[This Transcript is Unedited]
Hubert Humphrey Building
Room 305A
200 Independence Avenue, S.W.
Washington, DC
DR. STEINWACHS: I'd like to welcome everyone to the National Committee on Vital and Health Statistics, Populations Subcommittee Meeting, to gather information on health insurance data capabilities around access and coverage.
And we've got two very exciting panels here today that will help us address both those who are producing the information and those who are using information, and trying to understand areas where there may be -- certainly areas where there are strengths, but also where there are gaps that are important.
And I think as everyone realizes, the National Committee on Vital and Health Statistics is a federal advisory committee to the Secretary on health information policy. And so that we see this as part of what the Populations Subcommittee is doing to try and gather information and provide any advice that's appropriate dealing with information that helps support health reform, analyses, and evaluations as we move forward.
And so that we are looking for information particularly that help us understand from the data sources that we have, the capacity to know to what extent people are uninsured for short, longer, or extensive periods of time, something about the duration of uninsurance, to also be able to understand better what our growing concerns in many areas about underinsurance and that trends appear, at least this is my personal interpretation, that there is more shifting of the cost, both many times in premium and co-payments and deductibles to employees and families, and, therefore, creating a threat that may be stronger than it was before.
We're very interested in extent to which we can understand trends, and because as you look at turns in the economy and so on, the question is what's going to happen in the next year, as well as what has happened that has gotten us here?
So that we are very excited about having really some very great presenters here who can help us look at these issues, put the information together, and provide appropriate feedback.
In putting these panels together, I really want to express my appreciation to Dale Hitchcock and Rashida Dorsey who are in ASPI. And Dale tells me that he primarily fronted for the operation, which I don't believe totally, and that Rashida did it all. Is Rashida here? Thank you, Rashida.
So I do know that I got more e-mails from Rashida than I did from Dale. Is that an indication --
MR. HITCHCOCK: That it is. It is, yes.
DR. STEINWACHS: But I really do appreciate it, because this was put together, as many of you know, in pretty short order, trying to begin to build an information base that we feel that we need.
Before actually starting, I'd like to just go around the room and have everyone introduce themselves so we know a little bit. I'm Don Steinwachs. I'm from Johns Hopkins University. And I'm a member of the National Committee on Vital and Health Statistics and co-chair of the Populations Subcommittee.
DR. BILL SCANLON: I'm Bill Scanlon. I'm from Health Policy R&D, also a member of the National Committee, and also co-chair of the Populations Subcommittee.
MR. JIM SCANLON: Good afternoon. I'm Jim Scanlon, from the department here at ASPI, and I'm the Staff Director for the full NCVHS.
MR. HITCHCOCK: Hi. I'm Dale Hitchcock. I work for Jim in ASPI.
MR. JIM SCANLON: Enough said.
MS. GREENBERG: I'm Marjorie Greenberg from the National Center for Health Statistics, CDC, and the Executive Secretary to the Committee.
MS. JACKSON: I'm Debbie Jackson, and I work with Marjorie Greenberg, NCHS.
MR. LAND: I'm Garland Land, Secretary to the National Association for Public Health Statistics and Information Systems, member of the committee and Population Subcommittee.
MR. HORNBROOK: I‘m Mark Hornbrook, Kaiser Permanente Northwest and member of the National Committee.
MS. PAISANO: I am Edna Paisano, Indian Hills Service, and I am staff to the sub-committee.
DR. POWELL-GRINER: Eve Powell-Griner. I'm with the National Center for Health Statistics, the Division of Health Interview Statistics.
MR. COHEN: I'm Joel Cohen. I'm the Director of the Division of Social and Economic Research at the Agency for Healthcare Research and Quality. And the group that I'm in is the Center for Financing Access and Cost Trends to the Medical Expenditure Panels.
MR. NELSON: I'm Chuck Nelson. I work at the Census Bureau. I'm the Assistant Chief for Economic Characteristics at the Census Bureau's Housing and Household Economic Statistics Division, and we handle a lot of the household surveys that have health insurance information.
MR. BAUGH: I'm Dave Baugh from the Centers for Medicare and Medicaid Services, Office of Research, Development, and Information.
DR. STEINWACHS: Rashida, going back to you.
MS. DORSEY: I'm Rashida Dorsey. I work in ASPI with Dale and Jim.
MR. BROWN: I'm Jason Brown of the Treasury Department. I'm at the cost desk of the Office of Economic Policy.
MS. CASH: I'm Amanda Cash. I'm with the Health Resources and Services Administration, and I work in the Office of Planning and Evaluation.
MR. HAGAN: Stuart Hagan with Congressional Budget Office.
MR. PETERSON: Chris Peterson, Congressional Research Service.
MS. MORGAN: Paulette Morgan, Congressional Research Service.
MS. COUNT: Kathy Count, Centers for Medicare and Medicaid Services, Office of the Actuary National Health Statistics Group.
(Continued introductions around room)
DR. STEINWACHS: Well, thank you to everyone for being here, and I'm going to turn it over to Bill.
DR. BILL SCANLON: Okay. Let me also thank everyone for coming. And while the words health reform are used in some respects as sort of impetus for having this hearing, it's not just focused on sort of health -- data for health reform debate, which is already too late to develop a health reform, let's be honest about it, okay.
But it's out of a recognition that sort of health reform is more likely to be a process, that it's something where we need to be able to have adequate information to both monitor sort of what may be put into place, as well as to think about sort of the kinds of revisions that we might want to undertake in the future, probably the same thing we should have been doing for the last sort of 20 years. So the question -- we identify sort of gaps today. It's not that this is the time to do it; I've done it, but it's better late than never, okay.
The other context here is that I think that it's important that we put together a very good pair of panels that bridge both sort of the data coming from surveys as well as data coming from administrative sources because there's a recognition in many corners, including the National Committee, that our future is not just in trying to go out and collect through surveys information that's essential to some monitoring sort of the healthcare system and the health of Americans, and we really need to think about more efficient strategies. And that's probably going to involve some combination, sort of a data coming from administrative sources, and, hopefully, sort of much enhanced administrative sources.
That if the HIT sort of promise is ever realized that there's going to be better information flowing through sort of administrative systems and we really will be able to understand sort of better the kinds of services and impacts of services that are being delivered.
So, I think is an important thing to kind of keep in mind as we have sort of our discussion this afternoon.
The other thing about our two panels is while we divided them between sort of producers and users, I think probably virtually everybody on each panel has played a bit of both roles. And so, you -- I mean, feel free to comment from both perspectives. And I think the discussion, sort of having a free-flowing discussion from both perspectives throughout the afternoon is something that will help us all.
So let me stop. We don't have bios to sort of talk about, to provide. So I'm just going to say, let's turn this over to the first panel. Eve Powell-Griner is from NCHS. She's going to talk about the health interview survey. And I'll just go through the four of you and then leave it up to you. Joel Cohen from AHRQ is going to be talking about sort of MEPS. Chuck Nelson from the Census Bureau about the CPS, and then finally Dave Baugh about some of the data that come in to CMS from the Medicaid program.
So, Eve, the show is yours.
Agenda Item: PANEL 1 - Data Producers
DR. POWELL-GRINER: Okay. Thank you. Well, first of all, thank you for allowing us to come down and talk about our survey. We are always very interested in telling people about what we do. So basically what I'm going to do is just a little brief overview of what the NHIS is and then I'm going to give you some illustrations of how the data can be used. I'm not a techie, so Robin is going to help me out.
The National Health Interview Survey has been in existence since 1957, and we're household-based. We are in the field continuously and the Census Bureau is the organization that collects our data.
Periodically we redesign our survey; typically that's connected to the census, and the last redesign was in 2006. At that time, we decreased the size of the survey by about 20 percent. We now survey 35,000 households annually, and that results in about 87,000 persons. We do over sampling of minorities, as you see here.
We are representative of the US in the four census regions, and this is where the non-institution wide civilian population only.
Our insurance section of the survey is very extensive, over 80 questions in all. We collect information on the type of coverage within private coverage, the source of it, whether it's employer sponsored or comes from a direct purchase. Under public coverage, we identify the type of public coverage that our respondents have. And then finally, we also collect information on those persons who are uninsured. And the uninsured would include the Indian Health Service folks as well as those with only a single service plan.
In terms of the characteristics of the health insurance plans for those who are covered, we look at out-of-pocket premium amounts. This is relatively new. We also look at whether or not the deductible is exceeding the health service --savings account, which is 1,100 for individuals, 2,200 for families. And then the availability of these accounts for persons who have high deductible health plans. We also look at other characteristics of the coverage such as do they need to use certain networks, do they have to get prior authorization for care, does their policy also includes some dental coverage.
We do collect information on single service plans. Now again, keep in mind that people who would respond that they have these would not be counted as covered unless they had comprehensive coverage as well.
In terms of the uninsured, at the -- for those who say they do not have health insurance at the time that we interview them, we have a series of questions about how long they have been without health coverage. And these are the categories, at least part of the past year. If more than a year, we look at whether it's less than or more than three years or never. We ask them about why they don't have coverage. And we also asked them if coverage is offered through an employer for anyone in the family.
For all of our respondents, irrespective of their insurance coverage, we collect the basic sociodemographic characteristics. But we have a few other things that might be helpful for policymakers as well. One is that we collect extensive information upon the structure of the families, so we know what families have in terms of elderly, in terms of young people and so forth.
We also collect information on birthplace and citizenship so we can take a look, for example, at immigrants versus native-born persons.
Although it's not on our public use file, we have a number of geographic identifiers that might be useful for policymakers. Our data primarily is for national estimates. However, in any given year, we can do estimates for about 20 of the largest states. And by combining several years of data, we can provide estimates for most of the other states.
We do have a large selection of variables related to health status. And again, of course, all of these are self assessed, and like NHANES, which does the physical examinations. Among other things, we ask them about the presence of chronic conditions. If they have them, we ask more information about what type and how long. We have a whole series of questions about health behaviors which would impact health, such as smoking, obesity, and so forth. And then we also look at work days, sick days, or, for children, days missed from school.
We inquire about access to care that the past 12 months for all respondents. And we particularly are interested in the delayed medical care due to cost -- and I'll show you a slide about that later -- as well as those who didn't receive medical care at all because of the cost.
And then we also take a look at their access and use of physicians and other health professionals, including looking at items such as the length of time since they had seen a health professional, what their usual source of care is, whether they're relying on emergency rooms for most of their care, and also the extent to which they have access to dentists. We do inquire as to whether anyone in the family has a flexible savings account, because we know that that also can increase access to health care.
So on this next set of slides, I'm just going to give you some illustrations of our data. I'm not really going to go into the more than, less than. This is just to give you a flavor. But the main that I want to make is that the strength of the NHIS is that we are really a health survey, so that you can look at health characteristics and health outcome in the context of insurance status. And we're one -- I think that sets us apart from any of the other surveys.
You can take a look at how groups of people show up in terms of their insurance coverage. You can see, for example, on the green, that most people are covered. But if you look at the pink, you see that children have much higher percentages of coverage from public sources compared to adults, and that adults also have much higher rates of uninsurance.
You can look at it by time of interview. Again, you can see that there are differences between adults and children. You can look at the same variable; that is, length of time that they have been without insurance; if they are an insured, by race ethnicity. I've included here the broad groups that are available from our early release program which puts data out six months after date of collection. So in December, we will have a quarter one and quarter two 2008 data.
However, the NHIS also lets us take a closer look at disparities by going into some subgroups for Asians and also Hispanics. And even though we often treat them as a single entity, you can see that they have very differing -- different characteristics in terms of coverage.
We can do some mapping. This requires a combined data year. What I'm showing here is for uninsured children at the time of the interview, is that percentage lower, which would be the light blue, or higher which would be the dark blue, than the US average. The white would be folks who have -- or states that have such low numbers we can't produce estimates.
But you can see that by combining data years, we can get estimates for most of the states on many of the items.
MR. HORNBROOK: So even if you combine those four states, you couldn't get an estimate?
DR. POWELL-GRINER: If we combine the four states, we could. But we can't do it for the individual ones, so.
MR. HORNBROOK: Okay. Thank you.
DR. POWELL-GRINER: This is a similar kind of map. This is just the Medicaid SCHIP program. And again, the light blue are the states that have lower than the US overall, which is about 27.9 percent. The blue have significantly higher percentage of children who have this type of coverage than US. The gridded one, there is no statistical difference.
This is one of my favorite graphics from the NHIS. I think it really shows, particularly for children, what has happened over time. And what you are seeing here is something that is quite different from this next slide, which is for near-poor adults. And what you see is that, you know, unlike children, the uninsured near-poor adult population has been fairly stable across time, and you don't see this offsetting of private and public to the extent that you do for kids.
Of course, we know that chronic conditions increase the cost of healthcare in the US, and that access to healthcare is a very important component for minimizing those costs. Here we're looking at the insurance status and the type of insurance for people with chronic conditions who are in the age grouping of 18 to 64. And what you see is that the uninsured have much less access than the other two groups. You don't really see too much difference between Medicaid and private until you get to the prescription costs. And there you see substantial differences.
This is just another take on it. Again looking at it over time and these data points are for years. But one could also look at it quarterly if you wanted to do that.
And, in fact, this slide does exactly that. The orange line is quarter one of 2007. The blue line is quarter one of 2008. And what you can see is sort of an increase in these categories.
We also can look at regional data. These regions are fairly homogenous with respect to cultural socioeconomic characteristics, industry distribution, and so forth. And the blue is -- they tend to have lower than US average percentages of people taking these high deductible health plans. The pink, those two regions, the Rocky Mountain and the Plains, have greater uptake than the US as a whole, and then there's no difference in the rest of the regions.
So those lines, NHIS data. You can go to the web. As I said, we have as our major product, our early release program, which is updated every quarter. The next one comes out in early December. And it consists of two reports, one which is primarily health indicators, broad health indicators. And a second one, which is health insurance only. And then twice a year, we also have a report on the telephone status of our respondents. That is used in part to assess the amount of bias coming from using only phone interviews.
Our data for those who might want to find it are available on the web, with complete documentation, and we have lots of reports there as well. Okay.
DR. BILL SCANLON: Thank you. Joel.
MR. COHEN: I'm going to focus more on the health insurance data. There's also access data in the MEPS. But the structure of my presentation is very similar to what Eve just did, sort of a general, you know, introduction to the survey and then the different parts of it. And then a little bit of some of the research that's done with it.
And to get back to Bill's point earlier about researchers being involved in the data collection process. I know I actually worked with Bill at the Urban Institute probably what, 25 years ago.
MR. BILL SCANLON: Yes, you were still in high school.
MR. COHEN: Yes, that's right. And at the time, I probably never imagined that I would be involved in a group that actually collects data as opposed to analyzing it.
But I think one of the strengths of the Medical Expenditure Panel Survey in our group is that there's a staff of about 25 researchers who are both users of the data and have input into the design and collection of the data. So I think there's an iterative process there that allows the data to be very flexible.
And I think it's served us well. And I think you'll see that there's a, you know, in terms of the flexibility of the data for supporting a lot of different kinds of research is really a strength of the dataset.
Now, the MEPS, we tend to talk about it as a survey. It's actually -- we call it a family of surveys. But there are several different surveys in the MEPS. There's a household component which is a nationally representative survey of the civilian, non-institutionalized population. And I'll talk a little bit more about that in a minute. Actually, quite a bit more about that in a minute.
There's a medical provider component which is actually designed to support expenditure estimates in the household component. The reason we do that is because, you know, we're really interested -- it's Medical Expenditure Panel Survey, expenditures are really what we're looking for, and that's the major focus of the survey. And if you go to household respondents, they often had no clue of actually what was spent for their medical care. For example, if someone's in an HMO and you go and ask them, how much did you pay for your doctor visit, they'll say $15 or $20. They had no idea that the insurance company has spent $80 directly to the provider.
So you really have to do a different survey to get at that kind of information. And that's the medical provider component. And it's a survey of the providers, the people -- the providers who provided care to the people who are in the household component. That's all I'm going to say about that survey, just to mention that it's there and it's designed to support the expenditure estimates.
We also have an insurance component, and that's actually done by the Census Bureau. And that's a survey of establishments in state and local governments to get at basically insurance offerings. And that's the one aspect of the MEPS that's actually state representative. And also you can make local estimates, which I'll talk about in a couple minutes as well.
Starting with the household component of MEPS, it's basically, as I said, a national survey of the civilian non-institutionalized population. And the idea is to provide national estimates of different aspects of the healthcare system: use, expenditure, sources of payment, insurance coverage, access, quality, et cetera. So there's a lot of different components to it.
I think the strength of the MEPS really is that all of this information is internally consistent, so we're collecting information about expenditures, about peoples' characteristics, about their insurance, about their access, about their use, et cetera, all in one place. So, you know, there are other surveys where you can get a piece of information here, merge it with a piece of information there, and you have a larger dataset. But internal consistency is not necessarily a strength of those kind of datasets.
So there are weaknesses to the MEPS, of course. But I think one of the main strengths is that all of this information is collected at the same time and so that the relationships are preserved among the different aspects of it.
You know, basically the idea is -- and the strength there is really to support sort of behavioral estimates. We did make aggregate estimates. But aggregate estimates you can get from other places as well. For example, if you want to know total medical expenditures, you would go to the national health expenditure accounts. And that's a complete sort of estimate of what aggregate spending is in the United States. And we can do an aggregate estimate. But as I said, it's -- the MEPS is the non-institutionalized population. So already off the bat you're missing the institutionalized population, which is, you know, a component. If you're looking at total expenditures, you would want to look at them.
On the other hand, things you can't do with the national health accounts are look at the distribution of expenditures. You can't look at, you know, different people. You can't look at the concentration of expenditures among different people. You know, you can't look at the -- at, you know, what role demographics play in their use and expenditures, et cetera, or their insurance coverage.
We also -- there's been a real focus lately on expenditures for specific conditions. One of the sort of historical pieces of research that came out of the MEPS was this idea of concentration of expenditures, where about one percent of the population accounts for 25 percent of all medical expenditures. That's the kind of thing you can do with our survey. So this leads to, you know, looking at people with chronic conditions, who are the people who tend to be in the pale. And there are very few nationally -- actually, there are no other, I don't think, nationally representative datasets that would allow you to look at that kind of thing. So that's been a real focus recently.
And again we've been doing -- I talk again, going back to what Bill was talking about with healthcare reform. Actually, we do the MEPS every year now. And that was a function of the last bout of healthcare reform, where in 1992, people were getting together and finding out that the last information was from the National Medical Expenditure Survey which was done in 1987. So we were trying to project expenditures in ‘96, there was this huge gap. We used to do this survey every 10 years. At that point, we decided -- we started doing it -- we picked it up again in ‘96, and now we're doing it every year. I'll talk a little bit about the sample there.
The NHIS, the National Health Interview Survey is actually very important for us because we actually select our sample off of respondents to that survey. So when we did the survey, in previous years you'd have to do a screening survey in order to pick the sample, which was kind of expensive. So in that same time period when we were starting up the MEPS, we redesigned this in survey integration mode. And so, again, pick our sample off the previous year's National Health Interview Survey.
Now, that actually is -- it not only saves money, but is analytically very useful because you can actually trace MEPS participants back another year or even more, which I'll show you a piece of research that we've done doing that, so that you can put a profile together, for example, of insurance coverage over something like a four year period. And you can also pick up other information. There's a lot of health status information that we don't necessarily take up in the MEPS. You can go back to the NHIS and pick that up. So analytically, that's actually a very powerful linkage there.
We also -- I think Eve talked about some of the over samples. In the NHIS, since we select our sample off the NHIS, we also get those over samples. And at various times, we've also done some over sampling of our own. For example, people we expect to have high expenditures or who are going to be -- that we expect to be in poverty, et cetera, so. And that varies across years as to how we do that.
The way the survey is designed, it's called an overlapping panel design. They're basically what you do -- what we did in 1996, we picked up a cohort of individuals. And we're going to follow them for two years, okay. In the first year, we only had that one cohort to deal with. In the second year, we actually pick up a new cohort from, again selected off the previous year's NHIS. And so the annual estimate for 1997 consisted of one cohort which is the one we originally picked up in 1996, and that would be the second year of that one. And then we added a cohort, which would have been the first year of ‘97, and then we follow them for two years. So every year we're dropping off one panel and picking up a new panel. And again, that adds a longitudinal component to the survey that we didn't use to have. And that actually is very important analytically as well. For instance, we've done -- I've personally been involved in some research looking at predicting expenditures. And what you -- it's, you know, you can always predict the expenditures for the current year with characteristics in that year. The question is, if you knew what they were in the year before, can you predict what it's going to be next year? So having the two years worth of information, you can pick them up in one year and then try to look at, you know, risk adjustment strategies and expenditure prediction models in the next year. So having that overlapping panels allows us to do that.
In terms of the interview content, we basically -- it's an in-person interview. So we have this actually fairly long questionnaire that's programmed into a laptop computer. And we send interviewers out into the field to interview people in their homes. And the interviews tend to be fairly long. We follow people. The reference period is two years. It takes us about two and a half years to collect the information on those people. So we're going to them actually five times over that two and a half year period to collect information over two years, a two-year reference period.
And the questionnaire has various sections of it. There's what we call the core questionnaire, which is a set of questions that we ask about the various aspects of healthcare. We ask it every round. And then there are other parts of the questionnaire that are supplements. And they may come in at various rounds. Or at the end, we also have some self administered questionnaires that we mail to people. And those are for information that we want individuals to fill out for themselves. The main part of the interview, actually, we would talk to one individual for information about everyone in the family. And we try to find the person who is most knowledgeable about that.
But anyway, this slide kind of tells you what the main components of the questionnaire are. And, you know, we start with the family composition and characteristics of all the individuals in the family and then we ask about health status. And then health care use and expenditures, we ask about all visits that occurred over this period of time. Again, we're going to them five different times. So the recall period is only about, on average, about five months for each round of data collection.
We ask about employment, insurance status, and changes in insurance. And then we put the income and assets information at the end, because we don't want to ask that up front because people get a little touchy about -- when you ask them about their income. So that's the last thing to come in.
But again, the income data is very important for a lot of analyses of the healthcare system. For example, expenditure burdens and that kind of thing, which go back to the issue of under-insurance. I have a slide a little later on that looks at that issue as well.
Just to give you some sense of what questions we ask about about health insurance, the first -- the first we specifically ask about health insurance is when we're talking about -- we're asking people about their jobs. And of course, as you're all aware, you know, most people get their health insurance in this country through their employers. So the link between health insurance and an individual's job is very important.
Actually, this is the first time we ask about health insurance. But people have had an opportunity to think about health insurance before we get to this because we do the use and expenditure component of the questionnaire before we get to this part. So we're asking people for every doctor, hospital visit, et cetera, we ask them how much was paid for that and who paid for it. So they have an opportunity to tell us that their private insurance paid for something before we even get to this.
But in the job's questionnaire is where we really -- the employment questionnaire is where we really start talking about insurance and trying to link that up. And we ask whether they were offered insurance and whether they took it up, if they were offered.
Then we go into the insurance section of the instrument and we go back to what they had told us in the employment section, and, you know, confirm that the person had insurance at the job. And then we ask about other sources of insurance at that point too, private health insurance. So we get employment-related and non-group. And occasionally you'll find someone who may be, like a child, perhaps, who is covered by someone who's outside of the household. So basically we want to get all the information about, you know, who's got the private insurance, who's the policyholder, who are the dependants, et cetera. So we have all those relationships.
MR. HORNBROOK: Is there a smooth way of dealing with self-employed persons?
MR. COHEN: Yes. The self-employed?
MR. HORNBROOK: Yes.
MR. COHEN: Yes. Well, if they're self-employed -- it depends on the size of their firm.
MR. HORNBROOK: It could be one or it could be many.
MR. COHEN: It could be one, yes, or it could be many. So, we do sort that out. So we ask them about their insurance. So if they're self-insured, if it's above a certain amount, you know, you could classify it differently.
MR. HORNBROOK: Right.
MR. COHEN: But we do get all that information, even if they are self-insured.
DR. STEINWACHS: Joel, if the insurance is changed during the recall period how do you handle that?
MR. COHEN: As I said, we go to people five times over this period of time.
DR. STEINWACHS: Yes.
MR. COHEN: So when we come back to them, we ask them, last time you told us you had this insurance, has anything changed. So we basically have a profile at all points over this two-year reference period what their insurance was at all periods. And if it changed, we can track that.
DR. STEINWACHS: And you'd know why it changed? Do they tell you why it changed?
MR. COHEN: Well, yes, we -- sometimes we don't ask those kinds of questions. I mean, if it's a change in jobs, that can be one thing.
DR. STEINWACHS: Yes.
MR. COHEN: I don't think in the employment section or the insurance section we actually ask them why it changed.
DR. STEINWACHS: Yes.
MR. COHEN: We ask them -- we do have an access question -- section of the questionnaire, and we may ask them if they changed insurance, you know, why they did that, or if they had trouble getting insurance or whatever.
So there's a set of questions there. I don't think in the insurance component of the survey that we ask that.
DR. STEINWACHS: Okay.
MR. COHEN: So anyway, we get, you know, the time periods at all points along this two-year time frame. We ask about the general benefits and we ask about out-of-pocket premiums, which we didn't ask about originally. We added that a few years ago. The original design of the survey was to have a linked survey where we would actually go to their employers to ask about the benefits. And we were supposed to get the out-of-pocket premiums from that. We did that for a few years. The response rate on that, it was problematic to get enough of a response rate on that to be able to use the data in other than sort of an analytic sense. So our statisticians told us it wasn't valid enough to put out any annual estimates. So we sort of stopped doing that after a while.
So then we decided to pick up the out-of-pocket premium in the household survey. So we do have that now.
Again, we probe on source of public coverage. We asked people about, you know, the different programs. We have a set of managed care questions to get at whether they're in an HMO or whether if they're not in an HMO they have to select providers off of a list that's provided from the insurance company. We try to sort out -- people are often confused about Medicare and Medicaid. We try and sort that out.
We say people are often confused about Medicare and Medicaid. SCHIP, too, a lot of -- actually there's some research that shows that a lot of people are, for example, under an SCHIP program actually think they have non-group private insurance when it's actually an SCHIP program. So, you know, we try to sort that out. We have a list of names of Medicaid and SCHIP programs in the states and we present that to them. And so they'll pick that off of a list. And then if they do have Medicaid or SCHIP, recently they've been adding premiums to that. So we recently added a question to get at the premiums under the public programs as well.
Our definition of insurance is very similar to the NHIS. It's, you know, basically comprehensive, hospital medical insurance. So if you have like a vision plan only ore some kind of dread disease program or -- we always use the Maryland kidney disease program as our example. But something that's not comprehensive and doesn't really provide coverage, you know, general coverage for hospital and physician services, and then we don't really count that as insurance. Nor if people are -- don't have other insurance and are only eligible for IHS or VA programs, we don't count them as insured either.
The one thing about the uninsured is that that's -- to realize that's a residual category. We basically ask people about all the private insurance, all the public insurance, et cetera. If they've gone through all of that, and they don't have any insurance, then they're considered to be uninsured.
So we don't ask people are you uninsured; it basically comes out that way.
Now, in the first round of the interview, if we've gone through everything and they turn out to be uninsured, we have a question, a follow-up question that says, you know, we noticed you said you didn't have any insurance, when was the last time that you did have insurance. So we sort of prompt them at that point, you know, that you've told us you're uninsured and it will give them an opportunity to say no or to tell us when they last had insurance.
So as I said, we basically have followed the insurance coverage over this whole period of time. What we do in our public use files is we have monthly insurance coverage variables. So in every month over these two years, you can look at that. So it allows you to make a lot of different kinds of estimates of insurance. And this is particularly important, for example, when you're talking about the uninsured. We've done a lot of work with the department.
For example, under the affordable choices program, the secretary was trying to figure out, you know, what you might do to encourage people. One of the first questions to come up is, well, what constitutes uninsured that you would want to create a program for? Is it people have been uninsured for an entire year? Is it people who've been uninsured for six months? Is it three months? Do you want to -- you know, somebody's maybe in a transition period for only one month, do you want them to be eligible for these programs? So that's a very important policy variable that we're able to sort out because we have this insurance coverage information over this long period of time.
And as I said previously, since we follow people for two years and are linked to the NHIS, you can actually go back four years to sort out, you know, people who are uninsured, you know, for -- at some point in time versus a four year versus two years versus the whole four year period, et cetera. And so that's I think a very powerful advantage to the MEPS data.
Just some examples of, you know, what we've put together. These are from statistical briefs that we do ourselves. They're basically just sort of snapshots of the data, various things. In the insurance ones we do periodically. And, you know, this looks at insurance status over the period of the survey, ‘96 through 2007. And, you know, again, it's probably the public uninsured. And again, there are, you know, there's some decisions to be made when you're putting together tables like this as to what constitutes private, public, or uninsured, because people can have combinations of insurance. They could have private insurance in the beginning of the year and be uninsured at the end, or they could have, you know, public insurance for part of the time and be uninsured. You could have, you know, different combinations of public and private, et cetera. And, you know, depending on what you're looking at, you have to decide how you're going to define what kind of an insurance people have.
This is a very simple sort of hierarchical if people have private insurance at all during the year, we classify them as private. If they had no private, but public at some point, we classify them as public. And if they were uninsured for an entire year, they're uninsured. And this just shows you the trends there. You can see there's some decline in the private and increase in the public, which goes along with, you know what's been happening in -- with insurance policy over the last few years.
This just shows again the flexibility in terms of defining insurance status. And, you know, this looks at the number of uninsured any time in year -- first half of the year, which is basically our round estimate, which is, on average, about six months. It will vary by individual, but on average it's about five or six months. And then full year.
And as you can see, it makes a big difference in the number of uninsured, if you're talking about -- if you look at 2006, if you're talking about uninsured for a full year, that's about 37 million people. If you're talking about uninsured any time in the year, it's almost double that. So it does make a big difference how you define these.
This is just to show that, you know, you can look at insurance stats by characteristics of the population and as, you know, everyone's probably aware, young adults tend to be more likely to be uninsured than other people. Again, looking at race, ethnicity, Hispanics are more likely to be uninsured than other racial ethnic groups.
This is from a stat brief that Steve Cohen did where he linked up the MEPS to the NHIS. And you can look at, you know, the length of uninsurance, either uninsured for two years or covered for part of the time or uninsured for a full four-year period. And this particular chart is by income. We have pretty good income information in the MEPS. So you can do a lot with that. And it basically shows that, of course, low income people are more likely to be uninsured for longer periods of time.
MR. O'GRADY: Steve, just to go back one or two. On the one where you said before about the racial ethnic differences --
MR. COHEN: Yes.
MR. O'GRADY: Have you ever considered adding a question about immigration? You know, whether like on the Hispanic disproportion, what percentage of that is because they're Hispanic and what percentage of that is because they've only been in the short country a short time?
MR. COHEN: Yes, we do have that. And actually, that's -- the NHIS actually collects that information. So when we link back to the NHIS, we can get the information about how long they've been in the country. And Marilee Sing actually just wrote a paper on that, if you're interested. So, yes, absolutely.
And we can also, you know, in any individual year, sometimes the smaller, you know, population groups are not large enough to make an estimate on. But since we do it every year, you can pull across time. And sometimes even small groups you can make an estimate on as you're pooling. So you can do that too.
And people have looked at, you know, like in the Hispanic population, the different Hispanic subgroups and, you know, how that relates to being uninsured. So all of those issues are available to us on the survey.
This is something that -- this is from a paper that Jessica Banthon and Tina Bernard did, looking at out-of-pocket burdens. And this basically gets at the issue of uninsurance. There are different ways to define it. They defined it here as just persons who are spending more than 10 percent of their family income on health care. And this again looks by income status. And as you can see, poor people are more likely to spend more than 10 percent of their income on health care.
You can set this limit at different levels. You could -- if you think 10 percent's too low, you could look at 20 percent or whatever. But it allows you to address the issue of, you know, how many people really have insurance, but -- well, actually, the next graph will show you that. How many people have insurance but still end up spending a huge amount out of pocket for their health care. And this one looks by insurance status of individuals. And the thing, of course, we saw here, is that the non-group privately insured are the ones who are really at risk of spending, you know, large proportions of their family income on health care.
As I said, there's also the insurance component, which is an establishment survey. It's a survey about 42,000 private sector establishments and about 3,000 state and local government units. That varies a little bit across time. Again, we've been doing this survey since 1996, as well. But a few years ago, we actually upped the sample size so that we could make estimates for every state. Originally we could do like 30 states or 35 or something like that. But now we can do every state. And again, it looks at what employers are offering, you know, who of their employees are eligible, who's taking it up, what the cost of the insurance is, you know, what's the premium, how much of it's paid by the employer, how much is paid by the employee. You know, some information on the benefit provisions, you know, whether their co-payments and go insurance and deductibles in that kind of thing. So, you know, this survey is actually done, as I said, by the Census Bureau.
And the sample is selected off of Census Bureau frames. The private establishments are selected off the business register and then they have a register of state and local governments that they select that off of. This is -- it makes for a really nice survey. The Census Bureau has really good frames, the best, their response rates are very high, et cetera. It does create difficulty in terms of having access to the data, because these data are now under the Census Bureau's confidentiality requirements.
So, you know, even though we sponsored the survey and consider it to be ours, I think the Census thinks its theirs. And, in fact, we have trouble getting access to the individual data.
So, and in terms of publicly available data, there's no micro data available off of this. It's all in tape -- in tabular format, et cetera. It's all cleared by the Census Bureau, et cetera. So that's an issue with these data. You can't just go in and link up, you know, the household and the employer data and have a nice dataset there. It can be done, you just have to go through a process.
MR. JIM SCANLON: Joel, how much information do you get on the policy itself, benefit provisions, deductibles and co-pays?
MR. COHEN: It's -- actually, I will get to that, employer characteristics. Let's see. Premiums, contribution, plan types, enrollment, deductibles and co-payments. Some of the benefits, it's not real detailed because you just can't get that from the employers. So, you know, it's not the kind of thing -- if you're going to look at look, you know, an HSA or something and you want to know exactly, you know, what deductible each individual is facing and that kind of thing, it's not going to be all that good for that.
MR. BILL SCANLON: Is there a linkage between questions that you have and what Eve mentioned in terms of the characteristics of policies? I mean, HIS you captured some of that, right?
MR. COHEN: Yes. Yes. I don't know how detailed. I don't know how detailed the NHIS questions are. Plus a person's -- since this is a previous year, their insurance could have changed over that period of time as well.
As I said, originally, we had had a link survey designed where we were going to send house, you know, the names of household respondents -- well, I actually did it. We sent the names of the household respondents' employers to the Census Bureau and then they were going to interview those employers and then we'd be able to link up the plans and the characteristics, et cetera. That hasn't worked all that well. It's just difficult to get the response rates to work it out.
We do have some proposals in to do health insurance plan survey where for the individuals that are in our household survey, we will collect very detailed information about their health plans, which will allow us to get that information. We need a little bit of money to support that data collection effort. And, you know, we've gone through the Data Council and I think, you know, there's been agreement that that's an important piece of information. But, you know, so far, we haven't gotten money to do it.
MR. BILL SCANLON: Was that done at some point in the past?
MR. COHEN: Yes. Well, in ‘87, we did that. And I believe we did it in ‘96 as well, when we had the original link survey and then we had this health insurance plan survey as well and we coded up the information. But the linkages were such that, you know, the response rate was under 50 percent. And as I said, our statisticians didn't like that. So they wouldn't really release the data. Although, it's available in our data center, you know, as an analytic file, but it's not --
MR. BILL SCANLON: I'm going to stop interrupting you.
MR. O'GRADY: Can I just interrupt one more on this? I mean many of the people in the room are going to have to sort of bunker down and get ready for some notion of reform coming up. And whether it's, you know direct data collection or it's going to be how you model and simulate what you think's going to go on, we do know that there's -- I mean, certainly, I'm willing to put my hand on the Bible about privacy concerns. But, you know, many of these other people who would need to use this data are other feds. Is there not some way to think about if our colleagues at CBO needed to know what's on that IC, I mean, do they really have to go to Suitland every time they want to do a simulation? And if they want to link to the very rich, although private sector, AHIP, key on possibly the individual market, the small group market, how do we sort of get beyond, you know, or deal with the reasonable concerns that are out there, but get decision makers, sort of the analytics they need?
MR. COHEN: Stuart, do you want to add to the question?
MR. HAGAN: This is a real frustration. I mean, we really like MEPS and all of the data sources at AHRQ. But maybe because that data is so valuable to us it is very frustrating when we have to go through these hoops and we have to make a request to AHRQ for some sort of data and they say, well, you've got to go through Census and get it. And every time I keep forgetting, now why do we have to do this? And they say, well, there's confidentiality rules and all this kind of business.
It would be -- we don't -- we're not going to, when these proposals come up, we don't have the luxury of time. And we're waiting right now on a particular census proposal request that we made several weeks ago. And I can understand these things take time to turn around. But I wish that we would have some sort of a -- an arrangement up front where we could get some quick turnaround. If we're going to continue to have these constraints, then I'd like to have some way, some institutionalized method of having turnaround on data requests.
MR. COHEN: You know, I share your frustration. I mean, we, as I said earlier, we actually have trouble getting access to the data ourselves. And we -- it's really a Census Bureau issue. It's not anything that we're doing.
MR. NELSON: I think all agencies have their regulations.
MR. COHEN: Yes, exactly.
MR. NELSON: The whole routine is a pretty strong regulation. And I think our policy people try to work with the agencies to try to do what they can. I think maybe some high-level meetings have to be held at the -- this actually -- I think we'd be able to sort of talk these issues through.
MR. HAGAN: I think it's good. The problem is are we making requests?
MR. NELSON: Right.
MR. HAGAN: You know, we have a priority and then Census has a priority and they have limited human capital, but we do also. And so, you know, there's a question of where you put your human resources. We would like the programmers, of course, working on our stuff full time. And maybe we can meet and come to some sort of an agreement, you know, to do that.
MR. BILL SCANLON: Remember we had that hearing about two years ago, on data linkage, the same issue came up. And it was the question of bringing data, databases from different agencies together. And the testimony we heard then was the fact that you could do it, but very often, and if not always, involved a process which essentially started at step one, took a long period of time, and then an agreement was reached.
And we've written a letter to the Secretary suggesting that if you can reach this goal, why not sort of figure out sort of what are the standards there and streamline the process? So I think that's -- we'd like to see that happen. Linda.
MS. BILHEIMER: Well, it's interesting that you should say that, because yesterday was the annual meeting of the Federal Committee on Statistical Methodology. And the Federal Committee has a subcommittee that is looking specifically at the barriers to usage of administrative data across agencies and the barriers to sharing data across agencies.
And the subcommittee reported yesterday. There were two sessions yesterday to a packed room of federal statisticians from all agencies, I assure you, on the progress that they are making on facilitating data sharing across agencies. And one of the critical things that they are doing is developing a model agreement that agencies can use to facilitate the process. And the issues that came up and that are being discussed right now, what components -- I think there are 20 components that have to be in a model agreement. What components -- can there be actual boilerplate language that somebody can lift and put into an agreement. And what components can we at least give some examples that agencies can --
So this work is ongoing right now. They are hoping to have the model agreement draft out within a few months. I know that's not going to address the immediate issue. But it is certainly a very high priority for all the federal statistical agencies.
MR. O'GRADY: But it is clear that - these guys are running out of time. This can't take another two years, or we'll be in the same criticism that we heard 12 years ago, that the data wasn't ready when the policy was. And I know how the committee staff will -- you know, it'll be, what are we giving Census Bureau $50 million a year for if they sit on the data and we never get to use it to actually make policy.
So it'll come back to bite hard, for sure. Now, we've seen in other things where you come up with something that is not a public use file but is, in effect, a other trusted feds file or something like that, so that you give Stuart sort of an analytic abstract that meets Census' concerns about confident -- you know, whether you're aggregating off or using other things like that. But he doesn't have to start from step one every time he wants to do another run. He's got something he can work from.
And, I mean, I don't want to be a pain, but it's sort of we're running out of time here, and so it's got to -- you know, we can't have another hearing in another year and talk some more. It's got to happen pretty quickly.
MR. COHEN: I think there were methods, too, I mean --
MS. TURK: Doesn't census groups include agencies that are not statistical agencies? So the policy agencies would be involved. Because of that, then ASPI couldn't get focused statistical agency --
PARTICIPANT: I couldn't hear that.
DR. STEINWACHS: Joan, do you want to repeat that? There are some people that couldn't hear your question or comment.
MS. TURK: I was curious to know if the agreement they were talking about was broader than statistical agencies.
MS. BILHEIMER: It's a model for agencies to use. It's not sort of a binding agreement that is going to be put out there as a model.
DR. STEINWACH: That'll be for all agencies that want to use data.
PARTICIPANT: Is OMB involved in these discussions?
PARTICIPANT: Yes.
PARTICIPANT: Well, the committees under there are, yes.
DR. POWELL-GRINER: And this is a little bit different because the Title 13 is really sort of a universe of its own. But NCHS, I guess about 18 months ago or so, we developed what we call a sworn agent, which allows people to come in and to use our confidential files for research, so that they don't have to go through quite so many hurdles. And I -- Is Nancy Breen -- Nancy, aren't you our first sworn agent? So she's with NIH.
DR. STEINWACHS: So how does it feel to be a sworn agent?
MS. BREEN: Well, I haven't really taken advantage of the situation yet. But we do --
DR. POWELL-GRINER: But I'm glad that we can. And I know that at the federal linkage discussion, we did talk about that then you said you were developing a model. So my guess is that maybe the model you developed is the one that you were talking about that we have now.
MR. O'GRADY: Does the CBO sworn agent have to go out to Hyattsville?
MS. BILHEIMER: They're coming out to Hyattsville.
MR. O'GRADY: Is there any -- I mean, ideally it would be if there was something here in the Humphrey Building.
MR. PETERSON: Yes, because I'm in the same boat as Stuart. And, of course, Chuck's tired of hearing me whine about this.
(Laughter)
MR. PETERSON: I mean, from the days that Mike was there, the Director of CRS had sent the Census Bureau a memo saying, guys, you got to help us out here. And so it's been shot down annually. But sometimes I get enough angst in me that I'll try it again to get shot down again.
But the issue on the sworn special service or whatever it is, is exactly the point that you have to go there physically to do it. And so when we're in a time period where we've got to do something, the turnaround --
MR. HAGAN: That is completely unworkable. It's unworkable.
MR. PETERSON: And so, you know, and I had talked to Chuck about, geez, could we set up a data center at the Library of Congress? I mean, the Library of Congress is supposed to have the source of information for everybody.
MR. HAGAN: Or better yet at the Ford Building.
(Laughter)
MR. HAGAN: Or in the office next to yours.
MR. PETERSON: See, my argument works better.
(Laughter)
MR. HAGAN: You're forgetting your separation of power. There's such a thing as a secure internet, I think, which I thought, and we do need to develop the capability at CBO. We handle -- we have tax data at the individual level at CBO in our computers. And we have the security procedures and capabilities necessary to protect that level of confidentiality, which is pretty darn high.
It's not a matter of being able to put out our -- put in a single request or a series of requests early on and say, this is what we're going to need, just give us this and we'll walk away and be happy, because we really don't.
I mean, one that pops into my mind right now that's an example, this may be -- I don't know if this relates to Census or this if this is really an AHRQ thing. But, for example, the conditions, health conditions, you guys do, you only do it for the third, is it condition? Yes, I think it's only a third character.
MR. COHEN: The ICD-9 code's -- the public use file's only got three digits.
MR. HAGAN: We get a Congressional use file that has all five digits, because these guys, you know, they can get very specific about what they want to cover or not cover or when they want to do a particular proposal that gets this particular kind of breast cancer or this kind of recreational sports injury. These are just --
MR. COHEN: I would actually caution you on that, that the five digit ICD-9 code, I'm not sure how specific you want to get with that in terms of an analysis, because those codes are very, very specific. And the information that we're getting on the MEPS comes from a household respondent. And I don't think they know the difference between diabetes with a specific complication of this type versus diabetes with a specific complication of that type. And that's what you get with the five-digit codes. But I think, you know, in terms of like aggregating them to some level, I'm sure we could come to some kind of a, you know, an agreement.
I'm not the person who is in charge of, you know, the confidentiality requirements of the MEPS. But, I mean, I'm sure you've been talking to Delores, right?
MR. BILL SCANLON: I think this is the kind of discussion that we were hoping to generate, but we need to proceed with the presentations. This issue, though, probably is something that needs to go up to a much higher level, because I think that -- and what we were hoping for in the prior work with respect to data linkages is that we establish some uniform procedures and that somebody -- I mean, somebody at a high enough level has to be comfortable with them and sort of say that this -- we are going to be inherent to all the statutes that exist. We're going to provide the protections for confidentiality that are appropriate, and we can move forward.
Because, I mean, Stuart, I agree with you completely. Coming from GAO, I had no concern about the kinds of information we had within that building, and it was sort of the gamut in terms of tax returns, sort of confidential financial records, individual Medicare claims, you know, with full identification, et cetera. And it's basically because, I mean, the standards there for confidentiality are as high as they are sort of anywhere else in the government. So if the government can have the data, so the Congressional agencies can put in some of the same safeguards into place.
MS. BREEN: For the record, also, I think the other thing that needs to happen is there needs to be staffing at these various agencies to help with the data, because these datasets are quite complex, particularly when you try to move across the three datasets that the MEPS consist of.
And so part of the issue is that Census doesn't have staff to help people do these analyses. So I think that, you know, the expertise that's onsite is also important, as well as access to the data within the federal government and then outside the federal government as well, because becoming a sworn employee is kind of a big deal.
As Eve said, you know, I happen to have this because we're a funder of the National Health Interview Survey. But we haven't needed to use it. But if we did, we would have to go down to Suitland in order to use it, and that's not an easy thing to do. It's quite a big organizational stunt to pull off.
MR. BILL SCANLON: Okay. We're back to Joel.
MR. COHEN: All right.
MR. JIM SCANLON: Before you do, I have -- (laughter) Any ideas like this you have, I'd like to have them right away, because, if it's possible, we'll follow up with the other agencies. Here at the Humphrey Building we might be able to set up some sort of physical space for an AHRQ(?) actually, you do have space here at NCHS, possibly even at Census, but we'd have to talk to Census about -- it doesn't sound like it's the space so much as the -- that's one feature, but it's the review policy. But we'd be willing to follow up at a fairly high level among the agencies, what we could do to make this happen faster.
MR. O'GRADY: Right. I think, Chris, waiting for it to appear in the Library of Congress is going to be a long wait. But four blocks away sounds a lot better than --
MR. PETERSON: -- what you're talking about, though, because it sounds like you're thinking more on the HHS lines.
MR. O'GRADY: Yes.
MR. PETERSON: And, frankly, that's a lot easier. I mean, we get a lot of help, the folks at AHRQ, are very helpful, we don't have problems. In terms of, the difficulties are the Census Bureau.
And so I'm not sure, kind of going back to Bill's point about what your role can be here, if it's really a Census Bureau issue, and as soon as Joel has to involve the Census Bureau, then all bets are off, then that is a different question.
MR. BILL SCANLON: Yes. And I think it may not be an issue of branch of government, because, I mean -- Bruce can tell me if this is wrong. But I mean we have access into some of the Executive Branch computers at GAO when we were there. But the issue is, in part, GAO is set up to deal with secure data. I mean, there's a lot of --
MR. STEINWALD: We're the auditors.
MR. BILL SCANLON: Well, right.
MR. STEINWALD: Join the auditors.
MR. BILL SCANLON: You could be the auditors without setting up the safeguards, okay.
MR. STEINWALD: Yes.
MR. BILL SCANLON: And so they have devoted resources to that. So it's the question of putting in place the protections that are going to make people comfortable. And I think -- I mean, I never had any concern; you may never have had any concern about it, hopefully not, too.
PARTICIPANT: No. I mean, you actually know this area better than I do, I think. But, yes, we do have ready access to Medicare claims data that identifies the beneficiary and treating physician. So what could be more sensitive information than that?
MR. JIM SCANLON: Well, the Medicare data is a much different -- Medicare data is a much different -- it's a lot -- it's a lot different process and much faster to get the Medicare data.
MR. BILL SCANLON: Last word before we go back to Joel.
MS. BILHEIMER: Just one additional point. I think it's important to understand that it's not just access to personally identifiable information that is an issue. It's also the disclosure of the process that you have to go to before the release of your product.
And, you know, yes, you can have very secure data systems that stop people, unauthorized people accessing the data, but that is not addressing the disclosure of your issues. And it's important to realize that it's -- there are two components here.
MR. BILL SCANLON: That's another one of the safeguards we have, yes.
MR. J. SCANLON: But on the other hand, it may be a combination of protection, like a sworn agent, plus a secure site. That gets you to --
MS. BILHEIMER: But then you have to have -- you still have to have someone to do the disclosure review after the sworn agent has done the work.
MR. BILL SCANLON: Okay, Joel.
MR. COHEN: As I was saying -- Just in terms of the published estimates, as I said, there is no micro data sets that are publicly available from the IC as a previous discussion with the IC.
But we put out tables, and these are all reviewed and approved by the Census Bureau, and there are a lot of them. There's I think a -- I looked at last year's and there were something like 285 private sector establishment tables looking at -- you know, broken down different ways looking at premiums, contributions, enrollments, take-up rates, et cetera. So there are a lot of tables that are out on the web.
There is also an interactive data analysis tool called MEPS net, and there's an IC version and a household version. And you can go in there and do some simple calculations there. And the IC version would, you know, combine the tables differently, et cetera. So you can pull some things out that you're looking for.
As I said before, you know, we started in ‘96 with estimates for 40 states, but now we actually have increased the sample size and we can produce estimates for every state. And, in fact, since 2005, we've produced estimates for metro areas as well. And so, you know, we have even smaller than state area estimates, and those are available. Those data can be downloaded in Excel spreadsheets or CSV, which I believe is comma separated value spreadsheet format. So those can be downloaded and manipulated on your own.
And then at times, at various times, we've worked with HRSA and with states and other organizations to actually supplement the samples in particular states. So, you know, because of the sample design of the IC, you can easily just add sample in a particular state if somebody's interested in that.
Just to give you some ideas of, you know, what kind of data are available, this is just a table that we put together periodically. It shows you the types of information available off the IC. This is just the premiums for single coverage and employee plus one coverage and family coverage. And these are the top 10 states. And we usually mark, you know, the little asterisks are identifying the states that are above or below the national average. So you can see, you know, how states relate to one another in the national --
MR. JIM SCANLON: Are those mostly group plans or are there individual?
MR. COHEN: Yes, these are all private sector establishment plans.
MR. JIM SCANLON: So those are group?
MR. COHEN: Yes, group, right. This shows, you know, again, the information across SMSAs, large metropolitan areas, insurance offerings. And you can see nationally on the far right the yellow bar's about 87 percent of employees work at firms that offer insurance. And it doesn't vary a great deal across these different areas. We collect information on retiree coverage and this is from a paper that was done by Tom Buckmueller and Tony LoSasso a couple years ago, looked at retiree coverage over time. You can see that, you know, in general, it's the big firms that are more likely to offer retiree coverage. But even at that, all the firms, it's declining over time.
And I threw this in just as an example of a paper that Tom Zeldin did, and it did combine the insurance component and the household component data in order to look at the tax subsidy for private insurance and estimate, you know, what the, you know, how much that tax subsidy was and how it varied across various types of establishments. So, you know, this is, again, I think an example of how powerful these data are when you can link them together.
And again, almost everything -- there's a tremendous amount of information available on the MEPS website, which I should have had a slide. But it's www.meps.hrq.gov. And you can get, you know all of the tables that I showed, all of staff briefs. You can download the questionnaires. You know, you can download the data files, et cetera.
DR. STEINWACHS: Joel, let me ask -- I appeared in a couple talks not long ago to alumni at Johns Hopkins about health -- the uninsured and health reform. And in both cases, in New York and Boston, I got hit with a question of saying, well, how many of the uninsured are illegal aliens.
And so to tell the answer, I said, well, I really don't know. They told me, they said, well, there are eight million, or something. Now, presumption, that, I guess -- the estimate's about, depending on which estimate you use for --
PARTICIPANT: 11.25.
DR. STEINWACHS: Pardon?
PARTICIPANT: No.
(Laughter)
DR. STEINWACHS: Mike has it. Thank you. So the inspiration is, I assume this is an area which we really don't probably have or do we? That is what I am asking.
MR. COHEN: You can look at, you know, people who are uninsured based on how long they've been in the country, whether they're native born, et cetera.
When you're talking about illegal aliens, number one, you know, these are household surveys were somebody's calling and saying, I'm from the government, I want to talk to you, you know, I think there are probably some illegal aliens who participate. But I'm not sure, you know, how much confidence you could have that you're, you know, you're covering that population adequately there.
So I think you have to make some --
MR. O'GRADY: This has come up, though, in the policy division.
DR. STEINWACHS: Yes.
MR. O'GRADY: And it's not so much -- I mean, Chuck's numbers from -- the last one I saw was about 22 percent. And we don't know what percent, you know, what the split is between illegal and legal. The old INS guys used to think 50-50, roughly. But you certainly is -- again back to, you know, Chris and Stuart's world and what they're going to face here, the notion of, there's not a lot of money to spend here. So even if it's a legal, you know, if it's a non-citizen , there's going to have to be a discussion there about whether, you know, do you have a consensus about covering non-citizens. Now, I think as long as we stay with employment-based, probably there is. If you're a citizen and I'm not and we both work for Bethlehem Steel, we're going to get coverage, you know, the work-related -- if you think of it that way. But if it's taxpayer dollars, you know, the problem, the illegals are way down there. But I'm not even sure that there's a consensus about covering legals.
DR. STEINWACHS: Joel, it made me think about this point Mike was making too is that, since if you are an illegal alien, you're probably less likely to respond and be caught up in our surveys.
MR. COHEN: Right.
DR. STEINWACHS: Which also led me to the sort of interest of to what extent we underestimate the uninsured because of that bias. And so when we come up with our numbers of the uninsured, it may actually be lower than it would be if we were able to include a representative.
MR. COHEN: Right.
DR. STEINWACHS: We don't really know that answer either, I'm going to assume.
MR. COHEN: No. Well, there's actually a lot of debate in general about how many uninsured are out there.
MR. COHEN: And, again, you know --
DR. STEINWACHS: I understand that.
MR. COHEN: -- there are a lot of issues relative to that as well. But, yes, these are things that are difficult to sort out.
MR. BILL SCANLON: Maybe we should give Chuck --
MR. LAND: I just have one question left and then we can get to Chuck. With the proposal to reduce the sample size of NHIS, how's that going to affect you all?
MR. COHEN: Robin wants to talk. Currently I think it's not going to have an impact.
ROBIN: The way it's designed, it won't affect MEPS.
MR. COHEN: Basically, we select off certain, you know, parts of the HIS, and the parts that they're cutting are the other parts so far. So, you know, that's not affected us.
MS. BREEN: We had a meeting with NHIS a few weeks ago and asked that question. And they said it wouldn't affect the MEPS for a while. In other words, I think in the short run it won't; in the longer run, it will. Is that correct, Robin?
ROBIN: I'm not sure about that. But at least for the next year, the plan is to only cut parts which do not impact it.
MR. COHEN: Yes, at some point if they keep cutting, you know, willy-nilly, it might have an impact.
DR. POWELL-GRINER: And for those who don't know, NHIS is now at half sample. So we started that in October, and that's going to be in existence for the first quarter of 2009.
MS. BREEN: And I think that should be a big concern to this committee, because that's bringing the sample down to around 20 or 25 thousand adults.
DR. STEINWACHS: Households.
DR. POWELL-GRINER: That's right. It wipes out a lot of our ability to look at these areas. So it is a major problem.
MR. BILL SCANLON: Right. And it is a concern and is something that we raised at the full committee meeting this morning. So it's something that's definitely on the radar in the issues; so what's the best way to think about addressing it.
Okay. Let me welcome Chuck and tell you that it wasn't an ambush that was planned here.
(Laughter)
MR. NELSON: Okay. So thanks for inviting me here. I'll be talking about actually several surveys, not just the CPS. There are several other surveys the Census Bureau has that request health insurance information. But obviously, I'll probably spend most of my time on the CPS since it's a very popular data source.
In terms of the original questions you asked about the Census Bureau's and other agencies' capacity to measure things, the Census Bureau's main strength are the questions that have to do with coverage, characteristics of coverage, length of time uninsured, the impact of economic change on health insurance. A lot of these surveys that the Census Bureau takes our sort of economic surveys with health insurance information, so they are particularly well-suited to look at that impact, the impact of economic change on health insurance change. And we have a lot of state data, a lot of state data.
Not so much these our household surveys, so we wouldn't have as much on the characteristics of health insurance policies or under insurance. You know, like what's covered and what isn't. But certainly, there's a lot of information about health insurance coverage from household surveys.
The surveys all talk about, obviously the CPS, there is an annual economic and social and economic supplement that's the source of the official poverty essence in the US, and that supplement also has questions about health insurance coverage. CPS is a monthly unemployment survey. It's the source of the big news that, you know, the unemployment rate rose to six five; that was from the CPS, the monthly CPS. So it's a supplement to that survey.
I'll also talk about SIPP which is a longitudinal survey, so it's a good survey for looking at duration of uninsured or following people and what happens to them when they lose their jobs or go off programs, what happens to their health insurance status.
There's also a relatively new survey in terms of health insurance coverage, the American Community Survey. It's the replacement for the long form and it's been out there since 2000 collecting national and state and sub- state information. But 2008 is the first year in which that survey had questions about health insurance. So next year we'll be putting out health insurance estimates from this survey. It's a very large survey.
And there's also a small area of model-base estimates program that the Census Bureau has called the SAHIE program and that just put out estimates for every county in the US. And I'll be showing some data from that, so.
The first survey I'll talk about, the CPS. We've been asking about health insurance on the CPS since 1980. As I said before, it's a source of official poverty estimates in the US. So the questions were really first added as a whole series of questions that were added to look at non-cash benefits and their effect on poverty and health benefits was one of the subjects that we asked about, as well as food stamps and public housing and so on. And we've been publishing the data from the CPS on health insurance coverage since the early 1990s -- 1991.
CPS, it's about 78,000 interviewed households across the country, surveys are conducted in February through April. And we ask about coverage during the previous calendar year. They're state representative samples. So we put out estimates from the CPS. We use multiyear averages because they -- if you use individual years, the standard errors can be kind of large for some of the smaller states. We asked about coverage on the previous calendar year and the -- there is sort of a household-based question: does anybody in this household have coverage from, and then you get to the different sources.
Our latest estimates from the CPS, we released them in August of this year. And we actually showed a drop in the percentage of people that had health insurance for the first time since the late 1990s, and a drop in the uninsurance rate for children as well. It's actually the first time that the CPS has shown an increase in coverage that was driven by an increase in public coverage as, over the past few years, private coverage was down, again, in 2007, the public coverage was up from about 27 percent in 2006, to 27.8 percent in 2007, so enough to drive the overall coverage rate up.
It's the first time that we've seen that the -- an increase in coverage was really driven by an increase in public coverage. The increases that we saw in the late 1990s were really driven by an increase in private group coverage. So it's kind of a unique year.
MS. BREEN: Now, was that Medicare or Medicaid or both?
MR. NELSON: It was -- they all went up. Medicaid went up, Medicare went up, even military, military and VA coverage went up, so.
MS. BREEN: So I was wondering if part of that burden was on the states. It sounds like it is.
MR. NELSON: Yes, that's what -- Medicaid is up. Medicaid is definitely up. In the CPS, we showed three-year estimates of state numbers from the CPS. We showed this kind of -- as I'm sure everyone knows, the big diversity in health insurance coverage by state, you have states that are down in the eight percent range, like Hawaii, Massachusetts, Minnesota is also down there, Wisconsin, up to the mid-20s, Texas, New Mexico or up in the 20s with -- in terms of uninsured.
We also show -- we also found that of the 10 states with increases between ‘04 and ‘05 and ‘06 and ‘07, seven of those states were in the South and Midwest. So the South and Midwest appeared to be where uninsurance appears to be rising, at least based on these data.
So the strengths of the CPS is that it is a longtime series. We release the data very quickly. We take the survey in March, and then we release the results in August every year. It's a pretty large sample by survey standards. It's state representative, which is important.
Since it's largely an economic survey, the data on economic well-being, income, benefits received, work experience, labor force status, CPS is a labor force survey, is very good. So it's a very good survey for that. It's a very popular data file. It's easy to use. It's been out there a long time. And it has a high response rate. The response rate to the basic survey is around 92 percent. So, and the response rate to the supplement is around 90 percent. So the combined response rate is around 80, 80 percent. So it's, by service standard, it's pretty high.
The limitations of the CPS, health insurance is not a focus of the survey. We ask about in February, March or April, we ask about any coverage at all in the previous calendar year. So we ask people to remember a pretty long period of time. And we know that from other surveys, like SIPP, we can build an annual estimate from multiple interviews. We take interviews every four months in SIPP, and we sort of -- you can come up with an annual number. Well, that annual number of uninsured is much lower in SIPP. And we base it on these sort of multiple interviews as opposed to one interview in the following year.
And there's limited flexibility from -- for adding new content, because it really is not a health insurance survey. It's really a, you know, really a partially an economic status survey.
So the SIPP, SIPP is the Bureau's source for compatible based estimates. So the SIPP is a survey where we follow people for three or four years, depending on the panel. Sample size is pretty good, it's around 42,000 interviewed households from the ‘04 panel, for example. It uses a four-month reference period. So we have to go out three times a year. We ask about the coverage status of every person in the household. And there are -- one of SIPP's strength is that it's a very rich data source. There's a lot of information about other kinds of topics that you're interested in, medical expenditures, use of medical services, health and disability status, assets as questions on -- it's on one of the few federal surveys that has a full set of questions on assets. So if you want to look at program eligibility issues, SIPP is a good survey for that, besides the economic, you know, besides a very strong questions -- set of questions, on economic status.
It has a shorter reference period than the CPS, so it probably has more accurate estimates of coverage. And, you know, so it's a good survey for that, for those kinds of issues. It's obviously the strength is that we're following people over a period of time so you're able to look at the impact of individual events on a insurance status.
The limitations, it's definitely a more complicated survey. And it takes longer to put the data out. It's more complicated to use from a data user perspective. I'm sure people who are using the survey can tell you that. And it's not as good for looking at state. While the sample is state-based, the sample size really doesn't lend itself to looking at all 50 states. You can look at big states. You can group states together. But it's not as good as these other surveys for looking at individual -- for looking at all 50 states.
Okay. The ACS, the American Community Survey, it's a replacement for the long form. So in 2000, you know, one out of seven of you probably got a long form census. In 2010, no one will get a long form census. All the Census Bureau small area data needs, household survey data needs will be met by the American Community Survey.
So it's based on about three million addresses annually. It's a mail-out/mail-back survey. So we mail out a survey to these addresses, to this sample of addresses. And for those who don't respond, we follow-up with a computer-assisted, either telephone or personal interview.
And we added health insurance questions in 2008. They were justified by ASPI, actually. ASPI wrote the justification to OMB that allowed the Census Bureau to add these questions. The first data will be released in 2009, the summer of 2009, for the nation, all states, and all geographic areas of 65,000 or more, counties, cities, all areas, congressional districts, all areas.
There'll be -- and the person-based questions, we ask about your current coverage status. So it's your coverage status at the time of the interview. And we're asking every person -- we're asking that question of every person in the household.
Well, the strength of the ACS are obvious. This is a very large sample size. And we're much, much larger than any national survey out there right now. And they'll be -- CPS will be a relatively quick publication time. The survey like at the end, the surveys taken throughout the year and we'll be publishing the results sort of in the following September. So the interviews that we take throughout the year in 2008 will be -- will come out in September of 2009.
DR. STEINWACHS: What kind of response rate are you getting on the American Community Survey?
MR. NELSON: Well, when you, if you include follow-ups surveys, it's up over 95 percent.
DR. STEINWACHS: Is it?
MR. NELSON: Yes.
DR. STEINWACHS: Oh, fantastic.
MR. NELSON: Yes. Yes. And now we sub-sample the non-respondents. But when you do the weighting, after you do the weighting, after the, you know, it becomes -- it's up over 95 percent, yes. It's a mandatory survey, by the way. This is a survey -- it's part of the -- umbrella.
DR. STEINWACHS: Yes, I understand.
MR. NELSON: It's actually, when you get this in the mail, it says you have to answer, as opposed to these surveys. So it actually helps the individual item response rates too, because I think there's some fear out there that -- Going to jail I guess is a good incentive, I guess, yes.
(Laughter)
So, and as a relatively rich dataset. It has information about a lot of topics. It has income information, job information, has a lot of housing information, as well. Disability status, work status. It's a pretty rich dataset. It has a little information about a lot of topics, that's probably a good way -- it's a general purpose survey that has a lot of information that fits a lot of agency data needs. So it has information about a lot of topics.
And the questions are asked of every person -- they're at the beginning of the survey, which is good for response rates. So and current health insurance status is a much easier question to answer than coverage over the previous calendar year.
MR. HITCHCOCK: Chuck, do you know if it includes anything on the citizenship or --
MR. NELSON: Yes, there's a question on citizenship. There's where you were born. But there's again, it's not the piddly little -- I'm not asking about status.
MR. HITCHCOCK: Right.
MR. NELSON: But, yes, you can certainly get at citizen, non-citizen, foreign-born, non-foreign born issues.
So limitations, I mean, a lot of work that's gone on, a lot of research that's gone on over the last decade has been how to customize questions for the fact that every state has its own programs, every state has it's own SCHIP programs, Medicaid programs, and other kinds of health insurance programs. So this is, being a paper survey, there really is no opportunity to sort of customize a survey for the state you live in. Yes.
MR. HAGAN: So when you say about the customization and everything, is that getting to kind of verification and accuracy, so that you're more likely to have some measurement error when you're -- when somebody thinks that they have non-group instead of SCHIP, for example?
MR. NELSON: Yes. I mean, right. Right, there's no -- well, you know, there's two things. One thing, it's not a trained interviewer, who, you know, whether even if you had a non-customized set of questions in the CPS, they do have a trained interviewer who knows about state programs, they can sort of guide the respondent. Here, you know, over half the responses are based on these mail-out/mail-back, where it's just you. It's just based on your perception of what your insurance status and type is. So it's much different. It's a much different animal. I mean, it really is a much different animal. And we're very hopeful that the data will be good. But, obviously, this is, you know, this is the first time we're doing this.
And now, there's some ability on the computer assisted follow-up, we have some ability to sort of mention state programs and do that. So there's some ability, but it's not to the extent that we do on surveys like NHIS and CPS, and so.
And there's limited flexibility for content additions. I mean, you have to go through a big, fairly lengthy process to add questions to this -- to the ACS.
MR. PETERSON: What kind of imputations are you going to do in terms of like logical imputations, you know, people over 65, say they have Medicaid, but not Medicare?
MR. NELSON: Yes, we have a set of a proposed -- I mean, we haven't actually run the edits yet, so they'll be some changes. But we have a similar -- similar to our other surveys. They'll be a set of edits that will, hopefully, clean up some of the, you know, some of the errors that we can see are errors. But, you know, there's always going to be this difference. I think those first couple years, I think we'll have to see how aggressive we'll be in changing somebody's status. Cause right now I think they're probably more based on kind of a, you know, a CPS model, CPS and SIPP model that sort of doesn't change an answer unless we're pretty sure it's not the right answer.
And, again, like these other surveys, health insurance is not a focus of the survey. It really is a survey that's asking about a lot of topics, and health insurance is now one of those topics, so.
MS. BREEN: What exactly do you ask? Is it just one question: do you currently have health insurance coverage?
MR. NELSON: It is actually a series of questions. We tested two questions. One was an overall question, yes or no to health insurance coverage. And then if you said yes, we asked you what type you had, and there was a list of types.
And against another set of questions where we asked about every type, and you had to say yes or no to every type. And then an uninsured person was a person who said no to every type.
The yes and no questions worked better in our test than the overall. So that's the question -- the question went to. There are, I think eight types that we asked about.
MS. BREEN: And that will be permanently on the ACS, then? Because I know it's a rolling survey. So that's really important --
MR. NELSON: It's permanently on.
MS. BREEN: -- because it could take 10 years to accumulate adequate sample in order to be able to look at your municipality or whatever.
MR. NELSON: Right. It'll be on the ACS until there's no longer a need for it. You know, there's been a -- now there's a demonstrated need for it. And, I mean, the process -- there's one nice thing about a survey like the ACS, it takes a while to get a question off the survey too.
(Laughter)
MR. NELSON: Okay. The other source of data I wanted to talk about were these model based estimates. Since the Bureau does have a program, mostly funded by the CDC, and it's used right now for cancer screening, cervical and breast cancer screening, to get universes and outreach, to get CDC's -- to get data for the CDC so they know how to target their efforts for cancer screening.
And so we recently released estimates for every county in the US. These are based on several sources of data, including the CPS. And I'll show you right now what the model is based on. The model is based on the CPS census 2000. Our population estimates, the Bureau's population estimates, county data on business patterns. It's based on some data we get from CMS on SCHIP participation, some food stamp data that's available at the county level, IRS data also available at the county level, and Medicaid data.
So we combine these data in a model and -- to come up with estimates of coverage for every county in the US. It's controlled to the National CPSS.
So for every county we are able to put out information on the uninsured estimates for persons -- for all persons by poverty ratio, sex and age. And for states, we were actually able to put out estimates for those characteristics, as well as race, race and Hispanic origin.
And if you go to the Bureau's website right now, you can see there's -- it was produced in the form of an interactive table. So you can go to the Bureau's website and you can put in your state, what age group you want, sex group you want, any kind of groups you want, and you can come up with the estimates for every county, in those -- in that state or in those -- in that group of states. So it's a nice little system for looking at that.
I'll give you the website at the end of the presentation.
And so here's some information from this program. You're able to look at -- So if you look at a state like Nevada, it has pretty big county, so you can see that while the CPS data showed us quite a bit of diversity among states, we can see that there's actually quite a bit of diversity among counties within states, if you believe the model.
So in a state like Nevada, there are states with relatively high uninsured rates and states where it's pretty close to the national average. So it's, yes, it's a pretty neutral set of data.
MS. BREEN: What data is the basis for the model?
MR. NELSON: The CPS.
MS. BREEN: Okay.
MR. NELSON: The CPS estimates. So it's controlled, by the national CPS estimates, and the model uses the CPS.
So future plans for -- Yes, go on.
MR. O'GRADY: May I just ask a question. I mean, there's a notion here that we have certain ways of how we think about the uninsured. And they may or may not match with the sort of policy options that will be coming out.
MR. NELSON: Right. Right.
MR. O'GRADY: So like when I look at the New Mexico data, there's big areas and big variation there too. Is Indian Health Service being counted as insurance in this type of an analysis?
MR. NELSON: No. No, it's not. No, because it's based on the CPS --
MR. O'GRADY: That kind of definition.
MR. NELSON: This does not -- right, it's based on that definition.
MR. O'GRADY: Okay. So we're liable to see something that is a big reservation in a place like Arizona or New Mexico, and it looks like people have nothing, although they have Indian Health Services?
MR. NELSON: Right. Could be.
MR. O'GRADY: Okay.
MS. BREEN: Though, we have looked at the Indian Health Service in this committee, and it's very limited coverage.
MR. O'GRADY: I just have a hunch that as this debate goes and there's very little money that the notion of thinking of the Indian Health Service as uninsured probably won't hold up.
MS. BREEN: That may be true.
DR. STEINWACHS: Edna, did you want to say anything?
MS. PAISANO: Well, no. As I listen to all the presentations before, American Indian Alaskan Natives always don't have enough sample to produce data. So with all the things that come out from the surveys, very rarely do they have any data on American Indian Alaskan Natives.
MR. NELSON: Well, the ACS will probably be the first time I think we'll be able to show health insurance data for the small -- groups.
MR. PETERSON: I think on the CPS it asks about IHS and --
MR. NELSON: Oh, yes. Yes, so --
MR. PETERSON: But it's just not counted as any type of coverage. So you --
MR. NELSON: It'd be very easy --
MR. PETERSON: You can look at it, but you're on your own.
MR. NELSON: Yes. It's very easy with the CPS to count Indian Health Service as coverage when you look at the impact when we did -- when we changed the definition. So it's still collected. And I'm assuming the other survey is probably like that as well, we collect the information and it's just all included in --
DR. POWELL-GRINER: But NHIS actually does do a fair amount with Native Americans and they're part of our summary statistical reports every year. And then we also have some special reports that we do.
Now, again, sometimes we have to combine data years if we really want to get into things that are relatively rare, like diabetes and so forth. But we do make an effort to cover that.
MR. PETERSON: Chuck, on the CPS, one problem that we've had is you have indicators, geographic indicators for certain spots.
MR. NELSON: Right.
MR. PETERSON: And then we'll run those estimates and, boom, we have a number. But then kind of as a check, we'll see what the size of that county or that MSA actually is, and they're nowhere close to each other. And so, you know, I take that to mean you're not benchmarking to that.
MR. NELSON: Exactly.
MR. PETERSON: And is the same thing happening with the SAHIE, that you're not -- are you benchmarking ?
MR. NELSON: No, actually those are being benchmarked to the population. So you're actually going to see much better, much, much stronger correlation between the true populations of sub-state areas and the SAHIE numbers. CPS, there's no sub-state controls for the, you know, for pop control. So you're always going to have issues that you have to decide what to do about that. Whereas, SAHIE, SAHIE it should be much better for that.
MR. LAND: So are the white, what appears to be white up there, are those very low uninsured rates, or are you going to have data for those counties?
MR. NELSON: I think they're very low. I think that's very low.
PARTICIPANT: There's something wrong there.
MR. NELSON: Yes, there might be an issue there because I know that Boothill, Missouri does not have no uninsured rates. I'll have to look at that, yeah.
DR. STEINWACHS: Okay. We need to move on so we don't get into too detailed level analysis.
MR. NELSON: But anyway, we do plan on putting out data on next year for '06. And pending funding support for this program, we'd like to add more categories. We'd like to run this model using the ACS's input, once this ACS health insurance data -- it should be a much stronger estimate if you use the ACS as input into the model. And obviously, we want to make other inclusions to the model too.
The one thing about model-based estimates is that they're great if you're looking at that one number. You know, they're not, you know, they're somewhat inflexible because you have to model everything. And so if you want to look at particular race or particular groups or subgroups of the population, you have to think about whether or not your model actually works for that subgroup.
And so, but for formula purposes and other kind of purposes where you just want those few numbers, models can, you know, models really do work very well. And you can really use this other information to make those estimates stronger. So --
MS. BREEN: Chuck, did CDC fund that, and are they gong to continue to fund it?
MR. NELSON: Yes, they're funding -- they're going to -- as far as I know, they're going to continuing funding the creation of those estimates that I showed you. You know, we'd like more funding so we can expand the program.
MS. BREEN: Sure. Okay.
MR. NELSON: Yes. But right now it's, you know, the funding is sufficient to produce those estimates that are up there right now.
So I'm going to talk a little bit about under reporting of Medicaid. There was recently a study called the SNACC study, SNACC project, that looked at CPS, and it's actually been expanded to other surveys as well. But the first survey was the CPS that looked at when you had a file from CMS of people who actually were enrolled in Medicaid, how well, did those people report their Medicaid coverage on the CPS.
So there's a study up on the Bureau's website. I'll just kind of summarize the results. The SNACC stands for the funders of the project, SHADAC, NCHS, ASPI, Census Bureau, and CMS, and also Robert Wood Johnson Foundation gave a lot of the funding, as well as ASPI.
And the things that I was most concerned about the SNACC project is how often do Medicaid and people that we know are covered by Medicaid, how often do they report that coverage on the CPS. And for those who don't record it, since we know from looking at the CMS numbers, you always know that there are going to be people out there who the survey doesn't capture that are covered by Medicaid, because the CMS numbers are always a lot higher than any survey estimates of the Medicaid population.
So but the issue is, is it really reporting other types of coverage or is it really -- or is it under -- or is it people saying that they're not insured at all.
And there have been state studies that kind of vary the results. And then what are the characteristics of these people who are covered by Medicaid but don't -- that don't say so on the survey. There are other ways we can improve the survey to capture these people.
So it's a pretty complicated survey and has a lot of results, but they -- but one of the major findings was that when you lifted -- when you focused on these people who were -- who had Medicaid coverage indicated based on the MSIS files, that somewhere around 60 percent of them actually reported Medicaid on the survey, another 25 percent of them reported coverage, but not Medicaid, which is actually not too surprising on the CPS. In CPS, you know, it's not a health insurance survey. We give opportunities for people who are uncertain about coverage, to report some other type of coverage, some other government coverage. You know, we sort of -- the CPS questionnaire allows -- makes it relatively easy for people who are uncertain, and there are lots of Medicaid kinds of programs out there, and that's your programs that people are uncertain about, just sort of gives them an out. So it's not surprising that a lot of Medicaid recipients have reported something other than Medicaid.
What was important to us was the 16 percent number, 16.6 percent number, that's the percentage of people who were on the Medicaid files, but they didn't report any kind of coverage on the CPS. Yes?
MR. HORNBROOK: Did you or have you looked at whether any of the states have got special intermediary arrangements, maybe as care plans, that would seem more visible to the beneficiary of Medicaid?
MR. NELSON: Yes, there's a lot of information in the full report about states and how many group states as -- there's actually a very nice full report that goes into a lot of those issues.
MS. BREEN: Is that on your website?
MR. NELSON: Yes. Yes, I'll show you the site at the end.
DR. STEINWACHS: Chuck, I think we need to keep moving along.
MR. NELSON: Yes, exactly. Exactly.
DR. STEINWACHS: We've got lots of good questions here for you.
MR. NELSON: So the things that were associated with this were length of time, you know, things that these people who didn't report Medicaid coverage, these people who weren't enrolled, you know, for all -- a lot of the previous calendar year or people who just became enrolled, people who are higher up the income level, people who are sort of 18- to 44-year-old adults. You know, people who either weren't on Medicaid for a long -- for a lot of the previous year or weren't using Medicaid services a lot, were less likely to report they were covered by Medicaid.
So we'd like to change the questions in the CPS in the future to ask about current coverage first and then ask about previous calendar year coverage, since that seems to be an issue. That seems to be -- the sort of memory issue seems to be a big issue. So asking a set of easier questions first; what's your current status; and then using them to probe about coverage over the previous calendar year will, we think, yield better results in the CPS in the future.
And there's work that's going to go on in '09. There's a field test that's going to go on in '09, that has to do with that.
So here are the contacts for further information. That SNACC report is -- it's called the Phase II report. It's up there and it has all of the results of this mass study and the other things I've talked about are all listed on the website or you can contact me or Kevin for questions.
DR. POWELL-GRINER: NHIS is also part of that and so is MEPS.
MR. NELSON: Right. We're having a meeting tomorrow.
MS. TURK: Chuck, didn't the CPS at one point ask both current and retrospective?
MR. NELSON: Yes, but it wasn't done very well.
MS. TURK: I remember it being --
MR. NELSON: Right. They weren't integrated very well. There were two separate sets of questions.
MS. TURK: And they kind of didn't work.
MR. NELSON: And they didn't work very well, right. Right.
And I will say about these - Title 13 of the Bureau is very strong. And I'm, you know, I'm somehow I'm frustrated also with these -- where we talk to researchers and sort of can't help them out, as well as we'd like to.
The Census Bureau is sort of set up now that you have two classes of files. You have internal files and you have public use files. It's not a lot of in between, you know. So we, while we make it possible for researchers to come to Suitland and use these internal files, in fact, a lot do, it's not as good for -- so it's very good for specific projects. It's not as good for kind of just to be ready for things that may come up, as is true in the policy world.
So I don't know. I mean, I'm assuming that discussions could be had at the Bureau at the policy -- the Bureau policy level that would somehow -- you know, there is some precedent for research data centers. SSA, I know has the ability to do -- because we have a data sharing agreement, they have the ability to use datasets at their facility.
So there may be something, but it has to be worked out at, you know, between the, you know, the lawyers and policy people, because Title 13, you know, we have to always ensure that these guidelines are met and they're really strong. But there really is not much of a middle ground where people -- you know, where agencies can say, sort of say, yes, we're secure. I wish there was. It'd be great if there were.
MR. HAGAN: What you had said, there is precedent in proper agencies. I mean, it doesn't necessarily get --
MR. NELSON: Yes, there is -- right, it's part of data sharing agreement. And there is some precedent, and I, you know, I just, you know, it's probably --
MR. PETERSON: But I think the data sharing is important because what -- the reason they have to do this, if I'm remembering correctly, because everybody in the data census, and so --
MR. NELSON: Yes, there's definitely both ways.
MR. PETERSON: Now, if you were to view whatever data we have --
(Laughter)
DR. STEINWACHS: Chuck, this is an offer you shouldn't refuse. You should take whatever data they have.
MR. NELSON: Well, thank you, very, very much.
DR. STEINWACHS: Okay. Now Dave.
MR. BAUGH: Now, I just want to say at the outset, I'm sort of a bridge in a certain sense because I'm now talking about the administrative data rather than surveys.
We are a research organization. And we got into the data collection business and the data preparation business for research because nobody else would do it for us. So here we are. We're both users and producers.
I'm talking today about the Medicaid Analytic Extract, which is a derivative dataset from the source data that states supply to us on Medicaid under current federal law.
The purpose of these data, the MAX, the Medicaid Analytic Extract, is to produce data to support research and policy analysis on Medicaid in some SCHIP populations. MAX is needed because the source data are just not organized to support research.
MAX consists of person-level data by calendar year, eligibility for 2004, some 58 million covered lives, service utilization, Medicaid payments. And when we're talking about the services, we're talking about roughly two terabytes of data per year. So there's a lot of information. It includes individuals, whether or not they use any services in the year, and it includes Medicaid expansion SCHIP, but due to a quirk in the reporting requirements, only some eligibility data for the non-Medicaid standalone SCHIP programs.
As I said, MAX is derived from the Medicaid statistical information system. It exists for all states and the District of Columbia. The difference is from MSIS, this inability to use MSIS for research. What we do is we transform the data along several dimensions. We do an orientation according to calendar year. We organize data for services according to the dates when the services were rendered, not necessarily when they were processed for payment.
In order to do that, we use seven fiscal quarters of MSIS data to capture the lagging eligibility transactions that may occur, retroactive determinations of eligibility, and claims transactions for payment that are flagged.
We also transform the data from an extract of a bill paying system to something that we would try to model as health events. We take initial claims, voids, and other adjustments, to create a final action or the net result of that particular encounter with the health system. There are several file types available: one is a person summary file that includes person-level data on the eligibility, person demographics, eligibility characteristics in Medicaid, managed care enrollment, and an annual calendar year summary of utilization and payments by type of service.
Then for those who want greater detail, there are the service files, four types: inpatient hospital, long-term, institutional care, prescribed drug, and then all other services. These include fee-for-service claims, managed care premium payments, and encounter records, although encounter recording is not up to the quality standard I would like. And as appropriate, these files include diagnoses, procedures, drug cards, for the individual services provided.
There are various enhancements that we have done to the incoming data. One is that we are attempting now to validate the SSNs. In Medicaid, the data we receive from states, identification has not, clearly not been validated. We are not at a point where we're able to work out an arrangement with either Census, who could do it, or SSA, to do a full validation of identity. But we're doing a partial validation against something called the SSA high group list.
We know that an SSN was on the high group list, means it may have been issued. If it's not on the high group list, it was not issued, so it's clearly invalid.
DR. STEINWACHS: Dave?
MR. BAUGH: Yes?
DR. STEINWACHS: Is this going to help match the Medicare to the new eligibles, the pieces of the Medicaid to Medicare?
MR. BAUGH: Oh, yes. I'm going to talk about that here in a moment.
DR. STEINWACHS: Okay. Thanks.
MR. BAUGH: On this slide.
DR. STEINWACHS: Oh, okay.
MR. BAUGH: We put the retroactive determinations in proper chronology and we do some improvement on eligibility mapping. And so in answer to your question, we determine, through looking at the Medicare and the Medicaid systems, that neither one could tell us clearly and accurately who is duly enrolled in both systems, which is, to me, a little bit of a troubling notion, but, nevertheless, a real notion. So we decided the only real answer to that question was to take a person from the Medicaid system and see if would can find that person in the Medicare system and find them to be eligible at a contemporaneous time point. We've done that. We do that on an annual basis now.
And the results of that linkage activity are reflected in the MAX data. So we have confirmed people who have been found to be in both systems at the same time. And so we have variables about that.
On the services side, there's a lot of interest in services beyond the level of detail that is provided in the MSIS. I think one of the things to point out here is that we have added four additional service types to the list you could get from the MSIS data directly: durable medical equipment supplies, residential care, psychiatric services, and adult daycare.
It is possible for anyone who wants to, to go in and tally up information using service cards from each record, each service record. But that is a bit daunting for most people. So we've added four types here. We've talked about adding other types. A lot of people are interested in oxygen services. We haven't done that as of yet.
We do have a maternal delivery indicator. That is not as good as one would like because it is not very easy to clearly identify exactly which women deliver babies in Medicaid in a given year. If you want to talk about that privately, we can spend two or three hours and talk about the difficulties of doing so.
MR. HORNBROOK: Does that mean you can't match the mother and the baby either?
MR. BAUGH: Not completely. It's difficult, at best.
MR. PETERSON: And the services, are you all only able to do that for pay-for-service people?
MR. BAUGH: The mapping of the services into these types?
MR. PETERSON: Yes.
MR. BAUGH: The answer to your question is sort of yes and no. And the reason is that encounter reporting is incomplete for people who are in managed care. And the coding sometimes in encounter reporting is not up to what we would like it to be.
So if it is up to the standard we would like, we can do the mapping. If it's not, then we're stuck. So that's based on what you're getting from each plan in terms of the encounters that were reflective of the services delivered under the plan.
The agency doesn't really have the mindset to put pressure on states and plans to make improvements; otherwise, we'd have better data.
MS. TURK: If they go more by electronic medical records, will you be able to bring in the ones that are covered by Medicaid? I mean, you know, because I know a lot of public health clinics are now converting to electronic medical records, and you can --
MR. BAUGH: Well, our data are not based on the medical records themselves, but on the claims for service and the encounters reported under the prepaid arrangements. So the electronic medical record may help it with coding the claims, but it won't be a direct input to this process.
Another thing to add is that if you try to use drug data, you find that national drug codes are not organized in a way to help you understand the therapeutic uses of the drugs. We've gone to two commercial vendors, First Databank and Walter Sklor Health(?) to tag each drug record with a therapeutic use, for label use of that drug. If it's an off-label use, sorry, we can't help. But if it's an on-label use, you've got the therapeutic uses. And that's under a license agreement, so we have to be careful about handling data exchanges in such a way that we honor the license requirements.
For 2005 and beyond, we are putting forth this effort to improve verification of SSNs. MSIS is now collecting more detail on race and ethnicity that's captured here. We're capturing monthly dual status for the first time. Even though, as I said, the correct identification of duals requires a link, prior to this time, Medicaid captured dual status only on a quarterly basis through deficiency.
There's a lot of interest in waivers in Medicaid, alternatives to institutional long-term care. For the first time, we are now capturing up to three observations of waiver type and a waiver plan ID assigned by the state each month for the person. And we are capturing information on 1915(c) waivers annually.
Because of the interest in community alternatives to institutional long-term care, we've been working with a group from CMSO, our sister component in the agency, and with folks here at ASPI and a group of technical advisors to build in new variables that define various types of community long-term care. So that's an enhancement for 2005.
And then MSIS begins to collect national provider identifier and provider taxonomy in 2009, beginning October of this year. So as those variables become available we will implement them.
MAX availability and access. The first question everybody wants to know is why the lag, why cannot we be more current with the data we have available. First of all, the law says the states must submit data in the MSIS system, but there are no carrots or sticks; there are no incentives or penalties for failures. So states may lag substantially in terms of submitting data initially. The data that are received may be of poor quality, and there has to be several iterations to bring those data up to acceptable quality. Or, in some cases, accept them despite the poor quality. And then, as I mentioned early, we use seven quarters of data to put everything in the proper chronology, and then we do editing and cleaning. So this -- all this takes some time.
So at the present time, the slide says there are 29 states available in 2005; as of today, it's 32. We hope to have all states available by the end of the year, with the possible exception of two states that have been, I'll call it recalcitrant on their MSIS submissions. 2006 is projected for the fall of '09, and we have prior years available for longitudinal analysis.
These are Privacy Act data because they do include personal identifying information. So access is through a privacy board review at CMS, review of a protocol, minimum data necessary criteria, a data use agreement to be signed by the party, and, in many cases, but not all cases, a bit of a fee. The Research Data Assistant Center, ResDAC, is often helpful with these requests. I put their web address and phone number up here.
DR. STEINWACHS: David, we need to keep moving.
MR. BAUGH: Yes, okay.
DR. STEINWACHS: Sorry.
MR. BAUGH: I'll try to be much quicker here. There are a lot of resources available on the web for potential users. I won't highlight some of the details here. But to add to what's on the slide is to say we have now chart books on the Medicaid data from MAX; one covers the year 2002. There's another one covering 2004, that's soon to be posted on the web. We have under the second bullet here, a very rich array of data on prescribed drug usage in Medicaid for three populations, all Medicaid duals and non-dual enrollees.
Here's the fine print. I'll include this for your informational purposes about limitations in the data. But in the interest of time, I really won't go into detail. It's in your packet. If people have questions, they can let me know.
I've included a few charts from the chart book and the drug material just to show you -- to give you a feel for some of the things we can do. Medicaid, during the period from 1999 to 2004, went from covering about 15 percent of the national population up to now approximately 20 percent of the population. So its share of the national population is growing.
This slide shows the current enrollee fee-for-service expenditures among four major types of services for four different Medicaid sub-populations. And I think it's important to highlight that for the aged, the driver is institutional long-term care. For the disabled, it's really all types of care. The disabled have the highest average expenditures for inpatient care, drugs, and ambulatory services, and second only to the aged in institutional long-term care.
Just wanted to show you one slide that shows variation in per-enrollee expenditures among full benefit enrollees. This is 2004 data. Substantial variation again across states. These dollar amounts do include premium payments. So this reflects eligibility groups that are optional that states have chosen or not chosen. It reflects the richness of services provided. But it's amazing how much variation there is. How often do you see California in the low end; not very often.
PARTICIPANT: Never.
MR. BAUGH: The next slide just shows you how much more expensive dual enrollees are than non-duals. And this is based on the verification that we've done. Typically, for each of these measures, approximately a three-to-one or better ratio.
DR. STEINWACHS: So if you could add the Medicare expenditure on the duals, it'd even be higher, right?
MR. BAUGH: Actually, we are doing that.
DR. STEINWACHS: Yes.
MR. BAUGH: We have a project now that's looking at high-cost duals. And we've done a matched link between these data and something called the base annual summary file for Medicare. And for the Office of Policy in CMS, we're looking at individuals who fall into the eighth, ninth, and tenth deciles of spending, rated from low to high, both in Medicare and Medicaid, to begin to examine what can be done in terms of policy to coordinate across both programs.
The next one just shows you some of the split between community and institutional long-term care. Not surprising that nursing facility is over half, 55 percent; ICFMR, another institutional type, that's intermediate care for mentally retarded, is over 14 percent. But then you see an array of other community based services.
This is of great interest to the group of people who are studying community based long-term care. And work is being done in CMS on community-based long-term care in support of the demonstration to maintain independence in employment, and the money follows the person more.
I just wanted to show you one slide related to drug spending. We've done a lot of analysis over the years showing that drug spending has been growing much faster than other sectors of healthcare. Through the 1990s, we were seeing, for the aged and disabled, rates approaching 20 percent. We're seeing those continue through the early 2000 period; although, there does appear to be a bit of a drop off in 2004. It's still increasing, but not at the same rate.
And this is of great concern as duals move into Part D and we begin to look at expenditure trends under Part D.
I'm almost done.
This is a slide talking about aggregate state reporting on SCHIP. I mentioned earlier that not all SCHIP enrollees in standalone programs have enrollment data in the source data we have at a personal level. They have none of the service data or the expenditures. So these reports and the related schedules from them provide information on enrollment and expenditures for both Medicaid expansion SCHIP and standalone SCHIP.
Again, in the interest of time, I'll move on. Just wanted to show you one graph. And this was pulled down from the website. It's a little hard to read, but it shows you the trend in SCHIP enrollment, ever enrolled in a year from the beginning of the program, 1998, through 2007 fiscal year. It's been growing at a pretty rapid rate, leveled off during the 2003, ‘4, and ‘5 period, and appears to be accelerating again.
Something about MAX users in your handout; again, I'll skip over this.
And some concluding remarks. We will continue, as funding is available, to build MAX for future years. We are still in a developmental mode on community long-term care. We may be adding some new variables on that as we are advised from this advisory group.
We clearly see the value in data linkages. Linda talked about the meeting yesterday at COPAFS. A great deal of interest around federal sector and non-federal groups to expand our capabilities through linking data, working smarter because money is tight in these times.
We have a great deal of interest in linking to the American Community Survey, and we'll want to work with our brothers and sisters down at Census. We are already linking to the Medicare Chronic Condition Warehouse.
We want to thank ASPI for their support of MAX over the years. And here are some contacts, mine, and a colleague, Susan Reading.
DR. STEINWACHS: Thank you very much.
MR. BAUGH: Thank you all.
DR. STEINWACHS: Want to do a 10-minute break to 3:30?
MR. BILL SCANLON: Yes, right. I think, yes, we've had this fascinating discussion. We've proved beyond a shadow of a doubt that Don and I should not be hired to keep you to an agenda. So let's try and recoup at least five minutes of the time. We'll reconvene at 3:30.
(BREAK)
Agenda Item: PANEL 2 - Data Users
DR. STEINWACHS: I already promised the opportunity for Stuart to go first. And he said he's had so many frustrations about getting access to data, I should let him go first, or some other story; I don't remember. Something like that. Let me try to corral the group here. Just give me a moment.
MR. BILL SCANLON: We're ready to resume, and we're going to have a slight change in the order. So Stuart, you have a --
MR. HAGAN: Okay.
MR. BILL SCANLON: You've got to go make an estimate, right?
MR. HAGAN: Actually, as a matter of fact, I do. We're making together these -- a bunch of estimates right now for this health options volume coming out in a few weeks. So we're getting to the deadline part. It's really getting a little bit stressful.
DR. STEINWACHS: How many weeks is that?
MR. HAGAN: It's a few weeks. It's the middle of December.
Well, I -- you know, as I was thinking about this, I'll just be very quick. We use data from pretty much all the surveys that we've heard from today, with the exception of a couple. And the ones that we focus on the most and where we seem to get the most, are SIPP and MEPS. I always get the name wrong for what SIPP is, and I apologize for that. Survey of Income and Program Participation, is that -- that's it.
Just briefly on the current population survey, we do use that occasionally, and other people in my division and at CBO use it more. But I think there is some question as to how you interpret the health insurance question just because of the timing of that whole issue. So we've kind of -- we've stayed away from it with -- where we used to really get serious about using survey data on enrollment, we don't use CPS, and we use primarily SIPP for that. And so I'll talk more about that.
We have a simulation model at CBO that is fairly new. We've been working on it for a few years now, and it's based on SIPP. And the reason that we like SIPP for this model is that it has more observations than MEPS and it has a better, you know, better set of questions in terms of timing and everything than CPS. So it strikes a middle ground that we like. And the other reason that we use it is that we have used it. And it's kind of the devil we know, and so we're comfortable with it and we do hope it continues into the future. I understand there's some question about that. It's been very helpful for us.
MEPS has proven, as I mentioned earlier when I was -- had a comment, MEPS has been very helpful for us in terms of expenditures. We're -- a little bit because of the smaller number of observations, we have not used it for enrollment data, but it is pretty much the gold standard in terms of expenditures, and very helpful to us. And there are some interactive things on there that, I don't know if they're actually part of MEPS or not. The HCUP, H-C-U-P?
PARTICIPANT: HCUP is a separate --
MR. HAGAN: HCUP, that's really -- that's a great little tool. And I think it would be great if you guys could expand that.
(Laughter)
PARTICIPANT: I'll bring that message back to the HCUP people.
MR. HAGAN: Actually, if you could all expand everything, that'd be really helpful.
DR. POWELL-GRINER: How would you expand it?
MR. HAGAN: Well, I think right now it just covers inpatient pretty much. So I think if it covered outpatient, that would be really helpful.
MR. HITCHCOCK: They actually want to present to the data council next month, because I just got a note this afternoon that we'll hear more about HCUP and where they want to go. Maybe would could lend some departmental support, whatever it is that they want to do.
MR. BILL SCANLON: They definitely are interested in expanding their belt.
MR. HAGAN: Aren't they also limited to a certain number of states? Not all states participate in those conferences?
MR. COHEN: Yes, I think there's 23 or 24 states, I think.
MR. HAGAN: Right.
MR. COHEN: So, which, you know, for inpatient is like most of the inpatient states in the country. But for outpatient it may be more of an issue.
MR. BILL SCANLON: And it's also the universe of those inpatient states, right?
PARTICIPANT: Yes. It's discharge abstract data, which is actually collected by the states and then the, you know, the HCUP people pull it together to an analytic data set.
MR. HAGAN: It's very helpful. And actually, that gets me to another comment, that just as I was listening to the presentations earlier. I think one of the issues that we have at CBO, and I'm sure at some of the other places where they're data users and not creators, is that we are not survey experts. And I think for me, especially when I was listening to these presentations earlier, I was thinking, boy, what I'd really like to hear from these guys, because a lot of what they were saying, I had heard before I was fairly familiar with -- relatively familiar with the surveys. I would have liked to hear what their criticisms are of their surveys and what their critiques are of the other surveys.
And in particular, in -- kind of in line with this idea that HCUP does, which is kind of bringing in data from various sources is, you know, one thing that we had to do in creating our simulation model, we based it on SIPP, but we had to bring in data from other sources. We had to benchmark it and things like that. And a lot of that benchmarking was done by me with, you know, with some level of expertise, but certainly not an expert at this sort of thing. And it would have been helpful, and maybe this resource already exists. But if we had kind of a central organization that could help bring together and marry these different sources of data into forms that can be most useful to the user community. And I couldn't really tell you exactly what would be required for that. But I think if there was some sort of a, kind of a top-level effort at doing things like coming up with some datasets that would have good enrollment data, as best we could, that's benchmarked, to some extent, to administrative data where we have it, and then could also have expenditure data on it.
To the extent that we could kind of create a database out of all these different surveys that we have that would bring the strengths and try to avoid the weaknesses of the various datasets that would be very helpful to have that kind of a data source in a single place. And, you know, with users, I don't think I'm the only one, we tend to do that. We have to do that. But we're doing that by hook and by crook. And it would be nice if we could, if we had some experts there who could devote a little bit of their time to help us do that, in a way.
So, but at this point, I think a comment was made earlier that we really can't -- we're not going to be changing any surveys for the health reform things that might be coming up. And, in the same way, I think the train's already left the station in terms of our background preparations for these estimates. We've largely done what we can on that. And now we're just tightening the screws and kicking the tires to make sure that we have modeling capabilities that will allow us to do what Congress wants.
And let me just finish some other things. So that's just been very helpful. And the insurance component is especially helpful in terms of benchmarking. So we've used that a lot for benchmarking purposes, and that's a very good source of data for us.
We're less familiar with NHIS. I don't know that that's because it isn't useful or just because we just haven't used it. And what tends to happen, I think, is that you get a stock of human capital that knows how to use certain datasets, and that's what you go with, because you really just don't have the time to devote to learning a new dataset. And it does take time with all these to just learn how to use it and how to use it effectively.
And then we use the Medicare current beneficiary survey, but I don't know that that's really so much what we want to talk about today. And incidentally, in Dave's presentation on the MAX data, I think it was called; we do -- we have been using that at CBO, somewhat. And so that's been a very helpful source for us.
And I should mention also that the SNACC effort has been helpful for us too, and we've used some -- even though it wasn't done on SIPP and was done on CPS, we've take -- we've learned some things from that exercise that enabled us in our benchmarking of Medicaid enrollment to SIPP.
And actually, that's one of the strengths of SIPP is that as we've been trying to do that benchmarking and everything, SIPP is not that far off of the Medicaid administrative counts, after you make the adjustments for duplicates and institutional folks that are not included in SIPP to begin with. So that's something that we like about SIPP.
And then I already mentioned that we -- getting back to the data where we have to go through some of these hurdles to get. It is a little bit difficult in that it, you know, maybe it does require another meeting at a different level. And maybe that is necessary. I'm just -- I don't know exactly what form, what would be best. But it would be nice if we had that capability there, because these are things that we can't really predict what sort of things that we're going to need. And so it would be helpful if we could have some mechanism in place that would allow us to quickly get access to data that would be helpful to our cause, so.
MR. BILL SCANLON: Thank you. Snce you're going to have to leave, anybody want to raise any questions before we move on? Okay.
MR. STEINWALD: I think the idea of the Congressional agencies sort of getting together and creating a users group has been talked about before. But I'm not sure that anything much has come of it.
MR. HAGAN: Yes. I think that we tend to get very parochial in terms of what our needs are, and we go out for just what we need and there go thy neighbor. I think is the -- (laughter)
MR. STEINWALD: As much as we'd like to talk with each other, it's a bit of a luxury when you have immediate demands placed.
MR. HAGAN: And maybe we can do that. Maybe that's the first step is that we -- that the Congressional agencies meet to talk about what our uses are and what our needs are and maybe we can then meet with the Executive Branch agencies and come to some sort of an arrangement.
MR. PETERSON: Stuart, before you go, there's one of the concerns I was going to raise with MEPS, and that is the extent to which it appears to dramatically undercount expenditures.
MR. HAGAN: Yes. Actually, I forgot about this. Joel, you mentioned this bit about how MEPS is picking up the one percent that -- spend 50 percent. Now, I had always thought that that was a -- I had heard that as a criticism of MEPS that it wasn't picking up those people.
MR. COHEN: Well, there are two issues. Actually, we just did a study, one of the people on my staff did a study looking at Medicare claims. And so you match the claims data for the Medicare people to MEPS and did some analysis of, you know, the expenditures. And it's true that there's, you know, it comes in lower than the health account, certainly, even after you adjust for the included and excluded services, et cetera.
And I think the last -- there's a paper out, which I'm sure, Chris, you're familiar with the latest recon -- and periodically we reconcile the aggregate net expenditure estimates to the national health accounts. And the last one showed about 14 percent gap there.
And the more recent stuff that we looked at tended to -- we indicate that there's -- there are two issues there; one is that there's general under reporting of, you know, we're going to household response, they're trying to tell us about, you know, all of their, say physician visits, et cetera. So if they had five visits and they tell us about four of them, you know, you've got a 20 percent undercount right there.
So there's some -- this research suggested there was some general under reporting along the entire distribution.
And then at the very tail end, there's like, you know, cases -- if you look in like the claims databases, you'll find, you know, these cases of like two million dollar a year cases, et cetera. And we don't get those in MEPS.
MR. HAGAN: Because you just don't have a big enough sample.
MR. COHEN: There's not a big enough sample, exactly right. And in some cases there are things that probably wouldn't show up because they're never going to come into scope. You know, it'd be like, you know, a premature baby who ends up being in the hospital for the entire year. And that's, you know, the two million dollar case. Well, we're not going to get that because that baby is never going to come into the noninstitutionalized population.
MR. HAGAN: Oh, yes.
MR. COHEN: So it's both a little bit of under reporting across the entire distribution and some, you know, on the very tail, you know, not picking that up. But in general, behaviorally, doesn't make a whole lot of difference.
MR. PETERSON: Well, I think the issue, though, is in terms of level, right? And which is separate from matching aggregate totals with the national health accounts.
But, you know, I'll tell you about the project we did a few years ago. And, Joel, you probably remember our conversations. I think this was more with Tom, though. But we tried to update our actuarial evaluation model. And the problem was that with -- if we used MEPS, we could never get close to an average premium, so to speak.
So, in other words, you have people in MEPS, they've got expenditures paid by ESI, and so you say, okay, here's what I'm going to use as my basis. And then we make all kinds of adjustments for institutional lives and, you know, admin, and insurance and all this stuff. And you think that at that point you're average -- you can get close to an average premium. And even after we did all of that, you know, working with Tom, we were still 30 percent shy after a lot of adjustments. And so it's just, you know, one of the -- one of the things that we love about MEPS is because it has this expenditure information. But if the levels are low, then I think it's just a caveat that you all would need to consider if you're looking at the expenditures of healthcare in --
MR. HAGAN: Well, we're -- this is the thing where we have to benchmark. And it's not just level. It's the shape. You know, as you were pointing out, it's the shape of the distribution; you have to get that right. And we've done our best to adjust the shape of our distribution and to change the level to kind of match up with the appropriate national health accounts level, taking out the cost of the institutionalized and all that kind of stuff. And we relied a lot on some data that we got from you guys, which was helpful.
MR. COHEN: Yes. Well, I actually think the shape of the distribution is probably not bad, it's just low. You need to bump it up. So, I mean, you know, you can take these reconciliation papers and, you know, if --
MR. PETERSON: Well, but that's not quite it, though. But I think Tom has been working on something, and that's what I was going to raise is --
MR. COHEN: Well, he's working on putting some cases out in that tail, is that what you mean?
MR. PETERSON: Right. So that is something that might be useful for us as end users, even if it's kind of a draft basis, to said, look, we recognize there's this issue here, and we've had smart people look at this. So that when you run these numbers, you're not going to get two low cost estimates; although, maybe that might be useful in the end.
(Laughter)
MR. HAGAN: We go for the truth; you don't go for --
MR. PETERSON: So, anyway, I raise that because that might be something to consider if we could, you know, have a conversation about that, or --
MR. COHEN: Yes. Well, like I say, Sam did, you know, a lot of research, you know, preparing the Medicare claims. And, you know, we have some ideas as to what to do there. And Tom certainly has been working on that, so.
MR. PETERSON: But I do think it has to be a caveat to expenditure, you know, analyses on that.
MR. O'GRADY: Can I just say, historically, I mean, this was one of the big, you know, food fights 13, 14 years ago. That the administration -- it was NMIS at that point, came up with basically kind of a premium estimate for this set of benefits for this population. And then CRS and CBO had worked together on what they thought the -- you know, working off different datasets. And there was this 15, 20 percent gap. And, of course, when it got into a cost estimate, it was billions. But it's sort of, you know, it is one of those things that's liable to become a real rub later on if there's not, you know, good techniques to figure out how to --
MR. HAGAN: Well, and we think that in our simulating we have benchmarked it appropriately. But certainly now that we're talking about it a gain, it makes me nervous. It makes me want to go back and check it.
(Laughter)
MR. HAGAN: But I do, I need to take off. But I do want to say thank you to all of you guys who are doing this data creation for us; it's extremely useful. And we always want more, but we're grateful for what we get also. So thank you very much.
DR. STEINWACHS: Stuart, thank you.
MR. BILL SCANLON: Let's now turn to Gillian and Jason. I guess I put you into the category of both potentially data producers as well as data users, though I'm not sure -- if we had problems accessing census, we may have more problems thinking about accessing some of the data that you may use, so.
MS. HUNTER: Yes, I was cringing a little as people were talking about some of those areas. I feel like I'm always the ultimate consumer; I want everything, and people have been so good at this. And I have to say that I started working on health reform almost 20 years ago. And to give you the order of magnitude, we were talking about premiums of about $3,000, and that was to pay for an entire family plan.
PARTICIPANT: So what happened?
MS. HUNTER: So things have changed a little bit.
(Laughter)
MS. HUNTER: I'm also thinking that health reform will be here for quite awhile. As slow as the data process may be, it will be here in time to answer very important questions as we go forward.
And maybe -- I'm going to just jump and kind of segue back to this conversation we had, because we had a similar thing. And I was thinking in line with Stuart, that it would be nice to not necessarily to have a -- I mean, it would be great if people put a dataset together. But the problem I have is, over the last 20 years, the kinds of questions we've been asked requires so many different things put together in different ways, that I don't know what I'm going to need two years from now. I, you know, we've looked at the Obama plan. But when Congress gets hold of it, you know, and they need enough votes, it could swing around in very unpredictable sorts of ways when they compromise and take pieces from here and there.
So I think if you were to go in that way, we need to also have some flexibility and have people who work with the matching different datasets to be able to get us expertise on how to do it, because, after all, I work in the Office of Tax Analysis. So we're really good at doing tax things. But we're not quite as good doing the health thing. Although, I have to say we have developed a staff over the years because of the need to do that.
And on this particular issue, so far we actually -- I read the CBO paper and noticed they had bumped up, based on A-R-Q, ARQ. And so we called Tom Zeldin and then we got the papers and we did some adjustments ourselves. And that has been so helpful. And just thinking back to 1991, I guess, when I started, and, you know, the data problems we had then. And now when I send out an e-mail and said, what would you guys like me to bring up at the meeting, and I had two really minor questions, we still have big data problems that no one's going to have, but you won't be able to address those either on the employer dropping issues. And I'll get into those in a minute.
But back to these sorts of things. And also, with the SNACC and the whole question of measuring the uninsured over the different datasets, the fact that you -- that people have been doing these papers and you have been able to get hold of them, I mean, it's so much easier now than it was before.
And the MEPS website is just -- he said there were like 200 tables. And I know that's true because I think I've gone through almost every one of them at different times. And so I'm just so grateful at all of the progress that has been made over the years.
So anyway, if we could also just have more of a central place that maybe said, these are these issues or somewhere where we could go and have a little informal thing, oh, by the way, there's a problem between this dataset and that dataset, or you need to make this adjustment or is it slightly different population or whatever. Because I'm sure you know a lot of things. And if it isn't in a formal paper or if we don't find out about it from hearsay, we might be missing some things that are very important that you've spent a lot of money on developing and we just don't know because we don't have the time.
I mean, we sometimes have to turn things around in a matter of hours or days. You know, you would just cringe to think of some of the things we do in the process.
(Laughter)
MS. HUNTER: But I won't go through all of that. So, anyway, I'll just follow Stuart and kind of skip and just talk about the data sources that we use.
First of all, since we're the Office of Tax Analysis, we have to hook everything into our tax data. And we do primarily the revenue estimates and then other analysis that goes on. And -- because the employer tax preference for health insurance is just so huge, such a big incentive; we get pulled in even if there aren't necessarily tax provisions, such as under the Clinton mandate world, we were estimating what would happen to the tax revenues as a result of this.
And then, of course, discussions of caps or tax credits or the standard health insurance deduction, all of those health and savings accounts, all of that. And when we're doing all of this, of course, we have to be able to put together the employer market, the non-group market, the new tax proposals, the public health insurance, and then we have to be able to say how are all these going to change under the proposal.
So now we're looking at the Obama plan. And one thing that we haven't had to do in the past is to think about, well, if they do have this new pooling mechanism, what are those premiums going to look like, and how do we calculate that under the various alternatives. And as you change these tax subsidies just slightly, you can get a whole different behavior into this pooling mechanism.
So now, just to give you an idea, so we have a tax dataset and we've got to put all this health stuff over there. We actually have several different micro-simulation models that we've used over the past years, and we're developing a new one. So we're not necessarily just doing one at a time. And we also have to look at income distribution questions. And those have to be combined with all of the other tax provisions.
So sometimes we're looking at just health pieces and sometimes we're looking at the whole tax package, which makes us have to jump through a few different hoops. So basically, we have the tax data, we have the CPS looking at coverage, and we know that all the problems we have with that, so we've looked at other things too; sometimes we've used SIPP to look at the length of the spell in the past.
And now we're using a lot of the MEPS, and that's because we're -- we have a separate -- we're developing a separate employer dropping kind of component of our model. So we wanted to look at who has employer, who has an offer but doesn't take it; were they eligible or not; so all of those questions are really crucial. And then looking at estimating what kind of premiums they might have.
So we've been using the expenditure data to do that. So we have kind of the same sorts of questions about that, that Chris does.
And then we have used the NHIS to look at the non-group market and estimate what premiums might be for people with that, because we needed to do that with a different, new tax subsidy for non-group market.
In the past, I've used the RWJ when we have the employer survey, because that had all the wages of a person. So if we wanted to look at who that person's working with. And that's the one piece that we're kind of missing right now. And we are trying to use some tax data now to look up what kind of the wave structures are for different firms. But, there again, you'll have some other issues that -- firm size and industry that's reported in any of these surveys are going to be different than the firm size in industry as reported by a tax filer.
DR. STEINWACHS: Gillian, you mentioned RWJ. Is that the --
MS. HUNTER: The employee health insurance survey under the RWJ.
DR. STEINWACHS: Oh, they do the tracking --
MS. HUNTER: The tracking, community tracking --
DR. STEINWACHS: Yes, okay.
MS. HUNTER: -- but it's, you know, it's fairly old now. I think Lowen(?) has got it in its current model; I'm not sure, but.
DR. STEINWACHS: Okay.
MS. HUNTER: The point being, that was really nice because at the -- and it was hard to get people to move from the CPS; we know it really well.
DR. STEINWACHS: Yes.
MS. HUNTER: So it's just -- and it takes a long time for us to develop these simulations. And things just keep changing on us, so, you know.
So, anyway, the two minor requests that came from my office was one on the MEPS, for example, are these different datasets being able to identify tax units might be nice. And now that some of them are collecting tax information, that might be pretty easy to do. There's health insurance units, and we can look at those in the family structure as a proxy. So that's what we've done in the past.
And then the other is, if you have more than -- and this may be -- I didn't go and check, and I haven't used it myself, so I don't know if it's quite accurate or not. But sometimes when there's multiple insurance to identify on all the different datasets that are collected from the original survey, but just to identify which has the main source of data. Main source of insurance is good, because a lot of times we have the proposals and there might be overlapping.
Another issue we've had to deal with was, you know, in family coverage, you might, in a family, you might have two employer policies.
DR. STEINWACHS: Yes.
MS. HUNTER: So we have to deal with all of those sorts of issues. And then, you know, the world changed from single and family to single, two person policies, and family, or there could be other hybrids too.
DR. STEINWACHS: Yes.
MS. HUNTER: And trying to change in that world. And then if you have a different, say a tax credit, is it going to be based on family? So does the two classify that? And then you have to think about all the behavior. Well, if all of the sudden you have a tax credit instead of an exclusion, is the behavior going to change, where firms will stop having these two, because if they put the two in with their families, that changes the premium. So now these people won't be hit by a cap.
So I just want to kind of give you a flavor of some of the things we have to model as we go along. And it's really nice because, for example, MEPS has given us, not only the single, two person, and family plan, but the blended of the two person and the family. And so all those extras that people have done have really been used.
So I want to thank you for all of the hard work. And we've had really good turnaround when we've had questions. And, although I have to say, I mean, we did have this discussion, we don't have the time to send our programmers to Suitland because we need them in the office to be doing the other tax stuff. So we would love to get into that if it becomes available in the future.
But -- and we don't ask as many questions because we know how hard it is. But we've gotten great turnaround. So we really appreciate that. No, it's been very impressive, so. So I think if there aren't any questions, I just wanted to kind of give you a flavor of the issues that we deal with as users.
MR. BILL SCANLON: Very helpful. Thank you. Bruce, you want to talk about the other branch of government?
MR. STEINWALD: Sure.
MR. BILL SCANLON: One you know well.
MR. STEINWALD: The Legislative Branch. Well, Stuart kicked that off. I had a one-pager that I brought with me that was supposedly passed out. If anybody didn't get it, then I'm sure we have more.
GAO's healthcare team has a pretty diverse agenda. Anywhere there's a federal dollar spent on healthcare, eventually GAO has a role in identifying whether it was well spent, over spent, and what it was spent for. And that takes us into the various program areas that are listed on this page.
In addition to those domains and different people cover them, we have within a healthcare team what we call a research support group, whose function is to provide programming, but also data identification and management. And they prepared for me this sheet that just generally gives an overview of the kinds of data that we use in the different areas in which we do research for the Congress.
I also asked them, with a health insurance focus, to identify some areas where they would like to see expansion, based on requests for information that we've had recently, that we've had difficulty fulfilling. And the three things that they put in the back, at least two of them have already been talked about today. Apparently, they're the NHIS used to collect more information on disability or it did periodically; it hasn't done so in some time.
DR. POWELL-GRINER: We did have a supplement a few years back. What we're doing right now is we're actually testing two sets of questions that have been developed by the Citigroup effort. And those started in October and will continue through March of this coming year.
So I would not be surprised if in the future we didn't try to put something on disability back on the NHIS.
MR. STEINWALD: Okay. I'll tell them you're working on it.
DR. POWELL-GRINER: Yes, tell them to send money.
(Laughter)
MR. STEINWALD: And then some other things I won't go into. I personally live in a claims world, Medicare claims principally.
DR. STEINWACHS: How does the world look inside Medicare?
MR. STEINWALD: Fom a data standpoint, it's tasty. I mean, you know, there's no shortage -- there's no volume problem. And it's -- I'm of a generation where we used to take cards down to the computer center to run jobs. I'm just amazed at what can be accessed and then investigated at someone's -- on someone's desk. But that's another story.
DR. STEINWACHS: You're showing your age here.
MR. STEINWALD: Well, yes. Now, claims data are -- analyzing claims data are a little bit like the drunk looking for his keys under the streetlamp, you know, because they're so available, but they, of course, don't tell you the whole story. And we often do need to supplement them.
In addition to making use of the datasets that are available to us, GAO will do its own surveys and other data collection activities. Bill Scanlon, who was the managing director of the healthcare team until a few years ago, mentioned also something about our data access and our confidentiality procedures. And I think at some point it might be good to talk a little bit about access.
Let me see if there's anything else I wanted to talk about.
GAO does studies at the request of Congress. Those requests either come in the form of a letter or they're often in law. If they're in law, they're not really a request. We call those mandates.
But typically we have a little bit more time to do our work than, let's say CBO or CRS does. And so if we need to obtain data in order to do one of our engagements, if the engagement is six months to a year, that gives us a little bit more time to acquire data than if CBO is being asked to do a budget estimate over the weekend. And so maybe we have a little bit less of a feeling of urgency about our data issues.
If you talk to GAO's lawyers, and there are plenty of them, they have a very clear view of GAO's access to data, from the Executive Branch, and, in fact, from private organizations that accept federal money for providing health services, let's say, to its beneficiaries. They have a very clear view and it's a very simple view. We have access just about to almost anything that you can think of. We have subpoena power if organizations fail to provide data that we believe what we need in order to fulfill a Congressional request. We don't use that very often. It's kind of the stick in the closet, and we'd like to leave it there. But it has been used. And I think under Bill's watch, there was one with a pharmaceutical company, as I recall. Isn't that true?
MR. BILL SCANLON: Pharmaceutical company and hospitals and nursing homes.
MR. STEINWALD: Yes. Well, there you go, so.
MR. BILL SCANLON: Not yet to the stage of using it often, but more than one example.
MR. STEINWALD: More than one example. And yet there's a difference between, you know, our lawyers' view or having access to data and what you might call realized access. Now, I mentioned earlier that we can get, readily get, Medicare Part A and Part B data, and we have our own computer access to those data files.
Medicare Part C and Part D data, different story. And it's largely for reasons that I don't need to go into, and I'll be glad to if you're interested.
We have access to records of the public health service agencies, let's say, the Food and Drug Administration, National Institute of Health. Actually obtaining those records can sometimes take months and months and months and iterations and iterations. So there is an issue of access that affects GAO, as I'm sure it does the other Congressional agencies.
Looking forward to the 111th Congress, with a Democratic administration in power and both houses controlled by that same party, one might expect that things like access issues would become less issues than they have in the past. And certainly if healthcare reform measures are going to be actively debated in the Congress, my guess is that Congress will expect that the agencies that collect and maintain data will make those data available to the Congressional support agencies in order to do their analysis.
And Mike mentioned earlier if it becomes known, if this is, in fact, what happens, that there's a dataset that's maintained by the Census Bureau that wasn't made available in a timely way in order to do an analysis of a legislative proposal, that's probably, I think the way you put it, it could have an impact on their budget.
And without wanting to be cynical about it, I guess there -- as Bill said, you know, we should have -- there are certain things that we should have been doing 20 years ago. But then if there are resources -- and I have to admit I'm impressed at what I heard today about the data resources that are available, many of which I had not heard of at all, I'm going to take your PowerPoints back to my colleagues at GAO, and maybe they will realize that there's more capability than they had thought in the past. And I think we're just like CBO and CRS; we're sort of captives to what we have done yesterday, seems like a logical thing to do tomorrow.
So there do, indeed, appear to be some valuable resources. And the extent to which they'll be deployed in the upcoming healthcare debate, I think remains to be seen.
It does seem to me that there's a foundation for wanting the Congressional agencies and the Executive agencies to work together. That's something that one could have always said in any year, but maybe this year even more so as the 111th Congress gets underway.
That's all.
DR. STEINWACHS: Just I started thinking about taxes – I was thinking to ask a tax question.
MR. BILL SCANLON: No. Well, actually, I want to sort of confess about sort of my sort of tunnel vision sort of working in healthcare. And the last time you talked about when you got involved with health reform, and the last time I was in the Treasury Building, we just were able to walk in off the street, you know; and those days are past
DR. STEINWACHS: You can do it. You'll be shot, but you can -- there's an access problem now.
MR. BILL SCANLON: But I hadn't realized was that Jason was going to -- from a different office within the Treasury and had a different perspective. And I didn't mean to sort of go pass you up and move the group. So I want to give you a chance to add sort of your perspective.
mR. STEINWALD: Unless you had --
DR. STEINWACH: No, I was back to Treasury again.
MR. BROWN: See, Gillian and I work in difference offices, and --
DR. STEINWACHS: Do you have access to each other?
MS. HUNTER: I tried to hire him, but he wouldn't -- I mean, not to work for me, but alongside me.
MR. BROWN: We're allowed to talk under -- depending on the different supervisors we have.
Yes, I actually don't have a lot to add. I think the discussions has been sort of right on target with my thinking.
I just wanted to underscore how useful MEPS is. It's enormously useful for our work. We have to respond to a lot of requests on, you know, fairly short notice on primarily non-tax matters, public insurance expansion and health insurance regulations. I work in an office with a lot of people who don't work on health.
And I know that we're able to answer these kinds of short-term questions a lot better and a lot more convincingly than people who don't have the luxury of using a dataset like this.
But that's really about it. I don't have a whole lot to add. I guess we've been interested in some of the health insurance regulation issues, especially around the individual market, and a lot of that is -- some of the individual market depends on the state regulations. And I think the state level identifiers aren't publicly available, right? And that's, you know, that's our sort of, you know, that's the wish list of being able to use -- to be able to match states to individuals more easily.
DR. STEINWACHS: So sort of the question I have, which actually crossed over what you said. Many times in the simulation models and forecasting, you probably have a sort of standard approach as to how you stretch out the future years. And I was just wondering whether or not the concerns that certainly are on a lot of peoples' minds, that we're going into a period that may be very dynamic, very unstable, is something that at this point concerns any of you in terms of how you would do forecasting maybe in the near future. And the answer may be no because you may take sort of a standard approach that says we assume, on the average, certain kinds of things happening. But I know that there's also, you know, as people talk about this, that concern that we may be going into a very deep recession and what that scenario would look like, and it could be protracted versus, you know, other scenarios set out there.
So I was just curious whether in the tax area, you know, those sort of shorter term issues come up and, you know, GAO, CBO, others, is the short term of a particular concern as you think about what you're doing?
MS. HUNTER: Well, the economic estimates are developed by Detroyco(?) which is a group of Treasury OMEs.
PARTICIPANT: CEA.
DR. STEINWACHS: Yes.
MS. HUNTER: CEA, so --
(misc comments)
MS. HUNTER: So the Treasury follows the economic assumptions that we're locked into. And then there are other things that we have to look at for the health that might not be a part of the Detroyco. So we would look at, say the national health accounts and things like that to see we're going. But the financial crisis now, obviously, would be hard for us to say, well, we know how it's going to affect health insurance premiums, so.
MR. BROWN: But are you also asking about reform itself, depending on how the reform takes, we don't really know.
DR. STEINWACHS: Yes.
MR. BROWN: Like so if you allowed cross-state purchase of insurance and, you know, with standard deductions for health insurance, you know, what's that going to do to the Massachusetts experiment and people can buy insurance in Mississippi.
DR. STEINWACHS: That kind of interaction level.
MR. BROWN: Yes, I think not.
MS. HUNTER: Well, when we're doing a revenue estimate, it's over a 10-year period.
DR. STEINWACHS: Okay.
MS. HUNTER: And some of our modeling, we kind of do like when it would get to full effect. And then when revenue estimators come in, they have to come and estimate how it would affect each of those years. So they wouldn't necessarily have it going to full scale right away. It would be a phase-in period. So they do have to take those things into account.
MR. STEINWACH: You know, the things that are popping up in the news and whether or not they're really true trends or not. There was one news item about fewer prescriptions being filled, fewer doctor visits occurring because people are starting to feel the impact and they're worried about their finances, so they're not going to a doctor, doing some of those things as much.
You know, certainly another that says employers this year are really starting to adopt, you know, the large deductible health plans. Where there hadn't been much of an uptake for quite awhile, but now the health savings accounts, and, I mean, the high deductible plans are really -- and so I guess what I was getting at was sort of the dynamics as you think about what is happening to the economy, possibly, what that translates into in the health sector, possibly.
MS. HUNTER: And it can affect each of those levels. So it could be that if it's something major – it could be something that if it's the premium growth is going to be different, then the health accounts are going to be probably looking different. And then if it's some factor that isn't in this, then the person who's actually doing the relevant aspect would have to take that into consideration.
And I should say, I'm not on the revenue estimating staff. I do all the non-revenue estimating analyses.
DR. STEINWACHS: Are those easier or harder?
MS. HUNTER: Well, it's less predictable. And they -- actually, they have done some too. So they also were looking at coverage, how many people would be covered through the thing. And that was a key thing that people have been looking at over the years.
DR. STEINWACHS: Because a downturn in the economy, you would expect maybe a downturn in --
MS. HUNTER: Well, I'm not saying so much about the economy, but just in general under any proposal. That sort of thing.
PARTICIPANT: So you're asking questions about what's going to happen with the health sector and how the economy impacts it. So my group comes out with 10-year health spending projections each February. And we'll have a set that's coming out in a few months and so then I'll know what's going to happen.
DR. STEINWACHS: They might not know who your group is. Who's your group?
PARTICIPANT: The National Health Statistics Group and the House of the Actuary, CMS. So I don't have a lot of information for you now about what we think going to happen. But you can think about what's happened in the past, or economic downturns, and how the health sector relates. It tends to not move as much like the rest of the economy during cycles, particularly when you talk about what's going on on the public side and public programs, Medicaid, and so forth.
On the private side and employer side, a lot of times things are locked in well before the economy turns around. So typically when we're modeling, using economic variables and so forth, we build those in with lagged effects. So slow downs you see in '08 and '09, tend to show up in later years.
I will say that -- and we'll come out with our historical estimates through 2007, in January, early January. I will say that there are some unique features going on right now in the health sector that maybe haven't been there at other times and other recessions. And a lot of that is coming out in some of these news reports when you start hearing about how much it appears people are cutting back on their healthcare use, whether it be filling prescriptions, switching the types of prescriptions, moving across tiers, the types of visits that are occurring, how often they are visiting. Then a lot of that's tied to the number of people with or without insurance.
So, you know, we haven't been in this situation since 2001, and then you could go back to '91, to the prior time. So there are only a few data points over a couple of decades to really look at what the impacts are. But there are definitely some unique things going on right now compared to some of the prior recessions.
MS. BILHEIMER: Just to add to that. Eve might want to comment about the release data --
DR. POWELL-GRINER: We are seeing people delaying care more than they were in the same quarter in 2007, and also just simply not getting it at all.
MR. BILL SCANLON: Chris.
MR. PETERSON: All right. Chris Peterson with CRS. And I'm here with my colleague, Paulette Morgan. So she's worked on these issues as well. She's mostly focused on Medicare nowadays.
I just want to go back to the data matching thing. If he was talking about helping where links already exist, like MEPS and NHIS, it seems like there's already support within those agencies to help with that. If he's talking about something like matching NHIS to CPS, I'm not sure I want to get into that. So I'll just raise that as a cautionary note on that, because we actually paid a lot of money to have somebody try to do something along those lines.
And they had great models and it was very fancy and complicated. And at the end of the day, we decided it didn't meet our standard. So I just raise that as a cautionary note.
In terms of background, most of you probably know, but it usually helps to tell a little bit about CRS. That we are confidential experts and educators to staff and members of Congress on policy issues and confidential legislative consultants is what I call it.
So when Congressional staff are developing legislation, the way that I portray it is we often get to come up behind the backs of people at the poker table and they will ask us what we think they should do. And we sometimes get to see everybody's hand while, you know, acting as if we're not going to tell what the others have.
So it's an interesting position to be in. But there are times when, you know, we're able to tell things that come out. So I think there is one thing that's instructive that occurred recently with SCHIP, the Safe Children's Health Insurance Program, and the effort to reauthorize that.
So, you know, the committee staff knew I was a data wonk, and the SCHIP formula's based on CPS. And so bipartisan, bicameral, the committee staff asked me, you know, it was over recess, and we went to some senator's hideaway office in the Capitol. And they wanted to know the details about data and estimates and the uninsured and all this stuff.
They were engaged, very interested. And, you know, it was an opportunity to educate them. And, for example, one of the things that is used in determining how much states get in SCHIP money, is the number of low income, uninsured kids which is from the CPS. Unfortunately, at this point, the only sources of information for them. So fine, that information's there.
But -- and they're used to thinking of margins of error, you know, in terms of polls, right? So I said, well, let's take the state of Vermont, for example; 5,000 kids, low-income children, are uninsured, plus or minus 4,000. So that's a margin of error of 80 percent. And so this is the basis on which we're allotting billions of dollars in SCHIP.
So teachable moment.
(Laughter)
MR. PETERSON: And in the SCHIP legislation, that was twice vetoed by the President, they opted not to use the CPS for their source of data for allotting those funds.
But it did require the secretary of HHS, in collaboration with the Secretary of Commerce or Director of the Census, I forget which -- once the new ACS estimates come out, that the Secretary of HHS needs to collaborate and figure out which of these two is better for state level estimates of the uninsured.
So that might be something. I'm not sure if it's gotten up the food chain. But something to be aware of because, you know, some folks are talking about this legislation might re-emerge as it was, in a new creation.
It was also an opportunity to help them understand what data cannot answer, and illegal immigration was one that came up. Another one is the number of uninsured kids who are eligible for Medicaid or SCHIP. Because a lot of the staffers think, oh, just run me the numbers. How many are eligible? Well, you can't ask a person whether their child is eligible for coverage, because, ostensively, if they're uninsured and the kid's eligible, you'd think they'd have them enrolled.
So you have to explain to them, you have to take smart people who take this data and change things, you know, make estimates, look at state policies and all of that. And so that was helpful for them as well.
And so there are a couple things that came out of the SCHIP debate that were instructive and that they were suddenly well-informed. One was the administration had come out and said, you know, everybody's been saying that there are six million uninsured kids who are eligible for public coverage. It's really one point one million.
And it was at that point where, you know, the staff, staffers called me from both sides and said, what is going on here. So it was the opportunity to explain to them, you know, in a memo form, here's where these estimates are coming from, here's what people are changing. And so that was useful as well. And another thing is the administration also came out with an August 17th, it's known as the August 17th directive, that before you're allowed to expand your SCHIP program you have to enroll 95 percent of eligible kids. And again the staffers knew that that wasn't something that was -- something that you could take a federal data source off the shelf and run it. And so it raised questions about the validity of standards like that.
So my point is merely that to raise the issue of, even on the Hill, something as technical as data, staff care, they realize its importance.
And so let me talk a little bit about what we use for our survey selection in our analyses. My first report that I had to do when I came to CRS was to look at all the federal data sources for health insurance and to evaluate them. So I don't have that kind of inertia of, I've always used this one, so that's what I'm going to do. So, fortunately for me, I'm in a position where I'll use whatever works.
And so CPS is the default choice for several reasons; one is that it's what everybody knows. It's your 46 million. It provides a state level information. And it's the primary source for income and poverty, as Chuck talked about. So that's a very strong link.
You know, some of the draw backs are you really don't know what insurance you're measuring. Is it full year uninsured? Is it point in time? So even though the question says it's uninsured for the full year, we all kind of act like it's point in time uninsured.
And one thing that people tend to use it for is long-term trends of uninsurance. And we tend to not use it for that because there have been so many changes in the survey. And, you know, smart folks have gone back and tried to make the adjustments along the process. But we haven't felt comfortable, you know, just jumping onboard and pulling those numbers straight off the website as they are. So, you know, I'm just raising limitations.
But let me also say that the help that I get from these folks is phenomenal. And without them, we could not do what we do. And they help us in so many ways do our jobs a lot better. You know, I contact Chuck and, yeah, I whine some, but he also helps me get a lot of information. And, Joel, you know, the folks at AHRQ are great. And then, you know, NCHS, you guys are doing stuff for us as well. So I do want to mention that.
MEPS is probably our secondary because it does the monthly uninsurance. That gives us a lot of detail. And, of course, the utilization expenditures, and I already raised the concern that I have on that. And it's also very user friendly, everything's well organized, so that's good.
But MEPS, as Joel mentioned, it gives you a lot of information. But there are a couple of missing pieces to the puzzle. And so those two pieces that are missing out of the puzzle really stand out, in spite of the fact that it provides a lot more information as well.
So those pieces of the puzzle are, when you look at the household component, you don't have plan characteristics. You don't know what people face in deductibles. And there are good reasons for that in terms of how are you going to get that and it's going to be good. But, you know, we're at the point where you can take MEPS and you can almost do a lot of really cool stuff. But that is one big barrier.
MS. JACKSON: Would you go back and say, what was it you were missing? I just missed --
MR. PETERSON: Plan information.
MS. JACKSON: Plan.
MR. PETERSON: So, in other words, I'm enrolled in a plan, what's my deductible? What's my co-payment? You know, are drugs covered? Am I in the non-group market, and I don't get maternity benefits?
MS. JACKSON: All right.
MR. COHEN: That's the part we have a proposal to address.
MR. PETERSON: Oh, yes, that's right.
MR. COHEN: Because you don't need the link sample to do that. We can get it from the policy booklets and going to the household respondent. So we have a proposal to do that.
MR. PETERSON: Okay. Well, put me down as a supporter of that.
Another limitation, again, it's like you're almost there, this is the great place to get this stuff is, you don't know the employer's contribution to the health insurance coverage in the household component. So, in other words, you have this separate employer component, and that tells you what the employer's contributing over here. But when you interview these individual people, you don't know what their employer's contributing. So that would also be helpful.
But it's the lack of the plan information that you really can't get at underinsured in terms of how a lot of folks have called it, because they've used the size of the deductible relative to income as underinsurance. And if you don't have that information, then you don't -- it's not there.
NHIS, we use it for the health information, as I talked about. Have you ever had cancer? And then putting that together with health insurance.
I personally find the NHIS is most useful for longer trends. And can I mention what we've asked you all to do? So knowing that that data source is good for trends of uninsurance and I know I got this e-mail from them saying we're releasing our report on insurance coverage in 1963. And so I said, well, can you guys figure out and kind of show us what has happened over the long term, so they are going back to about then to begin with to do estimates of uninsurance and insurance coverage.
And simultaneously working out how health insurance itself has evolved over those periods. And I think that'll be very useful. But that's another example of how, you know, we're able to talk and engage the folks at the agency levels. So that's helpful.
SIPP is monthly, like MEPS. It has some useful information, and I've used it when I absolutely have to, which is assets, that's the big thing, because I don't think assets on MEPS is public.
PARTICIPANT: No.
MR. PETERSON: So that's when I use SIPP, because it's a real bear to use and poor documentation is an issue.
The ACS could be very good. I mean, those estimates come out in August. So we don't know, at this point. It will provide more geographic specificity. Although Chuck said, you know, it'll meet your -- what'd he say? -- meet your small health -- small area health insurance needs.
Well, the fact of the matter is, we will not have access to the full sample. You have to go to Census in order to get that stuff. And so what they will be releasing is kind of a redacted version. And as a result, you know, if I get a call from a member and he says, I want to know how many uninsured Hispanics there are in my district, I'm not so sure that with the data available I would get that. I might have to go out to Suitland to do that. So that is one caveat on that.
MR. NELSON: The ACS is a different kind of public use file. Because CPS, when you get the public use file, you get every record. You get every, you know, every -- the suppression of high income. You know, there's some things we do for disclosure reasons. But you get every record. The ACS, because it's just so large, there are disclosure issues with giving people the whole file. It's a sub-sample. So if you get the public use file, it's a pretty small sub-sample of cases. And but it's for disclosure reasons.
MR. PETERSON: That Title 13.
MR. NELSON: So if you have, you know, so we'll probably do a lot of tables because of this issue, because we know that, you know, for a lot of things the only way you can get them is through tables. And the ACS, if you look simply at subject areas, there are lots of tables, and lots have been crossed by race and age, and, you know, however it is that you want for small geographic areas. But that's, you know, the public use file is an issue as we talked about.
MR. PETERSON: Two more points in conclusion. What I call desperately needed information, which is essentially brick walls to great analysis beyond what we've talked about for the surveys. One is, and we talked about administrative data a little bit, it would be great to have private health insurance administrative data, claims data, so that folks at AHRQ can say, okay, we know where the deficiencies are; maybe we're losing this tail, maybe the whole curve needs to be shifted up by X percentage. But at this point it's really hard to get your handle on that.
And, you know, I think if you've heard this only for the first time, it might be like, oh, god, we can't get that. But, in fact, the Society of Actuaries, several years ago, had had a file, it was called the large claims database, but they ended up putting all claims on it. And they got enough insurers that it made up a substantial portion of the population. So it's doable, and it might, you know, it might have to go through AHIP or something; I'm not exactly sure. But I just raise that as something that would be very helpful for us.
And an example that I give is, let's say you want in health reform to create a connector, right, this exchange that everybody has to go through. Should it be a national connector? Well, if it's a national connector, what does that mean? We know that health insurance is about subsidizing, transferring risk and subsidizing. But if you're doing it at a national level instead of state level, really what you're talking about, to some extent, might be subsidizing high cost states from low cost states.
If we had private health insurance data, I think we could get a better sense to the extent to which that occurs. California, I think it was the administrative data where they showed it's very low on a per capita basis on Medicaid. But that's true. I mean, the private health insurance premiums are very low and it would be helpful to be able to get a handle on what is going on with that. So, you know, again, it would be helpful to have that.
And the second issue is Medicaid and SCHIP. And although, you know, this focus is often on administrative data and the details, really, in my mind, the deficiency is the big picture. Think about coverage and reform plans. Senator Bacchus just released his plan. He calls for Medicaid to go up to 100 percent of poverty for everybody. Most people, you know, a lot of people think, well, if you're poor, everybody gets Medicaid. Of course, that's not true. If you're a childless adult, you know, you're not going to get in unless you're disabled, so.
But there are 13 to 14 states that do cover childless adults up to 100 percent of poverty through the Medicaid program. You cannot -- It seems to me that CMS should have that information up. That you should be able to contact somebody and say, how many states are doing this and what are they doing; and you cannot get it.
Now, the best you can come up with is, they will post the waiver, the actual terms and conditions, these hundred page documents. But even then, you can't get it. And an example I give is, I was in a briefing with some Senate staff with, you know, folks from HHS, and the staffer said, I want to know how many states cover, you know, have SCHIP eligibility above 200 percent? Something very basic along those lines. And the person said, I don't know; ask CRS.
And it seems to me that if you want to -- if states are the laboratory of democracy for health reform, then where are the lab reports, right? We need to have somebody, and I understand CMS or whoever would do this would need the resources to do it. But I think they would be resources that are well placed.
MR. BROWN: Isn't that on the Kaiser website?
MR. PETERSON: That's the thing. So you've got -- Kaiser does a great job of it. But they have to contact the states individually and try to pull all this together. And, frankly, there are, one side of the aisle, folks might be less inclined to take that, even though the numbers are legit, I believe, there's an issue with that, versus, you know, if there's kind of a -- the federal government -- here's a number.
So that's what we use. You know, we have to use that, that's right.
And the last issue with regard to Medicaid and CHIP is the provider payments and the adequacy of provider supply. So how are plans paid under Medicaid and CHIP? You know, policymakers want to know how they're doing it. These are fundamental questions that, you know, you just don't know. How are the rates negotiated? How do these rates -- how are provider payments -- how do they differ if I'm an insurer and I have a commercial plan versus the Medicaid plan?
And an example I give is out of the Social Security Act. This is the Medicaid statute. And it says the Medicaid plan is supposed to, quote, assure that payments are sufficient to enlist enough providers so that care and services are available under the plan, at least to the extent that such care and services are available to the general population in the geographic area.
So, in other words, Medicaid and CHIP, you're supposed to pay enough so that you have an adequate supply of providers, dentists, doctors, hospitals. Can anybody tell me what those payments are to providers, and whether they're adequate? And I don't think that's possible.
MR. STEINWALD: No. I'd ask CRS, that's what I'd do.
(Laughter)
MR. PETERSON: So those are my comments.
MR. BILL SCANLON: Yes. Very good comments. And I think this whole issue with Medicaid, I'm sensitive from both GAO days and sort of in MEDTAG days, thinking about sort of giving more assignments to CMS, feeling that they're already overwhelmed with the assignments they've got. And this whole issue of asking the states for information, at times at GAO, when we were doing some Medicaid reports, we would have to go to the states ourselves, even though they had submitted data to CMS. But when you went to the states and you actually worked with them to get sort of more verifiable data, it was very different than the data they had given to CMS.
So, I mean, this is sort of the unfortunate situation is that we can have sort of requests or even mandates for information coming from CMS to the states, or from the states to the managed care plans, because -- Dave raised the issue of sort of the incompleteness of managed care, sort of information in Medicaid.
It's not that there's not a requirement. It is it hasn't been fulfilled or it's been fulfilled poorly and that there's an issue of state and -- states' efforts to enforce that at the plan level. So we've got these problems that -- in terms of getting the data to flow. And those have been long-term and these have really been sort of problematic for a while now.
MR. O'GRADY: But, I think you highlight something here, Bill, because we've talked about this notion from, you know, agencies that collect data to agencies that analyze data to policymaking. And we haven't really talked much about the kind of way that loops back.
So having drafted Medicare legislation, it's real easy to put in the thing that says, and if you don't turn in the data in a timely manner that meet the standards of, you know, we're going to hold that 10 percent of whatever your payments are until you do.
But I know that those committee staff, when they're doing a million different things to track, they're not hearing that they need to do that to have then that quality of data come back that they're going to need two years from now to, you know, to do the reauthorization or do whatever. So it's a little bit of a -- and the people who collect the data are not dealing day-to-day with the policymakers; the analytic agencies are.
So to some degree, it sounds like Chris has started some of those discussions about how to let them know that if they want to be able to have the information they need to make rational decisions, there is some -- I mean, we've also heard this theme, well, we don't have enough money to do this or we don't have -- we wanted to collect this, but we didn't.
So there's some of that disconnect that goes on between the policymakers thinking about what they'd really like, questions they'd like to be able to answer, and not realizing that they have, you know, there's no free lunch; you have to then fund it and hopefully you funded it two years ago so that it's ready when you have this hard decision in front of you.
So it's that loop that somehow seems to slip.
MR. BILL SCANLON: Right. Okay. Let's turn to Joe. I think this is -- a lot of times in healthcare, people among people who are sort of in the health policy area, we don't think of DOL. You know, we'll start off discussions of insurance, and we'll say the largest source of insurance is employers, and then we move on. And so we feel very fortunate today to have you here to talk about sort of what -- I think of you again sort of both a potential data producer as well as a data user.
MR. PIACENTINI: Thank you, and it's a pleasure to be here. So my name is Joe Piacentini. I'm with the Employee Benefits Security Administration in the Department of Labor. And what we do is administer the federal law that governs private employee benefits, health insurance and retirement benefits as well. And these are just the benefits that are offered by private companies. It doesn't cover the benefits that are offered by government, state and local governments or so forth, to their employees.
It is the biggest single source of health insurance. By our estimate, about 137 million Americans are insured in these ERISA-covered health plans. It's also a very decentralized system. You know, when people talk about a health plan, often they mean the insurer as an MD, right; it's Blue Cross of Ohio or it's Coverman Massachusetts; that's a health plan. But for us, a group health plan is the entity sitting at the level of the employer. And there are lots of small employers out there, lots of them offer health insurance. We think there are about two and a half million of these ERISA-covered health plans in operation. So --
MS. BREEN: Did you say you think there are?
MR. PIACENTINI: I did say I think there are. And let me go a little bit out of order in my comments to pick up on that. We are a data producer in a sense. There are national reporting requirements for employee benefit plans. They file annual reports with the federal government. But there's a very large exception built into the rules that we use currently that says that if the plan is small, fewer than 100 people, and the plan doesn't hold any assets in trust, so the most common model for a small plan is it's fully insured, right? All they're doing is paying premiums. They're not holding any assets. These plans don't have to file.
So while we estimate based largely on the MEPS IC, we estimate that there are about two and a half million of these things out there. There are only about 60,000 that actually file reports with us. They're mostly big plans and a few small plans that for some reason hold assets and have to file a report. So there is a big disconnect there.
We do have data on those plans, so we know something about how much money they take in and spend and how many people they cover. Even there there's a little bit of fuzz in the measures. So because the coverage of that reporting is incomplete and because of some of the fuzz and how you define employee benefit concepts versus health insurance concepts, the data don't get much used. We don't use them a whole lot. We do have some uses for them. I'm happy to talk at greater length with anybody who's interested in exploring the degree to which our data on large employment based health plans could be of use.
One good news about these data is they're public. By law, these reports are public. So anybody can get any of it from us.
So let me turn back for a minute to what it is my agency does in administering ERISA, so you'll have a sense of where we come from in terms of the data that we're interested in. So one thing to know about ERISA and health benefits is sort of what it does. There are some general provisions in ERISA that apply to health and pensions. Some of this reporting disclosure requirements provide certain notices to participants. Some general fiduciary standards of conduct for people who run the plants.
But then there are a lot of specific little requirements for group based health insurance, many of which you probably know at least a little bit about. It's where we go by an alphabet soup, right? We have COBRA for continuation of coverage when people leave the job and other circumstances. We have HIPAA, which in group insurance means some portability provisions, so if you move from group plan to group plan, you don't have a pre-existing condition exclusion. Also in HIPAA, we have some non-discrimination on health status, so that peoples' premiums that they pay in an employment based plan don't vary depending on their health status.
We have the Mental Health Parody Act. We have Newborns and Mothers Health Protection Act, Women's Health and Cancer Rights Act, all of which set certain minimum standards for the way group health plans coverage has to look.
And we have some new laws just coming out online. We have Genetic Information Nondiscrimination. We have a new Mental Health Parody Act. We have something called Michelle's Law about continuation of coverage for students who otherwise lose coverage because they're no longer students because they got sick and couldn't stay in school.
So we have these incremental things that make up now this sort of pool of federal mandates. All that are things that ERISA does in governing group health insurance.
The other thing important to know about ERISA is what it un-does. ERISA has a preemption provision. One of the motivations behind ERISA was to create some national uniform standards for employee benefits, where state standards have differed. So there's a preemption provision that essentially says state laws can't govern employee benefit plans.
The provision says states can go on regulating insurance. So as long as the employment-based health insurance plan is buying a group insurance policy from an insurance company that's regulated by the state, state laws apply, state benefit mandates, rating rules if it's a small group policy. But if the employer is self insured, as many, many larger employers are, there's no application of state law.
Recently, you may have seen this in the headlines, because it's had the effect of putting obstacles in the way of some state health reform efforts, some of what you hear about the Fair Share Plans that we try to get employers to pony up some role in providing health insurance. There's some question about whether those things can survive this preemption, and the courts are still pondering that in some cases.
So within the agency that administers this law, I run the office of policy and research. And among the things that we do in my office are supporting policymaking by the agency in both regulations the agency itself issues and in our participation in legislative debates and the development of legislative proposals by the administration.
So for me that means that we have, you know, in maybe a sort of a smaller pond, we have a scoring role like CBO does or like Gillian does at the Office of Tax Analysis. If we're going to promulgate a regulation, I have to try to figure out what the impact will be. If there's legislation pending in our area, then I have a role on behalf of the agency and the department for trying to figure out what the impact will be on our regulated community.
So in that sense, my interests, I think, and Gillian's overlap a lot. We're thinking about a lot of the same things in terms of possible reforms to change the structure of insurance markets, to change the distribution of tax subsidies and the structure of tax subsidies.
What impact would that have on employment-based health insurance and the role that employment-based health insurance plays in the overall insurance market?
So data, what does that mean for us in terms of our data needs? Well, we have some specific things that we need to be able to look at to get a handle on who our regulated community is and how different reforms will affect it. We care not only about whether people get insurance through a job, but we care about the sector, right? Is it a private sector job? Some data sources do a better job at telling you that than others.
We also care about whether the plan is insured or self insured, because of the difference in whether state laws apply or don't. We care not only about whether people have insurance from the job, but also about offers of insurance that may not be taken up. If somebody has insurance from a job, we care about whether they're an active employee, whether they're on COBRA continuation, whether they're covered as a retiree.
We're also interested in information on the plan sponsor. We're not just interested in individual decisions about where to get health insurance, but employer decisions about whether to offer health insurance. So we're interested in knowing about the characteristics of the overall entity that offers the plan. We have pretty good information on that from the MEPS IC. But establishments are not the same thing as firms, so there's a little bit of a disconnect sometimes there.
So with all that, what are we using as data? We make a lot of use of the MEPS IC, probably more than any other single source gets directly at some of these things, you know, what sector? Is it self-insured insurance? Many of these things are there. So, you know, our challenge is the one that we've heard about here from others. I think we would use it even more or get more out of it if we had better access to the microdata. But it is probably the most direct on-point of the datasets we've talked about.
We also use VLS data, which I won't go into at length. Some of you know about the national compensation survey. They also have detailed data on design of employee benefit plans and so forth. But it's also non-public data. You've got to go over to their shop and have their clearances to look at those microdata.
So we do need to be able to use microdata. And so as a result, partly as a result, we also rely a lot on household surveys. And what we've done is build our own sort of enhanced version of the March CPS, for that purpose. Why the March CPS? Well, for some of the reasons I think we've already heard. One is that it's popular, makes it possible for us to have numbers that reconcile with the ones that people mostly are used to seeing. It has good labor force variables, which, of course, ties to what we're interested in. It's quite current. Because of the preemption -- there is a preemption issue I talked about, we care about what state people are in. So the state breakdown supported by the CPS is helpful to us. We can get it as a microdata set. It's a relatively easy one to use. But it has lots of limits. And so we try to melt it with other datasets as best we can to fill in some of the gaps, to take our best guesses, for example, at whether people have offers of health insurance they're not taking up from jobs, to be able to make guesses about the characteristics of plan sponsors. Sometimes when it's not that the person who is the policyholder has it from their current job and they're in the household, if that's not the case, then you don't necessarily know about the characteristics of the plan sponsor and the CPS.
So that's sort of the short list. I guess, you know, we could come up with a long wish list of things we'd like to have. But a couple of the things that come up most often, one relates to COBRA. A lot of the proposals that we have either actually explicitly reference COBRA and COBRA-like things, or implicitly resemble it. And we don't know a whole lot about take-up. Most of our data sources don't tell us -- distinguish, for example, between people who have COBRA and people who might be covered as retirees. So we don't know much -- as much as we'd like to know about that.
And then the other ongoing challenge and frustration for us circles back to the guess that I cited earlier about two and a half million plans. I mean, these are the entities that we regulate. And so it seems kind of silly for me to sit here and tell you that we don't know how many of them there are. But we don't. I suppose we could amend our rules so that all of them would have to file an annual report. But, you know, there's a question about the efficiency of public burden of asking every small employer to fill out and file a report for their healthcare plan. So something that would help us get at that better would be helpful. I don't know that the MEPS IC can exactly answer it because of the disconnect between the employer and the establishment. But a start might be that the establishment survey could begin to get at how many separate plans might be offered. It's a little bit tricky because a plan for our purpose is a little different from do you have an HMO and a Blue Cross option. But I could elaborate on that some other time if that'd be helpful.
MR. COHEN: Yes, there's a little bit of information on the firm. I guess it depends on what you're looking for exactly and how it is classified.
MR. PIACENTINI: Yes. I mean, I understand that it tells you about -- something about the pay profile of the firm, the size of the firm. But for me it's really at the firm level how many plans are there. Some companies will have just one plan, might have different options offered in different places. Others might have different plans. So it's very difficult for me to get to that two and a half million number. So it's a bit of a guess.
MR. PETERSON: And, Joe, don't they -- I mean, it seems like we have requested COBRA estimates from you guys and you provide them. I just forget --
MR. COHEN: I think it's probably off the household survey. There might be something in the IC. We do have the data. I think it's small numbers probably.
MR. PETERSON: Yes.
MR. COHEN: And, you know, whether people can answer the question as to whether their insurance is coming from COBRA, which job it came off of, et cetera. I don't know. Jessica Business is probably the person to talk to about that. We'll pursue that a little bit. But it's possible that we have --
MR. PIACENTINI: We're frequently asked, and we do, you know, generate numbers by combining different data sources. Again, one challenge is if somebody has health insurance from a job and they're not at the job, can you tell if it's COBRA or something else, you know, the state mini-COBRA things, right. I don't think necessarily a survey response always can distinguish.
MR. COHEN: Right.
MR. PIACENTINI: And sometimes that matters, sometimes it doesn't, depending on what question you're asking.
But then there's a separate question of COBRA take-up. And I think you don't even have as much shot at getting that from a household survey, because you have a COBRA opportunity and not take it up.
MR. COHEN: Right.
MR. PIACENTINI: That's more the employer's perspective of how many people did they send COBRA notices to and how many of those actually elected COBRA, or didn't. But then, of course, from that perspective, then you don't know if they didn't elect COBRA, the employer doesn't know, well, is it because they had other insurance?
MR. COHEN: Other insurance, yes.
MR. PIACENTINI: So there's this displaced worker survey, one of the CPS supplements that DLS has done from time-to-time, it gets at parts of that. But there's no complete picture.
MS. BREEN: Listening to this, I'm really impressed with the creativity that you all bring to using the various surveys that are available and putting them together in order to try to get a complete picture.
I'm also really stuck, though. I guess it boils down to the paucity of data that seems to be available to the agencies that are in charge of regulating the parts of our economy that they'll have data available to them. I mean, it's very striking to me that everyone's depending on knowing about healthcare plans and expenditures on a survey of 30,000 Americans in a population of 300 million, with the exception of the ACS.
But, you know, still, it takes a long time to get that super size survey sample that -- which was talking about.
So I guess, is there a push or discussion to improve our data systems at all on the part of Congress or any of the other agencies? And I mean here the NHIS is suffering with, you know, a cutback of its sample to a half of what it was, and it never was that big. And it doesn't collect data even at the state level, much less at the county or a smaller level which would be useful. I believe one of the reasons that the CPS is such a popular tool is, you know, it's not known for its fabulous estimates of health insurance. In fact, they've come under a lot of criticism over the last decade. What it is good for is that it's a big, big sample, and it's released about three months after it's collected, and it's collected -- it's always in the field. So you've got annual estimates.
So I just wondered, is there any talk or how could we sort of, you know, is there a way to promote this? Is there anything we can do to help? Or is there anything you can do to help? I mean, how can we sort of improve our data systems so that you all can get the data that you need to do the estimates? And this isn't for tomorrow, but it does seem like it's a question that we should think about and address.
MR. BILL SCANLON: Well, I think, I mean, in part it is a discussion that's going on at full committee level of NCVHS. Mike is a member of the Board of Scientific Counselors for NCHS, and it's a similar kind of discussion there.
The NHIS cut today is, in part, a function of the fact that we're in a continuing resolution. And there was slated as -- tell me if I'm wrong, an $11 million increase for NCHS, which would be a contribution towards what might be more adequate data, whether it would be -- move all the way there is not clear.
But so we're kind of now in this sort of awkward situation where because the bigger appropriation couldn't go through, this part sort of gets stymied, okay. And the issue will be, come March, are we going to extend that continuing resolution or will we have an appropriation and will then the appropriation be -- sort of allow some of these things to be restored, because it's not just the NHIS. There are other things that are going on in terms of existing surveys.
But in looking at the bigger picture, there are sort of more kind of fundamental questions, which start with, what are the data needs for a variety of purposes. Then the second piece is what's the best way to satisfy them? And that's why we've started this discussion of, what's the combination of surveys and administrative data, and then what's the future sort of combination? How's that combination likely to change in the future as we get more information, technology, sort of into the healthcare sector? And how can we sort of combine these sort of various sources in the most efficient way possible to satisfy data needs.
But of course, you know, I mean, the MEPS, obviously everybody -- we've extolled it's -- sort of its value here today over and over again, and it's an expensive service. I mean, it's not sort of -- I mean, it's not cheap. And so --
MS. BREEN: And not that well integrated.
MR. BILL SCANLON: Well, but to really expand it, I mean would be -- could be very expensive. And to really get into some of the details that we might need. You know, one of the issues in health reform that's very big is the individual insurance market, okay. We have about five percent of the population that's in that market through today. So you think about any sort of representative sample of the population, and you're only going to get -- five percent of it's going to be people that are in the market.
Then we've got the uninsured. We really need to know about sort of what were their interactions with the market and why don't they have insurance? I mean, that's a whole different kind of set of questions than we've ever asked -- than I think that we've ever asked in the past, which is, what's your shopping experience been like in terms of trying to get insurance? Have you been denied because of underwriting? Did you get sticker shock and immediately walk away? I mean, those are the kinds of things that are going on.
And in terms of reform, those are the kinds of things that you want to think about, about having information so you can know what to do to address them. Joel.
MR. COHEN: Well, I was going to say, some of that like the, you know, you're talking about, you know, decisions by the uninsured. Some of that stuff is not that expensive to add on to a survey, because you can do, you know, self-administered questionnaires, et cetera, which are pretty cheap. So pieces of it aren't that expensive. Other things, you know, in order -- if you have to like up-sample by, you know, 10,000 people in order to get something that's rare in the population, then you're talking about a fair amount of money.
So it depends -- you know, the health insurance plan abstract stuff that we're talking about is not that expensive. You know, we could add it on, you know, without, you know, that much additional money. So there are pieces that, you know, the incremental cost is not very high. There are other things where if you have to sort of start over and sort of reformulate things, then you're talking about a lot of additional money.
MS. HUNTER: On the uninsured too, you're expanding that and there was some way to kind of have someone put in the state eligibility for Medicaid and SCHIP and that sort of thing. I mean, because we have to think about, who's going to take up of the uninsured. And so if they're already eligible for something and not participating, they're probably going to behave very different than if they're not eligible. Anyway -- and I had a couple other points, but I don't want to digress from where you are.
MR. BILL SCANLON: No, please. Go ahead.
MS. HUNTER: Okay. I'm not sure I mentioned this. But we do use the MEPS IC for the employer piece, in case I omitted that very important piece of information.
And on your benefits that you're going forward, I just remember from the '87, when we had the benefit information, I was so overwhelmed. We had all these characteristics, but I didn't know how to put it together in a kind of actuarial sort of way.
So if you just looked at deductibles and co-pays, is this a more generous plan or less generous plan? You're kind of missing a lot. Like is this plan restricting what doctors I can go to? So if I could get some sort of measure of the actuarial value. And this goes to the deductibles, because I remember the point earlier was about, was there a high deductible, so it was HSA.
You know, I went out on the web to look at all the non-group policies, and there are an awful lot of high deductible plans that are not HSA qualified because they let you go to the doctor without having to first go through the deductible.
So it has a deductible. It looks like it is fine. And that's probably more in the non-group market. I have no idea about the employer market, whether there's those plans. I'm assuming the employers would go for the HSA qualified, but I don't know. So that's a question that I have there.
And then in terms of thinking for the future, there have been proposals to have reporting on the W2s about employer contributions. And so if the MEPS were to pick that up, that might be a nice merge that we kind of have.
And if we go to health records, electronic, what does that mean for our data? I'm sure you guys have thought about that.
MR. STEINWALD: You know, we've always been pennywise and Tom-foolish about investing in data resources, haven't we? I can't think -- and when I was on the National Committee in the ‘90s, it was always a situation where the National Center was cutting corners here in order to preserve something over here.
I don't know that there's ever been a presidential administration that's really invested adequacy in data resources. And the Congress, for its part, you know, every Congress is two years long. Congressional staff median tenure isn't much more than that two years. So, you know, it's always the here-and-now focus and unwillingness to spend money today in order to have a resource three years from now that can help somebody else make good decisions.
MR. BILL SCANLON: I don't disagree with you at all. I guess, though, I mean, at the same time, in seating here today, and this is maybe a function of age, okay, thinking about sort of the richness of the data today, versus what we used to work with, okay, the first grant that I got sort of working on healthcare was to help HCFA at the time design physician payment policy. And we had a dataset from California about California physicians. And it was the only data that they had about physician behavior sort of in Medicare at the time.
And like other than, there was some data for about physicians in Arkansas. So national policy, you had a choice, you either make it on the basis of California or Arkansas, because in --
DR. STEINWACHS: And what'd you choose?
(Laughter)
MR. BILL SCANLON: We didn't have that. (Laughter) In 19, sort of '76 and ‘7, you couldn't process Medicare claims on a national level and produce sort of good, sort of evidence about sort of things by specialty, by geographic area, by the type of service, et cetera. That was not possible.
So I mean today, I mean, when we heard about the Medicaid data, I mean, this is such a movement forward compared to what we used to have with respect to Medicaid, so. But it doesn't mean that we should sit here being satisfied. We clearly need to move forward.
MR. PETERSON: But the word that you mentioned that stood out is oversight, which is a different approach than with the surveys. I mean, with oversight, at least, you know, I think about the Congressional committees, where Mr. Waxman said, okay, this Delmonte driver in Maryland, died, what are dentist getting paid in Maryland. How many dentists are there? And that information was really hard to come by.
And the committee, I don't think they actually subpoenaed, but they got the information from the insurers directly. And so when it comes to oversight, you know, that does -- it is a fundamental policy question. I mean, you know, I hear Medicare's costs or administrative costs are two to three percent of the total. Well, if lots of money's going out to people with laptops and we don't have a lot of people at CMS who are watching this stuff, wow, you can really lower your percentages.
So, you know, those are really fundamental policy questions.
DR. STEINWACHS: Joel, let me just ask a question. The 60,000 plans on which you get reports, what does that tell you about the health insurance coverage?
MR. PIACENTINI: Not a lot, I guess is the short answer.
DR. STEINWACHS: I was looking things about trend information, recognizing those are mainly big employers, you know.
MR. PIACENTINI: Right.
DR. STEINWACHS: I'm more interested in probably they'd be changing the nature of their policy offerings or contributions, things like that, as different from dropping it, which the smaller plans may just drop.
MR. PIACENTINI: Right. I mean, how much it tells you depends on how labor intensive you want to be about sifting through the data. You know, we get sort of general information, number of people covered. We get information about what is the financing mechanism of the plan, which sort of means is it self insured or does it buy insurance policies. If it buys insurance policies, they file an additional schedule for each insurance policy that they have. It tells you a certain amount about that, premiums, commissions they might have paid on the insurance.
The form is really designed not as much to answer research questions or even support policy development as it is to support the enforcement of standards of fiduciary conduct in managing plans. You know, who do you have relationships with? How much are you paying them? That sort of thing. That's sort of like what it grew up from.
Also, under ERISA there's this sort of concept that you have, pension plans, and you have what they call welfare benefit plans, which health plans are a subset. And so the reports that we get are for welfare plans, many of which are welfare plans that provide health benefits, but some of which are welfare plans that provide health and disability benefits, for example, or health and life insurance.
And so you can't always distinguish without really going in and sort of by hand looking through the insurance schedules how much of the people covered -- are covered under the health insurance and how much of the money is spent on the health insurance. And then it's not an insured plan. It's a self-insured plan. They have a big trust somewhere. You may not even be able to untangle how much of the money was health insurance and how much was disability.
So, I mean, it's really a reporting structure that's designed around, is the money being handled well and things like that. It's unfortunate. You know, I think it's to some degree an artifact of an employee benefit mindset that precedes sort of a modern health policy mindset.
DR. STEINWACHS: Yes.
MR. PIACENTINI: But it's got a certain amount of inertia to it, partly because those concerns and issues haven't gone away.
DR. STEINWACHS: Yes. What I'm talking about, you have to add on more data collection, and that would be not easy probably and still it's only 60,000 under your two and a half million, so it's not representative.
MR. PIACENTINI: More, different. And I think the value of improving it is limited by the lack of coverage of the state plans. If you were to take on both, then you could possibly turn it into a real source of data. But then the question becomes what is the burden to the public to all these small companies who are deciding whether to offer health insurance if now they've got this other thing they have to do. At least this is the questions that, you know, get asked whenever this comes up.
DR. STEINWACHS: Yes.
MR. PIACENTINI: And then from our perspective as an agency, okay, if it's mandatory that people file with us, and you think, well, that's great, compared to having to do a survey, you don't have to go through all the expense and so forth. But if you're receiving two and a half million or more filings, just the processing of that requires a pretty substantial apparatus, you know, between all the pension plans and health plans that do file and others, we get about a million of these filings a year now from various employee benefit plans, mostly pension plans. And the apparatus to process all of that data, make it publicly available, it's not a -- it's a fairly big piece of what my agency does.
DR. STEINWACHS: Yes. It may be cheaper to do a survey, right, if you were looking for additional information?
MR. PIACENTINI: Well, and that's where the conversation tends to go, is that, you know, it's not that much more expensive for the government to do a survey than it is to process all this stuff. And in addition, it's much less burdensome on the public.
DR. STEINWACHS: Yes. And you might get -- then you get -- select some of the information, what in addition to that which is fiduciary.
MR. PIACENTINI: Right. The survey wouldn't help us with our enforcement program.
DR. STEINWACHS: No.
MR. PIACENTINI: But it could be tailored to answer the research questions. And another thing people will say to me is, well, those surveys already exist. You know, the MEPS IC and the NCS and the Bureau of Labor Statistics.
I mean, from my point of view as a researcher in these issues, I would love to see mandatory reporting include more information that would help us with policy, help us get a real picture of what's going on in the market place. But, you know, I run the research office, somebody else runs the enforcement office, and so forth. So this is sort of where we are at the moment.
MR. O'GRADY: Can I go back to Bill's point for a second here, just to not lose it. Certainly things are much better than I think when -- especially those of us who have been doing it for a long time, sort of got started. But I would disagree with my old friend. I mean, they're not there yet, though.
I mean, when you think of what a policymaker really needs to make an informed decision; there's just too many big question marks there. So the question is sort of what do you do about it?
So there are groups like this and groups like the board that I sit on at NCHS. And we will do sort of the letter to the Secretary and to, you know, the Director and sort of make these recommendations and support that they go on.
But I think that there is, when you think, especially -- and it certainly sounds great if the Congressional guys sort of sit down, kind of caucus and think about sort of where is their joint efforts and, perhaps, even the Executive Branch, kind of users of the data, you know, similar kind of thing.
But one of the problems when we think about Congressional decision making is I don't -- I haven't seen much ownership of this issue. Like we can do hearings in a thing like this. Have you ever -- I don't ever remember a Congressional hearing on a, you know, what information does really -- you know, we're heading into the new whatever. I don't remember during the Clinton years or any of that that would sort of say, what do you really need to -- you know, what does Congress need? Is that data there? What new data collection? What modeling? Et cetera, et cetera.
I've never seen where there's -- you know, when I was on the finance committee staff, I knew I had Medicare HMOs. And if there was something that was going to affect the Medicare HMO, I better at least know about it, if not have my fingers in it. And I just don't see that in terms of this notion of data and then how data fits into analysis and how that feeds into policymaking. I just don't see anybody who knows that if something falls apart here, they're the ones who are sort of on the hook for. You know, some chairman's going to call up and say, what the hell happened? Does anybody remember there ever used to be a Congressional hearing on anything having to do with data?
MS. BILHEIMER: There was one. It was related to SCHIP and there was concern about the CPS sample size for estimating uninsured children. And it was given -- it was back in the 1990s. There was a hearing on the adequacy of the CPS for measuring the number of low income uninsured children. And there was additional funds put into the -- for that.
MR. PETERSON: I testified twice that I raised the CPS and ACS issues. And they put more money into the CPS, you know, I mean. So those issues have been raised. But when it comes to the oversight issues, I mean, it seems to me GAO would raise those kind of questions all the time, right? I mean, I think about 1115 waivers and the black hole that they are. And GAO says, look, you got to do something about this, right?
PARTICIPANT: You're HHS now, you don't call them black holes.
(Laughter)
MR. STEINWALD: I guess the ownership, agencies like ours, you know, have to communicate to our clients and say, if you want us to help you with your decision making, if you want us to be a decision support agency, then you have to make sure, we have to make sure that we have the tools. And that's -- so if there is ownership, I think, to some extent, it does belong to us. I mean, we're the ones who are likely to be there when the next generation of Hill staff arrive and, you know, they'll be even younger than the ones that are leaving. So I guess to some extent it is our responsibility.
MR. BILL SCANLON: Well, I do think that GAO has done that, in terms of being critical. And I think -- and I won't say this is the only time GAO has ever done this. But there are within health reports GAO recommendations to the Congress, spend more money on the administration of Medicare and Medicaid, that the two percent sort of, or three percent, is just not adequate, okay.
And that's the problem that we face. I mean, today we had sort of a panel of users and producers, okay. In the Congress we've got the authorizers and the appropriators. And it's the authorizers that are sitting there puzzled by the lack of information, and the appropriators are meeting sort of somewhere else, different sort of schedule, et cetera. And it's convincing those appropriators that you need to spend more is a big part of this.
You know, in Medicare we had a breakthrough in terms of oversight with HIPAA, because the appropriation was in a piece of authorizing legislation. There was no need to go through the Appropriations Committee. Now, that doesn't happen very often, and how it happened that time is not clear. But that, you know, it's that kind of thing that I think is a real problem with respect to something happening is you've got to bring in two parties. You've got to convince both the authorizers and the appropriators that we've got a problem here and to sort of move forward sort of on it.
I guess there's a question, kind of our time is waning, sort of next step for this group. I mean, I found -- I also said here kind of thinking how curious this is we've got these groups, this large group, think of it as one large group of federal sort of agencies here talking about sort of data, sort of sharing, et cetera, and here we are, this -- outside of government, and, you know, today we're sort of special government employees, people outside of government sort of having sort of called this hearing; and the question is, where do we go sort of as from a National Committee perspective?
We had a short discussion at the full committee hearing about the fact that perhaps sending a letter to the Secretary, the new Secretary, saying, hi, we're the National Committee; as a means of introduction would be sort of be an appropriate thing to do.
I'm wondering if also that sort of within, you know, as a way of introduction, to talk about some of the things that we've looked at over time. And given that this is now the second time we've faced this issue of interagency cooperation in terms of data, how can we facilitate it? How can we make it sort of happen in a much more rapid sort of fashion? Sort of avoid the process that every time we need to bring data together or share data, that it's an issue of reinventing the wheel, that this should be something that the administration think about sort of very early on, and particularly because it has relevance for something else that's going to be happening, potentially very early on, which is health reform.
So that this would be something, you know, not extensive, but sort of a potential sort of piece of what has the National Committee done and what is the National Committee focused on today that we think about sort of putting in this letter to the secretary, which I'm presuming they were aiming for us at either December or very early in the year, depending on sort of how we get it through the committee.
MS. GREENBERG: We have a Secretary now, apparently.
MR. BILL SCANLON: Well, not until they're confirmed.
(Laughter)
MR. BILL SCANLON: But anyway, but beyond that, I guess I'd sort of be interested in sort of other thoughts about potential next steps.
MS. JACKSON: Well, I heard, also at the full committee meeting today, terms about the sense of value, and that's a major theme that's been going around in hearing, with the linkage and someone mentioned just being able to communicate what some of the hot spot issues are somewhere.
There's some kind of communication link to show that the sense of value and importance across the board.
DR. STEINWACHS: In some of the -- highlighting some of the deficits, I guess, I was just trying to capture in my mind, go back from the user side. And I think you talked about private insurance and some of the things you'd like to have from private insurance companies in terms of data. And, I don't know, was that the major one that when you look at -- I don't remember now -- that we mentioned several times in terms of premiums, in terms of the differential earlier from a, you know, getting the MEPS and estimates of what the private insurance is and what it comes out.
MS. HUNTER: Well, the part that we'll have better information on are not very high cost cases.
DR. STEINWACHS: High cost cases. And then there is also the individual insurance market too. I think you were highlighting, you don't know a lot about or you don't have the kinds of data that you need to --
MS. HUNTER: Well, and we have 50 states regulating it, and so it's just all over the map. So I don't have time to call anybody, so I go in and if GAO has something that actually is useful for what I'm doing, you know, then I do this. But it's just --
DR. STEINWACHS: So just to extend that a little bit further, are there things that, you know, if we were to communicate them to the Secretary, be helpful in trying to think about how to get the kinds of information in the private sector? I know that you mentioned Society of Actuaries, that at one point, and they're health actuaries, pull together a dataset, sort of that private-public role. I'm just trying to get a sense of if there are some things that --
MR. O'GRADY: Well, and AHIC, their lobby extensive - I mean, they can give you all the deductibles, the co-pays in a fairly large sample; not scientific, but like --
MR. PETERSON: But that's been a non --
MR. O'GRADY: you know, 200 – A what?
MR. PETERSON: A non-group market.
MR. O'GRADY: Yes, but the individual and small group --
MR. PETERSON: Oh, okay.
MS. HUNTER: And we do use them. We do use them.
MR. PIACENTINI: I think it would be helpful if there was some more centralized way that the data that insurance companies have and maybe that insurance regulators have at the state level, on the group insurance market could be, you know, put together and made accessible and usable.
I mean, one of the rationales that I didn't mention why there's such a large exception from reporting under our body of law, is the argument that, well, you know, you don't have to have all these little companies reporting about their health insurance, because the insurers that are writing the policies are reporting to the states. So there's already a mechanism for some government oversight and government reporting of what's going on with this insurance -- these insurance policies.
But to my knowledge, that's not collected up and made available in a way that we could integrate with what we might know about the larger companies, for example, the self-insured companies, and piece it together.
MS. BILHEIMER: We had a meeting recently under the auspices of the (?) HCFA project, that was looking at data needs for modeling health reform options. And the issue of private -- access to private insurance data was a big one. And there were some options being discussed there. And that final report has not come out yet.
But one of the issues was access to Medstat data. Those are only large employers, but it is a very large database. And certainly it was the idea that that might be possible, that it might be possible to get access to that. So it wouldn't give all the issues we would be interested in, but it could certainly get you some information on things like -- some of the things like high cost cases, for example, so. Because it's a huge, huge claims base of large employers nationwide. And there certainly seem to be some thought that there was a possibility of you getting access to those data. And I think people are going to be talking to the Medstat people about that. I can keep you posted on that.
MS. BREEN: And that would -- would that be premiums and expenditures, or?
MS. BILHEIMER: It would certainly -- it would be claims. And I'm not sure whether the premium information would be in there or not, but it's certainly something I can explore and get back to you.
MR. PETERSON: The other thing is, I mean, we'll talk about kind of two things, one is the use of data for policy formulation, the other is use of data in terms of oversight. So I think for the oversight piece, one can couch that as almost under our fraud, waste abuse thing, right? I mean, we've got to do a better job of keeping our eye on this stuff and knowing whatever it is, states are doing, providers are doing, what they're paying. And I'm just thinking out loud, I mean, that's always a fuzz phrase that --
MR. BILL SCANLON: Well, I think it certainly implies there. But I also think it implies in terms of monitoring the impacts of policies and the need for change.
MR. PETERSON: Absolutely, yes.
MR. BILL SCANLON: I mean, we certainly do a certain amount of regulation of the insurance market, you know, in terms of guaranteed issue, pre-existing conditions, grading(?) rules -- and I'm thinking that health reform is going to involve potentially some more of that. And the question is, how far do you go? And if you've made a choice, what's the impact then.
And, you know, right now -- you know, the individual insurance market is the hardest thing I think to deal with because, as I said, it's five percent of the population and we need state specific estimates. I mean, that's kind of what it comes down to because it's all happening at the state level. HIPAA goes so far with respect to small groups, and even less far when it comes to dealing with the individual market at all. So that's really the province of the states, and so we really need that sort of state specific information, which I know it's like an impossible task when you're thinking of about five percent of the population.
MS. BREEN: Well, with the progress you've seen, just imagine what we could do.
MR. BILL SCANLON: In 50 years –
DR. STEINWACHS: Give Bill optimism.
(Laughter)
MR. PETERSON: Back to Mike's point, though. I mean, you may or may not know this. But CRS can't make recommendations. So we're essentially prohibited from weighing in on this. So we leave it to you; it is good opportunity.
MR. BILL SCANLON: Thanks.
MR. PETERSON: Well, Bruce is taking it seriously, I want you to notice.
PARTICIPANT: I mean, the CRS can't make official recommendations.
DR. STEINWACHS: But Chris, you can go grumbling up and down the aisle --
(Laughter)
MR. PETERSON: I do a -- you can probably tell I do enough grumbling.
MS. BREEN: And educate.
MR. BILL SCANLON: Okay then.
DR. STEINWACHS: Well, we want to thank everyone. This has really been fantastic, and appreciate peoples' flexibility and fitting into their schedules. And we look forward to taking next steps and sharing with you and we are also hoping to have some subsequent hearings. One, we're talking about having one on modeling, both the models that are being used internally in the government and the different agencies for projecting aspects of what is either insurance policy or the impact of the uninsured, as well as bringing in some individuals in academia, the private sector models that are out there. And some of the private sector models are being used too, as I understand, by government. And so trying to understand what the strengths and limitation of the models are, as also how they relate to the data, since the data, in part, just as you described to us aren't -- availability of the data drives those models or the assumptions which you have to make to adjust those models.
So we, hopefully, will see you soon again, and be working with you. But thanks very much.
(Whereupon, the meeting was adjourned at 5:27 p.m.)