Health Services Research

Accuracy of Medicare Expenditures in the Medical Expenditure Panel Survey. S. Zuvekas, G. Olin, Inquiry, Spring 2009; 46(1):92-108. Examines underreporting and underrepresentation of high expenditure cases in the Medical Expenditure Panel Survey (MEPS) and their implications for analyses. (AHRQ 09-R068)

Deriving SF-12v2 Physical and Mental Health Summary Scores: A Comparison of Different Scoring Algorithms. J. Fleishman, A. Selim, L. Kazis. Quality of Life Research, March 2010; 19(2):231-241. Examines alternative procedures for deriving the SF12v2 physical and mental health summary scores using recent, nationally representative U.S. data. (AHRQ 11-R071)

Health Expenditure Estimation and Functional Form: Applications of the Generalized Gamma and Extended Estimating Equations Models. S. Hill, G. Miller, Health Economics, May 2009; e-pub. Uses data from the Medical Expenditure Panel Survey (MEPS) to compare the bias, predictive accuracy, and marginal effects of generalized gamma models and extended estimating equation models with other commonly used regression models. (AHRQ 09-R067)

A Health Services Research Agenda for Cellular, Molecular, and Genomic Technologies in Cancer Care. L. Wideroff, K. Phillips, G. Randhawa, et al., Public Health Genomics, April 2009; 12(4):233-244. Summarizes the outcomes of a National Cancer Institute workshop that examined the state of health services research on cancer-related technologies and identified priorities for expanding the evidence base on their effectiveness in routine care. (AHRQ 09-R071)

Incidence of and Risk Factors for Bacteraemia in HIV-Infected Adults in the Era of Highly Active Antiretroviral Therapy. B. Yehia, J. Fleishman, I. Wilson, et al., HIV Medicine, March 2011; e-pub. Assesses the incidence of bacteraemia among HIV patients followed between 2000 and 2008 at 10 HIV Research Network sites and assesses risk factors for bacteraemia and trends over time in the odds of bacteraemia. (AHRQ 11-R041)

National Release of the Nursing Home Quality Report Cards: Implications of Statistical Methodology for Risk Adjustment. Y. Li, X. Cai, L. Glance, et al., Health Services Research, February 2009; 44(1):79-102. Examines how alternative statistical risk-adjustment methods may affect the quality measures used in nursing home report cards. (AHRQ 09-R089)

Patient Engagement in Health Care. C. Clancy, Health Services Research, April 2011; 46(2):389-393. Editorial discusses methods used by health services researchers to evaluate how individuals participate in health care at various decision points and introduces four papers that appear in the same journal issue. (AHRQ 11-R043)

Sensitivity of Household Reported Medical Conditions in the Medical Expenditure Panel Survey. S. Machlin, J. Cohen, A. Elixhauser, et al., Medical Care, June 2009; 47(6):618-625. Uses pooled data from 4 years (2002-2005) of the Medical Expenditure Panel Survey to estimate the extent to which household respondents may underreport 23 types of medical conditions. (AHRQ 09-R073)

Updated U.S. Population Standard for the Veterans RAND 12-Item Health Survey (VR-12). A. Selim, W. Rogers, J. Fleishman, et al., Quality of Life Research, February 2009; 18(1):43-52. Discusses the development of an updated U.S. population standard for use in quality of life assessments that accounts for the changes in the U.S. population that have taken place in the past 20 years. (AHRQ 09-R039)

Using Proxy Measures and Other Correlates of Survey Outcomes to Adjust for Non-Response: Examples from Multiple Surveys. F. Kreuter, K. Olson, J. Wagner, et al., Journal of the Royal Statistical Society, April 2010; 173(2):389-407. Examines traditional covariates and new auxiliary variables for use in various surveys, including AHRQ's Medical Expenditure Panel Survey, and provides empirical estimates of the association between proxy measures and response to the survey request. (AHRQ 10-R058)

What Have We Learned from the Application of Stochastic Frontier Analysis to U.S. Hospitals? M. Rosko, R. Mutter, Medical Care Research and Review, June 2010; e-pub. Discusses the lessons learned from studies of U.S. hospitals that used the statistical technique, stochastic frontier analysis, to examine hospital inefficiency. (AHRQ 10-R073)

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