This report was prepared by the Office of Applied Studies (OAS), Substance Abuse and Mental Health Services Administration (SAMHSA), and by RTI International (a trade name of Research Triangle Institute, Research Triangle Park, NC). Work by RTI was performed under contract number 280-03-2602.
All material appearing in this report is in the public domain and may be reproduced or copied without permission from SAMHSA. Citation of the source is appreciated. However, this publication may not be reproduced or distributed for a fee without the specific, written authorization of the Office of Communications, SAMHSA, U.S. Department of Health and Human Services.
Substance Abuse and Mental Health Services Administration, Office of Applied Studies. Drug Abuse Warning Network, 2008: Area Profiles of Drug-Related Mortality. Rockville, MD, 2010.
This publication may be downloaded from http://DAWNinfo.samhsa.gov or from http://oas.samhsa.gov.
Or, please call SAMHSA's Health Information Network at:
1-877-SAMHSA-7 (1-877-726-4727)
(English and Español).
Office of Applied Studies
Substance Abuse and Mental Health Services Administration
1 Choke Cherry Road, Rockville, MD 20857
May 2010
DAWN MORTALITY DATA
Drug-related deaths
Drugs
Deaths included in this publication
Standardized death rates
DESCRIPTION OF PROFILES
Full profiles
Limitations to data
Brief profiles for selected metropolitan areas
County profiles
State profiles
PROFILES
Alabama
Birmingham-Hoover, AL
Arizona
Phoenix-Mesa-Scottsdale, AZ
Arkansas
Fort Smith, AR-OK
California
Los Angeles-Long Beach-Santa Ana, CA
San Diego-Carlsbad-San Marcos, CA
San Francisco-Oakland-Fremont, CA
Contra Costa County
San Francisco-San Mateo-Redwood City, CA
San Francisco County
Colorado
Denver-Aurora-Broomfield, CO
Arapahoe County
Denver County
District of Columbia
Washington-Arlington-Alexandria, DC-VA-MD-WV
District of Columbia
Prince George's County
Florida
Miami-Fort Lauderdale-Pompano Beach, FL
Miami-Dade County
Palm Beach County
Georgia
Atlanta-Sandy Springs-Marietta, GA
Fulton County
Illinois
Chicago-Naperville-Joliet, IL-IN-WI
Cook County
Lake County
Indiana
Indianapolis-Carmel, IN
Marion County
Kentucky
Louisville-Jefferson County, KY-IN
Louisiana
New Orleans-Metairie-Kenner, LA
Jefferson Parish
Maine
Statewide
Augusta-Waterville, ME
Bangor, ME
Lewiston-Auburn, ME
Portland-South Portland-Biddeford, ME
Rockland, ME
Maryland
Statewide
Baltimore-Towson, MD
Anne Arundel County
Baltimore City
Baltimore County
Cambridge, MD
Cumberland, MD-WV
Easton, MD
Hagerstown-Martinsburg, MD-WV
Lexington Park, MD
Ocean Pines, MD
Salisbury, MD
Massachusetts
Statewide
Barnstable Town, MA
Boston-Cambridge-Quincy, MA-NH
Essex County
Middlesex County
Norfolk County
Plymouth County
Suffolk County
Pittsfield, MA
Springfield, MA
Hampden County
Worcester, MA
Michigan
Detroit-Warren-Livonia, MI
Macomb County
Wayne County
Minnesota
Brainerd, MN
Minneapolis-St. Paul-Bloomington, MN-WI
Hennepin County
Ramsey County
Missouri
Kansas City, MO-KS
Jackson County
St. Louis, MO-IL
St. Louis City
St. Louis County
New Hampshire
Statewide
Berlin, NH-VT
Claremont, NH
Concord, NH
Keene, NH
Laconia, NH
Lebanon, NH-VT
Manchester-Nashua, NH
New Mexico
Statewide
Alamogordo, NM
Albuquerque, NM
Bernalillo County
Carlsbad-Artesia, NM
Clovis, NM
Deming, NM
Española, NM
Farmington, NM
Gallup, NM
Grants, NM
Hobbs, NM
Las Cruces, NM
Las Vegas, NM
Los Alamos, NM
Portales, NM
Roswell, NM
Ruidoso, NM
Santa Fe, NM
Silver City, NM
Taos, NM
New York
Buffalo-Niagara Falls, NY
Erie County
New York-Northern New Jersey-Long Island, NY-NJ-PA
NY Suburban, NY
Suffolk County
NYC 5 Boroughs, NY
Bronx County
Kings County
New York County
Queens County
Richmond County
Newark-Edison, NJ-PA
North Dakota
Fargo, ND-MN
Grand Forks, ND-MN
Ohio
Cleveland-Elyria-Mentor, OH
Oklahoma
Statewide
Ada, OK
Altus, OK
Ardmore, OK
Bartlesville, OK
Duncan, OK
Durant, OK
Elk City, OK
Enid, OK
Guymon, OK
Lawton, OK
McAlester, OK
Miami, OK
Muskogee, OK
Oklahoma City, OK
Oklahoma County
Ponca City, OK
Shawnee, OK
Stillwater, OK
Tahlequah, OK
Tulsa, OK
Tulsa County
Woodward, OK
Oregon
Statewide
Albany-Lebanon, OR
Astoria, OR
Bend, OR
Brookings, OR
Coos Bay, OR
Corvallis, OR
Eugene-Springfield, OR
Grants Pass, OR
Hood River, OR
Klamath Falls, OR
La Grande, OR
Medford, OR
Ontario, OR-ID
Pendleton-Hermiston, OR
Portland-Vancouver-Beaverton, OR-WA
Multnomah County
Prineville, OR
Roseburg, OR
Salem, OR
The Dalles, OR
Pennsylvania
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
Delaware County
Montgomery County
Philadelphia County
Rhode Island
Statewide
Providence-New Bedford-Fall River, RI-MA
Bristol County
Providence County
South Dakota
Sioux Falls, SD
Tennessee
Kingsport-Bristol-Bristol, TN-VA
Texas
Dallas-Fort Worth-Arlington, TX
Houston-Sugar Land-Baytown, TX
Utah
Statewide
Brigham City, UT
Cedar City, UT
Heber, UT
Logan, UT-ID
Ogden-Clearfield, UT
Price, UT
Provo-Orem, UT
Salt Lake City, UT
Salt Lake County
St. George, UT
Vernal, UT
Vermont
Statewide
Barre, VT
Bennington, VT
Burlington-South Burlington, VT
Rutland, VT
Virginia
Statewide
Blacksburg-Christiansburg-Radford, VA
Charlottesville, VA
Culpeper, VA
Danville, VA
Harrisonburg, VA
Lynchburg, VA
Martinsville, VA
Richmond, VA
Roanoke, VA
Staunton-Waynesboro, VA
Virginia Beach-Norfolk-Newport News, VA-NC
Winchester, VA-WV
Washington
Seattle-Tacoma-Bellevue, WA
King County
Pierce County
Snohomish County
West Virginia
Statewide
Beckley, WV
Bluefield, WV-VA
Charleston, WV
Kanawha County
Clarksburg, WV
Fairmont, WV
Huntington-Ashland, WV-KY-OH
Morgantown, WV
Oak Hill, WV
Parkersburg-Marietta-Vienna, WV-OH
Point Pleasant, WV-OH
Weirton-Steubenville, WV-OH
Wheeling, WV-OH
Wisconsin
Milwaukee-Waukesha-West Allis, WI
List of Tables
Table 1. Participation of medical examiner/coroner jurisdictions in DAWN, 2008
Table 2. Rates of drug-related deaths and drug-related suicide deaths per 100,000 population, 2008
Table 3. Rates of drug-related deaths and percentage change, 2007 and 2008
List of Figures
Figure 1. Sample metropolitan area profile layout
List of Appendixes
Appendix A: Multum Licensing Agreement
Appendix B: Glossary of Terms
Appendix C: DAWN Mortality Data Collection
The Drug Abuse Warning Network (DAWN) is a public health surveillance system that monitors drug-related deaths referred to medical examiners and coroners (ME/Cs). In 2008, there were 544 participating ME/Cs who identified and reported to DAWN on all deaths referred to their offices that met the DAWN criteria for being a drug-related death. These ME/Cs represent the larger metropolitan and micropolitan areas in 36 states and, collectively, cover one third of the nation's population. Findings in this publication reflect data on drug-related deaths that occurred during calendar year 2008 and were reported by participating ME/Cs to DAWN. In selected tables, data from reporting year 2007 are included for comparison. The Office of Applied Studies (OAS) of the Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services, is responsible for DAWN.
The mortality component of DAWN does not rely on a statistical sample of ME/Cs. Findings cannot be considered representative of ME/Cs that did not participate, and results cannot be extrapolated to the United States as a whole. DAWN mortality data for 2003 and later are not comparable to mortality data for any years prior to 2003 because of changes introduced in the 2003 reporting year.
Since 2003, a DAWN case is any death reviewed by an ME/C that was related to recent drug use. Findings in this publication pertain to drug-related deaths and drug-related suicide deaths reported by participating death investigation jurisdictions as DAWN cases.1 The data items submitted on drug-related deaths are described in Appendix C.
DAWN cases are identified through a retrospective review of decedent case files in each participating death investigation jurisdiction. A DAWN case is any death that is determined by the ME/C as being related to drug use. The relationship between the death and the drug need not be causal; the drug need only be implicated in the death. The drug use may have been for legitimate, therapeutic purposes or for the purpose of drug abuse or misuse, but in either case, the drug use must have been recent.
These eligibility criteria for a DAWN case are intentionally broad and inclusive. Since death record documentation varies in clarity and comprehensiveness across jurisdictions, broad criteria reduce the potential for judgment calls that could cause data to vary systematically and unexpectedly across reporters and jurisdictions. Broad criteria also capture a diverse set of drug-related deaths that support a wide variety of analytical purposes and interests.
For decedents under the age of 21, DAWN cases include deaths where the only drug involved was alcohol. For those 21 or older, there must be at least one other drug involved besides alcohol for the death to be a DAWN case.
Drugs that make a death eligible for DAWN include:
Findings in this publication focus on two major categories of drug-related deaths, based on the manner of death as determined by the ME/C.
Findings reported in this publication are based on concluded investigations that were submitted by June 2009 for deaths that occurred during 2008. Death investigations that were not concluded by the ME/C by the end of the data collection period are excluded.
Death rates (i.e., the number of deaths per 100,000 population) are reported to permit comparisons within or across areas or across demographic subgroups. This use of death rates, as opposed to counts, is important because two areas with similar numbers of drug-related deaths may have vastly different populations. Rates, which take population differences into account, standardized these comparisons.
There are limitations to be considered when comparing death rates. While differences in rates may signify differences in underlying drug-related mortality (or a lack of differences may suggest similarity), other factors may confound such comparisons. For example, State laws dictate which deaths are subject to ME/C review. These laws vary by State and, within each State, by time. Within ME/C offices, toxicology testing practices vary depending on local concerns, funding, and testing technology. Such factors will affect the number of deaths determined to be DAWN cases and the number of deaths attributed to particular drugs. Even though there is no sampling error in DAWN ME/C data, the possibility of nonsampling errors (i.e., errors in reporting, differences in testing protocols) limits the interpretation of the findings.
In some circumstances, large percentage change values can be misleading because a small increase in the number of deaths can result in a large percentage difference if the base is small. For example, in 2008, Klamath Falls, OR, experienced an 800 percent increase in drug-related deaths compared with 2007. There was one death in 2007, and there were nine deaths in 2008. To get a sense of the significance of a large percentage change, it is helpful to also consider the absolute numbers of deaths, the population, and rates per 100,000 population.
DAWN relies on the voluntary cooperation of ME/Cs in selected areas of the United States to provide standardized data on drug-related deaths. For 2008, there were 373 jurisdictions in 153 metropolitan areas and 447 jurisdictions in 12 States that submitted mortality data to DAWN.3, 4
Table 1 provides information on the metropolitan areas and States that participated in 2008. It includes the following:
An awareness of the extent of DAWN's coverage within a given area is needed to interpret DAWN mortality data appropriately. ME/C participants in DAWN are not part of a scientific sample at either the metropolitan or the national level. Within a metropolitan area, findings based on participating jurisdictions are not representative of nonparticipating jurisdictions. Reports from only a portion of jurisdictions within a metropolitan area can be extrapolated neither to the metropolitan area as a whole nor to the nation as a whole.5
While the data do not support any representations at a national level, some generalizations can be made at a metropolitan level, even if some ME/Cs do not participate. For example, while only 1 (10%) of the 10 counties that make up the Houston metropolitan area participated in DAWN in 2008, that county is home to 70 percent of the area's total population. The important consideration is population coverage, not ME/C participation, per se.
Among the metropolitan areas listed in Table 1, population coverage exceeded 90 percent in 123 metropolitan areas, with 100 percent coverage in 118 of those areas. The remaining metropolitan areas had response rates that ranged from a low of 22 percent for Dallas-Fort Worth to 85 percent for St. Louis. Population coverage below 50 percent usually equates to the absence of large jurisdictions.
| State | Area* | Total jurisdictions (counties) |
Participating jurisdictions (counties) |
Percent of jurisdictions in area that participated | Population in participating jurisdictions | Percent of area population covered by participating ME/Cs |
|---|---|---|---|---|---|---|
| * Names in italics are submetropolitan areas within the larger metropolitan statistical area. NOTE: ME/Cs = medical examiners and coroners. SOURCE: Office of Applied Studies, SAMHSA, Drug Abuse Warning Network, 2008 (08/2009 update). |
||||||
| – | Twelve States | 447 | 447 | 100% | 38,172,645 | 100% |
| – | One hundred fifty-three metropolitan and micropolitan statistical areas | 525 | 373 | 71% | 111,888,133 | 76% |
| – | Four submetropolitan areas | 26 | 13 | 50% | 12,885,466 | 62% |
| – | Five hundred forty-four counties | 544 | 544 | 100% | 114,776,727 | 100% |
| Alabama | Birmingham-Hoover, AL | 7 | 1 | 14% | 659,503 | 59% |
| Arizona | Phoenix-Mesa-Scottsdale, AZ | 2 | 1 | 50% | 3,954,598 | 92% |
| Arkansas | Fort Smith, AR-OK | 5 | 2 | 40% | 90,836 | 31% |
| California | Los Angeles-Long Beach-Santa Ana, CA | 2 | 1 | 50% | 9,862,049 | 77% |
| California | San Diego-Carlsbad-San Marcos, CA | 1 | 1 | 100% | 3,001,072 | 100% |
| California | San Francisco-Oakland-Fremont, CA | 5 | 4 | 80% | 2,800,163 | 66% |
| California | San Francisco-San Mateo-Redwood City, CA | 3 | 3 | 100% | 1,770,460 | 100% |
| Colorado | Denver-Aurora-Broomfield, CO | 10 | 8 | 80% | 2,492,565 | 99% |
| District of Columbia | Washington-Arlington-Alexandria, DC-VA-MD-WV | 22 | 22 | 100% | 5,358,130 | 100% |
| Florida | Miami-Fort Lauderdale-Pompano Beach, FL | 3 | 2 | 67% | 3,663,538 | 68% |
| Georgia | Atlanta-Sandy Springs-Marietta, GA | 28 | 7 | 25% | 2,240,915 | 42% |
| Illinois | Chicago-Naperville-Joliet, IL-IN-WI | 14 | 9 | 64% | 8,758,561 | 92% |
| Indiana | Indianapolis-Carmel, IN | 10 | 2 | 20% | 1,019,538 | 59% |
| Kentucky | Louisville-Jefferson County, KY-IN | 13 | 1 | 8% | 713,877 | 57% |
| Louisiana | New Orleans-Metairie-Kenner, LA | 7 | 4 | 57% | 555,998 | 49% |
| Maine | Statewide | 16 | 16 | 100% | 1,316,456 | 100% |
| Maine | Augusta-Waterville, ME | 1 | 1 | 100% | 120,959 | 100% |
| Maine | Bangor, ME | 1 | 1 | 100% | 148,651 | 100% |
| Maine | Lewiston-Auburn, ME | 1 | 1 | 100% | 106,877 | 100% |
| Maine | Portland-South Portland-Biddeford, ME | 3 | 3 | 100% | 514,065 | 100% |
| Maine | Rockland, ME | 1 | 1 | 100% | 40,686 | 100% |
| Maryland | Statewide | 24 | 24 | 100% | 5,633,597 | 100% |
| Maryland | Baltimore-Towson, MD | 7 | 7 | 100% | 2,667,117 | 100% |
| Maryland | Cambridge, MD | 1 | 1 | 100% | 31,998 | 100% |
| Maryland | Cumberland, MD-WV | 2 | 2 | 100% | 99,033 | 100% |
| Maryland | Easton, MD | 1 | 1 | 100% | 36,215 | 100% |
| Maryland | Hagerstown-Martinsburg, MD-WV | 3 | 3 | 100% | 263,753 | 100% |
| Maryland | Lexington Park, MD | 1 | 1 | 100% | 101,578 | 100% |
| Maryland | Ocean Pines, MD | 1 | 1 | 100% | 49,274 | 100% |
| Maryland | Salisbury, MD | 2 | 2 | 100% | 120,165 | 100% |
| Massachusetts | Statewide | 14 | 14 | 100% | 6,497,967 | 100% |
| Massachusetts | Barnstable Town, MA | 1 | 1 | 100% | 221,049 | 100% |
| Massachusetts | Boston-Cambridge-Quincy, MA-NH | 7 | 7 | 100% | 4,522,858 | 100% |
| Massachusetts | Pittsfield, MA | 1 | 1 | 100% | 129,395 | 100% |
| Massachusetts | Springfield, MA | 3 | 3 | 100% | 687,558 | 100% |
| Massachusetts | Worcester, MA | 1 | 1 | 100% | 783,806 | 100% |
| Michigan | Detroit-Warren-Livonia, MI | 6 | 4 | 67% | 3,132,061 | 71% |
| Minnesota | Brainerd, MN | 2 | 1 | 50% | 28,732 | 32% |
| Minnesota | Minneapolis-St. Paul-Bloomington, MN-WI | 13 | 9 | 69% | 2,661,495 | 82% |
| Missouri | Kansas City, MO-KS | 15 | 4 | 27% | 1,068,449 | 53% |
| Missouri | St. Louis, MO-IL | 16 | 9 | 56% | 2,388,574 | 85% |
| New Hampshire | Statewide | 10 | 10 | 100% | 1,315,809 | 100% |
| New Hampshire | Berlin, NH-VT | 2 | 2 | 100% | 38,471 | 100% |
| New Hampshire | Claremont, NH | 1 | 1 | 100% | 42,591 | 100% |
| New Hampshire | Concord, NH | 1 | 1 | 100% | 148,161 | 100% |
| New Hampshire | Keene, NH | 1 | 1 | 100% | 77,170 | 100% |
| New Hampshire | Laconia, NH | 1 | 1 | 100% | 61,281 | 100% |
| New Hampshire | Lebanon, NH-VT | 3 | 3 | 100% | 171,404 | 100% |
| New Hampshire | Manchester-Nashua, NH | 1 | 1 | 100% | 402,042 | 100% |
| New Mexico | Statewide | 33 | 33 | 100% | 1,984,356 | 100% |
| New Mexico | Alamogordo, NM | 1 | 1 | 100% | 62,776 | 100% |
| New Mexico | Albuquerque, NM | 4 | 4 | 100% | 845,913 | 100% |
| New Mexico | Carlsbad-Artesia, NM | 1 | 1 | 100% | 51,360 | 100% |
| New Mexico | Clovis, NM | 1 | 1 | 100% | 43,755 | 100% |
| New Mexico | Deming, NM | 1 | 1 | 100% | 27,227 | 100% |
| New Mexico | Española, NM | 1 | 1 | 100% | 40,692 | 100% |
| New Mexico | Farmington, NM | 1 | 1 | 100% | 122,500 | 100% |
| New Mexico | Gallup, NM | 1 | 1 | 100% | 70,724 | 100% |
| New Mexico | Grants, NM | 1 | 1 | 100% | 27,285 | 100% |
| New Mexico | Hobbs, NM | 1 | 1 | 100% | 59,155 | 100% |
| New Mexico | Las Cruces, NM | 1 | 1 | 100% | 201,603 | 100% |
| New Mexico | Las Vegas, NM | 1 | 1 | 100% | 28,558 | 100% |
| New Mexico | Los Alamos, NM | 1 | 1 | 100% | 18,150 | 100% |
| New Mexico | Portales, NM | 1 | 1 | 100% | 18,889 | 100% |
| New Mexico | Roswell, NM | 1 | 1 | 100% | 63,060 | 100% |
| New Mexico | Ruidoso, NM | 1 | 1 | 100% | 20,793 | 100% |
| New Mexico | Santa Fe, NM | 1 | 1 | 100% | 143,937 | 100% |
| New Mexico | Silver City, NM | 1 | 1 | 100% | 29,844 | 100% |
| New Mexico | Taos, NM | 1 | 1 | 100% | 31,546 | 100% |
| New York | Buffalo-Niagara Falls, NY | 2 | 2 | 100% | 1,124,309 | 100% |
| New York | New York-Northern New Jersey-Long Island, NY-NJ-PA | 23 | 10 | 43% | 11,115,006 | 58% |
| New York | NY Suburban, NY | 5 | 2 | 40% | 1,611,468 | 38% |
| New York | NYC 5 Boroughs, NY | 5 | 5 | 100% | 8,363,710 | 100% |
| New York | Newark-Edison, NJ-PA | 13 | 3 | 23% | 1,139,828 | 18% |
| North Dakota | Fargo, ND-MN | 2 | 2 | 100% | 195,685 | 100% |
| North Dakota | Grand Forks, ND-MN | 2 | 1 | 50% | 30,694 | 32% |
| Ohio | Cleveland-Elyria-Mentor, OH | 5 | 1 | 20% | 1,283,925 | 61% |
| Oklahoma | Statewide | 77 | 77 | 100% | 3,642,361 | 100% |
| Oklahoma | Ada, OK | 1 | 1 | 100% | 36,999 | 100% |
| Oklahoma | Altus, OK | 1 | 1 | 100% | 25,236 | 100% |
| Oklahoma | Ardmore, OK | 2 | 2 | 100% | 57,134 | 100% |
| Oklahoma | Bartlesville, OK | 1 | 1 | 100% | 50,452 | 100% |
| Oklahoma | Duncan, OK | 1 | 1 | 100% | 43,498 | 100% |
| Oklahoma | Durant, OK | 1 | 1 | 100% | 40,109 | 100% |
| Oklahoma | Elk City, OK | 1 | 1 | 100% | 21,136 | 100% |
| Oklahoma | Enid, OK | 1 | 1 | 100% | 58,167 | 100% |
| Oklahoma | Guymon, OK | 1 | 1 | 100% | 20,283 | 100% |
| Oklahoma | Lawton, OK | 1 | 1 | 100% | 111,772 | 100% |
| Oklahoma | McAlester, OK | 1 | 1 | 100% | 45,115 | 100% |
| Oklahoma | Miami, OK | 1 | 1 | 100% | 31,849 | 100% |
| Oklahoma | Muskogee, OK | 1 | 1 | 100% | 71,278 | 100% |
| Oklahoma | Oklahoma City, OK | 7 | 7 | 100% | 1,206,142 | 100% |
| Oklahoma | Ponca City, OK | 1 | 1 | 100% | 45,632 | 100% |
| Oklahoma | Shawnee, OK | 1 | 1 | 100% | 69,616 | 100% |
| Oklahoma | Stillwater, OK | 1 | 1 | 100% | 78,280 | 100% |
| Oklahoma | Tahlequah, OK | 1 | 1 | 100% | 45,733 | 100% |
| Oklahoma | Tulsa, OK | 7 | 7 | 100% | 916,079 | 100% |
| Oklahoma | Woodward, OK | 1 | 1 | 100% | 19,838 | 100% |
| Oregon | Statewide | 36 | 36 | 100% | 3,790,060 | 100% |
| Oregon | Albany-Lebanon, OR | 1 | 1 | 100% | 115,348 | 100% |
| Oregon | Astoria, OR | 1 | 1 | 100% | 37,404 | 100% |
| Oregon | Bend, OR | 1 | 1 | 100% | 158,456 | 100% |
| Oregon | Brookings, OR | 1 | 1 | 100% | 21,523 | 100% |
| Oregon | Coos Bay, OR | 1 | 1 | 100% | 63,453 | 100% |
| Oregon | Corvallis, OR | 1 | 1 | 100% | 81,859 | 100% |
| Oregon | Eugene-Springfield, OR | 1 | 1 | 100% | 346,560 | 100% |
| Oregon | Grants Pass, OR | 1 | 1 | 100% | 81,618 | 100% |
| Oregon | Hood River, OR | 1 | 1 | 100% | 21,536 | 100% |
| Oregon | Klamath Falls, OR | 1 | 1 | 100% | 66,425 | 100% |
| Oregon | La Grande, OR | 1 | 1 | 100% | 24,961 | 100% |
| Oregon | Medford, OR | 1 | 1 | 100% | 201,138 | 100% |
| Oregon | Ontario, OR-ID | 2 | 1 | 50% | 30,907 | 57% |
| Oregon | Pendleton-Hermiston, OR | 2 | 2 | 100% | 84,666 | 100% |
| Oregon | Portland-Vancouver-Beaverton, OR-WA | 7 | 5 | 71% | 1,771,935 | 80% |
| Oregon | Prineville, OR | 1 | 1 | 100% | 23,023 | 100% |
| Oregon | Roseburg, OR | 1 | 1 | 100% | 104,059 | 100% |
| Oregon | Salem, OR | 2 | 2 | 100% | 391,680 | 100% |
| Oregon | The Dalles, OR | 1 | 1 | 100% | 23,775 | 100% |
| Pennsylvania | Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 11 | 5 | 45% | 3,500,631 | 60% |
| Rhode Island | Statewide | 5 | 5 | 100% | 1,050,788 | 100% |
| Rhode Island | Providence-New Bedford-Fall River, RI-MA | 6 | 6 | 100% | 1,596,611 | 100% |
| South Dakota | Sioux Falls, SD | 4 | 1 | 25% | 179,180 | 77% |
| Tennessee | Kingsport-Bristol-Bristol, TN-VA | 5 | 3 | 60% | 93,312 | 31% |
| Texas | Dallas-Fort Worth-Arlington, TX | 12 | 2 | 17% | 1,398,567 | 22% |
| Texas | Houston-Sugar Land-Baytown, TX | 10 | 1 | 10% | 3,984,349 | 70% |
| Utah | Statewide | 29 | 29 | 100% | 2,736,424 | 100% |
| Utah | Brigham City, UT | 1 | 1 | 100% | 49,015 | 100% |
| Utah | Cedar City, UT | 1 | 1 | 100% | 44,540 | 100% |
| Utah | Heber, UT | 1 | 1 | 100% | 21,066 | 100% |
| Utah | Logan, UT-ID | 2 | 1 | 50% | 112,616 | 90% |
| Utah | Ogden-Clearfield, UT | 3 | 3 | 100% | 531,488 | 100% |
| Utah | Price, UT | 1 | 1 | 100% | 19,549 | 100% |
| Utah | Provo-Orem, UT | 2 | 2 | 100% | 540,820 | 100% |
| Utah | Salt Lake City, UT | 3 | 3 | 100% | 1,115,692 | 100% |
| Utah | St. George, UT | 1 | 1 | 100% | 137,589 | 100% |
| Utah | Vernal, UT | 1 | 1 | 100% | 29,885 | 100% |
| Vermont | Statewide | 14 | 14 | 100% | 621,270 | 100% |
| Vermont | Barre, VT | 1 | 1 | 100% | 58,829 | 100% |
| Vermont | Bennington, VT | 1 | 1 | 100% | 36,382 | 100% |
| Vermont | Burlington-South Burlington, VT | 3 | 3 | 100% | 208,460 | 100% |
| Vermont | Rutland, VT | 1 | 1 | 100% | 63,331 | 100% |
| Virginia | Statewide | 134 | 134 | 100% | 7,769,089 | 100% |
| Virginia | Blacksburg-Christiansburg-Radford, VA | 4 | 4 | 100% | 158,328 | 100% |
| Virginia | Charlottesville, VA | 5 | 5 | 100% | 194,391 | 100% |
| Virginia | Culpeper, VA | 1 | 1 | 100% | 46,203 | 100% |
| Virginia | Danville, VA | 2 | 2 | 100% | 105,783 | 100% |
| Virginia | Harrisonburg, VA | 2 | 2 | 100% | 118,409 | 100% |
| Virginia | Lynchburg, VA | 6 | 6 | 100% | 245,809 | 100% |
| Virginia | Martinsville, VA | 2 | 2 | 100% | 69,859 | 100% |
| Virginia | Richmond, VA | 20 | 20 | 100% | 1,225,626 | 100% |
| Virginia | Roanoke, VA | 6 | 6 | 100% | 298,108 | 100% |
| Virginia | Staunton-Waynesboro, VA | 3 | 3 | 100% | 117,170 | 100% |
| Virginia | Virginia Beach-Norfolk-Newport News, VA-NC | 16 | 15 | 94% | 1,634,109 | 99% |
| Virginia | Winchester, VA-WV | 3 | 3 | 100% | 122,369 | 100% |
| Washington | Seattle-Tacoma-Bellevue, WA | 3 | 3 | 100% | 3,344,813 | 100% |
| West Virginia | Statewide | 55 | 55 | 100% | 1,814,468 | 100% |
| West Virginia | Beckley, WV | 1 | 1 | 100% | 79,357 | 100% |
| West Virginia | Bluefield, WV-VA | 2 | 2 | 100% | 105,287 | 100% |
| West Virginia | Charleston, WV | 5 | 5 | 100% | 303,944 | 100% |
| West Virginia | Clarksburg, WV | 3 | 3 | 100% | 92,212 | 100% |
| West Virginia | Fairmont, WV | 1 | 1 | 100% | 56,496 | 100% |
| West Virginia | Huntington-Ashland, WV-KY-OH | 5 | 2 | 40% | 135,713 | 48% |
| West Virginia | Morgantown, WV | 2 | 2 | 100% | 118,506 | 100% |
| West Virginia | Oak Hill, WV | 1 | 1 | 100% | 46,341 | 100% |
| West Virginia | Parkersburg-Marietta-Vienna, WV-OH | 4 | 3 | 75% | 99,111 | 62% |
| West Virginia | Point Pleasant, WV-OH | 2 | 1 | 50% | 25,678 | 45% |
| West Virginia | Weirton-Steubenville, WV-OH | 3 | 2 | 67% | 53,528 | 44% |
| West Virginia | Wheeling, WV-OH | 3 | 2 | 67% | 76,872 | 53% |
| Wisconsin | Milwaukee-Waukesha-West Allis, WI | 4 | 1 | 25% | 953,328 | 62% |
Table 2 reports the rates of drug-related deaths and drug-related suicide deaths per 100,000 population for metropolitan areas and States that participated in DAWN in 2008. Table 3 compares the rates of drug-related deaths in 2008 with those found for 2007 and reports the percentage change. (Comparisons are not made for drug-related suicide deaths because of their small numbers.) Table 3 is limited to those areas where the same jurisdictions participated in 2007 as in 2008. Both tables include indicators of the population coverage in DAWN for 2008.
| State | Area* | Rate of drug-related deaths per 100,000 population† | Rate of drug-related suicide deaths per 100,000 population | Population in participating jurisdictions | Percent of area population covered by participating ME/Cs |
|---|---|---|---|---|---|
| * Names in italics are submetropolitan areas within the larger metropolitan statistical area. † Drug-related deaths exclude drug-related suicide deaths. NOTE: ME/Cs = medical examiners and coroners. SOURCE: Office of Applied Studies, SAMHSA, Drug Abuse Warning Network, 2008 (08/2009 update). |
|||||
| Alabama | Birmingham-Hoover, AL | 12.1 | 0.8 | 659,503 | 59% |
| Arizona | Phoenix-Mesa-Scottsdale, AZ | 16.9 | 3.1 | 3,954,598 | 92% |
| Arkansas | Fort Smith, AR-OK | 16.5 | 2.2 | 90,836 | 31% |
| California | Los Angeles-Long Beach-Santa Ana, CA | 9.4 | 1.5 | 9,862,049 | 77% |
| California | San Diego-Carlsbad-San Marcos, CA | 12.7 | 2.8 | 3,001,072 | 100% |
| California | San Francisco-Oakland-Fremont, CA | 12.8 | 2.4 | 2,800,163 | 66% |
| California | Contra Costa County | 9.4 | 2.2 | 1,029,703 | 100% |
| California | San Francisco-San Mateo-Redwood City, CA | 14.8 | 2.5 | 1,770,460 | 100% |
| California | San Francisco County | 22.4 | 2.6 | 808,976 | 100% |
| Colorado | Denver-Aurora-Broomfield, CO | 14.8 | 2.9 | 2,492,565 | 99% |
| Colorado | Arapahoe County | 13.7 | 2.9 | 554,282 | 100% |
| Colorado | Denver County | 30.1 | 4.2 | 598,707 | 100% |
| District of Columbia | Washington-Arlington-Alexandria, DC-VA-MD-WV | 7.7 | 1.6 | 5,358,130 | 100% |
| District of Columbia | District of Columbia | 29.2 | 3.5 | 591,833 | 100% |
| District of Columbia | Prince George's County | 7.3 | 0.4 | 820,852 | 100% |
| Florida | Miami-Fort Lauderdale-Pompano Beach, FL | 10.0 | 1.9 | 3,663,538 | 68% |
| Florida | Miami-Dade County | 9.1 | 1.6 | 2,398,245 | 100% |
| Florida | Palm Beach County | 11.6 | 2.4 | 1,265,293 | 100% |
| Georgia | Atlanta-Sandy Springs-Marietta, GA | 9.1 | 1.7 | 2,240,915 | 42% |
| Georgia | Fulton County | 12.0 | 1.8 | 1,014,932 | 100% |
| Illinois | Chicago-Naperville-Joliet, IL-IN-WI | 9.7 | 0.9 | 8,758,561 | 92% |
| Illinois | Cook County | 10.7 | 0.7 | 5,294,664 | 100% |
| Illinois | Lake County | 11.2 | 1.1 | 712,453 | 100% |
| Indiana | Indianapolis-Carmel, IN | 16.4 | 1.7 | 1,019,538 | 59% |
| Indiana | Marion County | 16.8 | 1.9 | 880,380 | 100% |
| Kentucky | Louisville-Jefferson County, KY-IN | 14.0 | 2.8 | 713,877 | 57% |
| Louisiana | New Orleans-Metairie-Kenner, LA | 19.4 | 1.6 | 555,998 | 49% |
| Louisiana | Jefferson Parish | 20.6 | 1.4 | 436,181 | 100% |
| Maine | Statewide | 11.4 | 2.1 | 1,316,456 | 100% |
| Maine | Augusta-Waterville, ME | 7.4 | 2.5 | 120,959 | 100% |
| Maine | Bangor, ME | 14.8 | 2.0 | 148,651 | 100% |
| Maine | Lewiston-Auburn, ME | 15.0 | 1.9 | 106,877 | 100% |
| Maine | Portland-South Portland-Biddeford, ME | 13.8 | 2.5 | 514,065 | 100% |
| Maine | Rockland, ME | 12.3 | 4.9 | 40,686 | 100% |
| Maryland | Statewide | 11.6 | 1.0 | 5,633,597 | 100% |
| Maryland | Baltimore-Towson, MD | 15.6 | 1.0 | 2,667,117 | 100% |
| Maryland | Anne Arundel County | 12.3 | 1.0 | 512,790 | 100% |
| Maryland | Baltimore City | 30.0 | 0.8 | 636,919 | 100% |
| Maryland | Baltimore County | 11.8 | 1.5 | 785,618 | 100% |
| Maryland | Cambridge, MD | 12.5 | 0.0 | 31,998 | 100% |
| Maryland | Cumberland, MD-WV | 15.1 | 2.0 | 99,033 | 100% |
| Maryland | Easton, MD | 22.1 | 5.5 | 36,215 | 100% |
| Maryland | Hagerstown-Martinsburg, MD-WV | 18.2 | 1.1 | 263,753 | 100% |
| Maryland | Lexington Park, MD | 12.8 | 0.0 | 101,578 | 100% |
| Maryland | Ocean Pines, MD | 20.3 | 2.0 | 49,274 | 100% |
| Maryland | Salisbury, MD | 12.5 | 2.5 | 120,165 | 100% |
| Massachusetts | Statewide | 14.1 | 1.5 | 6,497,967 | 100% |
| Massachusetts | Barnstable Town, MA | 13.6 | 1.8 | 221,049 | 100% |
| Massachusetts | Boston-Cambridge-Quincy, MA-NH | 12.9 | 1.7 | 4,522,858 | 100% |
| Massachusetts | Essex County | 12.5 | 2.3 | 736,457 | 100% |
| Massachusetts | Middlesex County | 10.8 | 1.5 | 1,482,478 | 100% |
| Massachusetts | Norfolk County | 11.7 | 0.9 | 659,909 | 100% |
| Massachusetts | Plymouth County | 13.6 | 1.6 | 492,066 | 100% |
| Massachusetts | Suffolk County | 20.9 | 1.8 | 732,684 | 100% |
| Massachusetts | Pittsfield, MA | 10.0 | 3.1 | 129,395 | 100% |
| Massachusetts | Springfield, MA | 14.8 | 0.9 | 687,558 | 100% |
| Massachusetts | Hampden County | 17.8 | 0.9 | 460,840 | 100% |
| Massachusetts | Worcester, MA | 14.4 | 1.8 | 783,806 | 100% |
| Michigan | Detroit-Warren-Livonia, MI | 19.6 | 1.5 | 3,132,061 | 71% |
| Michigan | Macomb County | 17.6 | 2.3 | 830,663 | 100% |
| Michigan | Wayne County | 21.8 | 1.0 | 1,949,929 | 100% |
| Minnesota | Brainerd, MN | 17.4 | 0.0 | 28,732 | 32% |
| Minnesota | Minneapolis-St. Paul-Bloomington, MN-WI | 8.2 | 1.7 | 2,661,495 | 82% |
| Minnesota | Hennepin County | 8.6 | 1.2 | 1,140,988 | 100% |
| Minnesota | Ramsey County | 13.4 | 3.4 | 501,428 | 100% |
| Missouri | Kansas City, MO-KS | 12.4 | 2.4 | 1,068,449 | 53% |
| Missouri | Jackson County | 13.3 | 2.8 | 668,417 | 100% |
| Missouri | St. Louis, MO-IL | 12.1 | 1.6 | 2,388,574 | 85% |
| Missouri | St. Louis City | 21.7 | 2.3 | 354,361 | 100% |
| Missouri | St. Louis County | 10.9 | 1.9 | 991,830 | 100% |
| New Hampshire | Statewide | 9.3 | 2.0 | 1,315,809 | 100% |
| New Hampshire | Berlin, NH-VT | 13.0 | 0.0 | 38,471 | 100% |
| New Hampshire | Claremont, NH | 9.4 | 2.3 | 42,591 | 100% |
| New Hampshire | Concord, NH | 10.1 | 1.3 | 148,161 | 100% |
| New Hampshire | Keene, NH | 2.6 | 0.0 | 77,170 | 100% |
| New Hampshire | Laconia, NH | 13.1 | 1.6 | 61,281 | 100% |
| New Hampshire | Lebanon, NH-VT | 14.0 | 3.5 | 171,404 | 100% |
| New Hampshire | Manchester-Nashua, NH | 9.9 | 2.2 | 402,042 | 100% |
| New Mexico | Statewide | 22.0 | 4.0 | 1,984,356 | 100% |
| New Mexico | Alamogordo, NM | 12.7 | 8.0 | 62,776 | 100% |
| New Mexico | Albuquerque, NM | 27.1 | 4.4 | 845,913 | 100% |
| New Mexico | Bernalillo County | 31.6 | 4.9 | 635,139 | 100% |
| New Mexico | Carlsbad-Artesia, NM | 25.3 | 0.0 | 51,360 | 100% |
| New Mexico | Clovis, NM | 11.4 | 0.0 | 43,755 | 100% |
| New Mexico | Deming, NM | 0.0 | 3.7 | 27,227 | 100% |
| New Mexico | Española, NM | 61.4 | 2.5 | 40,692 | 100% |
| New Mexico | Farmington, NM | 11.4 | 2.4 | 122,500 | 100% |
| New Mexico | Gallup, NM | 14.1 | 2.8 | 70,724 | 100% |
| New Mexico | Grants, NM | 18.3 | 0.0 | 27,285 | 100% |
| New Mexico | Hobbs, NM | 23.7 | 0.0 | 59,155 | 100% |
| New Mexico | Las Cruces, NM | 13.4 | 3.0 | 201,603 | 100% |
| New Mexico | Las Vegas, NM | 35.0 | 0.0 | 28,558 | 100% |
| New Mexico | Los Alamos, NM | 11.0 | 5.5 | 18,150 | 100% |
| New Mexico | Portales, NM | 0.0 | 5.3 | 18,889 | 100% |
| New Mexico | Roswell, NM | 17.4 | 1.6 | 63,060 | 100% |
| New Mexico | Ruidoso, NM | 4.8 | 4.8 | 20,793 | 100% |
| New Mexico | Santa Fe, NM | 18.8 | 8.3 | 143,937 | 100% |
| New Mexico | Silver City, NM | 30.2 | 16.8 | 29,844 | 100% |
| New Mexico | Taos, NM | 28.5 | 3.2 | 31,546 | 100% |
| New York | Buffalo-Niagara Falls, NY | 10.1 | 1.2 | 1,124,309 | 100% |
| New York | Erie County | 9.9 | 1.0 | 909,845 | 100% |
| New York | New York-Northern New Jersey-Long Island, NY-NJ-PA | 10.0 | 1.1 | 11,115,006 | 58% |
| New York | NY Suburban, NY | 12.5 | 1.8 | 1,611,468 | 38% |
| New York | Suffolk County | 12.4 | 1.8 | 1,512,224 | 100% |
| New York | NYC 5 Boroughs, NY | 9.7 | 0.8 | 8,363,710 | 100% |
| New York | Bronx County | 13.6 | 0.5 | 1,391,903 | 100% |
| New York | Kings County | 9.1 | 0.6 | 2,556,598 | 100% |
| New York | New York County | 9.4 | 1.7 | 1,634,795 | 100% |
| New York | Queens County | 7.5 | 0.8 | 2,293,007 | 100% |
| New York | Richmond County | 13.3 | 0.4 | 487,407 | 100% |
| New York | Newark-Edison, NJ-PA | 9.1 | 1.8 | 1,139,828 | 18% |
| North Dakota | Fargo, ND-MN | 3.6 | 0.5 | 195,685 | 100% |
| North Dakota | Grand Forks, ND-MN | 9.8 | 0.0 | 30,694 | 32% |
| Ohio | Cleveland-Elyria-Mentor, OH | 17.5 | 1.9 | 1,283,925 | 61% |
| Oklahoma | Statewide | 16.0 | 2.1 | 3,642,361 | 100% |
| Oklahoma | Ada, OK | 13.5 | 2.7 | 36,999 | 100% |
| Oklahoma | Altus, OK | 0.0 | 4.0 | 25,236 | 100% |
| Oklahoma | Ardmore, OK | 17.5 | 0.0 | 57,134 | 100% |
| Oklahoma | Bartlesville, OK | 9.9 | 4.0 | 50,452 | 100% |
| Oklahoma | Duncan, OK | 18.4 | 0.0 | 43,498 | 100% |
| Oklahoma | Durant, OK | 24.9 | 5.0 | 40,109 | 100% |
| Oklahoma | Elk City, OK | 23.7 | 4.7 | 21,136 | 100% |
| Oklahoma | Enid, OK | 13.8 | 5.2 | 58,167 | 100% |
| Oklahoma | Guymon, OK | 14.8 | 0.0 | 20,283 | 100% |
| Oklahoma | Lawton, OK | 10.7 | 1.8 | 111,772 | 100% |
| Oklahoma | McAlester, OK | 11.1 | 6.6 | 45,115 | 100% |
| Oklahoma | Miami, OK | 15.7 | 3.1 | 31,849 | 100% |
| Oklahoma | Muskogee, OK | 33.7 | 2.8 | 71,278 | 100% |
| Oklahoma | Oklahoma City, OK | 14.1 | 1.8 | 1,206,142 | 100% |
| Oklahoma | Oklahoma County | 15.3 | 2.7 | 706,617 | 100% |
| Oklahoma | Ponca City, OK | 11.0 | 2.2 | 45,632 | 100% |
| Oklahoma | Shawnee, OK | 23.0 | 1.4 | 69,616 | 100% |
| Oklahoma | Stillwater, OK | 7.7 | 1.3 | 78,280 | 100% |
| Oklahoma | Tahlequah, OK | 37.2 | 2.2 | 45,733 | 100% |
| Oklahoma | Tulsa, OK | 19.4 | 1.9 | 916,079 | 100% |
| Oklahoma | Tulsa County | 20.1 | 2.0 | 591,982 | 100% |
| Oklahoma | Woodward, OK | 15.1 | 0.0 | 19,838 | 100% |
| Oregon | Statewide | 12.2 | 2.9 | 3,790,060 | 100% |
| Oregon | Albany-Lebanon, OR | 5.2 | 0.0 | 115,348 | 100% |
| Oregon | Astoria, OR | 10.7 | 5.3 | 37,404 | 100% |
| Oregon | Bend, OR | 13.9 | 4.4 | 158,456 | 100% |
| Oregon | Brookings, OR | 4.6 | 0.0 | 21,523 | 100% |
| Oregon | Coos Bay, OR | 3.2 | 1.6 | 63,453 | 100% |
| Oregon | Corvallis, OR | 3.7 | 1.2 | 81,859 | 100% |
| Oregon | Eugene-Springfield, OR | 14.4 | 4.3 | 346,560 | 100% |
| Oregon | Grants Pass, OR | 4.9 | 0.0 | 81,618 | 100% |
| Oregon | Hood River, OR | 13.9 | 0.0 | 21,536 | 100% |
| Oregon | Klamath Falls, OR | 13.5 | 3.0 | 66,425 | 100% |
| Oregon | La Grande, OR | 0.0 | 4.0 | 24,961 | 100% |
| Oregon | Medford, OR | 15.4 | 6.0 | 201,138 | 100% |
| Oregon | Ontario, OR-ID | 9.7 | 0.0 | 30,907 | 57% |
| Oregon | Pendleton-Hermiston, OR | 8.3 | 5.9 | 84,666 | 100% |
| Oregon | Portland-Vancouver-Beaverton, OR-WA | 13.7 | 3.3 | 1,771,935 | 80% |
| Oregon | Multnomah County | 25.2 | 4.5 | 714,567 | 100% |
| Oregon | Prineville, OR | 8.7 | 0.0 | 23,023 | 100% |
| Oregon | Roseburg, OR | 6.7 | 0.0 | 104,059 | 100% |
| Oregon | Salem, OR | 10.5 | 0.8 | 391,680 | 100% |
| Oregon | The Dalles, OR | 21.0 | 0.0 | 23,775 | 100% |
| Pennsylvania | Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 19.7 | 1.3 | 3,500,631 | 60% |
| Pennsylvania | Delaware County | 19.3 | 2.3 | 553,619 | 100% |
| Pennsylvania | Montgomery County | 9.8 | 1.4 | 778,048 | 100% |
| Pennsylvania | Philadelphia County | 30.5 | 1.0 | 1,447,395 | 100% |
| Rhode Island | Statewide | 18.4 | 3.2 | 1,050,788 | 100% |
| Rhode Island | Providence-New Bedford-Fall River, RI-MA | 18.7 | 2.5 | 1,596,611 | 100% |
| Rhode Island | Bristol County | 19.4 | 1.1 | 545,823 | 100% |
| Rhode Island | Providence County | 20.1 | 3.7 | 626,150 | 100% |
| South Dakota | Sioux Falls, SD | 0.6 | 2.2 | 179,180 | 77% |
| Tennessee | Kingsport-Bristol-Bristol, TN-VA | 12.9 | 2.1 | 93,312 | 31% |
| Texas | Dallas-Fort Worth-Arlington, TX | 5.8 | 1.7 | 1,398,567 | 22% |
| Texas | Houston-Sugar Land-Baytown, TX | 11.3 | 1.9 | 3,984,349 | 70% |
| Utah | Statewide | 15.1 | 2.2 | 2,736,424 | 100% |
| Utah | Brigham City, UT | 12.2 | 0.0 | 49,015 | 100% |
| Utah | Cedar City, UT | 13.5 | 0.0 | 44,540 | 100% |
| Utah | Heber, UT | 9.5 | 0.0 | 21,066 | 100% |
| Utah | Logan, UT-ID | 7.1 | 0.9 | 112,616 | 90% |
| Utah | Ogden-Clearfield, UT | 17.9 | 2.6 | 531,488 | 100% |
| Utah | Price, UT | 5.1 | 5.1 | 19,549 | 100% |
| Utah | Provo-Orem, UT | 10.9 | 0.7 | 540,820 | 100% |
| Utah | Salt Lake City, UT | 17.6 | 3.1 | 1,115,692 | 100% |
| Utah | Salt Lake County | 17.8 | 3.2 | 1,022,651 | 100% |
| Utah | St. George, UT | 12.4 | 0.7 | 137,589 | 100% |
| Utah | Vernal, UT | 3.3 | 0.0 | 29,885 | 100% |
| Vermont | Statewide | 11.6 | 1.9 | 621,270 | 100% |
| Vermont | Barre, VT | 10.2 | 1.7 | 58,829 | 100% |
| Vermont | Bennington, VT | 11.0 | 0.0 | 36,382 | 100% |
| Vermont | Burlington-South Burlington, VT | 10.6 | 1.9 | 208,460 | 100% |
| Vermont | Rutland, VT | 11.1 | 1.6 | 63,331 | 100% |
| Virginia | Statewide | 7.0 | 1.6 | 7,769,089 | 100% |
| Virginia | Blacksburg-Christiansburg-Radford, VA | 17.7 | 0.0 | 158,328 | 100% |
| Virginia | Charlottesville, VA | 3.6 | 1.5 | 194,391 | 100% |
| Virginia | Culpeper, VA | 13.0 | 2.2 | 46,203 | 100% |
| Virginia | Danville, VA | 10.4 | 0.0 | 105,783 | 100% |
| Virginia | Harrisonburg, VA | 2.5 | 2.5 | 118,409 | 100% |
| Virginia | Lynchburg, VA | 4.1 | 0.8 | 245,809 | 100% |
| Virginia | Martinsville, VA | 27.2 | 4.3 | 69,859 | 100% |
| Virginia | Richmond, VA | 7.8 | 1.1 | 1,225,626 | 100% |
| Virginia | Roanoke, VA | 12.4 | 3.0 | 298,108 | 100% |
| Virginia | Staunton-Waynesboro, VA | 13.7 | 2.6 | 117,170 | 100% |
| Virginia | Virginia Beach-Norfolk-Newport News, VA-NC | 6.2 | 1.1 | 1,634,109 | 99% |
| Virginia | Winchester, VA-WV | 11.4 | 4.1 | 122,369 | 100% |
| Washington | Seattle-Tacoma-Bellevue, WA | 17.0 | 2.1 | 3,344,813 | 100% |
| Washington | King County | 16.3 | 2.0 | 1,875,519 | 100% |
| Washington | Pierce County | 19.1 | 2.9 | 785,639 | 100% |
| Washington | Snohomish County | 16.7 | 1.5 | 683,655 | 100% |
| West Virginia | Statewide | 25.5 | 2.1 | 1,814,468 | 100% |
| West Virginia | Beckley, WV | 25.2 | 3.8 | 79,357 | 100% |
| West Virginia | Bluefield, WV-VA | 37.0 | 0.9 | 105,287 | 100% |
| West Virginia | Charleston, WV | 30.3 | 2.0 | 303,944 | 100% |
| West Virginia | Kanawha County | 33.5 | 2.1 | 191,018 | 100% |
| West Virginia | Clarksburg, WV | 18.4 | 4.3 | 92,212 | 100% |
| West Virginia | Fairmont, WV | 14.2 | 5.3 | 56,496 | 100% |
| West Virginia | Huntington-Ashland, WV-KY-OH | 31.7 | 1.5 | 135,713 | 48% |
| West Virginia | Morgantown, WV | 17.7 | 0.8 | 118,506 | 100% |
| West Virginia | Oak Hill, WV | 23.7 | 2.2 | 46,341 | 100% |
| West Virginia | Parkersburg-Marietta-Vienna, WV-OH | 13.1 | 4.0 | 99,111 | 62% |
| West Virginia | Point Pleasant, WV-OH | 23.4 | 0.0 | 25,678 | 45% |
| West Virginia | Weirton-Steubenville, WV-OH | 24.3 | 0.0 | 53,528 | 44% |
| West Virginia | Wheeling, WV-OH | 14.3 | 1.3 | 76,872 | 53% |
| Wisconsin | Milwaukee-Waukesha-West Allis, WI | 18.9 | 1.0 | 953,328 | 62% |
| State | Area* | Rate of drug-related deaths per 100,000 population, 2007† | Rate of drug-related deaths per 100,000 population, 2008† | Percent change in rate, 2007 to 2008 | Population in participating jurisdictions, 2008 | Percent of area population covered by participating ME/Cs, 2008 |
|---|---|---|---|---|---|---|
|
* Names in italics are submetropolitan areas within the larger metropolitan statistical area. † Drug-related deaths exclude drug-related suicide deaths. NOTE: ME/Cs = medical examiners and coroners. NC = no rate calculated because different jurisdictions participated in 2007 and 2008. NP = no percent change calculated, as there were no deaths in one or both years. SOURCE: Office of Applied Studies, SAMHSA, Drug Abuse Warning Network, 2008 (08/2009 update). |
||||||
| Alabama | Birmingham-Hoover, AL | 17.5 | 12.1 | -30.5 | 659,503 | 59% |
| Arizona | Phoenix-Mesa-Scottsdale, AZ | 17.3 | 16.9 | -2.0 | 3,954,598 | 92% |
| Arkansas | Fort Smith, AR-OK | 15.5 | 16.5 | 6.7 | 90,836 | 31% |
| California | Los Angeles-Long Beach-Santa Ana, CA | 10.0 | 9.4 | -6.3 | 9,862,049 | 77% |
| California | San Diego-Carlsbad-San Marcos, CA | 12.0 | 12.7 | 5.9 | 3,001,072 | 100% |
| California | San Francisco-Oakland-Fremont, CA | 13.9 | 12.8 | -7.5 | 2,800,163 | 66% |
| California | Contra Costa County | 8.1 | 9.4 | 16.6 | 1,029,703 | 100% |
| California | San Francisco-San Mateo-Redwood City, CA | 17.2 | 14.8 | -14.1 | 1,770,460 | 100% |
| California | San Francisco County | 28.8 | 22.4 | -22.3 | 808,976 | 100% |
| Colorado | Denver-Aurora-Broomfield, CO | NC | NC | NC | 2,492,565 | 99% |
| Colorado | Arapahoe County | 15.6 | 13.7 | -12.4 | 554,282 | 100% |
| Colorado | Denver County | 29.7 | 30.1 | 1.3 | 598,707 | 100% |
| District of Columbia | Washington-Arlington-Alexandria, DC-VA-MD-WV | NC | NC | NC | 5,358,130 | 100% |
| District of Columbia | District of Columbia | 36.4 | 29.2 | -19.7 | 591,833 | 100% |
| District of Columbia | Prince George's County | 7.3 | 7.3 | 0.5 | 820,852 | 100% |
| Florida | Miami-Fort Lauderdale-Pompano Beach, FL | NC | NC | NC | 3,663,538 | 68% |
| Florida | Miami-Dade County | NC | NC | NC | 2,398,245 | 100% |
| Florida | Palm Beach County | NC | NC | NC | 1,265,293 | 100% |
| Georgia | Atlanta-Sandy Springs-Marietta, GA | 8.2 | 9.1 | 10.7 | 2,240,915 | 42% |
| Georgia | Fulton County | 10.3 | 12.0 | 17.0 | 1,014,932 | 100% |
| Illinois | Chicago-Naperville-Joliet, IL-IN-WI | 9.2 | 9.7 | 6.0 | 8,758,561 | 92% |
| Illinois | Cook County | 10.3 | 10.7 | 4.0 | 5,294,664 | 100% |
| Illinois | Lake County | 10.8 | 11.2 | 4.4 | 712,453 | 100% |
| Indiana | Indianapolis-Carmel, IN | 12.0 | 16.4 | 37.0 | 1,019,538 | 59% |
| Indiana | Marion County | 12.0 | 16.8 | 40.3 | 880,380 | 100% |
| Kentucky | Louisville-Jefferson County, KY-IN | NC | NC | NC | 713,877 | 57% |
| Louisiana | New Orleans-Metairie-Kenner, LA | 25.3 | 19.4 | -23.2 | 555,998 | 49% |
| Louisiana | Jefferson Parish | 27.3 | 20.6 | -24.3 | 436,181 | 100% |
| Maine | Statewide | 10.0 | 11.4 | 13.5 | 1,316,456 | 100% |
| Maine | Augusta-Waterville, ME | 12.4 | 7.4 | -40.1 | 120,959 | 100% |
| Maine | Bangor, ME | 9.4 | 14.8 | 57.0 | 148,651 | 100% |
| Maine | Lewiston-Auburn, ME | 8.4 | 15.0 | 77.5 | 106,877 | 100% |
| Maine | Portland-South Portland-Biddeford, ME | 10.7 | 13.8 | 28.7 | 514,065 | 100% |
| Maine | Rockland, ME | 14.7 | 12.3 | -16.6 | 40,686 | 100% |
| Maryland | Statewide | 14.6 | 11.6 | -20.7 | 5,633,597 | 100% |
| Maryland | Baltimore-Towson, MD | 20.9 | 15.6 | -25.5 | 2,667,117 | 100% |
| Maryland | Anne Arundel County | 12.3 | 12.3 | -0.4 | 512,790 | 100% |
| Maryland | Baltimore City | 49.7 | 30.0 | -39.6 | 636,919 | 100% |
| Maryland | Baltimore County | 13.7 | 11.8 | -13.9 | 785,618 | 100% |
| Maryland | Cambridge, MD | 15.7 | 12.5 | -20.4 | 31,998 | 100% |
| Maryland | Cumberland, MD-WV | NC | NC | NC | 99,033 | 100% |
| Maryland | Easton, MD | 22.1 | 22.1 | -0.2 | 36,215 | 100% |
| Maryland | Hagerstown-Martinsburg, MD-WV | NC | NC | NC | 263,753 | 100% |
| Maryland | Lexington Park, MD | 5.0 | 12.8 | 156.6 | 101,578 | 100% |
| Maryland | Ocean Pines, MD | 22.3 | 20.3 | -9.1 | 49,274 | 100% |
| Maryland | Salisbury, MD | 12.6 | 12.5 | -0.7 | 120,165 | 100% |
| Massachusetts | Statewide | 15.8 | 14.1 | -10.7 | 6,497,967 | 100% |
| Massachusetts | Barnstable Town, MA | 18.5 | 13.6 | -26.5 | 221,049 | 100% |
| Massachusetts | Boston-Cambridge-Quincy, MA-NH | 15.5 | 12.9 | -16.8 | 4,522,858 | 100% |
| Massachusetts | Essex County | 19.2 | 12.5 | -35.1 | 736,457 | 100% |
| Massachusetts | Middlesex County | 12.0 | 10.8 | -9.9 | 1,482,478 | 100% |
| Massachusetts | Norfolk County | 11.1 | 11.7 | 4.8 | 659,909 | 100% |
| Massachusetts | Plymouth County | 13.7 | 13.6 | -0.5 | 492,066 | 100% |
| Massachusetts | Suffolk County | 26.3 | 20.9 | -20.5 | 732,684 | 100% |
| Massachusetts | Pittsfield, MA | 9.2 | 10.0 | 8.9 | 129,395 | 100% |
| Massachusetts | Springfield, MA | 16.9 | 14.8 | -12.1 | 687,558 | 100% |
| Massachusetts | Hampden County | 20.0 | 17.8 | -10.9 | 460,840 | 100% |
| Massachusetts | Worcester, MA | 13.0 | 14.4 | 10.5 | 783,806 | 100% |
| Michigan | Detroit-Warren-Livonia, MI | NC | NC | NC | 3,132,061 | 71% |
| Michigan | Macomb County | 15.7 | 17.6 | 12.1 | 830,663 | 100% |
| Michigan | Wayne County | 21.1 | 21.8 | 3.3 | 1,949,929 | 100% |
| Minnesota | Brainerd, MN | NC | NC | NC | 28,732 | 32% |
| Minnesota | Minneapolis-St. Paul-Bloomington, MN-WI | 7.1 | 8.2 | 15.5 | 2,661,495 | 82% |
| Minnesota | Hennepin County | 8.6 | 8.6 | 0.2 | 1,140,988 | 100% |
| Minnesota | Ramsey County | 11.2 | 13.4 | 19.0 | 501,428 | 100% |
| Missouri | Kansas City, MO-KS | 10.6 | 12.4 | 16.8 | 1,068,449 | 53% |
| Missouri | Jackson County | 10.8 | 13.3 | 23.2 | 668,417 | 100% |
| Missouri | St. Louis, MO-IL | 11.1 | 12.1 | 9.0 | 2,388,574 | 85% |
| Missouri | St. Louis City | 24.7 | 21.7 | -12.2 | 354,361 | 100% |
| Missouri | St. Louis County | 10.2 | 10.9 | 7.1 | 991,830 | 100% |
| New Hampshire | Statewide | 12.6 | 9.3 | -26.1 | 1,315,809 | 100% |
| New Hampshire | Berlin, NH-VT | 20.6 | 13.0 | -36.9 | 38,471 | 100% |
| New Hampshire | Claremont, NH | 4.7 | 9.4 | 99.9 | 42,591 | 100% |
| New Hampshire | Concord, NH | 16.2 | 10.1 | -37.5 | 148,161 | 100% |
| New Hampshire | Keene, NH | 7.8 | 2.6 | -66.6 | 77,170 | 100% |
| New Hampshire | Laconia, NH | 22.9 | 13.1 | -43.1 | 61,281 | 100% |
| New Hampshire | Lebanon, NH-VT | 12.9 | 14.0 | 8.9 | 171,404 | 100% |
| New Hampshire | Manchester-Nashua, NH | 10.0 | 9.9 | -0.2 | 402,042 | 100% |
| New Mexico | Statewide | 21.1 | 22.0 | 4.5 | 1,984,356 | 100% |
| New Mexico | Alamogordo, NM | 11.2 | 12.7 | 14.3 | 62,776 | 100% |
| New Mexico | Albuquerque, NM | 25.8 | 27.1 | 4.9 | 845,913 | 100% |
| New Mexico | Bernalillo County | 29.9 | 31.6 | 5.7 | 635,139 | 100% |
| New Mexico | Carlsbad-Artesia, NM | 17.7 | 25.3 | 43.3 | 51,360 | 100% |
| New Mexico | Clovis, NM | 15.6 | 11.4 | -26.5 | 43,755 | 100% |
| New Mexico | Deming, NM | 11.2 | 0.0 | NP | 27,227 | 100% |
| New Mexico | Española, NM | 61.4 | 61.4 | 0.0 | 40,692 | 100% |
| New Mexico | Farmington, NM | 15.5 | 11.4 | -26.5 | 122,500 | 100% |
| New Mexico | Gallup, NM | 10.0 | 14.1 | 41.4 | 70,724 | 100% |
| New Mexico | Grants, NM | 11.0 | 18.3 | 66.3 | 27,285 | 100% |
| New Mexico | Hobbs, NM | 20.7 | 23.7 | 14.4 | 59,155 | 100% |
| New Mexico | Las Cruces, NM | 12.6 | 13.4 | 6.1 | 201,603 | 100% |
| New Mexico | Las Vegas, NM | 27.9 | 35.0 | 25.3 | 28,558 | 100% |
| New Mexico | Los Alamos, NM | 10.8 | 11.0 | 1.9 | 18,150 | 100% |
| New Mexico | Portales, NM | 20.9 | 0.0 | NP | 18,889 | 100% |
| New Mexico | Roswell, NM | 11.2 | 17.4 | 55.6 | 63,060 | 100% |
| New Mexico | Ruidoso, NM | 29.0 | 4.8 | -83.4 | 20,793 | 100% |
| New Mexico | Santa Fe, NM | 19.7 | 18.8 | -4.6 | 143,937 | 100% |
| New Mexico | Silver City, NM | 16.8 | 30.2 | 79.1 | 29,844 | 100% |
| New Mexico | Taos, NM | 34.9 | 28.5 | -18.3 | 31,546 | 100% |
| New York | Buffalo-Niagara Falls, NY | 7.8 | 10.1 | 28.7 | 1,124,309 | 100% |
| New York | Erie County | 7.7 | 9.9 | 28.9 | 909,845 | 100% |
| New York | New York-Northern New Jersey-Long Island, NY-NJ-PA | NC | NC | NC | 11,115,006 | 58% |
| New York | NY Suburban, NY | NC | NC | NC | 1,611,468 | 38% |
| New York | Suffolk County | 12.1 | 12.4 | 2.7 | 1,512,224 | 100% |
| New York | NYC 5 Boroughs, NY | 8.8 | 9.7 | 10.5 | 8,363,710 | 100% |
| New York | Bronx County | 13.8 | 13.6 | -1.5 | 1,391,903 | 100% |
| New York | Kings County | 7.7 | 9.1 | 18.2 | 2,556,598 | 100% |
| New York | New York County | 9.8 | 9.4 | -4.3 | 1,634,795 | 100% |
| New York | Queens County | 6.1 | 7.5 | 21.3 | 2,293,007 | 100% |
| New York | Richmond County | 8.9 | 13.3 | 49.8 | 487,407 | 100% |
| New York | Newark-Edison, NJ-PA | 8.1 | 9.1 | 12.7 | 1,139,828 | 18% |
| North Dakota | Fargo, ND-MN | 8.3 | 3.6 | -57.1 | 195,685 | 100% |
| North Dakota | Grand Forks, ND-MN | NC | NC | NC | 30,694 | 32% |
| Ohio | Cleveland-Elyria-Mentor, OH | 15.3 | 17.5 | 14.6 | 1,283,925 | 61% |
| Oklahoma | Statewide | 15.0 | 16.0 | 6.4 | 3,642,361 | 100% |
| Oklahoma | Ada, OK | 16.4 | 13.5 | -17.8 | 36,999 | 100% |
| Oklahoma | Altus, OK | 11.7 | 0.0 | NP | 25,236 | 100% |
| Oklahoma | Ardmore, OK | 14.1 | 17.5 | 23.8 | 57,134 | 100% |
| Oklahoma | Bartlesville, OK | 6.0 | 9.9 | 64.4 | 50,452 | 100% |
| Oklahoma | Duncan, OK | 16.2 | 18.4 | 13.6 | 43,498 | 100% |
| Oklahoma | Durant, OK | 22.9 | 24.9 | 8.9 | 40,109 | 100% |
| Oklahoma | Elk City, OK | 28.9 | 23.7 | -18.0 | 21,136 | 100% |
| Oklahoma | Enid, OK | 3.5 | 13.8 | 295.4 | 58,167 | 100% |
| Oklahoma | Guymon, OK | 10.1 | 14.8 | 47.1 | 20,283 | 100% |
| Oklahoma | Lawton, OK | 8.8 | 10.7 | 22.3 | 111,772 | 100% |
| Oklahoma | McAlester, OK | 17.9 | 11.1 | -38.2 | 45,115 | 100% |
| Oklahoma | Miami, OK | 6.2 | 15.7 | 153.7 | 31,849 | 100% |
| Oklahoma | Muskogee, OK | 16.9 | 33.7 | 99.3 | 71,278 | 100% |
| Oklahoma | Oklahoma City, OK | 15.2 | 14.1 | -7.4 | 1,206,142 | 100% |
| Oklahoma | Oklahoma County | 20.3 | 15.3 | -24.8 | 706,617 | 100% |
| Oklahoma | Ponca City, OK | 6.6 | 11.0 | 67.0 | 45,632 | 100% |
| Oklahoma | Shawnee, OK | 13.0 | 23.0 | 76.8 | 69,616 | 100% |
| Oklahoma | Stillwater, OK | 6.4 | 7.7 | 19.1 | 78,280 | 100% |
| Oklahoma | Tahlequah, OK | 24.4 | 37.2 | 52.4 | 45,733 | 100% |
| Oklahoma | Tulsa, OK | 20.8 | 19.4 | -6.5 | 916,079 | 100% |
| Oklahoma | Tulsa County | 24.3 | 20.1 | -17.3 | 591,982 | 100% |
| Oklahoma | Woodward, OK | 0.0 | 15.1 | NP | 19,838 | 100% |
| Oregon | Statewide | 10.1 | 12.2 | 20.7 | 3,790,060 | 100% |
| Oregon | Albany-Lebanon, OR | 7.1 | 5.2 | -26.5 | 115,348 | 100% |
| Oregon | Astoria, OR | 16.1 | 10.7 | -33.5 | 37,404 | 100% |
| Oregon | Bend, OR | 9.1 | 13.9 | 52.5 | 158,456 | 100% |
| Oregon | Brookings, OR | 13.8 | 4.6 | -66.3 | 21,523 | 100% |
| Oregon | Coos Bay, OR | 15.8 | 3.2 | -80.0 | 63,453 | 100% |
| Oregon | Corvallis, OR | 1.2 | 3.7 | 197.4 | 81,859 | 100% |
| Oregon | Eugene-Springfield, OR | 9.6 | 14.4 | 49.9 | 346,560 | 100% |
| Oregon | Grants Pass, OR | 7.4 | 4.9 | -33.9 | 81,618 | 100% |
| Oregon | Hood River, OR | 0.0 | 13.9 | NP | 21,536 | 100% |
| Oregon | Klamath Falls, OR | 1.5 | 13.5 | 799.8 | 66,425 | 100% |
| Oregon | La Grande, OR | 4.0 | 0.0 | NP | 24,961 | 100% |
| Oregon | Medford, OR | 12.1 | 15.4 | 27.8 | 201,138 | 100% |
| Oregon | Ontario, OR-ID | 3.2 | 9.7 | 201.0 | 30,907 | 57% |
| Oregon | Pendleton-Hermiston, OR | 3.6 | 8.3 | 132.5 | 84,666 | 100% |
| Oregon | Portland-Vancouver-Beaverton, OR-WA | 11.6 | 13.7 | 17.6 | 1,771,935 | 80% |
| Oregon | Multnomah County | 23.0 | 25.2 | 9.4 | 714,567 | 100% |
| Oregon | Prineville, OR | 0.0 | 8.7 | NP | 23,023 | 100% |
| Oregon | Roseburg, OR | 14.4 | 6.7 | -53.4 | 104,059 | 100% |
| Oregon | Salem, OR | 9.1 | 10.5 | 15.2 | 391,680 | 100% |
| Oregon | The Dalles, OR | 0.0 | 21.0 | NP | 23,775 | 100% |
| Pennsylvania | Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 18.8 | 19.7 | 4.7 | 3,500,631 | 60% |
| Pennsylvania | Delaware County | 14.8 | 19.3 | 30.4 | 553,619 | 100% |
| Pennsylvania | Montgomery County | 9.0 | 9.8 | 8.2 | 778,048 | 100% |
| Pennsylvania | Philadelphia County | 28.8 | 30.5 | 6.1 | 1,447,395 | 100% |
| Rhode Island | Statewide | NC | NC | NC | 1,050,788 | 100% |
| Rhode Island | Providence-New Bedford-Fall River, RI-MA | NC | NC | NC | 1,596,611 | 100% |
| Rhode Island | Bristol County | 17.6 | 19.4 | 10.3 | 545,823 | 100% |
| Rhode Island | Providence County | NC | NC | NC | 626,150 | 100% |
| South Dakota | Sioux Falls, SD | 1.1 | 0.6 | -51.1 | 179,180 | 77% |
| Tennessee | Kingsport-Bristol-Bristol, TN-VA | 8.6 | 12.9 | 49.5 | 93,312 | 31% |
| Texas | Dallas-Fort Worth-Arlington, TX | 6.0 | 5.8 | -3.9 | 1,398,567 | 22% |
| Texas | Houston-Sugar Land-Baytown, TX | 14.7 | 11.3 | -23.1 | 3,984,349 | 70% |
| Utah | Statewide | 17.4 | 15.1 | -13.4 | 2,736,424 | 100% |
| Utah | Brigham City, UT | 18.8 | 12.2 | -35.0 | 49,015 | 100% |
| Utah | Cedar City, UT | 18.4 | 13.5 | -26.8 | 44,540 | 100% |
| Utah | Heber, UT | 24.5 | 9.5 | -61.2 | 21,066 | 100% |
| Utah | Logan, UT-ID | 9.2 | 7.1 | -22.6 | 112,616 | 90% |
| Utah | Ogden-Clearfield, UT | 15.8 | 17.9 | 12.8 | 531,488 | 100% |
| Utah | Price, UT | 25.5 | 5.1 | -79.9 | 19,549 | 100% |
| Utah | Provo-Orem, UT | 15.7 | 10.9 | -30.4 | 540,820 | 100% |
| Utah | Salt Lake City, UT | 20.3 | 17.6 | -13.3 | 1,115,692 | 100% |
| Utah | Salt Lake County | 20.6 | 17.8 | -13.6 | 1,022,651 | 100% |
| Utah | St. George, UT | 10.5 | 12.4 | 17.8 | 137,589 | 100% |
| Utah | Vernal, UT | 10.4 | 3.3 | -67.7 | 29,885 | 100% |
| Vermont | Statewide | 10.1 | 11.6 | 14.2 | 621,270 | 100% |
| Vermont | Barre, VT | 11.9 | 10.2 | -14.3 | 58,829 | 100% |
| Vermont | Bennington, VT | 2.7 | 11.0 | 300.3 | 36,382 | 100% |
| Vermont | Burlington-South Burlington, VT | 8.7 | 10.6 | 21.5 | 208,460 | 100% |
| Vermont | Rutland, VT | 20.5 | 11.1 | -46.0 | 63,331 | 100% |
| Virginia | Statewide | 5.9 | 7.0 | 17.3 | 7,769,089 | 100% |
| Virginia | Blacksburg-Christiansburg-Radford, VA | 13.3 | 17.7 | 32.5 | 158,328 | 100% |
| Virginia | Charlottesville, VA | 5.2 | 3.6 | -30.8 | 194,391 | 100% |
| Virginia | Culpeper, VA | 8.8 | 13.0 | 48.0 | 46,203 | 100% |
| Virginia | Danville, VA | 7.6 | 10.4 | 37.7 | 105,783 | 100% |
| Virginia | Harrisonburg, VA | 4.3 | 2.5 | -40.5 | 118,409 | 100% |
| Virginia | Lynchburg, VA | 5.3 | 4.1 | -23.8 | 245,809 | 100% |
| Virginia | Martinsville, VA | 12.9 | 27.2 | 111.6 | 69,859 | 100% |
| Virginia | Richmond, VA | 4.2 | 7.8 | 84.1 | 1,225,626 | 100% |
| Virginia | Roanoke, VA | 7.1 | 12.4 | 75.2 | 298,108 | 100% |
| Virginia | Staunton-Waynesboro, VA | 6.0 | 13.7 | 126.6 | 117,170 | 100% |
| Virginia | Virginia Beach-Norfolk-Newport News, VA-NC | 6.7 | 6.2 | -7.2 | 1,634,109 | 99% |
| Virginia | Winchester, VA-WV | NC | NC | NC | 122,369 | 100% |
| Washington | Seattle-Tacoma-Bellevue, WA | 15.0 | 17.0 | 13.6 | 3,344,813 | 100% |
| Washington | King County | 17.0 | 16.3 | -4.5 | 1,875,519 | 100% |
| Washington | Pierce County | 13.7 | 19.1 | 39.3 | 785,639 | 100% |
| Washington | Snohomish County | 10.8 | 16.7 | 54.1 | 683,655 | 100% |
| West Virginia | Statewide | NC | NC | NC | 1,814,468 | 100% |
| West Virginia | Beckley, WV | NC | NC | NC | 79,357 | 100% |
| West Virginia | Bluefield, WV-VA | NC | NC | NC | 105,287 | 100% |
| West Virginia | Charleston, WV | NC | NC | NC | 303,944 | 100% |
| West Virginia | Kanawha County | NC | NC | NC | 191,018 | 100% |
| West Virginia | Clarksburg, WV | NC | NC | NC | 92,212 | 100% |
| West Virginia | Fairmont, WV | NC | NC | NC | 56,496 | 100% |
| West Virginia | Huntington-Ashland, WV-KY-OH | NC | NC | NC | 135,713 | 48% |
| West Virginia | Morgantown, WV | NC | NC | NC | 118,506 | 100% |
| West Virginia | Oak Hill, WV | NC | NC | NC | 46,341 | 100% |
| West Virginia | Parkersburg-Marietta-Vienna, WV-OH | NC | NC | NC | 99,111 | 62% |
| West Virginia | Point Pleasant, WV-OH | NC | NC | NC | 25,678 | 45% |
| West Virginia | Weirton-Steubenville, WV-OH | NC | NC | NC | 53,528 | 44% |
| West Virginia | Wheeling, WV-OH | NC | NC | NC | 76,872 | 53% |
| Wisconsin | Milwaukee-Waukesha-West Allis, WI | 18.7 | 18.9 | 0.9 | 953,328 | 62% |
DAWN mortality data are reported for metropolitan and micropolitan areas6 with 30 or more drug-related deaths and participating ME/Cs covering more than 50 percent of the area population, and for all participating States in six figures and tables that span two or more pages. These are referred to as "full profiles." Metropolitan areas with fewer than 30 deaths or areas with less than 50 percent coverage receive a brief profile that includes just one of the six tables. Large, individual jurisdictions that are part of a multijurisdictional area and reported 60 or more deaths receive a full profile that is referred to as a "county profile."7
Among the 153 metropolitan areas, full profiles are provided for 46 metropolitan areas. Brief profiles are provided for 107 metropolitan areas that either submitted 30 or fewer drug-related deaths or had less than 50 percent population coverage. County profiles are provided for 50 individual counties. About one third of the nation's population is covered by the ME/Cs participating in DAWN.
The profiles are arranged by State, by metropolitan area within the State, and then by county within the metropolitan area. In several of the larger metropolitan areas, profiles are also provided for subareas within the metropolitan area that may be of interest. For example, while the New York metropolitan area spans multiple counties in New York, New Jersey, and Pennsylvania, a profile was developed just for New York City's 5 Boroughs.
The Contents to this publication lists the profiles in the order in which they appear.
The full profile is composed of six exhibits plus a map and demographic information on the State or metropolitan area and its constituent counties. Figure 1 shows the general layout of the full profile. All profiles observe the following conventions:
Each profile begins with a map displaying the boundaries of the metropolitan area or State and its component counties. In this publication, the terms "death investigation jurisdiction" (or, simply, "jurisdiction") and "county" are used interchangeably because ME/Cs' offices are typically organized by county. The one exception occurs in Niagara County, NY, which is divided into four districts. For reporting purposes, the four districts that make up Niagara County, NY, are treated collectively as a single jurisdiction.
Both participating and nonparticipating jurisdictions are shown in the map. Jurisdictions that provided mortality data for 2008 are colored white. Jurisdictions in the area that did not provide data are shaded light blue. Areas outside of the metropolitan area or State are shaded darker blue.
Metropolitan and micropolitan area definitions used in this publication are those established by the Office of Management and Budget (OMB) in 2003, based on the 2000 decennial U.S. Census and updated by OMB annually since then.8 By OMB convention, the name of each metropolitan statistical area (MSA) reflects the largest population centers (i.e., cities) in that MSA. If the relative population sizes of cities in an MSA change (i.e., the second largest becomes the largest), OMB changes the name of the MSA to reflect the new order of cities by size (i.e., the name of the larger city will appear first). This publication uses the name of the MSA that was current at the end of the data collection year. Other changes issued by OMB include the adding or deleting of a metropolitan or micropolitan area and changes in the composition of areas. This publication aggregates data and reports on all metropolitan and micropolitan areas identified by OMB at the end of the data collection year and for which DAWN has data from at least one participating ME/C.
Next to the map, the following items appear:
Below the map, Table A lists each of the component jurisdictions for the area. Each jurisdiction is numbered to correspond to the numbers shown on the area map. In metropolitan areas that cross State borders, jurisdictions are ordered first by State and then alphabetically by county name. Nonparticipating jurisdictions are included in the list with a shaded background to distinguish them from participating jurisdictions.
Information in Table A for each jurisdiction includes the following:
The top row of the table totals this information for just the participating jurisdictions.
Rates, because they are population adjusted, can be compared across jurisdictions, metropolitan areas, and States. This standardization does not take into account, however, the differences in applicable laws that specify which deaths are subject to ME/C review or other factors that may confound comparisons.
The subsequent tables and figures (B through F) are based on data aggregated across the participating jurisdictions in each metropolitan area or State.
Figure B is a pie chart that displays manner of death for drug-related deaths and drug-related suicide deaths. The manner of death reported here is that assigned by the ME/C using the categories provided on the U.S. Standard Certificate of Death. Solid-colored slices are reserved for drug-related deaths other than suicides; the patterned slice shows the suicide deaths. Reading clockwise, the manners of death are identified as follows:
Separate bar charts show the five most common types of drugs (e.g., opiates/opioids, benzodiazepines) reported to DAWN for drug-related deaths and drug-related suicide deaths across the participating jurisdictions. The number shown above each bar is the number of deaths reported for a specific drug type. The name of the drug type is printed below each bar. Each bar is partitioned to display separately the portion of deaths involving a single drug type (solid blue area in bottom portion of bar) versus multiple drug types (striped area in top of bar). A bar is not printed if there are fewer than four deaths associated with a drug type, and therefore, fewer than five bars may appear. The top 5 drug types are identified from among 17 different drug types, as listed in Table F (see below).
A single death that involved two drugs of different types (e.g., cocaine and heroin) would be counted in two bars (e.g., cocaine and heroin, respectively). As a result, summing the number of deaths reported in each bar will double-count deaths that involved multiple types of drugs. A death that involved two drugs of the same type (e.g., multiple opiates/opioids, such as methadone and heroin) will be counted once (e.g., in the bar for opiates/opioids).
Grouping drugs by drug type eliminates double counting due to the following causes: redundant drug reports (e.g., "cocaine" and its metabolite "benzoylecgonine" being reported for the same death); redundant reports from nonspecific terms (e.g., "heroin" and "opiates" being reported for the same death); and drug reports that may be indistinguishable (e.g., "heroin" and "morphine").
Figure D displays the gender and categorical age of decedents in drug-related deaths and drug-related suicide deaths, in terms of deaths per 100,000 population. Only population in participating jurisdictions is considered in the calculation of these rates. Taking population size into account enables comparisons to be made across age and gender subgroups.
Table E reports the place of death for drug-related deaths and drug-related suicide deaths. Deaths in emergency departments and other health care facilities have been combined into the single category "Health care facility."
Table F reports, by drug type or drug, the count of drug-related deaths and drug-related suicide deaths for 2007 and 2008. The first row of Table F summarizes deaths across all drug categories; the subsequent rows provide detail for 17 specific drug types or drugs of particular interest.
Data for both 2007 and 2008 are reported when the same jurisdictions participated in both years. If comparable data for 2007 are not available (e.g., the State of West Virginia did not participate in 2007) or are not comparable to those shown for 2008 (e.g., in the Denver metropolitan area, Broomfield and Park counties participated in 2008 but not in 2007), the 2007 columns are left blank.
Counts of drug-related deaths and drug-related suicide deaths include deaths that involved both single and multiple drugs. Summing these deaths across drug types or drugs could result in double counting deaths associated with multiple drug types. To help provide a better understanding of single-drug versus multidrug involvement, counts of single-drug deaths are reported. Single-drug deaths involve the listed drug type or drug and no other, and they are a subset of the total count of deaths.
The 17 drug categories shown in this table are derived from DAWN's standard drug classification scheme and include the following:10
The next six rows in Table F pertain to illicit drugs:
The remaining rows in Table F are devoted to prescription and over-the-counter pharmaceuticals. For this table, heroin is categorized and reported on as an opiate/opioid. Low-frequency drugs have been aggregated into higher-level categories:
Not every reported substance (drug) is, by itself, the cause of death or even a contributor to the death. DAWN's broad definition of drug involvement requires only that the drug is related to the death. Therefore, even in single-drug deaths, reported drugs may not be a direct cause of death. Furthermore, incidental reporting (i.e., reporting of drugs unrelated to the death) is unavoidable due to ambiguities and insufficiencies in the ME/C records.
The total number of deaths in some drug categories is often quite small and of limited significance. The intent in reporting small counts is primarily to indicate the relative occurrence of deaths in different drug categories.
Numbers less than four but greater than zero are suppressed.
To warrant a full profile, the participating jurisdictions of a metropolitan area in combination must have reported more than 30 drug-related deaths or drug-related suicide deaths, and the area's population coverage must exceed 50 percent. If either of these two conditions was not met, a brief profile is provided for the area. In contrast to full profiles, brief profiles include only a map and Table A (see above).
County profiles are produced for individual jurisdictions in which 60 or more drug-related deaths were reported. The purpose is to distinguish findings for a single location from those of the metropolitan area as a whole. County profiles may appear for jurisdictions even if the metropolitan area itself had less than 50 percent population coverage. In some instances, even if a jurisdiction has 60 or more deaths, a county profile may not be needed. Such is the case when a metropolitan area contains only one county or had only one county participating in DAWN.
County profiles have essentially the same format as the full metropolitan area profile. County profiles include the map; Figures B, C, and D; and Tables E and F, as described above. Because of the small numbers, drug-related suicide deaths have been removed from all exhibits except the jurisdiction summary and Figure B.
Twelve statewide ME/C systems participated in DAWN in 2008. A full profile is provided for each of the following States:
A Glossary of Terms used in this report appears in Appendix B. Additional detail on the DAWN data collection methodology is provided in Appendix C.
1 DAWN uses the terms "death investigation jurisdiction" (or, simply, "jurisdiction") and "county" interchangeably because ME/Cs' offices are typically organized by county. The one exception occurs in Niagara County, NY, which is divided into four districts. For reporting purposes, the four districts that make up Niagara County, NY, are treated collectively as a single jurisdiction.
2 To be reportable, a nonpharmaceutical substance must be consumed by inhalation, sniffing, or snorting and must have a psychoactive effect when inhaled. Carbon monoxide is excluded from the inhalants reportable to DAWN, as is accidental inhalation of a nonpharmaceutical. Additional information on inhalants is provided in Appendix B: Glossary of Terms.
3 There is overlap between the metropolitan areas and States. In total, usable reports were received from 544 jurisdictions: 97 are only in metropolitan areas, 171 are only in States, and 276 are in both.
4 DAWN uses the metropolitan area definitions established by the Office of Management and Budget in 2000 and updated in 2003. See Appendix C for additional detail.
5 Recruitment efforts to increase participation by ME/Cs are ongoing. However, there are no plans to make the mortality component of DAWN national in scope or representative of nonparticipating jurisdictions.
6 For brevity, when this publication refers to metropolitan areas, the term includes both metropolitan and micropolitan areas.
7 If a metropolitan area has only one participating county, a separate profile is not provided for the county, as the reported data would be identical to that provided for the metropolitan area.
8 Office of Management and Budget (OMB), Revised Definitions of Metropolitan Statistical Areas, New Definitions of Micropolitan Statistical Areas and Combined Statistical Areas, and Guidance on Uses of the Statistical Definitions of These Areas, Bulletin No. 03-04, June 6, 2003. (Available at http://www.whitehouse.gov/omb/bulletins/b03-04.html.). Updates describing new metropolitan or micropolitan areas and changes to existing area are provided annually by OMB. (Available at http://www.whitehouse.gov/omb/bulletins_default.)
9 Population estimates for 2007 and 2008 were obtained from the U.S. Census Bureau County-Level Population Estimates (CPOP file), Vintage 2008, released August 6, 2009. (Available at http://www.census.gov/popest/estimates.html.)
10 The classification of drugs used by DAWN is derived from the Multum Lexicon, © 2010, Multum Information Services, Inc. The classification has been modified to meet DAWN's unique requirements. The Multum Licensing Agreement governing use of the Lexicon is provided in Appendix A. (Also available at http://www.multum.com/.)
11 Some examples may assist readers in interpreting this classification. A death that involved heroin and methadone would be counted in the "Opiates/opioids" row, in the "Heroin (specified)" row, and in the "Methadone" row. A death that involved morphine would be counted in the "Opiates/opioids" row and in the "All other opiates/opioids" row. A death that involved both morphine and codeine would be counted in the "Opiates/opioids" row and in the "All other opiates/opioids" row.
12 Note that morphine and unspecified opiates are not grouped in the "Heroin (specified)" category. Morphine is not classified as heroin because it is not possible to differentiate morphine, the metabolite of heroin, from morphine itself. Most drugs in the category "Heroin (specified)" were reported to DAWN as heroin or its metabolite monoacetylmorphine. A few were reported as acetylmorphine, diacetylmorphine, acetylcodeine, monoacetylcodeine, heroin dope, or black tar heroin.
13 The term "morphine" or "free morphine" accounted for most drug reports classified as "morphine," and the term "opiates" accounted for most of the unspecified opiates.
