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Geographic Variation in Substance-Related Inpatient Stays Across States and Counties in the United States, 2013-2015
STATISTICAL BRIEF #245


November 2018


Geographic Variation in Substance-Related Inpatient Stays Across States and Counties in the United States, 2013-2015


Kathryn R. Fingar, Ph.D., M.P.H., Halcyon Skinner, Ph.D., M.P.H., Jayne Johann, M.B.A., Natalia Coenen, M.P.H., William J. Freeman, Dr.P.H., M.P.H., and Kevin C. Heslin, Ph.D.


Introduction

Substance use disorders contribute to the burden of disease at a higher rate in the United States than in other developed countries.1 Individuals with a substance use disorder are at greater risk of major medical conditions and adverse outcomes such as cardiovascular disease and cancer, mortality, and mental health problems, including suicidal thoughts and behaviors.2,3 On a societal level, substance use disorders are associated with greater health care utilization and costs, crime, and lost work productivity.4 Reducing the prevalence of these disorders is critical for fostering the physical and mental health, safety, and well-being of individuals, families, and communities across the United States.5

Alcohol is the most common type of substance abused, which may be in part because it is easier to access than other substances.6 In 2013, 17.3 million Americans were dependent on or had problems related to their use of alcohol, down from 18.1 million in 2002.7 Marijuana was the next most common type of substance involving abuse or dependence (4.2 million individuals), followed by prescription pain relievers (1.8 million individuals), cocaine (855,000 individuals), heroin (517,000 individuals), and stimulants other than cocaine (469,000 individuals).8 Use of multiple types of substances also is common and increases risk of hospitalization, overdose, and death.9 One in nine people with a substance use disorder have both alcohol and drug dependence.10

The proportion of the U.S. population with alcohol dependence decreased by 14 percent between 2002 and 2013 (from 7.7 percent to 6.6 percent of the population).11 However, other data show that the rate of alcohol-related inpatient stays increased by 33 percent between 2013 and 2014 alone (from 81.4 to 108.0 stays per 100,000 population).12 The opioid crisis—which has resulted from the abuse of both prescription and illegal drugs, including heroin—also has grown. Prescriptions for pain relievers did in fact decrease from 2012 through 2016, from 81.3 to 66.5 prescriptions per 100 persons—the lowest rate in over 10 years.13 Nevertheless, opioid-related emergency department visits have continued to rise (by 99 percent between 2005 and 2014), as have opioid-related hospitalizations (by 64 percent between 2005 and 2014), and deaths (by 180 percent between 2002 and 2015).14,15

An estimated 22.7 million Americans need treatment for a problem related to alcohol or drugs, but only a small fraction (<1 percent) receive it.16 Furthermore, access to treatment varies widely across the United States.17,18 Although substance use can be addressed effectively in ambulatory care and other community settings, hospitalization remains a key component of the continuum of care for individuals with a substance use disorder. A better understanding of geographic variation in types of substance-related hospitalizations across the country may inform State and local efforts to increase access to substance use disorder treatment.

This Healthcare Cost and Utilization Project (HCUP) Statistical Brief examines State- and county-level variation in substance-related inpatient stays in 31 States that, at the time this Statistical Brief was written, released data through the Community-Level Statistics path of HCUPnet, an online query tool for county- and substate region-level statistics.19 Aggregate data from 2013 through the third quarter of 2015 are presented. The fourth quarter of 2015 is excluded because of the transition of the International Classification of Diseases coding system from the 9th to the 10th revision.20

First, State-level rates of inpatient stays involving common types of substances are shown. Second, county-level rates of stays involving the four most common types of substances (alcohol, opioids, cannabis, and stimulants) are examined. Finally, the cost of substance-related stays overall and for these four substances is presented for each State. If an inpatient stay involved multiple substances, the stay was counted in each substance type. Data are suppressed for counties if they are based on a small number of inpatient stays or hospitals, if they are statistically unstable, or if there was incomplete reporting. Thus, in this Statistical Brief, the highest and lowest rates of substance-related stays are described only among counties with unsuppressed data. All differences noted in the text are 10 percent or greater.

Readers should note that the substance definitions used in this Statistical Brief were developed for the Community-Level Statistics path of HCUPnet.21 The definitions may differ from those in other Statistical Briefs. In particular, the opioid definition used here includes opioid dependence/abuse in remission and neonatal abstinence syndrome and does not include opioid substances causing adverse effects in therapeutic use. As a result, rates of substance-related inpatient stays may differ somewhat from similar rates reported elsewhere (e.g., opioid statistics available in HCUP Fast Stats, statistics based on the Clinical Classification Software category for substance-related disorders).

Findings

State-level rates of inpatient stays involving common types of substances, 2013-2015
Table 1 presents the leading types of substances involved in inpatient stays within 31 States and the nation overall. The State-level rate of stays per 100,000 population is presented for all substances combined, including the most common types of substances shown, as well as other types of substances that are not shown, such as sedatives. For each of the three most common types of substances, the State-level rate and share of stays, as a percentage of total stays for all substance types, are shown. Data are sorted in descending order by the rate of total stays for all types of substances combined. An inpatient stay may be counted under multiple categories if it involved more than one type of substance.
Highlights
  • From 2013 to 2015, there was an average of 1 substance-related inpatient stay annually for every 100 people in the United States. Alcohol, opioids, cannabis, and stimulants were the most common substances.


  • Of counties in the 31 States in this Statistical Brief, Baltimore City, Maryland had the highest rates of opioid (1,592 per 100,000 population), cannabis (843), and stimulant (931) stays and the third highest rate of alcohol-related stays (1,955).


  • Counties in Texas had the lowest rates of opioid (Starr, 15 per 100,000) and cannabis (Val Verde, 19) stays, and the second lowest rate of alcohol-related stays (Kendall, 139).


  • High rates (i.e., those in the top quintile) of substance-related stays contributed to costs:
    • Alcohol-related stays in Rhode Island and Massachusetts (80 and 71 percent of counties in the top quintile) cost an average of $98 and $95 per resident annually, respectively.
    • Opioid-related stays in West Virginia and Massachusetts (66 and 64 percent of counties in the top quintile) cost $33 and $39 per resident annually, respectively.
    • Cannabis-related stays in North Carolina, Maryland, and Rhode Island (45, 40, and 40 percent of counties in the top quintile) cost $16, $24, and $22 per resident annually, respectively.
    • Stimulant-related stays in California and North Carolina (63 and 56 percent of counties in the top quintile) cost $32 and $15 per resident annually, respectively.


Table 1. State-level rates of common types of substance-related inpatient stays, 2013-2015
State Ratea of stays for all substances Most common substance type Second most common substance type Third most common substance type
Type Ratea % Type Ratea % Type Ratea %
United States 1,064 Alcohol 588 55.3 Opioid 217 20.4 Cannabis 193 18.1
Rhode Island 1,503 Alcohol 894 59.5 Opioid 336 22.3 Cannabis 250 16.6
Massachusetts 1,387 Alcohol 839 60.5 Opioid 373 26.9 Cannabis 190 13.7
West Virginia 1,344 Alcohol 677 50.4 Opioid 401 29.9 Otherb 250 18.6
Maryland 1,329 Alcohol 723 54.4 Opioid 411 30.9 Cannabis 269 20.3
Florida 1,289 Alcohol 715 55.5 Opioid 218 16.9 Cannabis 217 16.9
Michigan 1,217 Alcohol 677 55.6 Cannabis 298 24.5 Opioid 236 19.4
Tennessee 1,204 Alcohol 574 47.7 Opioid 341 28.3 Other b 222 18.4
Minnesota 1,188 Alcohol 734 61.8 Cannabis 230 19.3 Opioid 214 18.0
Kentucky 1,173 Alcohol 559 47.6 Opioid 312 26.6 Otherb 239 20.3
Illinois 1,162 Alcohol 640 55.1 Opioid 267 23.0 Cannabis 234 20.2
New Mexico 1,139 Alcohol 674 59.2 Opioid 223 19.6 Cannabis 182 16.0
Pennsylvania 1,129 Alcohol 617 54.6 Opioid 280 24.7 Cannabis 163 14.5
Oregon 1,106 Alcohol 573 51.8 Opioid 270 24.4 Stimulant 201 18.1
Arizona 1,098 Alcohol 564 51.3 Opioid 223 20.3 Stimulant 184 16.7
North Carolina 1,087 Alcohol 574 52.8 Cannabis 227 20.9 Opioid 203 18.7
New Jersey 1,077 Alcohol 583 54.1 Opioid 294 27.3 Cannabis 173 16.1
Wisconsin 1,033 Alcohol 665 64.4 Opioid 173 16.7 Cannabis 153 14.8
Washington 1,021 Alcohol 495 48.5 Opioid 300 29.4 Cannabis 185 18.1
Indiana 976 Alcohol 536 55.0 Opioid 192 19.6 Cannabis 179 18.4
Oklahoma 970 Alcohol 485 49.7 Other b 185 19.1 Cannabis 150 15.5
California 945 Alcohol 503 53.2 Stimulant 221 23.4 Cannabis 169 17.9
North Dakota 945 Alcohol 634 67.2 Cannabis 166 17.6 Opioid 147 15.6
South Carolina 931 Alcohol 553 59.4 Cannabis 172 18.4 Stimulant 144 15.5
Nevada 923 Alcohol 501 54.2 Opioid 168 18.2 Stimulant 153 16.5
Wyoming 912 Alcohol 629 69.0 Cannabis 132 14.5 Opioid 101 11.0
Arkansas 881 Alcohol 432 49.0 Cannabis 161 18.3 Stimulant 147 16.6
Louisiana 867 Alcohol 425 49.0 Cannabis 167 19.2 Opioid 151 17.5
Hawaii 863 Alcohol 401 46.5 Stimulant 280 32.5 Cannabis 196 22.7
Texas 690 Alcohol 396 57.4 Stimulant 124 18.0 Cannabis 119 17.3
Utah 624 Alcohol 282 45.1 Opioid 187 29.9 Stimulant 119 19.1
Iowa 624 Alcohol 418 67.0 Cannabis 74 11.8 Otherb 63 10.1
a The rate of inpatient stays per 100,000 population was calculated annually and then averaged across the 3 years (2013-2015), weighted by the population total in each year. State-level rates are based on data from all counties, including those with suppressed county-level data in subsequent figures.
b Other drug abuse includes such things as combinations of or unspecified drug dependence, drug dependence complicating pregnancy, antidepressant abuse, and poisoning by common cold medicines. A full definition is provided in Table 3 starting on page 23.
Note: Inpatient stays may involve more than one type of substance. Thus, substance-specific rates may sum to more than the rate for all substances combined.
Source: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), 2013-2015 National Inpatient Sample (NIS) and 2013-2015 State Inpatient Databases (SID) for 31 States, which, at the time this Statistical Brief was written, released data through the Community-Level Statistics path on HCUPnet, an online query tool.


  • For every 100 people in the U.S. population, there was 1 substance-related inpatient stay per year. Rates of substance-related inpatient stays varied over two-fold across States.

    Using national data from 2013-2015, the average annual rate of inpatient stays involving all substances combined was 1,064 per 100,000 population (or 1 per 100). The three highest rates of substance-related stays in any of the 31 States included in this Brief were in the following States:
    • Rhode Island: 1,503 per 100,000 population—over twice the lowest rate in any State (624 in Utah and Iowa)
    • Massachusetts: 1,387 per 100,000 population
    • West Virginia: 1,344 per 100,000 population
    After Utah and Iowa (624 per 100,000 population), Texas and Hawaii had the next lowest State-level rates of substance-related stays (690 and 863 per 100,000 population, respectively).


  • Alcohol was the most common type of substance among inpatient stays overall and in each of the 31 States. At the national level, there were 588 alcohol-related stays per 100,000 population and alcohol was involved in 55.3 percent of all substance-related stays. At the State level, the rate of alcohol-related stays ranged from 282 per 100,000 population in Utah to 894 per 100,000 population in Rhode Island.
  • Nationally and in most States, opioids were the second most common type of substance among inpatient stays.

    Nationally, there were 217 opioid-related stays per 100,000 population, and opioids were involved in 1 in 5 substance-related stays (20.4 percent). State-level rates of opioid-related stays were highest in the following States:
    • Maryland: 411 per 100,000 population
    • West Virginia: 401 per 100,000 population
    • Massachusetts: 373 per 100,000 population
    Opioids did not rank among the three most common types of substances in Oklahoma, California, South Carolina, Arkansas, Hawaii, Texas, and Iowa. Notably, these States also had some of the lowest overall rates of stays for all substances combined (ranging from 624 per 100,000 population in Iowa to 970 per 100,000 population in Oklahoma).


  • Nationally, cannabis was the third most common type of substance among inpatient stays.

    Nationally, there were 193 cannabis-related stays per 100,000 population, and cannabis was involved in 18.1 percent of substance-related stays. State-level rates of cannabis-related stays were highest in the following States:
    • Michigan: 298 per 100,000 population (here, cannabis ranked as the second most common type of substance)
    • Maryland: 269 per 100,000 population
    • Rhode Island: 250 per 100,000 population


  • Stimulants ranked as the second most common type of substance among inpatient stays in California, Hawaii, and Texas.

    Nationally, stimulants were the fourth most common type of substance, involved in 15.8 percent of all substance-related stays (data not shown). The rate of stimulant-related stays was 168 per 100,000 population (data not shown). State-level rates of stimulant-related stays were highest in the following States, where stimulants ranked as the second or third most common type of substance among inpatient stays:
    • Hawaii: 280 per 100,000 population
    • California: 221 per 100,000 population
    • Oregon: 201 per 100,000 population
    Stimulants were involved in 32.5 percent of substance related stays in Hawaii, 23.4 percent of substance-related stays in California, and 18.1 percent of substance-related stays in Oregon.
Variation in county-level rates of inpatient stays involving common types of substances, 2013-2015
Figures 1 through 4 present characteristics of the distributions of county-level rates of inpatient stays involving the four most common types of substances: alcohol, opioids, cannabis, and stimulants. The figures display the minimum, mean, and maximum rate (per 100,000 population) across counties within the 31 States included in this Brief. The county name is listed alongside the minimum and maximum values. The States are ordered according to the mean county-level rate. Note that the means of county-level rates in Figures 1-4 (averaged across all counties in each State)22 may differ from the State-level rates presented in Table 1.


Figure 1. County-level variation in rates of alcohol-related inpatient stays, by State, 2013-2015

Abbreviation: N, number of counties in the State with unsuppressed data
Notes: The middle box represents the mean rate across all counties in the State. The lower and upper ends of the lines indicate the minimum and maximum values, respectively. Statistics are suppressed for counties if the reporting cell draws from fewer than two hospitals, contains fewer than 11 discharges, or has a relative standard error (standard error / weighted estimate) greater than 0.30 or equal to 0, or because the county is missing 2 percent or more of total discharges in the HCUP State Inpatient Database (SID) when compared with the Medicare Hospital Service Area File.
Source: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), 2013-2015 State Inpatient Databases (SID) for 31 States, which, at the time this Statistical Brief was written, released data through the Community-Level Statistics path on HCUPnet, an online query tool.

Cluster bar chart that shows rate of alcohol-related inpatient stays per 100,000 population in the county with the highest and lowest rate and the mean rate by State from 2013 to 2015.

Figure 1. County-level variation in rates of alcohol-related inpatient stays, by State, 2013-2015
State (Number of Counties) County With Minimum Rate State Mean Rate County With Maximum Rate
County Rate County Rate
Massachusetts (N=14) Nantucket 635 897 Berkshire 1,087
Rhode Island (N=5) Bristol 617 802 Providence 960
Florida (N=61) Lafayette 258 711 Monroe 1,494
Maryland (N=20) Howard 337 685 Baltimore City 1,955
Arizona (N=13) Santa Cruz 286 665 Graham 1,132
Wisconsin (N=72) Grant 247 660 Menominee 1,987
New Mexico (N=31) Los Alamos 401 659 Rio Arriba 1,169
Nevada (N=16) Lincoln 294 655 Mineral 1,369
North Carolina (N=98) Jones 268 619 Swain 1,393
New Jersey (N=19) Bergen 334 615 Cape May 876
Michigan (N=83) Ottowa 282 604 Keweenaw 1,390
West Virginia (N=55) Gilmer 278 598 Mercer 989
Minnesota (N=85) Red Lake 249 592 Cass 1,275
Oregon (N=30) Hood River 333 591 Wheeler 941
South Carolina (N=43) Pickens 333 581 Lee 845
Tennessee (N=31) Williamson 247 578 Washington 1,083
North Dakota (N=21) Oliver 310 571 Sioux 2,725
California (N=58) Kings 320 569 Lake 1,192
Pennsylvania (N=66) Clinton 246 555 Allegheny 998
Wyoming (N=10) Uinta 326 512 Laramie 827
Washington (N=35) Adams 212 503 Grays Harbor 760
Kentucky (N=114) Allen 233 501 Perry 1,523
Illinois (N=102) Edwards 233 489 Rock Island 952
Indiana (N=84) La Grange 208 456 Vanderburgh 815
Hawaii (N=4) Honolulu 346 438 Hawaii 534
Oklahoma (N=74) Major 173 429 Kiowa 744
Arkansas (N=64) Sevier 197 414 Garland 646
Texas (N=147) Kendall 139 402 Potter 1,104
Louisiana (N=19) St. James Parish 234 351 Cameron Parish 570
Iowa (N=90) Davis 141 350 Scott 656
Utah (N=26) Utah 136 254 Weber 478



  • The counties with the three highest rates of alcohol-related stays were in North Dakota, Wisconsin, and Maryland; whereas the three lowest county rates were in Utah, Texas, and Iowa.

    The following counties had the three highest rates of alcohol-related stays:
    • Sioux County, North Dakota: 2,725 per 100,000 population
    • Menominee County, Wisconsin: 1,987 per 100,000 population
    • Baltimore City, Maryland: 1,955 per 100,000 population
    Among the counties in these 31 States, the following counties had the lowest rates of alcohol-related stays:
    • Utah County, Utah: 136 per 100,000 population
    • Kendall County, Texas: 139 per 100,000 population
    • Davis County, Iowa: 141 per 100,000 population


  • County-level rates of alcohol-related stays varied widely in North Dakota, Wisconsin, and Texas, where the highest county rate was 8-9 times greater than the lowest county rate within each of the three States.
    • In North Dakota, the rate of alcohol-related stays was 9 times higher in Sioux County (2,725 per 100,000 population) than in Oliver County (310 per 100,000 population).
    • In Wisconsin, the rate of alcohol-related stays was 8 times higher in Menominee County (1,987 per 100,000 population) than in Grant County (247 per 100,000 population).
    • In Texas, the rate of alcohol-related stays was 8 times higher in Potter County (1,104 per 100,000 population) than in Kendall County (139 per 100,000 population).


Figure 2. County-level variation in rates of opioid-related inpatient stays, by State, 2013-2015

Abbreviation: N, number of counties in the State with unsuppressed data
Notes: The middle box represents the mean rate across all counties in the State. The lower and upper ends of the lines indicate the minimum and maximum values, respectively. Statistics are suppressed for counties if the reporting cell draws from fewer than two hospitals, contains fewer than 11 discharges, or has a relative standard error (standard error / weighted estimate) greater than 0.30 or equal to 0, or because the county is missing 2 percent or more of total discharges in the HCUP State Inpatient Database (SID) when compared with the Medicare Hospital Service Area File.
Source: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), 2013-2015 State Inpatient Databases (SID) for 31 States, which at the time this Statistical Brief was written released data through the Community-Level Statistics path on HCUPnet, an online query tool

Cluster bar chart that shows rate of opioid-related inpatient stays per 100,000 population in the county with the highest and lowest rate and the mean rate by State from 2013 to 2015.

Figure 2. County-level variation in rates of opioid-related inpatient stays, by State, 2013-2015
State (Number of Counties) County With Minimum Rate State Mean Rate County With Maximum Rate
County Rate County Rate
Massachusetts (N=14) Nantucket 179 399 Berkshire 1,091
West Virginia (N=55) Calhoun 94 353 McDowell 695
Kentucky (N=112) Trigg 38 352 Owsley 1,220
Tennessee (N=31) Williamson 80 349 Carter 759
Maryland (N=20) Prince George's 95 348 Baltimore City 1,592
New Jersey (N=19) Bergen 109 314 Atlantic 577
Rhode Island (N=5) Washington 189 269 Providence 391
Washington (N=35) Adams 54 259 Grays Harbor 543
Pennsylvania (N=66) Union 90 245 Armstrong 503
North Carolina (N=97) Hertford 53 238 Swain 745
Florida (N=61) Hardee 53 216 Bradford 495
Oregon (N=27) Benton 69 212 Multnomah 479
California (N=57) San Benito 67 210 Toulumne 759
New Mexico (N=29) Hidalgo 86 196 Rio Arriba 611
Indiana (N=84) La Grange 27 188 Jackson 1,028
Nevada (N=12) Humboldt 62 181 Carson City 274
Michigan (N=82) Ottowa 58 181 Wayne 415
Arizona (N=13) Santa Cruz 67 181 Mohave 330
North Dakota (N=11) Stutsman 62 181 Sioux 751
Wisconsin (N=72) Grant 40 160 Ashland 458
Minnesota (N=77) Yellow Medicine 41 156 Mahnomen 985
Utah (N=22) Grand 40 142 Weber 290
Oklahoma (N=66) Woodward 38 142 Adair 434
Louisiana (N=18) St. James Parish 34 141 Livingston Parish 328
Hawaii (N=4) Kauai 87 141 Maui 193
South Carolina (N=43) Allendale 40 130 Georgetown 361
Illinois (N=97) Jo Daviess 43 127 Cook 426
Arkansas (N=64) Bradley 48 122 Crawford 244
Wyoming (N=9) Goshen 55 112 Washakie 166
Texas (N=120) Starr 15 74 Nueces 227
Iowa (N=67) Buchanan 18 55 Marshall 169



  • Two of the three counties with the highest rates of opioid-related stays were in Kentucky. The counties with the three lowest rates of opioid-related stays were all in Texas.

    The following counties had the three highest rates of opioid-related stays:
    • Baltimore City, Maryland: 1,592 per 100,000 population
    • Owsley County, Kentucky: 1,220 per 100,000 population
    • Harlan County, Kentucky: 1,214 per 100,000 population (data not shown)
    The following three counties had the lowest rates of opioid-related stays:
    • Starr County, Texas: 15 per 100,000 population
    • Uvalde County, Texas: 17 per 100,000 population (data not shown)
    • Val Verde County, Texas: 18 per 100,000 population (data not shown)


  • County-level rates of opioid-related stays varied widely in Indiana, Kentucky, and Minnesota, where the highest county rate was over 24 times greater than the lowest county rate in each State.
    • In Indiana, the rate of opioid-related stays was 38 times higher in Jackson County (1,028 per 100,000 population) than in La Grange County (27 per 100,000 population).
    • In Kentucky, the rate of opioid-related stays was 32 times higher in Owsley County (1,220 per 100,000 population) than in Trigg County (38 per 100,000 population).
    • In Minnesota, the rate of opioid-related stays was 24 times higher in Mahnomen County (985 per 100,000 population) than in Yellow Medicine County (41 per 100,000 population).


Figure 3. County-level variation of rates of cannabis-related inpatient stays, by State, 2013-2015

Abbreviation: N, number of counties in the State with unsuppressed data
Notes: The middle box represents the mean rate across all counties in the State. The lower and upper ends of the lines indicate the minimum and maximum values, respectively. Statistics are suppressed for counties if the reporting cell draws from fewer than two hospitals, contains fewer than 11 discharges, or has a relative standard error (standard error / weighted estimate) greater than 0.30 or equal to 0, or because the county is missing 2 percent or more of total discharges in the HCUP State Inpatient Database (SID) when compared with the Medicare Hospital Service Area File.
Source: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), 2013-2015 State Inpatient Databases (SID) for 31 States, which, at the time this Statistical Brief was written, released data through the Community-Level Statistics path on HCUPnet, an online query tool.

Cluster bar chart that shows rate of cannabis-related inpatient stays per 100,000 population in the county with the highest and lowest rate and the mean rate by State from 2013 to 2015.

Figure 3. County-level variation in rates of cannabis-related inpatient stays, by State, 2013-2015
State (Number of Counties) County With Minimum Rate State Mean Rate County With Maximum Rate
County Rate County Rate
Maryland (N=20) Montgomery 103 245 Baltimore City 843
Massachusetts (N=14) Norfolk 98 230 Berkshire 736
North Carolina (N=98) Onslow 63 222 Robeson 635
Hawaii (N=4) Kauai 79 206 Hawaii 446
Rhode Island (N=5) Washington 130 205 Providence 294
Michigan (N=83) Leelanau 62 195 Genesee 647
California (N=57) Colusa 64 191 Trinity 557
Oregon (N=28) Hood River 77 190 Multnomah 291
New Mexico (N=29) Los Alamos 98 189 Quay 276
Florida (N=61) Lafayette 56 189 Escambia 420
Washington (N=35) Adams 57 189 Lewis 355
Illinois (N=101) Jo Daviess 31 185 Marion 498
Minnesota (N=80) Marshall 52 181 Mahnomen 563
South Carolina (N=43) Oconee 67 179 Darlington 308
New Jersey (N=19) Bergen 66 178 Camden 326
North Dakota (N=13) Stutsman 71 178 Sioux 697
Kentucky (N=113) Barren 42 164 Kenton 481
West Virginia (N=55) Wirt 80 162 Ohio 326
Arkansas (N=64) Sevier 43 149 Jefferson 318
Arizona (N=13) Santa Cruz 61 142 Mohave 229
Tennessee (N=31) Williamson 40 140 Washington 378
Pennsylvania (N=66) Wyoming 46 137 Armstrong 350
Indiana (N=83) Cass 47 134 Vanderburgh 346
Wisconsin (N=71) Grant 30 127 Menominee 403
Oklahoma (N=70) Major 51 126 Comanche 230
Nevada (N=13) Lincoln 77 126 Churchill 182
Wyoming (N=9) Big Horn 46 122 Laramie 227
Louisiana (N=18) St. Tammany Parish 61 121 West Baton Rouge Parish 179
Texas (N=129) Val Verde 19 109 Potter 376
Utah (N=19) Carbon 36 73 Weber 204
Iowa (N=67) Carroll 22 64 Pottawattamie 207



  • Counties with the three highest rates of cannabis-related stays were in Maryland, Massachusetts, and North Dakota. The three counties with the lowest rates of cannabis-related stays were in Texas and Iowa.

    The following counties had the highest county-level rates of cannabis-related stays:
    • Baltimore City, Maryland: 843 per 100,000 population
    • Berkshire County, Massachusetts: 736 per 100,000 population
    • Sioux County, North Dakota: 697 per 100,000 population
    The following counties had the lowest county-level rates of cannabis-related stays:
    • Val Verde County, Texas: 19 per 100,000 population
    • Carroll County, Iowa: 22 per 100,000 population
    • Clayton County, Iowa: 25 per 100,000 population (data not shown)


  • County-level rates of cannabis-related stays varied widely in Texas, Illinois, and Wisconsin, where the highest county rate was over 13 times greater than the lowest county rate in each State.
    • In Texas, the rate of cannabis-related stays was 20 times higher in Potter County (376 per 100,000 population) than in Val Verde County (19 per 100,000 population).
    • In Illinois, the rate of cannabis-related stays was 16 times higher in Marion County (498 per 100,000 population) than in Jo Daviess County (31 per 100,000 population).
    • In Wisconsin, the rate was 13 times higher in Menominee County (403 per 100,000 population) than in Grant County (30 per 100,000 population).


Figure 4. County-level variation in rates of stimulant-related inpatient stays, by State, 2013-2015

Abbreviation: N, number of counties in the State with unsuppressed data
Notes: The middle box represents the mean rate across all counties in the State. The lower and upper ends of the lines indicate the minimum and maximum values, respectively. Statistics are suppressed for counties if the reporting cell draws from fewer than two hospitals, contains fewer than 11 discharges, or has a relative standard error (standard error / weighted estimate) greater than 0.30 or equal to 0, or because the county is missing 2 percent or more of total discharges in the HCUP State Inpatient Database (SID) when compared with the Medicare Hospital Service Area File.
Source: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), 2013-2015 State Inpatient Databases (SID) for 31 States, which, at the time this Statistical Brief was written, released data through the Community-Level Statistics path on HCUPnet, an online query tool

Cluster bar chart that shows rate of stimulant-related inpatient stays per 100,000 population in the county with the highest and lowest rate and the mean rate by State from 2013 to 2015.

Figure 4. County-level variation in rates of stimulant-related inpatient stays, by State, 2013-2015
State (Number of Counties) County With Minimum Rate State Mean Rate County With Maximum Rate
County Rate County Rate
California (N=56) Mono 52 222 Lake 556
Hawaii (N=4) Maui 137 213 Honolulu 309
New Mexico (N=29) Los Alamos 48 182 Chaves 428
North Carolina (N=98) Onslow 43 181 Halifax 481
Oregon (N=27) Hood River 78 175 Multnomah 373
Maryland (N=20) Garrett 41 174 Baltimore City 931
Washington (N=35) Adams 59 169 Grays Harbor 391
Massachusetts (N=14) Dukes 63 167 Berkshire 495
Arizona (N=13) Santa Cruz 49 150 Graham 257
Nevada (N=12) Humboldt 90 149 Carson City 218
South Carolina (N=43) Oconee 63 148 Orangeburg 262
New Jersey (N=19) Sussex 55 148 Camden 328
Florida (N=61) Sumter 53 146 Escambia 403
Arkansas (N=63) Benton 59 138 Woodruff 332
Rhode Island (N=5) Washington 66 133 Providence 237
Oklahoma (N=71) Kingfisher 46 129 Kiowa 262
North Dakota (N=11) Richland 48 120 Sioux 449
Minnesota (N=73) Roseau 39 119 Mahnomen 429
Louisiana (N=18) Plaquemines Parish 32 111 Terrebonne Parish 234
Texas (N=129) Val Verde 23 110 Potter 488
Illinois (N=92) Warren 29 93 Marion 291
Utah (N=19) Wasatch 35 84 Weber 271
Kentucky (N=105) Calloway 29 82 Fayette 191
Tennessee (N=28) Williamson 26 82 Davidson 206
Wyoming (N=9) Big Horn 37 79 Laramie 161
Pennsylvania (N=61) Wayne 18 77 Philadelphia 469
West Virginia (N=45) Jackson 29 70 Ohio 195
Wisconsin (N=67) Oconto 12 68 Milwaukee 294
Indiana (N=80) Franklin 24 66 Marion 186
Michigan (N=71) Charlevoix 16 66 Wayne 315
Iowa (N=69) Winneshiek 21 55 Pottawattamie 196



  • Counties with the three highest rates of stimulant-related stays were in Maryland, California, and Massachusetts. The three counties with the lowest rates of stimulant-related stays were in Wisconsin and Michigan.

    The following counties had the highest rates of stimulant-related stays:
    • Baltimore City, Maryland: 931 per 100,000 population
    • Lake County, California: 556 per 100,000 population
    • Berkshire County, Massachusetts: 495 per 100,000 population
    The following counties had the lowest rates of stimulant-related stays:
    • Oconto County, Wisconsin: 12 per 100,000 population
    • Charlevoix County, Michigan: 16 per 100,000 population
    • Emmet County, Michigan: 17 per 100,000 population (data not shown)


  • County-level rates of stimulant-related stays varied widely in Pennsylvania, Wisconsin, and Maryland, where the highest county rate was 23-26 times greater than the lowest county rate in each State.
    • In Pennsylvania, the rate of stimulant-related stays was 26 times higher in Philadelphia County (469 per 100,000 population) than in Wayne County (18 per 100,000 population).
    • In Wisconsin, the rate of stimulant-related stays was 25 times higher in Milwaukee County (294 per 100,000 population) than in Oconto County (12 per 100,000 population).
    • In Maryland, the rate was 23 times higher in the county of Baltimore City (931 per 100,000 population) than in Garrett County (41 per 100,000 population).
Hot spots of county-level rates of inpatient stays involving common types of substances, 2013-2015
Figures 5 and 6 display maps of county-level rates of alcohol-, opioid-, cannabis-, and stimulant-related inpatient stays in 2013-2015 for the 31 States included in this Brief. Rates were categorized into quintiles after ranking all counties in the 31 States with data that were not suppressed. Counties with rates in the highest quintile (top 20 percent) were considered hot spots.


Figure 5. County-level rates of alcohol- and opioid-related inpatient stays per 100,000 population, 2013-2015



  • Hot spots of alcohol-related stays (i.e., counties with rates in the highest quintile) were particularly concentrated in Rhode Island and Massachusetts, where 80 and 71 percent of counties in the State, respectively, had a rate in the highest quintile.

  • Hot spots of opioid-related stays were particularly concentrated in West Virginia, as well as in Massachusetts. In these States, 66 and 64 percent of counties, respectively, had a rate in the highest quintile.

  • The following are examples of pockets of hot spots in some States with otherwise low rates:

    • In Oklahoma, most counties (81 percent) had an alcohol-related stay rate in the three lowest quintiles, yet two counties (Kiowa and Roger Mills) had an alcohol-related stay rate in the highest quintile.

    • In Utah, 82 percent of counties had an opioid-related stay rate in the three lowest quintiles, yet one county (Weber) had a rate in the highest quintile.

    • In South Carolina, 84 percent of counties had an opioid-related stay rate in the three lowest quintiles, yet two counties (Darlington and Georgetown) had a rate in the highest quintile.

Notes: County-level data are unavailable for States in grey. Data are suppressed if the reporting cell draws from fewer than two hospitals, contains fewer than 11 discharges, or has a relative standard error (standard error/weighted estimate) greater than 0.30 or equal to 0, or because the county is missing 2 percent or more of total discharges in the HCUP State Inpatient Database (SID) when compared with the Medicare Hospital Service Area File.
Source: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), 2013-2015 State Inpatient Databases (SID) for 31 States, which, at the time this Statistical Brief was written, released data through the Community-Level Statistics path on HCUPnet, an online query tool.

Two color-coded maps that show the county-level rates per 100,000 population for alcohol- and opioid-related inpatient stays from 2013 to 2015 for 31 States. For alcohol, high concentrations of counties with the highest rates are seen in Massachusetts, Rhode Island, Maryland, Florida, Wisconsin, Minnesota, and New Mexico. High concentrations of counties with the lowest rates are seen in Louisiana, Texas, Iowa, Arkansas, and Utah. For opioids, high concentrations of counties with the highest rates are seen in Massachusetts, Rhode Island, West Virginia, and Kentucky. High concentrations of counties with the lowest rates are seen in Texas and Iowa.

Figure 6. County-level rates of cannabis- and stimulant-related inpatient stays per 100,000 population, 2013-2015



  • The percentage of counties with a cannabis-related stay rate in the highest quintile reached 45 percent in North Carolina and 40 percent in Maryland and Rhode Island.

  • Hot spots of stimulant-related stays tended to be most concentrated in California and North Carolina. In these States, the percentage of hot-spot counties was 63 and 56 percent, respectively.

  • The following are examples of pockets of cannabis- and stimulant-related hot spots in some States with otherwise low rates:

    • In Oklahoma, most counties (73 percent) had a cannabis-related stay rate in the three lowest quintiles, yet two counties (Comanche and Greer) had a rate in the highest quintile.

    • Similarly, in Iowa (94 percent), Wisconsin (85 percent), and Pennsylvania (82 percent) the vast majority of counties had rates of stimulant-related stays in the three lowest quintiles, yet a small number of counties in these States had a rate in the highest quintile: Pottawattamie (Iowa); Burnett, Menominee, Douglas, and Milwaukee (Wisconsin); and Allegheny and Philadelphia (Pennsylvania).

Notes: County-level data are unavailable for States in grey. Data are suppressed if the reporting cell draws from fewer than two hospitals, contains fewer than 11 discharges, or has a relative standard error (standard error/weighted estimate) greater than 0.30 or equal to 0, or because the county is missing 2 percent or more of total discharges in the HCUP State Inpatient Database (SID) when compared with the Medicare Hospital Service Area File.
Source: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), 2013-2015 State Inpatient Databases (SID) for 31 States, which, at the time this Statistical Brief was written, released data through the Community-Level Statistics path on HCUPnet, an online query tool.

Two color-coded maps that show the county-level rates per 100,000 population for cannabis- and stimulant-related inpatient stays from 2013 to 2015 for 31 States. For cannabis, high concentrations of counties with the highest rates are seen in Massachusetts, Rhode Island, Maryland, Hawaii, and North Carolina. High concentrations of counties with the lowest rates are seen in Iowa, Wisconsin, Texas, and Utah. For stimulants, high concentrations of counties with the highest rates are seen in Hawaii, California, Oregon, Washington, New Mexico, and North Carolina. High concentrations of counties with the lowest rates are seen in Wisconsin, Michigan, Iowa, Indiana, and Pennsylvania.



Variation in average costs of common types of substance-related inpatient stays by State, 2013-2015
Figure 7 displays the average cost per type of substance-related inpatient stay by State in 2013-2015. States are ordered by the average cost per alcohol-related stay. The average cost of all inpatient stays (substance and nonsubstance-related) within each State is listed on the y-axis next to the State names.


Figure 7. Average cost per type of substance-related inpatient stay, by State, 2013-2015

Note: Average cost per stay was calculated annually and then averaged across the 3 years (2013-2015), weighted by the population total.
Source: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), 2013-2015 National Inpatient Sample (NIS) and 2013-2015 State Inpatient Databases (SID) for 31 States, which, at the time this Statistical Brief was written, released data through the Community-Level Statistics pathway on HCUPnet, an online query tool.

Clustered bar chart that shows average cost in thousands of dollars of substance-related inpatient stays for all stays and for alcohol, opioids, cannabis, and stimulants for 2013 to 2015.

Figure 7. Average cost per type of substance-related inpatient stay, by State, 2013-2015
State All Stays Cannabis Stimulants Alcohol Opioids
California 14.9 12.4 14.3 17.2 17.2
Hawaii 13.9 13.0 12.9 16.5 17.5
Washington 13.4 12.0 13.4 14.8 14.7
Oregon 12.8 10.8 12.8 14.7 13.6
Arizona 10.6 10.4 11.0 12.9 11.8
Nevada 10.7 10.1 10.7 12.7 12.7
Wyoming 12.5 10.0 10.8 12.5 13.5
Wisconsin 11.6 8.8 9.9 12.4 10.9
New Jersey 11.5 9.3 10.5 12.1 10.6
Minnesota 11.7 9.9 10.3 12.1 12.2
Texas 10.3 8.7 9.6 12.0 12.1
New Mexico 10.2 9.2 9.3 11.7 11.4
United States 11.0 8.9 10.0 11.6 10.5
Massachusetts 11.9 9.2 9.9 11.4 10.4
South Carolina 10.5 8.3 9.0 11.4 9.7
Indiana 10.6 7.8 9.4 11.2 10.2
Michigan 10.2 8.8 9.3 11.0 9.16
Rhode Island 11.5 8.9 9.5 11.0 10.5
Kentucky 10.3 7.7 8.8 10.8 8.7
Maryland 11.1 8.8 9.4 10.8 9.9
Iowa 10.4 8.4 9.8 10.8 11.9
Illinois 10.6 7.3 7.9 10.5 7.5
North Dakota 10.4 7.4 8.0 10.3 10.8
West Virginia 10.6 7.2 7.6 10.3 8.3
Pennsylvania 10.2 7.0 8.3 10.0 8.6
Florida 9.5 7.7 8.1 10.0 9.8
North Carolina 9.9 7.3 7.9 9.9 8.3
Oklahoma 9.3 7.0 7.6 9.4 8.3
Utah 9.3 7.2 7.2 8.7 8.4
Arkansas 8.4 5.9 6.3 8.5 9.2
Tennessee 8.6 6.4 7.4 8.3 7.4
Louisiana 9.3 5.9 6.0 8.1 6.6



Average costs of the four most common substance-related stays were lowest in Louisiana ($8,100 per alcohol-related stay, $6,600 per opioid-related stay, $6,000 per stimulant-related stay, and $5,900 per cannabis-related stay).

Table 2 presents the per capita cost of inpatient stays involving any substance type and of stays involving the four most common types of substances. The State-level statistics are sorted by the per capita cost of inpatient stays involving all types of substances combined. Per capita costs are presented alongside the percentage of counties in each State with a substance-related stay rate in the highest quintile (quintile 5), which were considered hot spots.


Table 2. Per capita costs of inpatient stays involving substance use and the prevalence of hot spots for substance-related stays, by State, 2013-2015
State Per capita cost, $a Counties in the State that were hot spotsb for types of substance-related stays, %
All substances Alcohol Opioids Cannabis Stimulants Alcohol Opioids Cannabis Stimulants
United States 119 68 23 17 17 c c c c
Rhode Island 165 98 35 22 18 80.0 40.0 40.0 20.0
Massachusetts 157 95 39 18 16 71.4 64.3 28.6 28.6
California 156 86 27 21 32 22.4 19.3 26.3 62.5
Oregon 155 84 37 21 26 33.3 18.5 35.7 44.4
Washington 150 73 44 22 24 8.6 42.9 37.1 42.9
Minnesota 143 89 26 23 16 27.1 11.7 21.3 17.8
Maryland 143 78 41 24 20 35.0 50.0 40.0 25.0
Hawaii 136 66 23 26 36 0.0 0.0 25.0 50.0
Arizona 135 73 26 17 20 38.5 15.4 7.7 46.2
West Virginia 130 70 33 14 6 25.5 65.5 20.0 2.2
New Mexico 129 79 26 17 16 41.9 10.3 27.6 44.8
Michigan 127 74 23 26 13 28.9 11.0 25.3 2.8
New Jersey 125 71 31 16 16 42.1 63.2 31.6 31.6
Wisconsin 123 82 19 13 10 34.7 9.7 14.1 6.0
Florida 123 71 21 17 16 59.0 27.9 37.7 27.9
Kentucky 117 61 27 14 9 17.5 51.8 24.8 5.7
Wyoming 114 79 14 13 10 10.0 0.0 11.1 0.0
Nevada 113 63 21 13 16 50.0 0.0 0.0 33.3
Illinois 111 67 20 17 14 12.7 4.1 24.8 12.0
Pennsylvania 107 61 24 11 12 22.7 31.8 13.6 3.3
North Carolina 103 57 17 16 15 29.6 35.1 44.9 56.1
Indiana 102 60 20 14 9 10.7 9.5 10.8 3.8
Indiana 102 60 20 14 9 10.7 9.5 10.8 3.8
South Carolina 101 63 13 14 13 27.9 4.7 23.3 30.2
North Dakota 99 65 16 12 9 9.5 9.1 15.4 18.2
Tennessee 96 47 25 12 9 25.8 61.3 12.9 3.6
Oklahoma 86 46 12 11 11 2.7 3.0 2.9 21.1
Texas 81 47 10 10 12 2.7 0.0 4.7 16.3
Arkansas 73 37 11 9 9 0.0 0.0 18.8 22.2
Iowa 68 45 7 6 6 0.0 0.0 0.0 1.4
Louisiana 65 34 10 10 9 0.0 5.6 0.0 22.2
Utah 55 25 16 7 9 0.0 4.5 0.0 5.3
a The cost per capita (cost per 1 individual in the population) was calculated annually for each State and then averaged across the 3 years, weighted by the population total.
b Hot spots were defined as counties with a substance-related inpatient stay rate in the highest quintile (top 20 percent).
cNot calculated because not all States provide county-level data for Community-Level Statistics
Note: Inpatient stays may involve more than one type of substance. Thus, substance-specific costs may sum to more than the cost for all substances combined.
Source: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), 2013--2015 National Inpatient Sample (NIS) and 2013-2015 State Inpatient Databases (SID) for 31 States, which, at the time this Statistical Brief was written, released data through the Community-Level Statistics pathway on HCUPnet, an online query tool.




About Statistical Briefs

Healthcare Cost and Utilization Project (HCUP) Statistical Briefs provide basic descriptive statistics on a variety of topics using HCUP administrative health care data. Topics include hospital inpatient, ambulatory surgery, and emergency department use and costs, quality of care, access to care, medical conditions, procedures, and patient populations, among other topics. The reports are intended to generate hypotheses that can be further explored in other research; the reports are not designed to answer in-depth research questions using multivariate methods.

Data Source

The estimates in this Statistical Brief are based on data from the HCUP National Inpatient Sample (NIS) and State Inpatient Databases (SID), 2013 through the third quarter of 2015. The statistics were generated from the Community-Level Statistics path of HCUPnet, a free, online query system that provides users with immediate access to the largest set of publicly available, all-payer national, regional, State- and county-level hospital care databases from HCUP.23 Supplemental sources included population denominator data for use with HCUP databases, derived from Claritas, a vendor that compiles and adds value to data from the U.S. Census Bureau.24 Because 2015 was based on three quarters of data, population estimates in this year were multiplied by three-fourths (i.e. 0.75) to obtain the denominator used to calculate population-based rates. State and county rates are based on the location of the patient's residence.

Data were suppressed if the reporting cell drew from fewer than two hospitals, contained fewer than 11 discharges, had a relative standard error (standard error divided by weighted estimate) greater than 0.30 or equal to 0, or because the county was missing 2 percent or more of total discharges in the HCUP SID when compared with the Medicare Hospital Service Area File.25 The Medicare Hospital Service Area file contains the number of Medicare inpatient hospital fee-for-service claims annually. This number was compared with the number of discharges in HCUP with an expected payer of Medicare to evaluate whether data for a given county should be suppressed. These rules were designed to protect patient and hospital identities, to reduce the influence of small counties with unstable rates on the results, and to ensure that HCUP data include most hospitalizations in an area. One limitation of these rules is that counties with substance-related inpatient stay rates of 0 or with low rates, which may reflect successful public health efforts to manage substance use, are suppressed.

For the national and State-level information presented in Tables 1 and 2 and Figure 7, the inpatient stay rate, average cost, and cost per capita were calculated annually at the national level and for each State. Then, to obtain the aggregate 3-year estimate, they were averaged across the 3 years, weighted by the population total in each year. These State-level data include information from all counties, including those with suppressed county-level data in the other figures.


Figures 2 through 6 are derived from county-level inpatient stay rates. The 3-year county-level estimates were calculated as follows:


For more information on methods used by Community-Level Statistics, please see https://hcupnet.ahrq.gov/downloadables/Methods-Community-Statistics-04-02-18.pdf

Definitions

Diagnoses and ICD-9-CM
The principal diagnosis is that condition established after study to be chiefly responsible for the patient's admission to the hospital. Secondary diagnoses are concomitant conditions that coexist at the time of admission or develop during the stay. All-listed diagnoses include the principal diagnosis plus these additional secondary conditions.

ICD-9-CM is the International Classification of Diseases, Ninth Revision, Clinical Modification, which assigns numeric codes to diagnoses. There are approximately 14,000 ICD-9-CM diagnosis codes.

Case definition
Substance-related ICD-9-CM codes were included in this Statistical Brief if they involved alcohol or illicit drug use, including any use of illegal drugs, or misuse of prescription drugs or other substances. With the exception of heroin (an illicit drug) causing adverse effects in therapeutic use (E935.0), ICD-9-CM codes for adverse effects of medications in therapeutic use were excluded from the definition of "substance-related" because these adverse effects were likely caused inadvertently by medical treatment, not by the illicit use of a substance. If it could not be determined from a given ICD-9-CM code whether the diagnosis reflected illicit use of a substance or an adverse effect of a medication, the code was included in the definition of a "substance-related" only if it referred to a substance that is likely to be abused, specifically barbiturates, benzodiazepines, sedatives, prescription opioids, dextromethorphan, pseudoephedrine, amphetamines, and methylphenidate. A full list of ICD-9-CM codes that were included is shown in Table 3. Identification of substance-related inpatient stays was based on all-listed diagnoses.

Note that the definition used in this Statistical Brief may differ from that in other Briefs. As a result, rates of substance-related inpatient stays may differ somewhat from similar rates reported elsewhere. In particular, the opioid definition used here includes opioid dependence/abuse in remission and neonatal abstinence syndrome and does not include opioid substances causing adverse effects in therapeutic use. The codes included in this Brief also differ from those in the Clinical Classification Software category for substance-related disorders available for query in other parts of HCUPnet.


Table 3. Definition of substance use
ICD-9-CM description ICD-9-CM code Type of substance or substance-related condition
Chapter 5: Mental disorders (290-319)
Alcohol-induced mental disorders (291)
Alcohol withdrawal delirium 291.0 Alcohol
Alcohol-induced persisting amnestic disorder 291.1 Alcohol
Alcohol-induced persisting dementia 291.2 Alcohol
Alcohol-induced psychotic disorder with hallucinations 291.3 Alcohol
Idiosyncratic alcohol intoxication 291.4 Alcohol
Alcohol-induced psychotic disorder with delusions 291.5 Alcohol
Other specified alcohol-induced mental disorders
Alcohol withdrawal 291.81 Alcohol
Alcohol-induced sleep disorders 291.82 Alcohol
Other alcohol-induced mental disorders 291.89 Alcohol
Unspecified alcohol-induced mental disorder 291.9 Alcohol
Drug-induced mental disorders (292)
Drug withdrawal 292.0 Drug-induced mental disorders
Drug-induced psychotic disorders with delusions 292.11 Drug-induced mental disorders
Drug-induced psychotic disorders with hallucinations 292.12 Drug-induced mental disorders
Pathological drug intoxication 292.2 Drug-induced mental disorders
Drug-induced delirium 292.81 Drug-induced mental disorders
Drug-induced persisting dementia 292.82 Drug-induced mental disorders
Drug-induced amnestic disorder 292.83 Drug-induced mental disorders
Drug-induced mood disorder 292.84 Drug-induced mental disorders
Drug-induced sleep disorders 292.85 Drug-induced mental disorders
Other specified drug-induced mental disorders 292.89 Drug-induced mental disorders
Unspecified drug-induced mental disorder 292.9 Drug-induced mental disorders
Alcohol and drug dependence (303, 304)
Acute alcohol intoxication 303.0x Alcohol
Other and unspecified alcohol dependence 303.9x Alcohol
Opioid type dependence 304.0x Opioids
Sedative, hypnotic or anxiolytic dependence 304.1x Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Cocaine dependence 304.2x Stimulants
Cannabis dependence 304.3x Cannabis
Amphetamine and other psychostimulant dependence 304.4x Stimulants
Hallucinogen dependence 304.5x Hallucinogens
Other specified drug dependence (absinthe, glue, inhalant, phencyclidine) 304.6x Other
Combinations of opioid type drug with any other drug dependence 304.7x Opioids
Combinations of drug dependence excluding opioid type drug 304.8x Other
Unspecified drug dependence 304.9x Other
Nondependent abuse of drugs (305)
Nondependent alcohol abuse 305.0x Alcohol
Nondependent cannabis abuse 305.2x Cannabis
Nondependent hallucinogen abuse 305.3x Hallucinogens
Nondependent sedative, hypnotic or anxiolytic abuse 305.4x Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Nondependent opioid abuse 305.5x Opioids
Nondependent cocaine abuse 305.6x Stimulants
Nondependent amphetamine or related acting sympathomimetic abuse 305.7x Stimulants
Nondependent anti-depressant abuse 305.8x Other
Nondependent other mixed or unspecified drug abuse 305.9x Other
Chapters 6, 7, and 9: Diseases of the nervous system and sense organs (320-389), Diseases of the circulatory system (390-459), and Diseases of the digestive system (520-579)
Alcoholic polyneuropathy 357.5 Alcohol
Alcoholic cardiomyopathy 425.5 Alcohol
Alcoholic gastritis, without mention of hemorrhage 535.30 Alcohol
Alcoholic gastritis, with hemorrhage 535.31 Alcohol
Fatty liver 571.0 Alcohol
Acute alcoholic hepatitis 571.1 Alcohol
Alcoholic cirrhosis of liver 571.2 Alcohol
Alcoholic liver damage unspecified 571.3 Alcohol
Chapter 11: Complications of pregnancy, childbirth and the puerperium (630-679)
Drug dependence complicating pregnancy 648.3x Other
Chapter 15: Newborn (perinatal) (760-779)
Noxious influences affecting fetus or newborn via placenta or breastmilk (760)
Fetal alcohol syndrome 760.71 Alcohol
Narcotics affecting newborn 760.72 Opioids
Hallucinogens affecting newborn 760.73 Hallucinogens
Cocaine affecting newborn 760.75 Stimulants
Other and ill-defined conditions originating in the perinatal period (779)
Drug withdrawal syndrome in newborn 779.5 Opioids
Chapter 17: Injury and poisoning (800-999)
Poisoning by drugs, medicinal substances, and biologicals (960-979)
Opium (alkaloids) 965.00 Opioids
Heroin 965.01 Opioids
Methadone 965.02 Opioids
Other opiates and related narcotics 965.09 Opioids
Barbiturates 967.0 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Chloral hydrate group 967.1 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Paraldehyde 967.2 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Bromine compounds 967.3 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Methaqualone compounds 967.4 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Glutethimide group 967.5 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Mixed sedatives, not elsewhere classified 967.6 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Other sedatives and hypnotics 967.8 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Unspecified sedative or hypnotic (sleeping pills) 967.9 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Surface [topical] and infiltration anesthetics 968.59 Stimulants
Benzodiazepine-based tranquilizers 969.4 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Other tranquilizer 969.5 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Hallucinogens 969.6 Hallucinogens
Psychostimulant NOS (Begin 2009) 969.70 Stimulants
Amphetamine (Begin 2009) 969.72 Stimulants
Methylphendate (Begin 2009) 969.73 Stimulants
Psychostimulant NEC (Begin 2009) 969.79 Stimulants
Opiate antagonist 970.1 Opioids
CNS stimulant NEC (only 2006-2010) 970.8 Stimulants
Cocaine (Begin 2010) 970.81 Stimulants
CNS stimulant NEC (Begin 2010) 970.89 Stimulants
CNS stimulant NOS 970.9 Stimulants
Antitussives 975.4 Other
Anti-common cold drugs 975.6 Other
Ethyl alcohol 980.0 Alcohol
Other specified alcohols 980.8 Alcohol
Unspecified alcohol 980.9 Alcohol
Supplemental classification of external causes of injury and poisoning (E-Codes)
Accidental poisoning by drugs, medicinal substances, and biologicals (E850-E858)
Accidental poisoning by heroin E850.0 Opioids
Accidental poisoning by methadone E850.1 Opioids
Accidental poisoning by other opiates and related narcotics E850.2 Opioids
Accidental poisoning by barbiturates E851 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Chlorl hydrate E852.0 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Paraldehyde E852.1 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Bromine compound E852.2 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Methaqualone compounds E852.3 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Glutethimide group E852.4 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Mixed sedatives NEC E852.5 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Other specified sedatives and hypnotics E852.8 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Unspecified sedative or hypnotic E852.9 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Benzodiazepine-based tranquilizers E853.2 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Tranquilizer NEC E853.8 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Tranquilizer NOS E853.9 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Accidental poisoning by hallucinogens E854.1 Hallucinogens
Accidental poisoning by psychostimulants E854.2 Stimulants
Accidental poisoning by central nervous system stimulants (analeptics, opiate antagonists) E854.3 Stimulants
Accidental poisoning by other solid and liquid substances, gases, and vapors (E860-E869)
Alcohol beverage E860.0 Alcohol
Ethyl alcohol E860.1 Alcohol
Alcohol NEC E860.8 Alcohol
Alcohol NOS E860.9 Alcohol
Drugs, medicinal substances, and biologicals causing adverse effects in therapeutic use (E930-E949)
Heroin causing adverse effects in therapeutic use E935.0 Opioids
Suicide and self-inflicted poisoning by solid or liquid substances (E950)
Suicide and self-inflicted poisoning by barbiturates E950.1 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Suicide and self-inflicted poisoning by other sedatives/hypnotics E950.2 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Suicide and self-inflicted poisoning by tranquilizers and other psychotropic agents E950.3 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Poisoning by solid or liquid substances, undetermined whether accidentally or purposely inflicted (E980-E989)
Undetermined poisoning by barbiturates E980.1 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Undetermined poisoning by other sedatives and hypnotics E980.2 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Undetermined poisoning by tranquilizers and other psychotropic agents E980.3 Sedatives, hypnotics, anxiolytics, tranquilizers, barbiturates
Classification of factors influencing health status and contact with health services (V-Codes)
Counseling, substance use V65.42 Other
Abbreviations: ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification; NEC, not elsewhere classifiable; NOS, not otherwise specified


Types of hospitals included in HCUP State Inpatient Databases
This analysis used State Inpatient Databases (SID) limited to data from community hospitals, which are defined as short-term, non-Federal, general, and other hospitals, excluding hospital units of other institutions (e.g., prisons). Community hospitals include obstetrics and gynecology, otolaryngology, orthopedic, cancer, pediatric, public, and academic medical hospitals. Excluded for this analysis are long-term care facilities such as rehabilitation, psychiatric, and alcoholism and chemical dependency hospitals. However, if a patient received long-term care, rehabilitation, or treatment for a psychiatric or chemical dependency condition in a community hospital, the discharge record for that stay was included in the analysis.

Additionally, discharges from long-term acute care facilities are excluded from data made available through the Community-Level Statistics path on HCUPnet.

Consistent with Steiner and Friedman (2013), we identified chronic conditions on the basis of the clinical coding criteria (listed in Table 2) indicated in either a principal or a secondary diagnosis code field on the discharge record. A chronic condition was counted only once per discharge regardless of the number of diagnosis codes (principal and secondary) that indicated the condition.

Unit of analysis
The unit of analysis is the hospital discharge (i.e., the hospital stay), not a person or patient. This means that a person who is admitted to the hospital multiple times in 1 year will be counted each time as a separate discharge from the hospital.

Costs and charges
Total hospital charges were converted to costs using HCUP Cost-to-Charge Ratios based on hospital accounting reports from the Centers for Medicare & Medicaid Services.26 Costs reflect the actual expenses incurred in the production of hospital services, such as wages, supplies, and utility costs; charges represent the amount a hospital billed for the case. For each hospital, a hospital-wide cost-to-charge ratio is used. Hospital charges reflect the amount the hospital billed for the entire hospital stay and do not include professional (physician) fees. For the purposes of this Statistical Brief, costs are reported to the nearest hundred.

About HCUP

The Healthcare Cost and Utilization Project (HCUP, pronounced "H-Cup") is a family of health care databases and related software tools and products developed through a Federal-State-Industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ). HCUP databases bring together the data collection efforts of State data organizations, hospital associations, and private data organizations (HCUP Partners) and the Federal government to create a national information resource of encounter-level health care data. HCUP includes the largest collection of longitudinal hospital care data in the United States, with all-payer, encounter-level information beginning in 1988. These databases enable research on a broad range of health policy issues, including cost and quality of health services, medical practice patterns, access to health care programs, and outcomes of treatments at the national, State, and local market levels.

HCUP would not be possible without the contributions of the following data collection Partners from across the United States:

Alaska Department of Health and Social Services
Alaska State Hospital and Nursing Home Association
Arizona Department of Health Services
Arkansas Department of Health
California Office of Statewide Health Planning and Development
Colorado Hospital Association
Connecticut Hospital Association
Delaware Division of Public Health
District of Columbia Hospital Association
Florida Agency for Health Care Administration
Georgia Hospital Association
Hawaii Health Information Corporation
Illinois Department of Public Health
Indiana Hospital Association
Iowa Hospital Association
Kansas Hospital Association
Kentucky Cabinet for Health and Family Services
Louisiana Department of Health
Maine Health Data Organization
Maryland Health Services Cost Review Commission
Massachusetts Center for Health Information and Analysis
Michigan Health & Hospital Association
Minnesota Hospital Association
Mississippi State Department of Health
Missouri Hospital Industry Data Institute
Montana Hospital Association
Nebraska Hospital Association
Nevada Department of Health and Human Services
New Hampshire Department of Health & Human Services
New Jersey Department of Health
New Mexico Department of Health
New York State Department of Health
North Carolina Department of Health and Human Services
North Dakota (data provided by the Minnesota Hospital Association)
Ohio Hospital Association
Oklahoma State Department of Health
Oregon Association of Hospitals and Health Systems
Oregon Office of Health Analytics
Pennsylvania Health Care Cost Containment Council
Rhode Island Department of Health
South Carolina Revenue and Fiscal Affairs Office
South Dakota Association of Healthcare Organizations
Tennessee Hospital Association
Texas Department of State Health Services
Utah Department of Health
Vermont Association of Hospitals and Health Systems
Virginia Health Information
Washington State Department of Health
West Virginia Department of Health and Human Resources, West Virginia Health Care Authority
Wisconsin Department of Health Services
Wyoming Hospital Association

About the NIS

The HCUP National (Nationwide) Inpatient Sample (NIS) is a nationwide database of hospital inpatient stays. The NIS is nationally representative of all community hospitals (i.e., short-term, non-Federal, nonrehabilitation hospitals). The NIS includes all payers. It is drawn from a sampling frame that contains hospitals comprising more than 95 percent of all discharges in the United States. The vast size of the NIS allows the study of topics at the national and regional levels for specific subgroups of patients. In addition, NIS data are standardized across years to facilitate ease of use. Over time, the sampling frame for the NIS has changed; thus, the number of States contributing to the NIS varies from year to year. The NIS is intended for national estimates only; no State-level estimates can be produced.

The 2012 NIS was redesigned to optimize national estimates. The redesign incorporates two critical changes:
The new sampling strategy is expected to result in more precise estimates than those that resulted from the previous NIS design by reducing sampling error: for many estimates, confidence intervals under the new design are about half the length of confidence intervals under the previous design. The change in sample design for 2012 necessitates recomputation of prior years' NIS data to enable analyses of trends that use the same definitions of discharges and hospitals.

About the SID

The HCUP State Inpatient Databases (SID) are hospital inpatient databases from data organizations participating in HCUP. The SID contain the universe of the inpatient discharge abstracts in the participating HCUP States, translated into a uniform format to facilitate multistate comparisons and analyses. Together, the SID encompass more than 95 percent of all U.S. community hospital discharges. The SID can be used to investigate questions unique to one State, to compare data from two or more States, to conduct market-area variation analyses, and to identify State-specific trends in inpatient care utilization, access, charges, and outcomes.

About HCUPnet

HCUPnet (www.hcupnet.ahrq.gov/) is an online query system that offers instant access to the largest set of all-payer health care databases that are publicly available. HCUPnet has an easy step-by-step query system that creates tables and graphs of national and regional statistics as well as data trends for community hospitals in the United States. HCUPnet generates statistics using data from HCUP's National (Nationwide) Inpatient Sample (NIS), the Kids' Inpatient Database (KID), the Nationwide Emergency Department Sample (NEDS), the Nationwide Readmissions Database (NRD), the State Inpatient Databases (SID), and the State Emergency Department Databases (SEDD).

For More Information

For other information on substance use, refer to the HCUP Statistical Briefs located at www.hcup-us.ahrq.gov/reports/statbriefs/sb_mhsa.jsp.

For additional HCUP statistics, visit:
For more information about HCUP, visit www.hcup-us.ahrq.gov/.

For a detailed description of HCUP and more information on the design of the National Inpatient Sample (NIS) and the State Inpatient Databases (SID), please refer to the following database documentation:

Agency for Healthcare Research and Quality. Overview of the National (Nationwide) Inpatient Sample (NIS). Healthcare Cost and Utilization Project (HCUP). Rockville, MD: Agency for Healthcare Research and Quality. Updated February 2018. www.hcup-us.ahrq.gov/nisoverview.jsp. Accessed February 12, 2018.

Agency for Healthcare Research and Quality. Overview of the State Inpatient Databases (SID). Healthcare Cost and Utilization Project (HCUP). Rockville, MD: Agency for Healthcare Research and Quality. Updated April 2017. www.hcup-us.ahrq.gov/sidoverview.jsp. Accessed January 18, 2018.

Suggested Citation

Fingar KR (IBM Watson Health), Skinner H (IBM Watson Health), Johann J (IBM Watson Health), Coenen N (IBM Watson Health), Freeman WJ (AHRQ), Heslin KC (AHRQ). Geographic Variation in Substance-Related Inpatient Stays Across States and Counties in the United States, 2013-2015. HCUP Statistical Brief #245. November 2018. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/reports/statbriefs/sb245-Substance-Inpatient-Stays-Across-US-Counties.pdf.

Acknowledgments

The authors would like to acknowledge the contributions of Minya Sheng, Veronica Hernandez, and Sylvia Hall of IBM Watson Health, and Anne Elixhauser formerly of the Agency for Healthcare Research and Quality (AHRQ).

***

AHRQ welcomes questions and comments from readers of this publication who are interested in obtaining more information about access, cost, use, financing, and quality of health care in the United States. We also invite you to tell us how you are using this Statistical Brief and other HCUP data and tools, and to share suggestions on how HCUP products might be enhanced to further meet your needs. Please e-mail us at hcup@ahrq.gov or send a letter to the address below:

Virginia Mackay-Smith, Acting Director
Center for Delivery, Organization, and Markets
Agency for Healthcare Research and Quality
5600 Fishers Lane
Rockville, MD 20857


This Statistical Brief was posted online on November 13, 2018.


1 Kamal R. What Are the Current Costs and Outcomes Related to Mental Health and Substance Abuse Disorders? Peterson-Kaiser Health System Tracker. Updated July 31, 2017. www.healthsystemtracker.org/chart-collection/current-costs-outcomes-related-mental-health-substance-abuse-disorders/?_sf_s=mental#item-start. Exit Disclaimer Accessed July 30, 2017.
2 Ibid.
3 Bahorik AL, Satre DD, Kline-Simon AH, Weisner CM, Campbell CI. Alcohol, cannabis, and opioid use disorders, and disease burden in an integrated health care system. Journal of Addiction Medicine. 2017;11(1):3-9.
4 Lipari RN, Van Horn SL. Trends in Substance Use Disorders Among Adults Aged 18 or Older. The CBHSQ Report. Substance Abuse and Mental Health Services Administration. June 29, 2017. www.samhsa.gov/data/sites/default/files/report_2790/ShortReport-2790.html. Accessed June 15, 2018.
5 Ibid.
6 Green M. 8 Most Commonly Abused Drugs in the U.S. [Infographic]. Absolute Advocacy. July 1, 2014. www.absoluteadvocacy.org/most-commonly-abused-drugs/. Exit Disclaimer Accessed June 15, 2018.
7 National Institute on Drug Abuse. Drug Facts: Nationwide Trends. Revised June 2015. www.drugabuse.gov/publications/drugfacts/nationwide-trends. Accessed June 15, 2018.
8 National Institute on Drug Abuse. Drug Facts: Nationwide Trends. Revised June 2015. www.drugabuse.gov/publications/drugfacts/nationwide-trends. Accessed June 15, 2018.
9 Substance Abuse and Mental Health Services Administration, Center for the Application of Prevention Technologies. Now What? The Role of Prevention Following a Nonfatal Opioid Overdose. January 26, 2018. www.samhsa.gov/capt/sites/default/files/resources/role_of_prevention_following_and_overdose-v02.pdf. Accessed June 15, 2018.
10 Lipari RN, Van Horn SL. Trends in Substance Use Disorders Among Adults Aged 18 or Older. The CBHSQ Report. Substance Abuse and Mental Health Services Administration. June 29, 2017.
11 National Institutes of Health, National Institute on Drug Abuse. Drug Facts: Nationwide Trends. Revised June 2015. www.drugabuse.gov/publications/drugfacts/nationwide-trends. Accessed June 15, 2018.
12 Agency for Healthcare Research and Quality. HCUPnet: Hospital Inpatient National Statistics. https://hcupnet.ahrq.gov/#query/eyJEQVRBU0VUX1NPVVJDRSI6WyJEU19OSVMiXSwiQU5BTFlTSVNfVFlQRSI6WyJBVF9UIl0sIkNBVEVHT1JJWkFUSU9OX1RZUEUiOlsiQ1RfQ0NTRCJdLCJDVF9DQ1NEIjpbIjU3NjciXX0=. Accessed August 10, 2018.
13 Centers for Disease Control and Prevention. U.S. Prescribing Rate Maps. July 31, 2017. www.cdc.gov/drugoverdose/maps/rxrate-maps.html. Accessed June 15, 2018.
14 Weiss AJ, Elixhauser A, Barrett ML, Steiner CA, Bailey MK, O'Malley L. Opioid-Related Inpatient Stays and Emergency Department Visits by State, 2009-2014. HCUP Statistical Brief #219. December 2016. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/reports/statbriefs/sb219-Opioid-Hospital-Stays-ED-Visits-by-State.pdf. Accessed July 30, 2018.
15 National Institute on Drug Abuse. Overdose Death Rates. September 2017. www.drugabuse.gov/related-topics/trends-statistics/overdose-death-rates. Accessed July 30, 2018.
16 National Institutes of Health, National Institute on Drug Abuse. Drug Facts: Nationwide Trends. June 2015. www.drugabuse.gov/publications/drugfacts/nationwide-trends. Accessed June 15, 2018.
17 Vestal C. Waiting Lists Grow for Medicine to Fight Opioid Addiction. Pew Charitable Trusts. February 11, 2016. www.pewtrusts.org/en/research-and-analysis/blogs/stateline/2016/02/11/waiting-lists-grow-for-medicine-to-fight-opioid-addiction. Exit Disclaimer Accessed June 15, 2018.
18 amfAR. Opioid and Health Indicators Database: Percent Needing but Not Receiving Addiction Treatment. http://opioid.amfar.org/indicator/pctunmetneed. Exit Disclaimer Accessed June 15, 2018.
19 Agency for Healthcare Research and Quality. HCUPnet Web site. www.hcupnet.ahrq.gov/. Accessed October 31, 2018.
20 Barrett ML, Heslin KC, Yoon F, Moore BJ. Case Study: National Healthcare Quality and Disparities Report (QDR) Sensitivity Analysis on Developing AHRQ Quality Indicator Estimates for 2015 Using Only ICD-9-CM Data. April 7, 2017. Agency for Healthcare Research and Quality. www.hcup-us.ahrq.gov/datainnovations/CaseStudy_QDRanalysis04072017.pdf. Accessed April 24, 2018.
21 Agency for Healthcare Research and Quality. HCUPnet Web site. www.hcupnet.ahrq.gov/. Accessed October 31, 2018.
22 The formula used to calculate county averages is provided on page 22.
23 Agency for Healthcare Research and Quality. HCUPnet Web site. www.hcupnet.ahrq.gov/. Accessed January 31, 2017.
24 Claritas. Claritas Demographic Profile. www.claritas.com. Exit Disclaimer Accessed June 23, 2017.
25 Centers for Medicare & Medicaid Services. Hospital Service Area File. July 26, 2018. www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Hospital-Service-Area-File/index.html. Accessed July 30, 2018.
26 Agency for Healthcare Research and Quality. HCUP Cost-to-Charge Ratio (CCR) Files. Healthcare Cost and Utilization Project (HCUP). 2001-2014. Rockville, MD: Agency for Healthcare Research and Quality. Updated November 2016. www.hcup-us.ahrq.gov/db/state/costtocharge.jsp. Accessed January 31, 2017.

Internet Citation: Statistical Brief #245. Healthcare Cost and Utilization Project (HCUP). November 2018. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/reports/statbriefs/sb245-Substance-Inpatient-Stays-Across-US-Counties.jsp.
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