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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.

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.

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.

• Nationally, of the four most common types of substance-related stays, those involving alcohol cost the most on average, followed by opioid-, stimulant-, and cannabis-related stays.
  
  Of the four most common types of substances involved in inpatient stays, alcohol-related stays had the highest average cost, followed by opioid-, stimulant, and cannabis-related stays.
  
  • Alcohol: $11,600 per stay
  • Opioids: $10,500 per stay
  • Stimulants: $10,000 per stay
  • Cannabis: $8,900 per stay
  The average cost per stay for some types of substances was higher than that of inpatient care in general. For instance, in California both alcohol- and opioid-related stays ($17,200 per stay) were on average costlier than all inpatient stays ($14,900 per stay).



• Among the 31 States, the average cost per alcohol-, opioid-, cannabis-, and stimulant-related stay was highest in California, Hawaii, and Washington, where the average cost of inpatient stays overall also was highest.
  
  • Alcohol: The highest average cost per alcohol-related stay was $17,200 in California, followed by $16,500 in Hawaii, and $14,800 in Washington.
  • Opioids and cannabis: Hawaii had the highest average cost of opioid- and cannabis-related stays ($17,500 and $13,000, respectively), followed by California ($17,200 and $12,400) and Washington ($14,700 and $12,000).
  • Stimulants: The highest average cost per stimulant-related stay was $14,300 in California, followed by $13,400 in Washington and $12,900 in Hawaii.
  • Inpatient stays overall: California, Hawaii, and Washington also had a higher average cost of all inpatient stays (including substance-related and non-substance-related stays) compared with other states ($14,900, $13,900, and $13,400, respectively).

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.

• On average, $119 was spent annually per U.S. resident on substance-related inpatient stays from 2013 through 2015.
  
  Nationally, the average annual per capita cost of inpatient stays involving all substances combined was $119 from January 2013 through September 2015. Alcohol-related stays constituted the greatest percentage of per capita costs, followed by opioids, cannabis, and stimulants.
  
  • Alcohol: 57.1 percent ($68 of $119 per capita)
  • Opioids: 19.3 percent ($23 of $119 per capita)
  • Cannabis: 14.3 percent ($17 of $119 per capita)
  • Stimulants: 14.3 percent ($17 of $119 per capita)



• Rhode Island, Massachusetts, and California had the highest per capita costs of substance-related stays.
  
  Rhode Island and Massachusetts had the highest per capita costs for substance-related stays ($165 and $157, respectively). These States also ranked among those with the highest rates of substance-related stays (as shown in Table 1).
  
  Notably, California had the third highest per capita cost of substance-related stays ($156), even though the rate of substance-related stays in California (945 per 100,000 population) fell below the national average (1,064 stays per 100,000 population, Table 1).



• Hot spots of substance stays were associated with substantial healthcare costs within States.
  
  Regarding alcohol-related stays:
  
  • Rhode Island and Massachusetts had the highest percentages of counties identified as hot spots of alcohol-related stays, where 80.0 and 71.4 percent of counties, respectively, had an alcohol-related stay rate in the highest quintile nationally.
    • In these States, alcohol-related stays cost nearly $100 per capita (compared with $68 nationally).
  • The per capita cost of alcohol-related stays was highest in Rhode Island ($98), Massachusetts ($95), and Minnesota ($89); and lowest in Utah ($25), Louisiana ($34), and Arkansas ($37).
  Regarding opioid-related stays:
  
  • West Virginia and Massachusetts had the highest percentages of counties identified as hot spots of opioid-related stays, where 65.5 and 64.3 percent of counties, respectively, had an opioid-related stay rate in the highest quintile nationally.
    • The per capita cost of opioid-related stays in these States was $33 and $39, respectively (compared with $23 nationally).
  • The per capita cost of opioid-related stays was highest in Washington ($44), Maryland ($41), and Massachusetts ($39); and lowest in Iowa ($7), Louisiana ($10), and Texas ($10).
  Regarding cannabis-related stays:
  
  • North Carolina (44.9 percent) and Maryland and Rhode Island (40.0 percent) had the highest percentages of counties identified as hot spots of cannabis-related stays.
    • In these States, the per capita cost of cannabis-related stays was $16, $24, and $22, respectively (compared with $17 nationally).
  • The per capita cost of cannabis-related stays was highest in Michigan ($26), Hawaii ($26), and Maryland ($24); and lowest in Iowa, Utah, and Arkansas (under $10).
  Finally, regarding stimulant-related stays:
  
  • California (62.5 percent) and North Carolina (56.1 percent) had the highest percentages of counties identified as hot spots of stimulant-related stays.
    • The per capita cost of stimulant-related stays was $32 and $15 in these States, respectively (compared with $17 nationally).
  • The per capita cost of stimulant-related stays was highest in Hawaii ($36), California ($32), and Oregon ($26); and lowest in Iowa and West Virginia ($6).

About Statistical Briefs

Healthcare Cost and Utilization Project (HCUP) Statistical Briefs provide basic descriptive statistics on a variety of topics using HCUP administrative healthcare 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.

Rate or cost_2013-2015 = Rate or cost₂₀₁₃ * (population estimate₂₀₁₃/population estimate_2013-2015) + rate or cost₂₀₁₄ * (population estimate₂₀₁₄/population estimate_2013-2015) + rate or cost₂₀₁₅ * (population estimate₂₀₁₅/population estimate_2013-2015)

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

Rate_2013-2015 = (Number of stays₂₀₁₃ + number of stays₂₀₁₄ + number of stays₂₀₁₅)/(population estimate₂₀₁₃ + population estimate₂₀₁₄ + population estimate₂₀₁₅) * 100,000

For more information on methods used by Community-Level Statistics, please see https://datatools.ahrq.gov/hcupnet/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.

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 healthcare 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 healthcare 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 healthcare 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:

• Revisions to the sample design—starting with 2012, the NIS is now a sample of discharge records from all HCUP-participating hospitals, rather than a sample of hospitals from which all discharges were retained (as is the case for NIS years before 2012).
• Revisions to how hospitals are defined—the NIS now uses the definition of hospitals and discharges supplied by the statewide data organizations that contribute to HCUP, rather than the definitions used by the American Hospital Association (AHA) Annual Survey of Hospitals.

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 (https://datatools.ahrq.gov/hcupnet) is an online query system that offers instant access to the largest set of all-payer healthcare 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:

• HCUP Fast Stats at https://datatools.ahrq.gov/hcup-fast-stats for easy access to the latest HCUP-based statistics for health information topics
• HCUPnet, HCUP's interactive query system, at https://datatools.ahrq.gov/hcupnet

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 healthcare 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. 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 healthcare 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/. 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.edc.org/sites/default/files/uploads/role_of_prevention_following_and_overdose-v02.pdf. Accessed October 14, 2019.
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://datatools.ahrq.gov/hcupnet/#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. Accessed June 15, 2018.
¹⁸ amfAR. Opioid and Health Indicators Database: Percent Needing but Not Receiving Addiction Treatment. http://opioid.amfar.org/indicator/pctunmetneed. Accessed June 15, 2018.
¹⁹ Agency for Healthcare Research and Quality. HCUPnet website. https://datatools.ahrq.gov/hcupnet. 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 website. https://datatools.ahrq.gov/hcupnet. 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 website. https://datatools.ahrq.gov/hcupnet. Accessed January 31, 2017.
²⁴ Claritas. Claritas Demographic Profile. www.claritas.com. 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.