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Opioid-Related Hospital Use

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graphic depiction of Opioid Use data which is available immediately following this image. **Self-pay/No charge: includes self-pay, no charge, charity, and no expected payment.
Note: Not all inpatient stays are included (see Data Notes & Methods).
U.S. National: Opioid-Related Hospital Use by Expected Payer
Year Qtr. Medicare Medicaid Private insurance Self-pay/No charge** All inpatient stays^
2010 1 38,300 47,750 30,150 23,000 146,200
2010 2 39,250 50,050 31,500 23,450 151,750
2010 3 41,400 52,100 31,500 24,250 157,450
2010 4 40,950 49,650 31,850 22,950 153,550
2011 1 45,650 45,850 32,650 21,600 152,950
2011 2 47,300 49,500 34,750 23,400 162,400
2011 3 48,500 51,000 35,750 25,350 168,650
2011 4 47,850 49,200 33,550 23,550 161,500
2012 1 48,800 52,100 34,950 20,700 164,600
2012 2 49,100 53,100 34,200 21,900 165,700
2012 3 49,700 52,300 35,400 22,950 168,100
2012 4 48,400 49,000 34,100 21,600 160,400
2013 1 49,050 48,950 33,450 21,500 159,550
2013 2 51,850 51,850 35,150 23,150 169,350
2013 3 53,350 55,300 35,550 24,850 177,100
2013 4 50,700 50,900 33,550 23,950 166,900
2014 1 52,500 59,600 33,550 17,800 169,150
2014 2 54,950 66,200 34,750 15,700 177,400
2014 3 56,700 70,300 35,950 15,600 184,050
2014 4 56,200 69,800 35,550 14,350 181,750
2015 1 56,600 70,750 36,250 12,950 182,050
2015 2 58,900 74,250 37,750 13,600 190,650
2015 3 62,400 79,850 39,250 14,700 203,150
2015 4 79,350 82,000 46,650 15,200 230,750
2016 1 82,700 83,700 45,550 14,250 233,850
2016 2 82,200 85,400 46,800 16,000 238,200
2016 3 84,550 90,450 46,600 16,950 246,900
2016 4 83,200 87,650 44,550 15,650 238,350
2017 1 87,000 89,000 44,950 14,800 243,000
2017 2 87,300 91,750 45,150 15,700 247,500
2017 3 84,900 93,650 44,100 16,900 247,150
2017 4 84,150 88,000 41,750 15,750 236,900
U.S. National: Opioid-Related Hospital Use by Expected Payer
Number of Inpatient Stays
Year Qtr. Medicare Medicaid Private insurance Self-pay/No charge** All inpatient stays^
2010 1 38,300 47,750 30,150 23,000 146,200
2010 2 39,250 50,050 31,500 23,450 151,750
2010 3 41,400 52,100 31,500 24,250 157,450
2010 4 40,950 49,650 31,850 22,950 153,550
2011 1 45,650 45,850 32,650 21,600 152,950
2011 2 47,300 49,500 34,750 23,400 162,400
2011 3 48,500 51,000 35,750 25,350 168,650
2011 4 47,850 49,200 33,550 23,550 161,500
2012 1 48,800 52,100 34,950 20,700 164,600
2012 2 49,100 53,100 34,200 21,900 165,700
2012 3 49,700 52,300 35,400 22,950 168,100
2012 4 48,400 49,000 34,100 21,600 160,400
2013 1 49,050 48,950 33,450 21,500 159,550
2013 2 51,850 51,850 35,150 23,150 169,350
2013 3 53,350 55,300 35,550 24,850 177,100
2013 4 50,700 50,900 33,550 23,950 166,900
2014 1 52,500 59,600 33,550 17,800 169,150
2014 2 54,950 66,200 34,750 15,700 177,400
2014 3 56,700 70,300 35,950 15,600 184,050
2014 4 56,200 69,800 35,550 14,350 181,750
2015 1 56,600 70,750 36,250 12,950 182,050
2015 2 58,900 74,250 37,750 13,600 190,650
2015 3 62,400 79,850 39,250 14,700 203,150
2015 4 79,350 82,000 46,650 15,200 230,750
2016 1 82,700 83,700 45,550 14,250 233,850
2016 2 82,200 85,400 46,800 16,000 238,200
2016 3 84,550 90,450 46,600 16,950 246,900
2016 4 83,200 87,650 44,550 15,650 238,350
2017 1 87,000 89,000 44,950 14,800 243,000
2017 2 87,300 91,750 45,150 15,700 247,500
2017 3 84,900 93,650 44,100 16,900 247,150
2017 4 84,150 88,000 41,750 15,750 236,900
**Self-pay/No charge: includes self-pay, no charge, charity, and no expected payment.
^The sum across the displayed expected payer groups does not equal the value in the "All inpatient stays" column because inpatient stays for some expected payers are not shown (e.g., discharges coded as "Other" or "Missing/Invalid"). (See Data Notes & Methods).

Transition from ICD-9-CM to ICD-10-CM/PCS Coding

On October 1, 2015, the United States transitioned from ICD-9-CM1 to ICD-10-CM/PCS2. The 2015 data in this section of HCUP Fast Stats include three quarters of information based on ICD-9-CM coding, whereas the fourth quarter is based on ICD-10-CM/PCS coding. Users may observe discontinuity in trends analyses that span the October 1, 2015 transition date. More information on the impact of ICD-10-CM/PCS is available on the HCUP User Support (HCUP-US) web page for ICD-10-CM/PCS Resources.

1 International Classification of Diseases, Ninth Revision, Clinical Modification
2 International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System

Opioid-Related Hospital Use

Inpatient stays and emergency department (ED) visits including opioid-related hospital use are identified by any diagnosis (all-listed) in the following ranges of ICD-10-CM and ICD-9-CM codes:

ICD-10-CM Codes Starting October 1, 2015
  • F11 series: Opioid-related disorders
    • All codes are included except F11.11 and F11.21
  • T40 series: Poisoning by, adverse effect of, and underdosing of narcotics
    • The following codes are included - encompassing accidental (unintentional) poisoning, intentional self-harm, assault, undetermined, and adverse effect (except heroin) - with a seventh digit indicating initial, subsequent encounter, or sequela
      • 0X1, 0X2, 0X3, 0X4, 0X5: Opium
      • 1X1, 1X2, 1X3, 1X4: Heroin
      • 2X1, 2X2, 2X3, 2X4, 2X5: Other opioids
      • 3X1, 3X2, 3X3, 3X4, 3X5: Methadone
      • 4X1, 4X2, 4X3, 4X4, 4X5: Other synthetic narcotics
      • 601, 602, 603, 604, 605: Unspecified narcotics
      • 691, 692, 693, 694, 695: Other narcotics
    • Codes with a sixth digit of "6", indicating underdosing, are excluded

We observed some differences in the reporting of opioid-related inpatient stays and ED visits identified using ICD-10-CM codes. These differences are explored within the Case Study: Exploring How Opioid-Related Diagnosis Codes Translate from ICD-9-CM to ICD-10-CM, which is found under "Doing Analysis with ICD-10 Data" on the ICD-10-CM/PCS Resources web page of HCUP-US.

ICD-9-CM Codes Prior to October 1, 2015
  • 304.00-304.02: Opioid type dependence (unspecified; continuous; episodic)
  • 304.70-304.72: Combinations of opioid type drug with any other drug dependence (unspecified; continuous; episodic)
  • 305.50-305.52: Opioid abuse (unspecified; continuous; episodic)
  • 965.00-965.02; 965.09: Poisoning by opium (alkaloids), unspecified; heroin; methadone; other opiates and related narcotics
  • 970.1: Poisoning by opiate antagonists
  • E850.0-E850.2: Accidental poisoning by heroin; methadone; other opiates and related narcotics
  • E935.0-E935.2: Heroin, methadone, other opiates and related narcotics causing adverse effects in therapeutic use
  • E940.1: Opiate antagonists causing adverse effects in therapeutic use

These codes include opioid-related use stemming from illicit opioids such as heroin, illegal use of prescription opioids, and the use of opioids as prescribed. Each type of opioid use is important for understanding and addressing the opioid epidemic in the United States.3 While there may be interest in examining how much each type of opioid use contributes to the overall opioid problem, many of the opioid-related codes under the ICD-9-CM clinical coding system do not allow heroin-related cases to be explicitly identified (e.g., in the 304.0x series, heroin is not distinguished from other opioids). In addition, the codes do not distinguish between illegal use of prescription drugs and their use as prescribed.

Excluded Codes

It should be noted that ICD-10-CM and ICD-9-CM diagnosis codes related to opioid dependence or abuse "in remission" are not used to identify opioid-related hospital use because remission does not indicate active use of opioids. Codes indicating neonatal abstinence syndrome (NAS) are also not included. HCUP Fast Stats provides a separate topic, Neonatal Abstinence Syndrome (NAS) Among Newborn Hospitalizations, for users interested in trends for NAS.

State-Specific Differences

It should be noted that for certain States, data differences or restrictions may impact the presented trends.

  • In the Iowa SID and SEDD prior to data year 2017, records for behavioral health patients treated in chemical dependency or psychiatric care units were prohibited from release, and therefore not reported within the definition of opioid-related hospital use. Beginning with the 2017 data year those records are included.
  • In the Georgia State databases, diagnoses indicating medical misadventures and adverse reactions, which include diagnosis codes specific to the adverse effects of opioids, are not available because of an HCUP Partner restriction. For this reason, rates and counts in this section of Fast Stats are under-estimated for Georgia data.

3 Compton WM, Jones CM, Baldwin GT. Relationship between nonmedical prescription-opioid use and heroin use. The New England Journal of Medicine. 2016;374:154-63.

Inpatient Stays

State-level statistics on inpatient stays are from the HCUP State Inpatient Databases (SID) and quarterly data if available. Information based on quarterly data should be considered preliminary. Quarterly data will be replaced by the State's complete annual SID for the year when it is available. Additionally, it is possible for a State's annual SID to be recreated. As a result of either the replacement of quarterly data with annual SID or the recreation of an annual SID, previously reported statistics for a given State may change. The SID are limited to patients treated in community hospitals in the State. Community hospitals are defined as short-term, non-Federal, general, and other hospitals, excluding hospital units of other institutions (e.g., prisons). Included among community hospitals are obstetrics and gynecology, otolaryngology, orthopedic, cancer, pediatric, public, and academic medical hospitals. Excluded are community hospitals that are also long-term care facilities such as rehabilitation, psychiatric, and alcoholism and chemical dependency hospitals.

We adjust the discharge counts for hospitals that were not included in the SID or quarterly data. Across all States, the SID are missing about 7 percent of community hospitals and about 1.5 percent of discharges. Weighting for missing hospitals uses the following information from the American Hospital Association (AHA) Annual Survey of Hospitals to define strata within the State:

  • Ownership: government, private nonprofit, and private investor-owned
  • Size of the hospital based on the number of beds: small, medium, and large categories defined within region
  • Location combined with teaching status: rural, urban nonteaching, urban teaching

If a stratum is missing one or more hospitals in the State data, then we set the discharge weight to the total number of discharges reported in the AHA divided by the total number of discharges in the State data. If all hospitals in a stratum are represented in the State data, then we set the discharge weight to 1. We also adjust the discharge weights for hospitals that have missing discharge quarters of data, provided there is no indication in the AHA Annual Survey that the facility had closed.

In this section of Fast Stats, discharge weights are specific to the data year for SID through 2013 (e.g., discharge weights for the 2013 SID use 2013 AHA data). Weighting for HCUP data starting in 2014 is based on AHA data from the prior year because current information is often unavailable (e.g., discharge weights for the 2014 SID use 2013 AHA data).

National statistics on inpatient stays are from the HCUP National (Nationwide) Inpatient Sample (NIS). The NIS is sampled from the HCUP State Inpatient Databases (SID). Beginning with the 2012 data year, the NIS is a 20 percent sample of discharges from community hospitals, excluding rehabilitation and long-term acute care (LTAC) hospitals, participating in HCUP in that data year. For data years 1988 through 2011, the NIS was a 20 percent sample of community, nonrehabilitation hospitals and included all discharges within sampled hospitals. The national estimates on inpatient stays presented in this section of Fast Stats were developed using the NIS Trend Weight Files for consistent estimates across all data years (e.g., LTACs were removed from earlier data years using trend weights).

Emergency Department Visits

Emergency department (ED) visits are defined as ED encounters that do not result in a hospital admission to the same hospital (i.e., treat-and-release ED visits).

State-level statistics on ED treat-and-release visits are from the HCUP State Emergency Department Databases (SEDD) and quarterly data if available. Information based on quarterly data should be considered preliminary. Quarterly data will be replaced by the State's complete annual SEDD for the year when it is available. Additionally, it is possible for a State's annual SEDD to be recreated. As a result of either the replacement of quarterly data with annual SEDD or the recreation of an annual SEDD, previously reported statistics for a given State may change. The SEDD are limited to patients treated in community hospital-owned EDs in the State.

We adjust the ED visit counts for hospital-owned EDs that are missing from the SEDD. Across all States, the SEDD are missing about 5 percent of EDs and about 2 percent of ED visits. Data from the following data sources are used to weight for missing information: the American Hospital Association (AHA) Survey of Hospitals and the Trauma Information Exchange Program (TIEP) database, a national inventory of trauma centers in the United States collected by the American Trauma Society. Weighting for missing EDs uses the following information to define strata within the State:

  • Ownership: government, private nonprofit, and private investor-owned (AHA)
  • Location: large metropolitan, small metropolitan, micropolitan, and rural (AHA)
  • Teaching status: nonteaching and teaching (AHA)
  • Trauma center designation: levels I, II, and III (TIEP)

If a stratum is missing one or more EDs in the State data, then we set the weight to the total number of ED visits reported in the AHA divided by the total number of ED visits in the State data. If all EDs in a stratum are represented in the State data, then we set the discharge weight to 1. We also adjust the discharge weights for EDs that have missing quarters of data, provided there is no indication in the AHA Annual Survey that the facility had closed.

In this section of Fast Stats, discharge weights are specific to the data year for ED visits through 2013 (e.g., discharge weights for the 2013 ED visits use 2013 AHA data). Weighting of HCUP data for ED visits starting in 2014 is based on AHA data from the prior year because current information is often unavailable (e.g., discharge weights for the 2014 ED visits use 2013 AHA data).

National statistics on ED treat-and-release visits are from the HCUP Nationwide Emergency Department Sample (NEDS). Treat-and-release records were selected from the NEDS using the HCUP data element HCUPFILE, which identifies the source of the ED record: the HCUP State Emergency Department Databases (SEDD) or the HCUP State Inpatient Databases (SID). All records where HCUPFILE was equal to SEDD are included in this analysis, that is, inpatient admissions from the ED were excluded since these cases are represented in the NIS.

Unit of Analysis

The unit of analysis is the hospital discharge (i.e., the hospital inpatient stay) or an emergency department (ED) visit, not a person or patient. This means that a person who is admitted to the hospital or visits the ED multiple times in one year is counted each time as a separate discharge from the hospital or a separate visit in the ED. Discharge and ED visit counts exclude transfers to another acute care hospital or ED. If the hospital inpatient stay or ED visit includes more than one diagnosis code for opioid-related hospital use, the encounter is only counted once.

For Fast Stats, all stays and visits are counted one time only, regardless of the number of relevant diagnosis or procedure codes that appear on the record. For instance, when identifying opioid-related inpatient stays and ED visits, a record may include more than one of the opioid-specific codes; in such a case, the record is only included once in the counts.

Rate or Count of Inpatient Stays or ED Visits

Population-based rates are presented for trends of opioid-related inpatient stays and ED visits reported overall and by age, sex, community-level income, and patient location. For expected payer, trends in opioid-related hospital use are presented as discharge/visit counts. Currently, there is no source of national population insurance estimates that align with HCUP's definition of expected primary payer. More information is available in HCUP Methods Series Reports by Topic "Population Denominator Data for Use with the HCUP Databases" (multiple documents; updated annually).

Discharge/visit counts for expected payer and numerator counts for age, sex, community-level income, and patient location are summarized by discharge quarter. For records where the discharge quarter is missing, the value is imputed based on the average quarterly discharge distribution in the United States between 2005 and 2014, as follows:

  • Inpatient - quarter 1: 23 percent; quarter 2: 25 percent; quarter 3: 27 percent; quarter 4: 25 percent
  • ED - quarter 1: 24 percent; quarter 2: 25 percent; quarter 3: 26 percent; quarter 4: 25 percent

For age, sex, community-level income, and patient location, denominator counts are consistently defined with the numerator (i.e., rates for females use HCUP counts and population counts specific to females). Population data are obtained from the Claritas, a vendor that produces population estimates and projections based on data from the U.S. Census Bureau. Claritas estimates intercensal annual household and demographic statistics for geographic areas.

The rate of inpatient stays or rate of ED visits includes the HCUP number of stays or ED visits in the numerator and the U.S. resident population in the denominator (with a multiplier of 100,000).

Annualized quarterly rates are calculated as the quarterly count of inpatient stays or ED visits divided by one-fourth the annual population, times 100,000. Rates are suppressed for confidentiality when numerator counts are less than 26.

Information based on quarterly data from less than a full year should be considered preliminary. Quarterly data will be replaced by quarterly counts from the State's complete annual State Inpatient Database (SID) or State Emergency Department Database (SEDD) for the year, when it is available.

The number of years of data reported for each individual State and the United States depends on the availability of the underlying HCUP database. For example, the HCUP nationwide databases for the most recent data year can only be created after all of the necessary State databases are available. State-level data are included in Fast Stats when they become available. Occasionally, the statistics previously reported in Fast Stats may change if the HCUP data used initially are recreated.

The discharge/visit counts and numerator counts are available in the exported data file, which can be downloaded by expanding "Show Data Export Options." Counts are rounded to the nearest 50 discharges or visits, with any counts less than 26 suppressed for confidentiality. The exported data file also includes rates calculated on an annual rather than quarterly basis for trends of opioid-related inpatient stays and ED visits reported overall and by age, sex, community-level income, and patient location.

Age

Age refers to the age of the patient at admission. Discharges or visits missing age are excluded from results reported by age.

It should be noted that beginning with the transition to the ICD-10-CM/PCS coding system on October 1, 2015, a noticeable jump in the rate of opioid-related inpatient stays was observed for adults aged 65 years and older in most States. Additional information regarding this pattern is available on the HCUP-US web page for ICD-10-CM/PCS Resources in the report Preliminary Case Study: Exploring How Opioid-Related Diagnosis Codes Translate from ICD-9-CM to ICD-10-CM.

Sex

All nonmale, nonfemale responses are set to missing. Discharges or visits with missing values for sex are excluded from results reported by sex.

Community-Level Income

Community-level income is based on the median household income of the patient's ZIP Code of residence. Quartiles are defined so that the total U.S. population is evenly distributed across four groups. The cut-offs for the quartile designation are determined annually using ZIP Code demographic data obtained from Claritas, a vendor that produces population estimates and projections based on data from the U.S. Census Bureau. Claritas estimates intercensal annual household and demographic statistics for geographic areas. The value ranges for the national income quartiles vary by year. Income quartile is missing if the patient is homeless or foreign. Discharges missing the income quartile are excluded from results reported by community-level income.

Patient Location

Patient location is based on the six-category, county-level scheme developed by the National Center for Health Statistics (NCHS) to study the relationship between urbanization and health:

  • Large central metropolitan: Counties in metropolitan statistical areas (MSAs) of 1 million or more population that contain the entire population of the largest principal city of the MSA, have their entire population contained in the largest principal city of the MSA, or contain at least 250,000 inhabitants of any principal city of the MSA
  • Large fringe metropolitan (suburbs): Counties in MSAs of 1 million or more population that did not qualify as large central metropolitan counties
  • Medium metropolitan: Counties in MSAs of populations of 250,000 to 999,999
  • Small metropolitan: Counties in MSAs of population less than 250,000
  • Micropolitan: Counties in micropolitan statistical areas
  • Noncore: Nonmetropolitan counties that did not qualify as micropolitan
In the NCHS scheme, the rural counties are divided into micropolitan and noncore categories, but in this section of Fast Stats, these two categories are combined into a single rural category in order to preserve results when cell sizes are too small. For rates prior to 2014, the NCHS classification is based on population density from the 2000 Census. Starting in 2014, the NCHS classification is based on population density from the 2010 Census.

In the 2007 Rhode Island SID, the reporting of patients residing in counties designated as large central metropolitan was inconsistent with prior and subsequent years. Therefore, the fluctuation between 2006 and 2008 in the inpatient rates for opioid-related inpatient stays by urban-rural location should be considered an anomaly.

Expected Payer

The "expected payer" data element in HCUP databases provides information on the type of payer that the hospital expects to be the source of payment for the hospital bill. Trends in inpatient and ED visit counts are provided by the following expected primary payers: Medicare, Medicaid, private insurance, and self-pay/no charge.

Patients identified as self-pay/no charge have an expected primary payer of self-pay, charity, no charge, or no expected payment. The self-pay/no charge category may also include patients with an expected payer of Indian Health Services, county indigent, migrant health programs, Ryan White Act, Hill-Burton Free Care, or other Federal, State, and local programs for the indigent when those programs are identifiable in the Partner-provided coding of expected payer. This reclassification of patients is only possible for some States and not for national estimates. More information on identifying programs reported in HCUP data that may cover the uninsured is available in HCUP Methods Series Reports by Topic "User Guide - An Examination of Expected Payer Coding in HCUP Databases" (multiple documents; updated annually).

Discharges/visits with the following expected primary payers are not reported in Fast Stats reporting for adult inpatient stays or ED visits by expected payer: other Federal, State, and local programs; missing; or invalid. In 2017, across all states, these excluded discharges/visits represented 3 percent (range of 0 to 19.4 percent) of all discharges and 3.6 percent (range of 0 to 26.7 percent) of all ED visits.

The total reflecting the number of discharges or ED visits across all expected payers (including those groups not presented in the graphs) is provided in the underlying data tables ("Show Underlying Data Tables") and in the Excel data download file ("Show Data Export Options"). This statistic was added to Fast Stats in April 2020. It was calculated using the currently available SID and SEDD for data years 2005 and forward. If the SID/SEDD initially used for Fast Stats has been recreated, then the total counts could be based on different versions of the SID/SEDD than the counts shown for Medicare, Medicaid, private insurance, and self-pay/no charge.

For comparison against the total described above for all expected payers, the Excel download file also provides the sum of the displayed expected payers (i.e., the sum of the rounded weighted quarterly expected payer counts of discharges/visits across the expected payers that are displayed in the graphs).

It should be noted that in certain data years and for certain States, data anomalies are identified that may impact the observed trends in inpatient stays and ED visits by expected primary payer:

  • In the New York SEDD prior to 2011, the coding of expected primary payer did not distinguish between patients covered by commercial managed care plans and patients covered by Medicaid managed care plans. Because of this ambiguity in the payer coding, ED visits for patients with Medicaid managed care plans are reported under private insurance in this section of Fast Stats. Starting in 2011, the expected payer coding in New York data separately identifies Medicaid managed care patients and therefore ED visits for these patients are reported under Medicaid.
  • In the Texas 2004-2011 SID, some Medicare records were incorrectly mapped to private insurance. Thus, the counts for Medicare are slightly underreported and the counts for private insurance are slightly overreported. This impacts roughly 1.5-3.5 percent of SID records between 2004-2011.
  • In the Nebraska SID and SEDD prior to 2016, some Medicaid managed care patients may have been categorized in the data under private insurance instead of Medicaid because the Medicaid program was managed by a commercial insurance company. Beginning with data year 2016, there are large increases in the number of Medicaid records and proportionate decreases in records categorized as private insurance because the Nebraska Partner organization improved the process for the identification of patients covered by Medicaid managed care programs managed by commercial insurance companies.
  • In the Vermont 2015 SID and SEDD, increases in Medicaid should be considered an anomaly. Vermont briefly modified their billing process in late 2014, which led to increases in records with a primary expected payer value of Medicaid. In 2016 data, the coding of Medicaid returns to a normal level that is consistent with historical data.

Interactive Map

The opioid-related hospital use map provides annual rates of opioid-related inpatient stays or ED visits per 100,000 population. States are color-coded to identify each State's opioid-related inpatient or ED rate relative to the distribution across all States providing data in 2015. States are classified into one of five groups based on the distribution of rates in 2015: lowest 20 percent, 2nd lowest 20 percent, middle 20 percent, 2nd highest 20 percent, highest 20 percent. The five groups are defined separately for opioid-related inpatient and ED rates. States in grey do not have data available; this may include States that are not currently HCUP Partners, do not provide the specific data type (e.g., ED) to HCUP, are not participating in Fast Stats, or participate but have not provided data for the year displayed.

Use this export feature to download all of the underlying data (quarterly and annual rates) for opioid-related hospital use for all available States and settings of care in Microsoft Excel (.xls) format.

  1. Select Excel Export to request the download.
  2. You must read and agree to the terms of the Data Use Agreement for HCUP Fast Stats that is displayed on the screen in order to obtain these data.
  3. Follow the prompts to save a copy of the Excel file to your computer. Prompting will vary by browser.
  4. If you decide to use these data for publishing purposes please refer to Requirements for Publishing with HCUP Data.


Internet Citation: HCUP Fast Stats. Healthcare Cost and Utilization Project (HCUP). April 2020. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/faststats/opioid/opioiduse.jsp?radio3=on&location1=US&characteristic1=06&setting1=IP&location2=&characteristic2=01&setting2=IP&expansionInfoState=hide&dataTablesState=show&definitionsState=hide&exportState=hide.
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Last modified 4/29/2020