User Support

April 2020

Audrey J. Weiss, Ph.D., Kevin C. Heslin, Ph.D., Carol Stocks, Ph.D., and Pamela L. Owens, Ph.D.


Introduction

Declared a public health emergency by the Department of Health and Human Services in 2017, the opioid crisis continues to negatively affect communities across the United States.¹ The 47,600 overdose deaths due to opioids in 2017 represented 67.8 percent of all drug overdose deaths in that year and a 12-percent increase in opioid overdose deaths from the previous year alone.² In addition to overdose, opioid-related diagnoses such as abuse/dependence, withdrawal, and medical complications arising from opioid use have become more frequent among patients in recent years, leading to sharp increases in the use of acute care services.³ Between 2005 and 2014, the national rate of opioid-related inpatient stays and emergency department visits increased 64.1 percent and 99.4 percent, respectively.⁴ Despite the dramatic rise in opioid-related problems, little research has focused on clinical conditions that may be associated with opioid dependence and long-term use.

Concurrent with the opioid epidemic, there has been a rise in hospital inpatient stays for individuals with both drug dependence and endocarditis.^5,6 Between 2002 and 2012, the number of inpatient stays involving opioid abuse/dependence and endocarditis increased by 46.1 percent.⁷ Endocarditis is an inflammation of the heart valves and the inner lining of the heart from bacteria or fungi that entered the bloodstream through a variety of mechanisms, including bleeding gums from brushing or dental procedures, infections, catheters, needles for body piercing and tattoos, and intravenous drug use.⁸ Endocarditis can lead to severe complications such as heart failure, stroke, and pulmonary embolism, and if left untreated, the condition can be fatal.⁹ At least half of individuals with endocarditis will require heart valve replacement surgery.^10,11

The specific relationship between opioid use disorder (defined here as opioid abuse, opioid dependence, opioid use, long-term opiate use, poisoning, and adverse effects) and endocarditis is not clear. For example, a bloodstream infection resulting in endocarditis may develop subsequent to a dental procedure and may co-occur in a patient being treated with opioids. Endocarditis also may result from illicit intravenous drug (opioid) use, one of many possible risk factors for endocarditis.

Using the National (Nationwide) Inpatient Sample (NIS), this Healthcare Cost and Utilization Project (HCUP) Statistical Brief presents statistics for three groups of inpatient stays for patients aged 18 years and older:

1. Those related to opioid use disorder and endocarditis (OUD-endocarditis stays)
2. Those related to opioid use disorder but not endocarditis (OUD stays)
3. Those related to endocarditis but not opioid use disorder (endocarditis stays)

Trends in inpatient stays for each group during 2005-2014 are presented, using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coded data. There are known discontinuities between the ICD-9-CM and the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding systems that impact the identification of cases. Estimates related to OUD and endocarditis for 2016 based on ICD-10-CM data are presented, establishing new baseline estimates of such cases. The distribution of stays for each group is provided by select patient characteristics in 2016, along with the population rates by U.S. census region and division. Finally, the most common principal diagnoses are presented for each group of inpatient stays. Because of the large sample size of the NIS data, small differences can be statistically significant. Thus, only percentage differences greater than or equal to 10 percent are discussed in the text.

Findings

Trends in inpatient stays related to opioid use disorder and/or endocarditis, 2005-2014
Figure 1 presents the total number of inpatient stays with any diagnosis related to opioid use disorder and/or endocarditis in 4 years: 2005, 2010, 2014, and 2016.¹²

• The number of OUD-endocarditis inpatient stays more than doubled between 2005 and 2014.
  
  Between 2005 and 2014, the number of OUD-endocarditis inpatient stays increased by 122 percent, from 3,007 stays in 2005 to 6,685 stays in 2014. In 2016, there were 13,125 OUD-endocarditis stays.



• Between 2005 and 2014, the percentage change in the number of OUD inpatient stays was ten times the percentage change in the number of endocarditis stays.
  
  The number of OUD inpatient stays increased 80 percent between 2005 and 2014, from 416,173 stays in 2005 to 747,735 stays in 2014. In contrast, the number of endocarditis stays increased 8 percent, from 61,081 in 2005 to 66,210 in 2014.

Characteristics of inpatient stays related to opioid use disorder and/or endocarditis, 2016
Table 1 summarizes the distribution of inpatient stays with any diagnosis of opioid use disorder and/or endocarditis, by select patient characteristics in 2016. Figures 2 and 3 present the distributions by expected primary payer (Figure 2) and discharge status (Figure 3).

• There were differences in the types of opioid diagnoses listed on an inpatient record, depending on whether there was a coexisting diagnosis of endocarditis.
  
  The majority of OUD-endocarditis inpatient stays included a diagnosis of opioid abuse/dependence (82.3 percent), whereas only about half of OUD stays involved an opioid abuse/dependence diagnosis (50.6 percent). Diagnoses of adverse effects and long-term use of opiates were more common among OUD stays than among OUD-endocarditis stays (adverse effects: 13.7 vs. 3.5 percent; long-term use of opiates: 25.8 vs. 6.9 percent).



• A larger percentage of OUD-endocarditis inpatient stays were for patients aged 18-34 years and for patients from the lowest-income communities compared with inpatient stays for either condition alone.
  
  Among OUD-endocarditis inpatient stays, approximately half (49.8 percent) involved patients aged 18-34 years. In contrast, this age group represented 21.6 percent of OUD stays and 8.2 percent of endocarditis stays.
  
  A larger percentage of OUD-endocarditis inpatient stays were for patients from the lowest-income communities (37.1 percent) compared with OUD stays (32.9 percent) or endocarditis stays (32.7 percent). In contrast, a larger percentage of endocarditis stays were for patients from the highest-income communities (18.3 percent) compared with OUD-endocarditis stays (14.9 percent).



• Compared with OUD-endocarditis stays and OUD stays, a larger percentage of endocarditis inpatient stays were for older patients.
  
  Patients aged 50 years and older constituted 79.9 percent of endocarditis inpatient stays, 56.2 percent of OUD stays, and 22.7 percent of OUD-endocarditis stays.

Figure 2 presents the distribution of inpatient stays with any diagnosis related to opioid use disorder and/or endocarditis by expected primary payer in 2016.

• Medicaid was the expected payer for more than half of OUD-endocarditis inpatient stays in 2016.
  
  More than half of OUD-endocarditis inpatient stays had Medicaid as the expected primary payer (54.1 percent). In contrast, Medicaid was the expected payer for 30.9 percent of OUD stays and 14.2 percent of endocarditis stays.
  
  The percentage of stays that were expected to be self-pay/no charge also was higher for OUD-endocarditis stays (13.3 percent) than for OUD stays (5.5 percent) or endocarditis stays (4.1 percent).

Figure 3 presents the distribution of inpatient stays related to opioid use disorder and/or endocarditis by discharge status in 2016.

• Nearly one-fifth of OUD-endocarditis inpatient stays were discharged against medical advice.
  
  Among OUD-endocarditis inpatient stays, 17.8 percent resulted in discharge against medical advice, compared with 5.4 percent of OUD stays and 1.9 percent of endocarditis stays.
  
  Just over one-third of OUD-endocarditis stays resulted in routine discharge or discharge to home health care (37.5 percent), compared with three-fourths of OUD stays (74.2 percent) and nearly half of endocarditis stays (48.6 percent).

Geographic variation in inpatient stays related to opioid use disorder and endocarditis, 2016
Table 2 presents the population rate of inpatient stays related to opioid use disorder and/or endocarditis, by the U.S. census region and division of the hospital in which patients were treated in 2016.

• The population rate of inpatient stays related to opioid use disorder and/or endocarditis varied across and within census regions.
  
  The Northeast had the highest population rate of OUD-endocarditis inpatient stays (6.4 per 100,000 population) compared with the other regions, whereas the Midwest and West had the lowest rates (4.7 and 4.8). The Northeast and Midwest had the highest rate of OUD stays (608.2 and 567.9), and the West and South had the lowest rates (481.4 and 497.6). The South had the highest rate of endocarditis stays (37.2), whereas the West had the lowest rate (24.3).
  
  The rate of stays related to opioid use disorder and/or endocarditis also varied within census regions. For example, within the South census region, the East South Central division had the highest rate compared with the other two divisions for all types of stays, with the rate of OUD-endocarditis stays nearly 5 times higher in the East South Central division versus the West South Central division.

Most common reason for inpatient stays related to opioid use disorder and/or endocarditis, 2016
Table 3 presents the top 10 principal diagnoses among inpatient stays with any diagnosis of opioid use disorder and/or endocarditis in 2016.

• Septicemia was the principal diagnosis for over 40 percent of OUD-endocarditis inpatient stays and one-fourth of endocarditis stays.
  
  Septicemia was the most common reason for OUD-endocarditis inpatient stays (43.0 percent of stays) and for endocarditis stays (25.2 percent of stays). Septicemia was the second most common reason for OUD stays (6.5 percent of stays).



• Endocarditis was the principal diagnosis for nearly one in four OUD-endocarditis inpatient stays and one in eight endocarditis stays.
  
  Behind septicemia, endocarditis was the second most common reason for OUD-endocarditis inpatient stays (23.4 percent of stays) and for endocarditis stays (12.5 percent of stays).



• Four of the top 10 reasons for OUD inpatient stays involved mental or substance use disorders.
  
  Among OUD inpatient stays, 4 of the top 10 principal diagnoses involved mental or substance use disorders—opioid-related disorders (11.0 percent of stays), depressive disorders (4.1 percent), alcohol-related disorders (2.1 percent), and bipolar and related disorders (2.1 percent)—accounting for approximately one in five OUD stays.



• The 10 most common principal diagnoses accounted for a larger percentage of OUD-endocarditis inpatient stays than OUD or endocarditis stays.
  
  Two-thirds of OUD-endocarditis inpatient stays were for either septicemia or endocarditis (43.0 and 23.4 percent of stays, respectively). The 10 most common principal diagnoses accounted for 82.9 percent of all OUD-endocarditis stays.
  
  In addition to opioid-related disorders (11.0 percent), the most common principal diagnoses for OUD stays were septicemia (6.5 percent) and depressive disorders (4.1 percent). The 10 most common principal diagnoses accounted for just over one-third (38.5 percent) of all OUD stays.
  
  Nearly half of endocarditis stays had a principal diagnosis of septicemia (25.2 percent of stays), endocarditis (12.5 percent), or complication of cardiovascular device (10.2 percent). The 10 most common principal diagnoses accounted for 65.1 percent of all endocarditis stays.

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 upon data from the HCUP 2016 National Inpatient Sample (NIS). Historical data were drawn from the 2005, 2010, and 2014 National (Nationwide) Inpatient Sample (NIS). Supplemental sources included population denominator data for use with HCUP databases, derived from information available from the U.S. Census Bureau. ¹³

Definitions

Diagnoses, ICD-9-CM, ICD-10-CM, and Clinical Classifications Software Refined (CCSR) for ICD-10-CM diagnoses
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.

ICD-10-CM is the International Classification of Diseases, Tenth Revision, Clinical Modification. In October 2015, ICD-10-CM replaced the ICD-9-CM diagnosis coding system with the ICD-10-CM diagnosis coding system for most inpatient and outpatient medical encounters. There are over 70,000 ICD-10-CM diagnosis codes.

The CCSR for ICD-10-CM aggregates the diagnosis codes into a manageable number of clinically meaningful categories.¹⁴ The CCSR for ICD-10-CM is intended to be used analytically to examine patterns of healthcare in terms of cost, utilization, and outcomes; rank utilization by diagnoses; and risk adjust by clinical condition. The CCSR capitalizes on the specificity of the ICD-10-CM coding scheme and allows ICD-10-CM codes to be classified in more than one category. Approximately 10 percent of diagnosis codes are associated with more than one CCSR category because the diagnosis code documents either multiple conditions or a condition along with a common symptom or manifestation. For this Statistical Brief, the principal diagnosis code is assigned to a single default CCSR based on clinical coding guidelines, etiology and pathology of diseases, and standards set by other Federal agencies. The assignment of the default CCSR for the principal diagnosis is available starting with v2020.2 of the software tool. For this Statistical Brief, a preliminary version of the default CCSR was used.

Case definition
Opioid use disorder and endocarditis were defined using the all-listed ICD-9-CM and ICD-10-CM diagnosis codes shown in Tables 4 and 5, respectively. Note that the specificity of ICD-10-CM allowed for the identification of cases with opioid use and long-term use that was not previously available in ICD-9-CM.

The definition of opioid use disorder in this Statistical Brief may differ from the definition used in other HCUP reports about opioid-related hospitalizations because of the specific focus on the relationship between opioids and endocarditis in this Statistical Brief. In particular, the definition used here includes not only the general opioid dependence, abuse and use, poisoning, and adverse effect codes as an initial or subsequent encounter but also opioid abuse/dependence in remission and long-term use of opiates. The definition does not include sequela codes for poisoning and adverse effects.

Types of hospitals included in the HCUP National (Nationwide) Inpatient Sample
The National (Nationwide) Inpatient Sample (NIS) is based on 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). The NIS includes obstetrics and gynecology, otolaryngology, orthopedic, cancer, pediatric, public, and academic medical hospitals. Excluded are long-term care facilities such as rehabilitation, psychiatric, and alcoholism and chemical dependency hospitals. Beginning in 2012, long-term acute care hospitals are also excluded. 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 will be included in the NIS.

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.

Location of patients' residence
Place of residence is based on the rural-urban continuum codes (RUCC) for U.S. counties developed by the United States Department of Agriculture (USDA).¹⁵ For this Statistical Brief, we collapsed the RUCC categories into the following three groups:

• Metro
  • Counties in metro areas of 1 million population or more
  • Counties in metro areas of 250,000 to 1 million population
  • Counties in metro areas of fewer than 250,000 population
• Rural (nonmetro), adjacent to metro area:
  • Urban population of 20,000 or more, adjacent to a metro area
  • Urban population of 2,500 to 19,999, adjacent to a metro area
  • Completely rural or less than 2,500 urban population, adjacent to a metro area
• Rural (nonmetro), remote area:
  • Urban population of 20,000 or more, not adjacent to a metro area
  • Urban population of 2,500 to 19,999, not adjacent to a metro area
  • Completely rural or less than 2,500 urban population, not adjacent to a metro area

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. Cut-offs for the quartiles 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.¹⁶ The value ranges for the income quartiles vary by year. The income quartile is missing for patients who are homeless or foreign.

Expected payer
To make coding uniform across all HCUP data sources, the primary expected payer for the hospital stay combines detailed categories into general groups:

• Medicare: includes fee-for-service and managed care Medicare
• Medicaid: includes fee-for-service and managed care Medicaid
• Private insurance: includes commercial nongovernmental payers, regardless of the type of plan (e.g., private health maintenance organizations [HMOs], preferred provider organizations [PPOs])
• Self-pay/No charge: includes self-pay, no charge, charity, and no expected payment
• Other payers: includes other Federal and local government programs (e.g., TRICARE, CHAMPVA, Indian Health Service, Black Lung, Title V) and Workers' Compensation

Hospital stays that were expected to be billed to the State Children's Health Insurance Program (SCHIP) may be classified as Medicaid or Other, depending on the structure of the State program. Because most State data do not identify SCHIP as a separate expected payer, it is not possible to present this information separately.

For this Statistical Brief, when more than one payer is listed for a hospital discharge, the first-listed payer is used.

Discharge status
Discharge status reflects the disposition of the patient at discharge from the hospital and includes the following six categories: routine (to home); transfer to another short-term hospital; other transfers (including skilled nursing facility, intermediate care, and another type of facility such as a nursing home); home health care; against medical advice (AMA); or died in the hospital.

Region and division
Region is one of the four regions defined by the U.S. Census Bureau. Division corresponds to the location of the hospital and is one of the nine divisions defined by the U.S. Census Bureau.

• Northeast:
  • New England: Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut
  • Middle Atlantic: New York, New Jersey, Pennsylvania
• Midwest:
  • East North Central: Ohio, Indiana, Illinois, Michigan, Wisconsin
  • West North Central: Minnesota, Iowa, Missouri, North Dakota, South Dakota, Nebraska, Kansas
• South:
  • South Atlantic: Delaware, Maryland, District of Columbia, Virginia, West Virginia, North Carolina, South Carolina, Georgia, Florida
  • East South Central: Kentucky, Tennessee, Alabama, Mississippi
  • West South Central: Arkansas, Louisiana, Oklahoma, Texas
• West:
  • Mountain: Montana, Idaho, Wyoming, Colorado, New Mexico, Arizona, Utah, Nevada
  • Pacific: Washington, Oregon, California, Alaska, Hawaii

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 Laulima Data Alliance, a subsidiary of the Healthcare Association of Hawaii
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 unweighted sample size for the 2016 NIS is 7,135,090 (weighted, this represents 35,675,421 inpatient stays).

For More Information

For other information on mental and substance use disorders, including opioid-related hospitalizations, 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 care information topics
• HCUPnet, HCUP's interactive query system, at 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 (Nationwide) Inpatient Sample (NIS), 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 August 2018. www.hcup-us.ahrq.gov/nisoverview.jsp. Accessed January 4, 2019.

Suggested Citation

Weiss AJ (IBM Watson Health), Heslin KC (George Washington University), Stocks C (West Virginia University), Owens PL (AHRQ). Hospital Inpatient Stays Related to Opioid Use Disorder and Endocarditis, 2016. HCUP Statistical Brief #256. April 2020. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/reports/statbriefs/sb256-Opioids-Endocarditis-Inpatient-Stays-2016.pdf.

Acknowledgments

The authors would like to acknowledge the contributions of Minya Sheng of IBM Watson Health.

***

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:

Joel W. Cohen, Ph.D., Director
Center for Financing, Access and Cost Trends
Agency for Healthcare Research and Quality
5600 Fishers Lane
Rockville, MD 20857


This Statistical Brief was posted online on April 14, 2020.


¹ U.S. Department of Health and Human Services. What is the U.S. Opioid Epidemic? Updated September 4, 2019. www.hhs.gov/opioids/about-the-epidemic/index.html. Accessed February 11, 2020.
² Scholl L, Seth P, Kariisa M, Wilson N, Baldwin G. Drug and opioid-involved overdose deaths - United States, 2013-2017. Morbidity and Mortality Weekly Report. 2019;67:1419-27.
³ 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 March 22, 2019.
⁴ Ibid.
⁵ McCarthy NL, Baggs J, Jernigan JA, Reddy SJ, See I, Fiore AE. National Trends of Infective Endocarditis and Other Infections Associated With Substance Use Disorder. AcademyHealth Annual Research Meeting, Washington, DC, June 2019.
⁶ Ronan MV, Herzig SJ. Hospitalizations related to opioid abuse/dependence and associated serious infections increased sharply, 2002-12. Health Affairs. 2016;35(5):832-7.
⁷ Ibid.
⁸ Mayo Clinic. Endocarditis. www.mayoclinic.org/diseases-conditions/endocarditis/symptoms-causes/syc-20352576. Accessed July 31, 2019.
⁹Yang E, Frazee BW. Infective endocarditis. Emergency Medicine Clinics of North America. 2018;36:645-63.
¹⁰ Prendergast BD, Tornos P. Surgery for infective endocarditis: who and when? Circulation. 2010;121(9):1141-52.
¹¹ Cleveland Clinic. Infective endocarditis surgery. Updated July 19, 2019. my.clevelandclinic.org/health/treatments/17125-infective-endocarditis-surgery. . Accessed July 31, 2019.
¹² Because of the change in clinical coding system in 2015, from the International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), the coding and identification of stays related to opioid use disorder and/or endocarditis changed. For example, beginning in 2016, ICD-10-CM codes became available to identify cases with opioid use and opiate long-term use. Thus, we describe the trends based on data from the period 2005-2014 and 2016 separately.
¹³ Barrett M, Coffey R, Levit K. Population Denominator Data for Use with the HCUP Databases (Updated with 2016 Population Data). HCUP Methods Series Report #2017-04. October 17, 2017. U.S. Agency for Healthcare Research and Quality. www.hcup-us.ahrq.gov/reports/methods/2017-04.pdf. Accessed January 4, 2019.
¹⁴ Agency for Healthcare Research and Quality. HCUP Clinical Classifications Software Refined (CCSR) for ICD-10-CM Diagnoses. Healthcare Cost and Utilization Project (HCUP). Agency for Healthcare Research and Quality. Updated January 2020. www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed February 3, 2020.
¹⁵ United States Department of Agriculture. Rural-Urban Continuum Codes. Updated October 25,2019. www.ers.usda.gov/data-products/rural-urban-continuum-codes/. Accessed June 26, 2019.
¹⁶ Claritas. Claritas Demographic Profile by ZIP Code. https://claritas360.claritas.com/mybestsegments/. Accessed January 4, 2019.