User Support

• The overall ED visit rate for injuries decreased from 2006 to 2014, whereas the rate for medical, mental health/substance abuse, and maternal/neonatal visits increased.
  
  The overall ED visit rate for injuries decreased 12.9 percent between 2006 and 2014, from 93.9 to 81.8 visits per 1,000 population. The ED visit rate for medical diagnoses increased 11.7 percent from 284.2 to 317.5 visits per 1,000 population, the ED visit rate for mental health/substance abuse diagnoses increased 44.1 percent from 14.1 to 20.3 visits per 1,000 population, and the ED visit rate for maternal/neonatal diagnoses increased 25.3 percent from 10.1 to 12.7 visits per 1,000 population.



• The rate of ED visits resulting in an inpatient admission increased from 2006 to 2014 for mental health/substance abuse and maternal/neonatal visits.
  
  The ED visit rate among mental health/substance abuse visits that resulted in an admission to the same hospital increased 31.8 percent between 2006 and 2014, from 3.4 to 4.5 visits per 1,000 population. The ED visit rate among maternal/neonatal visits that resulted in an admission increased 23.4 percent from 1.4 to 1.7 visits per 1,000 population.



• The rate of treat-and-release ED visits for injuries decreased from 2006 to 2014, whereas the rate for medical, mental health/substance abuse, and maternal/neonatal treat-and-release ED visits increased.
  
  The treat-and-release ED visit rate for injuries decreased 13.1 percent between 2006 and 2014, from 88.6 to 77.0 visits per 1,000 population. The treat-and-release ED visit rate increased for medical (15.2 percent), mental health/substance abuse (48.1 percent), and maternal/neonatal (25.6 percent) diagnoses.

ED visits by type of first-listed diagnosis and expected payer, 2006 and 2014
Figure 2 presents the distribution of expected primary payer for ED visits by type of first-listed diagnosis (injury, medical, mental health/substance abuse, and maternal/neonatal) comparing 2006 and 2014.

• For all types of first-listed diagnoses, the percentage of ED visits billed to Medicaid increased and the percentage of visits billed to private insurance and visits for the uninsured decreased between 2006 and 2014.
  
  Compared with the overall changes in the payer distribution from 2006 to 2014, there was—
  
  
  • A disproportionate increase in injury-related ED visits billed to Medicare (13.0 to 17.3 percent of injury-related ED visits in 2006 and 2014, respectively) and Medicaid (17.4 to 27.6 percent of injury-related ED visits).
  • A disproportionate decrease in the mental health/substance abuse-related ED visits for the uninsured (25.1 to 18.5 percent of mental health/substance abuse-related ED visits in 2006 and 2014, respectively).
  • A disproportionate decrease in maternal/neonatal-related ED visits billed to private insurance (31.2 to 27.4 percent of maternal/neonatal-related ED visits in 2006 and 2014, respectively) and for the uninsured (15.6 to 10.1 percent of maternal/neonatal ED visits). There was a smaller increase in maternal/neonatal ED visits billed to Medicaid (49.0 to 58.1 percent of maternal/neonatal ED visits).

Table 3 presents changes in the diagnoses most frequently seen in the ED in 2014 by type of first-listed diagnosis, comparing 2006 and 2014. Values represent national estimates of the number of ED visits in each year.

• Sprains and strains, superficial injury (contusions), and open wounds of extremities were the most frequent first-listed diagnoses for injury-related ED visits in 2014.
  
  The most common injury-related diagnoses for ED visits in 2014 were sprains and strains, superficial injuries (contusions), and open wounds of extremities. All of the five most frequent injury-related diagnoses for ED visits decreased between 2006 and 2014, ranging from a 9.6 percent decrease for sprains and strains to a 21.3 percent decrease for open wounds of extremities.



• Abdominal pain, nonspecific chest pain, and spondylosis (back problems) were the most frequent first-listed diagnoses for medical-related ED visits in 2014.
  
  The most frequent medical diagnoses for ED visits in 2014 were abdominal pain, nonspecific chest pain, and spondylosis (back problems). All of the five most frequent medical diagnoses for ED visits were more frequent in 2014 than in 2006, ranging from a 9.7 percent increase for skin and subcutaneous tissue infections to a 32.0 percent increase for abdominal pain.



• Alcohol-related disorders, mood disorders, and anxiety disorders were the most frequent first-listed diagnoses for mental health/substance abuse-related ED visits in 2014.
  
  The most frequent mental health/substance abuse diagnoses for ED visits in 2014 were alcohol-related disorders, mood disorders, and anxiety disorders. All of the five most frequent mental health/substance abuse diagnoses for ED visits increased between 2006 and 2014, ranging from a 33.5 percent increase for mood disorders to a 76.3 percent increase for alcohol-related disorders.



• Hemorrhage during pregnancy was the most frequent first-listed diagnosis for maternal/neonatal-related ED visits in 2014.
  
  The most frequent first-listed diagnoses in the maternal/neonatal ED visit category all were related to maternal conditions in 2014. Hemorrhage during pregnancy was the most frequent first-listed diagnosis and was the diagnosis on more ED visits than the remaining four diagnoses combined. Four of the five most frequent maternal/neonatal diagnoses for ED visits increased in frequency between 2006 and 2014, ranging from a 15.3 percent increase for hemorrhage during pregnancy to a 94.9 percent increase for hypertension complicating pregnancy.

• From 2006 to 2014, ED visits for intracranial injury (injury-related ED visits), acute post-hemorrhagic anemia (medical-related ED visits), suicidal ideation (mental health/substance abuse-related ED visits), and prolonged pregnancy (maternal/neonatal-related ED visits) increased in frequency more than other conditions.
  
  Among injury-related ED visits, those for intracranial injuries increased more than any other injury-related condition (38.8 percent) between 2006 and 2014.
  
  Among medically-related ED visits, those for acute post-hemorrhagic anemia increased the most (314.5 percent) between 2006 and 2014, followed by influenza (250.7 percent) and septicemia (161.3 percent).
  
  Comparing 2006 to 2014, ED visits associated with suicidal ideation increased more than any other condition (414.6 percent) across all types of diagnoses. Among other mental health/substance abuse-related ED visits, those for personality disorders and disorders diagnosed in childhood increased more than ED visits for other conditions (112.3 percent and 111.0 percent, respectively).
  
  Among maternal/neonatal-related ED visits, those for prolonged pregnancy increased the most (172.6 percent), followed by polyhydramnios (172.0 percent) and previous C-section (163.9 percent).

Table 5 presents the diagnoses seen in the ED with the greatest percentage decrease in frequency from 2006 to 2014.

• From 2006 to 2014, ED visits for open wound of extremities (injury-related ED visits), several medically-related conditions including coronary atherosclerosis and coma, and contraceptive and procreative management (maternal/neonatal ED visits) decreased by over 20 percent. There were no decreases in ED visits for mental health/substance abuse diagnosis categories.
  
  Among injury-related ED visits, those for open wound of extremities decreased the most (21.3 percent) between 2006 and 2014, followed by poisoning by other medications and drugs (18.5 percent) and open wounds of head, neck, and trunk (18.4 percent).
  
  Among medically-related ED visits, those for coronary atherosclerosis and coma decreased the most (both 34.0 percent), followed by meningitis (31.7 percent).
  
  There are 15 first-listed diagnoses included in the mental health/substance abuse ED visit category definition. None of them decreased in frequency from 2006 to 2014.
  
  ED visits for contraceptive and procreative management decreased the most among maternal/neonatal-related ED visits (78.6 percent).

About Statistical Briefs

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 Healthcare Cost and Utilization Project (HCUP) 2014 Nationwide Emergency Department Sample (NEDS). Historical data were drawn from the 2006-2013 Nationwide Emergency Department Sample (NEDS). Supplemental sources included population denominator data for use with HCUP databases, derived from information available from the U.S. Census Bureau.¹¹ Population denominator data from Claritas, a vendor that compiles and adds value to data from the U.S. Census Bureau, was used for the calculation of rates based on location of patients' residence and community-level income, as no denominator was available from the U.S. Census Bureau for these characteristics.¹²

Definitions

Diagnoses, ICD-9-CM, and Clinical Classifications Software (CCS)
The first-listed diagnosis is the condition, symptom, or problem identified in the medical record to be chiefly responsible for the emergency department (ED) services provided. For ED visits that result in an inpatient admission to the same hospital, the first-listed diagnosis is the principal diagnosis, the condition established after study to be chiefly responsible for the patient's admission to the hospital.

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.

CCS categorizes ICD-9-CM diagnosis codes into a manageable number of clinically meaningful categories.¹³ This clinical grouper makes it easier to quickly understand patterns of diagnoses. CCS categories identified as Other typically are not reported; these categories include miscellaneous, otherwise unclassifiable diagnoses that may be difficult to interpret as a group.

Type of first-listed ED diagnosis
First-listed diagnoses were classified into four types based on ICD-9-CM and CCS categories in Table 6.¹⁴ Each first-listed diagnosis was assigned to a single category, sequentially, in the following order: maternal/neonatal, mental health/substance abuse, injury, and medical.

Types of hospitals included in the HCUP Nationwide Emergency Department Sample
The Nationwide Emergency Department Sample (NEDS) 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 NEDS includes specialty, pediatric, public, and academic medical hospitals. Excluded are long-term care facilities such as rehabilitation, psychiatric, and alcoholism and chemical dependency hospitals. Hospitals included in the NEDS have hospital-owned EDs and no more than 90 percent of their ED visits resulting in admission.

ED visits
ED visits include information on all visits to hospital-owned EDs regardless of whether the patient was treated-and-released from that ED or admitted to the same hospital from the ED.

Treat-and-release ED visits
Treat-and-release ED visits were defined as those ED visits in which patients were treated and then released from the ED; that is, patients were not admitted to the specific hospital associated with the ED. In 2014, although the majority of patients discharged from the ED were discharged home (94.5 percent), some patients were transferred to another acute care facility (1.7 percent), left against medical advice (1.8 percent), were discharged to another type of long-term or intermediate care facility (nursing home or psychiatric treatment facility; 1.5 percent), were referred to home healthcare (0.2 percent), died (0.2 percent), or were discharged alive but the destination was unknown (< 0.1 percent).¹⁵

ED visits resulting in admission to the same hospital
ED visits resulting in admission to the same hospital included those patients initially seen in the ED who were then admitted to the specific hospital associated with that ED.

Unit of analysis
The unit of analysis is the ED encounter, not a person or patient. This means that a person who is seen in the ED multiple times in 1 year will be counted each time as a separate encounter in the ED.

Location of patients' residence
Place of residence is based on the urban-rural classification scheme for U.S. counties developed by the National Center for Health Statistics (NCHS). For this Statistical Brief, we collapsed the NCHS categories into the following categories:

• Large Central Metropolitan: includes metropolitan areas with 1 million or more residents
• Large Fringe Metropolitan: includes counties of metropolitan areas with 1 million or more residents
• Medium and Small Metropolitan: includes areas with 50,000 to 999,999 residents
• Micropolitan and Noncore: includes nonmetropolitan counties (i.e., counties with no town greater than 50,000 residents).

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 adds value to data from the U.S. Census Bureau.¹⁶ The value ranges for the income quartiles vary by year. Patients in the first quartile are designated as having low income, and patients in the upper three quartiles are designated as having not low income. The income quartile is missing for patients who are homeless or foreign.

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

• Medicare: includes patients covered by fee-for-service and managed care Medicare
• Medicaid: includes patients covered by fee-for-service and managed care Medicaid
• Private Insurance: includes Blue Cross, commercial carriers, and private health maintenance organizations (HMOs) and preferred provider organizations (PPOs)
• Uninsured: includes an insurance status of self-pay and no charge
• Other: includes Workers' Compensation, TRICARE/CHAMPUS, CHAMPVA, Title V, and other government programs

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

For this Statistical Brief, when more than one payer is listed for an ED visit, the fist-listed payer is used.

Region
Region is one of the four regions defined by the U.S. Census Bureau:

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

Comparisons
The majority of comparisons are based on 2 years of data, 2006 and 2014. Trends in the number of ED visits by expected primary payer from 2006 through 2014 are presented with the estimates provided for the intervening years.

Percentage change
Percentage change was calculated using the following formula:

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
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 Health Care Authority
Wisconsin Department of Health Services
Wyoming Hospital Association

About the NEDS

The HCUP Nationwide Emergency Department Database (NEDS) is a unique and powerful database that yields national estimates of emergency department (ED) visits. The NEDS was constructed using records from both the HCUP State Emergency Department Databases (SEDD) and the State Inpatient Databases (SID). The SEDD capture information on ED visits that do not result in an admission (i.e., patients who were treated in the ED and then released from the ED, or patients who were transferred to another hospital); the SID contain information on patients initially seen in the ED and then admitted to the same hospital. The NEDS was created to enable analyses of ED utilization patterns and support public health professionals, administrators, policymakers, and clinicians in their decision making regarding this critical source of care. The NEDS is produced annually beginning in 2006. Over time, the sampling frame for the NEDS has changed; thus, the number of States contributing to the NEDS varies from year to year. The NEDS is intended for national estimates only; no State-level estimates can be produced.

For More Information

For other information on emergency department visits, refer to the HCUP Statistical Briefs located at www.hcup-us.ahrq.gov/reports/statbriefs/sb_ed.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 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 Nationwide Emergency Department Sample (NEDS), please refer to the following database documentation:

Agency for Healthcare Research and Quality. Overview of the Nationwide Emergency Department Sample (NEDS). Healthcare Cost and Utilization Project (HCUP). Rockville, MD: Agency for Healthcare Research and Quality. Updated December 2016. www.hcup-us.ahrq.gov/nedsoverview.jsp. Accessed January 5, 2017.



Suggested Citation

Moore BJ (IBM Watson Health), Stocks C (AHRQ), Owens PL (AHRQ). Trends in Emergency Department Visits, 2006-2014. HCUP Statistical Brief #227. September 2017. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/reports/statbriefs/sb227-Emergency-Department-Visit-Trends.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:

Sharon B. Arnold, Ph.D., 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 September 12, 2017.


¹ Calculated from HCUPnet. Healthcare Cost and Utilization Project (HCUP). 2014. Rockville, MD: Agency for Healthcare Research and Quality. datatools.ahrq.gov/hcupnet Accessed March 24, 2017.
² Includes ED visits in which patients were not admitted to the hospital associated with the ED. Patients may have been discharged home, transferred to another acute care facility, left against medical advice, went to another long-term or immediate care facility (nursing home or psychiatric treatment facility), discharged to home health, or died.
³ Weiss AJ, Wier LM, Stocks C, Blanchard J. Overview of Emergency Department Visits in the United States, 2011. HCUP Statistical Brief #174. June 2014. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/reports/statbriefs/sb174-Emergency-Department-Visits-Overview.pdf.
⁴ National Academies of Sciences, Engineering, and Medicine. 2007. Hospital-Based Emergency Care: At a Breaking Point. Washington, DC: The National Academies Press.
⁵ Skinner HG, Blanchard J, Elixhauser A. Trends in Emergency Department Visits, 2006-2011. HCUP Statistical Brief #179. September 2014. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/reports/statbriefs/sb179-Emergency-Department-Trends.pdf.
⁶ National Academies of Sciences, Engineering, and Medicine, 2007. Op. cit.
⁷ Ibid.
⁸ Miller S. The effect of insurance on emergency room visits: an analysis of the 2006 Massachusetts health reform. Journal of Public Economics. December 2012;96(11-12):893-908.
⁹ Ginde AA, Lowe RA, Wiler JL. Health insurance status change and emergency department use among US adults. Archives of Internal Medicine. 2012;172(8):642-647.
¹⁰ Barrett M, McCarty J, Coffey R, Levit K. Population Denominator Data for Use With the HCUP Databases (Updated with 2015 Population Data). HCUP Methods Series Report #2016-04. September 29, 2016. Rockville, MD: Agency for Healthcare Research and Quality. www.hcup-us.ahrq.gov/reports/methods/2016-04.pdf. Accessed January 31, 2017.
¹¹ Barrett M, McCarty J, Coffey R, Levit K. Population Denominator Data for Use with the HCUP Databases (Updated with 2015 Population Data). HCUP Methods Series Report #2016-04. September 29, 2016. Rockville, MD: Agency for Healthcare Research and Quality. www.hcup-us.ahrq.gov/reports/methods/2016-04.pdf. Accessed January 31, 2017.
¹² Claritas. Claritas Demographic Profile. www.claritas.com. Accessed February 14, 2017.
¹³ Agency for Healthcare Research and Quality. HCUP Clinical Classifications Software (CCS) for ICD-9-CM. Healthcare Cost and Utilization Project (HCUP). Rockville, MD: Agency for Healthcare Research and Quality. Updated October 2016. www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed February 14, 2017.
¹⁴ ED visit types were adapted from the hospital service line variable used in the HCUP State Inpatient Databases (SID). Diagnosis-related groups (DRGs), which are used to distinguish surgical and medical stays with inpatient data, are not available with ED visit data; all ED visits that did not fall into one of the other ED visit types (maternal/neonatal, mental health/substance abuse, or injury) were categorized as medical. Agency for Healthcare Research and Quality. Central Distributor SID: Description of Data Elements-SERVICELINE. Healthcare Cost and Utilization Project (HCUP). Rockville, MD: Agency for Healthcare Research and Quality. Updated August 2008. www.hcup-us.ahrq.gov/db/vars/siddistnote.jsp?var=serviceline. Accessed February 14, 2017.
¹⁵ Statistics were obtained from an HCUPnet (datatools.ahrq.gov/hcupnet) query of discharge status in the 2014 Nationwide Emergency Department Sample (NEDS) among all discharges. Accessed August 31, 2017.
¹⁶ Claritas. Claritas Demographic Profile. www.claritas.com. Accessed February 14, 2017.