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HCUP Fast Stats FAQ

This page provides answers to commonly asked questions about HCUP Fast Stats.

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Contents

General Questions: HCUP Fast Stats

  • How often will HCUP Fast Stats be updated?

    Fast Stats topics are generally updated annually. Updates to each topic occur throughout the calendar year and typically are ordered based on the last topic update from the prior year as well as the availability of the underlying HCUP data.Update dates for existing Fast Stats topics are noted on the Fast Stats homepage. Typically, Fast Stats updates will include additional data years and/or quarters, if applicable, and occasionally changes to the underlying definitions or methodology that would affect older data. It is important to keep in mind that some differences may be observed when comparing statistics obtained prior to and at the time of a data update. If you would like to receive information regarding Fast Stats data updates, please sign up for the HCUP Mailing List. You can also check the HCUP User Support (HCUP-US) website Calendar of Database and Product Releases for updates.

 

  • Why don’t all States appear in the State-level portions of Fast Stats?

    HCUP is a voluntary partnership between the federal government and statewide data organizations. HCUP currently has agreements with 48 States and the District of Columbia, known as “HCUP Partners”. At this time, 49 of the HCUP Partners provide inpatient data to HCUP and 41 provide emergency department data. Because HCUP is a voluntary partnership, each State determines how their data are used in HCUP, thus not all States appear in Fast Stats. In addition, for effective presentation a State must have at least two contiguous recent years of data available to appear in HCUP Fast Stats trend topics.

 

  • Can you display more than two results when doing comparisons?

    To ensure that graphics and tables are readable, Fast Stats is limited to side-by-side comparisons for two sets of results.

 

  • What other HCUP resources can be used to examine aspects of hospital care?

    HCUP has various resources available for researching hospital care.

    • HCUPnet is an interactive online tool that provides access to national and State-level health statistics and information on hospital inpatient, emergency department, and ambulatory surgery utilization through a step-by-step query process.
    • HCUP Statistical Briefs provide simple descriptive statistics on selected topics with explanatory text. Statistical Briefs are static reports while Fast Stats will be updated on a regular basis.
    • The HCUP Central Distributor facilitates the purchase of HCUP Nationwide and State Databases for researchers who have a specific topic of interest.
    • The HCUP User Support website includes information on the HCUP databases, tools, software, reports, news, events, and technical assistance.

 

  • Why are population-based rates in some topics not reported by expected payer?

    Population-based rates are not reported by expected payer because there is currently no data source for national population insurance estimates that aligns 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).Rates are reported by expected payer in the Fast Stats topic Neonatal Abstinence Syndrome because the denominator is number of newborn hospitalizations and not population.

 

  • Why does information from HCUPnet sometimes differ from similar information on Fast Stats?

    In some cases, slightly different definitions are used to present the information in Fast Stats and HCUPnet. There are five specific instances of differences: (a) the definitions of the maternal and neonatal hospitalization types; (b) the version of the HCUP Clinical Classifications Software (CCS) for ICD-9-CM that is used to classify diagnoses and procedures; (c) the source of data used for the calculation of population-based rates; (d) how visits are counted when multiple, relevant diagnosis and procedure codes appear on a record; and (e) the definition of opioid-related hospitalizations.

 

Definition of Maternal and Neonatal Hospitalization Types:In two Fast Stats topics – State Trends in Inpatient Stays by Payer, and National Hospital Utilization and Costs (prior to 2016) – the definitions of maternal and neonatal rely on the CCS categories, whereas in HCUPnet the definitions of maternal and neonatal use Major Diagnostic Categories (MDC) to classify diagnosis codes for all data years. Compared with using MDC, the CCS approach assigns approximately 0.9 percent fewer cases to “maternal” because a maternal discharge is classified into a mental health CCS or a substance use CCS when the diagnosis code includes a mental health or substance abuse condition along with a maternal condition (e.g., drug dependence in pregnancy). Similarly, compared with the MDC approach, the CCS approach assigns 0.1 percent fewer cases to “neonatal” because a neonatal discharge is classified into a substance use CCS when the diagnosis code refers to a drug effect on the fetus or neonatal drug withdrawal. The CCS approach assigns another 0.1 percent fewer cases to “neonatal” than the MDC approach because neonatal septicemia is assigned to the septicemia CCS rather than a neonatal CCS.Refer to the Data Notes & Methods for the State Trends in Inpatient Stays by Payer and the National Hospital Utilization and Costs topics for specific definitions by data year.

Version of the HCUP Clinical Classifications Software (CCS) for ICD-9-CM: Statistics for ICD-9-CM in the National Hospital Utilization and Costs topic of HCUP Fast Stats are based on the most current version of the CCS software that was available at the time of the update. Because tables for HCUPnet were generated as soon as each year’s database was completed, HCUPnet used the CCS version provided on each year of the NIS. This may affect the numbering scheme, the counts, and the labels for the CCS.

Source Used for Population Data: For Fast Stats, all population data are 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. For HCUPnet, all population data except for community-level income are obtained from the Census; population data for community-level income are obtained from Claritas. Users will notice slight differences in the population-based rates presented in Fast Stats versus those in HCUPnet for characteristics other than community-level income. These differences are the result of different approaches used by the Census and Claritas to interpolate the rate of change in the population from year to year.

Counting Stays and Visits With Multiple, Relevant Diagnosis/Procedure Codes: 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. However, when performing a query with multiple, all-listed diagnosis or procedure codes in HCUPnet, individual stays or visits will be counted more than once if multiple, specified ICD-9-CM codes appear on the record.

Definition of Opioid-Related Hospitalizations and Neonatal Abstinence Syndrome (NAS) Among Newborn Hospitalizations: The definition of an opioid-related hospitalization, based on ICD-9-CM diagnosis codes, varies between Fast Stats’ Opioid-Related Hospital Use topic and HCUPnet’s Community-level Statistics pathway that presents statistics on inpatient stays for alcohol and other drugs (specifically, when drilling down to the opioids option under the type of substance or substance-related condition).

      • Fast Stats does not include ICD-9-CM codes indicating opioid dependence and abuse “in remission” (304.03, 304.73, and 305.53); HCUPnet does include these codes.
      • Fast Stats includes ICD-9-CM codes for methadone, other opiates and related narcotics, and opiate antagonists causing adverse effects in therapeutic use (E935.1, E935.2, and E940.1); HCUPnet does not include these codes.
      • The definition of NAS among newborn hospitalizations, based on ICD-9-CM diagnosis codes, in Fast Stats’ Neonatal Abstinence Syndrome (NAS) Among Newborn Hospitalizations topic also differs from HCUPnet’s Community-level Statistics pathway for alcohol and other drugs.
            • Fast Stats does not include the code for narcotics affecting fetus or newborn via placenta or breast milk (760.72); HCUPnet does include this code.

       

  • Why do some topics in HCUP Fast Stats define conditions in the emergency department setting using only the first-listed diagnosis?

While the first-listed diagnosis in the emergency department (ED) setting does not carry the same meaning as the principal diagnosis in the inpatient setting, the first-listed diagnosis on an ED record is often used for the definitions of ED-related conditions in Fast Stats. This decision was made so that an ED record is assigned to only one condition in the ED setting.

  • What might account for trend breaks or sudden changes in the ranking of conditions or procedures in some Fast Stats topics?

    There are many possible causes for noticeable trend breaks or sudden changes in the ranking of conditions or procedures over time in some Fast Stats topics. Three common causes are listed below.

Change in clinical coding systems: The clinical coding systems used to code diagnoses and/or procedures are periodically updated. For example, a major change occurred on October 1, 2015 when the United States transitioned from ICD-9-CM1 to ICD-10-CM/PCS2. Users may observe discontinuity in trend analyses that span the October 1, 2015 transition date. For additional information, refer to the ICD-10-CM/PCS Resources page on the HCUP-US website. Changes also can occur within a coding system over time. For example, a change in ICD-10-CM coding guidelines on which diagnosis to code as the principal diagnosis has caused a discontinuity in the ranking of inpatient stays related to chronic obstructive pulmonary disease (COPD) and pneumonia within the Most Common Diagnoses topic of the National Hospital Utilization and Costs section of Fast Stats. Effective October 1, 2016 precedence was given to the COPD diagnosis. A subsequent revision effective October 1, 2017 bases precedence on the discretion of a clinical coder.

Change in healthcare policy: Change in healthcare policies can have an impact on the delivery and reporting of healthcare. For example, the State Trends in Hospital Use by Payer topic illustrates that implementation of Medicaid expansion typically caused a significant increase in inpatient stays/ED visits expected to be covered by Medicaid and a concurrent decrease in inpatient stays/ED visits with a primary expected payer of self-pay/no charge.

Data anomalies: It is possible that data anomalies or differences are observed for a given State and year, which may impact trends in Fast Stats topics that provide State-level statistics. For example, in the Opioid-Related Hospital Use Topic, users may observe a trend break for Iowa beginning with data year 2017 as the result of the inclusion of records for behavioral health patients treated in chemical dependency or psychiatric care units. Prior to data year 2017, these records were not included in the Iowa State databases. When data anomalies or differences are significant and identified, they are described in the Data Notes & Methods for the topic in which they are observed.1 International Classification of Diseases, Ninth Revision, Clinical Modification2 International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System

 

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Topic Questions: State Trends in Hospital Use by Payer

 

  • Why don’t you summarize the results of the analyses shown in the State Trends in Hospital Use by Payer portion of Fast Stats?

    Fast Stats is intended to provide easy-to-read graphics and simple tables to convey complex information at a glance. Fast Stats focuses on a few preselected topics that are of general interest. The interpretation of the information in the State Trends in Hospital Use by Payer section of Fast Stats is left to the user. Changes in inpatient and emergency department utilization are influenced by many factors including health insurance expansion.

 

  • What is the data source used for State-level trends in inpatient stays and emergency department visits for patients?

    The estimates in the State-specific trends in inpatient stays are based on data from the HCUP State Inpatient Databases (SID) and inpatient quarterly data, which is available from some HCUP Partner organizations.The estimates in the State-specific trends in emergency department visits are based on data from both the HCUP State Emergency Department Databases (SEDD), selected records from the SID, and quarterly inpatient and emergency department data, which is available from some HCUP Partner organizations. The SEDD capture information on emergency department (ED) visits that do not result in an admission (i.e., the data include treat-and-release visits and transfers to another hospital). Information on patients initially seen in the ED and then admitted to the same hospital are obtained from the SID.The data are limited to patients treated in community, nonrehabilitation hospitals in the State.

 

  • Why do some Medicaid expansion dates occur earlier than others?

    Some States implemented Medicaid expansion under the Affordable Care Act earlier than others. Some of these States adopted the early expansion option provided for under the Affordable Care Act to expand their Medicaid programs before January 1, 2014, while others expanded on January 1, 2014, to coincide with the start of Qualified Health Plan Coverage through the Marketplaces. Other States have expanded after January 1, 2014, while others have not expanded their Medicaid programs to date. Finally, please note that some States had been covering the population now eligible as a result of the Medicaid expansion using demonstration authority prior to the passage of the Affordable Care Act. For many States, implementation of the Medicaid expansion under the Affordable Care Act in January 2014 is associated with shifts of people from uninsured status to Medicaid coverage.

 

  • What are your sources for the Medicaid expansion dates?

    We used information from the Kaiser Family Foundation1 for the Medicaid expansion dates referenced in the State Trends in Hospital Use by Payer section of Fast Stats:

Kaiser Family Foundation “Status of State Action on the Medicaid Expansion Decision”

Kaiser Family Foundation “States Getting a Jump Start on Health Reform’s Medicaid Expansion”

1 The U.S. Department of Health and Human Services (HHS) is offering these links for informational purposes only, and this fact should not be construed as an endorsement of the host organization’s programs or activities.

  • Why does it say expected payer?

    A hospital’s response to the question, “Who is expected to pay the hospital for a given service?” is reported under the category of expected payer in the HCUP databases. However, who is expected to pay the hospital may be different than who actually pays the bill. In addition, the hospital’s answer to this question may be different from the response to, “What company or agency is the patient’s insurer?” or “Is the patient insured?” This distinction applies particularly to uninsured patients, whose care may be paid by various State or local programs.

 

  • Who is included in the category of self-pay/no charge?

    Discharges with the expected primary payer of self-pay, charity, no charge, or no expected payment are classified as self-pay/no charge. 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. About one-third of the HCUP Partner organizations include this level of detail in their coding of expected payer.

 

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Inpatient Stay Trends by Payer

  • Why do you present information only for adults? Do you plan to extend this to children?

    Most hospital stays for patients under age 18 are for normal newborns; children are hospitalized at a much lower rate than adults. Excluding normal newborns, patients under age 18 account for roughly 5 percent of inpatient stays. In the future, the Inpatient Stay Trends by Payer section of Fast Stats may be expanded to include health care utilization for children as well.

 

  • Why are asthma, congestive heart failure (CHF), and diabetes no longer reported in HCUP Fast Stats?

    Trends in the number of adult inpatient stays for specific medical conditions is not currently being reported in HCUP Fast Stats. Three conditions, defined based on principal diagnosis, previously were included in HCUP Fast Stats: asthma, congestive heart failure (CHF), and diabetes. Reporting of CHF in HCUP Fast Stats was discontinued as of November 2017 because a change in the ICD-10-CM coding guidelines effective October 1, 2016 caused a discontinuity in the trend. Reporting of asthma and diabetes in HCUP Fast Stats was discontinued as of December 2019 because the framework for the inpatient data is focused around presenting payer trends for the five high-level hospitalization types. The specific medical conditions have been removed from the active query tool, but historical data previously reported in HCUP Fast Stats for CHF (with data reported through 2016 Q3 for some States) and for asthma and diabetes (with data reported through 2018 Q2 for some States) is offered in the Excel download file, which can be downloaded by expanding “Show Data Export Options.”

 

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Emergency Department Visit Trends by Payer

 

  • Why do you include emergency department visit results for children?

    Patients under the age of 18 account for roughly 20 percent of total emergency department visits compared to only about 5 percent of inpatient stays (excluding normal newborns).

 

  • What is the first-listed diagnosis?

    For treat-and-release ED visits in the SEDD, the first-listed diagnosis represents the condition, symptom, or problem identified in the medical record to be chiefly responsible for the ED services provided. In cases where the first-listed diagnosis is a symptom or problem, a diagnosis has not been established (confirmed) by the provider. For ED visits that result in an inpatient admission in the SID, the first-listed diagnosis is the principal diagnosis, which reflects the condition established to be chiefly responsible for a patient’s admission to the hospital. For example, chest pain may be the reason for a patient’s visit to the emergency department, but if admitted, the principal diagnosis might be acute myocardial infarction.

 

  • Why is stomach flu no longer reported in HCUP Fast Stats?

    Reporting of stomach flu in this section of Fast Stats was discontinued in December 2020. At that time, changes were made to the ICD-10-CM definitions for each ED visit type. One specific ICD-10-CM diagnosis code, R19.7 (Diarrhea, unspecified), which was previously included in the definition for stomach flu and responsible for the majority of ED visits with this first-listed diagnosis, is now included in the definition for abdominal pain. As a result, trends in pediatric ED visits for stomach flu significantly declined. Stomach flu has been removed from the active query tool, but historical data previously released in HCUP Fast Stats for this ED visit type (with data reported through 2018 Q4 for some States) is offered in the Excel download file, which can be downloaded by expanding “Show Data Export Options.”

 

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Topic Questions: National Hospital Utilization and Costs

 

  • What is the data source used for national estimates of hospital utilization and costs?

    The estimates for inpatient stays in the national topic are based on data from the HCUP National (Nationwide) Inpatient Sample (NIS). In 2012, the NIS was redesigned to optimize national estimates. As a result, there can be a discontinuity in national trends. In order to generate consistent national estimates across 10 and 20 years, the NIS Trend Weight Files were used. These files include revised weights for the NIS that ensure that weighted national estimates for data years 1993-2011 are comparable to weighted national estimates for data year 2012 and later.The estimates in the emergency department portion of the national topic are based on data from the HCUP Nationwide Emergency Department Sample (NEDS).

 

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National Trends in Inpatient Stays

 

  • Which adjustment year was chosen for the average inflation-adjusted costs?

    The adjustment year used to calculate the average inflation-adjusted costs is 2010.

 

  • Why is cost information only available beginning with the 2000 data year?

    Average costs are calculated using the HCUP Cost-to-Charge Ratios (CCR), which are unavailable prior to 2000. For additional information on the average actual and inflation-adjusted costs per stay, refer to the Data Notes & Methods section of the Trends in Inpatient Stays portion of Fast Stats.

 

  • Why is information on community-level income only available beginning with the 2002 data year?

    Information by community-level income is only reported from 2002 forward because of inconsistent definitions over time in the income-related data elements in the HCUP National (Nationwide) Inpatient Sample (NIS).

 

  • Why are population-based rates only available beginning with the 2002 data year?

    Population-based rates are only reported from 2002 forward because the population denominators for age, sex, and community-level income were unavailable prior to 2002.

 

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National Trends in Emergency Department Visits

 

  • Why are cost trends not reported for emergency department utilization?

    Cost-related statistics in HCUP Fast Stats are calculated using the HCUP Cost-to-Charge Ratio (CCR) files. The CCR files are available for the HCUP inpatient databases, including the State Inpatient Databases (SID) and the National (Nationwide) Inpatient Sample (NIS), beginning with data year 2001. CCR files have not historically been available for the HCUP emergency department databases but have been newly developed for recent data years. Cost-related statistics for emergency department data may be considered for possible reporting in HCUP Fast Stats in the future.

 

  • Why are trends reported for three different emergency department visit types?

    The statistics reported in this section of Fast Stats are based on data from the HCUP Nationwide Emergency Department Sample (NEDS). The NEDS is sampled from the HCUP State Emergency Department Databases (SEDD), which capture information on ED encounters that do not result in an admission (i.e., treat-and-release visits and transfers to other hospitals), and the State Inpatient Databases (SID), which contain information on patients initially seen in the ED and then admitted to the same hospital. As a result, trends are reported for three different ED visit types:

    • All ED visits
      Includes treat-and-release ED visits and ED visits resulting in admission to the same hospital.

 

    • Treat-and-release ED visits
      Includes ED visits that did not result in admission to the same hospital and ED visits transferred to another hospital or ED.

 

    • ED visits resulting in admission to the same hospital
      Includes ED visits for patients initially seen in the ED and then admitted to the same hospital.

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Most Common Diagnoses for Inpatient Stays

 

  • What is the principal diagnosis?

    The principal diagnosis is the condition established after study to be chiefly responsible for the patient’s admission to the hospital for care. The principal diagnosis is considered to be the main reason for the hospital stay.

 

  • What are the diagnoses based on?

    For data years 2015 and earlier, diagnoses are identified using the Clinical Classifications Software (CCS) for ICD-9-CM. Beginning with data year 2016, diagnoses are identified using the Clinical Classifications Software Refined (CCSR) for ICD-10-CM default categorization scheme for the principal diagnosis, which categorizes patient diagnoses into a manageable number of clinically meaningful categories. For additional information, please refer to the Data Notes & Methods section.

 

  • Why are there options to include and exclude maternal/neonatal stays?

    Maternal/Neonatal stays generally account for nearly a fourth of all inpatient hospitalizations in the United States and the majority are low complexity, low cost stays. When examining all inpatient stays, it is important to consider whether the results should factor in maternal and neonatal hospitalizations, which tend to have different characteristics than other types of hospital stays. For instance, if the focus is on hospital use for the treatment of illnesses, it would make sense to exclude maternal/neonatal stays from the analysis.

 

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Most Common Operations During Inpatient Stays

 

  • What are all-listed operating room procedures?

    Operating room (OR) procedures are identified using Procedure Classes for ICD-9-CM for data years 2015 and earlier and the Procedure Classes Refined for ICD-10-PCS beginning data year 2016. The Procedure Classes tools identify procedures as diagnostic or therapeutic and whether they would be expected to be performed in an operating room. OR procedures are identified using all-listed procedures (principal and secondary) that are available on the discharge record.

 

 

  • Why are there options to include and exclude maternal/neonatal stays?

    Maternal/Neonatal stays generally account for nearly a fourth of all inpatient hospitalizations in the United States and the majority are low complexity, low cost stays. When examining all inpatient stays, it is important to consider whether the results should factor in maternal and neonatal hospitalizations, which tend to have different characteristics than other types of hospital stays. For instance, if the focus is on hospital use for the treatment of illnesses, it would make sense to exclude maternal/neonatal stays from the analysis.

 

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Topic Questions: Opioid-Related Hospital Use

 

  • What are the data sources used for the estimates of opioid-related hospital use?

    The national estimates in the Opioid-Related Hospital Use portion of Fast Stats are based on data from the HCUP National (Nationwide) Inpatient Sample (NIS) and the HCUP Nationwide Emergency Department Sample (NEDS).The State-level estimates in the Opioid-Related Hospital Use portion of Fast Stats are based on data from the HCUP State Inpatient Databases (SID) and HCUP State Emergency Department Databases (SEDD).

 

  • Why are the emergency department (ED) estimates restricted to only ED visits that do not result in an admission to the same hospital?

    This approach is used to avoid double counting of inpatient stays. The HCUP Nationwide Emergency Department Sample (NEDS) includes ED visits that result in admission to the same hospital, but the HCUP State Emergency Department Databases (SEDD) do not include these records; instead, the SEDD report only “treat-and-release” ED visits. Reporting of opioid-related ED visits was defined consistently for both national and State data to include only ED encounters that do not result in admission to the same hospital (i.e., treat-and-release ED visits). Estimates of opioid-related ED visits resulting in admission to the same hospital are included in the inpatient stay statistics, which are captured consistently in both the HCUP National (Nationwide) Inpatient Sample (NIS) and the State Inpatient Databases (SID).

 

  • Why are the results presented as population-based rates? What if we are interested in obtaining the discharge counts?

    Since both national and State-level estimates are provided, rates per 100,000 population were chosen in order to make the estimates comparable. In addition, reporting by rates allows comparisons across population subgroups, such as age and sex. Rates are calculated based on the actual quarterly discharge counts, which are available as rounded values in the exported data file and can be downloaded by expanding “Show Data Export Options” on the main query page. The exported data file also includes rates calculated based on annual discharge counts.

 

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Topic Questions: Neonatal Abstinence Syndrome (NAS) Among Newborn Hospitalizations

 

  • What is neonatal abstinence syndrome (NAS)?

    NAS refers to newborns exhibiting withdrawal symptoms due to prenatal exposure to opioids.22 Hudak ML, Tan RC; The Committee on Drugs; The Committee on Fetus and Newborn; American Academy of Pediatrics. Neonatal drug withdrawal. Pediatrics 2012;129:e540-60. https://doi.org/10.1542/peds.2011-3212.

 

  • What are withdrawal symptoms in newborns?

    Withdrawal symptoms in newborns may include tremors, irritability, diarrhea, seizures, jitteriness, excessive and inconsolable crying, difficulty sleeping, exaggerated Moro reflex, hypertonia, and myoclonic jerks.33 Kocherlakota P (2014). Neonatal abstinence syndrome. Pediatrics, 134(2):e547-e561.

 

  • Why are only newborn hospitalizations reported?

    To limit the potential for counting multiple NAS hospital stays for the same newborn, we have focused this topic on the newborn hospitalization record, i.e. the initial hospitalization related to the birth (in hospital or outside the hospital) and not any subsequent hospital stay with a diagnosis of NAS within the neonatal period.

 

  • Why does the NAS Among Newborn Hospitalizations topic only present statistics for the inpatient setting when the Opioid-Related Hospital Use topic presents statistics for both the inpatient and ED settings?

    The NAS topic is limited to the inpatient setting because the NAS-related rates are calculated per 1,000 newborn hospitalizations. In contrast, the opioid topic presents opioid-related rates for both the inpatient and ED settings because the rates are calculated per 100,000 population.

 

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Topic Questions: Severe Maternal Morbidity (SMM)

 

 

  • Why are clinical codes with only blood transfusions not included in the definition of SMM?

    Although blood transfusions can be an indicator of SMM, this is not always the case when other indicators of SMM are absent.6,7 Additionally, as a result of changes from the 9th to 10th revision of the International Classification of Diseases (ICD) clinical coding system in 2015, blood transfusions may not be consistently reported in clinically coded data.6,7 At this time, the U.S. Department of Health & Human Services recommends that the definition of SMM not include discharges with blood transfusions alone in the absence of other SMM indicators.6,7 For example, delivery discharges with an SMM indicator of disseminated intravascular coagulation (DIC) will be identified as SMM records, regardless of whether these discharges also have a blood transfusion code or not. However, delivery discharges with a blood transfusion code and no other SMM indicators will not be identified as SMM records.6 Office of the Surgeon General. The Surgeon General’s Call to Action to Improve Maternal Health. December 2020. U.S. Department of Health & Human Services: Washington, DC. www.hhs.gov/sites/default/files/call-to-action-maternal-health.pdf.
    7 U.S. Department of Health & Human Services. Healthy Women, Healthy Pregnancies, Healthy Futures: Action Plan to Improve Maternal Health in America. December 2020. U.S. Department of Health & Human Services: Washington, DC. aspe.hhs.gov/system/files/aspe-files/264076/healthy-women-healthy-pregnancies-healthy-future-action-plan_0.pdf.

 

  • Why are State-level statistics based on the State in which the hospital is located and not the State of the patient’s residence?

    State-level statistics on SMM in HCUP Fast Stats are derived from the State Inpatient Databases (SID). Each State’s SID includes all inpatient discharges from hospitals in that State; this includes discharges from residents of the State as well as discharges from non-residents who obtained treatment at a hospital in the State.

 

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Topic Questions: Hurricane Impact on Hospital Use

 

  • What do direct path, near path, remote/FEMA disaster, and remote/not disaster mean?

    These four categories describe county proximity to a hurricane (i.e., in the direct path of the hurricane, near the path of the hurricane, or remote from the hurricane), and for remote counties, whether a county was declared a disaster area by the Federal Emergency Management Agency (FEMA). A county may be declared a disaster area by FEMA if any one or more of the following types of assistance are needed: individual and household assistance, individual assistance, public assistance, or hazard mitigation. Classification of counties into the four proximity categories is based on information from two data sources: National Oceanic Atmospheric Administration’s (NOAA’s) Best Track data and FEMA’s Disaster Declaration Summary. For additional information on hurricane proximity, refer to the Data Notes & Methods section.

 

  • Is it possible to share the list of counties included in the four proximity categories for each hurricane: direct path, near path, remote identified as a disaster area by FEMA, and remote not identified as a disaster area by FEMA?

At this time, AHRQ is not presenting information about which counties were in each proximity category. AHRQ will consider the feasibility of doing so in a future enhancement of this Fast Stats topic. Additional information about the designation of counties into proximity categories is available under the Data Notes & Methods section, and hurricane-specific maps can be obtained under the Data Export Options section.

 

  • What is a population rate, and why is percent change in population rate presented rather than the actual number of stays/visits or rate?

    The population rate measures hospital utilization in a population. The denominator of the rate is the population of interest. The numerator is the number of inpatient stays or emergency department (ED) visits of the same population. The population rate facilitates comparison across populations of different sizes. The percent change in population rate demonstrates the changes that can occur in hospital utilization rates following a hurricane. Specifically, a pre-hurricane four-week average is used as a baseline with percent change presented for the week of hurricane landfall and the following seven weeks. The number of stays/visits and population counts are available in the exportable data file under “Show Data Export Options”.

 

  • Why is ED limited to treat-and-release?

    This approach is used to avoid double counting of any emergency department visit that resulted in an inpatient admission. Statistics on ED visits are derived from the HCUP State Emergency Department Databases (SEDD), which include only “treat-and-release” ED visits. Statistics on inpatient stays are derived from the HCUP State Inpatient Databases (SID), which include both inpatient stays and ED visits resulting in admission to the same hospital.

 

  • What types of conditions are included?

    Hospital utilization statistics are provided separately for circulatory conditions, infections, injuries, and respiratory conditions, and overall for all conditions. Each condition category encompasses a broad range of conditions that may or may not be hurricane related. To be included in any category, the condition has to be reported as the principal diagnosis on an inpatient stay or the first-listed diagnosis on an ED visit. For a full list of diagnoses included in the definition of each condition, refer to the Data Notes & Methods section.

 

  • Why are certain hurricanes included and not others (e.g., Katrina)?

    The inclusion of these 11 U.S. hurricanes is based on the availability of HCUP data corresponding to the time and place of the hurricane. For Hurricane Katrina, which heavily impacted Louisiana and Mississippi in August 2005, HCUP data are not available for either State during that time period. For more recent hurricanes (e.g., Florence and Michael, which occurred in 2018), sufficient pre- and post-hurricane HCUP data from impacted States are not yet available.

 

  • Why are some trend lines and data points missing, and why are there no ED data for certain hurricanes?

    If the average number of encounters in the pre-hurricane period is less than or equal to 10, or represents fewer than two hospitals, the percent changes for the hurricane week and each post-hurricane week are suppressed. This will result in one or more missing trend lines in a graph. If the number of encounters for the hurricane week or any post-hurricane week is less than or equal to 10, or represents fewer than two hospitals, the percent change is suppressed. This will result in one or more missing data points in a graph and will cause a discontinuity in the trend lines. When three or more data points are suppressed, the entire trend line is missing from the graph and none of the data values are provided in the underlying data tables and exported data file. For some hurricanes, all trend lines may be missing from certain graphs due to data suppression; in these instances, the graph includes a note indicating that “Data are insufficient for presentation.” Information on emergency department utilization is not presented for hurricanes Gustav, Ike, Isaac, and Rita because none of the States impacted by these hurricanes provided emergency department data to HCUP during the hurricane time periods. For additional information, refer to the Data Notes & Methods section.

 

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