HepVu visualizes data related to Hepatitis C in the U.S., including standardized state-level estimates of people living with Hepatitis C infection. These estimates were generated by researchers affiliated with the Emory University Coalition for Applied Modeling for Prevention (CAMP) project, including from the University of Albany and Georgia State University, and researchers from the Centers for Disease Control and Prevention (CDC). HepVu is led by Patrick Sullivan, Ph.D., Professor of Epidemiology and Co-Director of the CFAR Prevention Science Core. HepVu currently maps Hepatitis C prevalence and mortality data for all U.S. states and Hepatitis C mortality at the county-level. Hepatitis C prevalence and mortality maps at the state-level can be viewed overall and by age group, race, and sex. At the county level HepVu displays Hepatitis C mortality which can be viewed overall and by age and two county- level opioid indicators: overdose mortality and opioid prescription rate. HepVu also displays three state-level opioid indicators: overdose mortality rate, opioid prescription rate, and pain reliever misuse prevalence, which are available as maps and as side-by-side comparisons to hepatitis C prevalence and mortality. Additional data comparisons include social determinants of health, such as poverty, high school education, and people without health insurance as well as maps on opioid indicators at the state and county level and Hepatitis C treatment restrictions such as fibrosis, sobriety, and prescriber restrictions at the state level.
Other HepVu resources including state profile pages, blogs with viral hepatitis experts, and infographics provide supplementary visualizations and complement the data presented in the maps and existing surveillance reports. Downloadable resources include map images, infographic panels that can be used for presentations or other materials, and downloadable datasets.
Detailed below are HepVu’s data methods and sources.
Hepatitis C Prevalence: The data reflect persons with a positive or indeterminate anti-Hepatitis C virus (HCV) test and positive HCV RNA test.
Hepatitis C Mortality: The data reflect deaths among persons with acute viral hepatitis C or chronic viral hepatitis C as an underlying cause of death. The map title “Deaths related to Hepatitis C” has been used to encompass deaths of persons diagnosed with acute or chronic Hepatitis C.
Opioid Prescription Rate: The data reflect the number of opioid prescriptions dispensed in the U.S. per 100 persons. A prescription is considered a pharmaceutical dispensed initially or as a refill.
Overdose Mortality Rate: The data reflect deaths of persons from narcotic overdose per 100,000 persons.
Pain Reliever Misuse Prevalence: The data reflect the percentage of persons who self-report indicators of misusing prescription psychotherapeutics (defined as use in any way not directed by a doctor) in the past year.
Hepatitis C Prevalence Estimates:
State-level Hepatitis C prevalence estimates presented on HepVu were published by the Coalition for Applied Modeling for Prevention (CAMP) researchers. These estimates were calculated using four data sources – National Health and Nutrition Examination Survey (NHANES) (1999-2016), National Vital Statistics System (NVSS) (1999-2016), American Community Survey (ACS) Public Use Microdata Samples (PUMS) (2012-2016), and U.S. Census intercensal data (1999-2016). Additional populations not sampled in NHANES, including those who are incarcerated, experiencing unsheltered homelessness, and living in nursing homes, were taken into account using values from a systematic literature review.
- Weighted NHANES data represent all non-institutionalized people six years of age and older residing in the 50 states and the District of Columbia (D.C.). Demographic information and blood specimens are collected by NHANES. For these estimates, persons with a positive or indeterminate anti-HCV test and positive HCV RNA test were considered to have prevalent Hepatitis C infection. As NHANES data and corresponding sampling weights are released every two years, data from nine cycles were used, representing an 18-year span (1999-2016). NHANES responses were grouped by demographic information. Sex was categorized into male and female. Race/ethnicity was categorized into non-Hispanic Black, and other races/ethnicities. Age was categorized into three birth cohorts: born before, during, and after 1945-1969. Poverty was categorized into three groups: below the federal poverty level, 1.0 to 1.9 times the federal poverty level, and 2.0 times the federal poverty level or more.
- The NVSS data are collected using information from death certificates of all U.S. residents within the 50 states and D.C. Deaths of residents of Puerto Rico, other U.S. territories, and fetal deaths are not included. Demographic, geographic, and cause-of-death information for each individual is recorded. Any records that included the ICD-10 code for acute viral hepatitis C (B17.1) or chronic viral hepatitis C (B18.2) as the underlying or multiple cause of death were considered deaths related to Hepatitis C. Narcotic overdose mortality was classified using ICD-10 codes for unintentional poisoning by and exposure to narcotics and psychodysleptics (hallucinogens) (X42), unknown unintentional poisoning by and exposure to narcotics and psychodysletpics (hallucinogens) (Y12), unintentional poisoning by and exposure to other and unspecified drugs, medicaments, and biological substances (X44), and unknown intention poisoning by and exposure to other and unspecified drugs, medicaments, and biological substances (Y14).
- The ACS provides PUMS data as records at the individual person or housing unit level with response information such as sex, age, and other characteristics. The five-year PUMS files were used for the modeled estimates and are the multiyear combinations of the single year ACS data. State-by-demographic group population totals from the 2012-2016 five-year ACS estimates were used in calculations to determine Hepatitis C prevalence in each state in 2016.
- The U.S. Census Bureau produces demographic and geographic data through decennial censuses, annual surveys, and population estimates and projections. Intercensal data were used to provide denominators for HCV mortality rates for each year during 1999-2012.
Additional analyses accounted for populations not included in NHANES. Researchers conducted a systematic literature review of articles published between 2013 and 2017 to estimate HCV prevalence among incarcerated and homeless populations. For nursing home residents, state-level population size estimates were obtained from public data sources and multiplied by age- and sex-specific national HCV prevalence rates to estimate the number of people with prevalent HCV infection.
The Hepatitis C prevalence analyses were restricted to people aged 18 years or older, living within the 50 states and D.C. The NHANES data were used to calculate direct weighted estimates of national Hepatitis C prevalence. The 2012-2016 ACS PUMS data were used to generate estimated population totals within each state, stratified by era (pre- and post-2013), sex, race/ethnicity, and birth cohort. These state-by-strata population totals were multiplied by the stratified Hepatitis C prevalence estimates to generate crude state-level estimates. NVSS mortality data and intercensal population totals were used to model the HCV-related and narcotic overdose death rates in the same strata by state. Within strata, these two mortality rates were combined using weights computed from data-driven assumptions about the proportion of HCV prevalence in a given birth cohort that was likely to be attributable to injection drug use. The combined mortality rates were multiplied by state-by-strata prevalence estimates to generate mortality-adjusted HCV prevalence totals in each stratum in each state. These were summed within states across strata, and the numbers of additional prevalent HCV infections estimated from populations unsampled by NHANES were added to state-specific estimates. For the stratified estimates, state- and strata-specific hepatitis C prevalence among populations unsampled by NHANES were estimated in a way that reflected state- and strata specific hepatitis C estimates derived from NHANES. This difference in estimation methods may result in differences between summed totals of stratified estimates and overall state-level estimates. Last, these estimates were combined with 2012-2016 ACS population totals to generate HCV prevalence estimates for each state.
Rates are expressed as the number of estimated cases per 100,000 people in the population, rounded to the nearest 10 persons. It is an expression of the relative concentration of people with current Hepatitis C infection in an area (state). Estimated cases are rounded to the nearest hundred persons.
Hepatitis C State Mortality Data
The state-level Hepatitis C mortality data presented on HepVu (single-year data from 2013-2017) were obtained from the Centers for Disease Control and Prevention (CDC) WONDER Online Database System and compiled by researchers at the Rollins School of Public Health at Emory University. The CDC WONDER data are collected using information from death certificates of all U.S. residents within the 50 states and D.C. Deaths of residents of other U.S. territories and fetal deaths are not included. Demographic, geographic, and cause-of-death information for each individual is recorded. Any records that included the ICD-10 code for acute viral hepatitis C (B17.1) or chronic viral hepatitis C (B18.2) as the underlying or multiple cause of death were used to identify deaths related to hepatitis C.
If the death count for any particular group/state is less than 10, data are suppressed and appear as no color (white) on the maps. If the death count for any particular group/state is 10 or greater and less than 20, the rate generated from that count is considered unreliable.
Hepatitis C County Mortality Data
The county-level hepatitis C mortality data presented on HepVu (single-year data from 2005-2017) were published by Eric Hall, et. al., in a paper titled “County-Level Variation in Hepatitis C Virus Mortality and Trends in the United States, 2005-2017.” These estimates were calculated using data from the National Center for Health Statistics Detailed Multiple Cause of Death (1999-2017) using deaths that listed ICD-10 codes for acute viral hepatitis C (B17.1) and chronic viral hepatitis C (B18.2) as a multiple cause of death. National Center for Health Statistics bridged-race annual county-level were used for the population denominators. A Bayesian multivariate model space-time conditional autoregressive model was used to model the annual number of hepatitis C deaths. The modeled counts were aggregated to estimates overall and age-stratified (<40 years and 40+ years) age-standardized county-level hepatitis C death rates.
From 2005 to 2017, several county definitions changed (counties were created and consolidated), so a common set of 3115 counties were included in the analysis. For counties that were created out of another county during the time period, HepVu took the death rate from the original county and applied it to the new county. Several counties that were created could not be associated to a singular county and were therefore suppressed. The analysis grouped the New York counties Bronx, Kings, New York, Queens, and Richmond together for the analysis, and therefore the rates listed for each of those counties is the combined death rate of all five counties. Below is a list of counties which were created during the time period paired with the old counties from which their data was pulled:
- La Paz County, AZ (GEO ID 4012) – created from Yuma County, AZ (GEO ID 4027)
- Cibola County, NM (GEO ID 35006) – created from Valencia County, NM (GEO ID 35061)
County Opioid Indicators
There are two opioid indicators mapped on HepVu, both at the county level as individual maps and as side-by-side data comparison maps. The two indicators are overdose mortality rate during 2014-2018 and opioid prescription rate in 2018.
County-level data on drug overdose mortality were obtained from “Describing the changing relationship between opioid prescribing rates and overdose mortality: A novel county-level metric” and National Vital Statistics System (NVSS). Data from NVSS classified drug overdose death using International Classification of Diseases, Tenth Revision codes: X40-X44, X60-X64, X85, and Y10-Y14.
The opioid prescriptions rate data from 2014 to 2018 for all U.S. counties were obtained from the CDC’s National Center for Injury Prevention and Control. CDC derived the data from the IQVIA Transactional Data Warehouse to obtain the number of opioid prescriptions dispensed in the U.S. via retail. Please see the full CDC report for more details.
State Opioid Indicators
There are three opioid indicators mapped on HepVu, both at the state level as individual maps and as side-by-side data comparison maps. The three indicators are overdose mortality rate during 2014-2019, opioid prescription rate in 2019, and pain reliever misuse prevalence during 2018-2019.
State-specific data for overdose mortality rates were obtained from the CDC injury center and the NVSS. Deaths were classified using the International Classification of Diseases, Tenth Revision. Drug overdose deaths were identified using underlying cause-of-death codes X40–X44, X60–X64, X85, and Y10–Y14. Rates are age-adjusted using the 2000 U.S. standard population, except for age-specific crude rates. All rates are per 100,000 population.
The opioid prescription rate data were obtained from the CDC report, “2019 Annual Surveillance Report of Drug-Related Risks and Outcomes.” Data were collected from Table 1C reporting “Rates of opioid prescription dispensed per 100 persons by dosage, type, and state – United States, 2018.” CDC derived the data from the IQVIA Transactional Data Warehouse to obtain the number of opioid prescriptions dispensed in the U.S. via retail. Please see the full CDC report for more details.
Pain reliever misuse data were obtained from a report produced by the Substance Abuse and Mental Health Service Administration (SAMHSA). The data come from Table 11, “Pain Reliever Misuse in the Past Year,” in the report, “2018-2019 National Survey on Drug Use and Health: Model-Based Prevalence Estimates (50 States and the District of Columbia).” The data are a yearly average of the 2018 and 2019 data collected in the National Survey on Drug Use and Health. Misuse was described as anyone 12 and older using a prescription psychotherapeutic drug in any way not directed by a doctor and does not include over-the-counter drugs. Please see the full SAMHSA report for more details.
Treatment Restriction Data
The treatment restriction data published on HepVu (single-year data from 2017-2021) come the Center for Health Law and Policy Innovation at Harvard Law School (CHLPI). CHLPI, in collaboration with the National Viral Hepatitis Roundtable (NVHR), evaluated the status of Hepatitis C treatment restrictions for each state in the United States. To find these data, CHLPI evaluated the Medicaid reimbursement criteria for available direct-acting antivirals (DAAs) in each state, Washington DC, and Puerto Rico. A survey form was sent to each state’s Medicaid officials requesting their coverage criteria for DAAs. If a state was unresponsive, then the state’s Medicaid website was searched for any publicly available information. If the data in the survey form conflicted with publicly available data, then it was resolved either by communicating with the Medicaid officials in that state or by consensus. Along with Medicaid fee-for-service programs, data was also evaluated from managed care organizations (MCOs).
HepVu allows viewers to examine Hepatitis C state-level prevalence and mortality data at the overall state level for cases and rates, as well as stratified by age group, race/ethnicity, and sex. The county-level mortality data displays rates overall and by two age groups, under 40 and 40 and older. The prevalence data display age as three birth cohorts – younger than Baby Boomers (born after 1969), Baby Boomers (born during 1945-1969), and older than Baby Boomers (born before 1945). The state=level mortality data also display age as three birth cohorts – younger than Baby Boomers (0-49 years), Baby Boomers (50-74), and older than Baby Boomers (75+). The prevalence data display race/ethnicity as non-Hispanic Black and persons of other races/ethnicities. The state mortality data display race/ethnicity as non-Hispanic American Indian or Alaska Native, non-Hispanic Asian or Pacific Islander, non-Hispanic Black/African American, non-Hispanic White, and Hispanic/Latinx. The state prevalence and mortality data display sex as male or female.
Caution should be exercised when viewing and interpreting these maps because scales vary across different demographic breakdowns.
Range intervals were developed by calculating cut-points in SAS analytic software (SAS Institute, Cary, NC) and rounding to create the range values for each group. In order to illustrate the variation in rates and case counts, values were developed specific to each demographic grouping (i.e., overall, and separately by sex, by race, and by age group). Values were also developed for each of the opioid indicators maps and the five social determinants of health data comparison maps (see section below for details). The treatment restrictions legends were developed based on the different values for each map. The exception is the overall prevalence range values, which were created to match the range values of those in the paper. Thus, a total of 40 map scales exist (rate/case count by overall and by age group/race/sex for prevalence, rate/case by overall and by age group/race/sex for state mortality, rate by overall and by age group for county mortality, rate by overall for overdose mortality and opioid prescription, percent by overall for pain reliever misuse, overall for all five social determinants of health, and overall for all three treatment restrictions).
Social determinants of health, opioid indicators, and Hepatitis C treatment restrictions are displayed on a secondary map and their associated scales were developed using the same method described above. The five social determinants displayed include: poverty (percent of population living in poverty), high school education (percent of population with a high school degree or equivalent), median household income, income inequality (measured by the Gini Coefficient, a measure of income inequality where 0 reflects complete equality and 1 reflects complete inequality), and people without health insurance (percent of population lacking health insurance). The three opioid indicators are overdose mortality rate, opioid prescription rate, and pain reliever misuse prevalence. The three Hepatitis C treatment restrictions are fibrosis restrictions, sobriety restrictions, and prescriber restrictions.
National data are available for download. The state data available are 2013-2016 Hepatitis C prevalence, single-year 2013-2017 Hepatitis C state mortality, 2014-2019 overdose mortality rate, 2019 opioid prescription rate, 2018-2019 pain reliever misuse prevalence, 2016 social determinants of health, and Hepatitis C 2017-2021 treatment restrictions. At the county-level single-year 2005-2017 Hepatitis C mortality data, 2014-2018 overdose mortality rate, and 2018 opioid prescription rate are available for download.
Data, maps, and information from HepVu may be used, provided credit is given to HepVu and the Rollins School of Public Health at Emory University. Recommended citations:
Data Source: HepVu (hepvu.org). Emory University, Rollins School of Public Health.
Hepatitis C Prevalence Manuscript Source: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2719137
Stratified Hepatitis C Prevalence Manuscript Source: https://aasldpubs.onlinelibrary.wiley.com/doi/full/10.1002/hep4.1457
Hepatitis C County Mortality Manuscript
Hepatitis C and Opioid Data
|Data Element||Location on HepVu||Data Source||Anticipated Update Frequency on HepVu Website|
|Estimated Rates and Case Counts of People Living with Hepatitis C Overall, State Data||Maps; Downloadable Datasets; State Profile Pages||Rosenberg ES, Rosenthal EM, Hall EW, et al. Prevalence of Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016. JAMA Netw Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371||When available|
|Estimated Stratified Rates and Case Counts of People Living with Hepatitis C, State Data||Maps; Downloadable Datasets; State Profile Pages||Bradley H, Hall EW, Rosenthal EM, et al. Hepatitis C Virus Prevalence in 50 U.S. States and D.C. by Sex, Birth Cohort, and Race: 2013-2016. Hepatology Communications. 2020. doi:10.1002/hep4.1457||When available|
|Rates and Case Counts of Hepatitis C Mortality, State Data||Maps; Downloadable Datasets; State Profile Pages||Centers for Disease Control and Prevention. National Center for Health Statistics. CDC WONDER Online Database.||Annually|
|Rates of Hepatitis C Mortality, County Data||Maps; Downloadable Datasets||County‐Level Variation in Hepatitis C Virus Mortality and Trends in the United States, 2005‐2017||When Available|
|Rate of Opioid Prescriptions, State Data||Maps; State Profile Pages; Downloadable Datasets||Centers for Disease Control and Prevention; 2019 Annual Surveillance Report of Drug-Related Risks and Outcomes||When Available|
|Rate of Overdose Mortality, State Data||Maps; State Profile Pages; Downloadable Datasets||Centers for Disease Control and Prevention. National Vital Statistics System, CDC WONDER Online Database.
|Percent of People Misusing Pain Relievers, State Data||Maps; State Profile Pages; Downloadable Datasets||Substance Abuse and Mental Health Services Administration; 2018-2019 National Survey on Drug Use and Health: Model-Based Prevalence Estimates (50 States and the District of Columbia)||When Available|
|Rate of Opioid Prescriptions, County Data||Maps; Downloadable Datasets||Centers for Disease Control and Prevention; National Center for Injury Prevention and Control||When Available|
|Rate of Overdose Mortality, County Data||Maps; Downloadable Datasets||CDC WONDER Online Database||When Available|
|Data Element||Location on HepVu||Data Source||Anticipated Update Frequency on HepVu Website|
|Hepatitis C Treatment Restrictions, State Data||Maps; State Profile Pages; Downloadable Datasets||The Center for Health Law and Policy Innovation at Harvard Law School (CHLPI); Data Request, March 2021.||Annually|
|Poverty (Percent of population, all ages, living in poverty), State Data||Maps; State Profile Pages; Downloadable Datasets||U.S. Census Bureau, Small Area Income and Poverty Estimates, Table 1: 2016 Poverty and Median Income Estimates – States.||Annually|
|Educational Attainment (Percent of people over age 25 with at least a high school diploma or equivalent), State Data||Maps; State Profile Pages; Downloadable Datasets||U.S. Census Bureau, American Community Survey 1-Year Estimates, 2016, Table C15003: Educational attainment for the population 25 years and over – States.||Annually|
|Median Household Income, State Data||Maps; State Profile Pages; Downloadable Datasets||U.S. Census Bureau, Small Area Income and Poverty Estimates, Table 1: 2016 Poverty and Median Income Estimates – States.||Annually|
|Percent of population under age 65 lacking health insurance, State Data||Maps; State Profile Pages; Downloadable Datasets||U.S. Census Bureau, Small Area Health Insurance Estimates, Model Based SAHIE Estimates for Counties and States: 2016.||Annually|
|Gini Coefficient of income inequality, State Data||Maps; State Profile Pages; Downloadable Datasets||U.S. Census Bureau, American Community Survey 1-Year Estimates, 2016, Table B19083: Gini Index of Income Inequality.||Annually|
|State Population by Age||State Profile Pages||U.S. Census Bureau, 2010 Census.||When Available|