Prevalence of Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016

Eli S. Rosenberg; Elizabeth M. Rosenthal; Eric W. Hall; Laurie Barker; Megan G. Hofmeister; Patrick S. Sullivan; Patricia Dietz; Jonathan Mermin; A. Blythe Ryerson

doi:10.1001/jamanetworkopen.2018.6371

Prevalence of Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016

Rosenberg, Eli S.; Rosenthal, Elizabeth M.; Hall, Eric W.; Barker, Laurie; Hofmeister, Megan G.; Sullivan, Patrick S.; Dietz, Patricia; Mermin, Jonathan; Ryerson, A. Blythe 2018-12-21 00:00:00 Key Points Question During 2013 to 2016, what IMPORTANCE Infection with hepatitis C virus (HCV) is a major cause of morbidity and mortality in proportion of adults were living with the United States, and incidence has increased rapidly in recent years, likely owing to increased hepatitis C virus (HCV) infection in each injection drug use. Current estimates of prevalence at the state level are needed to guide prevention US state? and care efforts but are not available through existing disease surveillance systems. Findings In this survey study, US national HCV prevalence during 2013 to OBJECTIVE To estimate the prevalence of current HCV infection among adults in each US state and 2016 was 0.93% and varied by the District of Columbia during the years 2013 to 2016. jurisdiction between 0.45% and 2.34%. Three of the 10 states with the highest DESIGN, SETTING, AND PARTICIPANTS This survey study used a statistical model to allocate prevalence and 5 of the 9 states with the nationally representative HCV prevalence from the National Health and Nutrition Examination highest number of HCV infections were Survey (NHANES) according to the spatial demographics and distributions of HCV mortality and in the Appalachian region. narcotic overdose mortality in all National Vital Statistics System death records from 1999 to 2016. Additional literature review and analyses estimated state-level HCV infections among populations Meaning Regions with long-standing not included in the National Health and Nutrition Examination Survey sampling frame. HCV epidemics, and those with newly emergent ones partly driven by the EXPOSURES State, accounting for birth cohort, biological sex, race/ethnicity, federal poverty level, opioid crisis, face substantial HCV and year. prevalence. MAIN OUTCOMES AND MEASURES State-level prevalence estimates of current HCV RNA. Supplemental content RESULTS In this study, the estimated national prevalence of HCV from 2013 to 2016 was 0.84% Author affiliations and article information are listed at the end of this article. (95% CI, 0.75%-0.96%) among adults in the noninstitutionalized US population represented in the NHANES sampling frame, corresponding to 2 035 100 (95% CI, 1 803 600-2 318 000) persons with current infection; accounting for populations not included in NHANES, there were 231 600 additional persons with HCV, adjusting prevalence to 0.93%. Nine states contained 51.9% of all persons living with HCV infection (California [318 900], Texas [202 500], Florida [151 000], New York [116 000], Pennsylvania [93 900], Ohio [89 600], Michigan [69 100], Tennessee [69 100], and North Carolina [66 400]); 5 of these states were in Appalachia. Jurisdiction-level median (range) HCV RNA prevalence was 0.88% (0.45%-2.34%). Of 13 states in the western United States, 10 were above this median. Three of 10 states with the highest HCV prevalence were in Appalachia. CONCLUSIONS AND RELEVANCE Using extensive national survey and vital statistics data from an 18-year period, this study found higher prevalence of HCV in the West and Appalachian states for 2013 to 2016 compared with other areas. These estimates can guide state prevention and treatment efforts. JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 1/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 Introduction Hepatitis C virus (HCV) infection is the most frequently reported bloodborne infection in the United States and a leading cause of liver-related morbidity, transplantation, and mortality. Transmission of HCV occurs through exposure to infected body fluids, principally blood. Untreated, between 15% 2-4 and 42% of infected persons resolve infection ; about half of those chronically infected develop 5,6 progressive liver disease, which may include cirrhosis and hepatocellular carcinoma. 5-7 Approximately 18 000 people died in 2016 because of HCV infection. Historically, HCV prevalence has been highest among persons in the birth cohort born between 1945 and 1965, and the number 8,9 of people living with chronic infection was estimated to be 3.5 million in the late 2000s. Changes over the past decade have reshaped the US HCV epidemic. US Food and Drug Administration approval and increased availability of direct-acting antivirals have cured many people 10-12 of infection. However, high all-cause and HCV-related mortality rates among persons in the highest-prevalence birth cohort for HCV infection remain. There has concomitantly been a tripling of HCV incidence, due primarily to an increase in persons injecting drugs and associated unsafe 7,14,15 sharing of injection equipment related to the opioid crisis. With the increasing availability of direct-acting antivirals, national and state-level public health strategies have raised elimination of HCV as a possible goal. Accurate estimates of the current burden of HCV infection in each US jurisdiction are critical to the policy, programmatic, and resource planning of elimination strategies. However, national case surveillance provides an incomplete picture of the burden of HCV infection. Although HCV infection is reportable to the Centers for Disease Control and Prevention’s (CDC’s) National Notifiable Diseases Surveillance System, acute and chronic infections reported through this program represent a small proportion of cases, and in some states neither are 7,16 reportable. Some jurisdictions maintain enhanced surveillance programs funded by the CDC or other sources, yet a comprehensive jurisdiction-specific picture for the nation remains inestimable from case surveillance data. The current approach for estimating national HCV prevalence involves analysis of the US National Health and Nutrition Examination Survey (NHANES), which conducts HCV 8,17 testing among noninstitutionalized persons aged 6 years or older. An updated national HCV prevalence for 2013 to 2016 has been estimated using NHANES, reflecting the previously mentioned age-bimodal epidemic patterns, yielding an estimated 2.4 million persons with HCV RNA–positive results, indicating current (acute or chronic) infection. This estimate used methods to account for populations unrepresented in NHANES-based estimates, including individuals experiencing incarceration and unsheltered homelessness, groups that represent 11% of HCV prevalence. Current subnational estimates are needed to guide local HCV elimination efforts, as previous estimates are no longer valid owing to changes in HCV epidemiology over the past few years. We present an updated approach to our previous methodology for state-specific HCV prevalence estimation that reflects current changes to the epidemic. This method uses newly released NHANES and vital statistics data through 2016 and incorporates HCV-related and narcotic overdose deaths to yield updated estimates that reflect overlaid spatial patterns in HCV infection attributable to previous and recent transmission. Methods We used a multistep statistical approach (eFigure 1 in the Supplement), first generating direct estimates for each state using NHANES national prevalence in sex, race/ethnicity, birth cohort, and poverty strata. We next examined the distribution of each state’s cause-specific death rates relative to the US average as signals for local patterns of HCV infection. Within demographic strata, we applied 2 sets of state-specific mortality ratios relative to the nation, mortality rates from HCV infection and narcotic overdose, to represent older and recent infections, respectively. We then estimated additional infections among populations not included in NHANES’ sampling frame by applying literature-based estimates of prevalence in these groups to state-specific population JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 2/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 estimates. All analyses were limited to persons aged 18 years or older. In the following section, we describe this approach in detail. This study was reported according to the American Association for Public Opinion Research (AAPOR) reporting guideline. Because the study used publicly available data, institutional review board approval was not sought per organizational policy. Data Sources NHANES (1999-2016) Every 2-year cycle, NHANES samples approximately 10 000 individuals through a complex multistage design that represents the noninstitutionalized civilian US population. The survey 19,20 collects demographic characteristics and specimens for HCV RNA and antibody testing. Additional details, including response rates, are in eAppendix 1 in the Supplement. Race/ethnicity was categorized into non-Hispanic black and other race/ethnicities. Birth year was categorized as before 1945, 1945 to 1969, and after 1969. The typical 1945 to 1965 birth cohort with the highest HCV prevalence was expanded by 4 years because preliminary NHANES analyses showed similar prevalence to the traditional birth cohort (not shown). Income was represented as a ratio comparing family income with the US Department of Health and Human Services poverty guidelines for each year and categorized in the following 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. Missing income data (n = 3931 [8.30%]) were imputed using a process described in eAppendix 2 in the Supplement. We pooled 9 data cycles (1999-2016) to ensure sufficient stratum-level data (Table 1). American Community Survey Public Use Microdata Sample (2012-2016) The American Community Survey (ACS) samples nearly 3 million addresses annually, collecting demographic and economic characteristics of the US population. We used the 2012 to 2016 five- year ACS Public Use Microdata Sample to estimate population denominators for the noninstitutionalized population in each stratum and state. Race/ethnicity, birth year, and income were categorized as we have described, and we conducted imputation analyses for missing income data (n = 237 600 [1.98%]) (eAppendix 2 in the Supplement). Table 1. Data Sources Data Source Years Included Purpose Individuals Represented, No. Cases, No. Data Extraction Notes NHANES 1999-2016 National HCV RNA prevalence 47 387 With nonmissing HCV 575 With positive HCV NHANES 2000, 2002, 2004, 2006, overall and by strata of sex, RNA test results; 47 590 RNA test; 874 with 2008, 2010, 2012, 2014, 2016 data race/ethnicity, birth cohort, with nonmissing HCV positive HCV antibody sets a a and poverty; trends in HCV antibody test results test antibody inform analysis weights US Census 1999-2016 Population structure for 4 109 869 228 Person-years NA US Vintage 2000, Vintage 2009, intercensal data modeling HCV- and overdose- aged ≥18 y Vintage 2016 data sets related mortality rates US Census American 2012-2016 Noninstitutionalized US 12 023 450 Observations of NA 5-y Public Use Microdata Sample Community Survey population structure for final noninstitutionalized persons estimates aged ≥18 y National Vital 1999-2016 Distribution of hepatitis 44 071 310 Decedents aged 261 858 With HCV as ICD-10 codes included acute viral Statistics System C–related mortality, signaling ≥18 y who resided in the 50 underlying or multiple hepatitis C (B17.1) and chronic viral underlying HCV prevalence, to states or the District of cause of death hepatitis C (B18.2) inform distribution of older Columbia HCV infections National Vital 1999-2016 Distribution of narcotic 44 071 310 Decedents aged 541 130 With ICD-10 codes included poisoning by Statistics System overdose mortality, signaling ≥18 y who resided in the 50 unintentional or and exposure to narcotics and underlying injection patterns, states or the District of undetermined cause psychodysleptics (hallucinogens) to inform distribution of newer Columbia narcotic or unknown drug (X42 unintentional, Y12 HCV infections as underlying or multiple undetermined intent); poisoning by cause of death and exposure to other and unspecified drugs, medicaments, and biological substances (X44 unintentional, Y14 undetermined intent) Abbreviations: HCV, hepatitis C virus; ICD-10, International Classification of Diseases, Hepatitis C virus antibody screening test data are included for all years. Confirmatory Tenth Revision; NA, not applicable; NHANES, National Health and Nutrition test data for HCV antibodies are not publicly available for 2015 to 2016. Examination Survey. JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 3/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 National Vital Statistics System Multiple Cause of Death Mortality Data (1999-2016) Multiple Cause of Death Mortality Microdata files (1999-2016), including individual death records for persons who lived in a US state or the District of Columbia, were requested from the National Vital Statistics System (NVSS). These records contained International Classification of Diseases, Tenth Revision (ICD-10) codes for multiple underlying causes of deaths (N = 44 071 310). Hepatitis C virus–related mortality was classified using the ICD-10 code for acute viral hepatitis C (B17.1) or chronic viral hepatitis C (B18.2) as an underlying or multiple cause of death (n = 261 858). We earlier demonstrated that although HCV is underreported on death certificates, HCV prevalence estimates were not meaningfully affected because underreporting was insufficiently differential by jurisdiction. Narcotic overdose mortality, an outcome highly correlated with local acute HCV infection, was classified using the ICD-10 codes for unintentional poisoning by and exposure to narcotics and psychodysleptics (hallucinogens) (X42), unknown intention poisoning by and exposure to narcotics and psychodysleptics (hallucinogens) (Y12), unintentional poisoning by and exposure to other and unspecified drugs, medicaments, and biological substances (X44), or unknown intention poisoning by and exposure to other and unspecified drugs, medicaments, and biological substances (Y14) (n = 541 130). This algorithm is more specific to injection-related overdose deaths than others, while robust to missingness, with full considerations described in eAppendix 3, eFigure 2, and eTables 1 25-27 and 2 in the Supplement. Analysis NHANES-Eligible Population The equation in eAppendix 4 in the Supplement details our estimator for the total persons with HCV in each state in the NHANES population, depicted visually in eFigure 1 in the Supplement. Within 12 strata representing previously defined levels of sex, race/ethnicity, and birth year, we computed the standardized estimate by direct estimation from a weighted logistic regression model of NHANES, which included the terms for these strata, era (1999-2012 and 2013-2016), and poverty. To yield standardized estimates for the 12 demographic strata that accounted for poverty, we output logistic model estimates for the 2013 to 2016 era and weighted them according to the ACS poverty distribution for the 12 strata in each state. Next, we estimated the state stratum-specific likelihood of HCV-related mortality, using a logistic model of NVSS-derived mortality counts, per person-years, that approximated full- stratification with main effects for state, sex, race/ethnicity, birth cohort, era; 2-way interactions for state by each sex, race/ethnicity, birth cohort, and era; 2-way and 3-way interactions for each combination of sex, race/ethnicity, birth cohort, and era; and 4-way interaction of sex, race/ethnicity, birth cohort, and era. These state stratum-specific mortality estimates were divided by the national stratum-specific average, yielding a mortality ratio for the state stratum. This process was repeated for the narcotic overdose mortality. The 2 mortality ratios per stratum were averaged according to weights w (values described in the following section) and then multiplied by the standardization- based value to yield adjusted totals. Summing these across all 12 state strata yielded the estimated number of persons with HCV, which when divided by the ACS state population N yielded the estimated prevalence rate. Weights In the primary analysis, 3 weights w were used, with the same w applied to the 4 sex–race/ethnicity j j strata within birth cohort, representing the proportion of that birth cohort’s current infections allocated as prevalent in 1999 to 2012 (w ) vs incident during 2013 to 2016 (1 − w ). For persons born j j before 1945, we assumed no recent infections due to injection (w = 1). Based on additional analyses of biannual NHANES trends in HCV-antibody and literature estimates, we set w = 0.875 for those born from 1945 to 1969, and w = 0.378 for those born after 1969 (eAppendix 5 and eTables 3 and 4 in the Supplement). To facilitate comparisons with our earlier approach for 2010, which considered JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 4/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 only HCV mortality, we conducted a sensitivity analysis with all w = 100%. An additional sensitivity analysis considered an upper bound for incidence among persons born from 1945 to 1969, with w = 0.80 (eAppendix 5 and eTables 3 and 5 in the Supplement). Confidence Intervals Confidence intervals accounted for the joint statistical uncertainty from the 3 logistic regression models and 2 poverty imputation models. This was done with a Monte Carlo simulation that resampled parameter estimates from logit-normal distributions, using the standard errors for each, and recomputed all modeling steps (k = 10 000 runs) to produce 95% CIs. Additional Populations The National Health and Nutrition Examination Survey does not sample persons who are incarcerated, experiencing unsheltered homelessness, or residing in nursing homes. We expanded to states the earlier-described method for including these populations nationally. In brief, for incarcerated and homeless populations, HCV prevalence was estimated based on values identified in a systematic literature review of articles published from January 1, 2013, to December 31, 2017. For incarcerated populations, the mean prevalence of the literature estimates was generated using a random-effects model with study sample size as weight. For nursing home residents, the age-sex standardized NHANES prevalence was used. State-level population size estimates for these groups as of December 31, 2016, were obtained from public data sources. Additional detail on data sources and prevalence estimates appears in eTable 6 in the Supplement. For each state, within each population, we multiplied the national HCV prevalence rate by the state-specific population size to yield the number infected, which was then summed across populations to yield the state total persons with HCV among additional populations. We also conducted a secondary analysis that further adjusted by state-specific prevalence rates in the NHANES-represented population (eAppendix 6 in the Supplement). State-level estimates for populations not represented in NHANES were added to the model results within each state, allowing calculation of point estimates for prevalence in the total state population. Unlike the national analysis, we did not account for active-duty military populations in our state approach because this group consists of persons originating from multiple states residing in facilities outside of state jurisdiction, for whom data on origin states are unavailable and for whom there exists insufficient evidence of increased HCV risk. This population represents an estimated 6900 persons with HCV infection in the United States (0.3%). Results For the years 2013 to 2016, we estimated an HCV RNA prevalence of 0.84% (95% CI, 0.75%-0.96%) among adults in the noninstitutionalized US population represented in the NHANES sampling frame, corresponding to 2 035 100 (95% CI, 1 803 600-2 318 000) persons with current infection (Table 2). Accounting for populations not included in NHANES, there were 231 600 additional persons with HCV, adjusting prevalence to 0.93% (10% relative increase nationally with a state increase range of 2%-23%), with prevalence relatively increasing by more than 20% in Georgia and South Dakota and less than 5% in Rhode Island and the District of Columbia. These deviations were largely attributable to respectively higher and lower proportions of persons incarcerated in these jurisdictions (data not shown). Using the alternative method that adjusted additional populations for background state prevalence in the NHANES population, the relative proportional change from the primary method was minimal (state median [range] change, −0.5% [−7.6% to 8.4%]) (eTable 7 in the Supplement). Large variations were observed in total population HCV prevalence by state (median [range], 0.88% [0.45%-2.34%]) (Figure 1). Of 13 states in the US West census region, 10 were above this median rate, and the region contained 27.1% of infected persons, despite constituting 23.4% of the JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 5/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 Table 2. Estimated Total and Prevalence of Persons With Current HCV Infection, US States and District of Columbia, 2013 to 2016 Population Included in With Additional Populations Not Included in NHANES Sampling Frame NHANES Sampling Frame 2016 Adult HCV RNA Total Adult Population 2016, a b c b State Population, No. HCV RNA Positive (95% CI), No. % (95% CI) Positive, No. No. (%) Alabama 3 671 100 26 100 (23 100-29 600) 0.71 (0.63-0.81) 30 700 3 736 700 (0.82) Alaska 542 500 4700 (3900-5700) 0.86 (0.72-1.05) 5200 548 000 (0.95) Arizona 5 020 500 55 300 (48 000-64 100) 1.10 (0.96-1.28) 61 500 5 090 500 (1.21) Arkansas 2 215 500 19 100 (16 800-21 800) 0.86 (0.76-0.99) 21 800 2 258 700 (0.97) California 29 160 200 288 500 (253 500-331 800) 0.99 (0.87-1.14) 318 900 29 544 700 (1.08) Colorado 4 057 000 32 500 (28 000-38 400) 0.80 (0.69-0.95) 36 300 4 108 500 (0.88) Connecticut 2 771 800 16 500 (14 200-19 700) 0.60 (0.51-0.71) 18 300 2 812 700 (0.65) Delaware 719 400 5600 (4800-6500) 0.78 (0.67-0.90) 6300 730 500 (0.86) District of Columbia 537 500 12 400 (10 500-14 800) 2.32 (1.95-2.76) 12 700 542 400 (2.34) Florida 15 620 600 133 200 (117 700-152 100) 0.85 (0.75-0.97) 151 000 15 860 200 (0.95) Georgia 7 465 900 46 400 (41 300-52 300) 0.62 (0.55-0.70) 56 800 7 597 700 (0.75) Hawaii 1 094 200 5700 (4700-7000) 0.52 (0.43-0.64) 6700 1 107 400 (0.60) Idaho 1 187 300 9900 (8400-11 800) 0.84 (0.71-0.99) 11 200 1 203 300 (0.93) Illinois 9 703 700 47 700 (42 200-54 300) 0.49 (0.44-0.56) 54 900 9 842 400 (0.56) Indiana 4 915 800 35 400 (30 900-40 700) 0.72 (0.63-0.83) 40 200 5 000 100 (0.80) Iowa 2 339 900 11 100 (9 500-13 100) 0.47 (0.40-0.56) 12 600 2 379 300 (0.53) Kansas 2 137 000 12 600 (10 900-14 800) 0.59 (0.51-0.69) 14 600 2 173 600 (0.67) Kentucky 3 331 500 38 600 (33 600-44 800) 1.16 (1.01-1.34) 42 500 3 390 700 (1.25) Louisiana 3 445 000 44 900 (40 000-50 400) 1.30 (1.16-1.46) 50 000 3 518 500 (1.42) Maine 1 058 600 6500 (5400-7800) 0.61 (0.51-0.74) 7000 1 069 400 (0.65) Maryland 4 547 800 37 300 (32 700-43 100) 0.82 (0.72-0.95) 40 600 4 602 900 (0.88) Massachusetts 5 283 400 35 800 (30 600-42 500) 0.68 (0.58-0.80) 38 100 5 346 600 (0.71) Michigan 7 578 400 62 800 (55 800-70 900) 0.83 (0.74-0.94) 69 100 7 676 600 (0.90) Minnesota 4 115 000 22 300 (19 400-26 000) 0.54 (0.47-0.63) 24 300 4 159 900 (0.58) Mississippi 2 205 500 19 600 (17 500-22 200) 0.89 (0.79-1.01) 22 900 2 251 700 (1.02) Missouri 4 575 700 35 200 (31 100-40 200) 0.77 (0.68-0.88) 40 300 4 660 800 (0.86) Montana 787 100 6800 (5700-8000) 0.86 (0.73-1.02) 7400 798 100 (0.93) Nebraska 1 391 400 6900 (6000-8200) 0.50 (0.43-0.59) 7900 1 412 800 (0.56) Nevada 2 148 500 19 300 (16 800-22 400) 0.90 (0.78-1.04) 21 900 2 177 400 (1.00) New Hampshire 1 046 300 7200 (5900-8900) 0.69 (0.57-0.85) 7700 1 058 000 (0.73) New Jersey 6 810 300 43 400 (37 900-50 300) 0.64 (0.56-0.74) 47 200 6 890 900 (0.68) New Mexico 1 557 100 25 000 (21 600-29 100) 1.61 (1.39-1.87) 26 700 1 578 000 (1.69) New York 15 260 100 107 100 (94 900-121 600) 0.70 (0.62-0.80) 116 000 15 448 400 (0.75) North Carolina 7 545 400 60 200 (53 600-68 100) 0.80 (0.71-0.90) 66 400 7 640 100 (0.87) North Dakota 559 100 2200 (1800-2800) 0.39 (0.32-0.50) 2600 568 300 (0.45) Ohio 8 787 100 81 500 (71 800-93 200) 0.93 (0.82-1.06) 89 600 8 938 500 (1.00) Oklahoma 2 862 800 48 900 (42 700-56 500) 1.71 (1.49-1.97) 53 300 2 922 700 (1.82) Oregon 3 086 200 45 700 (39 400-53 700) 1.48 (1.28-1.74) 48 700 3 120 900 (1.56) Pennsylvania 9 888 700 84 500 (74 300-97 000) 0.86 (0.75-0.98) 93 900 10 055 600 (0.93) Rhode Island 829 900 9600 (8300-11 400) 1.16 (1.00-1.37) 10 000 841 300 (1.19) South Carolina 3 689 100 31 900 (28 400-36 100) 0.87 (0.77-0.98) 35 600 3 740 300 (0.95) South Dakota 628 400 3000 (2500-3700) 0.48 (0.39-0.59) 3700 641 000 (0.57) Tennessee 4 972 200 63 500 (56 200-72 100) 1.28 (1.13-1.45) 69 100 5 053 700 (1.37) Texas 19 455 200 178 000 (157 500-203 100) 0.91 (0.81-1.04) 202 500 19 777 300 (1.02) Utah 2 024 600 11 000 (9300-13 100) 0.54 (0.46-0.65) 12 300 2 042 200 (0.60) Vermont 499 100 3500 (2900-4200) 0.70 (0.58-0.85) 3700 503 800 (0.73) Virginia 6 348 500 33 500 (29 400-38 500) 0.53 (0.46-0.61) 39 900 6 436 400 (0.62) Washington 5 412 700 50 000 (43 100-58 900) 0.92 (0.80-1.09) 54 200 5 468 900 (0.99) West Virginia 1 439 300 19 500 (16 700-23 000) 1.35 (1.16-1.60) 20 600 1 459 400 (1.41) (continued) JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 6/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 Table 2. Estimated Total and Prevalence of Persons With Current HCV Infection, US States and District of Columbia, 2013 to 2016 (continued) Population Included in With Additional Populations Not Included in NHANES Sampling Frame NHANES Sampling Frame 2016 Adult HCV RNA Total Adult Population 2016, a b c b State Population, No. HCV RNA Positive (95% CI), No. % (95% CI) Positive, No. No. (%) Wisconsin 4 384 900 24 000 (21 000-27 700) 0.55 (0.48-0.63) 27 900 4 449 600 (0.63) Wyoming 437 600 3200 (2600-3900) 0.73 (0.60-0.90) 3700 444 300 (0.82) d,e f Total 241 152 600 2 035 100 (1 803 600-2 318 000) 0.84 (0.75-0.96) 2 266 700 244 681 600 (0.93) Abbreviations: HCV, hepatitis C virus; NHANES, National Health and Nutrition The NHANES prevalence percentage estimates are based on results from 2013 to 2016 Examination Survey. NHANES. Population size includes noninstitutionalized adults eligible for NHANES from the 2012 to 2016 American Community Survey. Population sizes are estimated as of December 2016 based on American Community Survey 5-year estimates from 2012 to 2016 and include noninstitutionalized adults Values may not sum to total due to rounding. eligible for NHANES. This estimate includes 1 288 600 active-duty military personnel e Results are based on a regression model that incorporates data for the period 1999 to ineligible for NHANES, which cannot be removed at the state level because population 2016 and generates estimates via simulations. Accordingly, these results do not sizes are unavailable by home state of personnel. Therefore, this assumes a mean 11 precisely sum to previous national totals for the 2013 to 2016 period. prevalence value for this group, adding 5000 infections nationally. Does not sum to previous 2013 to 2016 US total due to the exclusion of persons Number of infected persons is calculated by multiplying the prevalence percentage incarcerated in federal prisons who are not assigned to state-specific populations. estimate by the adult population size before rounding for presentation. US population. Three of the 10 states with the highest rates are members of the US Appalachian Regional Commission (Kentucky, Tennessee, and West Virginia) and together constituted 5.8% of persons with HCV and 4.0% of the population. Nine states (California [318 900], Texas [202 500], Florida [151 000], New York [116 000], Pennsylvania [93 900], Ohio [89 600], Michigan [69 100], Tennessee [69 100], and North Carolina [66 400] each contained more than 65 000 persons with HCV and together constituted 51.9% of all persons with HCV nationally. Of these 9 states, 5 are in the Appalachian region (New York, North Carolina, Ohio, Pennsylvania, and Tennessee). Tennessee and Arizona were the only states represented in the top 10 for both HCV rates and persons with HCV. Figure 2 displays the impact of the revised methodology for the NHANES population that incorporates the distribution of narcotic overdose mortality, relative to considering HCV mortality only. States experiencing higher rates of overdose mortality saw relative increases in estimated HCV prevalence, whereas those with lower rates saw declines in prevalence. A sensitivity analysis that considered maximally increased weighting of overdose mortality (and incidence) in the 1945 to 1969 birth cohort yielded small proportional changes from the default weighting (median [range], 0.3% [−4.8% to 5.7%]) (eTable 5 in the Supplement). Discussion Using newly available data for 2013 to 2016 and methods that account for changes in HCV epidemiology, we observed large variation in HCV prevalence and burden across the United States. There was a particularly high prevalence in the West and Appalachia. These findings were consistent across analyses that considered alternative incidence rates for the highest-prevalence 1945 to 1969 cohort and alternative approaches for populations not included in the NHANES sampling frame. The state patterns for areas of high burden, particularly the Appalachian region, closely echo recent reports of direct, local (but incomplete) measures of HCV burden using acute HCV surveillance in the National Notifiable Diseases Surveillance System and maternal HCV status on birth certificates 29,30 in NVSS. In Appalachia, it is likely that HCV prevalence reflects recent increases in injection drug use, high densities of counties vulnerable to HCV and HIV infection outbreaks, large outbreaks of 7,14,24,31 these infections among persons who inject drugs (PWID), and elevated reports of acute HCV. These estimates help to quantify the need for investments in efficacious direct and indirect services for the prevention of HCV acquisition and transmission. This includes syringe services programs, which are associated with decreased HCV spread, especially when combined with linkage to medication-assisted substance use treatment. Although increasing, the number of syringe services programs remains low in 2018 in many states, with programs often geographically dispersed JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 7/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 within states. Direct medical services such as HCV testing and curative treatments remain cornerstones for extending life and averting transmission. Furthermore, testing and treatment are 35-37 cost-effective, with earlier treatment possibly yielding greater cost savings. Despite availability of these services, some policies restrict their use. A recent analysis found substantial variation in the comprehensiveness of laws supporting access to clean injection equipment and sobriety requirement–based restrictions of Medicaid fee-for-service HCV treatment. Some of the highest-incidence states had the lowest levels of prevention and treatment access overall, with 47 states lacking comprehensive laws and Medicaid policies for effective prevention and treatment of HCV among PWID. Additionally, restrictions based on fibrosis score remain prevalent, and a 45-state analysis of 2016 to 2017 pharmacy data found treatment had been denied for many patients with Medicaid (34.5%) and private insurance (52.4%). Finally, indirect Figure 1. Estimated Hepatitis C Virus (HCV) RNA Prevalence and Total Persons With HCV RNA, Indicating Current Infection, United States and District of Columbia, 2013 to 2016 A HCV RNA prevalence 2013-2016 HCV RNA prevalence (per 100 population) 1.25-2.34 1.00-1.25 0.85-1.00 0.65-0.85 0.45-0.65 B Total persons with HCV RNA Total persons with HCV RNA 75 000-318 900 50 000-75 000 Prevalence of HCV (A) and total number of persons 25 000-50 000 with HCV (B) in the full US adult population defined by 10 000-25 000 noninstitutionalized adults included in the National 2600-10 000 Health and Nutrition Examination Survey sampling frame and additional populations not in the sampling frame (those incarcerated, in nursing homes, and experiencing homelessness). JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 8/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 HCV prevention is achievable by addressing opioid use disorder using efficacious individual approaches, like medication-assisted treatment, and numerous state- and systems-level 39,40 policies. Even with effective tools for addressing the HCV epidemic, substantial challenges remain in their application to rural PWID. The evidence base for understanding the unique HCV risk, prevention, and care context of these areas remains limited. Others have prioritized areas for 42,43 further research and recent federal commitments are promising. Figure 2. Hepatitis C Virus (HCV) RNA Prevalence, Accounting for the Distribution of Both HCV and Narcotic Overdose Deaths or HCV Deaths Only, by US State and Census Region, 2013 to 2016 Illinois ICD-10 codes for HCV Indiana and narcotic overdose Iowa Kansas ICD-10 codes for HCV only Michigan Minnesota Missouri Nebraska North Dakota Ohio South Dakota Wisconsin 0 0.5 1.0 1.5 2.0 2.5 3.0 Connecticut Maine Massachusetts New Hampshire New Jersey New York Pennsylvania Rhode Island Vermont 0 0.5 1.0 1.5 2.0 2.5 3.0 Alabama Arkansas Delaware District of Columbia Florida Georgia Kentucky Louisiana Maryland Mississippi North Carolina Oklahoma South Carolina Tennessee Texas Virginia West Virginia 0 0.5 1.0 1.5 2.0 2.5 3.0 Alaska Arizona California Colorado Hawaii Idaho Montana Nevada New Mexico Oregon Utah Washington Wyoming 0 0.5 1.0 1.5 2.0 2.5 3.0 Estimated HCV RNA Prevalence (per 100 Population) Prevalence in the US adult population defined by noninstitutionalized adults included in represent 95% confidence intervals. ICD-10 indicates International Classification of the National Health and Nutrition Examination Survey sampling frame. Error bars Diseases, Tenth Revision. JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 9/14 State and Region West South Northeast Midwest JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 Limitations Key strengths of our approach include anchoring to robust and comprehensive national data systems, use of highly specific markers of local HCV infection that reflect the bimodal epidemic pattern, and a near-exact standardization approach, yet several limitations remain. It is possible that HCV increases associated with PWID are not well represented in national NHANES estimates. However, earlier analyses demonstrated robustness for this subgroup, lifetime exposure among those born in 1970 or later (per eTable 4 in the Supplement) indicate dramatic increases consistent with acute surveillance trends, and estimated national totals are consistent with projections from a population-based dynamic model. A recently published analysis of laboratory databases reports a number of persons diagnosed that exceeds previous national prevalence estimates, likely due to incomplete deduplication of infections across deidentified databases. Mortality caused by HCV may be an imperfect spatial marker given underreporting, although we previously demonstrated that 13,18 the method is robust to this. Likewise, limitations may exist with the use of narcotic overdoses to represent recent infection. First, further specificity for likely injected narcotic, per toxicology codes, remains challenging because of substantial data toxicology code missingness. Additionally, to the extent that more lethal narcotics such as fentanyl are more prevalent in certain jurisdictions, this may bias estimates upward. Further refinements of toxicology code data are required to account for this. Second, local variations may exist in the relationship between overdose deaths and HCV-risky injection. State-specific variations in laws and funding of interventions that avert overdose deaths, like naloxone, and those that reduce HCV risks associated with injection while influencing 33,39,45 mortality less, like syringe services programs, may bias estimates in opposing directions. Estimates for populations not included in NHANES are based on systematic approaches, but still may not be representative. Explorations of the impact of variations in these estimates found this contributed little overall variation. Ultimately, one of the best ways to overcome these limitations, particularly as jurisdictions wish to monitor progress in shorter time frames, is to strengthen core surveillance registries through standardized reporting definitions, active case finding, and rigorous 46-48 linkages to understand mortality, treatment, and migration. Third, our state estimate sum is slightly lower than the recent updated national estimate, owing to 2 methodological differences: use of a weighted regression model that pools a broader time period and noninclusion of active-duty military persons. Conclusions Prevalence of HCV infection varies widely in the United States. Highest rates are frequently in states deeply affected by the opioid crisis or with a history of increased levels of injection drug use and chronic HCV infection, particularly in the West. Progress toward hepatitis C elimination is 34,47,49,50 theoretically possible with the right investments in prevention, diagnosis, and cure. The urgency for action and the resources necessary will vary by jurisdiction. ARTICLE INFORMATION Accepted for Publication: November 7, 2018. Published: December 21, 2018. doi:10.1001/jamanetworkopen.2018.6371 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Rosenberg ES et al. JAMA Network Open. Corresponding Author: Eli S. Rosenberg, PhD, Department of Epidemiology and Biostatistics, University at Albany School of Public Health, State University of New York, Rensselaer, NY 12144 (erosenberg2@albany.edu). Author Affiliations: Department of Epidemiology and Biostatistics, University at Albany School of Public Health, State University of New York, Rensselaer (Rosenberg, Rosenthal); Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia (Hall, Sullivan); Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 10/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 (Barker, Hofmeister, Ryerson); Office of the Director, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia (Dietz, Mermin). Author Contributions: Dr Rosenberg had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Rosenberg, Rosenthal, Hall, Barker, Sullivan, Dietz, Mermin, Ryerson. Acquisition, analysis, or interpretation of data: Rosenberg, Rosenthal, Hall, Barker, Hofmeister, Mermin, Ryerson. Drafting of the manuscript: Rosenberg, Rosenthal, Hall, Barker, Ryerson. Critical revision of the manuscript for important intellectual content: Rosenberg, Hall, Barker, Hofmeister, Sullivan, Dietz, Mermin, Ryerson. Statistical analysis: Rosenberg, Rosenthal, Hall. Obtained funding: Rosenberg, Sullivan, Mermin. Administrative, technical, or material support: Rosenberg, Barker, Sullivan, Ryerson. Supervision: Rosenberg, Dietz, Ryerson. Conflict of Interest Disclosures: Dr Rosenberg reported personal fees from Cengage Learning and from Statistics.com outside the submitted work. Mr Hall reported grants from the Center for AIDS Research at Emory University during the conduct of the study. Ms Barker reported employment with the Centers for Disease Control and Prevention (CDC) Division of Viral Hepatitis, which funds state and local governments and community health centers to prevent and control hepatitis C virus infections, and strengthen surveillance for viral hepatitis B and C. Dr Sullivan reported grants from the CDC during the conduct of the study and personal fees from the CDC, grants and personal fees from the National Institutes of Health, and grants from Gilead Sciences outside the submitted work. No other disclosures were reported. Funding/Support: This study was supported by the CDC National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention Epidemic and Economic Modeling Agreement (grant U38 PS004646) (Dr Rosenberg, Ms Rosenthal, and Mr Hall) and the Center for AIDS Research at Emory University (grant P30AI050409). Role of the Funder/Sponsor: As a cooperative agreement, CDC scientific coauthors were involved in the design and conduct of the study; analysis and interpretation of the data; preparation, review, and approval of the manuscript; and the decision to submit the manuscript for publication. Collection and management of the original source National Health and Nutrition Examination Survey and National Vital Statistics System data were conducted prior to this study by CDC staff not affiliated with this project. The Center for AIDS Research at Emory University had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the CDC. Additional Contributions: Meredith Barranco, BS, and Bahareh Ansari, MBA, both of the University at Albany, Rensselaer, New York, assisted with literature review. Jane Kelly, MD, and Greg Felzien, MD, of the Emory Coalition for Applied Modeling for Prevention Public Health Advisory Group reviewed the manuscript. Colleagues at the Illinois Department of Public Health (Mai Tuyet Pho, MD, Tristan Jones, BS, Judy Kauerauf, MPH, and Megan Patel, MPH), Oklahoma State Department of Health (Terrainia Harris, MPH, Sally Bouse, MPH, and Kristen Eberly, MPH), Tennessee Department of Health (Carolyn Wester, MD, and Lindsey Sizemore, MPH), and Washington State Department of Health (Jon Stockton, MHA, Emalie Huriaux, MPH, and Tessa Fairfortune, MPH) provided input during the development of the methods. None of these individuals were compensated for their contributions. REFERENCES 1. Ditah I, Ditah F, Devaki P, et al. 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Centers for Disease Control and Prevention Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention. Progress toward viral hepatitis elimination in the United States, 2017. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, Office of Infectious Diseases, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention; 2017. https://www.cdc. gov/hepatitis/policy/PDFs/NationalProgressReport.pdf. Accessed January 1, 2018. 50. Dan C. New York State coalition hepatitis C consensus statement leads to governor’s action. https://www.hhs. gov/hepatitis/blog/2018/06/21/new-york-commits-to-eliminate-hepatitis-c.html. Published June 21, 2018. Accessed June 22, 2018. SUPPLEMENT. eAppendix 1. NHANES Methodological Details eAppendix 2. Imputation for Missing Poverty Data in the National Health and Nutrition Examination Survey (NHANES) and the American Community Survey (ACS) eAppendix 3. Drug Overdose Mortality eAppendix 4. Equation for Estimator of the Total Persons With HCV Infection in Each US State eAppendix 5. Description of Analytic Weight Derivation eAppendix 6. Further Descriptions of Analyses for Additional Populations Not in NHANES Sampling Frame eFigure 1. Conceptual Overview of Method for Estimating Hepatitis C Virus (HCV) RNA Prevalence in US States eFigure 2. Schematic for Levels of Specificity in Coding Injection-Related Overdose Deaths in the National Vital Statistics System eTable 1. National Distribution of Drug Deaths by Intentionality and Narcotic Involvement, National Vital Statistics System, 2013-2016 eTable 2. State-Level Total Drug Deaths and Narcotic Deaths by Intentionality, National Vital Statistics System 2013-2016 eTable 3. Values of Three Analytic Weighting Schemas eTable 4. Estimated Prevalence of HCV Antibody, NHANES 1999-2012 and 2013-2016, by Birth Cohort eTable 5. Sensitivity Analysis of Results Under Two Assumptions for Cumulative Mortality for 1945-1969 Birth Cohort, Among Population Included in NHANES Sampling Frame eTable 6. Summary of Additional Population Analytic Considerations eTable 7. Comparison Between Primary and Alternative Approach to Additional Population Estimates eReferences JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 14/14 Supplementary Online Content 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 eAppendix 1. NHANES Methodological Details eAppendix 2. Imputation for Missing Poverty Data in the National Health and Nutrition Examination Survey (NHANES) and the American Community Survey (ACS) eAppendix 3. Drug Overdose Mortality eAppendix 4. Equation for Estimator of the Total Persons With HCV Infection in Each US State eAppendix 5. Description of Analytic Weight Derivation eAppendix 6. Further Descriptions of Analyses for Additional Populations Not in NHANES Sampling Frame eFigure 1. Conceptual Overview of Method for Estimating Hepatitis C Virus (HCV) RNA Prevalence in US States eFigure 2. Schematic for Levels of Specificity in Coding Injection-Related Overdose Deaths in the National Vital Statistics System eTable 1. National Distribution of Drug Deaths by Intentionality and Narcotic Involvement, National Vital Statistics System, 2013-2016 eTable 2. State-Level Total Drug Deaths and Narcotic Deaths by Intentionality, National Vital Statistics System 2013-2016 eTable 3. Values of Three Analytic Weighting Schemas eTable 4. Estimated Prevalence of HCV Antibody, NHANES 1999-2012 and 2013-2016, by Birth Cohort eTable 5. Sensitivity Analysis of Results Under Two Assumptions for Cumulative Mortality for 1945-1969 Birth Cohort, Among Population Included in NHANES Sampling Frame eTable 6. Summary of Additional Population Analytic Considerations eTable 7. Comparison Between Primary and Alternative Approach to Additional Population Estimates eReferences © 2018 Rosenberg ES et al. JAMA Network Open. This supplementary material has been provided by the authors to give readers additional information about their work. © 2018 Rosenberg ES et al. JAMA Network Open. eAppendix 1: NHANES methodological details This analysis utilized NHANES data from 1999-2016. Response rates for NHANES were: 76% in 1999-2000, 80% in 2001-2002, 76% in 2003-2004, 77.36% in 2005-2006, 75.4% in 2007-2008, 77.3% in 2009-2010, 69.5% in 2011- 2012, 68.5% in 2013-2014, and 58.7% in 2015-2016. Written consent was obtained for participants aged 12 and older and parents or guardians of participants younger than 18, and written assent was obtained for youth 7 to 11 years old. NHANES protocol was approved by the National Center for Health Statistics Research Ethics Review Board. eAppendix 2: Imputation for missing poverty data in the National Health and Nutrition Examination Survey (NHANES) and the American Community Survey (ACS) NHANES collects data on household and family income as part of the demographic questionnaire administered to all respondents. NHANES uses annual poverty guidelines that vary by state and family size from the Department of Health and Human Services to calculate a ratio of family income to the poverty level. Of all NHANES respondents from 1999-2016 (N=75,974), 8.30% (n=6,303) were missing data for poverty ratio. We used a multiple imputation regression process to impute income-to-poverty ratio for all observations with missing values. First, we categorized all values of income-to-poverty ratio into: below the poverty level, 1.0-1.9 times the poverty level and 2 times the poverty level. Second, we used polytomous logistic regression to model the predicted probability of being within each income-to-poverty ratio categorization using the same race, sex and birth year categories from the primary analysis as predictor variables. This resulted in income-to-poverty ratio probability distributions specific to the covariate pattern of each individual observation. For observations that were missing income-to-poverty ratio values, we randomly drew a poverty-to-income value (from the individual defined distribution) to be imputed. Each run of the Monte Carlo simulation incorporated a new random draw from each individual income-to-poverty ratio distribution. The final analytic dataset used in the primary analysis included 47,387 respondents 18 years of age or older with non-missing HCV RNA test results. In the final analytic dataset, 3,931 (8.30%) or respondents had imputed income-to-poverty ratio values. Data used to generate ACS population estimates also contain comparable income-to-poverty level ratios for individual respondents. Of the 8,369,036 adult respondents who are not on active military duty in the 2012-2016 ACS PUMS dataset (the denominators for national NHANES analyses), 1.98% (n=237,600) were missing values for income-to-poverty level ratio. To impute these values, we followed an analogous process as the NHANES imputation model. We fit a polytomous regression model to predict the income-to-poverty level ratio distribution using state, race, sex and birth year as predictor variables. Each run of the Monte Carlo simulation randomly selected an income-to-poverty level value from the individual distributions for observations with missing values. eAppendix 3: Drug overdose mortality Injection drug use is the most commonly reported risk factor for acute HCV. Death certificate records submitted to the National Vital Statistics System (NVSS) contain useful data on drug overdose deaths that may specifically inform HCV-related risks. However, injection-specific drug use death, which is most ideal to signal HCV infection due to injection drug use, is neither reported as an ICD-10 code on death certificates or consistently in the open-text portions of certificates. We depict in eFigure 2 a conceptual model of the levels of detail available in mortality data in order to reach the underlying ideal construct of injection-related deaths. Not all of these levels are readily available from the NVSS Multiple Cause of Death microdata files and we present our analytic case-definition as the optimal combination of specificity and sensitivity, given this challenge. Level 1: Overdose deaths by state Level 1 depicts the most basic information regarding deaths with underlying cause of death drug poisoning codes available from NVSS mortality data. Drug poisoning ICD-10 codes are classified into four categories of intentionality [unintentional (X40-44), suicide (X60-64), homicide (X65), and undetermined intent (Y10-Y14)]. 5-7 Many publications that describe the opioid epidemic focus on all drug poisonings of all intentionalities. The case definition described in the Methods focuses on overdoses of unintentional and undetermined intent, which have been 8,9 the focus of additional recent assessments of opioid mortality. Overdoses of undetermined intent are included to increase the sensitivity of this measure as it would include potentially accidental or non-accidental overdoses for © 2018 Rosenberg ES et al. JAMA Network Open. which there was not enough information to record these otherwise. There were some differences in the proportion of overdoses of undetermined intent by state, which are shown in eTable 1. We explored the potential for some drug deaths coded as suicides to be accidental overdoses. The proportion of narcotic and unknown drug deaths coded as suicides varies by state (eTable 2). Since these vary only modestly, we did not include suicides in the primary analysis. Additionally, drug intoxication does not result in a majority of suicide deaths, relative to other (more violent) methods. It is actually possible that our inclusion of deaths of 10,11 undetermined intent includes misclassified suicides that did not have enough evidence to be reported as suicides. Level 2: Overdose deaths by drug class by state Level 2 depicts a bit more detail that is available in NVSS mortality data with regards to drug overdose deaths. Within each category of intentionality, ICD-10 codes are separated by drug class. These classes include: poisoning by and exposure to non-opioid analgesics, antipyretics, and antirheumatics; poisoning by and exposure to antiepileptic, sedative-hypnotic, antiparkinsonism and psychotropic drugs, not elsewhere classified; poisoning by and exposure to narcotics and psychodysleptics (hallucinogens), not elsewhere classified; poisoning by and exposure to other drugs acting on the autonomic nervous system; and poisoning by and exposure to other and unspecified drugs, medicaments, and biological substances. For this analysis, our definition was restricted to deaths due to poisoning by narcotics and psychodysleptics (X42, Y12) and exposure to other and unspecified drugs (X44, Y14). Drug overdoses due to narcotics were included because this drug class includes cannabis, cocaine, codeine, heroin, methadone, morphine, and opium. While not all of these drugs are typically administered via injection, this class provides a more specific definition than merely using all drugs. 12-14 Death investigation and drug toxicology processes vary by state. In order to account for this variation, and to 8,15 provide a more sensitive definition, we included overdoses due to other and unspecified drugs. Level 3: Overdose deaths by specific drugs by state The third level describes specific drugs that are more likely to be used via injection than other drugs. NVSS mortality data includes specific drug toxicology codes (T codes) for heroin, natural and semisynthetic opioids (morphine, codeine, hydrocodone, and oxycodone), methadone, synthetic opioids excluding methadone (fentanyl, fentanyl analogs, and tramadol), cocaine, and psychostimulants with abuse potential (methamphetamine, amphetamine, Ritalin, caffeine, and ecstasy). While inclusion of T codes for injection-related drugs such as heroin and synthetic opioids excluding methadone would be an improved signal of injection-related overdose, the 12-14,16,17 toxicology completion regarding specific drug codes on death certificates varies greatly by state and by year. A second issue with using specific drug codes for this analysis is that these are not mutually exclusive. Many drugs, particularly heroin and fentanyl, are found together in toxicology and subsequent death certificates. This becomes an issue since some drugs (i.e., fentanyl) have higher fatality rates from injection than others, and some drugs are 19,20 more frequently used via injection than other routes of administration. This is particularly important for assessing the geographic distribution of overdose deaths since the distribution of fentanyl varies, in part due to the relative ease of incorporating fentanyl into the white powder heroin supply east of the Mississippi River, compared to black tar heroin. These have not been incorporated into the present analysis due to the variation of completion by state, but this is a critical issue that should be explored in future research, in order to reduce biases introduced by non-specificity for injected drugs and by spatial heterogeneity in highly-lethal substances such as fentanyl. Level 4: Overdose deaths by specific drugs and injection status by state Finally, for the fourth level, the ideal measurement of injection-related overdose is overdose by drug by injection. The most relevant literature-based estimate of opioid overdose deaths attributable to injection is not generalizable to all states. This is the ideal measure to use for those born after 1970 for the HCV work. However, due to the lack of literature as well as the above-mentioned limitations in the specific drugs, we cannot achieve this level of detail. © 2018 Rosenberg ES et al. JAMA Network Open. eAppendix 4: Equation for Estimator of the Total Persons With HCV Infection in Each US State The above equation details our estimator for the total persons with HCV in each state ( ) in the NHANES population. Within 12 strata representing above-defined levels of sex, race/ethnicity, and birth year, we computed the standardization estimate , where is the 2016 ACS population in the state’s stratum (“state-stratum”) and the national 2013-2016 HCV RNA prevalence in stratum by direct estimation from a weighted logistic regression model of NHANES, which included terms for these strata, era (1999-2012, 2013-2016), and poverty. To yield standardized estimates for the 12 demographic strata that accounted for poverty, we weighted logistic model estimates according to the ACS poverty distribution for the 12 strata in each state. Next, we estimated the state-stratum-specific likelihood of HCV-related mortality ( ), using a logistic model of NVSS-derived mortality counts, per person-years ( ), that approximated full-stratification with main effects for state, sex, race/ethnicity, birth cohort, era; two-way interactions for state by each sex/ethnicity, race, birth cohort, and era; two-way and three-way interactions for each combination of sex, race/ethnicity, birth cohort, and era; and four-way interaction of sex, race/ethnicity, birth cohort, and era. These were divided by the national stratum-specific average, yielding a mortality ratio for the state-stratum. This process was repeated for the narcotic overdose mortality ( ). The two mortality ratios per stratum were averaged according to weights (values described in the main manuscript text and eAppendix 5) and then multiplied by the standardization-based value to yield adjusted totals . Summing these across all 12 state-strata yielded , which when divided by the ACS state population yielded the estimated prevalence rate. eAppendix 5: Description of analytic weight derivation To represent the spatial distribution of both older prevalent HCV infections (those existing during 1999-2012) and newer HCV infections (during 2013-2016) resulting from injection drug use, we separately modeled mortality rates from HCV infection and narcotic overdose. Mortality rates were used to calculate state-level mortality ratios for both HCV infection and narcotic overdose. Within each age group (defined by birth year), we calculated a single weighted state-level mortality ratio. For the first weighting scenario, we only used state-level mortality ratios from the HCV death model in order to compare to our previous method (eTable 3, results depicted in Figure 2). © 2018 Rosenberg ES et al. JAMA Network Open. For the second scenario, we used available data and expert knowledge of HCV epidemiology to derive the weights. First, we assumed all HCV infections among persons born <1945 are a result of older exposure and used a weight of 1.0 for that age group (w ). For the other two age groups, we used trends of HCV antibody prevalence in NHANES data to make inferences and assumptions about HCV incidence from 2013-2016. For persons born 1970, we estimated there were 411,449 persons with a history of infection (HCV antibody) prior to 2013 and 1,253,938 after 2013 (eTable 4). We assumed ~0% mortality for this age group during this time frame, suggesting 37.8% of persons with HCV exposure in this age group acquired HCV prior to 2013 and 62.2% thereafter. This is similar to other observations around 70%. Therefore, we used a weight of 0.378 for the HCV death state effect ratio for persons born 1970 (w ). The estimated number of persons born from 1945-1969 with HCV antibody did not meaningfully change between 1999-2012 (n=3,092,027) and 2013-2016 (n=2,848,019), which suggests that total number of incident infections is approximately equal to the total number of persons in this cohort who died between the midpoint of 1999-2012 to the midpoint of 2013-2016. Thus, estimating the cumulative death rate of persons born 1945-1969 with HCV antibody would provide an estimate of incidence to inform our weighting. Using a life-table approach incorporating all-cause mortality NVSS data and ACS population sizes for those born 1945-1969, we estimated the all-cause likelihood of death in the general population to be 0.047 between 1/1/2007 and 12/31/2014. We assumed persons in this age group who spontaneously clear their HCV infection or do not have a positive HCV RNA diagnosis experience the same death rate as the general population. From 2013-2016 NHANES data, 53.6% of persons born between 1945-1969 with HCV antibodies had a positive HCV RNA test and 62.0% of HCV RNA+ individuals had an HCV diagnosis. A previous paper reported that persons born between 1945-1964 with an HCV RNA diagnosis had an estimated mortality of 0.282. From this, we used the following calculation to estimate the mortality rate for persons born 1945-1969 with HCV antibody: Based on the assumption of a stable number of HCV infections within this age group, we used a weight of 0.875 for persons born 1945-1969 (w ). Persons with HCV antibody, but who are not currently infected, and those who are currently infected but are not diagnosed (and presumably relatively asymptomatic), may experience death rates higher than the background mortality rates, although such data are unavailable. Recognizing that this age group (persons born 1945-1969) has the highest HCV burden and may disproportionally impact prevalence results, we conducted a sensitivity analysis examining a third scenario that considered an overall mortality rate of 20% for person with HCV antibody (w = 0.80) (eTable 5). eAppendix 6: Further descriptions of analyses for additional populations not in NHANES sampling frame This analysis used the results of a literature review from Hofmeister et al., which used articles that reported HCV prevalence from 1/1/2013-12/31/2017. Search terms used for the incarcerated population were (“hepatitis C” or “HCV”) and (“prison” or “jail” or “correctional”) and for the homeless population were (“hepatitis C” or “HCV”) and (“homeless” or “homeless persons” or “housing unstable” or “housing insecure”). Details on motivation for additional populations and prevalence and population size sources used in Hofmeister et al. are described in eTable Alternative Approach to Additional Population Estimates The alternative approach to estimating state-level HCV RNA prevalence among additional populations involved two steps. First, we generated a national prevalence ratio for each population component (incarcerated, unsheltered homeless, and nursing home residents) by taking the national HCV prevalence in the population component divided by the national HCV prevalence in NHANES. Then, we multiplied this national prevalence ratio by the each state’s HCV prevalence in the NHANES population and each state’s population size of each population component. This provided an estimate of HCV infections among additional populations that reflects each state’s underlying HCV © 2018 Rosenberg ES et al. JAMA Network Open. prevalence rather than the national HCV estimate. This assumes that the state epidemics are echoed in these additional populations. Full results including both the primary approach for the additional population estimate and the alternative are shown in eTable 7. There was a median difference in prevalence between methods 1 and 2 of 0.004% (relative multiplicative change of -0.5%). © 2018 Rosenberg ES et al. JAMA Network Open. eFigure 1: Conceptual overview of method for estimating Hepatitis C virus (HCV) RNA prevalence in US states We used a multistep, statistical approach that first generated estimates for each state using National Health and Nutrition Examination Survey (NHANES) national prevalence in sex, race, birth cohort, and poverty strata (1A). To represent the spatial distribution of older and recent infections respectively, we separately modeled mortality rates from HCV infection and narcotic overdose in the National Vital Statistics System (NVSS), yielding stratified state-level mortality ratios (1B). We weighted these ratios according to birth cohort-specific trends in HCV exposure history (1C) and used them to adjust initial NHANES-based estimates (1D). Finally, we estimated additional infections among populations not included in NHANES’ sampling frame, by applying literature- based estimates of prevalence in these groups to state-specific population estimates (1E). eFigure 2: Schematic for levels of specificity in coding injection-related overdose deaths in the National Vital Statistics System Level 4 (Ideal) • Overdose Level 3 deaths by specific drugs • Overdose Level 2 and injection deaths by by state specific drugs • Overdose Level 1 by state deaths by drug class by • Overdose state deaths by state © 2018 Rosenberg ES et al. JAMA Network Open. eTable 1. National distribution of drug deaths by intentionality and narcotic involvement, National Vital Statistics System, 2013-2016 Intentionality of death and drug class n (%) All-intention deaths, all drug classes 221,710 (100%) Homicide 497 (0.22%) Unintentional, suicide, undetermined deaths 221,213 (99.8%) o Narcotics and unspecified drugs 195,134 (88%) Suicide deaths 17,017 (9%) Narcotics only 2,869 (17%) Unspecified drugs only 14162 (83%) Unintentional and undetermined cause deaths 178,122 (91%) Unintentional deaths 166,822 (85%) Narcotics only 82,288 (49%) Unspecified drugs only 84,609 (51%) Undetermined deaths 11,300 (6%) Narcotics only 5,449 (48%) Unspecified drugs only 5,857 (52%) Drug intentions are defined by ICD-10 codes: Unintentional: X40-X44 Intentional self-poisoning (suicide): X60-X64 Homicide includes: X85 Undetermined intent: Y10-Y14 Drug classes are defined by ICD-10 codes: Poisoning by and exposure to nonopioid analgesics, antipyretics, and antirheumatics: X40, X60, Y10 Poisoning by and exposure to antiepileptic, sedative-hypnotic, antiparkinsonism and psychotropic drugs, not elsewhere classified: X41, X61, Y11 Poisoning by and exposure to narcotics and psychodysleptics (hallucinogens), not elsewhere classified: X42, X62, Y12 Poisoning by and exposure to other drugs acting on the autonomic nervous system: X43, X63, Y13 Poisoning by and exposure to other and unspecified drugs, medicaments, and biological substances: X44, X64, Y14 b 5-7 This is how many publications often describe drug overdoses This is the definition used in this analysis. © 2018 Rosenberg ES et al. JAMA Network Open. eTable 2. State-level total drug deaths and narcotic deaths by intentionality, National Vital Statistics System 2013-2016 Deaths from narcotic or unspecified drugs Deaths from all drugs Unintentional, (unintentional, suicide, and suicide, and undetermined Unintentional Suicide Undetermined a a a undetermined intent) intent only only intent only Total 221,213 195,134 166,822 (85%) 17,017 (9%) 11,300 (6%) State Alabama 2,958 2,767 2,474 (89%) 169 (6%) 124 (4%) Alaska 527 450 378 (84%) 49 (11%) 23 (5%) Arizona 5,531 4,390 3,470 (79%) 536 (12%) 384 (9%) Arkansas 1,610 1,264 926 (73%) 178 (14%) 160 (13%) California 21,077 15,322 12,975 (85%) 2,000 (13%) 348 (2%) Colorado 3,823 3,229 2,564 (79%) 541 (17%) 125 (4%) Connecticut 3,027 2,865 2,627 (92%) 200 (7%) 38 (1%) Delaware 860 811 716 (88%) 53 (7%) 42 (5%) District of Columbia 613 574 497 (87%) 24 (4%) 53 (9%) Florida 13,777 12,641 11,123 (88%) 1,387 (11%) 131 (1%) Georgia 5,227 4,517 4,047 (90%) 374 (8%) 97 (2%) Hawaii 754 422 308 (73%) 69 (16%) 45 (11%) Idaho 945 748 534 (71%) 133 (18%) 81 (11%) Illinois 8,059 7,448 6,604 (89%) 571 (8%) 273 (4%) Indiana 5,428 5,052 4,241 (84%) 384 (8%) 427 (8%) Iowa 1,341 1,043 813 (78%) 173 (17%) 57 (5%) Kansas 1,506 1,202 947 (79%) 187 (16%) 68 (6%) Kentucky 5,073 4,769 4,325 (91%) 198 (4%) 246 (5%) Louisiana 3,789 3,533 3,114 (88%) 182 (5%) 237 (7%) Maine 1,052 963 854 (89%) 91 (9%) 18 (2%) Maryland 5,399 5,107 1,396 (27%) 214 (4%) 3,497 (68%) Massachusetts 6,493 6,208 5,857 (94%) 267 (4%) 84 (1%) Michigan 7,915 7,522 6,001 (80%) 580 (8%) 941 (13%) Minnesota 2,426 1,985 1,658 (84%) 248 (12%) 79 (4%) Mississippi 1,467 1,316 1,147 (87%) 108 (8%) 61 (5%) Missouri 4,791 4,119 3,540 (86%) 385 (9%) 194 (5%) Montana 553 444 283 (64%) 83 (19%) 78 (18%) Nebraska 552 438 349 (80%) 70 (16%) 19 (4%) Nevada 2,865 2,152 1,794 (83%) 311 (14%) 47 (2%) New Hampshire 1,487 1,403 1,273 (91%) 101 (7%) 29 (2%) New Jersey 6,334 6,066 5,628 (93%) 333 (5%) 105 (2%) New Mexico 2,195 1,828 1,588 (87%) 205 (11%) 35 (2%) New York 11,543 10,845 9,458 (87%) 779 (7%) 608 (6%) North Carolina 6,289 5,840 5,109 (87%) 580 (10%) 151 (3%) North Dakota 229 192 155 (81%) 22 (11%) 15 (8%) © 2018 Rosenberg ES et al. JAMA Network Open. Deaths from narcotic or unspecified drugs Deaths from all drugs Unintentional, (unintentional, suicide, and suicide, and undetermined Unintentional Suicide Undetermined a a a undetermined intent) intent only only intent only Ohio 13,397 12,777 11,975 (94%) 602 (5%) 200 (2%) Oklahoma 3,360 2,401 2,098 (87%) 172 (7%) 131 (5%) Oregon 2,129 1,650 1,239 (75%) 273 (17%) 138 (8%) Pennsylvania 13,336 12,852 11,730 (91%) 786 (6%) 337 (3%) Rhode Island 1,167 1,090 1,002 (92%) 70 (6%) 18 (2%) South Carolina 3,203 2,874 2,600 (90%) 226 (8%) 48 (2%) South Dakota 268 192 135 (70%) 46 (24%) 11 (6%) Tennessee 5,860 5,319 4,742 (89%) 364 (7%) 213 (4%) Texas 11,732 9,615 8,314 (86%) 979 (10%) 322 (3%) Utah 2,622 2,276 1,567 (69%) 305 (13%) 405 (18%) Vermont 433 389 311 (80%) 42 (11%) 36 (9%) Virginia 4,401 3,963 3,471 (88%) 368 (9%) 124 (3%) Washington 4,496 3,541 2,955 (83%) 460 (13%) 126 (4%) West Virginia 3,095 2,847 2,601 (91%) 126 (4%) 120 (4%) Wisconsin 3,769 3,503 3,006 (86%) 363 (10%) 134 (4%) Wyoming 430 370 303 (82%) 50 (14%) 17 (5%) 4,338 3,826 3,271 334 222 Mean 3,095 2,847 2,474 214 120 Median 1,254 1,067 890 105 46 Lower Quartile 5,480 5,080 4,283 385 207 Upper Quartile Denominator for percentage is deaths from narcotics and unspecified drugs of unintentional, suicide, and undetermined intent. © 2018 Rosenberg ES et al. JAMA Network Open. eTable 3: Values of three analytic weighting schemas Weight ( applied to drug Weight applied to Scenario HCV death state effect overdose death state effect Birth Year (standardized ratio) (standardized ratio) 1. ICD-10 Codes for HCV Only <1945 100% 0% 1945-1969 100% 0% 1970 100% 0% 2. ICD-10 Codes for HCV and Narcotic Overdose (primary analysis) <1945 100% 0% 1945-1969 87.5% 12.5% 1970 37.8% 62.2% 3. ICD-10 Codes for HCV and Narcotic Overdose (sensitivity analysis) <1945 100% 0% 1945-1969 80.0% 20.0% 1970 37.8% 62.2% Abbreviations: HCV, hepatitis C virus; ICD-10, International Classification of Diseases, Tenth Revision eTable 4: Estimated prevalence of HCV antibody, NHANES 1999-2012 and 2013- 2016, by birth cohort 2005-2007 ACS NHANES anti-HCV+ 1999-2012 N Per 100 95% CI n 95%CI <1945 43,453,450 0.66 0.50 0.88 287,749 216,876 381,521 1945-1969 100,776,581 3.07 2.70 3.48 3,092,027 2,721,572 3,511,056 1970 76,820,211 0.54 0.38 0.75 411,449 292,378 578,533 2012-2016 ACS NHANES anti-HCV+ 2013-2016 N Per 100 95% CI n 95%CI <1945 29,693,961 0.52 0.28 0.94 152,983 83,648 279,272 1945-1969 97,702,202 2.92 2.38 3.57 2,848,019 2,321,697 3,489,239 1970 113,756,485 0.96 0.69 1.32 1,087,171 783,782 1,506,363 Abbreviations: HCV, hepatitis C virus; NHANES, National Health and Nutrition Examination Survey; ACS, American Community Survey; anti-HCV, hepatitis C virus antibody; CI, confidence interval © 2018 Rosenberg ES et al. JAMA Network Open. eTable 5. Sensitivity analysis of results under two assumptions for cumulative mortality for 1945-1969 birth cohort, among population included in NHANES sampling frame Primary Analysis Results (12.5% mortality) Sensitivity Analysis Results (20.0% mortality) State ACS 2012- HCV 95% CI % (95% CI) HCV 95% CI % (95% CI) a b b 2016 RNA+ RNA+ Alabama 3,671,100 26,100 (23,100 - 29,600) 0.71 (0.63 - 0.81) 26,100 (23,100 - 29,600) 0.71 (0.63 - 0.81) Alaska 542,500 4,700 (3,900 - 5,700) 0.86 (0.72 - 1.05) 4,600 (3,800 - 5,600) 0.85 (0.70 - 1.03) Arizona 5,020,500 55,300 (48,000 - 64,100) 1.10 (0.96 - 1.28) 54,800 (47,600 - 63,500) 1.09 (0.95 - 1.26) Arkansas 2,215,500 19,100 (16,800 - 21,800) 0.86 (0.76 - 0.99) 18,600 (16,400 - 21,300) 0.84 (0.74 - 0.96) California 29,160,200 288,500 (253,500 331,800) 0.99 (0.87 - 1.14) 281,200 (247,200 323,100) 0.96 (0.85 - 1.11) - - Colorado 4,057,000 32,500 (28,000 - 38,400) 0.80 (0.69 - 0.95) 32,100 (27,600 - 37,800) 0.79 (0.68 - 0.93) Connecticut 2,771,800 16,500 (14,200 - 19,700) 0.60 (0.51 - 0.71) 17,200 (14,700 - 20,500) 0.62 (0.53 - 0.74) Delaware 719,400 5,600 (4,800 - 6,500) 0.78 (0.67 - 0.90) 5,700 (4,900 - 6,600) 0.79 (0.68 - 0.92) District of 537,500 12,400 (10,500 - 14,800) 2.32 (1.95 - 2.76) 12,600 (10,600 - 15,000) 2.35 (1.98 - 2.80) Columbia Florida 15,620,600 133,200 (117,700 152,100) 0.85 (0.75 - 0.97) 132,700 (117,300 151,600) 0.85 (0.75 - 0.97) - - Georgia 7,465,900 46,400 (41,300 - 52,300) 0.62 (0.55 - 0.70) 46,500 (41,400 - 52,400) 0.62 (0.56 - 0.70) Hawaii 1,094,200 5,700 (4,700 - 7,000) 0.52 (0.43 - 0.64) 5,600 (4,600 - 6,800) 0.51 (0.42 - 0.62) Idaho 1,187,300 9,900 (8,400 - 11,800) 0.84 (0.71 - 0.99) 9,600 (8,200 - 11,400) 0.81 (0.69 - 0.96) Illinois 9,703,700 47,700 (42,200 - 54,300) 0.49 (0.44 - 0.56) 50,500 (44,700 - 57,400) 0.52 (0.46 - 0.59) Indiana 4,915,800 35,400 (30,900 - 40,700) 0.72 (0.63 - 0.83) 36,000 (31,500 - 41,500) 0.73 (0.64 - 0.84) Iowa 2,339,900 11,100 (9,500 - 13,100) 0.47 (0.40 - 0.56) 10,900 (9,300 - 12,900) 0.47 (0.40 - 0.55) Kansas 2,137,000 12,600 (10,900 - 14,800) 0.59 (0.51 - 0.69) 12,500 (10,700 - 14,700) 0.58 (0.50 - 0.69) Kentucky 3,331,500 38,600 (33,600 - 44,800) 1.16 (1.01 - 1.34) 39,600 (34,500 - 45,900) 1.19 (1.04 - 1.38) Louisiana 3,445,000 44,900 (40,000 - 50,400) 1.30 (1.16 - 1.46) 44,400 (39,700 - 49,900) 1.29 (1.15 - 1.45) Maine 1,058,600 6,500 (5,400 -7,800) 0.61 (0.51 - 0.74) 6,600 (5,600 - 8,000) 0.63 (0.53 - 0.75) Maryland 4,547,800 37,300 (32,700 - 43,100) 0.82 (0.72 - 0.95) 38,600 (33,800 - 44,600) 0.85 (0.74 - 0.98) Massachuse 5,283,400 35,800 (30,600 - 42,500) 0.68 (0.58 - 0.80) 37,200 (31,900 - 44,200) 0.70 (0.60 - 0.84) tts Michigan 7,578,400 62,800 (55,800 - 70,900) 0.83 (0.74 - 0.94) 64,400 (57,200 - 72,700) 0.85 (0.76 - 0.96) Minnesota 4,115,000 22,300 (19,400 - 26,000) 0.54 (0.47 - 0.63) 22,000 (19,200 - 25,600) 0.53 (0.47 - 0.62) Mississippi 2,205,500 19,600 (17,500 - 22,200) 0.89 (0.79 - 1.01) 19,300 (17,100 - 21,800) 0.87 (0.78 - 0.99) Missouri 4,575,700 35,200 (31,100 - 40,200) 0.77 (0.68 - 0.88) 35,700 (31,400 - 40,700) 0.78 (0.69 - 0.89) Montana 787,100 6,800 (5,700 - 8,000) 0.86 (0.73 - 1.02) 6,600 (5,600 - 7,800) 0.84 (0.71 - 0.99) Nebraska 1,391,400 6,900 (6,000 - 8,200) 0.50 (0.43 - 0.59) 6,700 (5,800 - 7,900) 0.48 (0.41 - 0.57) Nevada 2,148,500 19,300 (16,800 - 22,400) 0.90 (0.78 - 1.04) 19,500 (17,000 - 22,700) 0.91 (0.79 - 1.06) New 1,046,300 7,200 (5,900 - 8,900) 0.69 (0.57 - 0.85) 7,400 (6,100 - 9,200) 0.71 (0.58 - 0.88) Hampshire New Jersey 6,810,300 43,400 (37,900 - 50,300) 0.64 (0.56 - 0.74) 44,200 (38,600 - 51,400) 0.65 (0.57 - 0.75) New Mexico 1,557,100 25,000 (21,600 - 29,100) 1.61 (1.39 - 1.87) 24,900 (21,500 - 29,000) 1.60 (1.38 - 1.86) © 2018 Rosenberg ES et al. JAMA Network Open. Primary Analysis Results (12.5% mortality) Sensitivity Analysis Results (20.0% mortality) c c State ACS 2012- HCV 95% CI % (95% CI) HCV 95% CI % (95% CI) a b b 2016 RNA+ RNA+ New York 15,260,100 107,100 (94,900 - 121,600) 0.70 (0.62 - 0.80) 108,300 (95,900 - 123,100) 0.71 (0.63 - 0.81) North 7,545,400 60,200 (53,600 - 68,100) 0.80 (0.71 - 0.90) 59,900 (53,300 - 67,800) 0.79 (0.71 - 0.90) Carolina North 559,100 2,200 (1,800 - 2,800) 0.39 (0.32 - 0.50) 2,200 (1,700 - 2,700) 0.39 (0.31 - 0.49) Dakota Ohio 8,787,100 81,500 (71,800 - 93,200) 0.93 (0.82 - 1.06) 85,200 (75,200 - 97,400) 0.97 (0.86 - 1.11) Oklahoma 2,862,800 48,900 (42,700 - 56,500) 1.71 (1.49- 1.97) 47,400 (41,400 - 54,700) 1.66 (1.45 - 1.91) Oregon 3,086,200 45,700 (39,400 - 53,700) 1.48 (1.28 - 1.74) 43,500 (37,500 - 51,100) 1.41 (1.21 - 1.65) Pennsylvani 9,888,700 84,500 (74,300 - 97,000) 0.86 (0.75 - 0.98) 87,300 (76,800 - 100,200) 0.88 (0.78 - 1.01) Rhode 829,900 9,600 (8,300 - 11,400) 1.16 (1.00 - 1.37) 9,800 (8,400 - 11,600) 1.18 (1.01 - 1.40) Island South 3,689,100 31,900 (28,400 - 36,100) 0.87 (0.77 - 0.98) 31,900 (28,400 - 36,000) 0.86 (0.77 - 0.98) Carolina South 628,400 3,000 (2,500 - 3,700) 0.48 (0.39 - 0.59) 2,900 (2,400 - 3,600) 0.46 (0.38 - 0.57) Dakota Tennessee 4,972,200 63,500 (56,200 - 72,100) 1.28 (1.13 - 1.45) 63,400 (56,100 - 72,000) 1.27 (1.13 - 1.45) Texas 19,455,200 178,000 (157,500 203,100) 0.91 (0.81 - 1.04) 172,500 (152,700 196,600) 0.89 (0.79 - 1.01) - - Utah 2,024,600 11,000 (9,300 - 13,100) 0.54 (0.46 - 0.65) 11,400 (9,700 - 13,600) 0.56 (0.48 - 0.67) Vermont 499,100 3,500 (2,900 - 4,200) 0.70 (0.58 - 0.85) 3,500 (2,900 - 4,200) 0.69 (0.57 - 0.84) Virginia 6,348,500 33,500 (29,400 - 38,500) 0.53 (0.46 - 0.61) 33,400 (29,400 - 38,400) 0.53 (0.46 - 0.61) Washington 5,412,700 50,000 (43,100 - 58,900) 0.92 (0.80 - 1.09) 48,700 (42,000 - 57,400) 0.90 (0.78 - 1.06) West 1,439,300 19,500 (16,700 - 23,000) 1.35 (1.16 - 1.60) 20,400 (17,500 - 23,900) 1.41 (1.22 - 1.66) Virginia Wisconsin 4,384,900 24,000 (21,000 - 27,700) 0.55 (0.48 - 0.63) 24,600 (21,600 - 28,300) 0.56 (0.49 - 0.65) Wyoming 437,600 3,200 (2,600 - 3,900) 0.73 (0.60 - 0.90) 3,200 (2,600 - 3,900) 0.73 (0.60 - 0.89) d,e Total 241,152,60 2,035,1 (1,803,60 2,318,000) 0.84 (0.75 - 0.96) 2,033,800 (1,802,40 2,316,600) 0.84 (0.75 - 0.96) 0 00 0 - 0 - Population sizes are estimated as of December 2016 based on American Community Survey 5-year estimates from 2012-2016 and include noninstitutionalized adults eligible for NHANES. This estimate includes 1,288,600 active-duty military personnel ineligible for NHANES, which cannot be removed at the state-level because population sizes are unavailable by home state of personnel. Number of infected persons is calculated by multiplying the prevalence percentage estimate by the adult population size, before rounding for presentation. NHANES prevalence percentage estimates are based on results from 2013-2016 NHANES. Population size includes noninstitutionalized adults eligible for NHANES from the 2012- 2016 American Community Survey. Values may not sum to total due to rounding. Results are based on a regression model that incorporates data for the time period 1999-2016 and generates estimates via simulations. Accordingly, these results do not precisely sum to previous national totals for the 2013-2016 period. Abbreviations: NHANES, National Health and Nutrition Examination Survey; ACS, American Community Survey; HCV, hepatitis C virus; CI, confidence interval © 2018 Rosenberg ES et al. JAMA Network Open. eTable 6. Summary of additional population analytic considerations Population features evaluated for Data sources used in analysis analytic decisions Population Included Included Evidence HCV Mean Population- in in ACS of prevalenc prevalenc size source NHANE populatio Differentia e source e S n size l HCV samplin estimates Risk g frame used for NHANES analyses Residential, Yes Yes N/A NHANES 0.9% ACS, 2012 – noninstitutionalize 2016 d, civilian population Incarcerated No No Yes Literature 10.7% Bureau of Justice Statistics, Unsheltered No No Yes Literature 10.8% U.S. homeless Department of Housing and Urban Development , 2016 Nursing homes No No No NHANES 0.5% National Survey of Long Term Care Providers, 30,c People living in Yes Yes Yes N/A N/A N/A d, e AI/AN areas Hospitalized Yes Yes No N/A N/A N/A Other high risk Yes Yes Yes N/A N/A N/A populations (e.g., persons who inject drugs, sheltered homeless) a 31 32 Estimated mean prevalence calculated using a random effects model with prevalence inputs from Akiyama et al., Cocoros et al., 33 34 35 36 37 de la Flor et al., Kuncio et al., Mahowald et al., Schoenbachler et al., Stockman et al. For Akiyama, de la Flor, and Kuncio, RNA prevalence was calculated as (reported HCV Antibody Prevalence) x (NHANES 2013-2016 HCV RNA prevalence), where NHANES 2013-2016 HCV RNA prevalence among antibody positives= 0.575. For Cocoros, Mahowald, Schoenbachler, and Stockman, RNA prevalence was calculated as (Number HCV RNA-Positive/Number Tested HCV RNA) x (reported HCV Antibody Prevalence). b 38 Literature prevalence from Coyle et al. Scaled for population growth to 2016 Residents of Native American reservations and tribal lands and Alaska Native village statistical areas Excluded from analysis due to inclusion in both NHANES (prevalence numerator) and ACS (population size denominator) For persons who inject drugs, we assessed likely bias and determined that national NHANES estimates sufficiently represented HCV prevalence in this subpopulation Abbreviations: NHANES, National Health and Nutrition Examination Survey; ACS, American Community Survey; HCV, hepatitis C virus; AI/AN, American Indian/Alaska Native © 2018 Rosenberg ES et al. JAMA Network Open. eTable 7. Comparison between primary and alternative approach to additional population estimates Additional Populations Estimation: Additional Populations Estimation: Comparison Primary Method Alternative Method between methods Population RNA+ RNA+ Difference b b b State ACS 2012- Additional Total Among In Total % Among In Total % RNA+ % 2016 Populatio NHANES Additiona NHANES Additional ns population l population Population c c Populatio s ns Alabama 3,671,100 65,600 3,736,700 26,100 4,600 30,700 0.82 26,100 3,900 29,900 0.80 732 0.02 Alaska 542,500 5,500 548,000 4,700 500 5,200 0.95 4,700 500 5,200 0.95 (10) (0.00) Arizona 5,020,500 70,000 5,090,500 55,300 6,300 61,500 1.21 55,300 8,200 63,400 1.25 (1,889) (0.04) Arkansas 2,215,500 43,200 2,258,700 19,100 2,700 21,800 0.97 19,100 2,800 21,900 0.97 (60) (0.00) California 29,160,20 384,500 29,544,700 288,500 30,400 318,900 1.08 288,500 35,500 324,000 1.10 (5,119) (0.02) Colorado 4,057,000 51,500 4,108,500 32,500 3,800 36,300 0.88 32,500 3,600 36,100 0.88 190 0.00 Connecticut 2,771,800 40,900 2,812,700 16,500 1,800 18,300 0.65 16,500 1,300 17,800 0.63 510 0.02 Delaware 719,400 11,100 730,500 5,600 700 6,300 0.86 5,600 700 6,300 0.86 57 0.01 District of 537,500 4,800 542,400 12,400 200 12,700 2.34 12,400 700 13,100 2.42 (414) (0.08) Columbia Florida 15,620,60 239,600 15,860,200 133,200 17,800 151,000 0.95 133,200 18,000 151,200 0.95 (200) (0.00) Georgia 7,465,900 131,700 7,597,700 46,400 10,500 56,800 0.75 46,400 7,700 54,100 0.71 2,776 0.04 Hawaii 1,094,200 13,200 1,107,400 5,700 1,000 6,700 0.60 5,700 600 6,300 0.57 391 0.04 Idaho 1,187,300 15,900 1,203,300 9,900 1,300 11,200 0.93 9,900 1,300 11,200 0.93 14 0.00 Illinois 9,703,700 138,700 9,842,400 47,700 7,100 54,900 0.56 47,700 4,100 51,800 0.53 3,015 0.03 Indiana 4,915,800 84,300 5,000,100 35,400 4,900 40,200 0.80 35,400 4,200 39,500 0.79 705 0.01 Iowa 2,339,900 39,400 2,379,300 11,100 1,500 12,600 0.53 11,100 900 12,000 0.50 658 0.03 Kansas 2,137,000 36,600 2,173,600 12,600 1,900 14,600 0.67 12,600 1,400 14,000 0.64 577 0.03 Kentucky 3,331,500 59,200 3,390,700 38,600 3,900 42,500 1.25 38,600 5,300 44,000 1.30 (1,461) (0.04) Louisiana 3,445,000 73,500 3,518,500 44,900 5,100 50,000 1.42 44,900 7,900 52,700 1.50 (2,739) (0.08) Maine 1,058,600 10,800 1,069,400 6,500 500 7,000 0.65 6,500 400 6,800 0.64 124 0.01 Maryland 4,547,800 55,000 4,602,900 37,300 3,300 40,600 0.88 37,300 3,200 40,500 0.88 101 0.00 Massachuset 5,283,400 63,100 5,346,600 35,800 2,300 38,100 0.71 35,800 1,900 37,600 0.70 440 0.01 ts Michigan 7,578,400 98,200 7,676,600 62,800 6,300 69,100 0.90 62,800 6,200 69,000 0.90 93 0.00 Minnesota 4,115,000 44,900 4,159,900 22,300 1,900 24,300 0.58 22,300 1,300 23,600 0.57 672 0.02 Mississippi 2,205,500 46,100 2,251,700 19,600 3,200 22,900 1.02 19,600 3,400 23,000 1.02 (169) (0.01) Missouri 4,575,700 85,100 4,660,800 35,200 5,100 40,300 0.86 35,200 4,600 39,800 0.85 458 0.01 Montana 787,100 11,000 798,100 6,800 700 7,400 0.93 6,800 700 7,500 0.93 (13) (0.00) Nebraska 1,391,400 21,400 1,412,800 6,900 1,000 7,900 0.56 6,900 600 7,500 0.53 405 0.03 Nevada 2,148,500 29,000 2,177,400 19,300 2,600 21,900 1.00 19,300 2,700 22,000 1.01 (157) (0.01) Additional Populations Estimation: Additional Populations Estimation: Comparison Primary Method Alternative Method between methods Population RNA+ RNA+ Difference © 2018 Rosenberg ES et al. JAMA Network Open. b b b State ACS 2012- Additional Total Among In Total % Among In Total % RNA+ % 2016 Populatio NHANES Additiona NHANES Additional ns population l population Population c c Populatio s ns New 1,046,300 11,700 1,058,000 7,200 500 7,700 0.73 7,200 400 7,600 0.72 87 0.01 Hampshire New Jersey 6,810,300 80,600 6,890,900 43,400 3,800 47,200 0.68 43,400 2,900 46,200 0.67 933 0.01 New Mexico 1,557,100 20,900 1,578,000 25,000 1,600 26,700 1.69 25,000 3,100 28,200 1.78 (1,485) (0.09) New York 15,260,10 188,400 15,448,400 107,100 8,900 116,000 0.75 107,100 7,400 114,500 0.74 1,485 0.01 North 7,545,400 94,800 7,640,100 60,200 6,200 66,400 0.87 60,200 5,800 66,000 0.86 332 0.00 Carolina North Dakota 559,100 9,200 568,300 2,200 400 2,600 0.45 2,200 200 2,400 0.42 194 0.03 Ohio 8,787,100 151,400 8,938,500 81,500 8,100 89,600 1.00 81,500 8,900 90,300 1.01 (759) (0.01) Oklahoma 2,862,800 59,900 2,922,700 48,900 4,400 53,300 1.82 48,900 8,800 57,800 1.98 (4,460) (0.15) Oregon 3,086,200 34,800 3,120,900 45,700 2,900 48,700 1.56 45,700 5,200 50,900 1.63 (2,216) (0.07) Pennsylvani 9,888,700 166,900 10,055,600 84,500 9,300 93,900 0.93 84,500 9,500 94,000 0.94 (183) (0.00) Rhode Island 829,900 11,500 841,300 9,600 400 10,000 1.19 9,600 500 10,200 1.21 (152) (0.02) South 3,689,100 51,200 3,740,300 31,900 3,700 35,600 0.95 31,900 3,800 35,700 0.95 (100) (0.00) Carolina South 628,400 12,600 641,000 3,000 700 3,700 0.57 3,000 400 3,400 0.53 279 0.04 Dakota Tennessee 4,972,200 81,600 5,053,700 63,500 5,600 69,100 1.37 63,500 8,500 72,000 1.42 (2,883) (0.06) Texas 19,455,20 322,100 19,777,300 178,000 24,500 202,500 1.02 178,000 26,600 204,500 1.03 (2,036) (0.01) Utah 2,024,600 17,500 2,042,200 11,000 1,300 12,300 0.60 11,000 800 11,800 0.58 473 0.02 Vermont 499,100 4,700 503,800 3,500 200 3,700 0.73 3,500 200 3,700 0.73 34 0.01 Virginia 6,348,500 87,900 6,436,400 33,500 6,400 39,900 0.62 33,500 4,000 37,500 0.58 2,379 0.04 Washington 5,412,700 56,200 5,468,900 50,000 4,200 54,200 0.99 50,000 4,600 54,600 1.00 (391) (0.01) West Virginia 1,439,300 20,100 1,459,400 19,500 1,100 20,600 1.41 19,500 1,800 21,300 1.46 (692) (0.05) Wisconsin 4,384,900 64,700 4,449,600 24,000 3,900 27,900 0.63 24,000 2,600 26,600 0.60 1,371 0.03 Wyoming 437,600 6,700 444,300 3,200 500 3,700 0.82 3,200 400 3,600 0.81 59 0.01 b,d Total 241,152,6 3,529,000 244,681,60 2,035,100 231,600 2,266,70 0.93 2,035,100 239,600 2,274,800 0.93 (8,043) 0.00 00 0 0 Population sizes are estimated as of December 2016 based on American Community Survey 5-year estimates from 2012-2016 and include noninstitutionalized adults eligible for NHANES. 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Screening for Hepatitis C as a Prevention Enhancement (SHAPE) for HIV: an integration pilot initiative in a Massachusetts County correctional facility. Public Health Rep. 2014;129 Suppl 1:5 11. 33. de la Flor C, Porsa E, Nijhawan AE. Opt out HIV and Hepatitis C Testing at the Dallas County Jail: Uptake, Prevalence, and Demographic Characteristics of Testers. Public Health Rep. 2017;132(6):617 621. 34. Kuncio DE, Newbern EC, Fernandez Vina MH, Herdman B, Johnson CC, Viner KM. Comparison of risk based hepatitis C screening and the true seroprevalence in an urban prison system. J Urban Health. 2015;92(2):379 386. 35. Mahowald MK, Larney S, Zaller ND, et al. Characterizing the Burden of Hepatitis C Infection Among Entrants to Pennsylvania State Prisons, 2004 to 2012. J Correct Health Care. 2016;22(1):41 45. 36. Schoenbachler BT, Smith BD, Sena AC, et al. Hepatitis C Virus Testing and Linkage to Care in North Carolina and South Carolina Jails, 2012 2014. Public Health Rep. 2016;131 Suppl 2:98 104. 37. Stockman LJ, Greer J, Holzmacher R, et al. Performance of Risk Based and Birth Cohort Strategies for Identifying Hepatitis C Virus Infection Among People Entering Prison, Wisconsin, 2014. Public Health Rep. 2016;131(4):544 551. 38. Coyle C, Viner K, Hughes E, et al. Identification and Linkage to Care of HCV Infected Persons in Five Health Centers Philadelphia, Pennsylvania, 2012 2014. MMWR Morb Mortal Wkly Rep. 2015;64(17):459 463. © 2018 Rosenberg ES et al. JAMA Network Open. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA Network Open American Medical Association http://www.deepdyve.com/lp/american-medical-association/prevalence-of-hepatitis-c-virus-infection-in-us-states-and-the-Nk9DTkp4iu

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Publisher: American Medical Association
Copyright: Copyright 2018 Rosenberg ES et al. JAMA Network Open.
eISSN: 2574-3805
DOI: 10.1001/jamanetworkopen.2018.6371
Publisher site: See Article on Publisher Site

Abstract

Key Points Question During 2013 to 2016, what IMPORTANCE Infection with hepatitis C virus (HCV) is a major cause of morbidity and mortality in proportion of adults were living with the United States, and incidence has increased rapidly in recent years, likely owing to increased hepatitis C virus (HCV) infection in each injection drug use. Current estimates of prevalence at the state level are needed to guide prevention US state? and care efforts but are not available through existing disease surveillance systems. Findings In this survey study, US national HCV prevalence during 2013 to OBJECTIVE To estimate the prevalence of current HCV infection among adults in each US state and 2016 was 0.93% and varied by the District of Columbia during the years 2013 to 2016. jurisdiction between 0.45% and 2.34%. Three of the 10 states with the highest DESIGN, SETTING, AND PARTICIPANTS This survey study used a statistical model to allocate prevalence and 5 of the 9 states with the nationally representative HCV prevalence from the National Health and Nutrition Examination highest number of HCV infections were Survey (NHANES) according to the spatial demographics and distributions of HCV mortality and in the Appalachian region. narcotic overdose mortality in all National Vital Statistics System death records from 1999 to 2016. Additional literature review and analyses estimated state-level HCV infections among populations Meaning Regions with long-standing not included in the National Health and Nutrition Examination Survey sampling frame. HCV epidemics, and those with newly emergent ones partly driven by the EXPOSURES State, accounting for birth cohort, biological sex, race/ethnicity, federal poverty level, opioid crisis, face substantial HCV and year. prevalence. MAIN OUTCOMES AND MEASURES State-level prevalence estimates of current HCV RNA. Supplemental content RESULTS In this study, the estimated national prevalence of HCV from 2013 to 2016 was 0.84% Author affiliations and article information are listed at the end of this article. (95% CI, 0.75%-0.96%) among adults in the noninstitutionalized US population represented in the NHANES sampling frame, corresponding to 2 035 100 (95% CI, 1 803 600-2 318 000) persons with current infection; accounting for populations not included in NHANES, there were 231 600 additional persons with HCV, adjusting prevalence to 0.93%. Nine states contained 51.9% of all persons living with HCV infection (California [318 900], Texas [202 500], Florida [151 000], New York [116 000], Pennsylvania [93 900], Ohio [89 600], Michigan [69 100], Tennessee [69 100], and North Carolina [66 400]); 5 of these states were in Appalachia. Jurisdiction-level median (range) HCV RNA prevalence was 0.88% (0.45%-2.34%). Of 13 states in the western United States, 10 were above this median. Three of 10 states with the highest HCV prevalence were in Appalachia. CONCLUSIONS AND RELEVANCE Using extensive national survey and vital statistics data from an 18-year period, this study found higher prevalence of HCV in the West and Appalachian states for 2013 to 2016 compared with other areas. These estimates can guide state prevention and treatment efforts. JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 1/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 Introduction Hepatitis C virus (HCV) infection is the most frequently reported bloodborne infection in the United States and a leading cause of liver-related morbidity, transplantation, and mortality. Transmission of HCV occurs through exposure to infected body fluids, principally blood. Untreated, between 15% 2-4 and 42% of infected persons resolve infection ; about half of those chronically infected develop 5,6 progressive liver disease, which may include cirrhosis and hepatocellular carcinoma. 5-7 Approximately 18 000 people died in 2016 because of HCV infection. Historically, HCV prevalence has been highest among persons in the birth cohort born between 1945 and 1965, and the number 8,9 of people living with chronic infection was estimated to be 3.5 million in the late 2000s. Changes over the past decade have reshaped the US HCV epidemic. US Food and Drug Administration approval and increased availability of direct-acting antivirals have cured many people 10-12 of infection. However, high all-cause and HCV-related mortality rates among persons in the highest-prevalence birth cohort for HCV infection remain. There has concomitantly been a tripling of HCV incidence, due primarily to an increase in persons injecting drugs and associated unsafe 7,14,15 sharing of injection equipment related to the opioid crisis. With the increasing availability of direct-acting antivirals, national and state-level public health strategies have raised elimination of HCV as a possible goal. Accurate estimates of the current burden of HCV infection in each US jurisdiction are critical to the policy, programmatic, and resource planning of elimination strategies. However, national case surveillance provides an incomplete picture of the burden of HCV infection. Although HCV infection is reportable to the Centers for Disease Control and Prevention’s (CDC’s) National Notifiable Diseases Surveillance System, acute and chronic infections reported through this program represent a small proportion of cases, and in some states neither are 7,16 reportable. Some jurisdictions maintain enhanced surveillance programs funded by the CDC or other sources, yet a comprehensive jurisdiction-specific picture for the nation remains inestimable from case surveillance data. The current approach for estimating national HCV prevalence involves analysis of the US National Health and Nutrition Examination Survey (NHANES), which conducts HCV 8,17 testing among noninstitutionalized persons aged 6 years or older. An updated national HCV prevalence for 2013 to 2016 has been estimated using NHANES, reflecting the previously mentioned age-bimodal epidemic patterns, yielding an estimated 2.4 million persons with HCV RNA–positive results, indicating current (acute or chronic) infection. This estimate used methods to account for populations unrepresented in NHANES-based estimates, including individuals experiencing incarceration and unsheltered homelessness, groups that represent 11% of HCV prevalence. Current subnational estimates are needed to guide local HCV elimination efforts, as previous estimates are no longer valid owing to changes in HCV epidemiology over the past few years. We present an updated approach to our previous methodology for state-specific HCV prevalence estimation that reflects current changes to the epidemic. This method uses newly released NHANES and vital statistics data through 2016 and incorporates HCV-related and narcotic overdose deaths to yield updated estimates that reflect overlaid spatial patterns in HCV infection attributable to previous and recent transmission. Methods We used a multistep statistical approach (eFigure 1 in the Supplement), first generating direct estimates for each state using NHANES national prevalence in sex, race/ethnicity, birth cohort, and poverty strata. We next examined the distribution of each state’s cause-specific death rates relative to the US average as signals for local patterns of HCV infection. Within demographic strata, we applied 2 sets of state-specific mortality ratios relative to the nation, mortality rates from HCV infection and narcotic overdose, to represent older and recent infections, respectively. We then estimated additional infections among populations not included in NHANES’ sampling frame by applying literature-based estimates of prevalence in these groups to state-specific population JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 2/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 estimates. All analyses were limited to persons aged 18 years or older. In the following section, we describe this approach in detail. This study was reported according to the American Association for Public Opinion Research (AAPOR) reporting guideline. Because the study used publicly available data, institutional review board approval was not sought per organizational policy. Data Sources NHANES (1999-2016) Every 2-year cycle, NHANES samples approximately 10 000 individuals through a complex multistage design that represents the noninstitutionalized civilian US population. The survey 19,20 collects demographic characteristics and specimens for HCV RNA and antibody testing. Additional details, including response rates, are in eAppendix 1 in the Supplement. Race/ethnicity was categorized into non-Hispanic black and other race/ethnicities. Birth year was categorized as before 1945, 1945 to 1969, and after 1969. The typical 1945 to 1965 birth cohort with the highest HCV prevalence was expanded by 4 years because preliminary NHANES analyses showed similar prevalence to the traditional birth cohort (not shown). Income was represented as a ratio comparing family income with the US Department of Health and Human Services poverty guidelines for each year and categorized in the following 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. Missing income data (n = 3931 [8.30%]) were imputed using a process described in eAppendix 2 in the Supplement. We pooled 9 data cycles (1999-2016) to ensure sufficient stratum-level data (Table 1). American Community Survey Public Use Microdata Sample (2012-2016) The American Community Survey (ACS) samples nearly 3 million addresses annually, collecting demographic and economic characteristics of the US population. We used the 2012 to 2016 five- year ACS Public Use Microdata Sample to estimate population denominators for the noninstitutionalized population in each stratum and state. Race/ethnicity, birth year, and income were categorized as we have described, and we conducted imputation analyses for missing income data (n = 237 600 [1.98%]) (eAppendix 2 in the Supplement). Table 1. Data Sources Data Source Years Included Purpose Individuals Represented, No. Cases, No. Data Extraction Notes NHANES 1999-2016 National HCV RNA prevalence 47 387 With nonmissing HCV 575 With positive HCV NHANES 2000, 2002, 2004, 2006, overall and by strata of sex, RNA test results; 47 590 RNA test; 874 with 2008, 2010, 2012, 2014, 2016 data race/ethnicity, birth cohort, with nonmissing HCV positive HCV antibody sets a a and poverty; trends in HCV antibody test results test antibody inform analysis weights US Census 1999-2016 Population structure for 4 109 869 228 Person-years NA US Vintage 2000, Vintage 2009, intercensal data modeling HCV- and overdose- aged ≥18 y Vintage 2016 data sets related mortality rates US Census American 2012-2016 Noninstitutionalized US 12 023 450 Observations of NA 5-y Public Use Microdata Sample Community Survey population structure for final noninstitutionalized persons estimates aged ≥18 y National Vital 1999-2016 Distribution of hepatitis 44 071 310 Decedents aged 261 858 With HCV as ICD-10 codes included acute viral Statistics System C–related mortality, signaling ≥18 y who resided in the 50 underlying or multiple hepatitis C (B17.1) and chronic viral underlying HCV prevalence, to states or the District of cause of death hepatitis C (B18.2) inform distribution of older Columbia HCV infections National Vital 1999-2016 Distribution of narcotic 44 071 310 Decedents aged 541 130 With ICD-10 codes included poisoning by Statistics System overdose mortality, signaling ≥18 y who resided in the 50 unintentional or and exposure to narcotics and underlying injection patterns, states or the District of undetermined cause psychodysleptics (hallucinogens) to inform distribution of newer Columbia narcotic or unknown drug (X42 unintentional, Y12 HCV infections as underlying or multiple undetermined intent); poisoning by cause of death and exposure to other and unspecified drugs, medicaments, and biological substances (X44 unintentional, Y14 undetermined intent) Abbreviations: HCV, hepatitis C virus; ICD-10, International Classification of Diseases, Hepatitis C virus antibody screening test data are included for all years. Confirmatory Tenth Revision; NA, not applicable; NHANES, National Health and Nutrition test data for HCV antibodies are not publicly available for 2015 to 2016. Examination Survey. JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 3/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 National Vital Statistics System Multiple Cause of Death Mortality Data (1999-2016) Multiple Cause of Death Mortality Microdata files (1999-2016), including individual death records for persons who lived in a US state or the District of Columbia, were requested from the National Vital Statistics System (NVSS). These records contained International Classification of Diseases, Tenth Revision (ICD-10) codes for multiple underlying causes of deaths (N = 44 071 310). Hepatitis C virus–related mortality was classified using the ICD-10 code for acute viral hepatitis C (B17.1) or chronic viral hepatitis C (B18.2) as an underlying or multiple cause of death (n = 261 858). We earlier demonstrated that although HCV is underreported on death certificates, HCV prevalence estimates were not meaningfully affected because underreporting was insufficiently differential by jurisdiction. Narcotic overdose mortality, an outcome highly correlated with local acute HCV infection, was classified using the ICD-10 codes for unintentional poisoning by and exposure to narcotics and psychodysleptics (hallucinogens) (X42), unknown intention poisoning by and exposure to narcotics and psychodysleptics (hallucinogens) (Y12), unintentional poisoning by and exposure to other and unspecified drugs, medicaments, and biological substances (X44), or unknown intention poisoning by and exposure to other and unspecified drugs, medicaments, and biological substances (Y14) (n = 541 130). This algorithm is more specific to injection-related overdose deaths than others, while robust to missingness, with full considerations described in eAppendix 3, eFigure 2, and eTables 1 25-27 and 2 in the Supplement. Analysis NHANES-Eligible Population The equation in eAppendix 4 in the Supplement details our estimator for the total persons with HCV in each state in the NHANES population, depicted visually in eFigure 1 in the Supplement. Within 12 strata representing previously defined levels of sex, race/ethnicity, and birth year, we computed the standardized estimate by direct estimation from a weighted logistic regression model of NHANES, which included the terms for these strata, era (1999-2012 and 2013-2016), and poverty. To yield standardized estimates for the 12 demographic strata that accounted for poverty, we output logistic model estimates for the 2013 to 2016 era and weighted them according to the ACS poverty distribution for the 12 strata in each state. Next, we estimated the state stratum-specific likelihood of HCV-related mortality, using a logistic model of NVSS-derived mortality counts, per person-years, that approximated full- stratification with main effects for state, sex, race/ethnicity, birth cohort, era; 2-way interactions for state by each sex, race/ethnicity, birth cohort, and era; 2-way and 3-way interactions for each combination of sex, race/ethnicity, birth cohort, and era; and 4-way interaction of sex, race/ethnicity, birth cohort, and era. These state stratum-specific mortality estimates were divided by the national stratum-specific average, yielding a mortality ratio for the state stratum. This process was repeated for the narcotic overdose mortality. The 2 mortality ratios per stratum were averaged according to weights w (values described in the following section) and then multiplied by the standardization- based value to yield adjusted totals. Summing these across all 12 state strata yielded the estimated number of persons with HCV, which when divided by the ACS state population N yielded the estimated prevalence rate. Weights In the primary analysis, 3 weights w were used, with the same w applied to the 4 sex–race/ethnicity j j strata within birth cohort, representing the proportion of that birth cohort’s current infections allocated as prevalent in 1999 to 2012 (w ) vs incident during 2013 to 2016 (1 − w ). For persons born j j before 1945, we assumed no recent infections due to injection (w = 1). Based on additional analyses of biannual NHANES trends in HCV-antibody and literature estimates, we set w = 0.875 for those born from 1945 to 1969, and w = 0.378 for those born after 1969 (eAppendix 5 and eTables 3 and 4 in the Supplement). To facilitate comparisons with our earlier approach for 2010, which considered JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 4/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 only HCV mortality, we conducted a sensitivity analysis with all w = 100%. An additional sensitivity analysis considered an upper bound for incidence among persons born from 1945 to 1969, with w = 0.80 (eAppendix 5 and eTables 3 and 5 in the Supplement). Confidence Intervals Confidence intervals accounted for the joint statistical uncertainty from the 3 logistic regression models and 2 poverty imputation models. This was done with a Monte Carlo simulation that resampled parameter estimates from logit-normal distributions, using the standard errors for each, and recomputed all modeling steps (k = 10 000 runs) to produce 95% CIs. Additional Populations The National Health and Nutrition Examination Survey does not sample persons who are incarcerated, experiencing unsheltered homelessness, or residing in nursing homes. We expanded to states the earlier-described method for including these populations nationally. In brief, for incarcerated and homeless populations, HCV prevalence was estimated based on values identified in a systematic literature review of articles published from January 1, 2013, to December 31, 2017. For incarcerated populations, the mean prevalence of the literature estimates was generated using a random-effects model with study sample size as weight. For nursing home residents, the age-sex standardized NHANES prevalence was used. State-level population size estimates for these groups as of December 31, 2016, were obtained from public data sources. Additional detail on data sources and prevalence estimates appears in eTable 6 in the Supplement. For each state, within each population, we multiplied the national HCV prevalence rate by the state-specific population size to yield the number infected, which was then summed across populations to yield the state total persons with HCV among additional populations. We also conducted a secondary analysis that further adjusted by state-specific prevalence rates in the NHANES-represented population (eAppendix 6 in the Supplement). State-level estimates for populations not represented in NHANES were added to the model results within each state, allowing calculation of point estimates for prevalence in the total state population. Unlike the national analysis, we did not account for active-duty military populations in our state approach because this group consists of persons originating from multiple states residing in facilities outside of state jurisdiction, for whom data on origin states are unavailable and for whom there exists insufficient evidence of increased HCV risk. This population represents an estimated 6900 persons with HCV infection in the United States (0.3%). Results For the years 2013 to 2016, we estimated an HCV RNA prevalence of 0.84% (95% CI, 0.75%-0.96%) among adults in the noninstitutionalized US population represented in the NHANES sampling frame, corresponding to 2 035 100 (95% CI, 1 803 600-2 318 000) persons with current infection (Table 2). Accounting for populations not included in NHANES, there were 231 600 additional persons with HCV, adjusting prevalence to 0.93% (10% relative increase nationally with a state increase range of 2%-23%), with prevalence relatively increasing by more than 20% in Georgia and South Dakota and less than 5% in Rhode Island and the District of Columbia. These deviations were largely attributable to respectively higher and lower proportions of persons incarcerated in these jurisdictions (data not shown). Using the alternative method that adjusted additional populations for background state prevalence in the NHANES population, the relative proportional change from the primary method was minimal (state median [range] change, −0.5% [−7.6% to 8.4%]) (eTable 7 in the Supplement). Large variations were observed in total population HCV prevalence by state (median [range], 0.88% [0.45%-2.34%]) (Figure 1). Of 13 states in the US West census region, 10 were above this median rate, and the region contained 27.1% of infected persons, despite constituting 23.4% of the JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 5/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 Table 2. Estimated Total and Prevalence of Persons With Current HCV Infection, US States and District of Columbia, 2013 to 2016 Population Included in With Additional Populations Not Included in NHANES Sampling Frame NHANES Sampling Frame 2016 Adult HCV RNA Total Adult Population 2016, a b c b State Population, No. HCV RNA Positive (95% CI), No. % (95% CI) Positive, No. No. (%) Alabama 3 671 100 26 100 (23 100-29 600) 0.71 (0.63-0.81) 30 700 3 736 700 (0.82) Alaska 542 500 4700 (3900-5700) 0.86 (0.72-1.05) 5200 548 000 (0.95) Arizona 5 020 500 55 300 (48 000-64 100) 1.10 (0.96-1.28) 61 500 5 090 500 (1.21) Arkansas 2 215 500 19 100 (16 800-21 800) 0.86 (0.76-0.99) 21 800 2 258 700 (0.97) California 29 160 200 288 500 (253 500-331 800) 0.99 (0.87-1.14) 318 900 29 544 700 (1.08) Colorado 4 057 000 32 500 (28 000-38 400) 0.80 (0.69-0.95) 36 300 4 108 500 (0.88) Connecticut 2 771 800 16 500 (14 200-19 700) 0.60 (0.51-0.71) 18 300 2 812 700 (0.65) Delaware 719 400 5600 (4800-6500) 0.78 (0.67-0.90) 6300 730 500 (0.86) District of Columbia 537 500 12 400 (10 500-14 800) 2.32 (1.95-2.76) 12 700 542 400 (2.34) Florida 15 620 600 133 200 (117 700-152 100) 0.85 (0.75-0.97) 151 000 15 860 200 (0.95) Georgia 7 465 900 46 400 (41 300-52 300) 0.62 (0.55-0.70) 56 800 7 597 700 (0.75) Hawaii 1 094 200 5700 (4700-7000) 0.52 (0.43-0.64) 6700 1 107 400 (0.60) Idaho 1 187 300 9900 (8400-11 800) 0.84 (0.71-0.99) 11 200 1 203 300 (0.93) Illinois 9 703 700 47 700 (42 200-54 300) 0.49 (0.44-0.56) 54 900 9 842 400 (0.56) Indiana 4 915 800 35 400 (30 900-40 700) 0.72 (0.63-0.83) 40 200 5 000 100 (0.80) Iowa 2 339 900 11 100 (9 500-13 100) 0.47 (0.40-0.56) 12 600 2 379 300 (0.53) Kansas 2 137 000 12 600 (10 900-14 800) 0.59 (0.51-0.69) 14 600 2 173 600 (0.67) Kentucky 3 331 500 38 600 (33 600-44 800) 1.16 (1.01-1.34) 42 500 3 390 700 (1.25) Louisiana 3 445 000 44 900 (40 000-50 400) 1.30 (1.16-1.46) 50 000 3 518 500 (1.42) Maine 1 058 600 6500 (5400-7800) 0.61 (0.51-0.74) 7000 1 069 400 (0.65) Maryland 4 547 800 37 300 (32 700-43 100) 0.82 (0.72-0.95) 40 600 4 602 900 (0.88) Massachusetts 5 283 400 35 800 (30 600-42 500) 0.68 (0.58-0.80) 38 100 5 346 600 (0.71) Michigan 7 578 400 62 800 (55 800-70 900) 0.83 (0.74-0.94) 69 100 7 676 600 (0.90) Minnesota 4 115 000 22 300 (19 400-26 000) 0.54 (0.47-0.63) 24 300 4 159 900 (0.58) Mississippi 2 205 500 19 600 (17 500-22 200) 0.89 (0.79-1.01) 22 900 2 251 700 (1.02) Missouri 4 575 700 35 200 (31 100-40 200) 0.77 (0.68-0.88) 40 300 4 660 800 (0.86) Montana 787 100 6800 (5700-8000) 0.86 (0.73-1.02) 7400 798 100 (0.93) Nebraska 1 391 400 6900 (6000-8200) 0.50 (0.43-0.59) 7900 1 412 800 (0.56) Nevada 2 148 500 19 300 (16 800-22 400) 0.90 (0.78-1.04) 21 900 2 177 400 (1.00) New Hampshire 1 046 300 7200 (5900-8900) 0.69 (0.57-0.85) 7700 1 058 000 (0.73) New Jersey 6 810 300 43 400 (37 900-50 300) 0.64 (0.56-0.74) 47 200 6 890 900 (0.68) New Mexico 1 557 100 25 000 (21 600-29 100) 1.61 (1.39-1.87) 26 700 1 578 000 (1.69) New York 15 260 100 107 100 (94 900-121 600) 0.70 (0.62-0.80) 116 000 15 448 400 (0.75) North Carolina 7 545 400 60 200 (53 600-68 100) 0.80 (0.71-0.90) 66 400 7 640 100 (0.87) North Dakota 559 100 2200 (1800-2800) 0.39 (0.32-0.50) 2600 568 300 (0.45) Ohio 8 787 100 81 500 (71 800-93 200) 0.93 (0.82-1.06) 89 600 8 938 500 (1.00) Oklahoma 2 862 800 48 900 (42 700-56 500) 1.71 (1.49-1.97) 53 300 2 922 700 (1.82) Oregon 3 086 200 45 700 (39 400-53 700) 1.48 (1.28-1.74) 48 700 3 120 900 (1.56) Pennsylvania 9 888 700 84 500 (74 300-97 000) 0.86 (0.75-0.98) 93 900 10 055 600 (0.93) Rhode Island 829 900 9600 (8300-11 400) 1.16 (1.00-1.37) 10 000 841 300 (1.19) South Carolina 3 689 100 31 900 (28 400-36 100) 0.87 (0.77-0.98) 35 600 3 740 300 (0.95) South Dakota 628 400 3000 (2500-3700) 0.48 (0.39-0.59) 3700 641 000 (0.57) Tennessee 4 972 200 63 500 (56 200-72 100) 1.28 (1.13-1.45) 69 100 5 053 700 (1.37) Texas 19 455 200 178 000 (157 500-203 100) 0.91 (0.81-1.04) 202 500 19 777 300 (1.02) Utah 2 024 600 11 000 (9300-13 100) 0.54 (0.46-0.65) 12 300 2 042 200 (0.60) Vermont 499 100 3500 (2900-4200) 0.70 (0.58-0.85) 3700 503 800 (0.73) Virginia 6 348 500 33 500 (29 400-38 500) 0.53 (0.46-0.61) 39 900 6 436 400 (0.62) Washington 5 412 700 50 000 (43 100-58 900) 0.92 (0.80-1.09) 54 200 5 468 900 (0.99) West Virginia 1 439 300 19 500 (16 700-23 000) 1.35 (1.16-1.60) 20 600 1 459 400 (1.41) (continued) JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 6/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 Table 2. Estimated Total and Prevalence of Persons With Current HCV Infection, US States and District of Columbia, 2013 to 2016 (continued) Population Included in With Additional Populations Not Included in NHANES Sampling Frame NHANES Sampling Frame 2016 Adult HCV RNA Total Adult Population 2016, a b c b State Population, No. HCV RNA Positive (95% CI), No. % (95% CI) Positive, No. No. (%) Wisconsin 4 384 900 24 000 (21 000-27 700) 0.55 (0.48-0.63) 27 900 4 449 600 (0.63) Wyoming 437 600 3200 (2600-3900) 0.73 (0.60-0.90) 3700 444 300 (0.82) d,e f Total 241 152 600 2 035 100 (1 803 600-2 318 000) 0.84 (0.75-0.96) 2 266 700 244 681 600 (0.93) Abbreviations: HCV, hepatitis C virus; NHANES, National Health and Nutrition The NHANES prevalence percentage estimates are based on results from 2013 to 2016 Examination Survey. NHANES. Population size includes noninstitutionalized adults eligible for NHANES from the 2012 to 2016 American Community Survey. Population sizes are estimated as of December 2016 based on American Community Survey 5-year estimates from 2012 to 2016 and include noninstitutionalized adults Values may not sum to total due to rounding. eligible for NHANES. This estimate includes 1 288 600 active-duty military personnel e Results are based on a regression model that incorporates data for the period 1999 to ineligible for NHANES, which cannot be removed at the state level because population 2016 and generates estimates via simulations. Accordingly, these results do not sizes are unavailable by home state of personnel. Therefore, this assumes a mean 11 precisely sum to previous national totals for the 2013 to 2016 period. prevalence value for this group, adding 5000 infections nationally. Does not sum to previous 2013 to 2016 US total due to the exclusion of persons Number of infected persons is calculated by multiplying the prevalence percentage incarcerated in federal prisons who are not assigned to state-specific populations. estimate by the adult population size before rounding for presentation. US population. Three of the 10 states with the highest rates are members of the US Appalachian Regional Commission (Kentucky, Tennessee, and West Virginia) and together constituted 5.8% of persons with HCV and 4.0% of the population. Nine states (California [318 900], Texas [202 500], Florida [151 000], New York [116 000], Pennsylvania [93 900], Ohio [89 600], Michigan [69 100], Tennessee [69 100], and North Carolina [66 400] each contained more than 65 000 persons with HCV and together constituted 51.9% of all persons with HCV nationally. Of these 9 states, 5 are in the Appalachian region (New York, North Carolina, Ohio, Pennsylvania, and Tennessee). Tennessee and Arizona were the only states represented in the top 10 for both HCV rates and persons with HCV. Figure 2 displays the impact of the revised methodology for the NHANES population that incorporates the distribution of narcotic overdose mortality, relative to considering HCV mortality only. States experiencing higher rates of overdose mortality saw relative increases in estimated HCV prevalence, whereas those with lower rates saw declines in prevalence. A sensitivity analysis that considered maximally increased weighting of overdose mortality (and incidence) in the 1945 to 1969 birth cohort yielded small proportional changes from the default weighting (median [range], 0.3% [−4.8% to 5.7%]) (eTable 5 in the Supplement). Discussion Using newly available data for 2013 to 2016 and methods that account for changes in HCV epidemiology, we observed large variation in HCV prevalence and burden across the United States. There was a particularly high prevalence in the West and Appalachia. These findings were consistent across analyses that considered alternative incidence rates for the highest-prevalence 1945 to 1969 cohort and alternative approaches for populations not included in the NHANES sampling frame. The state patterns for areas of high burden, particularly the Appalachian region, closely echo recent reports of direct, local (but incomplete) measures of HCV burden using acute HCV surveillance in the National Notifiable Diseases Surveillance System and maternal HCV status on birth certificates 29,30 in NVSS. In Appalachia, it is likely that HCV prevalence reflects recent increases in injection drug use, high densities of counties vulnerable to HCV and HIV infection outbreaks, large outbreaks of 7,14,24,31 these infections among persons who inject drugs (PWID), and elevated reports of acute HCV. These estimates help to quantify the need for investments in efficacious direct and indirect services for the prevention of HCV acquisition and transmission. This includes syringe services programs, which are associated with decreased HCV spread, especially when combined with linkage to medication-assisted substance use treatment. Although increasing, the number of syringe services programs remains low in 2018 in many states, with programs often geographically dispersed JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 7/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 within states. Direct medical services such as HCV testing and curative treatments remain cornerstones for extending life and averting transmission. Furthermore, testing and treatment are 35-37 cost-effective, with earlier treatment possibly yielding greater cost savings. Despite availability of these services, some policies restrict their use. A recent analysis found substantial variation in the comprehensiveness of laws supporting access to clean injection equipment and sobriety requirement–based restrictions of Medicaid fee-for-service HCV treatment. Some of the highest-incidence states had the lowest levels of prevention and treatment access overall, with 47 states lacking comprehensive laws and Medicaid policies for effective prevention and treatment of HCV among PWID. Additionally, restrictions based on fibrosis score remain prevalent, and a 45-state analysis of 2016 to 2017 pharmacy data found treatment had been denied for many patients with Medicaid (34.5%) and private insurance (52.4%). Finally, indirect Figure 1. Estimated Hepatitis C Virus (HCV) RNA Prevalence and Total Persons With HCV RNA, Indicating Current Infection, United States and District of Columbia, 2013 to 2016 A HCV RNA prevalence 2013-2016 HCV RNA prevalence (per 100 population) 1.25-2.34 1.00-1.25 0.85-1.00 0.65-0.85 0.45-0.65 B Total persons with HCV RNA Total persons with HCV RNA 75 000-318 900 50 000-75 000 Prevalence of HCV (A) and total number of persons 25 000-50 000 with HCV (B) in the full US adult population defined by 10 000-25 000 noninstitutionalized adults included in the National 2600-10 000 Health and Nutrition Examination Survey sampling frame and additional populations not in the sampling frame (those incarcerated, in nursing homes, and experiencing homelessness). JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 8/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 HCV prevention is achievable by addressing opioid use disorder using efficacious individual approaches, like medication-assisted treatment, and numerous state- and systems-level 39,40 policies. Even with effective tools for addressing the HCV epidemic, substantial challenges remain in their application to rural PWID. The evidence base for understanding the unique HCV risk, prevention, and care context of these areas remains limited. Others have prioritized areas for 42,43 further research and recent federal commitments are promising. Figure 2. Hepatitis C Virus (HCV) RNA Prevalence, Accounting for the Distribution of Both HCV and Narcotic Overdose Deaths or HCV Deaths Only, by US State and Census Region, 2013 to 2016 Illinois ICD-10 codes for HCV Indiana and narcotic overdose Iowa Kansas ICD-10 codes for HCV only Michigan Minnesota Missouri Nebraska North Dakota Ohio South Dakota Wisconsin 0 0.5 1.0 1.5 2.0 2.5 3.0 Connecticut Maine Massachusetts New Hampshire New Jersey New York Pennsylvania Rhode Island Vermont 0 0.5 1.0 1.5 2.0 2.5 3.0 Alabama Arkansas Delaware District of Columbia Florida Georgia Kentucky Louisiana Maryland Mississippi North Carolina Oklahoma South Carolina Tennessee Texas Virginia West Virginia 0 0.5 1.0 1.5 2.0 2.5 3.0 Alaska Arizona California Colorado Hawaii Idaho Montana Nevada New Mexico Oregon Utah Washington Wyoming 0 0.5 1.0 1.5 2.0 2.5 3.0 Estimated HCV RNA Prevalence (per 100 Population) Prevalence in the US adult population defined by noninstitutionalized adults included in represent 95% confidence intervals. ICD-10 indicates International Classification of the National Health and Nutrition Examination Survey sampling frame. Error bars Diseases, Tenth Revision. JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 9/14 State and Region West South Northeast Midwest JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 Limitations Key strengths of our approach include anchoring to robust and comprehensive national data systems, use of highly specific markers of local HCV infection that reflect the bimodal epidemic pattern, and a near-exact standardization approach, yet several limitations remain. It is possible that HCV increases associated with PWID are not well represented in national NHANES estimates. However, earlier analyses demonstrated robustness for this subgroup, lifetime exposure among those born in 1970 or later (per eTable 4 in the Supplement) indicate dramatic increases consistent with acute surveillance trends, and estimated national totals are consistent with projections from a population-based dynamic model. A recently published analysis of laboratory databases reports a number of persons diagnosed that exceeds previous national prevalence estimates, likely due to incomplete deduplication of infections across deidentified databases. Mortality caused by HCV may be an imperfect spatial marker given underreporting, although we previously demonstrated that 13,18 the method is robust to this. Likewise, limitations may exist with the use of narcotic overdoses to represent recent infection. First, further specificity for likely injected narcotic, per toxicology codes, remains challenging because of substantial data toxicology code missingness. Additionally, to the extent that more lethal narcotics such as fentanyl are more prevalent in certain jurisdictions, this may bias estimates upward. Further refinements of toxicology code data are required to account for this. Second, local variations may exist in the relationship between overdose deaths and HCV-risky injection. State-specific variations in laws and funding of interventions that avert overdose deaths, like naloxone, and those that reduce HCV risks associated with injection while influencing 33,39,45 mortality less, like syringe services programs, may bias estimates in opposing directions. Estimates for populations not included in NHANES are based on systematic approaches, but still may not be representative. Explorations of the impact of variations in these estimates found this contributed little overall variation. Ultimately, one of the best ways to overcome these limitations, particularly as jurisdictions wish to monitor progress in shorter time frames, is to strengthen core surveillance registries through standardized reporting definitions, active case finding, and rigorous 46-48 linkages to understand mortality, treatment, and migration. Third, our state estimate sum is slightly lower than the recent updated national estimate, owing to 2 methodological differences: use of a weighted regression model that pools a broader time period and noninclusion of active-duty military persons. Conclusions Prevalence of HCV infection varies widely in the United States. Highest rates are frequently in states deeply affected by the opioid crisis or with a history of increased levels of injection drug use and chronic HCV infection, particularly in the West. Progress toward hepatitis C elimination is 34,47,49,50 theoretically possible with the right investments in prevention, diagnosis, and cure. The urgency for action and the resources necessary will vary by jurisdiction. ARTICLE INFORMATION Accepted for Publication: November 7, 2018. Published: December 21, 2018. doi:10.1001/jamanetworkopen.2018.6371 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Rosenberg ES et al. JAMA Network Open. Corresponding Author: Eli S. Rosenberg, PhD, Department of Epidemiology and Biostatistics, University at Albany School of Public Health, State University of New York, Rensselaer, NY 12144 (erosenberg2@albany.edu). Author Affiliations: Department of Epidemiology and Biostatistics, University at Albany School of Public Health, State University of New York, Rensselaer (Rosenberg, Rosenthal); Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia (Hall, Sullivan); Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 10/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 (Barker, Hofmeister, Ryerson); Office of the Director, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia (Dietz, Mermin). Author Contributions: Dr Rosenberg had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Rosenberg, Rosenthal, Hall, Barker, Sullivan, Dietz, Mermin, Ryerson. Acquisition, analysis, or interpretation of data: Rosenberg, Rosenthal, Hall, Barker, Hofmeister, Mermin, Ryerson. Drafting of the manuscript: Rosenberg, Rosenthal, Hall, Barker, Ryerson. Critical revision of the manuscript for important intellectual content: Rosenberg, Hall, Barker, Hofmeister, Sullivan, Dietz, Mermin, Ryerson. Statistical analysis: Rosenberg, Rosenthal, Hall. Obtained funding: Rosenberg, Sullivan, Mermin. Administrative, technical, or material support: Rosenberg, Barker, Sullivan, Ryerson. Supervision: Rosenberg, Dietz, Ryerson. Conflict of Interest Disclosures: Dr Rosenberg reported personal fees from Cengage Learning and from Statistics.com outside the submitted work. Mr Hall reported grants from the Center for AIDS Research at Emory University during the conduct of the study. Ms Barker reported employment with the Centers for Disease Control and Prevention (CDC) Division of Viral Hepatitis, which funds state and local governments and community health centers to prevent and control hepatitis C virus infections, and strengthen surveillance for viral hepatitis B and C. Dr Sullivan reported grants from the CDC during the conduct of the study and personal fees from the CDC, grants and personal fees from the National Institutes of Health, and grants from Gilead Sciences outside the submitted work. No other disclosures were reported. Funding/Support: This study was supported by the CDC National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention Epidemic and Economic Modeling Agreement (grant U38 PS004646) (Dr Rosenberg, Ms Rosenthal, and Mr Hall) and the Center for AIDS Research at Emory University (grant P30AI050409). Role of the Funder/Sponsor: As a cooperative agreement, CDC scientific coauthors were involved in the design and conduct of the study; analysis and interpretation of the data; preparation, review, and approval of the manuscript; and the decision to submit the manuscript for publication. Collection and management of the original source National Health and Nutrition Examination Survey and National Vital Statistics System data were conducted prior to this study by CDC staff not affiliated with this project. The Center for AIDS Research at Emory University had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the CDC. Additional Contributions: Meredith Barranco, BS, and Bahareh Ansari, MBA, both of the University at Albany, Rensselaer, New York, assisted with literature review. Jane Kelly, MD, and Greg Felzien, MD, of the Emory Coalition for Applied Modeling for Prevention Public Health Advisory Group reviewed the manuscript. Colleagues at the Illinois Department of Public Health (Mai Tuyet Pho, MD, Tristan Jones, BS, Judy Kauerauf, MPH, and Megan Patel, MPH), Oklahoma State Department of Health (Terrainia Harris, MPH, Sally Bouse, MPH, and Kristen Eberly, MPH), Tennessee Department of Health (Carolyn Wester, MD, and Lindsey Sizemore, MPH), and Washington State Department of Health (Jon Stockton, MHA, Emalie Huriaux, MPH, and Tessa Fairfortune, MPH) provided input during the development of the methods. None of these individuals were compensated for their contributions. REFERENCES 1. Ditah I, Ditah F, Devaki P, et al. 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Drug overdose deaths in the United States, 1999-2016. NCHS Data Brief. 2017;(294):1-8. 26. Katz J. Drug deaths in America are rising faster than ever. New York Times.2017. https://www.nytimes.com/ interactive/2017/06/05/upshot/opioid-epidemic-drug-overdose-deaths-are-rising-faster-than-ever.html. Accessed June 5, 2017. JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 12/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 27. Seth P, Scholl L, Rudd RA, Bacon S. Overdose deaths involving opioids, cocaine, and psychostimulants—United States, 2015-2016. MMWR Morb Mortal Wkly Rep. 2018;67(12):349-358. doi:10.15585/mmwr.mm6712a1 28. Appalachian Regional Commission. The Appalachian Region. https://www.arc.gov/appalachian_region/ TheAppalachianRegion.asp. Accessed June 13, 2018. 29. Campbell CA, Canary L, Smith N, Teshale E, Ryerson AB, Ward JW. State HCV incidence and policies related to HCV preventive and treatment services for persons who inject drugs—United States, 2015-2016. MMWR Morb Mortal Wkly Rep. 2017;66(18):465-469. doi:10.15585/mmwr.mm6618a2 30. Patrick SW, Bauer AM, Warren MD, Jones TF, Wester C. Hepatitis C virus infection among women giving birth— Tennessee and United States, 2009-2014. MMWR Morb Mortal Wkly Rep. 2017;66(18):470-473. doi:10.15585/ mmwr.mm6618a3 31. Evans ME, Labuda SM, Hogan V, et al. Notes from the field: HIV infection investigation in a rural area—West Virginia, 2017. MMWR Morb Mortal Wkly Rep. 2018;67(8):257-258. doi:10.15585/mmwr.mm6708a6 32. Platt L, Minozzi S, Reed J, et al. Needle and syringe programmes and opioid substitution therapy for preventing HCV transmission among people who inject drugs: findings from a Cochrane Review and meta-analysis. Addiction. 2018;113(3):545-563. doi:10.1111/add.14012 33. amfAR. Opioid & Health Indicators Database. http://opioid.amfar.org/. Accessed July 21, 2018. 34. National Academies of Sciences, Engineering, and Medicine. A National Strategy for the Elimination of Hepatitis B and C. Washington, DC: National Academies Press; 2016. 35. Barocas JA, Tasillo A, Eftekhari Yazdi G, et al. Population level outcomes and cost-effectiveness of expanding the recommendation for age-based hepatitis C testing in the United States. Clin Infect Dis. 2018;67(4):549-556. doi:10.1093/cid/ciy098 36. Morgan JR, Kim AY, Naggie S, Linas BP. The effect of shorter treatment regimens for hepatitis C on population health and under fixed budgets. Open Forum Infect Dis. 2017;5(1):ofx267. 37. Chhatwal J, Chen Q, Aggarwal R. Estimation of hepatitis C disease burden and budget impact of treatment using health economic modeling. Infect Dis Clin North Am. 2018;32(2):461-480. doi:10.1016/j.idc.2018.02.008 38. Gowda C, Lott S, Grigorian M, et al. Absolute insurer denial of direct-acting antiviral therapy for hepatitis C: a national specialty pharmacy cohort study. Open Forum Infect Dis. 2018;5(6):ofy076. doi:10.1093/ofid/ofy076 39. Haegerich TM, Paulozzi LJ, Manns BJ, Jones CM. What we know, and don’t know, about the impact of state policy and systems-level interventions on prescription drug overdose. Drug Alcohol Depend. 2014;145:34-47. doi: 10.1016/j.drugalcdep.2014.10.001 40. Connery HS. Medication-assisted treatment of opioid use disorder: review of the evidence and future directions. Harv Rev Psychiatry. 2015;23(2):63-75. doi:10.1097/HRP.0000000000000075 41. Paquette CE, Pollini RA. Injection drug use, HIV/HCV, and related services in nonurban areas of the United States: a systematic review. Drug Alcohol Depend. 2018;188:239-250. doi:10.1016/j.drugalcdep.2018.03.049 42. Grebely J, Bruneau J, Lazarus JV, et al; International Network on Hepatitis in Substance Users. Research priorities to achieve universal access to hepatitis C prevention, management and direct-acting antiviral treatment among people who inject drugs. Int J Drug Policy. 2017;47:51-60. doi:10.1016/j.drugpo.2017.05.019 43. National Institute on Drug Abuse. Grants awarded to address opioid crisis in rural regions. https://www. drugabuse.gov/news-events/news-releases/2017/08/grants-awarded-to-address-opioid-crisis-in-rural-regions. Published August 16, 2017. Accessed January 1, 2018. 44. Chirikov VV, Marx SE, Manthena SR, Strezewski JP, Saab S. Development of a comprehensive dataset of hepatitis C patients and examination of disease epidemiology in the United States, 2013-2016. Adv Ther. 2018;35 (7):1087-1102. doi:10.1007/s12325-018-0721-1 45. McClellan C, Lambdin BH, Ali MM, et al. Opioid-overdose laws association with opioid use and overdose mortality. Addict Behav. 2018;86:90-95. doi:10.1016/j.addbeh.2018.03.014 46. Canzater S, Crowley JS. Monitoring the Hepatitis C Epidemic in the United States: What Tools Are Needed to Achieve Elimination? Washington, DC: O’Neill Institute/Georgetown Law; 2017. 47. US Department of Health and Human Services. National Viral Hepatitis Action Plan: 2017-2020. https://www. hhs.gov/hepatitis/viral-hepatitis-action-plan/index.html. Published 2017. Accessed January 1, 2018. 48. Hart-Malloy R, Carrascal A, Dirienzo AG, Flanigan C, McClamroch K, Smith L. Estimating HCV prevalence at the state level: a call to increase and strengthen current surveillance systems. Am J Public Health. 2013;103(8): 1402-1405. doi:10.2105/AJPH.2013.301231 JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 13/14 JAMA Network Open | Public Health Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016 49. Centers for Disease Control and Prevention Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention. Progress toward viral hepatitis elimination in the United States, 2017. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, Office of Infectious Diseases, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention; 2017. https://www.cdc. gov/hepatitis/policy/PDFs/NationalProgressReport.pdf. Accessed January 1, 2018. 50. Dan C. New York State coalition hepatitis C consensus statement leads to governor’s action. https://www.hhs. gov/hepatitis/blog/2018/06/21/new-york-commits-to-eliminate-hepatitis-c.html. Published June 21, 2018. Accessed June 22, 2018. SUPPLEMENT. eAppendix 1. NHANES Methodological Details eAppendix 2. Imputation for Missing Poverty Data in the National Health and Nutrition Examination Survey (NHANES) and the American Community Survey (ACS) eAppendix 3. Drug Overdose Mortality eAppendix 4. Equation for Estimator of the Total Persons With HCV Infection in Each US State eAppendix 5. Description of Analytic Weight Derivation eAppendix 6. Further Descriptions of Analyses for Additional Populations Not in NHANES Sampling Frame eFigure 1. Conceptual Overview of Method for Estimating Hepatitis C Virus (HCV) RNA Prevalence in US States eFigure 2. Schematic for Levels of Specificity in Coding Injection-Related Overdose Deaths in the National Vital Statistics System eTable 1. National Distribution of Drug Deaths by Intentionality and Narcotic Involvement, National Vital Statistics System, 2013-2016 eTable 2. State-Level Total Drug Deaths and Narcotic Deaths by Intentionality, National Vital Statistics System 2013-2016 eTable 3. Values of Three Analytic Weighting Schemas eTable 4. Estimated Prevalence of HCV Antibody, NHANES 1999-2012 and 2013-2016, by Birth Cohort eTable 5. Sensitivity Analysis of Results Under Two Assumptions for Cumulative Mortality for 1945-1969 Birth Cohort, Among Population Included in NHANES Sampling Frame eTable 6. Summary of Additional Population Analytic Considerations eTable 7. Comparison Between Primary and Alternative Approach to Additional Population Estimates eReferences JAMA Network Open. 2018;1(8):e186371. doi:10.1001/jamanetworkopen.2018.6371 (Reprinted) December 21, 2018 14/14 Supplementary Online Content 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 eAppendix 1. NHANES Methodological Details eAppendix 2. Imputation for Missing Poverty Data in the National Health and Nutrition Examination Survey (NHANES) and the American Community Survey (ACS) eAppendix 3. Drug Overdose Mortality eAppendix 4. Equation for Estimator of the Total Persons With HCV Infection in Each US State eAppendix 5. Description of Analytic Weight Derivation eAppendix 6. Further Descriptions of Analyses for Additional Populations Not in NHANES Sampling Frame eFigure 1. Conceptual Overview of Method for Estimating Hepatitis C Virus (HCV) RNA Prevalence in US States eFigure 2. Schematic for Levels of Specificity in Coding Injection-Related Overdose Deaths in the National Vital Statistics System eTable 1. National Distribution of Drug Deaths by Intentionality and Narcotic Involvement, National Vital Statistics System, 2013-2016 eTable 2. State-Level Total Drug Deaths and Narcotic Deaths by Intentionality, National Vital Statistics System 2013-2016 eTable 3. Values of Three Analytic Weighting Schemas eTable 4. Estimated Prevalence of HCV Antibody, NHANES 1999-2012 and 2013-2016, by Birth Cohort eTable 5. Sensitivity Analysis of Results Under Two Assumptions for Cumulative Mortality for 1945-1969 Birth Cohort, Among Population Included in NHANES Sampling Frame eTable 6. Summary of Additional Population Analytic Considerations eTable 7. Comparison Between Primary and Alternative Approach to Additional Population Estimates eReferences © 2018 Rosenberg ES et al. JAMA Network Open. This supplementary material has been provided by the authors to give readers additional information about their work. © 2018 Rosenberg ES et al. JAMA Network Open. eAppendix 1: NHANES methodological details This analysis utilized NHANES data from 1999-2016. Response rates for NHANES were: 76% in 1999-2000, 80% in 2001-2002, 76% in 2003-2004, 77.36% in 2005-2006, 75.4% in 2007-2008, 77.3% in 2009-2010, 69.5% in 2011- 2012, 68.5% in 2013-2014, and 58.7% in 2015-2016. Written consent was obtained for participants aged 12 and older and parents or guardians of participants younger than 18, and written assent was obtained for youth 7 to 11 years old. NHANES protocol was approved by the National Center for Health Statistics Research Ethics Review Board. eAppendix 2: Imputation for missing poverty data in the National Health and Nutrition Examination Survey (NHANES) and the American Community Survey (ACS) NHANES collects data on household and family income as part of the demographic questionnaire administered to all respondents. NHANES uses annual poverty guidelines that vary by state and family size from the Department of Health and Human Services to calculate a ratio of family income to the poverty level. Of all NHANES respondents from 1999-2016 (N=75,974), 8.30% (n=6,303) were missing data for poverty ratio. We used a multiple imputation regression process to impute income-to-poverty ratio for all observations with missing values. First, we categorized all values of income-to-poverty ratio into: below the poverty level, 1.0-1.9 times the poverty level and 2 times the poverty level. Second, we used polytomous logistic regression to model the predicted probability of being within each income-to-poverty ratio categorization using the same race, sex and birth year categories from the primary analysis as predictor variables. This resulted in income-to-poverty ratio probability distributions specific to the covariate pattern of each individual observation. For observations that were missing income-to-poverty ratio values, we randomly drew a poverty-to-income value (from the individual defined distribution) to be imputed. Each run of the Monte Carlo simulation incorporated a new random draw from each individual income-to-poverty ratio distribution. The final analytic dataset used in the primary analysis included 47,387 respondents 18 years of age or older with non-missing HCV RNA test results. In the final analytic dataset, 3,931 (8.30%) or respondents had imputed income-to-poverty ratio values. Data used to generate ACS population estimates also contain comparable income-to-poverty level ratios for individual respondents. Of the 8,369,036 adult respondents who are not on active military duty in the 2012-2016 ACS PUMS dataset (the denominators for national NHANES analyses), 1.98% (n=237,600) were missing values for income-to-poverty level ratio. To impute these values, we followed an analogous process as the NHANES imputation model. We fit a polytomous regression model to predict the income-to-poverty level ratio distribution using state, race, sex and birth year as predictor variables. Each run of the Monte Carlo simulation randomly selected an income-to-poverty level value from the individual distributions for observations with missing values. eAppendix 3: Drug overdose mortality Injection drug use is the most commonly reported risk factor for acute HCV. Death certificate records submitted to the National Vital Statistics System (NVSS) contain useful data on drug overdose deaths that may specifically inform HCV-related risks. However, injection-specific drug use death, which is most ideal to signal HCV infection due to injection drug use, is neither reported as an ICD-10 code on death certificates or consistently in the open-text portions of certificates. We depict in eFigure 2 a conceptual model of the levels of detail available in mortality data in order to reach the underlying ideal construct of injection-related deaths. Not all of these levels are readily available from the NVSS Multiple Cause of Death microdata files and we present our analytic case-definition as the optimal combination of specificity and sensitivity, given this challenge. Level 1: Overdose deaths by state Level 1 depicts the most basic information regarding deaths with underlying cause of death drug poisoning codes available from NVSS mortality data. Drug poisoning ICD-10 codes are classified into four categories of intentionality [unintentional (X40-44), suicide (X60-64), homicide (X65), and undetermined intent (Y10-Y14)]. 5-7 Many publications that describe the opioid epidemic focus on all drug poisonings of all intentionalities. The case definition described in the Methods focuses on overdoses of unintentional and undetermined intent, which have been 8,9 the focus of additional recent assessments of opioid mortality. Overdoses of undetermined intent are included to increase the sensitivity of this measure as it would include potentially accidental or non-accidental overdoses for © 2018 Rosenberg ES et al. JAMA Network Open. which there was not enough information to record these otherwise. There were some differences in the proportion of overdoses of undetermined intent by state, which are shown in eTable 1. We explored the potential for some drug deaths coded as suicides to be accidental overdoses. The proportion of narcotic and unknown drug deaths coded as suicides varies by state (eTable 2). Since these vary only modestly, we did not include suicides in the primary analysis. Additionally, drug intoxication does not result in a majority of suicide deaths, relative to other (more violent) methods. It is actually possible that our inclusion of deaths of 10,11 undetermined intent includes misclassified suicides that did not have enough evidence to be reported as suicides. Level 2: Overdose deaths by drug class by state Level 2 depicts a bit more detail that is available in NVSS mortality data with regards to drug overdose deaths. Within each category of intentionality, ICD-10 codes are separated by drug class. These classes include: poisoning by and exposure to non-opioid analgesics, antipyretics, and antirheumatics; poisoning by and exposure to antiepileptic, sedative-hypnotic, antiparkinsonism and psychotropic drugs, not elsewhere classified; poisoning by and exposure to narcotics and psychodysleptics (hallucinogens), not elsewhere classified; poisoning by and exposure to other drugs acting on the autonomic nervous system; and poisoning by and exposure to other and unspecified drugs, medicaments, and biological substances. For this analysis, our definition was restricted to deaths due to poisoning by narcotics and psychodysleptics (X42, Y12) and exposure to other and unspecified drugs (X44, Y14). Drug overdoses due to narcotics were included because this drug class includes cannabis, cocaine, codeine, heroin, methadone, morphine, and opium. While not all of these drugs are typically administered via injection, this class provides a more specific definition than merely using all drugs. 12-14 Death investigation and drug toxicology processes vary by state. In order to account for this variation, and to 8,15 provide a more sensitive definition, we included overdoses due to other and unspecified drugs. Level 3: Overdose deaths by specific drugs by state The third level describes specific drugs that are more likely to be used via injection than other drugs. NVSS mortality data includes specific drug toxicology codes (T codes) for heroin, natural and semisynthetic opioids (morphine, codeine, hydrocodone, and oxycodone), methadone, synthetic opioids excluding methadone (fentanyl, fentanyl analogs, and tramadol), cocaine, and psychostimulants with abuse potential (methamphetamine, amphetamine, Ritalin, caffeine, and ecstasy). While inclusion of T codes for injection-related drugs such as heroin and synthetic opioids excluding methadone would be an improved signal of injection-related overdose, the 12-14,16,17 toxicology completion regarding specific drug codes on death certificates varies greatly by state and by year. A second issue with using specific drug codes for this analysis is that these are not mutually exclusive. Many drugs, particularly heroin and fentanyl, are found together in toxicology and subsequent death certificates. This becomes an issue since some drugs (i.e., fentanyl) have higher fatality rates from injection than others, and some drugs are 19,20 more frequently used via injection than other routes of administration. This is particularly important for assessing the geographic distribution of overdose deaths since the distribution of fentanyl varies, in part due to the relative ease of incorporating fentanyl into the white powder heroin supply east of the Mississippi River, compared to black tar heroin. These have not been incorporated into the present analysis due to the variation of completion by state, but this is a critical issue that should be explored in future research, in order to reduce biases introduced by non-specificity for injected drugs and by spatial heterogeneity in highly-lethal substances such as fentanyl. Level 4: Overdose deaths by specific drugs and injection status by state Finally, for the fourth level, the ideal measurement of injection-related overdose is overdose by drug by injection. The most relevant literature-based estimate of opioid overdose deaths attributable to injection is not generalizable to all states. This is the ideal measure to use for those born after 1970 for the HCV work. However, due to the lack of literature as well as the above-mentioned limitations in the specific drugs, we cannot achieve this level of detail. © 2018 Rosenberg ES et al. JAMA Network Open. eAppendix 4: Equation for Estimator of the Total Persons With HCV Infection in Each US State The above equation details our estimator for the total persons with HCV in each state ( ) in the NHANES population. Within 12 strata representing above-defined levels of sex, race/ethnicity, and birth year, we computed the standardization estimate , where is the 2016 ACS population in the state’s stratum (“state-stratum”) and the national 2013-2016 HCV RNA prevalence in stratum by direct estimation from a weighted logistic regression model of NHANES, which included terms for these strata, era (1999-2012, 2013-2016), and poverty. To yield standardized estimates for the 12 demographic strata that accounted for poverty, we weighted logistic model estimates according to the ACS poverty distribution for the 12 strata in each state. Next, we estimated the state-stratum-specific likelihood of HCV-related mortality ( ), using a logistic model of NVSS-derived mortality counts, per person-years ( ), that approximated full-stratification with main effects for state, sex, race/ethnicity, birth cohort, era; two-way interactions for state by each sex/ethnicity, race, birth cohort, and era; two-way and three-way interactions for each combination of sex, race/ethnicity, birth cohort, and era; and four-way interaction of sex, race/ethnicity, birth cohort, and era. These were divided by the national stratum-specific average, yielding a mortality ratio for the state-stratum. This process was repeated for the narcotic overdose mortality ( ). The two mortality ratios per stratum were averaged according to weights (values described in the main manuscript text and eAppendix 5) and then multiplied by the standardization-based value to yield adjusted totals . Summing these across all 12 state-strata yielded , which when divided by the ACS state population yielded the estimated prevalence rate. eAppendix 5: Description of analytic weight derivation To represent the spatial distribution of both older prevalent HCV infections (those existing during 1999-2012) and newer HCV infections (during 2013-2016) resulting from injection drug use, we separately modeled mortality rates from HCV infection and narcotic overdose. Mortality rates were used to calculate state-level mortality ratios for both HCV infection and narcotic overdose. Within each age group (defined by birth year), we calculated a single weighted state-level mortality ratio. For the first weighting scenario, we only used state-level mortality ratios from the HCV death model in order to compare to our previous method (eTable 3, results depicted in Figure 2). © 2018 Rosenberg ES et al. JAMA Network Open. For the second scenario, we used available data and expert knowledge of HCV epidemiology to derive the weights. First, we assumed all HCV infections among persons born <1945 are a result of older exposure and used a weight of 1.0 for that age group (w ). For the other two age groups, we used trends of HCV antibody prevalence in NHANES data to make inferences and assumptions about HCV incidence from 2013-2016. For persons born 1970, we estimated there were 411,449 persons with a history of infection (HCV antibody) prior to 2013 and 1,253,938 after 2013 (eTable 4). We assumed ~0% mortality for this age group during this time frame, suggesting 37.8% of persons with HCV exposure in this age group acquired HCV prior to 2013 and 62.2% thereafter. This is similar to other observations around 70%. Therefore, we used a weight of 0.378 for the HCV death state effect ratio for persons born 1970 (w ). The estimated number of persons born from 1945-1969 with HCV antibody did not meaningfully change between 1999-2012 (n=3,092,027) and 2013-2016 (n=2,848,019), which suggests that total number of incident infections is approximately equal to the total number of persons in this cohort who died between the midpoint of 1999-2012 to the midpoint of 2013-2016. Thus, estimating the cumulative death rate of persons born 1945-1969 with HCV antibody would provide an estimate of incidence to inform our weighting. Using a life-table approach incorporating all-cause mortality NVSS data and ACS population sizes for those born 1945-1969, we estimated the all-cause likelihood of death in the general population to be 0.047 between 1/1/2007 and 12/31/2014. We assumed persons in this age group who spontaneously clear their HCV infection or do not have a positive HCV RNA diagnosis experience the same death rate as the general population. From 2013-2016 NHANES data, 53.6% of persons born between 1945-1969 with HCV antibodies had a positive HCV RNA test and 62.0% of HCV RNA+ individuals had an HCV diagnosis. A previous paper reported that persons born between 1945-1964 with an HCV RNA diagnosis had an estimated mortality of 0.282. From this, we used the following calculation to estimate the mortality rate for persons born 1945-1969 with HCV antibody: Based on the assumption of a stable number of HCV infections within this age group, we used a weight of 0.875 for persons born 1945-1969 (w ). Persons with HCV antibody, but who are not currently infected, and those who are currently infected but are not diagnosed (and presumably relatively asymptomatic), may experience death rates higher than the background mortality rates, although such data are unavailable. Recognizing that this age group (persons born 1945-1969) has the highest HCV burden and may disproportionally impact prevalence results, we conducted a sensitivity analysis examining a third scenario that considered an overall mortality rate of 20% for person with HCV antibody (w = 0.80) (eTable 5). eAppendix 6: Further descriptions of analyses for additional populations not in NHANES sampling frame This analysis used the results of a literature review from Hofmeister et al., which used articles that reported HCV prevalence from 1/1/2013-12/31/2017. Search terms used for the incarcerated population were (“hepatitis C” or “HCV”) and (“prison” or “jail” or “correctional”) and for the homeless population were (“hepatitis C” or “HCV”) and (“homeless” or “homeless persons” or “housing unstable” or “housing insecure”). Details on motivation for additional populations and prevalence and population size sources used in Hofmeister et al. are described in eTable Alternative Approach to Additional Population Estimates The alternative approach to estimating state-level HCV RNA prevalence among additional populations involved two steps. First, we generated a national prevalence ratio for each population component (incarcerated, unsheltered homeless, and nursing home residents) by taking the national HCV prevalence in the population component divided by the national HCV prevalence in NHANES. Then, we multiplied this national prevalence ratio by the each state’s HCV prevalence in the NHANES population and each state’s population size of each population component. This provided an estimate of HCV infections among additional populations that reflects each state’s underlying HCV © 2018 Rosenberg ES et al. JAMA Network Open. prevalence rather than the national HCV estimate. This assumes that the state epidemics are echoed in these additional populations. Full results including both the primary approach for the additional population estimate and the alternative are shown in eTable 7. There was a median difference in prevalence between methods 1 and 2 of 0.004% (relative multiplicative change of -0.5%). © 2018 Rosenberg ES et al. JAMA Network Open. eFigure 1: Conceptual overview of method for estimating Hepatitis C virus (HCV) RNA prevalence in US states We used a multistep, statistical approach that first generated estimates for each state using National Health and Nutrition Examination Survey (NHANES) national prevalence in sex, race, birth cohort, and poverty strata (1A). To represent the spatial distribution of older and recent infections respectively, we separately modeled mortality rates from HCV infection and narcotic overdose in the National Vital Statistics System (NVSS), yielding stratified state-level mortality ratios (1B). We weighted these ratios according to birth cohort-specific trends in HCV exposure history (1C) and used them to adjust initial NHANES-based estimates (1D). Finally, we estimated additional infections among populations not included in NHANES’ sampling frame, by applying literature- based estimates of prevalence in these groups to state-specific population estimates (1E). eFigure 2: Schematic for levels of specificity in coding injection-related overdose deaths in the National Vital Statistics System Level 4 (Ideal) • Overdose Level 3 deaths by specific drugs • Overdose Level 2 and injection deaths by by state specific drugs • Overdose Level 1 by state deaths by drug class by • Overdose state deaths by state © 2018 Rosenberg ES et al. JAMA Network Open. eTable 1. National distribution of drug deaths by intentionality and narcotic involvement, National Vital Statistics System, 2013-2016 Intentionality of death and drug class n (%) All-intention deaths, all drug classes 221,710 (100%) Homicide 497 (0.22%) Unintentional, suicide, undetermined deaths 221,213 (99.8%) o Narcotics and unspecified drugs 195,134 (88%) Suicide deaths 17,017 (9%) Narcotics only 2,869 (17%) Unspecified drugs only 14162 (83%) Unintentional and undetermined cause deaths 178,122 (91%) Unintentional deaths 166,822 (85%) Narcotics only 82,288 (49%) Unspecified drugs only 84,609 (51%) Undetermined deaths 11,300 (6%) Narcotics only 5,449 (48%) Unspecified drugs only 5,857 (52%) Drug intentions are defined by ICD-10 codes: Unintentional: X40-X44 Intentional self-poisoning (suicide): X60-X64 Homicide includes: X85 Undetermined intent: Y10-Y14 Drug classes are defined by ICD-10 codes: Poisoning by and exposure to nonopioid analgesics, antipyretics, and antirheumatics: X40, X60, Y10 Poisoning by and exposure to antiepileptic, sedative-hypnotic, antiparkinsonism and psychotropic drugs, not elsewhere classified: X41, X61, Y11 Poisoning by and exposure to narcotics and psychodysleptics (hallucinogens), not elsewhere classified: X42, X62, Y12 Poisoning by and exposure to other drugs acting on the autonomic nervous system: X43, X63, Y13 Poisoning by and exposure to other and unspecified drugs, medicaments, and biological substances: X44, X64, Y14 b 5-7 This is how many publications often describe drug overdoses This is the definition used in this analysis. © 2018 Rosenberg ES et al. JAMA Network Open. eTable 2. State-level total drug deaths and narcotic deaths by intentionality, National Vital Statistics System 2013-2016 Deaths from narcotic or unspecified drugs Deaths from all drugs Unintentional, (unintentional, suicide, and suicide, and undetermined Unintentional Suicide Undetermined a a a undetermined intent) intent only only intent only Total 221,213 195,134 166,822 (85%) 17,017 (9%) 11,300 (6%) State Alabama 2,958 2,767 2,474 (89%) 169 (6%) 124 (4%) Alaska 527 450 378 (84%) 49 (11%) 23 (5%) Arizona 5,531 4,390 3,470 (79%) 536 (12%) 384 (9%) Arkansas 1,610 1,264 926 (73%) 178 (14%) 160 (13%) California 21,077 15,322 12,975 (85%) 2,000 (13%) 348 (2%) Colorado 3,823 3,229 2,564 (79%) 541 (17%) 125 (4%) Connecticut 3,027 2,865 2,627 (92%) 200 (7%) 38 (1%) Delaware 860 811 716 (88%) 53 (7%) 42 (5%) District of Columbia 613 574 497 (87%) 24 (4%) 53 (9%) Florida 13,777 12,641 11,123 (88%) 1,387 (11%) 131 (1%) Georgia 5,227 4,517 4,047 (90%) 374 (8%) 97 (2%) Hawaii 754 422 308 (73%) 69 (16%) 45 (11%) Idaho 945 748 534 (71%) 133 (18%) 81 (11%) Illinois 8,059 7,448 6,604 (89%) 571 (8%) 273 (4%) Indiana 5,428 5,052 4,241 (84%) 384 (8%) 427 (8%) Iowa 1,341 1,043 813 (78%) 173 (17%) 57 (5%) Kansas 1,506 1,202 947 (79%) 187 (16%) 68 (6%) Kentucky 5,073 4,769 4,325 (91%) 198 (4%) 246 (5%) Louisiana 3,789 3,533 3,114 (88%) 182 (5%) 237 (7%) Maine 1,052 963 854 (89%) 91 (9%) 18 (2%) Maryland 5,399 5,107 1,396 (27%) 214 (4%) 3,497 (68%) Massachusetts 6,493 6,208 5,857 (94%) 267 (4%) 84 (1%) Michigan 7,915 7,522 6,001 (80%) 580 (8%) 941 (13%) Minnesota 2,426 1,985 1,658 (84%) 248 (12%) 79 (4%) Mississippi 1,467 1,316 1,147 (87%) 108 (8%) 61 (5%) Missouri 4,791 4,119 3,540 (86%) 385 (9%) 194 (5%) Montana 553 444 283 (64%) 83 (19%) 78 (18%) Nebraska 552 438 349 (80%) 70 (16%) 19 (4%) Nevada 2,865 2,152 1,794 (83%) 311 (14%) 47 (2%) New Hampshire 1,487 1,403 1,273 (91%) 101 (7%) 29 (2%) New Jersey 6,334 6,066 5,628 (93%) 333 (5%) 105 (2%) New Mexico 2,195 1,828 1,588 (87%) 205 (11%) 35 (2%) New York 11,543 10,845 9,458 (87%) 779 (7%) 608 (6%) North Carolina 6,289 5,840 5,109 (87%) 580 (10%) 151 (3%) North Dakota 229 192 155 (81%) 22 (11%) 15 (8%) © 2018 Rosenberg ES et al. JAMA Network Open. Deaths from narcotic or unspecified drugs Deaths from all drugs Unintentional, (unintentional, suicide, and suicide, and undetermined Unintentional Suicide Undetermined a a a undetermined intent) intent only only intent only Ohio 13,397 12,777 11,975 (94%) 602 (5%) 200 (2%) Oklahoma 3,360 2,401 2,098 (87%) 172 (7%) 131 (5%) Oregon 2,129 1,650 1,239 (75%) 273 (17%) 138 (8%) Pennsylvania 13,336 12,852 11,730 (91%) 786 (6%) 337 (3%) Rhode Island 1,167 1,090 1,002 (92%) 70 (6%) 18 (2%) South Carolina 3,203 2,874 2,600 (90%) 226 (8%) 48 (2%) South Dakota 268 192 135 (70%) 46 (24%) 11 (6%) Tennessee 5,860 5,319 4,742 (89%) 364 (7%) 213 (4%) Texas 11,732 9,615 8,314 (86%) 979 (10%) 322 (3%) Utah 2,622 2,276 1,567 (69%) 305 (13%) 405 (18%) Vermont 433 389 311 (80%) 42 (11%) 36 (9%) Virginia 4,401 3,963 3,471 (88%) 368 (9%) 124 (3%) Washington 4,496 3,541 2,955 (83%) 460 (13%) 126 (4%) West Virginia 3,095 2,847 2,601 (91%) 126 (4%) 120 (4%) Wisconsin 3,769 3,503 3,006 (86%) 363 (10%) 134 (4%) Wyoming 430 370 303 (82%) 50 (14%) 17 (5%) 4,338 3,826 3,271 334 222 Mean 3,095 2,847 2,474 214 120 Median 1,254 1,067 890 105 46 Lower Quartile 5,480 5,080 4,283 385 207 Upper Quartile Denominator for percentage is deaths from narcotics and unspecified drugs of unintentional, suicide, and undetermined intent. © 2018 Rosenberg ES et al. JAMA Network Open. eTable 3: Values of three analytic weighting schemas Weight ( applied to drug Weight applied to Scenario HCV death state effect overdose death state effect Birth Year (standardized ratio) (standardized ratio) 1. ICD-10 Codes for HCV Only <1945 100% 0% 1945-1969 100% 0% 1970 100% 0% 2. ICD-10 Codes for HCV and Narcotic Overdose (primary analysis) <1945 100% 0% 1945-1969 87.5% 12.5% 1970 37.8% 62.2% 3. ICD-10 Codes for HCV and Narcotic Overdose (sensitivity analysis) <1945 100% 0% 1945-1969 80.0% 20.0% 1970 37.8% 62.2% Abbreviations: HCV, hepatitis C virus; ICD-10, International Classification of Diseases, Tenth Revision eTable 4: Estimated prevalence of HCV antibody, NHANES 1999-2012 and 2013- 2016, by birth cohort 2005-2007 ACS NHANES anti-HCV+ 1999-2012 N Per 100 95% CI n 95%CI <1945 43,453,450 0.66 0.50 0.88 287,749 216,876 381,521 1945-1969 100,776,581 3.07 2.70 3.48 3,092,027 2,721,572 3,511,056 1970 76,820,211 0.54 0.38 0.75 411,449 292,378 578,533 2012-2016 ACS NHANES anti-HCV+ 2013-2016 N Per 100 95% CI n 95%CI <1945 29,693,961 0.52 0.28 0.94 152,983 83,648 279,272 1945-1969 97,702,202 2.92 2.38 3.57 2,848,019 2,321,697 3,489,239 1970 113,756,485 0.96 0.69 1.32 1,087,171 783,782 1,506,363 Abbreviations: HCV, hepatitis C virus; NHANES, National Health and Nutrition Examination Survey; ACS, American Community Survey; anti-HCV, hepatitis C virus antibody; CI, confidence interval © 2018 Rosenberg ES et al. JAMA Network Open. eTable 5. Sensitivity analysis of results under two assumptions for cumulative mortality for 1945-1969 birth cohort, among population included in NHANES sampling frame Primary Analysis Results (12.5% mortality) Sensitivity Analysis Results (20.0% mortality) State ACS 2012- HCV 95% CI % (95% CI) HCV 95% CI % (95% CI) a b b 2016 RNA+ RNA+ Alabama 3,671,100 26,100 (23,100 - 29,600) 0.71 (0.63 - 0.81) 26,100 (23,100 - 29,600) 0.71 (0.63 - 0.81) Alaska 542,500 4,700 (3,900 - 5,700) 0.86 (0.72 - 1.05) 4,600 (3,800 - 5,600) 0.85 (0.70 - 1.03) Arizona 5,020,500 55,300 (48,000 - 64,100) 1.10 (0.96 - 1.28) 54,800 (47,600 - 63,500) 1.09 (0.95 - 1.26) Arkansas 2,215,500 19,100 (16,800 - 21,800) 0.86 (0.76 - 0.99) 18,600 (16,400 - 21,300) 0.84 (0.74 - 0.96) California 29,160,200 288,500 (253,500 331,800) 0.99 (0.87 - 1.14) 281,200 (247,200 323,100) 0.96 (0.85 - 1.11) - - Colorado 4,057,000 32,500 (28,000 - 38,400) 0.80 (0.69 - 0.95) 32,100 (27,600 - 37,800) 0.79 (0.68 - 0.93) Connecticut 2,771,800 16,500 (14,200 - 19,700) 0.60 (0.51 - 0.71) 17,200 (14,700 - 20,500) 0.62 (0.53 - 0.74) Delaware 719,400 5,600 (4,800 - 6,500) 0.78 (0.67 - 0.90) 5,700 (4,900 - 6,600) 0.79 (0.68 - 0.92) District of 537,500 12,400 (10,500 - 14,800) 2.32 (1.95 - 2.76) 12,600 (10,600 - 15,000) 2.35 (1.98 - 2.80) Columbia Florida 15,620,600 133,200 (117,700 152,100) 0.85 (0.75 - 0.97) 132,700 (117,300 151,600) 0.85 (0.75 - 0.97) - - Georgia 7,465,900 46,400 (41,300 - 52,300) 0.62 (0.55 - 0.70) 46,500 (41,400 - 52,400) 0.62 (0.56 - 0.70) Hawaii 1,094,200 5,700 (4,700 - 7,000) 0.52 (0.43 - 0.64) 5,600 (4,600 - 6,800) 0.51 (0.42 - 0.62) Idaho 1,187,300 9,900 (8,400 - 11,800) 0.84 (0.71 - 0.99) 9,600 (8,200 - 11,400) 0.81 (0.69 - 0.96) Illinois 9,703,700 47,700 (42,200 - 54,300) 0.49 (0.44 - 0.56) 50,500 (44,700 - 57,400) 0.52 (0.46 - 0.59) Indiana 4,915,800 35,400 (30,900 - 40,700) 0.72 (0.63 - 0.83) 36,000 (31,500 - 41,500) 0.73 (0.64 - 0.84) Iowa 2,339,900 11,100 (9,500 - 13,100) 0.47 (0.40 - 0.56) 10,900 (9,300 - 12,900) 0.47 (0.40 - 0.55) Kansas 2,137,000 12,600 (10,900 - 14,800) 0.59 (0.51 - 0.69) 12,500 (10,700 - 14,700) 0.58 (0.50 - 0.69) Kentucky 3,331,500 38,600 (33,600 - 44,800) 1.16 (1.01 - 1.34) 39,600 (34,500 - 45,900) 1.19 (1.04 - 1.38) Louisiana 3,445,000 44,900 (40,000 - 50,400) 1.30 (1.16 - 1.46) 44,400 (39,700 - 49,900) 1.29 (1.15 - 1.45) Maine 1,058,600 6,500 (5,400 -7,800) 0.61 (0.51 - 0.74) 6,600 (5,600 - 8,000) 0.63 (0.53 - 0.75) Maryland 4,547,800 37,300 (32,700 - 43,100) 0.82 (0.72 - 0.95) 38,600 (33,800 - 44,600) 0.85 (0.74 - 0.98) Massachuse 5,283,400 35,800 (30,600 - 42,500) 0.68 (0.58 - 0.80) 37,200 (31,900 - 44,200) 0.70 (0.60 - 0.84) tts Michigan 7,578,400 62,800 (55,800 - 70,900) 0.83 (0.74 - 0.94) 64,400 (57,200 - 72,700) 0.85 (0.76 - 0.96) Minnesota 4,115,000 22,300 (19,400 - 26,000) 0.54 (0.47 - 0.63) 22,000 (19,200 - 25,600) 0.53 (0.47 - 0.62) Mississippi 2,205,500 19,600 (17,500 - 22,200) 0.89 (0.79 - 1.01) 19,300 (17,100 - 21,800) 0.87 (0.78 - 0.99) Missouri 4,575,700 35,200 (31,100 - 40,200) 0.77 (0.68 - 0.88) 35,700 (31,400 - 40,700) 0.78 (0.69 - 0.89) Montana 787,100 6,800 (5,700 - 8,000) 0.86 (0.73 - 1.02) 6,600 (5,600 - 7,800) 0.84 (0.71 - 0.99) Nebraska 1,391,400 6,900 (6,000 - 8,200) 0.50 (0.43 - 0.59) 6,700 (5,800 - 7,900) 0.48 (0.41 - 0.57) Nevada 2,148,500 19,300 (16,800 - 22,400) 0.90 (0.78 - 1.04) 19,500 (17,000 - 22,700) 0.91 (0.79 - 1.06) New 1,046,300 7,200 (5,900 - 8,900) 0.69 (0.57 - 0.85) 7,400 (6,100 - 9,200) 0.71 (0.58 - 0.88) Hampshire New Jersey 6,810,300 43,400 (37,900 - 50,300) 0.64 (0.56 - 0.74) 44,200 (38,600 - 51,400) 0.65 (0.57 - 0.75) New Mexico 1,557,100 25,000 (21,600 - 29,100) 1.61 (1.39 - 1.87) 24,900 (21,500 - 29,000) 1.60 (1.38 - 1.86) © 2018 Rosenberg ES et al. JAMA Network Open. Primary Analysis Results (12.5% mortality) Sensitivity Analysis Results (20.0% mortality) c c State ACS 2012- HCV 95% CI % (95% CI) HCV 95% CI % (95% CI) a b b 2016 RNA+ RNA+ New York 15,260,100 107,100 (94,900 - 121,600) 0.70 (0.62 - 0.80) 108,300 (95,900 - 123,100) 0.71 (0.63 - 0.81) North 7,545,400 60,200 (53,600 - 68,100) 0.80 (0.71 - 0.90) 59,900 (53,300 - 67,800) 0.79 (0.71 - 0.90) Carolina North 559,100 2,200 (1,800 - 2,800) 0.39 (0.32 - 0.50) 2,200 (1,700 - 2,700) 0.39 (0.31 - 0.49) Dakota Ohio 8,787,100 81,500 (71,800 - 93,200) 0.93 (0.82 - 1.06) 85,200 (75,200 - 97,400) 0.97 (0.86 - 1.11) Oklahoma 2,862,800 48,900 (42,700 - 56,500) 1.71 (1.49- 1.97) 47,400 (41,400 - 54,700) 1.66 (1.45 - 1.91) Oregon 3,086,200 45,700 (39,400 - 53,700) 1.48 (1.28 - 1.74) 43,500 (37,500 - 51,100) 1.41 (1.21 - 1.65) Pennsylvani 9,888,700 84,500 (74,300 - 97,000) 0.86 (0.75 - 0.98) 87,300 (76,800 - 100,200) 0.88 (0.78 - 1.01) Rhode 829,900 9,600 (8,300 - 11,400) 1.16 (1.00 - 1.37) 9,800 (8,400 - 11,600) 1.18 (1.01 - 1.40) Island South 3,689,100 31,900 (28,400 - 36,100) 0.87 (0.77 - 0.98) 31,900 (28,400 - 36,000) 0.86 (0.77 - 0.98) Carolina South 628,400 3,000 (2,500 - 3,700) 0.48 (0.39 - 0.59) 2,900 (2,400 - 3,600) 0.46 (0.38 - 0.57) Dakota Tennessee 4,972,200 63,500 (56,200 - 72,100) 1.28 (1.13 - 1.45) 63,400 (56,100 - 72,000) 1.27 (1.13 - 1.45) Texas 19,455,200 178,000 (157,500 203,100) 0.91 (0.81 - 1.04) 172,500 (152,700 196,600) 0.89 (0.79 - 1.01) - - Utah 2,024,600 11,000 (9,300 - 13,100) 0.54 (0.46 - 0.65) 11,400 (9,700 - 13,600) 0.56 (0.48 - 0.67) Vermont 499,100 3,500 (2,900 - 4,200) 0.70 (0.58 - 0.85) 3,500 (2,900 - 4,200) 0.69 (0.57 - 0.84) Virginia 6,348,500 33,500 (29,400 - 38,500) 0.53 (0.46 - 0.61) 33,400 (29,400 - 38,400) 0.53 (0.46 - 0.61) Washington 5,412,700 50,000 (43,100 - 58,900) 0.92 (0.80 - 1.09) 48,700 (42,000 - 57,400) 0.90 (0.78 - 1.06) West 1,439,300 19,500 (16,700 - 23,000) 1.35 (1.16 - 1.60) 20,400 (17,500 - 23,900) 1.41 (1.22 - 1.66) Virginia Wisconsin 4,384,900 24,000 (21,000 - 27,700) 0.55 (0.48 - 0.63) 24,600 (21,600 - 28,300) 0.56 (0.49 - 0.65) Wyoming 437,600 3,200 (2,600 - 3,900) 0.73 (0.60 - 0.90) 3,200 (2,600 - 3,900) 0.73 (0.60 - 0.89) d,e Total 241,152,60 2,035,1 (1,803,60 2,318,000) 0.84 (0.75 - 0.96) 2,033,800 (1,802,40 2,316,600) 0.84 (0.75 - 0.96) 0 00 0 - 0 - Population sizes are estimated as of December 2016 based on American Community Survey 5-year estimates from 2012-2016 and include noninstitutionalized adults eligible for NHANES. This estimate includes 1,288,600 active-duty military personnel ineligible for NHANES, which cannot be removed at the state-level because population sizes are unavailable by home state of personnel. Number of infected persons is calculated by multiplying the prevalence percentage estimate by the adult population size, before rounding for presentation. NHANES prevalence percentage estimates are based on results from 2013-2016 NHANES. Population size includes noninstitutionalized adults eligible for NHANES from the 2012- 2016 American Community Survey. Values may not sum to total due to rounding. Results are based on a regression model that incorporates data for the time period 1999-2016 and generates estimates via simulations. Accordingly, these results do not precisely sum to previous national totals for the 2013-2016 period. Abbreviations: NHANES, National Health and Nutrition Examination Survey; ACS, American Community Survey; HCV, hepatitis C virus; CI, confidence interval © 2018 Rosenberg ES et al. JAMA Network Open. eTable 6. Summary of additional population analytic considerations Population features evaluated for Data sources used in analysis analytic decisions Population Included Included Evidence HCV Mean Population- in in ACS of prevalenc prevalenc size source NHANE populatio Differentia e source e S n size l HCV samplin estimates Risk g frame used for NHANES analyses Residential, Yes Yes N/A NHANES 0.9% ACS, 2012 – noninstitutionalize 2016 d, civilian population Incarcerated No No Yes Literature 10.7% Bureau of Justice Statistics, Unsheltered No No Yes Literature 10.8% U.S. homeless Department of Housing and Urban Development , 2016 Nursing homes No No No NHANES 0.5% National Survey of Long Term Care Providers, 30,c People living in Yes Yes Yes N/A N/A N/A d, e AI/AN areas Hospitalized Yes Yes No N/A N/A N/A Other high risk Yes Yes Yes N/A N/A N/A populations (e.g., persons who inject drugs, sheltered homeless) a 31 32 Estimated mean prevalence calculated using a random effects model with prevalence inputs from Akiyama et al., Cocoros et al., 33 34 35 36 37 de la Flor et al., Kuncio et al., Mahowald et al., Schoenbachler et al., Stockman et al. For Akiyama, de la Flor, and Kuncio, RNA prevalence was calculated as (reported HCV Antibody Prevalence) x (NHANES 2013-2016 HCV RNA prevalence), where NHANES 2013-2016 HCV RNA prevalence among antibody positives= 0.575. For Cocoros, Mahowald, Schoenbachler, and Stockman, RNA prevalence was calculated as (Number HCV RNA-Positive/Number Tested HCV RNA) x (reported HCV Antibody Prevalence). b 38 Literature prevalence from Coyle et al. Scaled for population growth to 2016 Residents of Native American reservations and tribal lands and Alaska Native village statistical areas Excluded from analysis due to inclusion in both NHANES (prevalence numerator) and ACS (population size denominator) For persons who inject drugs, we assessed likely bias and determined that national NHANES estimates sufficiently represented HCV prevalence in this subpopulation Abbreviations: NHANES, National Health and Nutrition Examination Survey; ACS, American Community Survey; HCV, hepatitis C virus; AI/AN, American Indian/Alaska Native © 2018 Rosenberg ES et al. JAMA Network Open. eTable 7. Comparison between primary and alternative approach to additional population estimates Additional Populations Estimation: Additional Populations Estimation: Comparison Primary Method Alternative Method between methods Population RNA+ RNA+ Difference b b b State ACS 2012- Additional Total Among In Total % Among In Total % RNA+ % 2016 Populatio NHANES Additiona NHANES Additional ns population l population Population c c Populatio s ns Alabama 3,671,100 65,600 3,736,700 26,100 4,600 30,700 0.82 26,100 3,900 29,900 0.80 732 0.02 Alaska 542,500 5,500 548,000 4,700 500 5,200 0.95 4,700 500 5,200 0.95 (10) (0.00) Arizona 5,020,500 70,000 5,090,500 55,300 6,300 61,500 1.21 55,300 8,200 63,400 1.25 (1,889) (0.04) Arkansas 2,215,500 43,200 2,258,700 19,100 2,700 21,800 0.97 19,100 2,800 21,900 0.97 (60) (0.00) California 29,160,20 384,500 29,544,700 288,500 30,400 318,900 1.08 288,500 35,500 324,000 1.10 (5,119) (0.02) Colorado 4,057,000 51,500 4,108,500 32,500 3,800 36,300 0.88 32,500 3,600 36,100 0.88 190 0.00 Connecticut 2,771,800 40,900 2,812,700 16,500 1,800 18,300 0.65 16,500 1,300 17,800 0.63 510 0.02 Delaware 719,400 11,100 730,500 5,600 700 6,300 0.86 5,600 700 6,300 0.86 57 0.01 District of 537,500 4,800 542,400 12,400 200 12,700 2.34 12,400 700 13,100 2.42 (414) (0.08) Columbia Florida 15,620,60 239,600 15,860,200 133,200 17,800 151,000 0.95 133,200 18,000 151,200 0.95 (200) (0.00) Georgia 7,465,900 131,700 7,597,700 46,400 10,500 56,800 0.75 46,400 7,700 54,100 0.71 2,776 0.04 Hawaii 1,094,200 13,200 1,107,400 5,700 1,000 6,700 0.60 5,700 600 6,300 0.57 391 0.04 Idaho 1,187,300 15,900 1,203,300 9,900 1,300 11,200 0.93 9,900 1,300 11,200 0.93 14 0.00 Illinois 9,703,700 138,700 9,842,400 47,700 7,100 54,900 0.56 47,700 4,100 51,800 0.53 3,015 0.03 Indiana 4,915,800 84,300 5,000,100 35,400 4,900 40,200 0.80 35,400 4,200 39,500 0.79 705 0.01 Iowa 2,339,900 39,400 2,379,300 11,100 1,500 12,600 0.53 11,100 900 12,000 0.50 658 0.03 Kansas 2,137,000 36,600 2,173,600 12,600 1,900 14,600 0.67 12,600 1,400 14,000 0.64 577 0.03 Kentucky 3,331,500 59,200 3,390,700 38,600 3,900 42,500 1.25 38,600 5,300 44,000 1.30 (1,461) (0.04) Louisiana 3,445,000 73,500 3,518,500 44,900 5,100 50,000 1.42 44,900 7,900 52,700 1.50 (2,739) (0.08) Maine 1,058,600 10,800 1,069,400 6,500 500 7,000 0.65 6,500 400 6,800 0.64 124 0.01 Maryland 4,547,800 55,000 4,602,900 37,300 3,300 40,600 0.88 37,300 3,200 40,500 0.88 101 0.00 Massachuset 5,283,400 63,100 5,346,600 35,800 2,300 38,100 0.71 35,800 1,900 37,600 0.70 440 0.01 ts Michigan 7,578,400 98,200 7,676,600 62,800 6,300 69,100 0.90 62,800 6,200 69,000 0.90 93 0.00 Minnesota 4,115,000 44,900 4,159,900 22,300 1,900 24,300 0.58 22,300 1,300 23,600 0.57 672 0.02 Mississippi 2,205,500 46,100 2,251,700 19,600 3,200 22,900 1.02 19,600 3,400 23,000 1.02 (169) (0.01) Missouri 4,575,700 85,100 4,660,800 35,200 5,100 40,300 0.86 35,200 4,600 39,800 0.85 458 0.01 Montana 787,100 11,000 798,100 6,800 700 7,400 0.93 6,800 700 7,500 0.93 (13) (0.00) Nebraska 1,391,400 21,400 1,412,800 6,900 1,000 7,900 0.56 6,900 600 7,500 0.53 405 0.03 Nevada 2,148,500 29,000 2,177,400 19,300 2,600 21,900 1.00 19,300 2,700 22,000 1.01 (157) (0.01) Additional Populations Estimation: Additional Populations Estimation: Comparison Primary Method Alternative Method between methods Population RNA+ RNA+ Difference © 2018 Rosenberg ES et al. JAMA Network Open. b b b State ACS 2012- Additional Total Among In Total % Among In Total % RNA+ % 2016 Populatio NHANES Additiona NHANES Additional ns population l population Population c c Populatio s ns New 1,046,300 11,700 1,058,000 7,200 500 7,700 0.73 7,200 400 7,600 0.72 87 0.01 Hampshire New Jersey 6,810,300 80,600 6,890,900 43,400 3,800 47,200 0.68 43,400 2,900 46,200 0.67 933 0.01 New Mexico 1,557,100 20,900 1,578,000 25,000 1,600 26,700 1.69 25,000 3,100 28,200 1.78 (1,485) (0.09) New York 15,260,10 188,400 15,448,400 107,100 8,900 116,000 0.75 107,100 7,400 114,500 0.74 1,485 0.01 North 7,545,400 94,800 7,640,100 60,200 6,200 66,400 0.87 60,200 5,800 66,000 0.86 332 0.00 Carolina North Dakota 559,100 9,200 568,300 2,200 400 2,600 0.45 2,200 200 2,400 0.42 194 0.03 Ohio 8,787,100 151,400 8,938,500 81,500 8,100 89,600 1.00 81,500 8,900 90,300 1.01 (759) (0.01) Oklahoma 2,862,800 59,900 2,922,700 48,900 4,400 53,300 1.82 48,900 8,800 57,800 1.98 (4,460) (0.15) Oregon 3,086,200 34,800 3,120,900 45,700 2,900 48,700 1.56 45,700 5,200 50,900 1.63 (2,216) (0.07) Pennsylvani 9,888,700 166,900 10,055,600 84,500 9,300 93,900 0.93 84,500 9,500 94,000 0.94 (183) (0.00) Rhode Island 829,900 11,500 841,300 9,600 400 10,000 1.19 9,600 500 10,200 1.21 (152) (0.02) South 3,689,100 51,200 3,740,300 31,900 3,700 35,600 0.95 31,900 3,800 35,700 0.95 (100) (0.00) Carolina South 628,400 12,600 641,000 3,000 700 3,700 0.57 3,000 400 3,400 0.53 279 0.04 Dakota Tennessee 4,972,200 81,600 5,053,700 63,500 5,600 69,100 1.37 63,500 8,500 72,000 1.42 (2,883) (0.06) Texas 19,455,20 322,100 19,777,300 178,000 24,500 202,500 1.02 178,000 26,600 204,500 1.03 (2,036) (0.01) Utah 2,024,600 17,500 2,042,200 11,000 1,300 12,300 0.60 11,000 800 11,800 0.58 473 0.02 Vermont 499,100 4,700 503,800 3,500 200 3,700 0.73 3,500 200 3,700 0.73 34 0.01 Virginia 6,348,500 87,900 6,436,400 33,500 6,400 39,900 0.62 33,500 4,000 37,500 0.58 2,379 0.04 Washington 5,412,700 56,200 5,468,900 50,000 4,200 54,200 0.99 50,000 4,600 54,600 1.00 (391) (0.01) West Virginia 1,439,300 20,100 1,459,400 19,500 1,100 20,600 1.41 19,500 1,800 21,300 1.46 (692) (0.05) Wisconsin 4,384,900 64,700 4,449,600 24,000 3,900 27,900 0.63 24,000 2,600 26,600 0.60 1,371 0.03 Wyoming 437,600 6,700 444,300 3,200 500 3,700 0.82 3,200 400 3,600 0.81 59 0.01 b,d Total 241,152,6 3,529,000 244,681,60 2,035,100 231,600 2,266,70 0.93 2,035,100 239,600 2,274,800 0.93 (8,043) 0.00 00 0 0 Population sizes are estimated as of December 2016 based on American Community Survey 5-year estimates from 2012-2016 and include noninstitutionalized adults eligible for NHANES. 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Prevalence of Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016

Prevalence of Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016

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Prevalence of Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016

Prevalence of Hepatitis C Virus Infection in US States and the District of Columbia, 2013 to 2016

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