Sociodemographic Disparities in Hepatitis C Care Utilization and Testing in the United States: A Nationwide Survey Analysis

Sociodemographic Disparities in Hepatitis C Care Utilization and Testing in the United States: A Nationwide Survey Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Sociodemographic Disparities in Hepatitis C Care Utilization and Testing in the United States: A Nationwide Survey Analysis Adam Buckholz, Xiaohan Ying, Yihan Liu, Brian McSteen, Olivia Blocker, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6378738/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Abstract Introduction Hepatitis C infection (HCV) is a leading cause of liver disease and mortality. Despite curative treatment options, eradication remains elusive. Although the US has national screening recommendations, HCV remains under-screened and under-diagnosed. We utilized two national surveys to estimate trends in overall HCV care utilization and testing in the US. Methods Data from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey from 2010-2019 were analyzed via inverse probability weighting to generate national estimates of visits and testing. Weighted chi-square and logistic regression analyses adjusted for demographics, payor type and time assessed the primary outcome of ambulatory care utilization as well as the secondary outcome of office-based hepatitis C screening. Results Between 2010-2019, 23,469,344 HCV ambulatory visits were identified with higher overall adjusted visit rates for men (OR 1.54), people born 1945-1965 (OR 4.00), and insured by Medicare (OR 1.98) with increased utilization by White, privately insured patients in the office since 2016. Identified HCV cases who were covered by Medicaid (OR 6.05) or had associated substance use disorder (SUD) (OR 3.30) were more likely to utilize the ER than office care. Since 2016 per-visit screening rates were low both overall (1%) and when restricting to initial primary care preventive health visits (2.1%). Discussions In a nationally representative study of ambulatory care utilization, we identified increasing rates of HCV visits, largely in White privately insured patients seen in office. Low overall screening rates and disproportionate ER utilization among rural, racial/ethnic minorities, Medicaid insured and patients with SUD highlight the importance of updated policy and practice guidelines to improve identification and care linkage for HCV. Health sciences/Gastroenterology/Hepatology/Hepatitis/Viral hepatitis/Hepatitis C Health sciences/Health care/Public health/Population screening Health sciences/Medical research/Epidemiology Health sciences/Health care/Public health/Epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hepatitis C virus (HCV) infection is among the leading causes of liver disease and mortality, with over 70 million people infected worldwide. The prevalence of patients with chronic hepatitis C (CHC) infection is between 1–2% in the United States, making it the most common bloodborne infection in the nation. 1 , 2 CHC is a major cause of mortality, and if untreated, patients with CHC can develop cirrhosis and hepatocellular carcinoma. 3 , 4 The approval of direct-acting antivirals has revolutionized the treatment for patients with HCV, with estimated cure rates of 97–99%. 5 However, despite advances in medical therapy, HCV remains a leading cause of liver-related death and transplantation and is rising in incidence in younger individuals. One major factor in the persistent burden of CHC is under-screening, with studies estimating that less than half of those with CHC are aware of their positive status. 6 In order to be treated, the disease must be identified, which generally requires an infected individual to undergo medical care. Most medical care occurs in the ambulatory or emergency setting, marking these as the most likely places for hepatitis C positive individuals to seek care, and thus be screened and linked to care. In 2012, the Centers for Disease Control (CDC) recommended one-time screening for HCV in all individuals born between 1945 and 1965, hereafter “baby boomers”, due to the high prevalence of CHC in this cohort in addition to risk factor based screening at any age. 7 Recently, studies found marked increases in HCV infection in those aged 19–39 as well as those with a pattern of injection drug use, coinciding with the ongoing opioid epidemic. In response to such trends, the CDC and United States Preventive Services Task Force in 2020 released revised recommendations suggesting that all adults receive one-time screening for HCV regardless of risk factors. 8 Despite national efforts, significant racial, socioeconomic, and geographic disparities in HCV screening and treatment remain in the United States. 9 , 10 One critical aspect driving disparities in the CHC diagnosis and care continuum is inadequate access to care. One study found that patients without a regular source of care are 19 times more likely to be unaware of their CHC status. 11 As we continue to strive towards HCV elimination goals, it is critical to better understand care utilization, especially for patients with CHC. The goal of this study is to understand national demographic trends in hepatitis C care utilization during a critical era of change in the landscape of hepatitis C diagnosis and management. We hypothesized that increased utilization of care among individuals with identified HCV and HCV screening would be predominantly White, baby boomers and privately insured population. To assess these rates of utilization, we analyzed two national databases to estimate overall rates of use among those with identified HCV in the United States and to identify disparities in access and utilization of HCV screening. Methods Data Sources Data from the National Ambulatory Medical Care Survey (NAMCS) and the National Hospital Ambulatory Medical Care Survey (NHAMCS), two publicly available databases, were extracted. NAMCS tracks care utilization in the outpatient, or ambulatory, setting, while NHAMCS uses similar sampling techniques to track care visits in the emergency room setting. We utilized data between 2010–2019. Due to changes in the sampling technique, the NAMCS 2017 data remains unreleased and was therefore excluded. NAMCS/NHAMCS are publicly funded survey-based datasets that include annual, national probability samples of ambulatory care visits and are designed to estimate national utilization of ambulatory health services. The basic sampling unit is the patient visit, with the overall raw sample size varying by year according to the number of hospitals/physician offices sampled and the response rate. Only visits by adults at least 18 years old at time of visit were included. Surveys include patient demographics, reason for visit, visit diagnoses, procedures performed, diagnostic tests ordered, and medications prescribed. 12 Medical coding and adjudication are provided by the National Center for Health Statistics. Weights are provided for each observation in the data sets to allow researchers to extrapolate and produce national estimates. A model-based single imputation is used to fill in missing data. Data Usage Survey data were analyzed using inverse probability weighting for each sample visit or each physician response, as appropriate. Sample weights were adjusted for the nonresponse rate at certain times of year, by geographic region, urban/rural location, and ownership designations to create unbiased national estimates. Due to concern over bias from overweighting of individual samples, NAMCS/NHAMCS do not recommend making conclusions based on weighted data with fewer than 30 unweighted instances. To increase the number of unweighted instances, NAMCS/NHAMCS datasets were combined as performed in prior studies and as recommended in documentation provided by NAMCS/NHAMCS. 13,14 As the survey design can vary slightly by annual iteration, coded variables may change and were accounted for according to accompanying public use documentation. Identification of Variables of Interest Study Outcomes The primary variable of interest in this study was a care visit for a patient with identified CHC. Presence of CHC was identified either according to International Classification of Diseases (ICD)-9-CM (2010–2015) or ICD-10-CM coding (2016–2019) in one of the 5 visit diagnoses, or by direct survey-level checkbox for current hepatitis C infection, available since 2016. Our secondary outcome measure was performance of a hepatitis C screening test in an ambulatory care setting (NAMCS), which was also available in survey checkbox format under “testing performed” for all years after 2013. Additional Variables of Interest To assess trends in utilization and testing, we collected demographic data including sex, birth year, self-identified race/ethnicity, expected payment method for visit, and metropolitan statistical area (MSA) status. Of note, in the 2018 NAMCS survey, the total number of responses was considered too low to accurately weight based on region. Therefore, the region (Northeast, South, Midwest, West) variable was not published and is therefore unavailable for observations from 2018. Acceptance of private insurance as a payment type was not collected in 2010 for NAMCS. Concomitant presence of substance abuse or substance use disorder were identified using ICD-9/10 codes as well as survey direct identification through the “does the patient now have opioid abuse or dependence” questions. A complete list of variables of interest including ICD codes utilized by year and dataset can be found in Supplemental Table 1. Primary Outcome Analysis To assess rates of utilization among those with identified CHC, after extraction of variables of interest, descriptive statistics were computed using nationally weighted estimates with corresponding 95% confidence intervals (CIs). To account for the complex survey design, a single-unit certainty ultimate cluster method was used for variance estimation in strata containing only a single primary sampling unit (PSU). Differences in categorical variables were assessed using chi-square tests, implemented via the “svychisq” function in the R “survey” package. Differences in characteristics associated with HCV-related visits were analyzed using both univariable and multivariable logistic regression models (adjusted for significant variables identified in univariable analysis), adjusting for the complex survey design. To assess temporal trends, data from two consecutive years were combined when necessary to ensure adequate raw sample sizes. Significant differences in utilization over two-year periods were assessed by modeling time as a linear variable in weighted logistic regression. The total number of visits and visit rates were estimated by aggregating all available data from NAMCS and NHAMCS over the study period. Secondary Outcome Analysis To assess rates of hepatitis C testing among groups at risk for hepatitis C, we included only ambulatory visits considered “at risk” for HCV screening. Visits prior to 2014, when HCV testing data were not collected, as well as visits where the patient already had a diagnosis of hepatitis B or C, likely cirrhosis, or was actively pregnant, were excluded. To better describe the utilization of HCV testing in routine clinical settings, further analysis was conducted excluding visits to surgical subspecialists, visits for acute problems, flares of chronic conditions, pre- or post-surgical management, or visits where the provider indicated they were not the patient’s primary care provider. Subgroup analyses were performed to evaluate testing patterns among individuals who would have been eligible for screening under the nationwide guidelines, which in 2012 were expanded to include all baby boomers, those with end-stage renal disease, and those with substance use disorder. Finally, a more restrictive assessment of HCV screening rates was performed by including only visits in which the provider identified themselves as the patient’s primary care provider, the visit was for either routine chronic disease management or preventive care, and the patient was new to the provider. This strict definition was applied to reduce potential confounding from prior HCV testing, ensuring that only true screening visits were considered. However, the stringent inclusion criteria limited the number of raw visits available for analysis, restricting further subgroup analyses. All statistical analyses were conducted using R (Version 4.3.1 GUI 1.79 Big Sur ARM Build 8238). The “survey” package in R was used for weighting, variance estimation, chi-square testing, and logistic regression modeling to ensure accurate representation of national estimates. Results Utilization of HCV Care Between 2010 and 2019, a total of 9.7 billion care visits were identified. Of these, 23,469,344 visits (0.3%) were for patients with identified CHC, and 1.29 million visits were for patients with CHC and cirrhosis. A higher percentage of visits for those with already identified HCV occurred in the office than for the overall cohort without CHC (94.8% vs 87.3%, p < 0.001). Across care locations, visits for patients with identified CHC were more common amongst men, baby boomers, people with private insurance, in the West region, and in MSAs, p < 0.001 for all. In contrast to office visits, ER visits were more frequent among individuals with CHC born after 1965, non-Hispanic Blacks, and insured by Medicaid (Table 1 ). Table 1 Total care utilization by HCV + patients from 2010 to 2019 Office Visits (NAMCS) Emergency Visits (NHAMCS) Total Visits in 1000s [SEM] % of All HCV + visits [SEM] p value Total Visits in 1000s [SEM] % of All HCV + visits [SEM] ˆ p value Gender 0.030 < 0.001 Male 11654 [1634] 52.4% [5.0%] 752 [115] 61.4% [4.3%] Female 10589 [1617] 47.6% [5.0%] 473 [73] 38.6% [4.3%] Age < 0.001 < 0.001 Born before 1945 2254 [1063] 10.1% [4.4%] 49 [31] 4.0% [2.4%] Born 1945–1965 14575 [1836] 65.5% [5.1%] 688 [103] 56.2% [4.8%] Born 1965–1985 4658 [972] 20.9% [4.0%] 389 [68] 31.8% [3.8%] Born 1985–2005 709 [273] 3.2% [1.2%] 92 [33] 7.6% [2.6%] Age range < 0.001 = 40 20273 [2336] 91.1% [1.9%] 1020 [145] 83.3% [3.9%] < 40 1971 [414] 8.9% [1.9%] 205 [50] 16.7% [3.9%] Region 0.004 0.007 Northeast 2097 [350] 14.9% [2.6%] 141 [43] 11.5% [3.4%] Midwest 2267 [545] 16.1% [3.6%] 208 [55] 17.0% [4.2%] South 4384 [679] 31.1% [4.3%] 431 [93] 35.2% [6.1%] West 5346 [1088] 37.9% [5.4%] 445 [101] 36.3% [6.3%] Race/Ethnicity 0.294 0.957 Non-Hispanic White 14494 [1973] 65.2% [4.7%] 711 [122] 58.0% [4.9%] Non-Hispanic Black 3271 [671] 14.7% [2.9%] 269 [52] 22.0% [4.1%] Hispanic 3524 [788] 15.8% [3.2%] 211 [45] 17.3% [3.3%] Other 955 [482] 4.3% [2.1%] 34 [16] 2.8% [1.2%] Insurance 0.011 0.002 Private 11062 [1509] 55.0% [5.5%] 242 [69] 25.1% [5.3%] Medicare 2665 [451] 31.7% [5.8%] 196 [48] 20.3% [3.8%] Medicaid 6384 [1567] 13.3% [2.3%] 527 [70] 54.6% [5.5%] MSA Status 0.012 0.262 MSA 21163 [2377] 95.1% [1.3%] 1026 [140] 90.0% [4.9%] Non-MSA 1081 [279] 4.9% [1.3%] 120 [61] 10.5% [4.9%] ^ estimate based on less than 30 raw observations. The National Center for Health Statistics deems estimates based on < 30 raw observations to be unreliable. Abbreviations: HCV, hepatitis C virus; MSA, metropolitan statistical area; NAMCS, National Ambulatory Medical Care Survey; NHAMCS, National Hospital Ambulatory Medical Care Survey; SEM, standard error of the mean In weighted univariable logistic regression analysis, visit rates for CHC compared to no CHC were higher among men (OR 1.54, 95% CI [1.06, 2.22]), baby boomers (OR 4.00, 95% CI [1.57, 10.15]), and those insured by Medicare (OR 1.98, 95% CI [1.17, 3.35]). Younger age was inversely associated with HCV visits. Regional differences were also evident, with higher odds of HCV visits in the West region (OR 2.23, 95% CI [1.38, 3.60]), whereas living in a non-MSA was associated with reduced odds of HCV visits (OR 0.49, 95% CI [0.30, 0.80]) ( Table 2 ). In weighted multivariable logistic regression analysis, odds of CHC visit were higher for male (OR 2.05, 95% CI [1.57, 2.69]), non-Hispanic Black (OR 1.71, 95% CI [1.19, 2.45]), baby boomer (OR 9.04, 95% CI [4.87, 16.82]), covered by Medicare (OR 2.19, 95% CI [1.50, 3.20]) or Medicaid (OR 2.48, 95% CI [1.71, 3.59]), and in the West region (OR 2.49, 95% CI [1.51, 4.12]) compared to those without CHC. Table 2 Univariate and multivariable analysis of factors associated with identified HCV 2010 to 2019 Variable Univariable OR for HCV+ (95% CI) p value Multivariable OR for HCV+ (95% CI) p value Male Gender 1.54 (1.06–2.22) 0.023 2.05 (1.57–2.69) < 0.001 Birth Cohort Before 1945 (Reference) (Reference) (Reference) (Reference) Born 1945–1965 4.0 (1.57–10.15) 0.004 9.04 (4.87–16.81) < 0.001 Born 1965–1985 1.98 (0.75–5.27) 0.170 4.24 (2.13–8.45) < 0.001 Born 1985–2005 0.41 (0.13–1.25) 0.116 0.76 (0.33–1.73) 0.515 Region Northeast (Reference) (Reference) (Reference) (Reference) Midwest 1.11 (0.66–1.89) 0.688 1.30 (0.73–2.32) 0.376 South 1.14 (0.75–1.71) 0.540 1.10 (0.70–1.73) 0.680 West 2.23 (1.38–3.60) 0.001 2.49 (1.51–4.12) < 0.001 Race/Ethnicity Non-Hispanic White (Reference) (Reference) (Reference) (Reference) Non-Hispanic Black 1.37 (0.88–2.12) 0.163 1.71 (1.19–2.45) 0.004 Hispanic 1.23 (0.76–2.00) 0.397 1.40 (0.85–2.31) 0.187 Other 0.85 (0.32–2.27) 0.740 1.24 (0.44–3.48) 0.685 Insurance Type Private 1 (n/a) (Reference) 1 (n/a) (Reference) 1 (n/a) (Reference) 1 (n/a) (Reference) Medicaid 1.15 (0.80–1.65) 0.446 2.48 (1.71–3.59) < 0.001 Medicare 1.98 (1.17–3.35) 0.011 2.19 (1.50–3.20) < 0.001 Non-MSA 0.49 (0.30–0.80) 0.004 0.72 (0.43–1.19) 0.202 Multivariable model contains all factors in column 1 as indicator variables, adjusted odds ratios are reported. Abbreviations: CI, confidence interval; HCV, hepatitis C virus; MSA, metropolitan statistical area When comparing odds of care utilization for those with identified CHC by practice setting (ER vs office), only Medicaid was significantly associated with ER usage (OR 6.05, 95% CI [2.88, 12.72]). The ER accounted for 16.5% of all CHC visits for those with Medicaid, compared to 2.1% for those with private insurance (Fig. 1). Among all visits to both clinician offices and ERs, non-Hispanic Black patients were more likely to have Medicaid as their primary payer type (29.7% vs 10.5%, p < 0.001). This was particularly true for Black patients born after 1965, for whom Medicaid covered 44.8% of visits. Medicaid was also less likely to be accepted by primary care providers for new patient visits than Medicare or private insurance across all study periods (Fig. 2). In multivariable logistic regression analysis adjusted for age, sex, race/ethnicity, region, residing in an MSA, and care location across the entire study population to evaluate factors associated with Medicaid as the payment method for visits, non-Hispanic Black (OR 2.58, 95% CI [2.40, 2.77], p < 0.001), Hispanic (OR 3.31, 95% CI [2.99, 3.66], p < 0.001) or “Other” (OR 1.63, 95% CI [1.37, 1.94], p < 0.001) were associated with increased odds of having Medicaid as their primary payment type. Trends in Utilization of HCV Care Over the course of the study period, the percentage of total visits for identified HCV increased in both the office setting (OR 1.35, 95% CI [1.15, 1.60], p < 0.001) and the ER setting (OR 1.27, 95% CI [1.11, 1.45], p < 0.001). In a multivariable logistic regression controlling for age, gender, race, ethnicity, region, MSA status, and insurance coverage, the odds of an identified HCV visit were highest for the combined year period of 2016 and 2018 (OR 2.05, 95% CI [1.11, 3.78]), the years following FDA approval of direct acting antivirals and national screening recommendations. The largest increase in visit rate, from 0.22–0.43%, was also observed in this period, representing a 98.8% increase from 2012–2014 (Fig. 3 ) . However, when evaluating visits by race / ethnicity, the total number of visits by Black patients remained unchanged with an annual percentage change (APC) of -0.44% (95% CI [-10.46, 10.71], p < 0.001), compared to non-Hispanic White (APC 17.99%, 95% CI [10.28, 26.23], p = 0.041) and Hispanic (APC 27.71%, 95% CI [26.31, 31.15], p < 0.001) (Fig. 4 ) . HCV Testing Between 2014–2019, a total of 3.7 billion office visits were made by non-pregnant adults without known hepatitis B, hepatitis C, or cirrhosis. During these visits, 36.6 million HCV tests were performed, for a testing rate of 1%. In univariable analysis, non-Hispanic Black patients had a higher likelihood of HCV testing, whereas males, individuals born before 1945, and those covered by Medicare had lower testing rates (Table 3 ). Estimates from multivariable logistic regression indicated that non-Hispanic Blacks patients had higher per-visit odds of HCV testing (OR 3.03, 95% CI [2.10, 4.37]) compared to patients of other races/ethnicities. Table 3 Association of demographic factors with office based with hepatitis C testing from 2016 to 2019 Factor Number of Tests Performed (1000s) [SEM] Total Visits in 1000s [SEM] % of total visits [SEM] p value (testing rate) Gender 0.023 Male 11973 [2308] 1546555 [40712] 0.8% [0.1%] Female 24599 [4651] 2177400 [53378] 1.1% [0.2%] Birth Cohort 0.038 1 Pre-1945 3333 [1026] 845433 [27171] 0.4% [0.1%] 1945–1965 16457 [2745] 1622115 [38677] 1.0% [0.2%] 1965–1985 12165 [4425] 893099 [27724] 1.4% [0.5%] 1985–2005 4618 [892] 363308 [14232] 1.3% [0.2%] Region 0.146 Northeast 4276 [959] 450767 [21846] 1.0% [0.2%] Midwest 2602 [660] 429582 [17325] 0.6% [0.2%] South 10890 [4756] 785059 [34673] 1.4% [0.6%] West 2977 [898] 507472 [34197] 0.6% [0.2%] Race/Ethnicity < 0.001 Non-Hispanic White 16927 [2187] 2673532 [56158] 0.6% [0.1%] Non-Hispanic Black 7341 [1645] 364484 [19345] 2.0% [0.4%] Hispanic 6165 [1645] 464260 [29628] 1.3% [0.3%] Other 6138 [2623] 221678 [26235] 2.8% [1.1%] Insurance 0.027 Private 22848 [3894] 2154271 [54362] 1.1% [0.2%] Medicare 5340 [1293] 249727 [16172] 0.6% [0.2%] Medicaid 4322 [1509] 829385 [30459] 1.7% [0.6%] MSA Status 0.343 MSA 34483 [6259] 3421607 [81320] 1.0% [0.2%] Non-MSA 2089 [816] 302348 [30188] 0.7% [0.3%] Testing rates were higher for visits during which the provider identified themselves as the patient’s primary care provider (1.7 vs 0.5%, OR 3.23, 95%CI [2.93, 3.56], p < 0.001) as well as during visits to general practitioners when compared to medical or surgical specialists (1.7% vs 0.5% vs 0.2% respectively, OR 3.15 95%CI [3.14, 3.15], p < 0.001, OR 7.41 95%CI [7.40, 7.42], p < 0.001). Additionally, patients who presented for initial visits (1.6 vs 0.9%, OR 1.92 95%CI [1.91, 1.93], p < 0.001) and preventative visits (2.7 vs 0.7%, OR 3.91 95%CI [3.90, 3.92] p < 0.001) were more likely to undergo HCV testing when compared to follow-up visits and visits for acute medical problems, respectively. After applying more stringent criteria excluding surgical specialties, visits where the physician indicated that they were not the patient’s primary care provider, and visits for an acute problem, a flare of a chronic problem, or a pre/post-operative evaluation, hepatitis testing was performed during 20 million of 887 million visits (2.2%). Black patients were tested at a significantly higher rate than non-Hispanic White and Hispanic and other (3.6 vs 1.5 vs 3.0 vs 6.5%, p < 0.001. Regarding birth cohort, the highest testing rate was for those born between 1965 to 1985 (3.4%) compared to those born between 1945-65 (2.1%), before 1945 (1.0%) and those born 1985–2005 (3.3%), p = 0.035. Among insurance categories, patients covered by Medicaid had the highest rate of hepatitis testing while those covered by Private insurance lower and by Medicare the lowest (4.9 vs 2.4 vs 1.4%, p = 0.023). In multivariable regression, age and race differences were retained while testing rates by primary insurance were no longer significantly different. In subgroup analysis based on 2012 screening recommendations, there were 406 million visits for baby boomers, 33.2 million visits for people with substance use disorder (SUD) (as documented by the treating provider), and 1.5 million visits for patients with end stage renal disease. In total, 426 million visits occurred for those who met high-risk screening criteria, and hepatitis testing was performed during 2.1% of these visits, no higher than testing rates in those who did not meet screening criteria (2.4%). In contrast, a complete blood count was ordered during 22.7% of these visits, suggesting that the reason for not performing hepatitis testing was not lack of access to phlebotomy/laboratory evaluations. Finally, when more stringent criteria was applied by only including those visits for which a self-identified primary care provider was seeing the patient for the first time for a preventive or routine care visit was applied, we estimated a testing rate of 9.8%. In comparison, complete blood count (40.0% of visits), complete metabolic panel (39.8% of visits) and glycosylated hemoglobin level (19.1% of visits) were ordered at significantly higher rates ( p < 0.01 for all). Care Utilization in Patients with Substance Use Disorder From 2010–2019, 69.6 million visits were identified for substance use disorder (SUD), and 1.3 million (1.8%) had identified HCV. Compared to those without SUD, those with SUD used the ER for a higher proportion of total visits (14.3 million of 69.6 million, 20.6% vs 1.2 billion of 9.6 billion, 12.6%, OR 1.8 95% CI [1.4, 2.2], p < 0.001). Similarly, among patients with identified HCV, those with SUD were more likely to utilize care in the ER compared to those without (17.1% [of all HCV visits] vs 4.5%, OR 4.4 95% CI [2.1, 9.0], p < 0.001). Only 3.6% of visits for people with SUD were of “baby boomer” age, compared to 19.3% of overall visits. In the baby boomer cohort with HCV, 0.1% of visits were for those with SUD compared to 0.5 vs 1.5 vs 1.2 in those born [before 1945 or 1965–1985 or 1985–2005] (Fig. 5 ) . In the period 2010–2012, 0.2 million (4.5%) of visits for HCV were also associated with SUD compared to 0.6 million (4.8%) in 2016–2019. Medicaid was overrepresented as the primary payer type for those with SUD (33% of total). In multivariable regression analysis adjusting for age, gender, race, and primary insurance, there were increased odds of SUD among HCV-identified users of the ER (OR 3.48, 95% CI [1.23, 9.90], p = 0.02). Similarly, in adjusted multivariable analysis those with both identified HCV and SUD had higher odds of using the ER for care compared to those without SUD (OR 3.30 95% CI [1.23, 9.02], p = 0.02). Discussion In this nationally representative study of ambulatory care use, our findings provide a comprehensive and contemporary evaluation of hepatitis C testing and ambulatory care utilization. Between 2010 and 2019, 36.6 million HCV tests were performed, and patients with HCV had 22 million office visits and 1.2 million ER visits. There are several key findings in this study. First, there were demographic differences in the most common care setting for those with HCV, namely the higher rates of ER use among Black patients, baby boomers, those in the West region, and those covered under Medicare and Medicaid. There was also disproportionate ER utilization among those with SUD, including those with already identified HCV. Second, there was an observed increase in visit proportions for those with hepatitis C, but primarily among White privately patients in the office setting. This may reflect access to office-based care as White patients were more likely to have private insurance. Third, HCV testing rates remained low. While on a per-visit basis Black patients were tested at a higher rate, this may be mitigated by lower overall access and visit rates. Taken together, these findings suggest that the current screening paradigm, which relies on office-based testing by primary care physicians, may have increasingly poor effectiveness in identifying hepatitis C and linking patients to treatment. Key strengths of this study include nationally representative nationwide estimates based on billions of setting-agnostic primary care visits, as well as low levels of missing data and consistency of data collection. Our findings complement those of previous studies examining demographic shifts in hepatitis C in the US. Since 2010, a significant increase in HCV incidence in the United States has been reported, with new cases nearly quadrupling between 2011 and 2019. 15 This dramatic increase in HCV has largely been attributed to the nation’s opioid epidemic, with some studies estimating that up to one-third of persons who inject drugs (PWID) will develop HCV within the first year. 16 , 17 Patients with SUD, along with Black patients and those covered by Medicaid and Medicare were more likely to utilize care from the ER rather than from an office visit. These findings are consistent with prior studies that found that Black patients and those with Medicaid are less likely to utilize primary care as their usual source of care and are more likely to visit the ER for non-urgent care. 18 – 21 While the ER plays a crucial role in caring for patients with SUD and identifying HCV cases, SUD and HCV treatment often require longitudinal outpatient management. As the prevalence of CHC decreases in baby boomers and those with higher socioeconomic status, case identification may increasingly rely on emergency care. Prior studies have indicated significant patient attrition along the HCV care cascade, highlighting the need for structured linkage-to-care programs, particularly for patients who primarily obtain care from the ER. 22 23 Ensuring effective follow-up for these patients is essential for improving treatment uptake and outcomes. Innovative approaches, such as integrating patient navigators and expanding telemedicine-based follow-up, may enhance care continuity and treatment initiation. 24 One recent study has shown high levels of satisfaction with telemedicine and high levels of HCV treatment completion amongst those enrolled in opioid treatment programs. 25 Additionally, telemedicine may help mitigate non-financial barriers faced by underserved populations. 21 , 26 It is critical to continue to implement strategies to mitigate access barriers despite financial challenges that may be faced by healthcare providers. There has also been a dramatic demographic shift among individuals with hepatitis C, largely driven by the opioid epidemic as well as more successful screening and eradication in the baby boomer population. However, it is concerning that the HCV testing rates may not reflect this demographic shift. In 2021, the CDC estimated that rural areas had significantly higher rates of HCV incidence (58 per 100,000) when compared to urban areas (36 per 100,000). 27 The same report found White patients and male patients had higher rates of HCV. In this study, we found similar rates of HCV testing in metropolitan and non-metropolitan areas. This may be the result of persistent geographic disparities in access to care and patients often need to travel further to obtain care, 27 as many specialists who treat patients with CHC are more likely to be in urban areas 28 – 30 . Additionally, we found a significantly higher testing rate for Black patients (2.0%) when compared to White patients (0.6%). While the exact reason for this is unclear, it is possible that a larger proportion of White patients live in rural areas that have lower geographic access to care. However, as noted earlier, Black patients were more likely to obtain care at the ER, which may fragment care and reduce linkage to outpatient treatment, even when identified as a case. 21 36 Screening is a critical first step towards our goal of HCV elimination, and there are many barriers that must be addressed as efforts continue towards achieving this goal. One such barrier is access to care. For example, as mentioned above, there are significant geographic disparities to access care, especially in areas that have been most impacted by HCV in recent years. Point of care (POC) HCV testing has shown promise in other countries and was recently approved by the FDA. 31 The implementation of POC HCV screening at community centers, supervised consumption services, and non-clinical justice settings could improve HCV screening rates, especially in at-risk areas. 22 , 32 , 33 This study has several limitations. First, due to the cross-sectional nature of NAMCS/NHAMCS, our study lacked longitudinal information. This could lead to inaccurate estimations of testing as we cannot account for individuals who previously underwent screening; any bias in utilization would be expected to trend towards the null in our analysis. However, to our knowledge, there are no nationally representative surveys that provide longitudinal data on this population. Additionally, the lack of longitudinal data prevents us from better understanding the impact of different care utilization practices on clinical outcomes. An additional limitation of this study was that NAMCS/NHAMCS data estimate healthcare utilization rather than true HCV prevalence. As such, visits by HCV positive patients may be under-identified due to reliance on ICD-9 and ICD-10 codes. Previous validation studies suggest these codes have moderate sensitivity (50–70%) but high specificity, meaning some undiagnosed or undocumented cases were likely missed. Likewise, NHAMCS does not capture visits to freestanding urgent care centers that are not affiliated with hospitals. As such, we may underestimate care utilization, particularly among patients who seek low-cost, walk-in care outside of traditional emergency or office-based settings. However, cases were unlikely to have been systematically missing due to specific patient characteristics or visit type, mitigating concerns of potential bias. This limitation underscores the need for future studies using biomarker-based databases or electronic health records with longitudinal follow-up. Finally, a critical transition in hepatitis C screening and management took place during this study period, including widespread adoption of universal screening. Regardless, our study serves to detail demographic shifts in usage rates, including among younger individuals and those in rural areas or with SUD who are less likely to seek care in commonly researched settings. Lastly, our most recent data were from 2019. The onset of the COVID-19 pandemic has led to a halt in data collection and the subsequent decision to discontinue NHAMCS permanently. The future lack of public, nationally representative and comparable data between care settings, however, adds the importance of this contemporary analysis of HCV care in the US. In this study using two large, nationally representative databases, we estimated ambulatory care utilization for individuals with HCV. Our study showed greater ER utilization among patients who were younger, Black, and covered by Medicaid. Additionally, we found that HCV screening practices did not address current patient demographic trends for those with HCV. Policy and practice updates must be implemented to enhance providers awareness of the current state of HCV infection in the United States and to improve patient access to adequate screening and linkage to care. As efforts continue toward the goal of HCV elimination, it is crucial for future research and further initiatives to focus on at risk populations. References Hall, E. W. et al. Estimating hepatitis C prevalence in the United States, 2017–2020. Hepatology 81, 625–636 (2025). Blach, S. et al. Global prevalence and genotype distribution of hepatitis C virus infection in 2015: a modelling study. Lancet Gastroenterol Hepatol 2, 161–176 (2017). Ly, K. N. et al. Deaths Associated With Hepatitis C Virus Infection Among Residents in 50 States and the District of Columbia, 2016–2017. Clin Infect Dis 71, 1149–1160 (2020). Westbrook, R. H. & Dusheiko, G. Natural history of hepatitis C. J Hepatol 61, S58–S68 (2014). Afdhal, N. et al. Ledipasvir and Sofosbuvir for Untreated HCV Genotype 1 Infection. New England Journal of Medicine 370, 1889–1898 (2014). Ha, S. & Timmerman, K. Awareness and knowledge of hepatitis C among health care providers and the public: A scoping review. Canada Communicable Disease Report 44, 157–165 (2018). Smith, B. D. et al. Recommendations for the identification of chronic hepatitis C virus infection among persons born during 1945–1965. MMWR Recomm Rep 61, 1–32 (2012). Schillie, S., Wester, C., Osborne, M., Wesolowski, L. & Ryerson, A. B. CDC Recommendations for Hepatitis C Screening Among Adults — United States, 2020. MMWR. Recommendations and Reports 69, 1–17 (2020). Biondi, B. E. et al. Racial and Ethnic Disparities in Hepatitis C Care in Reproductive-Aged Women With Opioid Use Disorder. Clinical Infectious Diseases 79, 1428–1436 (2024). Nili, M., Luo, L., Feng, X., Chang, J. & Tan, X. Disparities in hepatitis C virus infection screening among Baby Boomers in the United States. Am J Infect Control 46, 1341–1347 (2018). Artenie, A. A., Bruneau, J., Lévesque, A. & Wansuanganyi, J.-M. B. Role of primary care providers in hepatitis C prevention and care: one step away from evidence-based practice. Can Fam Physician 60, 881–2, e468-70 (2014). McCaig LF, McLemore T Plan and Operation of the National Hospital Ambulatory Medical Care Survey. National Center for Health Statistics. Vital Health Stat 1 (34) 1994.. Fleming-Dutra, K. E. et al. Prevalence of Inappropriate Antibiotic Prescriptions Among US Ambulatory Care Visits, 2010–2011. JAMA 315, 1864 (2016). Center for Disease Control and Prevention. NAMCS and NHAMCS Survey Methods and Analytic Guidelines. https://archive.cdc.gov/www_cdc_gov/nchs/ahcd/survey-methods-archived.htm . February 6, 2019. Accessed March 13, 2025. Nguyen, I., Moussa, K. & Gutierrez, J. Hepatitis C Virus Elimination in the United States: Challenges, Progress, and Future Steps. Gastroenterol Hepatol (N Y) 19, 700–707 (2023). Gonzalez, S. A. & Trotter, J. F. The rise of the opioid epidemic and hepatitis C–positive organs. Hepatology 67, 1600–1608 (2018). Hagan, H., Pouget, E. R., Des Jarlais, D. C. & Lelutiu-Weinberger, C. Meta-Regression of Hepatitis C Virus Infection in Relation to Time Since Onset of Illicit Drug Injection: The Influence of Time and Place. Am J Epidemiol 168, 1099–1109 (2008). Kim, H., McConnell, K. J. & Sun, B. C. Comparing Emergency Department Use Among Medicaid and Commercial Patients Using All-Payer All-Claims Data. Popul Health Manag 20, 271–277 (2017). Doty, M. M. & Holmgren, A. L. Health care disconnect: gaps in coverage and care for minority adults. Findings from the Commonwealth Fund Biennial Health Insurance Survey (2005). Issue Brief (Commonw Fund) 21, 1–12 (2006). Parast, L. et al. Racial/Ethnic Differences in Emergency Department Utilization and Experience. J Gen Intern Med 37, 49–56 (2022). Ying, X. et al. Racial Disparities in Cost and Non-Cost Barriers to Care: An Analysis of the All of Us Survey. J Gen Intern Med 39, 2875–2877 (2024). Trickey, A., Fajardo, E., Alemu, D., Artenie, A. A. & Easterbrook, P. Impact of hepatitis C virus point-of-care RNA viral load testing compared with laboratory-based testing on uptake of RNA testing and treatment, and turnaround times: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol 8, 253–270 (2023). Tran, L., Jung, J., Feldman, R. & Riley, T. Disparities in the quality of care for chronic hepatitis C among Medicare beneficiaries. PLoS One 17, e0263913 (2022). Hyde, Z. et al. Evaluation of a pilot emergency department linkage to care program for patients previously diagnosed with Hepatitis C. J Viral Hepat 30, 129–137 (2023). Talal, A. H. et al. High Satisfaction with Patient-Centered Telemedicine for Hepatitis C Virus Delivered to Substance Users: A Mixed-Methods Study. Telemedicine and e-Health 29, 395–407 (2023). Reed, M. E. et al. Patient Characteristics Associated With Choosing a Telemedicine Visit vs Office Visit With the Same Primary Care Clinicians. JAMA Netw Open 3, e205873 (2020). Centers for Disease Control and Prevention. Viral Hepatitis Surveillance Report – United States , 2021. Https://Www.Cdc.Gov/Hepatitis-Surveillance-2021/about/Index . Html. Published August 2023. Accessed February 12, 2025 . Estadt, A. T. et al. Differences in hepatitis C virus (HCV) testing and treatment by opioid, stimulant, and polysubstance use among people who use drugs in rural U.S. communities. Harm Reduct J 21, 214 (2024). Zhang, D. et al. Assessment of Changes in Rural and Urban Primary Care Workforce in the United States From 2009 to 2017. JAMA Netw Open 3, e2022914 (2020). Ying, X., Yao, L., Mathis, W. S., Congly, S. E. & Jesudian, A. B. Geographic Disparities in Access to Gastroenterologists in the United States. Gastroenterology (2025) doi: 10.1053/j.gastro.2025.01.232 . Food and Drug Administration. FDA Permits Marketing of First Point-of-Care Hepatitis C RNA Test. June 27, 2024. Accessed February 17 , 2025. Harvey, L. et al. Feasibility and Performance of a Point-of-Care Hepatitis C RNA Assay in a Community Supervision Cohort. JAMA Netw Open 7, e2438222 (2024). Lettner, B. et al. Rapid hepatitis C virus point-of-care RNA testing and treatment at an integrated supervised consumption service in Toronto, Canada: a prospective, observational cohort study. Lancet regional health. Americas 22, 100490 (2023). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementalTable1.docx Supplemental Table 1 Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6378738","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":451056499,"identity":"f177add5-46ed-4cfb-b2ba-e61d209c5202","order_by":0,"name":"Adam 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4","display":"","copyAsset":false,"role":"figure","size":29745,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure4HCVvisitsbyrace2new2.png","url":"https://assets-eu.researchsquare.com/files/rs-6378738/v1/98e3243a7d02c3704ee97bd4.png"},{"id":82309955,"identity":"21169c1d-a427-4e9a-9f56-4363c1a4d5c6","added_by":"auto","created_at":"2025-05-09 01:50:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32711,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6378738/v1/07ab9328827efad563e3a649.png"},{"id":105039664,"identity":"af8ef630-8c72-4892-9187-93a4d50f8d3b","added_by":"auto","created_at":"2026-03-20 07:46:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1222817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6378738/v1/073ab5da-86e2-4d4b-8ad6-1871596a2f3f.pdf"},{"id":82311587,"identity":"f9c9573d-c89b-4ba4-9780-f9c54620fc02","added_by":"auto","created_at":"2025-05-09 01:58:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20326,"visible":true,"origin":"","legend":"Supplemental Table 1","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6378738/v1/c4b46582f7b480136ff47c4e.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Sociodemographic Disparities in Hepatitis C Care Utilization and Testing in the United States: A Nationwide Survey Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatitis C virus (HCV) infection is among the leading causes of liver disease and mortality, with over 70\u0026nbsp;million people infected worldwide. The prevalence of patients with chronic hepatitis C (CHC) infection is between 1\u0026ndash;2% in the United States, making it the most common bloodborne infection in the nation.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e CHC is a major cause of mortality, and if untreated, patients with CHC can develop cirrhosis and hepatocellular carcinoma.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe approval of direct-acting antivirals has revolutionized the treatment for patients with HCV, with estimated cure rates of 97\u0026ndash;99%.\u003csup\u003e5\u003c/sup\u003e However, despite advances in medical therapy, HCV remains a leading cause of liver-related death and transplantation and is rising in incidence in younger individuals. One major factor in the persistent burden of CHC is under-screening, with studies estimating that less than half of those with CHC are aware of their positive status.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In order to be treated, the disease must be identified, which generally requires an infected individual to undergo medical care. Most medical care occurs in the ambulatory or emergency setting, marking these as the most likely places for hepatitis C positive individuals to seek care, and thus be screened and linked to care.\u003c/p\u003e \u003cp\u003eIn 2012, the Centers for Disease Control (CDC) recommended one-time screening for HCV in all individuals born between 1945 and 1965, hereafter \u0026ldquo;baby boomers\u0026rdquo;, due to the high prevalence of CHC in this cohort in addition to risk factor based screening at any age.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Recently, studies found marked increases in HCV infection in those aged 19\u0026ndash;39 as well as those with a pattern of injection drug use, coinciding with the ongoing opioid epidemic. In response to such trends, the CDC and United States Preventive Services Task Force in 2020 released revised recommendations suggesting that all adults receive one-time screening for HCV regardless of risk factors.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Despite national efforts, significant racial, socioeconomic, and geographic disparities in HCV screening and treatment remain in the United States.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOne critical aspect driving disparities in the CHC diagnosis and care continuum is inadequate access to care. One study found that patients without a regular source of care are 19 times more likely to be unaware of their CHC status.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e As we continue to strive towards HCV elimination goals, it is critical to better understand care utilization, especially for patients with CHC. The goal of this study is to understand national demographic trends in hepatitis C care utilization during a critical era of change in the landscape of hepatitis C diagnosis and management. We hypothesized that increased utilization of care among individuals with identified HCV and HCV screening would be predominantly White, baby boomers and privately insured population. To assess these rates of utilization, we analyzed two national databases to estimate overall rates of use among those with identified HCV in the United States and to identify disparities in access and utilization of HCV screening.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eData from the National Ambulatory Medical Care Survey (NAMCS) and the National Hospital Ambulatory Medical Care Survey (NHAMCS), two publicly available databases, were extracted. NAMCS tracks care utilization in the outpatient, or ambulatory, setting, while NHAMCS uses similar sampling techniques to track care visits in the emergency room setting. We utilized data between 2010\u0026ndash;2019. Due to changes in the sampling technique, the NAMCS 2017 data remains unreleased and was therefore excluded. NAMCS/NHAMCS are publicly funded survey-based datasets that include annual, national probability samples of ambulatory care visits and are designed to estimate national utilization of ambulatory health services. The basic sampling unit is the patient visit, with the overall raw sample size varying by year according to the number of hospitals/physician offices sampled and the response rate. Only visits by adults at least 18 years old at time of visit were included.\u003c/p\u003e \u003cp\u003eSurveys include patient demographics, reason for visit, visit diagnoses, procedures performed, diagnostic tests ordered, and medications prescribed.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Medical coding and adjudication are provided by the National Center for Health Statistics. Weights are provided for each observation in the data sets to allow researchers to extrapolate and produce national estimates. A model-based single imputation is used to fill in missing data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Usage\u003c/h3\u003e\n\u003cp\u003eSurvey data were analyzed using inverse probability weighting for each sample visit or each physician response, as appropriate. Sample weights were adjusted for the nonresponse rate at certain times of year, by geographic region, urban/rural location, and ownership designations to create unbiased national estimates. Due to concern over bias from overweighting of individual samples, NAMCS/NHAMCS do not recommend making conclusions based on weighted data with fewer than 30 unweighted instances. To increase the number of unweighted instances, NAMCS/NHAMCS datasets were combined as performed in prior studies and as recommended in documentation provided by NAMCS/NHAMCS.\u003csup\u003e13,14\u003c/sup\u003e As the survey design can vary slightly by annual iteration, coded variables may change and were accounted for according to accompanying public use documentation.\u003c/p\u003e\n\u003ch3\u003eIdentification of Variables of Interest\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy Outcomes\u003c/h2\u003e \u003cp\u003eThe primary variable of interest in this study was a care visit for a patient with identified CHC. Presence of CHC was identified either according to International Classification of Diseases (ICD)-9-CM (2010\u0026ndash;2015) or ICD-10-CM coding (2016\u0026ndash;2019) in one of the 5 visit diagnoses, or by direct survey-level checkbox for current hepatitis C infection, available since 2016. Our secondary outcome measure was performance of a hepatitis C screening test in an ambulatory care setting (NAMCS), which was also available in survey checkbox format under \u0026ldquo;testing performed\u0026rdquo; for all years after 2013.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAdditional Variables of Interest\u003c/h3\u003e\n\u003cp\u003eTo assess trends in utilization and testing, we collected demographic data including sex, birth year, self-identified race/ethnicity, expected payment method for visit, and metropolitan statistical area (MSA) status. Of note, in the 2018 NAMCS survey, the total number of responses was considered too low to accurately weight based on region. Therefore, the region (Northeast, South, Midwest, West) variable was not published and is therefore unavailable for observations from 2018. Acceptance of private insurance as a payment type was not collected in 2010 for NAMCS. Concomitant presence of substance abuse or substance use disorder were identified using ICD-9/10 codes as well as survey direct identification through the \u0026ldquo;does the patient now have opioid abuse or dependence\u0026rdquo; questions. A complete list of variables of interest including ICD codes utilized by year and dataset can be found in \u003cb\u003eSupplemental Table\u0026nbsp;1.\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrimary Outcome Analysis\u003c/h2\u003e \u003cp\u003eTo assess rates of utilization among those with identified CHC, after extraction of variables of interest, descriptive statistics were computed using nationally weighted estimates with corresponding 95% confidence intervals (CIs). To account for the complex survey design, a single-unit certainty ultimate cluster method was used for variance estimation in strata containing only a single primary sampling unit (PSU). Differences in categorical variables were assessed using chi-square tests, implemented via the \u0026ldquo;svychisq\u0026rdquo; function in the R \u0026ldquo;survey\u0026rdquo; package.\u003c/p\u003e \u003cp\u003eDifferences in characteristics associated with HCV-related visits were analyzed using both univariable and multivariable logistic regression models (adjusted for significant variables identified in univariable analysis), adjusting for the complex survey design. To assess temporal trends, data from two consecutive years were combined when necessary to ensure adequate raw sample sizes. Significant differences in utilization over two-year periods were assessed by modeling time as a linear variable in weighted logistic regression. The total number of visits and visit rates were estimated by aggregating all available data from NAMCS and NHAMCS over the study period.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSecondary Outcome Analysis\u003c/h3\u003e\n\u003cp\u003eTo assess rates of hepatitis C testing among groups at risk for hepatitis C, we included only ambulatory visits considered \u0026ldquo;at risk\u0026rdquo; for HCV screening. Visits prior to 2014, when HCV testing data were not collected, as well as visits where the patient already had a diagnosis of hepatitis B or C, likely cirrhosis, or was actively pregnant, were excluded. To better describe the utilization of HCV testing in routine clinical settings, further analysis was conducted excluding visits to surgical subspecialists, visits for acute problems, flares of chronic conditions, pre- or post-surgical management, or visits where the provider indicated they were not the patient\u0026rsquo;s primary care provider. Subgroup analyses were performed to evaluate testing patterns among individuals who would have been eligible for screening under the nationwide guidelines, which in 2012 were expanded to include all baby boomers, those with end-stage renal disease, and those with substance use disorder.\u003c/p\u003e \u003cp\u003eFinally, a more restrictive assessment of HCV screening rates was performed by including only visits in which the provider identified themselves as the patient\u0026rsquo;s primary care provider, the visit was for either routine chronic disease management or preventive care, and the patient was new to the provider. This strict definition was applied to reduce potential confounding from prior HCV testing, ensuring that only true screening visits were considered. However, the stringent inclusion criteria limited the number of raw visits available for analysis, restricting further subgroup analyses.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using R (Version 4.3.1 GUI 1.79 Big Sur ARM Build 8238). The \u0026ldquo;survey\u0026rdquo; package in R was used for weighting, variance estimation, chi-square testing, and logistic regression modeling to ensure accurate representation of national estimates.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUtilization of HCV Care\u003c/h2\u003e \u003cp\u003eBetween 2010 and 2019, a total of 9.7\u0026nbsp;billion care visits were identified. Of these, 23,469,344 visits (0.3%) were for patients with identified CHC, and 1.29\u0026nbsp;million visits were for patients with CHC and cirrhosis. A higher percentage of visits for those with already identified HCV occurred in the office than for the overall cohort without CHC (94.8% vs 87.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Across care locations, visits for patients with identified CHC were more common amongst men, baby boomers, people with private insurance, in the West region, and in MSAs, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all. In contrast to office visits, ER visits were more frequent among individuals with CHC born after 1965, non-Hispanic Blacks, and insured by Medicaid (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal care utilization by HCV\u0026thinsp;+\u0026thinsp;patients from 2010 to 2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOffice Visits (NAMCS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eEmergency Visits (NHAMCS)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Visits in 1000s [SEM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of All HCV\u0026thinsp;+\u0026thinsp;visits [SEM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal Visits in 1000s [SEM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% of All HCV\u0026thinsp;+\u0026thinsp;visits [SEM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eˆ\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11654 [1634]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.4% [5.0%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e752 [115]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.4% [4.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10589\u003c/p\u003e \u003cp\u003e[1617]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.6% [5.0%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e473 [73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.6% [4.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn before 1945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2254 [1063]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1% [4.4%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 [31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0% [2.4%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn 1945\u0026ndash;1965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14575 [1836]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.5% [5.1%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e688 [103]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.2% [4.8%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn 1965\u0026ndash;1985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4658 [972]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.9% [4.0%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e389 [68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.8% [3.8%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn 1985\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e709 [273]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2% [1.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92 [33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.6% [2.6%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge range\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20273 [2336]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.1% [1.9%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1020 [145]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.3% [3.9%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1971 [414]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9% [1.9%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205 [50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.7% [3.9%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2097 [350]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9% [2.6%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 [43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.5% [3.4%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2267 [545]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.1% [3.6%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e208 [55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.0% [4.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4384 [679]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.1% [4.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e431 [93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.2% [6.1%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5346 [1088]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.9% [5.4%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e445 [101]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.3% [6.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/Ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14494 [1973]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.2% [4.7%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e711 [122]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.0% [4.9%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3271 [671]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.7% [2.9%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e269 [52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.0% [4.1%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3524 [788]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8% [3.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211 [45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.3% [3.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e955 [482]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3% [2.1%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 [16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8% [1.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11062 [1509]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.0% [5.5%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e242 [69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.1% [5.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2665 [451]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.7% [5.8%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e196 [48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.3% [3.8%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6384 [1567]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3% [2.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e527 [70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.6% [5.5%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMSA Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21163 [2377]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.1% [1.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1026 [140]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.0% [4.9%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-MSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1081 [279]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9% [1.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120 [61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.5% [4.9%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e^ estimate based on less than 30 raw observations. The National Center for Health Statistics deems estimates based on \u0026lt;\u0026thinsp;30 raw observations to be unreliable.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAbbreviations: HCV, hepatitis C virus; MSA, metropolitan statistical area; NAMCS, National Ambulatory Medical Care Survey; NHAMCS, National Hospital Ambulatory Medical Care Survey; SEM, standard error of the mean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn weighted univariable logistic regression analysis, visit rates for CHC compared to no CHC were higher among men (OR 1.54, 95% CI [1.06, 2.22]), baby boomers (OR 4.00, 95% CI [1.57, 10.15]), and those insured by Medicare (OR 1.98, 95% CI [1.17, 3.35]). Younger age was inversely associated with HCV visits. Regional differences were also evident, with higher odds of HCV visits in the West region (OR 2.23, 95% CI [1.38, 3.60]), whereas living in a non-MSA was associated with reduced odds of HCV visits (OR 0.49, 95% CI [0.30, 0.80]) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e In weighted multivariable logistic regression analysis, odds of CHC visit were higher for male (OR 2.05, 95% CI [1.57, 2.69]), non-Hispanic Black (OR 1.71, 95% CI [1.19, 2.45]), baby boomer (OR 9.04, 95% CI [4.87, 16.82]), covered by Medicare (OR 2.19, 95% CI [1.50, 3.20]) or Medicaid (OR 2.48, 95% CI [1.71, 3.59]), and in the West region (OR 2.49, 95% CI [1.51, 4.12]) compared to those without CHC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariable analysis of factors associated with identified HCV 2010 to 2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariable OR for HCV+ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariable OR for HCV+ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale Gender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54 (1.06\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05 (1.57\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth Cohort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore 1945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn 1945\u0026ndash;1965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0 (1.57\u0026ndash;10.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.04 (4.87\u0026ndash;16.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn 1965\u0026ndash;1985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98 (0.75\u0026ndash;5.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.24 (2.13\u0026ndash;8.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn 1985\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41 (0.13\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76 (0.33\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (0.66\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (0.73\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14 (0.75\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.70\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23 (1.38\u0026ndash;3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.49 (1.51\u0026ndash;4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/Ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37 (0.88\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71 (1.19\u0026ndash;2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (0.76\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40 (0.85\u0026ndash;2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.32\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24 (0.44\u0026ndash;3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (n/a) (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (n/a) (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (n/a) (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (n/a) (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.80\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.48 (1.71\u0026ndash;3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.17\u0026ndash;3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19 (1.50\u0026ndash;3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-MSA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.30\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.43\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMultivariable model contains all factors in column 1 as indicator variables, adjusted odds ratios are reported.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAbbreviations: CI, confidence interval; HCV, hepatitis C virus; MSA, metropolitan statistical area\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen comparing odds of care utilization for those with identified CHC by practice setting (ER vs office), only Medicaid was significantly associated with ER usage (OR 6.05, 95% CI [2.88, 12.72]). The ER accounted for 16.5% of all CHC visits for those with Medicaid, compared to 2.1% for those with private insurance (Fig.\u0026nbsp;1). Among all visits to both clinician offices and ERs, non-Hispanic Black patients were more likely to have Medicaid as their primary payer type (29.7% vs 10.5%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This was particularly true for Black patients born after 1965, for whom Medicaid covered 44.8% of visits. Medicaid was also less likely to be accepted by primary care providers for new patient visits than Medicare or private insurance across all study periods (Fig.\u0026nbsp;2). In multivariable logistic regression analysis adjusted for age, sex, race/ethnicity, region, residing in an MSA, and care location across the entire study population to evaluate factors associated with Medicaid as the payment method for visits, non-Hispanic Black (OR 2.58, 95% CI [2.40, 2.77], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Hispanic (OR 3.31, 95% CI [2.99, 3.66], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or \u0026ldquo;Other\u0026rdquo; (OR 1.63, 95% CI [1.37, 1.94], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with increased odds of having Medicaid as their primary payment type.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTrends in Utilization of HCV Care\u003c/h2\u003e \u003cp\u003eOver the course of the study period, the percentage of total visits for identified HCV increased in both the office setting (OR 1.35, 95% CI [1.15, 1.60], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the ER setting (OR 1.27, 95% CI [1.11, 1.45], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In a multivariable logistic regression controlling for age, gender, race, ethnicity, region, MSA status, and insurance coverage, the odds of an identified HCV visit were highest for the combined year period of 2016 and 2018 (OR 2.05, 95% CI [1.11, 3.78]), the years following FDA approval of direct acting antivirals and national screening recommendations. The largest increase in visit rate, from 0.22\u0026ndash;0.43%, was also observed in this period, representing a 98.8% increase from 2012\u0026ndash;2014 (Fig.\u0026nbsp;3\u003cb\u003e)\u003c/b\u003e. However, when evaluating visits by race / ethnicity, the total number of visits by Black patients remained unchanged with an annual percentage change (APC) of -0.44% (95% CI [-10.46, 10.71], \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), compared to non-Hispanic White (APC 17.99%, 95% CI [10.28, 26.23], \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.041) and Hispanic (APC 27.71%, 95% CI [26.31, 31.15], \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) (Fig.\u0026nbsp;4\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHCV Testing\u003c/h2\u003e \u003cp\u003eBetween 2014\u0026ndash;2019, a total of 3.7\u0026nbsp;billion office visits were made by non-pregnant adults without known hepatitis B, hepatitis C, or cirrhosis. During these visits, 36.6\u0026nbsp;million HCV tests were performed, for a testing rate of 1%. In univariable analysis, non-Hispanic Black patients had a higher likelihood of HCV testing, whereas males, individuals born before 1945, and those covered by Medicare had lower testing rates (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Estimates from multivariable logistic regression indicated that non-Hispanic Blacks patients had higher per-visit odds of HCV testing (OR 3.03, 95% CI [2.10, 4.37]) compared to patients of other races/ethnicities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of demographic factors with office based with hepatitis C testing from 2016 to 2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Tests Performed (1000s) [SEM]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Visits in 1000s [SEM]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% of total visits\u003c/p\u003e \u003cp\u003e[SEM]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003cp\u003e(testing rate)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11973 [2308]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1546555 [40712]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8% [0.1%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24599 [4651]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2177400 [53378]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1% [0.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth Cohort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-1945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3333 [1026]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e845433 [27171]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4% [0.1%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1945\u0026ndash;1965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16457 [2745]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1622115 [38677]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0% [0.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1965\u0026ndash;1985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12165 [4425]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e893099 [27724]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4% [0.5%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1985\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4618 [892]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363308 [14232]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3% [0.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4276 [959]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450767 [21846]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0% [0.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2602 [660]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e429582 [17325]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6% [0.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10890 [4756]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e785059 [34673]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4% [0.6%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2977 [898]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e507472 [34197]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6% [0.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/Ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16927 [2187]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2673532 [56158]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6% [0.1%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7341 [1645]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e364484 [19345]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0% [0.4%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6165 [1645]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e464260 [29628]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3% [0.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6138 [2623]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221678 [26235]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8% [1.1%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22848 [3894]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2154271 [54362]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1% [0.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5340 [1293]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249727 [16172]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6% [0.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4322 [1509]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e829385 [30459]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7% [0.6%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMSA Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34483 [6259]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3421607 [81320]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0% [0.2%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-MSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2089 [816]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302348 [30188]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7% [0.3%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTesting rates were higher for visits during which the provider identified themselves as the patient\u0026rsquo;s primary care provider (1.7 vs 0.5%, OR 3.23, 95%CI [2.93, 3.56], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as well as during visits to general practitioners when compared to medical or surgical specialists (1.7% vs 0.5% vs 0.2% respectively, OR 3.15 95%CI [3.14, 3.15], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR 7.41 95%CI [7.40, 7.42], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, patients who presented for initial visits (1.6 vs 0.9%, OR 1.92 95%CI [1.91, 1.93], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and preventative visits (2.7 vs 0.7%, OR 3.91 95%CI [3.90, 3.92] \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were more likely to undergo HCV testing when compared to follow-up visits and visits for acute medical problems, respectively.\u003c/p\u003e \u003cp\u003eAfter applying more stringent criteria excluding surgical specialties, visits where the physician indicated that they were not the patient\u0026rsquo;s primary care provider, and visits for an acute problem, a flare of a chronic problem, or a pre/post-operative evaluation, hepatitis testing was performed during 20\u0026nbsp;million of 887\u0026nbsp;million visits (2.2%). Black patients were tested at a significantly higher rate than non-Hispanic White and Hispanic and other (3.6 vs 1.5 vs 3.0 vs 6.5%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Regarding birth cohort, the highest testing rate was for those born between 1965 to 1985 (3.4%) compared to those born between 1945-65 (2.1%), before 1945 (1.0%) and those born 1985\u0026ndash;2005 (3.3%), \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.035. Among insurance categories, patients covered by Medicaid had the highest rate of hepatitis testing while those covered by Private insurance lower and by Medicare the lowest (4.9 vs 2.4 vs 1.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). In multivariable regression, age and race differences were retained while testing rates by primary insurance were no longer significantly different.\u003c/p\u003e \u003cp\u003eIn subgroup analysis based on 2012 screening recommendations, there were 406\u0026nbsp;million visits for baby boomers, 33.2\u0026nbsp;million visits for people with substance use disorder (SUD) (as documented by the treating provider), and 1.5\u0026nbsp;million visits for patients with end stage renal disease. In total, 426\u0026nbsp;million visits occurred for those who met high-risk screening criteria, and hepatitis testing was performed during 2.1% of these visits, no higher than testing rates in those who did not meet screening criteria (2.4%). In contrast, a complete blood count was ordered during 22.7% of these visits, suggesting that the reason for not performing hepatitis testing was not lack of access to phlebotomy/laboratory evaluations.\u003c/p\u003e \u003cp\u003eFinally, when more stringent criteria was applied by only including those visits for which a self-identified primary care provider was seeing the patient for the first time for a preventive or routine care visit was applied, we estimated a testing rate of 9.8%. In comparison, complete blood count (40.0% of visits), complete metabolic panel (39.8% of visits) and glycosylated hemoglobin level (19.1% of visits) were ordered at significantly higher rates (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01 for all).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCare Utilization in Patients with Substance Use Disorder\u003c/h2\u003e \u003cp\u003eFrom 2010\u0026ndash;2019, 69.6\u0026nbsp;million visits were identified for substance use disorder (SUD), and 1.3\u0026nbsp;million (1.8%) had identified HCV. Compared to those without SUD, those with SUD used the ER for a higher proportion of total visits (14.3\u0026nbsp;million of 69.6\u0026nbsp;million, 20.6% vs 1.2\u0026nbsp;billion of 9.6\u0026nbsp;billion, 12.6%, OR 1.8 95% CI [1.4, 2.2], \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001). Similarly, among patients with identified HCV, those with SUD were more likely to utilize care in the ER compared to those without (17.1% [of all HCV visits] vs 4.5%, OR 4.4 95% CI [2.1, 9.0], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Only 3.6% of visits for people with SUD were of \u0026ldquo;baby boomer\u0026rdquo; age, compared to 19.3% of overall visits. In the baby boomer cohort with HCV, 0.1% of visits were for those with SUD compared to 0.5 vs 1.5 vs 1.2 in those born [before 1945 or 1965\u0026ndash;1985 or 1985\u0026ndash;2005] (Fig.\u0026nbsp;5\u003cb\u003e)\u003c/b\u003e. In the period 2010\u0026ndash;2012, 0.2\u0026nbsp;million (4.5%) of visits for HCV were also associated with SUD compared to 0.6\u0026nbsp;million (4.8%) in 2016\u0026ndash;2019. Medicaid was overrepresented as the primary payer type for those with SUD (33% of total). In multivariable regression analysis adjusting for age, gender, race, and primary insurance, there were increased odds of SUD among HCV-identified users of the ER (OR 3.48, 95% CI [1.23, 9.90], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). Similarly, in adjusted multivariable analysis those with both identified HCV and SUD had higher odds of using the ER for care compared to those without SUD (OR 3.30 95% CI [1.23, 9.02], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e In this nationally representative study of ambulatory care use, our findings provide a comprehensive and contemporary evaluation of hepatitis C testing and ambulatory care utilization. Between 2010 and 2019, 36.6\u0026nbsp;million HCV tests were performed, and patients with HCV had 22\u0026nbsp;million office visits and 1.2\u0026nbsp;million ER visits. There are several key findings in this study. First, there were demographic differences in the most common care setting for those with HCV, namely the higher rates of ER use among Black patients, baby boomers, those in the West region, and those covered under Medicare and Medicaid. There was also disproportionate ER utilization among those with SUD, including those with already identified HCV. Second, there was an observed increase in visit proportions for those with hepatitis C, but primarily among White privately patients in the office setting. This may reflect access to office-based care as White patients were more likely to have private insurance. Third, HCV testing rates remained low. While on a per-visit basis Black patients were tested at a higher rate, this may be mitigated by lower overall access and visit rates. Taken together, these findings suggest that the current screening paradigm, which relies on office-based testing by primary care physicians, may have increasingly poor effectiveness in identifying hepatitis C and linking patients to treatment. Key strengths of this study include nationally representative nationwide estimates based on billions of setting-agnostic primary care visits, as well as low levels of missing data and consistency of data collection.\u003c/p\u003e \u003cp\u003eOur findings complement those of previous studies examining demographic shifts in hepatitis C in the US. Since 2010, a significant increase in HCV incidence in the United States has been reported, with new cases nearly quadrupling between 2011 and 2019.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e This dramatic increase in HCV has largely been attributed to the nation\u0026rsquo;s opioid epidemic, with some studies estimating that up to one-third of persons who inject drugs (PWID) will develop HCV within the first year.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Patients with SUD, along with Black patients and those covered by Medicaid and Medicare were more likely to utilize care from the ER rather than from an office visit. These findings are consistent with prior studies that found that Black patients and those with Medicaid are less likely to utilize primary care as their usual source of care and are more likely to visit the ER for non-urgent care.\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e While the ER plays a crucial role in caring for patients with SUD and identifying HCV cases, SUD and HCV treatment often require longitudinal outpatient management. As the prevalence of CHC decreases in baby boomers and those with higher socioeconomic status, case identification may increasingly rely on emergency care. Prior studies have indicated significant patient attrition along the HCV care cascade, highlighting the need for structured linkage-to-care programs, particularly for patients who primarily obtain care from the ER. \u003csup\u003e22 23\u003c/sup\u003e Ensuring effective follow-up for these patients is essential for improving treatment uptake and outcomes. Innovative approaches, such as integrating patient navigators and expanding telemedicine-based follow-up, may enhance care continuity and treatment initiation.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e One recent study has shown high levels of satisfaction with telemedicine and high levels of HCV treatment completion amongst those enrolled in opioid treatment programs.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Additionally, telemedicine may help mitigate non-financial barriers faced by underserved populations.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e It is critical to continue to implement strategies to mitigate access barriers despite financial challenges that may be faced by healthcare providers.\u003c/p\u003e \u003cp\u003eThere has also been a dramatic demographic shift among individuals with hepatitis C, largely driven by the opioid epidemic as well as more successful screening and eradication in the baby boomer population. However, it is concerning that the HCV testing rates may not reflect this demographic shift. In 2021, the CDC estimated that rural areas had significantly higher rates of HCV incidence (58 per 100,000) when compared to urban areas (36 per 100,000).\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e The same report found White patients and male patients had higher rates of HCV. In this study, we found similar rates of HCV testing in metropolitan and non-metropolitan areas. This may be the result of persistent geographic disparities in access to care and patients often need to travel further to obtain care, \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003eas many specialists who treat patients with CHC are more likely to be in urban areas\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Additionally, we found a significantly higher testing rate for Black patients (2.0%) when compared to White patients (0.6%). While the exact reason for this is unclear, it is possible that a larger proportion of White patients live in rural areas that have lower geographic access to care. However, as noted earlier, Black patients were more likely to obtain care at the ER, which may fragment care and reduce linkage to outpatient treatment, even when identified as a case.\u003csup\u003e21 36\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eScreening is a critical first step towards our goal of HCV elimination, and there are many barriers that must be addressed as efforts continue towards achieving this goal. One such barrier is access to care. For example, as mentioned above, there are significant geographic disparities to access care, especially in areas that have been most impacted by HCV in recent years. Point of care (POC) HCV testing has shown promise in other countries and was recently approved by the FDA.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e The implementation of POC HCV screening at community centers, supervised consumption services, and non-clinical justice settings could improve HCV screening rates, especially in at-risk areas.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, due to the cross-sectional nature of NAMCS/NHAMCS, our study lacked longitudinal information. This could lead to inaccurate estimations of testing as we cannot account for individuals who previously underwent screening; any bias in utilization would be expected to trend towards the null in our analysis. However, to our knowledge, there are no nationally representative surveys that provide longitudinal data on this population. Additionally, the lack of longitudinal data prevents us from better understanding the impact of different care utilization practices on clinical outcomes. An additional limitation of this study was that NAMCS/NHAMCS data estimate healthcare utilization rather than true HCV prevalence. As such, visits by HCV positive patients may be under-identified due to reliance on ICD-9 and ICD-10 codes. Previous validation studies suggest these codes have moderate sensitivity (50\u0026ndash;70%) but high specificity, meaning some undiagnosed or undocumented cases were likely missed. Likewise, NHAMCS does not capture visits to freestanding urgent care centers that are not affiliated with hospitals. As such, we may underestimate care utilization, particularly among patients who seek low-cost, walk-in care outside of traditional emergency or office-based settings. However, cases were unlikely to have been systematically missing due to specific patient characteristics or visit type, mitigating concerns of potential bias. This limitation underscores the need for future studies using biomarker-based databases or electronic health records with longitudinal follow-up. Finally, a critical transition in hepatitis C screening and management took place during this study period, including widespread adoption of universal screening. Regardless, our study serves to detail demographic shifts in usage rates, including among younger individuals and those in rural areas or with SUD who are less likely to seek care in commonly researched settings. Lastly, our most recent data were from 2019. The onset of the COVID-19 pandemic has led to a halt in data collection and the subsequent decision to discontinue NHAMCS permanently. The future lack of public, nationally representative and comparable data between care settings, however, adds the importance of this contemporary analysis of HCV care in the US.\u003c/p\u003e \u003cp\u003eIn this study using two large, nationally representative databases, we estimated ambulatory care utilization for individuals with HCV. Our study showed greater ER utilization among patients who were younger, Black, and covered by Medicaid. Additionally, we found that HCV screening practices did not address current patient demographic trends for those with HCV. Policy and practice updates must be implemented to enhance providers awareness of the current state of HCV infection in the United States and to improve patient access to adequate screening and linkage to care. As efforts continue toward the goal of HCV elimination, it is crucial for future research and further initiatives to focus on at risk populations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHall, E. W. \u003cem\u003eet al.\u003c/em\u003e Estimating hepatitis C prevalence in the United States, 2017\u0026ndash;2020. Hepatology 81, 625\u0026ndash;636 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlach, S. \u003cem\u003eet al.\u003c/em\u003e Global prevalence and genotype distribution of hepatitis C virus infection in 2015: a modelling study. Lancet Gastroenterol Hepatol 2, 161\u0026ndash;176 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLy, K. N. \u003cem\u003eet al.\u003c/em\u003e Deaths Associated With Hepatitis C Virus Infection Among Residents in 50 States and the District of Columbia, 2016\u0026ndash;2017. Clin Infect Dis 71, 1149\u0026ndash;1160 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWestbrook, R. H. \u0026amp; Dusheiko, G. Natural history of hepatitis C. J Hepatol 61, S58\u0026ndash;S68 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfdhal, N. \u003cem\u003eet al.\u003c/em\u003e Ledipasvir and Sofosbuvir for Untreated HCV Genotype 1 Infection. New England Journal of Medicine 370, 1889\u0026ndash;1898 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHa, S. \u0026amp; Timmerman, K. Awareness and knowledge of hepatitis C among health care providers and the public: A scoping review. Canada Communicable Disease Report 44, 157\u0026ndash;165 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith, B. D. \u003cem\u003eet al.\u003c/em\u003e Recommendations for the identification of chronic hepatitis C virus infection among persons born during 1945\u0026ndash;1965. MMWR Recomm Rep 61, 1\u0026ndash;32 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchillie, S., Wester, C., Osborne, M., Wesolowski, L. \u0026amp; Ryerson, A. B. CDC Recommendations for Hepatitis C Screening Among Adults \u0026mdash; United States, 2020. MMWR. Recommendations and Reports 69, 1\u0026ndash;17 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiondi, B. E. \u003cem\u003eet al.\u003c/em\u003e Racial and Ethnic Disparities in Hepatitis C Care in Reproductive-Aged Women With Opioid Use Disorder. Clinical Infectious Diseases 79, 1428\u0026ndash;1436 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNili, M., Luo, L., Feng, X., Chang, J. \u0026amp; Tan, X. Disparities in hepatitis C virus infection screening among Baby Boomers in the United States. Am J Infect Control 46, 1341\u0026ndash;1347 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArtenie, A. A., Bruneau, J., L\u0026eacute;vesque, A. \u0026amp; Wansuanganyi, J.-M. B. Role of primary care providers in hepatitis C prevention and care: one step away from evidence-based practice. Can Fam Physician 60, 881\u0026ndash;2, e468-70 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCaig LF, McLemore T Plan and Operation of the National Hospital Ambulatory Medical Care Survey. National Center for Health Statistics. Vital Health Stat 1 (34) 1994..\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleming-Dutra, K. E. \u003cem\u003eet al.\u003c/em\u003e Prevalence of Inappropriate Antibiotic Prescriptions Among US Ambulatory Care Visits, 2010\u0026ndash;2011. JAMA 315, 1864 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenter for Disease Control and Prevention. NAMCS and NHAMCS Survey Methods and Analytic Guidelines. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://archive.cdc.gov/www_cdc_gov/nchs/ahcd/survey-methods-archived.htm\u003c/span\u003e\u003cspan address=\"https://archive.cdc.gov/www_cdc_gov/nchs/ahcd/survey-methods-archived.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. February 6, 2019. Accessed March 13, 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen, I., Moussa, K. \u0026amp; Gutierrez, J. Hepatitis C Virus Elimination in the United States: Challenges, Progress, and Future Steps. Gastroenterol Hepatol (N Y) 19, 700\u0026ndash;707 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonzalez, S. A. \u0026amp; Trotter, J. F. The rise of the opioid epidemic and hepatitis C\u0026ndash;positive organs. Hepatology 67, 1600\u0026ndash;1608 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagan, H., Pouget, E. R., Des Jarlais, D. C. \u0026amp; Lelutiu-Weinberger, C. Meta-Regression of Hepatitis C Virus Infection in Relation to Time Since Onset of Illicit Drug Injection: The Influence of Time and Place. Am J Epidemiol 168, 1099\u0026ndash;1109 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, H., McConnell, K. J. \u0026amp; Sun, B. C. Comparing Emergency Department Use Among Medicaid and Commercial Patients Using All-Payer All-Claims Data. Popul Health Manag 20, 271\u0026ndash;277 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoty, M. M. \u0026amp; Holmgren, A. L. Health care disconnect: gaps in coverage and care for minority adults. Findings from the Commonwealth Fund Biennial Health Insurance Survey (2005). Issue Brief (Commonw Fund) 21, 1\u0026ndash;12 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParast, L. \u003cem\u003eet al.\u003c/em\u003e Racial/Ethnic Differences in Emergency Department Utilization and Experience. J Gen Intern Med 37, 49\u0026ndash;56 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYing, X. \u003cem\u003eet al.\u003c/em\u003e Racial Disparities in Cost and Non-Cost Barriers to Care: An Analysis of the All of Us Survey. J Gen Intern Med 39, 2875\u0026ndash;2877 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrickey, A., Fajardo, E., Alemu, D., Artenie, A. A. \u0026amp; Easterbrook, P. Impact of hepatitis C virus point-of-care RNA viral load testing compared with laboratory-based testing on uptake of RNA testing and treatment, and turnaround times: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol 8, 253\u0026ndash;270 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTran, L., Jung, J., Feldman, R. \u0026amp; Riley, T. Disparities in the quality of care for chronic hepatitis C among Medicare beneficiaries. PLoS One 17, e0263913 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHyde, Z. \u003cem\u003eet al.\u003c/em\u003e Evaluation of a pilot emergency department linkage to care program for patients previously diagnosed with Hepatitis C. J Viral Hepat 30, 129\u0026ndash;137 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalal, A. H. \u003cem\u003eet al.\u003c/em\u003e High Satisfaction with Patient-Centered Telemedicine for Hepatitis C Virus Delivered to Substance Users: A Mixed-Methods Study. Telemedicine and e-Health 29, 395\u0026ndash;407 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReed, M. E. \u003cem\u003eet al.\u003c/em\u003e Patient Characteristics Associated With Choosing a Telemedicine Visit vs Office Visit With the Same Primary Care Clinicians. JAMA Netw Open 3, e205873 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eCenters for Disease Control and Prevention. Viral Hepatitis Surveillance Report \u0026ndash; United States\u003c/em\u003e, 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eHttps://Www.Cdc.Gov/Hepatitis-Surveillance-2021/about/Index\u003c/span\u003e\u003cspan address=\"http://Https://Www.Cdc.Gov/Hepatitis-Surveillance-2021/about/Index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003cem\u003eHtml. Published August 2023. Accessed February 12, 2025\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEstadt, A. T. \u003cem\u003eet al.\u003c/em\u003e Differences in hepatitis C virus (HCV) testing and treatment by opioid, stimulant, and polysubstance use among people who use drugs in rural U.S. communities. Harm Reduct J 21, 214 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, D. \u003cem\u003eet al.\u003c/em\u003e Assessment of Changes in Rural and Urban Primary Care Workforce in the United States From 2009 to 2017. JAMA Netw Open 3, e2022914 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYing, X., Yao, L., Mathis, W. S., Congly, S. E. \u0026amp; Jesudian, A. B. Geographic Disparities in Access to Gastroenterologists in the United States. Gastroenterology (2025) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.gastro.2025.01.232\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2025.01.232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFood \u003cem\u003eand Drug Administration. FDA Permits Marketing of First Point-of-Care Hepatitis C RNA Test. June 27, 2024. Accessed February 17\u003c/em\u003e, 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarvey, L. \u003cem\u003eet al.\u003c/em\u003e Feasibility and Performance of a Point-of-Care Hepatitis C RNA Assay in a Community Supervision Cohort. JAMA Netw Open 7, e2438222 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLettner, B. \u003cem\u003eet al.\u003c/em\u003e Rapid hepatitis C virus point-of-care RNA testing and treatment at an integrated supervised consumption service in Toronto, Canada: a prospective, observational cohort study. Lancet regional health. Americas 22, 100490 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6378738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6378738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction \u003c/strong\u003eHepatitis C infection (HCV) is a leading cause of liver disease and mortality. Despite curative treatment options, eradication remains elusive. Although the US has national screening recommendations, HCV remains under-screened and under-diagnosed. We utilized two national surveys to estimate trends in overall HCV care utilization and testing in the US.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eData from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey from 2010-2019 were analyzed via inverse probability weighting to generate national estimates of visits and testing. Weighted chi-square and logistic regression analyses adjusted for demographics, payor type and time assessed the primary outcome of ambulatory care utilization as well as the secondary outcome of office-based hepatitis C screening.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eBetween 2010-2019, 23,469,344 HCV ambulatory visits were identified with higher overall adjusted visit rates for men (OR 1.54), people born 1945-1965 (OR 4.00), and insured by Medicare (OR 1.98) with increased utilization by White, privately insured patients in the office since 2016. Identified HCV cases who were covered by Medicaid (OR 6.05) or had associated substance use disorder (SUD) (OR 3.30) were more likely to utilize the ER than office care. Since 2016 per-visit screening rates were low both overall (1%) and when restricting to initial primary care preventive health visits (2.1%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussions \u003c/strong\u003eIn a nationally representative study of ambulatory care utilization, we identified increasing rates of HCV visits, largely in White privately insured patients seen in office. Low overall screening rates and disproportionate ER utilization among rural, racial/ethnic minorities, Medicaid insured and patients with SUD highlight the importance of updated policy and practice guidelines to improve identification and care linkage for HCV.\u003c/p\u003e","manuscriptTitle":"Sociodemographic Disparities in Hepatitis C Care Utilization and Testing in the United States: A Nationwide Survey Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 01:41:54","doi":"10.21203/rs.3.rs-6378738/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4a6ba15-fef9-422d-8248-1d0bb5c3677e","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47989658,"name":"Health sciences/Gastroenterology/Hepatology/Hepatitis/Viral hepatitis/Hepatitis C"},{"id":47989659,"name":"Health sciences/Health care/Public health/Population screening"},{"id":47989660,"name":"Health sciences/Medical research/Epidemiology"},{"id":47989661,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2026-03-20T07:36:54+00:00","versionOfRecord":{"articleIdentity":"rs-6378738","link":"https://doi.org/10.1038/s43856-025-01352-1","journal":{"identity":"communications-medicine","isVorOnly":false,"title":"Communications Medicine"},"publishedOn":"2026-03-19 04:00:00","publishedOnDateReadable":"March 19th, 2026"},"versionCreatedAt":"2025-05-09 01:41:54","video":"","vorDoi":"10.1038/s43856-025-01352-1","vorDoiUrl":"https://doi.org/10.1038/s43856-025-01352-1","workflowStages":[]},"version":"v1","identity":"rs-6378738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6378738","identity":"rs-6378738","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}