ABSTRACT
People with hepatitis C should be identified and promptly linked to care after diagnosis to eliminate hepatitis C virus (HCV) infection by 2030. An implementation intervention, an HCV screening and linkage to care intervention, the FOCUS program, highlighted by four pillars (normalisation of routine testing, promotion of testing based on electronic medical records [EMR], procedure overseen by linkage coordinator, and quality improvement training/feedback among staff), has been conducted in a community health center in South Carolina from 2018 to 2020. We aim to assess the impacts of this intervention on linkage to care time and its sustainability. We analysed a cohort (n = 593) of adults who tested positive for hepatitis C infection, all of whom received hepatitis C care services from the Cooperative Health Center (January 2015 to March 2023) from the EMR data. Descriptive analysis was employed for outcome (linkage to care days [LTC days]) and sociodemographic variables (i.e., race, sex, age, health insurance). We compared the sociodemographic and average LTC days among pre‐intervention (2015–2017), during‐intervention (2018–2020), and post‐intervention (2021–2023) phases. Multivariate linear regressions were conducted on LTC days and intervention phases, controlling for relevant covariates. Most (59.5%) of the participants were 45 to 64 years of age. Sixty percent were males, and 57% were African Americans. Around 45% of the participants did not have health insurance. The mean LTC days at pre‐intervention were 66.68 (SD = 72.29). LTC days reduced by 31 days during the intervention compared to pre‐intervention (ß = −31.21, p < 0.001). Similarly, in the post‐intervention phase, LTC days reduced by 30 days compared to pre‐intervention (ß = −29.96, p < 0.001). Age was associated with LTC days, with middle‐aged people (45–64 years) having the longest LTC days. Our study suggests that the intervention had a robust immediate effect, which was maintained in the post‐intervention period, thus highlighting its lasting impact. Since its implementation, the FOCUS program has significantly shortened the days of linkage to care for people with hepatitis C. We noted the positive impacts across the different demographics studied. More importantly, the impacts were sustainable through the COVID‐19 pandemic. We need future efforts to engage middle‐aged groups to further reduce the hepatitis C LTC days.
1. Introduction
Hepatitis C virus infection is one of the leading causes of liver diseases worldwide and thus remains a significant public health concern [1]. Chronic hepatitis C is a significant cause of cirrhosis, end‐stage liver disease, and liver cancer, and it carries a significant socioeconomic impact on society [2]. Globally, in 2022, an estimated 50 million people had chronic hepatitis C virus infection, with about 1.0 million new infections occurring per year and approximately 242,000 deaths from hepatitis C, mostly from cirrhosis and hepatocellular carcinoma (primary liver cancer) [3]. According to the Centers for Disease Control and Prevention (CDC), 43 U.S. states and the District of Columbia reported 93,805 newly identified chronic hepatitis C cases, equating to 40.2 cases per 100,000 population [4]. Death rates were notably higher among non‐Hispanic American Indian/Alaska Native (AI/AN) and non‐Hispanic Black individuals, at 3.3 times and 1.7 times higher, respectively, compared to non‐Hispanic White individuals [4]. The disparities in mortality warrant improving outcomes of hepatitis C treatment and care for each step in the treatment cascade, especially hepatitis C testing and linkage to treatment.
In the past, treatment for hepatitis C was interferon‐based therapy, which had less efficacy and was associated with significant adverse effects, often leading to treatment discontinuation. However, in recent years, the highly efficacious, interferon‐free, all‐oral direct‐acting antiviral (DAA) treatments provided therapeutic efficacy in more than 95% of patients, including patients with Substance Abuse Disorders (SUDs), within 8–12 weeks [5]. Timely linkage to care after hepatitis C diagnosis within 12 weeks is one critical step to ensure benefits from this advanced treatment [5]. There is a dearth of literature that investigates the linkage to care time within the United States, but a study based on the University of Indiana database in 2019 showed that the median time to treatment in the early days of direct‐acting antivirals was 300 days [6]. However, studies have found that several challenges impede the linkage to care process, particularly in community health centres serving vulnerable populations [7]. These challenges include limited awareness and knowledge about hepatitis C among patients and healthcare providers, stigma associated with hepatitis C infection, and inadequate healthcare infrastructure to support comprehensive hepatitis C care [7].
Intervention strategies designed to enhance the adoption, implementation, and sustainability of evidence‐based practices have shown promise in improving healthcare delivery outcomes, including linkage to care in hepatitis C treatment. Strebe et al. examined an intervention that leveraged patient navigation to improve linkage to hepatitis C treatment in Dallas County, Texas, a socioeconomically disadvantaged, racially diverse patient population [8]. Patient navigation was associated with significantly increased linkage to care and treatment initiation among patients with hepatitis C infection. Another strategy that they tried was to screen at the Emergency Departments (ED). A study evaluated the efficacy of non‐targeted (i.e., non‐risk‐based) screening in emergency departments and other healthcare settings for hepatitis C and then linkage to hepatitis C care. They found that non‐targeted hepatitis C screening in acute care settings can identify many people with hepatitis C infection. However, among patients who had hepatitis C infection, the linkage to care rate was lower for patients screened in the ED compared with outpatient clinics [9]. In 2019, Louisiana and Washington states initiated a hepatitis C elimination program that promoted hepatitis C screening through public awareness, expansion of screening, surveillance enhancement (including electronic reporting and task force development), and harm reduction [9]. The program included screening the incarcerated population, and they noted an increase in the monthly average number of people linked to care from 62 to 275.
Although there are growing efforts in the implementation science research and practices aimed to promote linkage to hepatitis C treatment, existing literature suggests several gaps: (1) a lack of robust evidence for efficacy evaluation based on clinical data; (2) a dearth of evaluation about the long‐term implementation impacts based on longitudinal data; and (3) limited assessment of the sustainability of implementation projects, especially in the context of public health emergencies such as the COVID‐19 pandemic. To address these knowledge gaps, we used electronic medical record (EMR) data to assess the long‐term impacts of an implementation project on linkage to hepatitis C treatment among patients in a community health center in South Carolina. An estimated 65,000 people in South Carolina are living with chronic hepatitis C [10]. In 2023, there were an estimated 2482 cases of chronic hepatitis C diagnosed in South Carolina [10]. The implementation project, i.e., the Frontlines of Communities in the United States (FOCUS) program, conducted in South Carolina, applied four main implementation strategies (four pillars) to reduce the time it takes to link to care after diagnosis, with the hope of getting an early cure and reducing morbidities and mortalities related to hepatitis C. The current study aims to assess the impact of the FOCUS program on linkages to care among people diagnosed with hepatitis C in the long term, controlling for the sociodemographic characteristics of the patients, and investigating if the impacts were sustainable during the COVID‐19 pandemic.
2. Method
2.1. Study Site
The Cooperative Health (Eau Claire Cooperative Health Center) is a Federally Qualified Health Center (FQHC) in South Carolina that provides accessible, high‐quality, compassionate primary and specialty medical services across four counties in the Midlands of South Carolina. It caters to insured, underinsured, and uninsured patients. Cooperative Health operates out of 24 sites, spread over four counties. The providers referred all patients with positive HCV results to one of these sites for treatment. The center currently has over 65 highly qualified providers delivering integrated healthcare services, including paediatrics, adult and family practice, obstetrics, and gynaecology, dental, podiatry, behavioural medicine, orthopaedics, and pharmacy services to more than 58,000 patients annually in Richland, Lexington, Newberry, and Fairfield Counties.
2.2. Intervention Description
In response to the observed prevalence of chronic hepatitis C, the South Carolina Viral Hepatitis Committee developed the South Carolina Viral Hepatitis Strategic Plan to combat the burden of hepatitis C in the state. They employed the efforts of the workgroups focused on policy, advocacy, data, prevention, screening, and treatment through collaboration with healthcare agencies statewide, including Cooperative Health. We urgently needed to increase hepatitis C screening among the adult population, given the prevalence of hepatitis C exposure of about 12% among Cooperative Health patients living with HIV. We, therefore, launched the campaign to screen cooperative‐wide by educating the Providers about the need to screen all adults at our quarterly meetings. Although there was a buy‐in among the Providers, the workflow was not smooth. Before implementing the FOCUS pillars at Cooperative Health in 2018, hepatitis C screening was mainly risk‐based and only done at a few Cooperative Health offices. Since there was no automation, we did not screen many patients. We have made significant improvements since adopting the four pillars of the FOCUS program in 2018.
The FOCUS, i.e., Frontlines of Communities in the United States, is a public health initiative funded by Gilead Sciences that enables partners to develop and share best practices in routine blood‐borne virus (HIV, HCV, HBV) screening, diagnosis, and linkage to care in accordance with the screening guidelines promulgated by the U.S. Centers for Disease Control and Prevention (CDC), the U.S. Preventive Services Task Force (USPSTF), and state and local public health departments. FOCUS funding focuses on HIV, HCV, and HBV screening and linkage to the first medical appointment after diagnosis without aims beyond linkage to their first medical appointment. Appendix 1 describes the four pillars of FOCUS. We tailored and integrated the four pillars into the routine workflow of testing and linkage to care (Figure 1) in the context of South Carolina, even after the program funding ended in 2020.
FIGURE 1.
Testing and linkage to care workflow.
2.2.1. Pillar 1—Integration of Hepatitis C Testing
The first intervention pillar was to integrate hepatitis C testing into the standard clinic workflow seamlessly. We enhanced the Electronic Medical Record (EMR) system to automatically prompt testing if a patient is 18 years and older who has never been screened for hepatitis C at all, streamline processes, populate lab orders, and monitor performance metrics. The system created encounter types with preset orders to ease workflow. We automatically reflexed all positive screening tests to confirmatory tests to shorten diagnosis time. We also developed and integrated a specific appointment type for HIV/hepatitis care into the EMR system to standardise the lab orders and make tracking easier.
2.2.2. Pillar 2—Normalisation and Sustaining Routine Testing
We launched a cooperative‐wide campaign to increase disease awareness and encourage routine screening, with posters to motivate patients at sites. The Cooperative provided education on the need for increased screening and prompt linkage to care of all patients who tested positive for hepatitis C during regular meetings and sent e‐mail reminders to make hepatitis C screening a routine opt‐out test as part of the workflow. The providers tested all patients at least once unless there were risk factors that may dictate repeat testing.
2.2.3. Pillar 3—Systemic Policy Changes
The Cooperative made systemic policy changes at multiple organisational levels to support standardised and routine hepatitis C testing and ensure linkage to care. At the administration level, the Cooperative Health Center supported by authorising EMR modification, creating hepatitis C screening materials and circuit TV messages. They created a linkage coordinator position as well. The linkage coordinator was responsible for tracking all patients who tested positive for the hepatitis C virus and ensuring that the care team scheduled appointments timely. The linkage coordinator worked closely with screening providers and treatment providers. The FOCUS Award made financial assistance available to offset screening costs, especially for the uninsured and underinsured. We also adopted an open‐door policy to encourage walk‐ins regardless of insurance status. The Cooperative also encouraged all primary care providers (PCPs) to discuss positive results with patients and ensure a swift linkage to care. The administration adjusted the schedules to accommodate same‐day or early appointments for positive patients.
2.2.4. Pillar 4—Training, Feedback, and Quality Improvement
The FOCUS core team members (the Project Director, Project Coordinator, Linkage Coordinator and Data Manager) met bi‐weekly to review our numbers and identify gaps and how to improve screening and reduce barriers to linkage into care. Cooperative Health introduced comprehensive training, feedback, and quality improvement measures to identify and promote the best practices. The data manager pulled all screening tests and identified positive results. The linkage coordinator ensured timely (the set goal was 2 weeks from the positive result) treatment appointments, coordinating with PCPs. The team met bi‐weekly to discuss data, identified barriers and solutions to screening and linkage, and set goals to improve practices.
2.3. Data Source and Measurement
The outcome of this study is the time of linkage to hepatitis C care measured by days. The linkage to care time, i.e., the linkage to care days (LTC days), is the period between a positive hepatitis C virus result and the time the patient was seen to discuss the management of hepatitis C. For this analysis, we included the EMR data from a cohort (n = 593) of adults who tested positive for hepatitis C infection from the Cooperative Health Center or from outside referrals (January 2015 to March 2023) and who were successfully linked to care. Note that some people tested positive but did not show up for their appointment and therefore were not linked to care (about 6%).
The hepatitis nurse kept and updated a concurrent spreadsheet, which captured hepatitis C‐positive patients, their demographics, and whether they were linked to care or not. The Cooperative created an EMR encounter template that included diagnosis and test codes for uniformity and simplification to avoid misclassification and simplify the screening process. In cases when providers do not use the created EMR Encounter template, the data analyst was able to pull test results that included a range of hepatitis C screening test codes, as present on LabCorp.
We extracted basic demographics (age, sex, race, and ethnicity), health insurance status, HIV status, and year of hepatitis C testing. We categorised the intervention into three phases to assess the impact of the intervention by comparing the pre‐intervention and intervention phases, and the program's sustainability by comparing the intervention and the post‐intervention phases. Given the intervention phases (i.e., pre‐, during‐, and post‐intervention), we created the FOCUS intervention group variable by categorising patients based on their hepatitis C diagnosis year into three groups, including a ‘pre‐intervention group (2015‐2017)’, a ‘during‐intervention group (2018‐2020)’, and a ‘post‐intervention group (2021‐2023)’. The University of South Carolina's Institutional Review Board approved the study protocol.
2.4. Data Analysis
We screened the data for proper coding, outliers, and normality (kurtosis and skewness). We removed the outliers based on the distribution of the LTC days (top 1%, i.e., > 299) since including these extreme values could disproportionately influence the analysis and obscure general trends. We conducted a sensitivity analysis with and without the outliers to assess the consistency of the estimates and computed the descriptive statistics to summarise the sociodemographic variables, HIV status, and LTC days across the intervention groups. We applied the chi‐square test for the categorical variables and used ANOVA for the continuous variables to examine their differences by intervention groups. Furthermore, we used correlation analysis to evaluate the relationship between the dependent variable (LTC days) and independent variables, including the intervention groups and other demographic and control variables. We conducted multiple linear regression analyses to investigate these associations thoroughly. We determined the multicollinearity using variance inflation (VIF) analysis, with values lower than 10 suggesting no multicollinearity. Lastly, we conducted a post hoc test for pairwise comparisons among the intervention groups. We set the statistical significance at p < 0.05, with 95% confidence intervals. We conducted all analyses with SPSS.30 analytical software.
3. Results
3.1. Demographics
A total of 593 patients tested positive for hepatitis C infection between 2015 and 2023. After removing the outliers, we included 560 patients in our analysis. Table 1 shows demographic information on these patients. The mean age of patients linked was 52.3 years (SD = 12.91), and the majority (59.5%) were within the 45–64 years age group. The next most represented age group was the 25–44 years age group (26.8%). Most of the participants were males (59.8%), the majority were insured (54.8%), and most were African Americans (56.6%). Approximately 1 out of 10 patients (8.2%) had HIV. Notably, the intervention groups are different in terms of age, race, and HIV status (p < 0.001 for all three variables). The proportion of the age group 45 to 64 declined across the intervention phases, with 68.40% in the pre‐intervention, 60.80% during the intervention, and 51.80% post‐intervention. African Americans were the majority race in the pre‐and during‐intervention (pre: 81%, during: 57%), but in the post‐intervention phase, most patients were Caucasians (54.9%). The percentage of patients with HIV decreased from 16.8% in the pre‐intervention phase to 8% during thentervention phase and 3.7% in the post‐intervention phase. The average time to link positive patients to care significantly decreased from 67 days before the intervention (i.e., 2015–2017) to 33 days during the intervention (2018–2020). The reduction of LTC days was sustained post‐intervention (2021–2023), with average LTC days of 32 days.
TABLE 1.
Sociodemographic characteristics of Hepatitis C Virus patients across intervention groups.
| Pre (n = 95) | During (n = 301) | Post (n = 164) | Total (n = 560) | X2/T | p | |
|---|---|---|---|---|---|---|
| n (%) | n (%) | n (%) | n (%) | |||
| Age, mean (SD) | 57.95 (10.30) | 51.60 (13.38) | 50.40 (12.58) | 52.33 (12.91) | 0.906 | 0.749 |
| Age group | ||||||
| 18–24 | 1 (1.10) | 7 (2.30) | 0 (0.00) | 8 (1.40) | 34.69 | < 0.001 |
| 25–44 | 8 (8.40) | 81 (26.90) | 61 (37.20) | 150 (26.80) | ||
| 45–64 | 65 (68.40) | 183 (60.80) | 85 (51.80) | 333 (59.50) | ||
| 65+ | 21 (22.10) | 30 (10.00) | 18 (11.00) | 69 (12.30) | ||
| Sex | ||||||
| Males | 58 (61.10) | 176 (58.59) | 101 (61.60) | 335 (59.80) | 0.5 | 0.78 |
| Females | 37 (38.90) | 125 (41.50) | 63 (38.40) | 225 (40.20) | ||
| Race | ||||||
| African Americans | 77 (81.10) | 173 (59.50) | 67 (40.90) | 317 (56.60) | 42.87 | < 0.001 |
| Caucasians | 15 (15.80) | 118 (39.20) | 90 (54.90) | 223 (39.80) | ||
| Hispanics | 3 (3.20) | 10 (3.30) | 6 (3.70) | 19 (3.40) | ||
| Indians | 0 (0.00) | 0 (0.00) | 1 (0.60) | 1 (0.20) | ||
| Insurance | ||||||
| No | 33 (34.70) | 137 (46.00) | 82 (50.00) | 252 (45.20) | 5.8 | 0.055 |
| Yes | 62 (65.30) | 161 (54.00) | 82 (50.50) | 305 (54.80) | ||
| HIV status | ||||||
| Negative | 79 (83.20) | 277 (92.00) | 158 (96.30) | 514 (91.80) | 13.92 | < 0.001 |
| Positive | 16 (16.80) | 24 (8.00) | 6 (3.70) | 46 (8.20) | ||
| LTC days, mean (SD) | 66.68 (72.29) | 33.08 (42.15) | 32.26 (44.76) | 38.54 (50.78) | 18.70 | < 0.001 |
Table 2 presents a bivariate analysis examining the relationships between LTC days, Year of Screening, Age, Insurance, Race, and Gender. The correlation matrix shows that LTC had a significant positive correlation with age (0.143, p < 0.001) and a significant negative correlation with the year of screening (−0.22, p < 0.001) and race (−0.093, p < 0.05). These results indicated that it took longer to link older and African American patients to care. However, LTC days decreased in recent years. The year of screening was negatively correlated with age (−0.218, p < 0.001), meaning that as the year progressed, the people linked to care were getting younger. This finding corresponded to the effect of the opioid epidemic noted around the intervention years, which impacted younger age groups. Also, the year of screening was negatively correlated with insurance status (−0.124, p < 0.001) and positively correlated with race (0.253, p < 0.001), suggesting a higher proportion of younger and non‐African American patients and patients without health insurance in recent years. Age was positively correlated with insurance (0.349, < 0.001) and negatively correlated with race (−0.510, < 0.001), which means older people were more likely to have health insurance and more likely to be African American. Also, insurance status negatively correlated with race (−0.321, < 0.001), which means fewer African Americans had health insurance.
TABLE 2.
Correlation analysis summary.
| LTC days | Year of screening | Age | Insurance | Race | Sex | |
|---|---|---|---|---|---|---|
| LTC days | 1 | |||||
| Year of screening | −0.221** | 1 | ||||
| Age | 0.143** | −0.218** | 1 | |||
| Insurance | 0.039 | −0.124** | 0.349** | 1 | ||
| Race | −0.093* | 0.253** | −0.510** | −0.321** | 1 | |
| Sex | 0.027 | 1.70% | −6.20% | −0.50% | 0.006 | 1 |
p < 0.05.
p < 0.01.
Multivariate linear regression models suggest that the intervention group was significantly associated with LTC days (Table 3). The LTC was reduced by 33 days during the intervention phase compared to the pre‐intervention (ß = −33.60, p < 0.001). Similarly, the post‐intervention LTC was reduced by 34 days compared to the pre‐intervention phase (ß = −34.43, p < 0.001). Controlling for demographics, the average LTC days in the intervention phase decreased by over 31 days compared to pre‐intervention (ß = −31.21, p < 0.001), and the post‐intervention average LTC days decreased by nearly 30 days compared to the pre‐intervention phase (ß = −29.96, p < 0.001). Compared to the age group 45–64, patients in the age group 25–44 (ß = −13.64, p < 0.05) and patients older than 65 (ß = −16.58, p < 0.05) took less time to access care.
TABLE 3.
Logistic Regression Model Predicting the impact of intervention on the Linkage to Care of Hepatitis C Virus Patients.
| Variables | Model 1 | Model 2 |
|---|---|---|
| b (95% CI) | b (95% CI) | |
| Intercept | 66.68 (56.76, 76.61)*** | 69.99 (57.35, 82.62) *** |
| During intervention (ref: pre‐intervention) | −33.604 (−44.99, −22.22)*** | −31.21 (−42.88, −19.54) *** |
| Post Intervention (ref: pre‐intervention) | −34.43 (−46.90, −21.96)*** | −29.96 (−43.07, −16.85) |
| Gender (ref: males) | 3.31 (−5.07, 11.69) | |
| Caucasian (ref: African American) | −5.38 (−15.64, 4.89) | |
| Hispanic (ref: African American) | 16.83 (−6.74, 40.40) | |
| Asians/Native Indians (ref: African American) | −15.39 (−112.64, 81.86) | |
| Health Insurance (ref: no insurance) | 0.33 (−8.71, 9.38) | |
| HIV status (ref: HIV negative) | 2.99 (−12.34, 18.33) | |
| Age grp 18–24 (ref: Age grp 45–64) | −17.47 (−52.55, 17.62) | |
| Age grp 25–44 (ref: Age grp 45–64) | −13.64 (−24.62, −2.66)* | |
| Age 65+ (ref: Age grp 45–64) | −16.58 (−29.92, −3.25)* |
p < 0.05.
p < 0.001.
It is also notable that females, Hispanics, and patients with HIV had LTC days, although the magnitude did not reach statistical significance. It was unexpected to notice that it took people with HIV longer to get linked to hepatitis C care compared to patients without HIV. An explanation for this difference was that during the intervention phase, the practice was to wait for people with HIV to become virally suppressed before commencing treatment for hepatitis C. Notably, health insurance status did not affect the LTC days.
Table 4 presents multiple comparisons between the pre‐, during‐, and post‐intervention groups and how they affect the average LTC days. Following the significant ANOVA result in the regression analysis, we conducted Scheffe's test to identify the specific group differences. The analysis revealed significant differences between the average LTC days in the during‐intervention and pre‐intervention phases and non‐significant differences between the average LTC days during the intervention and post‐intervention phases. These indicated that our intervention significantly reduced the average LTC days, and this efficacy was sustained post‐intervention.
TABLE 4.
Post Hoc test of LTC days by intervention groups.
| Intervention group notations | Group comparison | Mean difference | Std. error | Sig. | 95% confidence interval | |
|---|---|---|---|---|---|---|
| Lower bound | Upper bound | |||||
| Pre‐Intervention (0) | 0–1 | 33.604 a | 5.795 | < 0.001 | 19.38 | 47.83 |
| 0–2 | 34.428 a | 6.350 | < 0.001 | 18.84 | 50.01 | |
| During Intervention (1) | 1–0 | −33.604 a | 5.795 | < 0.001 | −47.83 | −19.38 |
| 1–2 | 0.824 | 4.780 | 0.985 | −10.91 | 12.55 | |
| Post Intervention (2) | 2–0 | −34.428 a | 6.350 | < 0.001 | −50.01 | −18.84 |
| 2–1 | −0.824 | 4.780 | 0.985 | −12.55 | 10.91 | |
The mean difference is significant at the 0.05 level.
4. Discussion
Leveraging the power of EMR data, our study evaluated the efficacy of the implementation program FOCUS conducted in the Deep South. The program's four pillars, which included integration of hepatitis C testing into the workflow, normalisation and sustaining routine hepatitis C testing, systemic policy changes, and training, feedback, and quality improvement, were instrumental in significantly reducing the time it took to link hepatitis C positive patients to care. This success encouraged Cooperative Health to maintain the policy changes during the intervention and fostered a continued cooperative‐wide buy‐in among the Clinicians. Creating a dedicated team to monitor hepatitis C screening and ensure the timely linkage of patients into treatment care yielded a positive result. Cooperative Health significantly reduced linkage to care time from an average of 67 to 33 days. The integration of the four pillars of FOCUS into our routine workflow sustained LTC reduction even during the COVID‐19 pandemic.
Many programs are disrupted and not sustainable when funding ends. The FOCUS intervention significantly reduced the linkage to care days during implementation, and its continuity also sustained a reduced linkage to care time after the intervention period. FOCUS funding supported screening and confirmatory lab costs and a portion of staff FTE (Full Time Equivalent), but the funding was available only for the intervention phase. Cooperative health was able to sustain the implementation. The Linkage Coordinator and data analysts were members of staff whose job descriptions were repurposed to include FOCUS; therefore, their roles were sustained after funding ended.
We could attribute the success of this intervention to many factors. Dedication and collaboration among the team members were key. Also, the team met frequently, initially bi‐weekly and later monthly, to assess and discuss the challenges faced and find solutions. The Chief Medical Officer periodically reminded the providers to screen and link positive patients to care promptly, contributing to the success of the FOCUS intervention. This achievement will increase the chances that hepatitis positive patients will be linked, treated, and hopefully cured on time, which may lead to a reduction in the community burden of hepatitis C and its comorbidities. Although we did not assess the impact of timely linkage to care on the reduction of hepatitis C prevalence in the community in this study, we can consider it for future research.
The intervention period covered the early stage of the COVID‐19 pandemic, and despite that, we achieved significantly lower LTC days and maintained them. Telehealth services were available in our facilities during COVID‐19 (2020–2022). Specifically, we converted physical appointments to virtual appointments for those who could not commute. We also conducted phone visits for those who could not negotiate a virtual call easily or whose mobile devices could not accommodate a virtual visit. We offered patients the opportunity to draw their labs at closer facilities and have their labs sent over to us. The office also assisted in arranging transportation for some clients when all other options had been exhausted. Therefore, we were able to mitigate the impact of COVID‐19 on linkage to care with our flexible strategies. In addition, telehealth services helped to solve some challenges with other social determinants of health, like transportation availability, which was exacerbated during COVID‐19. The lessons obtained during the lived experiences of adopting telehealth in our clinics, although those experiences are beyond the scope of this study, are worth further reflection, synthesis, and discussion for better preparedness for future public health emergencies.
Our study suggested that age was associated with hepatitis C linkage to care days. We noted that people older than 65 were linked to care faster than people between the ages of 45–65. Many of those above 65 years were surprised by the diagnosis and tended to seek care as quickly as possible. Many did not know because they were not symptomatic, and they did not have the high prevalence of opioid misuse noted in the young adults who tested positive for hepatitis C. In addition, as the year progressed, we noted that the ages of enrolled patients were getting younger. Although we did not report in this paper, the younger age group tended to have contracted hepatitis C through IV drug use. Fostering collaboration between Medication‐Assisted Treatment centres and the Cooperative encouraged timely referral to our centres. Other studies have shown that collaboration between testing and treatment improved linkage to hepatitis C care [11]. We also found cases when parents accompanied their young adults to the office to start treatment, which shows that strong positive family support can lead to prompt hepatitis care.
Existing literature suggests complicated associations between age and LTC days. One study conducted by the University of Virginia found lower LTC rates among people younger than 40 years [12]. In the study, 82% of patients older than 60 were successfully linked compared to 67% of patients younger than 40. A retrospective study of patients screened for HCV within the University of California, Los Angeles Health Care System between 22 February and 9 July 2018, showed that younger patients were less likely to be referred for specialty care [11]. Middle‐aged groups may face more barriers, such as a lack of health insurance and a lack of flexible schedules for medical appointments, against timely linkage to care.
Our hepatitis C population had an 8.2% HIV infection rate. In a retrospective review of 615 people with both HIV and hepatitis C from HIV clinics at Parkland Health and Hospital Systems between 2014 and 2017, only 34% were linked to HCV care [13]. It was unexpected to notice that it took people with HIV longer to get treated for hepatitis C care compared to patients without HIV. In disease management practice, it was common to ensure people with HIV achieved HIV viral suppression before commencing treatment for hepatitis C to demonstrate compliance with care.
The study was limited in some ways. First, the sample size was small, which could impact the power of the analysis. Second, the study was not a randomised controlled trial but applied a time‐series design. Therefore, it was hard for us to control all potential confounding factors, especially the impacts of historical events among the patients and/or their communities. Third, Cooperative Health had multiple testing sites, but we were not able to track back patients' testing sites. Thus, we could not assess how the characteristics of testing sites (size, location, nature) may affect the linkage to care duration. Fourth, we were not able to examine how the referral type may affect LTC, given that almost half of the data was missing. For example, some internal diagnoses may experience shorter scheduling times and immediate linkage to services compared to those from outside referrals due to delays resulting from additional administrative processes or communication barriers. Some of the internal referrals would still face challenges, just as outside referrals would, because the sites are spread out, leading to potential transportation problems.
Fifth, the number of hepatitis C cases may bring selection biases in our analysis of the LTC and affect representativeness. For example, it is notable that most patients were from Cooperative Health, and few were outside referrals. In addition, we have fewer positive patients. Newly employed Providers may not be familiar with the hepatitis C testing policies. Therefore, they may not routinely screen all adult patients, leading to fewer new hepatitis C patient diagnoses. Another potential explanation is the ‘saturation’ effect; we reached a point where most of the established patients had been screened, and we identified fewer positive patients as the intervention went on. Finally, we were not able to access patients' location information for privacy protection reasons. Therefore, we were not able to explore the impacts of geographical disparities or other key social determinants of health on LTC.
Despite these limitations, our findings highlight the efficacy and sustainability of implementing the FOCUS program in a community health center regarding reducing linkage to care days for hepatitis C patients in the Deep South. The hepatitis C infection constitutes a community burden in South Carolina. The success of FOCUS is grounded in the integrated efforts of multiple key stakeholders, including government, healthcare workers, and public health professionals. It is also critical to have the policy and changes built into the daily workflow. The EMR data collection, monitoring, and management facilitate the program's implementation, suggesting a promising way to leverage the power of EMR data in daily medical practices and project evaluation. Initiatively, we set a goal of reducing LTC to 14 days, as the clinical guideline recommends. Although we fell short, we still achieved significantly lower LTC days and sustained relatively low LTC days after the funding ended. In the future, we will continue to shorten the linkage to care days as much as possible. We will also conduct qualitative studies to understand the challenges and lessons from the lived experiences of healthcare providers and patients in their daily practices to further adapt the implementation strategies. For example, we may take advantage of telehealth strategies to reduce linkage to care disparities caused by some SDOH factors. We hope the findings will inform tailored interventions to address healthcare services utilisation disparities by age, gender, and race/ethnicity in the Deep South.
Conflicts of Interest
The authors declare no conflicts of interest.
Appendix 1. A Summary of the FOCUS Intervention Pillars
| Pillars | Main intervention activities |
|---|---|
| Pillar 1: Testing integrated into regular clinic or workflow |
|
| Pillar 2: Electronic Medical Record (EMR) modification |
|
| Pillar 3: Systemic policy change |
|
| Pillar 4: Training feedback and continuous policy improvement |
|
Funding: This study was supported by Gilead Sciences and NIH/NIAID under Grant No. (3R25AI164581‐04S1)
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.