The Hepatitis C Cascade of Care: Identifying Priorities to Improve Clinical Outcomes

Background: As highly effective hepatitis C virus (HCV) therapies emerge, data are needed to inform the development of interventions to improve HCV treatment rates. We used simulation modeling to estimate the impact of loss to follow-up on HCV treatment outcomes and to identify intervention strategies likely to provide good value for the resources invested in them. Methods: We used a Monte Carlo state-transition model to simulate a hypothetical cohort of chronically HCV-infected individuals recently screened positive for serum HCV antibody. We simulated four hypothetical intervention strategies (linkage to care; treatment initiation; integrated case management; peer navigator) to improve HCV treatment rates, varying efficacies and costs, and identified strategies that would most likely result in the best value for the resources required for implementation. Main measures Sustained virologic responses (SVRs), life expectancy, quality-adjusted life expectancy (QALE), costs from health system and program implementation perspectives, and incremental cost-effectiveness ratios (ICERs). Results: We estimate that imperfect follow-up reduces the real-world effectiveness of HCV therapies by approximately 75%. In the base case, a modestly effective hypothetical peer navigator program maximized the number of SVRs and QALE, with an ICER compared to the next best intervention of $48,700/quality-adjusted life year. Hypothetical interventions that simultaneously addressed multiple points along the cascade provided better outcomes and more value for money than less costly interventions targeting single steps. The 5-year program cost of the hypothetical peer navigator intervention was$14.5 million per 10,000 newly diagnosed individuals. Conclusions: We estimate that imperfect follow-up during the HCV cascade of care greatly reduces the real-world effectiveness of HCV therapy. Our mathematical model shows that modestly effective interventions to improve follow-up would likely be cost-effective. Priority should be given to developing and evaluating interventions addressing multiple points along the cascade rather than options focusing solely on single points

“Community members have more impact on their neighbors than celebrities”: Leveraging community partnerships to build COVID-19 vaccine confidence

Vaccines are a strong public health tool to protect against severe disease, hospitalization, and death from COVID-19. Still, inequities in COVID-19 vaccination rates and health outcomes continue to exist among Black and Latino populations. Boston Medical Center (BMC) has played a significant role in vaccinating medically underserved populations, and organized a series of community-engaged conversations to better understand community concerns regarding the COVID-19 vaccine. We accessed and analyzed nine publicly available recordings of the community-engaged conversations which were held between Mar 2021-Sep 2021 (n=8-122 attendees). We employed a Consolidated Framework for Implementation Research-driven codebook to code our data and utilized a modified version of qualitative rapid analysis methods. Five main themes emerged: (1) Structural factors are important barriers to COVID-19 vaccination; (2) Mistrust exists due to the negative impact of systemic oppression and perceived motivation of the government; (3) There is a desire to learn more about biological and clinical characteristics of the COVID-19 vaccine as well as the practical implications of being vaccinated; (4) Community engagement is important for delivering COVID-19 information and education and; (5) Community leaders believe that the COVID-19 vaccine is a solution to address the pandemic. In highlighting the themes which resulted from these community-engaged conversations, this study illustrates a need for community-engaged COVID-19 vaccine messaging which reflects the nuances of the COVID-19 vaccine and pandemic without oversimplifying information and underscores important considerations for public health and healthcare leadership in the development of initiatives which work to advance health equity

Cost Effectiveness and Cost Containment in the Era of Interferon-Free Therapies to Treat Hepatitis C Virus Genotype 1

Abstract Background. Interferon-free regimens to treat hepatitis C virus (HCV) genotype 1 are effective but costly. At this time, payers in the United States use strategies to control costs including (1) limiting treatment to those with advanced disease and (2) negotiating price discounts in exchange for exclusivity. Methods. We used Monte Carlo simulation to investigate budgetary impact and cost effectiveness of these treatment policies and to identify strategies that balance access with cost control. Outcomes included nondiscounted 5-year payer cost per 10000 HCV-infected patients and incremental cost-effectiveness ratios. Results. We found that the budgetary impact of HCV treatment is high, with 5-year undiscounted costs of $1.0 billion to 2.3 billion per 10000 HCV-infected patients depending on regimen choices. Among noncirrhotic patients, using the least costly interferon-free regimen leads to the lowest payer costs with negligible difference in clinical outcomes, even when the lower cost regimen is less convenient and/or effective. Among cirrhotic patients, more effective but costly regimens remain cost effective. Controlling costs by restricting treatment to those with fibrosis stage 2 or greater disease was cost ineffective for any patient type compared with treating all patients. Conclusions. Treatment strategies using interferon-free therapies to treat all HCV-infected persons are cost effective, but short-term cost is high. Among noncirrhotic patients, using the least costly interferon-free regimen, even if it is not single tablet or once daily, is the cost-control strategy that results in best outcomes. Restricting treatment to patients with more advanced disease often results in worse outcomes than treating all patients, and it is not preferred

Development and Validation of the HIV-CARDIO-PREDICT Score to Estimate the Risk of Cardiovascular Events in HIV-Infected Patients

Importance: Commonly used risk assessment tools for cardiovascular disease might not be accurate for HIV-infected patients. Objective: We aimed to develop a model to accurately predict the 10-year cardiovascular disease (CV) risk of HIV-infected patients. Design: In this retrospective cohort study, adult HIV-infected patients seen at Boston Medical Center between March 2012 and January 2017 were divided into model development and validation cohorts. Setting: Boston Medical Center, a tertiary, academic medical center. Participants: Adult HIV-infected patients, seen in inpatient and outpatient setting. Main Outcomes and Measures: We used logistic regression to create a prediction risk model for cardiovascular events using data from the development cohort. Using a point-based risk-scoring system, we summarized the relationship between risk factors and cardiovascular disease (CVD) risk. We then used the area under the receiver operating characteristics curve (AUC) to evaluate model discrimination. Finally, we tested the model using a validation cohort. Results: 1914 individuals met the inclusion criteria. The model had excellent discrimination for CVD risk [AUC 0.989; (95% CI: 0.986–0.993)] and included the following 11 variables: male sex (95% CI: 2.53–3.99), African American race/ethnicity (95% CI: 1.50–3.13), current age (95% CI: 0.07–0.13), age at HIV diagnosis (95% CI: −0.10–(−0.02)), peak HIV viral load (95% CI: 9.89 × 10−7–3.00 × 10−6), nadir CD4 lymphocyte count (95% CI: −0.03–(−0.02)), hypertension (95% CI: 0.20–1.54), hyperlipidemia (95% CI: 3.03–4.60), diabetes (95% CI: 0.61–1.89), chronic kidney disease (95% CI: 1.26–2.62), and smoking (95% CI: 0.12–2.39). The eleven-parameter multiple logistic regression model had excellent discrimination [AUC 0.957; (95% CI: 0.938–0.975)] when applied to the validation cohort. Conclusions and Relevance: Our novel HIV-CARDIO-PREDICT Score may provide a rapid and accurate evaluation of CV disease risk among HIV-infected patients and inform prevention measures

Factors associated with inpatient complications among patients with obesity and COVID‐19 at an urban safety‐net hospital: A retrospective cohort study

Abstract Objective Obesity increases morbidity and mortality from Coronavirus disease 2019 (COVID‐19). This study characterized inpatient complications among patients with obesity and COVID‐19—including myocardial infarction, renal failure requiring dialysis, stroke, secondary bacterial infection, and venous thromboembolism—and identified factors associated with developing at least one inpatient complication at a safety‐net hospital with a diverse cohort. Methods A retrospective review was performed of all patients admitted for ≥3 days with COVID‐19 between 16 March 2020, and 8 April 2020. Logistic regression identified factors associated with developing at least one COVID‐19‐related complication among patients with obesity (body mass index ≥30 kg/m2). Results 374 patients were included; 53.7% were classified as having obesity, 43.9% identified as Black, and 38.5% identified as Latino or Hispanic. Obesity was not associated with having at least one inpatient complication on multivariable analysis, but increased age (aOR 1.02, [95% CI 1.01–1.04], p = 0.010) and obstructive sleep apnea (aOR 2.25, [1.08–4.85], p = 0.034) were associated with this outcome. Conclusions Obesity was not associated with specified inpatient complications among patients with COVID‐19 admitted to a health system caring for diverse patients. Future studies should incorporate larger cohorts and reflect newer treatment protocols

At the intersection of trust and mistrust: A qualitative analysis of motivators and barriers to research participation at a safety‐net hospital

Abstract Introduction The underrepresentation of Black, Indigenous, and People of Color (BIPOC) individuals in healthcare research limits generalizability and contributes to healthcare inequities. Existing barriers and attitudes toward research participation must be addressed to increase the representation of safety net and other underserved populations. Methods We conducted semi‐structured qualitative interviews with patients at an urban safety net hospital, focusing on facilitators, barriers, motivators, and preferences for research participation. We conducted direct content analysis guided by an implementation framework and used rapid analysis methods to generate final themes. Results We completed 38 interviews and identified six major themes related to preferences for engagement in research participation: (1) wide variation in research recruitment preferences; (2) logistical complexity negatively impacts willingness to participate; (3) risk contributes to hesitation toward research participation; (4) personal/community benefit, interest in study topic, and compensation serve as motivators for research participation; (5) continued participation despite reported shortcomings of informed consent process; and (6) mistrust could be overcome by relationship or credibility of information sources. Conclusion Despite barriers to participation in research studies among safety‐net populations, there are also facilitators that can be implemented to increase knowledge and comprehension, ease of participation, and willingness to join research studies. Study teams should vary recruitment and participation methods to ensure equal access to research opportunities. Patient/Public Contribution Our analysis methods and study progress were presented to individuals within the Boston Medical Center healthcare system. Through this process community engagement specialists, clinical experts, research directors, and others with significant experience working with safety‐net populations supported data interpretation and provided recommendations for action following the dissemination of data

Percent vaccinated and boosted according to individual ZIP code characteristics.

Note: Data on vaccine and booster coverage are as of October 10, 2022. Fitted lines are from bivariate regressions. Coefficients and confidence intervals for bivariate and multivariable models are presented in Table 2. To preserve the scale across the plots, three outliers with estimated vaccine coverage over 115% were suppressed in the scatter plots but contribute to the fitted lines.</p

Characteristics of the study population.

Characteristics of the study population.</p