Nasarawa State Ministry of Health

Background: Viral hepatitis B and C (HBV and HCV) pose significant public health concern in Nigeria, where access to healthcare and treatment affordability are limited. This study investigated sociodemographic and clinical predictors of health insurance coverage and access to care among patients with HBV and HCV in Nasarawa State, Nigeria. Methods: A cross-sectional facility-based study was conducted at two secondary hospitals in Nasarawa State, Nigeria. Participants included patients diagnosed with HBV, HCV, or both who were ≥18 years old. Data were collected using a structured questionnaire covering sociodemographic and clinical information, health insurance details, and economic impact. Binary logistic regression was used to analyze the relationship between sociodemographic/clinical factors and health insurance status. Results: Out of 303 participants, 68% had health insurance, which mostly covered hepatitis screening and vaccination. Significant predictors of health insurance coverage included being aged 36–40 years (adjusted odds ratio [aOR]: 11.01, 95% confidence interval [CI]: 2.38–50.89, p = 0.002), having post-secondary education (aOR: 25.2, 95% CI: 9.67–65.68, p < 0.001), being employed (aOR: 27.83, 95% CI: 8.85–87.58, p < 0.001), and being HIV-positive (aOR: 4.06, 95% CI: 1.55–10.61, p = 0.004). Nearly all those insured (99%) faced restrictions in insurance coverage for viral hepatitis services. Conclusions: This study reveals that while health insurance coverage is relatively high among viral hepatitis patients in Nasarawa State, significant restrictions hinder access to comprehensive services, especially for vulnerable groups like younger adults, the unemployed, and PLHIV. Key factors influencing coverage include age, education, employment, and HIV status. Expanding benefit packages to include viral hepatitis diagnosis and treatment, raising awareness about viral hepatitis as part of insurance strategy, improving access for underserved populations, and integrating hepatitis services into existing HIV programs with strong policy implementation monitoring frameworks are crucial to advancing universal health coverage and meeting the WHO’s 2030 elimination goals.

In 2016, the World Health Organization introduced global targets for the care and management of hepatitis C virus (HCV) infection to eliminate hepatitis C as a public health threat by 2030. Despite significant improvements in testing and treatment, in 2020 only 23% of all persons infected with HCV globally were diagnosed. We explore examples from global hepatitis C programs in Georgia, Rwanda, and Nigeria that have used decentralized and integrated models to increase access to HCV testing. Georgia established the world’s first national hepatitis C elimination program in 2015. In 2022, 2.6 million people (80% of the adults) have been screened for antibodies for HCV infection, and 80 000 persons with HCV virus detected were treated. To achieve these results, Georgia implemented HCV core antigen testing, utilization of point-of-care (POC) HCV ribonucleic acid (RNA), and simplification of HCV viremia detection by qualitative HCV RNA. Rwanda was the first country in sub-Saharan Africa to commit to HCV elimination in 2018, and as of 2022 it has achieved its screening target of 7 million people and initiated approximately 60 000 patients on hepatitis C treatment by rapid decentralization and integration of HCV services. In Nigeria, the integrated near-POC testing approach in Nasarawa State has been effective in expanding access to HCV viremia testing and enabling the possibility of same-day testing and treatment initiation. Examples of decentralization and integration of HCV testing and linkage to care in Georgia, Rwanda, and Nigeria could help inform effective strategies to reach 2030 hepatitis C elimination goals in other countries.

Although there is increasing importance placed on the use of mathematical models for the effective design and management of long-term parasite elimination, it is becoming clear that transmission models are most useful when they reflect the processes pertaining to local infection dynamics as opposed to generalized dynamics. Such localized models must also be developed even when the data required for characterizing local transmission processes are limited or incomplete, as is often the case for neglected tropical diseases, including the disease system studied in this work, viz. lymphatic filariasis (LF). Here, we draw on progress made in the field of computational knowledge discovery to present a reconstructive simulation framework that addresses these challenges by facilitating the discovery of both data and models concurrently in areas where we have insufficient observational data. Using available data from eight sites from Nigeria and elsewhere, we demonstrate that our data-model discovery system is able to estimate local transmission models and missing pre-control infection information using generalized knowledge of filarial transmission dynamics, monitoring survey data, and details of historical interventions. Forecasts of the impacts of interventions carried out in each site made by the models estimated using the reconstructed baseline data matched temporal infection observations and provided useful information regarding when transmission interruption is likely to have occurred. Assessments of elimination and resurgence probabilities based on the models also suggest a protective effect of vector control against the reemergence of LF transmission after stopping drug treatments. The reconstructive computational framework for model and data discovery developed here highlights how coupling models with available data can generate new knowledge about complex, data-limited systems, and support the effective management of disease programs in the face of critical data gaps.