RESUMO
Background: Nigeria is among the top five countries that have the highest gap between people reported as diagnosed and estimated to have developed tuberculosis (TB). To bridge this gap, there is a need for innovative approaches to identify geographical areas at high risk of TB transmission and targeted active case finding (ACF) interventions. Leveraging community-level data together with granular sociodemographic contextual information can unmask local hotspots that could be otherwise missed. This work evaluated whether this approach helps to reach communities with higher numbers of undiagnosed TB. Methodology: A retrospective analysis of the data generated from an ACF intervention program in four southwestern states in Nigeria was conducted. Wards (the smallest administrative level in Nigeria) were further subdivided into smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. This data were then combined with open-source high-resolution contextual data to train a Bayesian inference model. The model predicted TB positivity rates on the community level (population cluster level), and these were visualised on a customised geoportal for use by the local teams to identify communities at high risk of TB transmission and plan ACF interventions. The TB positivity yield (proportion) observed at model-predicted hotspots was compared with the yield obtained at other sites identified based on aggregated notification data. Results: The yield in population clusters that were predicted to have high TB positivity rates by the model was at least 1.75 times higher (p-value < 0.001) than the yield in other locations in all four states. Conclusions: The community-level Bayesian predictive model has the potential to guide ACF implementers to high-TB-positivity areas for finding undiagnosed TB in the communities, thus improving the efficiency of interventions.
RESUMO
BACKGROUND: In sub-Saharan Africa, cardiovascular disease is becoming a leading cause of death, with high blood pressure as number one risk factor. In Nigeria, access and adherence to hypertension care are poor. A pharmacy-based hypertension care model with remote monitoring by cardiologists through mHealth was piloted in Lagos to increase accessibility to quality care for hypertensive patients. OBJECTIVES: To describe patients' and healthcare providers' perceptions and practices regarding hypertension, pharmacy-based care, and mHealth and explore how this information may improve innovative hypertension service delivery. METHODS: This study consisted of observations of patient-pharmacy staff interactions and hypertension care provided, four focus group discussions and in-depth interviews with 30 hypertensive patients, nine community pharmacists, and six cardiologists, and structured interviews with 328 patients. RESULTS: Most patients were knowledgeable about biomedical causes and treatment of hypertension, but often ignorant about the silent character of hypertension. Reasons mentioned for not adhering to treatment were side effects, financial constraints, lack of health insurance, and cultural or religious reasons. Pharmacists additionally mentioned competition with informal, cheaper healthcare providers. Patients highly favored pharmacy-based care, because of the pharmacist-patient relationship, accessibility, small-scale, and a pharmacy's registration at an association. The majority of respondents were positive towards mHealth. CONCLUSION: Facilitating factors for innovative pharmacy-based hypertension care were: patients' biomedical perceptions, pharmacies' strong position in the community, and respondents' positive attitude towards mHealth. We recommend health education and strengthening pharmacists' role to address barriers, such as misperceptions that hypertension always is symptomatic, treatment nonadherence, and unfamiliarity with mHealth. Future collaboration with insurance providers or other financing mechanisms may help diminish patients' financial barriers to appropriate hypertension treatment.