부산대학교 도서관

Background: Preventive interventions with post-exposure prophylaxis (PEP) are needed in leprosy high-endemic areas to interrupt the transmission of Mycobacterium leprae. Program managers intend to use Geographic Information Systems (GIS) to target preventive interventions considering efficient use of public health resources. Statistical GIS analyses are commonly used to identify clusters of disease without accounting for the local context. Therefore, we propose a contextualized spatial approach that includes expert consultation to identify clusters and compare it with a standard statistical approach. Methodology/Principal findings: We included all leprosy patients registered from 2014 to 2020 at the Health Centers in Fatehpur and Chandauli districts, Uttar Pradesh State, India (n = 3,855). Our contextualized spatial approach included expert consultation determining criteria and definition for the identification of clusters using Density Based Spatial Clustering Algorithm with Noise, followed by creating cluster maps considering natural boundaries and the local context. We compared this approach with the commonly used Anselin Local Moran's I statistic to identify high-risk villages. In the contextualized approach, 374 clusters were identified in Chandauli and 512 in Fatehpur. In total, 75% and 57% of all cases were captured by the identified clusters in Chandauli and Fatehpur, respectively. If 100 individuals per case were targeted for PEP, 33% and 11% of the total cluster population would receive PEP, respectively. In the statistical approach, more clusters in Chandauli and fewer clusters in Fatehpur (508 and 193) and lower proportions of cases in clusters (66% and 43%) were identified, and lower proportions of population targeted for PEP was calculated compared to the contextualized approach (11% and 11%). Conclusion: A contextualized spatial approach could identify clusters in high-endemic districts more precisely than a standard statistical approach. Therefore, it can be a useful alternative to detect preventive intervention targets in high-endemic areas. Author summary: Leprosy is chronic infectious disease characterized by skin and peripheral nerve lesions. Despite the efforts to eliminate leprosy, around 210,000 new cases are still found annually of which 60% is reported by India alone. To reduce the incidence significantly, new active case finding approaches in combination with preventive treatment to at-risk populations are needed in high-endemic areas in India. Geospatial methods can support program managers and policy makers to identify clusters of leprosy patients and target at-risk populations for preventive interventions. However, often standard spatial methods do not account sufficiently for the local context (i.e., local barriers and social determinants). In this study, we describe a contextualized spatial approach that includes expert consultation to identify context specific clusters and compared it with a standard approach. Overall, our results show that the contextualized approach is able to identify more clusters precisely and covers a larger proportion of the population in clusters that would need to be targeted for preventive interventions. For program managers and policy makers, the contextualized approach can be useful to target at-risk populations in high-endemic areas while ensuring efficient use of public health resources. Further research is needed to test the scalability in different endemic settings and to apply to other Neglected Tropical Diseases. [ABSTRACT FROM AUTHOR]