Less is more: Developing an approach for assessing clustering at the lower administrative boundaries that increases the yield of active screening for leprosy in Bihar, India.

BACKGROUND: In India, leprosy clusters at hamlet level but detailed information is lacking. We aim to identify high-incidence hamlets to be targeted for active screening and post-exposure prophylaxis. METHODOLOGY: We paid home visits to a cohort of leprosy patients registered between April 1st, 2020, and March 31st, 2022. Patients were interviewed and household members were screened for leprosy. We used an open-source app(ODK) to collect data on patients' mobility, screening results of household members, and geographic coordinates of their households. Clustering was analysed with Kulldorff's spatial scan statistic(SaTScan). Outlines of hamlets and population estimates were obtained through an open-source high-resolution population density map(https://data.humdata.org), using kernel density estimation in QGIS, an open-source software. RESULTS: We enrolled 169 patients and screened 1,044 household contacts in Bisfi and Benipatti blocks of Bihar. Median number of years of residing in the village was 17, interquartile range(IQR)12-30. There were 11 new leprosy cases among 658 household contacts examined(167 per 10,000), of which seven had paucibacillary leprosy, one was a child under 14 years, and none had visible disabilities. We identified 739 hamlets with a total population of 802,788(median 163, IQR 65-774). There were five high incidence clusters including 12% of the population and 46%(78/169) of the leprosy cases. One highly significant cluster with a relative risk (RR) of 4.7(p<0.0001) included 32 hamlets and 27 cases in 33,609 population. A second highly significant cluster included 32 hamlets and 24 cases in 33,809 population with a RR of 4.1(p<0.001). The third highly significant cluster included 16 hamlets and 17 cases in 19,659 population with a RR of 4.8(p<0.001). High-risk clusters still need to be screened door-to-door. CONCLUSIONS: We found a high yield of active household contact screening. Our tools for identifying high-incidence hamlets appear effective. Focusing labour-intensive interventions such as door-to-door screening on such hamlets could increase efficiency.

Does appreciative inquiry decrease false positive diagnosis during leprosy case detection campaigns in Bihar, India? An operational research study.

BACKGROUND: India contributes ~60% to the global leprosy burden. The country implements 14-day community-based leprosy case detection campaigns (LCDC) periodically in all high endemic states. Paramedical staff screen the population and medical officers of primary health centres (PHCs) diagnose and treat leprosy cases. Several new cases were detected during the two LCDCs held in September-2016 and February-2018. Following these LCDCs, a validation exercise was conducted in 8 Primary health centres (PHCs) of 4 districts in Bihar State by an independent expert group, to assess the correctness of case diagnosis. Just before the February 2018 LCDC campaign, we conducted an "appreciative inquiry" (AI) involving the health care staff of these 8 PHCs using the 4-D framework (Discovery-Dream-Design-Destiny). OBJECTIVES: To assess whether the incorrect case diagnosis (false positive diagnosis) reduced as a result of AI in the 8 PHCs between the two LCDC conducted in September-2016 and February-2018. METHODOLOGY/PRINCIPAL FINDINGS: A three-phase quantitative-qualitative-quantitative mixed methods research (embedded design) with the two validation exercises conducted following September-2016 and February-2018 LCDCs as quantitative phases and AI as qualitative phase. In September-2016 LCDC, 303 new leprosy cases were detected, of which 196 cases were validated and 58 (29.6%) were false positive diagnosis. In February-2018 LCDC, 118 new leprosy cases were detected of which 96 cases were validated and 22 cases (23.4%) were false positive diagnosis. After adjusting for the age, gender, type of cases and individual PHCs fixed effects, the proportion of false positive diagnosis reduced by -9% [95% confidence intervals (95%CI): -20.2% to 1.7%, p = 0.068]. CONCLUSION: False positive diagnosis is a major issue during LCDCs. Though the decline in false positive diagnosis is not statistically significant, the findings are encouraging and indicates that appreciative inquiry can be used to address this deficiency in programme implementation.