Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study

Abstract

Background Leprosy is known to be unevenly distributed between and within countries. High risk areas or ‘hotspots’ are potential targets for preventive interventions, but the underlying epidemiologic mechanisms that enable hotspots to emerge, are not yet fully understood. In this study, we identified and characterized leprosy hotspots in Bangladesh, a country with one of the highest leprosy endemicity levels globally. Methods We used data from four high-endemic districts in northwest Bangladesh including 20 623 registered cases between January 2000 and April 2019 (among ~ 7 million population). Incidences per union (smallest administrative unit) were calculated using geospatial population density estimates. A geospatial Poisson model was used to detect incidence hotspots over three (overlapping) 10-year timeframes: 2000–2009, 2005–2014 and 2010–2019. Ordinal regression models were used to assess whether patient characteristics were significantly different for cases outside hotspots, as compared to cases within weak (i.e., relative risk (RR) of one to two), medium (i.e., RR of two to three), and strong (i.e., RR higher than three) hotspots. Results New case detection rates dropped from 44/100 000 in 2000 to 10/100 000 in 2019. Statistically significant hotspots were identified during all timeframes and were often located at areas with high population densities. The RR for leprosy was up to 12 times higher for inhabitants of hotspots than for people living outside hotspots. Within strong hotspots (1930 cases among less than 1% of the population), significantly more child cases (i.e., below 15 years of age) were detected, indicating recent transmission. Cases in hotspots were not significantly more likely to be detected actively. Conclusions Leprosy showed a heterogeneous distribution with clear hotspots in northwest Bangladesh throughout a 20-year period of decreasing incidence. Findings confirm that leprosy hotspots represent areas of higher transmission activity and are not solely the result of active case finding strategies.

Leprosy Relapse After Polychemotherapy: A Systematic Review and Meta-Analysis

“…Thus, in a context of controlling spread of the bacillus, correct and early diagnosis and extended surveillance of cured cases and contacts are crucial for reducing the number of new cases and relapse of the disease 3,6,17,22,37,46,76 . The unified treatment (PCT-U) that would waive the need for operational classification, recommended in 2020 by the Ministry of Health in Brazil, is not yet a national or global reality and was not analyzed in this review 62, 63,64,72,78,79 .…”

Determining target populations for leprosy prophylactic interventions: a hotspot analysis in Indonesia

“…Our sensitivity analysis showed that between 21 and 90% of the cases of the next years fall in a defined cluster. This may indicate that in high-endemic areas with a stable leprosy incidence, clusters will remain in the same areas over the next years [ 22 ]. Interventions should therefore be prioritized in these clusters.…”

“…Kulldorf 's spatial scan statistic identifies the most likely cluster(s) of cases [20]. The SaTScan [21] has the added value to scan for space-time clustering and is often used in studies to monitor leprosy transmission and clustering over a period [22][23][24][25].…”

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