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.

Keywords: Epidemiology; Geospatial; Hotspots; Leprosy; Neglected tropical diseases; Patient characteristics.

Fig. 1

Overview of the study area in northwest Bangladesh, which covers four districts, Nilphamari (64 unions), Panchagarh (45 unions), Rangpur (86 unions), and Thakurgoan (54 unions)

Fig. 2

Trends over time of leprosy incidence. The 2019 data include cases that were registered over a four-month timeframe (from 1 January 2019 to 30 April 2019) and were extrapolated (multiplied by three) to represent the estimated number of cases for the whole year of 2019 (*)

Fig. 3

Cumulative (smoothed) incidence levels (panel a, b and c) and hotspots detected with spatial scan statistics (panel d, e and f) in northwest Bangladesh between January 2000 and April 2019 for three overlapping 10-year timeframes

Fig. 4

Demographic, disease and location characteristics of leprosy cases outside of hotspots and within weak, medium or strong hotspots and in northwest Bangladesh