Epidemiological Characteristics and Factors Associated with Cure of Leprosy in Chongqing, China, from 1949 to 2019

Chongqing is one of the focuses of leprosy control in China. Although leprosy control in Chongqing has achieved remarkable results over the years, there are also some problems, such as recurrent epidemics and insufficient early detection in some areas. The aim of this study was to analyze the epidemiological characteristics of leprosy in Chongqing, from 1949 to 2019 and explore the potential factors sociated with cure of leprosy to provide a basis for improving leprosy prevention and treatment strategies in Chongqing. Epidemiological indicators such as incidence and prevalence rates were used to evaluate the prevalence of leprosy. The epidemiological characteristics and control situation of leprosy in patients were analyzed using demographic characteristics, diagnosis, and treatment. Survival analysis was conducted to explore factors associated with the cure of leprosy. From 1949 to 2019, 3,703 cases of leprosy were registered in Chongqing. The incidence of leprosy in the city peaked at 0.853/105 in 1960 and remained below 0.100/105 after 2003. The number of high incidence areas decreased significantly, but they were mainly concentrated in the northeast and southeast regions. The early detection rate increased yearly from 1949 to 2019, and the rate of grade 2 disability ranged from 38.2% to 21.7%, with a fluctuating downward trend after 1960. Male, young age, employment as a farmer, delayed diagnosis, and multibacillary leprosy were risk factors for leprosy cure. Chongqing should continue to strengthen leprosy monitoring to improve the early detection of leprosy and focus on sociated risk factors to carry out multiple strategies.

Identifying clusters of leprosy patients in India: A comparison of methods

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.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-021-00817-4.

Spatial distribution of leprosy in India: an ecological study

Background

As leprosy elimination becomes an increasingly realistic goal, it is essential to determine the factors that contribute to its persistence. We evaluate social and economic factors as predictors of leprosy annual new case detection rates within India, where the majority of leprosy cases occur.

Methods

We used correlation and linear mixed effect regressions to assess whether poverty, illiteracy, nighttime satellite radiance (an index of development), and other covariates can explain district-wise annual new case detection rate and Grade 2 disability diagnoses.

Results

We find only weak evidence of an association between poverty and annual new case detection rates at the district level, though illiteracy and satellite radiance are statistically significant predictors of leprosy at the district level. We find no evidence of rapid decline over the period 2008–2015 in either new case detection or new Grade 2 disability.

Conclusions

Our findings suggest a somewhat higher rate of leprosy detection, on average, in poorer districts; the overall effect is weak. The divide between leprosy case detection and true incidence of clinical leprosy complicates these results, particularly given that the detection rate is likely disproportionately lower in impoverished settings. Additional information is needed to distinguish the determinants of leprosy case detection and transmission during the elimination epoch.