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

Unmasking the Hidden Burden: A Delayed Diagnosis of Leprosy Patients With Grade 2 Disability and Its Effects on the Healthcare System in Bangladesh

Introduction Leprosy remains a significant cause of preventable disability worldwide. Early diagnosis and treatment of leprosy are critical not only to stop its spread but also to prevent physical and social complications and reduce the disease burden. Objectives The study aims to evaluate the factors that lead to a delayed leprosy diagnosis. Methods This study was conducted in the outpatient departments of Leprosy Control Institute and Hospital, Dhaka, Bangladesh, and at Medical College for Women and Hospital, Dhaka, Bangladesh, from March 2023 to June 2023. A total number of 252 male (148) and female (104) patients were selected with any sign of leprosy, including disability, age ranging from 15 to 74 years. Data was collected in a pre-designed structured questionnaire by the researchers. To assess the risk of independent exposures of Grade 2 leprosy disabilities, we used a logistic regression model. A chi-square test showed the association between significant effects and leprosy disabilities. A p-value of 0.05 was considered as significant. For statistical analysis, STATA version 15 (StataCorp LLC, College Station, Texas, USA) was used. Results The study participants exhibited a higher percentage of disability, with a rate of 25.8% for Grade 2 disabilities. In addition to this, males represented a more considerable proportion, 58.7%, than females among leprosy and disability patients across all levels of disability. In our study, lack of money and painless symptoms showed a significant association (p<0.001) with Grade 2 disability. Conclusion The study reveals that Grade 2 disabilities are more common in males and are particularly prevalent in lower socioeconomic groups.

Spatiotemporal pattern of leprosy in southwest China from 2010 to 2020: an ecological study

BACKGROUND

Despite many efforts to control leprosy worldwide, it is still a significant public health problem in low- and middle-income regions. It has been endemic in China for thousands of years, and southwest China has the highest leprosy burden in the country.

METHODS

This observational study was conducted with all newly detected leprosy cases in southwest China from 2010 to 2020. Data were extracted from the Leprosy Management Information System (LEPMIS) database in China. The Joinpoint model was used to determine the time trends in the study area. Spatial autocorrelation statistics was performed to understand spatial distribution of leprosy cases. Spatial scan statistics was applied to identify significant clusters with high rate.

RESULTS

A total of 4801 newly detected leprosy cases were reported in southwest China over 11 years. The temporal trends declined stably. The new case detection rate (NCDR) dropped from 4.38/1,000,000 population in 2010 to 1.25/1,000,000 population in 2020, with an average decrease of 12.24% (95% CI: -14.0 to - 10.5; P < 0.001). Results of global spatial autocorrelation showed that leprosy cases presented clustering distribution in the study area. Most likely clusters were identified during the study period and were frequently located at Yunnan or the border areas between Yunnan and Guizhou Provinces. Secondary clusters were always located in the western counties, the border areas between Yunnan and Sichuan Provinces.

CONCLUSIONS

Geographic regions characterized by clusters with high rates were considered as leprosy high-risk areas. The findings of this study could be used to design leprosy control measures and provide indications to strengthen the surveillance of high-risk areas. These areas should be prioritized in the allocation of resources.

Intra-urban differences underlying leprosy spatial distribution in central Brazil: geospatial techniques as potential tools for surveillance

This ecological study identified an aggregation of urban neighbourhoods spatial patterns in the cumulative new case detection rate (NCDR) of leprosy in the municipality of Rondonópolis, central Brazil, as well as intra-urban socioeconomic differences underlying this distribution. Scan statistics of all leprosy cases reported in the area from 2011 to 2017 were used to investigate spatial and spatiotemporal clusters of the disease at the neighbourhood level. The associations between the log of the smoothed NCDR and demographic, socioeconomic, and structural characteristics were explored by comparing multivariate models based on ordinary least squares (OLS) regression, spatial lag, spatial error, and geographically weighted regression (GWR). Leprosy cases were observed in 84.1% of the neighbourhoods of Rondonópolis, where 848 new cases of leprosy were reported corresponding to a cumulative NCDR of 57.9 cases/100,000 inhabitants. Spatial and spatiotemporal high-risk clusters were identified in western and northern neighbourhoods, whereas central and southern areas comprised low-risk areas. The GWR model was selected as the most appropriate modelling strategy (adjusted R²: 0.305; AIC: 242.85). By mapping the GWR coefficients, we identified that low literacy rate and low mean monthly nominal income per household were associated with a high NCDR of leprosy, especially in the neighbourhoods located within high-risk areas. In conclusion, leprosy presented a heterogeneous and peripheral spatial distribution at the neighbourhood level, which seems to have been shaped by intra-urban differences related to deprivation and poor living conditions. This information should be considered by decision-makers while implementing surveillance measures aimed at leprosy control.

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.

[Epidemiology of leprosy in Chad from 2015 to 2019]

INTRODUCTION

leprosy is a disease found and unevenly distributed in Chad. Since 1997, the annual national prevalence has been less than 1/10000 inhabitants, the elimination threshold set by the World Health Organization (WHO). The purpose of this study is to describe epidemiological trends of leprosy in Chad between 2015 and 2019, in order to provide the necessary data for the development of more effective strategies for leprosy control.

METHODS

we conducted a retrospective, descriptive study of patients with leprosy diagnosed between 2015 and 2019 at national level. Data were collected from the database of the National Program for the Control of Leprosy in Chad (NPCLC).

RESULTS

a total of 1896 new cases of leprosy were detected in Chad between 2015 and 2019. The rates of patients aged 15 to 70 years and children under 15 years were 92.08% and 7.92% respectively. Sex ratio (M/F) was 1.68. The annual average detection rate was 2.6/100 000, with an average rate of multi-bacillary leprosy of 83.10% and degree 2 disability (2DD) of 20.38%. The average rate of degree 2 disability in children under 15 years of age was 0.92% . However, our study identified five districts as endemic (Adré, Abéché, Aboudeia, Koukou, et Bebedjia) in 2019, where the prevalence rate was above 1/10000 inhabitants.

CONCLUSION

epidemiological trends are in favor of the persistence of the disease and a delay in diagnosis and in the management of leprosy cases.

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

Background

Leprosy incidence remained at around 200,000 new cases globally for the last decade. Current strategies to reduce the number of new patients include early detection and providing post-exposure prophylaxis (PEP) to at-risk populations. Because leprosy is distributed unevenly, it is crucial to identify high-risk clusters of leprosy cases for targeting interventions. Geographic Information Systems (GIS) methodology can be used to optimize leprosy control activities by identifying clustering of leprosy cases and determining optimal target populations for PEP.

Methods

The geolocations of leprosy cases registered from 2014 to 2018 in Pasuruan and Pamekasan (Indonesia) were collected and tested for spatial autocorrelation with the Moran’s I statistic. We did a hotspot analysis using the Heatmap tool of QGIS to identify clusters of leprosy cases in both areas. Fifteen cluster settings were compared, varying the heatmap radius (i.e., 500 m, 1000 m, 1500 m, 2000 m, or 2500 m) and the density of clustering (low, moderate, and high). For each cluster setting, we calculated the number of cases in clusters, the size of the cluster (km²), and the total population targeted for PEP under various strategies.

Results

The distribution of cases was more focused in Pasuruan (Moran’s I = 0.44) than in Pamekasan (0.27). The proportion of total cases within identified clusters increased with heatmap radius and ranged from 3% to almost 100% in both areas. The proportion of the population in clusters targeted for PEP decreased with heatmap radius from > 100% to 5% in high and from 88 to 3% in moderate and low density clusters. We have developed an example of a practical guideline to determine optimal cluster settings based on a given PEP strategy, distribution of cases, resources available, and proportion of population targeted for PEP.

Conclusion

Policy and operational decisions related to leprosy control programs can be guided by a hotspot analysis which aid in identifying high-risk clusters and estimating the number of people targeted for prophylactic interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07103-0.