Epidemiological, temporal and spatial dynamics of leprosy in a municipality in northeastern Brazil (2008-2017): an ecological study

Epidemiological characteristics of leprosy from 2000 to 2019 in a state with low endemicity in southern Brazil

BACKGROUND

Leprosy is an infectious and contagious disease caused by Mycobacterium leprae and is mainly characterized by lesions in the skin and peripheral nerves. In Brazil, it is a public health problem due to its high endemicity. However, the state of Rio Grande do Sul presents low endemicity of this disease.

OBJECTIVE

To characterize the epidemiological profile of leprosy in the state of Rio Grande do Sul from 2000 to 2019.

METHODS

This was a retrospective observational study. Epidemiological data were collected from the Notifiable Diseases Information System (SINAN, Sistema de Informação de Agravos de Notificação).

RESULTS

Among the 497 municipalities in the state, 357 (71.8%) registered cases of leprosy in the assessed period, with an average of 212 (81.5%) new cases per year. The average detection rate was 1.61 new cases per 100,000 inhabitants. The male sex was predominant (51.9%) and the mean age was 50.4 years. Regarding the epidemiological clinical profile; 79.0% of the patients were multibacillary; 37.5% presented the borderline clinical form; 16% had grade 2 physical disability at diagnosis and bacilloscopy was positive in 35.4% of cases. As for treatment, 73.8% of the cases were treated with the standard multibacillary therapeutic regimen.

STUDY LIMITATIONS

There were missing/inconsistent data in the database available.

CONCLUSIONS

The findings observed in this study indicate that the state presents a low endemicity profile of the disease and these results can support adequate health policies relevant to the reality of Rio Grande do Sul, inserted in a national scenario of highly endemic leprosy.

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.

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.