03095nas a2200325   4500000000100000008004100001260001200042653001500054653001300069653001200082653003000094653002700124100001100151700001100162700001400173700001300187700001600200700001400216700001800230700001100248700001700259700001600276700001600292245010800308856008400416300000800500490000700508520224000515022001402755       2022                            d  c02/202210aGeospatial10aHotspots10aleprosy10aPost-exposure prophylaxis10aTargeted interventions1 aTaal A1 aBlok D1 aHandito A1 aWibowo S1 aSumarsono S1 aWardana A1 aPontororing G1 aSari D1 avan Brakel W1 aRichardus J1 aPrakoeswa C00aDetermining target populations for leprosy prophylactic interventions: a hotspot analysis in Indonesia.  uhttps://bmcinfectdis.biomedcentral.com/track/pdf/10.1186/s12879-022-07103-0.pdf  a1310 v223 a<p><strong>BACKGROUND: </strong>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.</p>

<p><strong>METHODS: </strong>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&nbsp;m, 1000&nbsp;m, 1500&nbsp;m, 2000&nbsp;m, or 2500&nbsp;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 (km), and the total population targeted for PEP under various strategies.</p>

<p><strong>RESULTS: </strong>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 &gt; 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.</p>

<p><strong>CONCLUSION: </strong>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.</p>
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