The geographical distribution and socioeconomic risk factors of COVID-19, tuberculosis and leprosy in Fortaleza, Brazil.

BACKGROUND: Fortaleza (Brazil) is high endemic for coronavirus disease 2019 (COVID-19), tuberculosis (TB) and leprosy. These three diseases share respiratory droplets through coughing or sneezing as the main mode of transmission but differ in incubation time, with COVID-19 having a short and leprosy a long incubation time. Consequently, contacts of a patient are at higher risk of infection and developing these diseases. There might be scope for combined preventive measures, but a better understanding of the geographical distribution and relevant socioeconomic risk factors of the three diseases is needed first. This study aims to describe the geographic distribution of COVID-19, TB and leprosy incidence and to identify common socioeconomic risk factors. METHODS: The total number of new cases of COVID-19, TB and leprosy, as well as socioeconomic and demographic variables, were retrieved from official registers. The geographical distribution of COVID-19, TB and leprosy rates per neighbourhood was visualised in Quantum GIS, and spatial autocorrelation was measured with Moran's I in GeoDa. A spatial regression model was applied to understand the association between COVID-19, TB, leprosy rates, and socioeconomic factors. RESULTS: COVID-19 and TB showed a more homogenous distribution, whereas leprosy is located more in the south and west of Fortaleza. One neighbourhood (Pedras) in the southeast was identified as high endemic for all three diseases. Literacy was a socioeconomic risk factor for all three diseases: a high literacy rate increases the risk of COVID-19, and a low literacy rate (i.e., illiteracy) increases the risk of TB and leprosy. In addition, high income was associated with COVID-19, while low income with TB. CONCLUSIONS: Despite the similar mode of transmission, COVID-19, TB and leprosy show a different distribution of cases in Fortaleza. In addition, associated risk factors are related to wealth in COVID-19 and to poverty in TB and leprosy. These findings may support policymakers in developing (partially combined) primary and secondary prevention considering the efficient use of resources.

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.