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BACKGROUND: The pandemic caused by COVID-19 has seriously affected global health, resulting in the suspension of many regular health services, making the diagnosis of other infections difficult. Therefore, this study aimed to assess the impact of the COVID-19 pandemic on the diagnosis of leprosy in Brazil during the year 2020. METHODS: We evaluated the monthly incidence of leprosy and calculated the percentage change to verify whether there was an increase or decrease in the number of leprosy cases in 2020, considering the monthly average of cases over the previous 5 years. We used interrupted time series analysis to assess the trend in the diagnosis of leprosy before and after the start of COVID-19 in Brazil and prepared spatial distribution maps, considering the percentage variation in each state. FINDINGS: We verified a reduction of 41.4% of leprosy cases in Brazil in 2020. Likewise, there was a reduction of leprosy notifications in children under 15 years-old (-56.82%). Conversely, the diagnosis of multibacillary leprosy increased (8.1%). There was a decreasing trend in the leprosy incidence in the general population between 2015 and 2020 in Brazil. Spatial distribution maps depicted a reduction of up to 100% in new cases of leprosy in some states. INTERPRETATION: Along with COVID-19 spread there was a reduction in leprosy diagnosis in the general population and children under 15 years-old, and also an increase in multibacillary cases diagnosed, signalling a serious impact of the pandemic on leprosy control strategies in Brazil. FUNDING: This research received no specific grants.
RESUMEN
OBJECTIVES: To analyse and map the leprosy risk areas in the state of Alagoas, an endemic region in the Northeastern Brazil, between 2001 and 2019. METHODS: Ecological and time series study, using spatial analysis techniques. First, we analyse the epidemiological aspects of leprosy cases, using the data available in the Notifiable Diseases Information System; then, we used the segmented log-linear regression model to assess time trends. Spatial distribution was analysed by the Local Empirical Bayesian Estimator and by calculating the Global and Local Moran Index. Finally, spatiotemporal clusters were identified through scanning statistics, using the Kulldorf method of retrospective analysis. RESULTS: We observed that Alagoas showed an average new case detection rate of 14.43/100,000 inhabitants between 2001 and 2019, being classified as highly endemic. The area of highest risk was the 9th health region (state hinterland), with increasing time trend (Annual Percentage Change/APC = 7.2; p-value < 0.05). Several clusters of high risk of leprosy transmission were verified in Alagoas, including the state capital and hinterland municipalities. CONCLUSIONS: Our data indicate that active M. leprae transmission persists in Alagoas; that diagnosis is delayed and that there are high-risk areas, especially in inland municipalities.