Temporal trends and spatial analysis of leprosy surveillance indicators in the municipalities of the state of Mato Grosso, 2008-2022

2008-2022年马托格罗索州各市麻风病监测指标的时间趋势和空间分析

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Abstract

BACKGROUND: In 2022, Mato Grosso (MT, Brazil) reported the highest detection rate of new leprosy cases (66.20 per 100,000 inhabitants) among all Brazilian states. Monitoring of leprosy indicators is an important control strategy in hyperendemic areas. We aimed to describe the temporal trends and identify clusters of municipalities according to leprosy surveillance indicators in MT between 2008 and 2022. METHODS: Data from the Notifiable Diseases Information System were used to analyze new case detection rate of leprosy (NCDR), new case detection rate of leprosy among children aged <15 years (NCD15), and rate of new cases with grade 2 physical disability (G2DR). Spatial scan statistics with pure spatial analysis and spatial autocorrelation maps were used to analyze the spatial patterns. Joinpoint regression was used to estimate the annual percentage change (APC) in these indicators. RESULTS: The NCDR decreased (APC: -20.2%, 95% confidence interval (CI): -38.7% to -7.4%) between 2019 and 2021. The NCD15 also decreased (APC: -19.2%, 95% CI: -36.4% to -10.3%) between 2018 and 2022. Conversely, the G2DR remained stable throughout the study (APC: 3.2%, 95% CI: -0.1% to 6.7%). Global Moran's index (Moran's I) confirmed the existence of spatial dependence among the municipalities for NCDR (Moran's I=0.348), NCD15 (Moran's I=0.269), and G2DR (Moran's I=0.275). Clusters with high NCDR levels included 13 municipalities in the northern and eastern macroregions, while clusters with high G2DR levels included 12 municipalities in the northwestern, northern, and eastern macroregions. CONCLUSIONS: The NCDR and NCD15 decreased, but the G2DR remained stable between 2018 and 2022. The coronavirus disease 2019 (COVID-19) pandemic had a potential negative impact on leprosy case detection, highlighting the need to strengthen leprosy surveillance efforts. The identified clusters of MT municipalities can significantly assist in this task.

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