02710nas a2200265 4500000000100000008004100001260001200042653002000054653002800074653001200102653001200114653002100126653002500147100001600172700001800188700001600206700001600222700001400238245018000252856008800432300000700520490000700527520189600534022001402430 2022 d c02/202210aContact Tracing10aEpidemiological profile10aleprosy10aPoverty10aSpatial analysis10aContact surveillance1 aMachado LMG1 ados Santos ES1 aCavaliero A1 aSteinmann P1 aIgnotti E00aSpatio-temporal analysis of leprosy risks in a municipality in the state of Mato Grosso-Brazilian Amazon: results from the leprosy post-exposure prophylaxis program in Brazil. uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8862266/pdf/40249_2022_Article_943.pdf a210 v113 a
BACKGROUND: Leprosy post-exposure prophylaxis (LPEP) with single dose rifampicin (SDR) can be integrated into different leprosy control program set-ups once contact tracing has been established. We analyzed the spatio-temporal changes in the distribution of index cases (IC) and co-prevalent cases among contacts of leprosy patients (CP) over the course of the LPEP program in one of the four study areas in Brazil, namely the municipality of Alta Floresta, state of Mato Grosso, in the Brazilian Amazon basin.
METHODS: Leprosy cases were mapped, and socioeconomic indicators were evaluated to explain the leprosy distribution of all leprosy cases diagnosed in the period 2016-2018. Data were obtained on new leprosy cases [Notifiable diseases information system (Sinan)], contacts traced by the LPEP program, and socioeconomic variables [Brazilian Institute of Geography and Statistics (IBGE)]. Kernel, SCAN, factor analysis and spatial regression were applied to analyze changes.
RESULTS: Overall, the new case detection rate (NCDR) was 20/10 000 inhabitants or 304 new cases, of which 55 were CP cases among the 2076 examined contacts. Changes over time were observed in the geographic distribution of cases. The highest concentration of cases was observed in the northeast of the study area, including one significant cluster (Relative risk = 2.24; population 27 427, P-value < 0.001) in an area characterized by different indicators associated with poverty as identified through spatial regression (Coefficient 3.34, P-value = 0.01).
CONCLUSIONS: The disease distribution was partly explained by poverty indicators. LPEP influences the spatial dynamic of the disease and results highlighted the relevance of systematic contact surveillance for leprosy elimination.
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