Padrões epidemiológicos da hanseníase em área de alto risco de transmissão nos Estados do Maranhão, Pará, Tocantins e Piauí, 2001-2009

Abstract

In recent years, the National Leprosy Control has focused its actions on defined geographic areas with high leprosy detection rates. This study aimed to characterize epidemiological, spatial and temporal patterns in a high risk leprosy cluster in municipalities in the states of Maranhão, Pará, Piauí and Tocantins. Different methods of spatial analysis were applied (Descriptive, Local Bayesian Approach, Spatial Scan Statistics), and the spatial dependence of various epidemiological and operational indicators was quantified. In an additional study, I identified the flow of leprosy-affected individuals, and the reasons for migration after diagnosis. In the period 2001–2009, 82,463 new cases were detected in the endemic cluster (mean detection rate: 95.9/100mil inhabitants per year). In the rest of Brazil, the mean rate was 21.0 (RR=4.56, 95% CI: 4.45 - 4.66; p<0.0001). There was a directed flow of patients who were reported by a municipality other than their residence. Araguaína, Imperatriz, Marabá and Floriano notified a considerable number of cases from the municipalities in the proximity. São Luís, Teresina and Belém received also cases from other states. Goiânia and Brasília are distant from the cluster, but reported cases from the cluster. After first diagnosis, in 53.5% of cases migration was related to lifestyle changes. Spatial Scan analysis identified 23 clusters of high detection rates, most of them located in Pará and Maranhão. These clusters included only 32% of the population but 55.4% of new cases, and 101 (27.1%) municipalities. There were also 14 significant clusters for high detection rates in children, and 11 clusters of new cases with grade 2 disabilities/100.000 population. The most significant cluster, in the centre of Maranhão, had a RR of 2.24 and an annual detection of grade 2 cases of 10.4/100.000 population. The local auto-correlation method showed overlapping with high-risk areas identified by Local Bayesian and Spatial Scan Statistics. The study shows that leprosy is hyperendemic in the study area, without an expected trend of reduced detection rates in the coming years. In addition to late diagnosis in a considerable number of cases, there were shortcomings in the decentralization of the health system, evidenced by the flow of affected people. The use of maps based on other indicators than detection rates and the overlap of these maps highlighted previously unknown risk areas for transmission and for cases with advanced disabilities. This approach can be applied in other endemic areas to identify clusters of high risk for leprosy.