Correlation between the spatial distribution of leprosy and socioeconomic indicators in the city of Vitória, State of ES, Brazil.

INTRODUCTION Leprosy is a disease that is directly linked to poverty. The number of cases in Vit6ria, the capital city of Espírito Santo, has been decreasing in recent years, but the disease remains highly endemic. This research aimed to identify relationships between the epidemiological status of leprosy and socioeconomic indicators during the period from 2005 to 2009. METHODS An ecological study was performed based on the spatial distribution of leprosy in Vit6ria, Espírito Santo, between 2005 and 2009. The source data used were records available at the Secretary of State for Health of the Espírito Santo. We used the Urban Quality Index (IQU) as the leprosy-associated socioeconomic variable. The data were analysed with covariate and spatial effects by the WinBugs programme (Version 1.4) and R (Version 2.12). RESULTS The spatial distribution of leprosy in the district is not uniform. By studying the geographic distribution of leprosy cases, and the risks estimated by the complete Bayesian model, it was possible to gain further insight into the distribution of leprosy cases. It was noted that neighbourhoods with a low IQU have a higher leprosy case detection rate than neighbourhoods with a higher IQU. This result reinforced the theory that a low IQU is associated with the emergence of leprosy. CONCLUSION The model methodology adopted enabled the verification of the effect of the influence of covariates related to the social determinants of health as well as the spatial structure, in contrast to the gross rate method that does not aggregate this information. The results obtained suggest that leprosy control may be promoted by improving the socioeconomic indicators of neighbourhoods, and highlights the need for implementation of health policies aimed at people who live in areas where they are at greatest risk of getting sick.

[1]  F. Chiaravalloti-Neto,et al.  Spatial analysis of leprosy incidence and associated socioeconomic factors. , 2012, Revista de saude publica.

[2]  E. Maciel,et al.  Prevalência de HIV em gestantes e transmissão vertical segundo perfil socioeconômico, Vitória, ES , 2011 .

[3]  J. Teixeira,et al.  Análise da associação entre saneamento e saúde nos estados Brasileiros: estudo comparativo entre 2001 e 2006 , 2011 .

[4]  E. Declercq Leprosy statistics 2009: some thoughts. , 2011, Leprosy review.

[5]  E. G. Gonçalves,et al.  [Leprosy in Buriticupu, State of Maranhão: active search for cases in the adult population]. , 2010, Revista da Sociedade Brasileira de Medicina Tropical.

[6]  R Dietze,et al.  Spatial patterns of pulmonary tuberculosis incidence and their relationship to socio-economic status in Vitoria, Brazil. , 2010, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[7]  Jenefer M Blackwell,et al.  Geographic information systems and applied spatial statistics are efficient tools to study Hansen's disease (leprosy) and to determine areas of greater risk of disease. , 2010, The American journal of tropical medicine and hygiene.

[8]  E. Castilho,et al.  [The aids epidemic in the State of São Paulo: application of the full Bayesian space-time model]. , 2009, Revista da Sociedade Brasileira de Medicina Tropical.

[9]  Antônio Levino da Silva Neto,et al.  Social inequality, urban growth and leprosy in Manaus: a spatial approach. , 2009, Revista de saude publica.

[10]  M. Hatta,et al.  Risk factors for developing leprosy--a population-based cohort study in Indonesia. , 2006, Leprosy review.

[11]  D. Lockwood,et al.  Leprosy: too complex a disease for a simple elimination paradigm. , 2005, Bulletin of the World Health Organization.

[12]  P. Elliott,et al.  Spatial Epidemiology: Current Approaches and Future Challenges , 2004, Environmental health perspectives.

[13]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[14]  R. Assunção,et al.  [Maps of epidemiological rates: a Bayesian approach]. , 1998, Cadernos de saude publica.

[15]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[16]  R. Marshall Mapping disease and mortality rates using empirical Bayes estimators. , 1991, Journal of the Royal Statistical Society. Series C, Applied statistics.

[17]  G. Penna,et al.  Spatial Distribution of Leprosy in the , 2009 .

[18]  L Bernardinelli,et al.  Empirical Bayes versus fully Bayesian analysis of geographical variation in disease risk. , 1992, Statistics in medicine.