Spatial analysis for stratifi cation of priority malaria control areas ,

The goal of this study was to stratify priority areas for malaria control in the State of Mato Grosso, Brazil, based on spatial analysis. The variables used were: Annual Parasite Index (API), Plasmodium falciparum/Plasmodium vivax ratio, population variation, number of families settled, and percent of deforested area. The Moran’s I and Local Moran Test were applied, visualized with the Box Map and Moran Map, for 19861991, 19921997, and 1998-2003. Box Map identified areas with high, low, and intermediate priority for control, and Moran Map identified municipalities with significant autocorrelation. In the high priority area, located in the North of Mato Grosso, malaria incidence decreased drastically despite the increase in the number of municipalities from the first to the last period. Other municipalities were added to the lower priority area, from the Southeast, Southwest, and Central-South of the State. The intermediate priority area was located along the border with neighboring States and municipalities classified as high and low priority. Spatial analysis showed the importance of the neighboring phenomenon between municipalities in defining priority areas, thus highlighting the technique’s advantages for use in malaria control and surveillance. Moran’s I; Malaria; Communicable Disease Control; Spatial Analysis Introduction In the late 1980s and early 1990s, the State of Mato Grosso, Brazil, witnessed a clear epidemic peak in malaria, reaching its maximum in 1992 with a total of 198,392 positive blood smears recorded, or an Annual Parasite Index (API) of 96.07 positive smears/1,000 inhabitants. This trend then reversed, and the decline was constant, with a drop to 3.03 positive smears/1,000 inhabitants by the year 2003. However, the reduction was not homogeneous across the State, with areas of greater and lesser epidemiological relevance 1. Malaria is known as an eminently focal disease 2 whose distribution pattern is influenced by such factors as vector dispersal, human-vector contact, human mobility, economic production, and social reproduction of human hosts 3, with no regard for political and administrative boundaries. Demarcation of areas for intervention according to risk distribution patterns is thus a key issue, especially for health system managers and professionals involved in malaria control programs. In the 1980s, the Superintendency for Public Health Campaigns (SUCAM) stratified the Amazon Region according to risk areas for malaria, defining the so-called Priority Areas I and II 4. In its initial design, this stratification sought to prioritize efforts and resources according to the concentration, scattering, and spread of malaria cases 5. ARTIGO ARTICLE Atanaka-Santos M et al. 1100 Cad. Saúde Pública, Rio de Janeiro, 23(5):1099-1112, mai, 2007 One criterion now used to define priority control areas is the intensity of malaria transmission based on the API. This stratification, adopted by the Health Surveillance Secretariat of the Ministry of Health (SVS/MS), classifies areas as low risk (API < 10 positive blood smears/1,000 inhabitants), medium risk (10 to 50 positive smears/1,000), and high risk (> 50/1,000) 6. Malaria control programs have recommended constructing maps based on this classification in order to visualize areas aggregated according to risk level 7. Thus, the demarcation of priority control areas by mapping has become strategic for health services to both organize and implement surveillance practices. The correct localization and identification of these areas, especially regarding differentiated risks for malaria, orients the transfer of financial resources and control activities. However, although the criterion adopted by the SVS/MS allows spatial visualization of locations by risk stratification, it does not allow grasping the structure of spatial dependence in the disease’s incidence. This stratification does not consider the possible existence of a spatial association between the occurrence of malaria in one municipality and that of its neighbor, i.e., between neighboring municipalities (counties) 8. The current study is intended to analyze the stratification of priority malaria control areas based on a spatial data analysis method that takes neighboring municipalities into account, and using data on malaria distribution for the State of Mato Grosso from 1986 to 2003.