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01.12.2017 | Research | Ausgabe 1/2017 Open Access

Malaria Journal 1/2017

Socioeconomic and demographic characterization of an endemic malaria region in Brazil by multiple correspondence analysis

Zeitschrift:
Malaria Journal > Ausgabe 1/2017
Autoren:
Raquel M. Lana, Thais I. S. Riback, Tiago F. M. Lima, Mônica da Silva-Nunes, Oswaldo G. Cruz, Francisco G. S. Oliveira, Gilberto G. Moresco, Nildimar A. Honório, Cláudia T. Codeço
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12936-017-2045-z) contains supplementary material, which is available to authorized users.
The original version of the article was revised to correct an error in figure 6.
A correction to this article is available online at https://​doi.​org/​10.​1186/​s12936-017-2058-7.

Abstract

Background

In the process of geographical retraction of malaria, some important endemicity pockets remain. Here, we report results from a study developed to obtain detailed community data from an important malaria hotspot in Latin America (Alto Juruá, Acre, Brazil), to investigate the association of malaria with socioeconomic, demographic and living conditions.

Methods

A household survey was conducted in 40 localities (n = 520) of Mâncio Lima and Rodrigues Alves municipalities, Acre state. Information on previous malaria, schooling, age, gender, income, occupation, household structure, habits and behaviors related to malaria exposure was collected. Multiple correspondence analysis (MCA) was applied to characterize similarities between households and identify gradients. The association of these gradients with malaria was assessed using regression.

Results

The first three dimensions of MCA accounted for almost 50% of the variability between households. The first dimension defined an urban/rurality gradient, where urbanization was associated with the presence of roads, basic services as garbage collection, water treatment, power grid energy, and less contact with the forest. There is a significant association between this axis and the probability of malaria at the household level, OR = 1.92 (1.23–3.02). The second dimension described a gradient from rural settlements in agricultural areas to those in forested areas. Access via dirt road or river, access to electricity power-grid services and aquaculture were important variables. Malaria was at lower risk at the forested area, OR = 0.55 (1.23–1.12). The third axis detected intraurban differences and did not correlate with malaria.

Conclusions

Living conditions in the study area are strongly geographically structured. Although malaria is found throughout all the landscapes, household traits can explain part of the variation found in the odds of having malaria. It is expected these results stimulate further discussions on modelling approaches targeting a more systemic and multi-level view of malaria dynamics.
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