Mayaro virus infection in amazonia: a multimodel inference approach to risk factor assessment.

• Main Authors: Fernando Abad-Franch (Author), Gustavo H Grimmer (Author), Vanessa S de Paula (Author), Luiz T M Figueiredo (Author), Wornei S M Braga (Author), Sérgio L B Luz (Author)
• Format: Book
• Published: Public Library of Science (PLoS), 2012-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1935-2735; 10.1371/journal.pntd.0001846
• Keywords: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270
• Source: PLoS Neglected Tropical Diseases, Vol 6, Iss 10, p e1846 (2012)

BACKGROUND: Arboviral diseases are major global public health threats. Yet, our understanding of infection risk factors is, with a few exceptions, considerably limited. A crucial shortcoming is the widespread use of analytical methods generally not suited for observational data--particularly null hypothesis-testing (NHT) and step-wise regression (SWR). Using Mayaro virus (MAYV) as a case study, here we compare information theory-based multimodel inference (MMI) with conventional analyses for arboviral infection risk factor assessment. METHODOLOGY/PRINCIPAL FINDINGS: A cross-sectional survey of anti-MAYV antibodies revealed 44% prevalence (n = 270 subjects) in a central Amazon rural settlement. NHT suggested that residents of village-like household clusters and those using closed toilet/latrines were at higher risk, while living in non-village-like areas, using bednets, and owning fowl, pigs or dogs were protective. The "minimum adequate" SWR model retained only residence area and bednet use. Using MMI, we identified relevant covariates, quantified their relative importance, and estimated effect-sizes (β ± SE) on which to base inference. Residence area (β(Village) = 2.93 ± 0.41; β(Upland) = -0.56 ± 0.33, β(Riverbanks) = -2.37 ± 0.55) and bednet use (β = -0.95 ± 0.28) were the most important factors, followed by crop-plot ownership (β = 0.39 ± 0.22) and regular use of a closed toilet/latrine (β = 0.19 ± 0.13); domestic animals had insignificant protective effects and were relatively unimportant. The SWR model ranked fifth among the 128 models in the final MMI set. CONCLUSIONS/SIGNIFICANCE: Our analyses illustrate how MMI can enhance inference on infection risk factors when compared with NHT or SWR. MMI indicates that forest crop-plot workers are likely exposed to typical MAYV cycles maintained by diurnal, forest dwelling vectors; however, MAYV might also be circulating in nocturnal, domestic-peridomestic cycles in village-like areas. This suggests either a vector shift (synanthropic mosquitoes vectoring MAYV) or a habitat/habits shift (classical MAYV vectors adapting to densely populated landscapes and nocturnal biting); any such ecological/adaptive novelty could increase the likelihood of MAYV emergence in Amazonia.