Statistical modeling of surveillance data to identify correlates of urban malaria risk: A population-based study in the Amazon Basin

被引:17
|
作者
Corder, Rodrigo M. [1 ]
Paula, Gilberto A. [2 ]
Pincelli, Anaclara [1 ]
Ferreira, Marcelo U. [1 ]
机构
[1] Univ Sao Paulo, Inst Biomed Sci, Dept Parasitol, Sao Paulo, Brazil
[2] Univ Sao Paulo, Inst Math & Stat, Dept Stat, Sao Paulo, Brazil
来源
PLOS ONE | 2019年 / 14卷 / 08期
基金
巴西圣保罗研究基金会; 美国国家卫生研究院;
关键词
CENTRAL-AFRICA; TRANSMISSION; URBANIZATION; EPIDEMIOLOGY; BRAZZAVILLE; PREVALENCE; DIVERSITY; CHILDREN; EXAMPLE; REGION;
D O I
10.1371/journal.pone.0220980
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Despite the recent malaria burden reduction in the Americas, focal transmission persists across the Amazon Basin. Timely analysis of surveillance data is crucial to characterize high-risk individuals and households for better targeting of regional elimination efforts. Here we analyzed 5,480 records of laboratory-confirmed clinical malaria episodes combined with demographic and socioeconomic information to identify risk factors for elevated malaria incidence in Mancio Lima, the main urban transmission hotspot of Brazil. Overdispersed malaria count data clustered into households were fitted with random-effects zero-inflated negative binomial regression models. Random-effect predictors were used to characterize the spatial heterogeneity in malaria risk at the household level. Adult males were identified as the population stratum at greatest risk, likely due to increased occupational exposure away of the town. However, poor housing and residence in the less urbanized periphery of the town were also found to be key predictors of malaria risk, consistent with a substantial local transmission. Two thirds of the 8,878 urban residents remained uninfected after 23,975 person-years of follow-up. Importantly, we estimated that nearly 14% of them, mostly children and older adults living in the central urban hub, were free of malaria risk, being either unexposed, naturally unsusceptible, or immune to infection. We conclude that statistical modeling of routinely collected, but often neglected, malaria surveillance data can be explored to characterize drivers of transmission heterogeneity at the community level and provide evidence for the rational deployment of control interventions.
引用
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页数:14
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