Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging.
Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging.
复制标题
使用贝叶斯模型平均检测加纳北部残留疟疾的局部危险因素。
DOI：
10.1186/s12936-018-2491-2
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发表时间：
2018-09-29
期刊：
影响因子：
3
通讯作者：
Valle D
中科院分区：
文献类型：
--
作者：
Millar J;Psychas P;Abuaku B;Ahorlu C;Amratia P;Koram K;Oppong S;Valle D
有必要对造成撒哈拉以南非洲地区残余疟疾的潜在当地因素进行全面评价。然而，在考虑季节差异和非线性关系的同时，很难使用标准统计方法比较与疟疾传播相关的各种人口、社会经济和环境变量。本文使用贝叶斯模型平均（BMA）方法来识别和比较与残留疟疾相关的潜在风险和保护因素。人口、社会经济、环境和疟疾干预等一系列变量对疟疾流行的相对影响采用BMA模型进行变量选择。在2010年至2013年期间进行的6次半年一次的调查中，在加纳东北部的农村地区bunkpuruguu - yunyoo收集了数据，该地区经历了全面流行的季节性疟疾传播。共有10022名年龄在6到59个月之间的儿童被用于分析。开发了多个模型来识别重要的风险和保护因素，考虑到季节模式和非线性关系。这些模型显示，疟疾风险与离最近的城市中心和卫生设施的距离之间存在明显的非线性关联。此外，在雨季和旱季，疟疾风险与年龄和某些民族之间的关联存在显著差异。BMA优于其他常用的回归方法在样本外预测能力使用季节到季节的验证方法。该建模框架为疾病风险因素分析提供了另一种方法，可以生成可解释的模型，可以揭示复杂的非线性关系，在模型选择中纳入不确定性，并产生准确的预测。某些建模应用，如设计有针对性的局部干预措施，需要更复杂的统计方法，这些方法能够处理广泛的相关数据，同时保持可解释性和预测性能，并直接表征不确定性。为此目的，BMA是一种宝贵的工具，可用于构建信息更丰富的模型，以了解疟疾以及其他病媒传播和环境介导的疾病的风险因素。本文的在线版本（10.1186/s12936-018-2491-2）包含补充材料，授权用户可使用。
There is a need for comprehensive evaluations of the underlying local factors that contribute to residual malaria in sub-Saharan Africa. However, it is difficult to compare the wide array of demographic, socio-economic, and environmental variables associated with malaria transmission using standard statistical approaches while accounting for seasonal differences and nonlinear relationships. This article uses a Bayesian model averaging (BMA) approach for identifying and comparing potential risk and protective factors associated with residual malaria. The relative influence of a comprehensive set of demographic, socio-economic, environmental, and malaria intervention variables on malaria prevalence were modelled using BMA for variable selection. Data were collected in Bunkpurugu-Yunyoo, a rural district in northeast Ghana that experiences holoendemic seasonal malaria transmission, over six biannual surveys from 2010 to 2013. A total of 10,022 children between the ages 6 to 59 months were used in the analysis. Multiple models were developed to identify important risk and protective factors, accounting for seasonal patterns and nonlinear relationships. These models revealed pronounced nonlinear associations between malaria risk and distance from the nearest urban centre and health facility. Furthermore, the association between malaria risk and age and some ethnic groups was significantly different in the rainy and dry seasons. BMA outperformed other commonly used regression approaches in out-of-sample predictive ability using a season-to-season validation approach. This modelling framework offers an alternative approach to disease risk factor analysis that generates interpretable models, can reveal complex, nonlinear relationships, incorporates uncertainty in model selection, and produces accurate predictions. Certain modelling applications, such as designing targeted local interventions, require more sophisticated statistical methods which are capable of handling a wide range of relevant data while maintaining interpretability and predictive performance, and directly characterize uncertainty. To this end, BMA represents a valuable tool for constructing more informative models for understanding risk factors for malaria, as well as other vector-borne and environmentally mediated diseases. The online version of this article (10.1186/s12936-018-2491-2) contains supplementary material, which is available to authorized users.
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