I-BioStat, Hasselt University, 3500 Hasselt, Belgium.
Centre of Excellence in Data Science, Bio-Statistics, College of Business and Economics, University of Rwanda, Kigali 4285, Rwanda.
Int J Environ Res Public Health. 2023 Feb 28;20(5):4283. doi: 10.3390/ijerph20054283.
Malaria is a life-threatening disease ocuring mainly in developing countries. Almost half of the world's population was at risk of malaria in 2020. Children under five years age are among the population groups at considerably higher risk of contracting malaria and developing severe disease. Most countries use Demographic and Health Survey (DHS) data for health programs and evaluation. However, malaria elimination strategies require a real-time, locally-tailored response based on malaria risk estimates at the lowest administrative levels. In this paper, we propose a two-step modeling framework using survey and routine data to improve estimates of malaria risk incidence in small areas and enable quantifying malaria trends.
To improve estimates, we suggest an alternative approach to modeling malaria relative risk by combining information from survey and routine data through Bayesian spatio-temporal models. We model malaria risk using two steps: (1) fitting a binomial model to the survey data, and (2) extracting fitted values and using them in the Poison model as nonlinear effects in the routine data. We modeled malaria relative risk among under-five-year old children in Rwanda.
The estimation of malaria prevalence among children who are under five years old using Rwanda demographic and health survey data for the years 2019-2020 alone showed a higher prevalence in the southwest, central, and northeast of Rwanda than the rest of the country. Combining with routine health facility data, we detected clusters that were undetected based on the survey data alone. The proposed approach enabled spatial and temporal trend effect estimation of relative risk in local/small areas in Rwanda.
The findings of this analysis suggest that using DHS combined with routine health services data for active malaria surveillance may provide provide more precise estimates of the malaria burden, which can be used toward malaria elimination targets. We compared findings from geostatistical modeling of malaria prevalence among under-five-year old children using DHS 2019-2020 and findings from malaria relative risk spatio-temporal modeling using both DHS survey 2019-2020 and health facility routine data. The strength of routinely collected data at small scales and high-quality data from the survey contributed to a better understanding of the malaria relative risk at the subnational level in Rwanda.
疟疾是一种主要发生在发展中国家的危及生命的疾病。2020 年，全球近一半人口面临疟疾风险。五岁以下儿童是感染疟疾和出现严重疾病风险较高的人群之一。大多数国家使用人口与健康调查（DHS）数据来开展卫生项目和进行评估。然而，疟疾消除策略需要根据最低行政级别的疟疾风险估计值，进行实时的、有针对性的响应。本文提出了一种两步建模框架，使用调查和常规数据来提高小区域疟疾风险发生率的估计值，并量化疟疾趋势。
为了提高估计值，我们建议通过贝叶斯时空模型结合调查和常规数据信息来改进疟疾相对风险模型。我们使用两步法来建模疟疾风险：（1）拟合二项式模型进行调查数据；（2）提取拟合值，并将其用作常规数据中的非线性效应。我们在卢旺达五岁以下儿童中建模疟疾相对风险。
仅使用卢旺达 2019-2020 年人口与健康调查数据估计五岁以下儿童的疟疾流行率，结果显示卢旺达西南部、中部和东北部的流行率高于该国其他地区。结合常规卫生机构数据，我们检测到了仅根据调查数据无法检测到的聚类。该方法可用于估计卢旺达局部/小区域疟疾相对风险的时空趋势效应。
该分析结果表明，使用 DHS 结合常规卫生服务数据进行主动疟疾监测，可以提供更准确的疟疾负担估计值，有助于实现疟疾消除目标。我们比较了使用 DHS 2019-2020 年调查数据进行五岁以下儿童疟疾流行率的地统计建模结果，以及使用 DHS 2019-2020 年调查数据和卫生机构常规数据进行疟疾相对风险时空建模的结果。小尺度常规数据和调查高质量数据的优势，有助于更好地了解卢旺达次国家一级的疟疾相对风险。
