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首页> 外文期刊>Epidemiologic Perspectives and Innovations >Using variable importance measures from causal inference to rank risk factors of schistosomiasis infection in a rural setting in China
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Using variable importance measures from causal inference to rank risk factors of schistosomiasis infection in a rural setting in China

机译：使用因果关系的变量重要性量度对中国农村地区血吸虫病感染的危险因素进行排序

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Background Schistosomiasis infection, contracted through contact with contaminated water, is a global public health concern. In this paper we analyze data from a retrospective study reporting water contact and schistosomiasis infection status among 1011 individuals in rural China. We present semi-parametric methods for identifying risk factors through a comparison of three analysis approaches: a prediction-focused machine learning algorithm, a simple main-effects multivariable regression, and a semi-parametric variable importance (VI) estimate inspired by a causal population intervention parameter. Results The multivariable regression found only tool washing to be associated with the outcome, with a relative risk of 1.03 and a 95% confidence interval (CI) of 1.01-1.05. Three types of water contact were found to be associated with the outcome in the semi-parametric VI analysis: July water contact (VI estimate 0.16, 95% CI 0.11-0.22), water contact from tool washing (VI estimate 0.88, 95% CI 0.80-0.97), and water contact from rice planting (VI estimate 0.71, 95% CI 0.53-0.96). The July VI result, in particular, indicated a strong association with infection status - its causal interpretation implies that eliminating water contact in July would reduce the prevalence of schistosomiasis in our study population by 84%, or from 0.3 to 0.05 (95% CI 78%-89%). Conclusions The July VI estimate suggests possible within-season variability in schistosomiasis infection risk, an association not detected by the regression analysis. Though there are many limitations to this study that temper the potential for causal interpretations, if a high-risk time period could be detected in something close to real time, new prevention options would be opened. Most importantly, we emphasize that traditional regression approaches are usually based on arbitrary pre-specified models, making their parameters difficult to interpret in the context of real-world applications. Our results support the practical application of analysis approaches that, in contrast, do not require arbitrary model pre-specification, estimate parameters that have simple public health interpretations, and apply inference that considers model selection as a source of variation.
机译：背景技术血吸虫病感染是通过与被污染的水接触而感染的，这是全球公共卫生问题。在本文中，我们分析了一项回顾性研究的数据，该研究报告了中国农村地区1011个人中的水接触和血吸虫病感染状况。通过比较三种分析方法，我们提出了用于识别风险因素的半参数方法：预测为重点的机器学习算法，简单的主效应多元回归以及因果种群启发的半参数变量重要性（VI）估计干预参数。结果多变量回归发现仅工具清洗与结果相关，相对风险为1.03，95％置信区间（CI）为1.01-1.05。在半参数VI分析中，发现三种类型的水接触与结果相关：7月水接触（VI估计为0.16，95％CI 0.11-0.22），工具清洗产生的水接触（VI估计为0.88，95％CI） 0.80-0.97），以及水稻种植带来的水接触（VI估计为0.71，95％CI 0.53-0.96）。特别是7月VI的结果表明，它与感染状况密切相关-因果关系的解释表明，7月消除水接触将使我们研究人群中血吸虫病的患病率降低84％，即从0.3降至0.05（CI为95％） ％-89％）。结论7月VI估计值表明血吸虫病感染风险可能在季节内变化，但回归分析未发现这一关联。尽管这项研究有很多局限性限制了因果解释的可能性，但是如果可以在接近实时的情况下检测出高风险时间段，则将开辟新的预防选择。最重要的是，我们强调传统的回归方法通常基于任意预先指定的模型，这使得它们的参数难以在实际应用中进行解释。相反，我们的结果支持了分析方法的实际应用，这些分析方法不需要任意的模型预先指定，估计具有简单公共卫生解释的参数以及应用将模型选择视为差异源的推断。

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