Using variable importance measures from causal inference to rank risk factors of schistosomiasis infection in a rural setting in China.
Using variable importance measures from causal inference to rank risk factors of schistosomiasis infection in a rural setting in China.
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DOI：
10.1186/1742-5573-7-3
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发表时间：
2010-07-14
期刊：
影响因子：
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通讯作者：
Hubbard AE
中科院分区：
文献类型：
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作者：
Sudat SE;Carlton EJ;Seto EY;Spear RC;Hubbard AE
通过接触受污染的水而感染血吸虫病是一个全球性的公共卫生问题。本文分析了中国农村地区1011名居民的水接触和血吸虫病感染状况的回顾性研究数据。我们通过比较三种分析方法提出了识别风险因素的半参数方法：以预测为中心的机器学习算法，简单的主效应多变量回归，以及受因果人群干预参数启发的半参数变量重要性（VI）估计。多变量回归发现，只有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估计值0.16，95%CI 0.11-0.22），工具清洗水接触（VI估计值0.88，95%CI 0.80-0.97）和水稻种植水接触（VI估计值0.71，95%CI 0.53-0.96）。特别是7月VI的结果，表明与感染状态有很强的关联-其因果解释意味着在7月消除水接触将使我们研究人群中的血吸虫病患病率降低84%，或从0.3降低到0.05（95%CI 78%-89%）。7月VI日的估计表明血吸虫病感染风险可能存在季节内变异，回归分析未检测到这种关联。虽然这项研究有许多限制，限制了因果解释的可能性，但如果可以在接近真实的时间内检测到高风险时间段，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是基于任意的预先指定的模型，使其参数难以解释在现实世界中的应用程序。我们的研究结果支持分析方法的实际应用，相比之下，不需要任意的模型预规范，估计参数，有简单的公共卫生解释，并应用推理，认为模型选择的变异来源。
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. 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%). 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.