Lower socioeconomic position (SEP) is associated with increased risk of developing chronic pain, experiencing more severe pain, and suffering greater pain-related disability. However, SEP is a multidimensional construct; there is a dearth of research on which SEP features are most strongly associated with high-impact chronic pain, the relative importance of SEP predictive features compared to established chronic pain correlates, and whether the relative importance of SEP predictive features differs by race and sex. This study used 3 machine learning algorithms to address these questions among adults in the 2019 National Health Interview Survey. Gradient boosting decision trees achieved the highest accuracy and discriminatory power for high-impact chronic pain. Results suggest that distinct SEP dimensions, including material resources (eg, ratio of family income to poverty threshold) and employment (ie, working in the past week, number of working adults in the family), are highly relevant predictors of high-impact chronic pain. Subgroup analyses compared the relative importance of predictive features of high-impact chronic pain in non-Hispanic Black vs White adults and men vs women. Whereas the relative importance of body mass index and owning/renting a residence was higher for non-Hispanic Black adults, the relative importance of working adults in the family and housing stability was higher for non-Hispanic White adults. Anxiety symptom severity, body mass index, and cigarette smoking had higher relevance for women, while housing stability and frequency of anxiety and depression had higher relevance for men. Results highlight the potential for machine learning algorithms to advance health equity research.

中文翻译：

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富人和穷人：社会经济地位提高了机器学习算法预测高影响慢性疼痛的准确性。


较低的社会经济地位 （SEP） 与患慢性疼痛、经历更严重疼痛和遭受更严重疼痛相关残疾的风险增加有关。然而，SEP 是一种多维结构;缺乏关于哪些 SEP 特征与高影响慢性疼痛最密切相关、SEP 预测特征与已建立的慢性疼痛相关性相比的相对重要性以及 SEP 预测特征的相对重要性是否因种族和性别而异的研究。这项研究在 2019 年全国健康访谈调查中使用了 3 种机器学习算法来解决成年人的这些问题。梯度提升决策树对高影响慢性疼痛实现了最高的准确性和判别能力。结果表明，不同的 SEP 维度，包括物质资源（例如，家庭收入与贫困线的比率）和就业（即，过去一周的工作，家庭中在职成年人的数量），是高影响慢性疼痛的高度相关预测因子。亚组分析比较了非西班牙裔黑人与白人成人和男性与女性高影响慢性疼痛的预测特征的相对重要性。虽然体重指数和拥有/租赁住宅的相对重要性对于非西班牙裔黑人成年人来说更高，但对于非西班牙裔白人成年人来说，在职成年人在家庭和住房稳定性中的相对重要性更高。焦虑症状严重程度、体重指数和吸烟与女性的相关性较高，而住房稳定性以及焦虑和抑郁频率与男性的相关性较高。结果突出了机器学习算法推进健康公平研究的潜力。