AIMS We contribute to the methodological literature on the assessment of health inequalities by applying an algorithmic approach to evaluate the capabilities of socioeconomic variables in predicting the prevalence of non-communicable diseases in a Norwegian health survey. METHODS We use data from the seventh survey of the population based Tromsø Study (2015-2016), including 11,074 women and 10,009 men aged 40 years and above. We apply the random forest algorithm to predict four non-communicable disease outcomes (heart attack, cancer, diabetes and stroke) based on information on a number of social root causes and health behaviours. We evaluate our results using the classification error, the mean decrease in accuracy, partial dependence statistics. RESULTS Results suggest that education, household income and occupation to a variable extent contribute to predicting non-communicable disease outcomes. Prediction misclassification ranges between 25.1% and 35.4% depending on the non-communicable diseases under study. Partial dependences reveal mostly expected health gradients, with some examples of complex functional relationships. Out-of-sample model validation shows that predictions translate to new data input. CONCLUSIONS Algorithmic modelling can provide additional empirical detail and metrics for evaluating heterogeneous inequalities in morbidity. The extent to which education, income and occupation contribute to predicting binary non-communicable disease outcomes depends on both non-communicable diseases and socioeconomic indicator. Partial dependences reveal that social gradients in non-communicable disease outcomes vary in shape between combinations of non-communicable disease outcome and socioeconomic status indicator. Misclassification rates highlight the extent of variation within socioeconomic groups, suggesting that future studies may improve predictive accuracy by exploring further subpopulation heterogeneity.

中文翻译：

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特罗姆瑟研究探索社会经济指标与非传染性疾病之间关联的变化：一种算法方法。


目标 我们通过应用算法方法来评估社会经济变量在挪威健康调查中预测非传染性疾病患病率的能力，为健康不平等评估的方法论文献做出贡献。方法 我们使用基于特罗姆瑟研究的第七次人口调查（2015-2016 年）的数据，其中包括年龄 40 岁及以上的 11,074 名女性和 10,009 名男性。我们应用随机森林算法根据一些社会根源和健康行为的信息来预测四种非传染性疾病的结果（心脏病、癌症、糖尿病和中风）。我们使用分类误差、准确率平均下降、部分依赖统计来评估我们的结果。结果 结果表明，教育、家庭收入和职业在不同程度上有助于预测非传染性疾病的结果。根据所研究的非传染性疾病，预测错误分类范围在 25.1% 到 35.4% 之间。部分依赖性揭示了大部分预期的健康梯度，以及一些复杂功能关系的例子。样本外模型验证表明预测会转化为新的数据输入。结论 算法建模可以提供额外的经验细节和指标来评估发病率的异质不平等。教育、收入和职业对预测二元非传染性疾病结果的贡献程度取决于非传染性疾病和社会经济指标。部分依赖性表明，非传染性疾病结果的社会梯度在非传染性疾病结果和社会经济地位指标的组合之间存在变化。 错误分类率凸显了社会经济群体内的变异程度，表明未来的研究可能通过进一步探索亚群异质性来提高预测准确性。