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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.
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Background: There now exists a rich body of literature on the relationship between income, income inequality, and health. The discussion about the impact of income and income inequality on health includes psychosocial mechanisms, such as long-term perceptions of inferiority and social positioning, material advantage from income, and the structural conditions that define what people can do with their material resources. Aims: This study investigated the extent to which income's effects on health are mediated by psychosocial stress, and to what extent those effects are moderated by country-level income inequality and economic development. Methods: Data were collected from The European Social Survey, round 7. Multilevel moderated mediation analysis was applied to estimate the extent of psychosocial stress mediation of the effects of income on self-rated health. Moderated parameters were estimated over country-level income inequality and economic development. Results: Significant full or partial meditation by psychosocial stress was found in all 20 countries studied. Effects moderated by income inequality and GDP per capita showed expected relationships but failed to reach conventional levels of statistical significance. Conclusions: Individual-level income remains important for explaining the income-health gradient in self-rated health in Europe. The income-health relationship and the extent to which it is mediated by psychosocial stress varies among countries but is not significantly moderated by contextual income or income inequality. Policies should be aimed at allowing a greater proportion of people to live in material comfort and reduced sense of financial precarity, and protecting individuals from harmful consequences of low income.
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OBJECTIVE: To examine the association between area-level education and the local growth trajectories in antibacterial dispensing rates in Norwegian municipalities among children under 3 years old. DESIGN: Retrospective, longitudinal study using individual primary care prescription data from the Norwegian Prescription Database for the period 2006-2016. Data were collected on the date of dispensing, the type and amount of antibiotic, the patient's age, sex and municipality of residence and linked to municipality-level statistics on education available from Statistics Norway. We used multilevel growth curve modelling, with a linear trend variable modelled as a random effect and a cross-level interaction between linear trends and the proportion of the population in the municipality having received a university or college education. SETTING: The local government level in Norway. The sample includes all municipalities over the study period. OUTCOME MEASURE: Number of dispensed antibacterial prescriptions per 100 children in individual primary care by municipality and year. RESULTS: We identified a significant negative linear trend in the square root of the dispensing rate for children under 3 years old during the period. This trend varied between municipalities. A negative cross-level interaction term between population education levels and random trends showed that municipalities with an average level of population education saw a reduction in their square root dispensing rates of -0.053 (95% CI -0.066 to -0.039) prescriptions per 100 children. Each additional percentage point in population education contributed a further -0.0034 (95% CI -0.006 to -0.001) reduction to the square root dispensing rate. CONCLUSIONS: Municipalities in which a larger proportion of the local population have high educational achievements have been more successful in reducing antibacterial dispensing rates in children under 3 years old. Adopting area-level strategies and addressing local community disadvantages may help to optimise practices and prescribing patterns across local communities.