Neighbourhood deprivation and intersectional inequalities in biomarkers of healthy ageing in England

Geographical variation, demographic and socioeconomic disparities in Active Ageing: The situation in Thailand

“…This study highlights the influence of location on health behaviour and outcomes, supported by previous research indicating different patterns of mortality and long-term illness [ 12 ], self-rated health and functional limitations [ 17 ], and biomarkers of chronic disease and healthy ageing such as HbA1c, systolic blood pressure, and grip strength [ 18 ]. Additionally, geographical factors impact physical activity, and sedentary behaviour [ 15 ], and are intertwined with gender disparities in health, and socio-economic inequalities [ 14 ].…”

“…Measuring these variations and disparities is crucial for evaluating the impact of policies on health equity. Geographic location significantly influences health-related outcomes, affecting self-rated health [ 15 , 16 ], functional limitations [ 17 ], health behaviours [ 15 ], and healthy ageing [ 12 , 18 ].…”

A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

“…Finally, researchers could envision a cross-classified multilevel model (CCMM) where individuals (level 1) are nested in both intersectional strata (level 2) and a context such as schools (level 2), which are cross-classified with each other. This might be done simply to test the robustness of “core” MAIHDA results when context is adjusted for ( Evans, 2019a ), or it might be to evaluate the relative importance of stratum and contexts ( Holman, Bell, Green, & Salway, 2022 ; Khalaf et al, 2020 ; Prior and Leckie 2023 ). The latter, though intriguing, is more challenging to interpret.…”

Extending intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to study individual longitudinal trajectories, with application to mental health in the UK

“…The algebraic specification of the models would similar to the strictly hierarchical case demonstrated here, and in R/lme4, the code to do so would be the same too. Such an approach could in theory be further extended with the addition of other cross-classified levels of analysis (such as household and neighbourhood) -past work has considered this with crosssectional data (Holman et al, 2022). Whilst the focus of this paper has been panel data, a version of the method could be used with birth-cohort studies (although the latter would not have variation between generations to explore).…”