Do socioeconomic health gradients persist over time and beyond income? A distributional analysis using UK biomarker data

Sinha, Kompal; Davillas, Apostolos; Jones, Andrew M.; Sharma, Anurag

doi:10.1016/j.ehb.2021.101036

Cited by 9 publications

(12 citation statements)

References 70 publications

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“…Although this observed correlation cannot be interpreted causally, the relationship is nearly identical for employed and unemployed adults across both genders (Figure 3c), suggesting that potential links between T3 and earnings in the job market are not driving the income-T3 correlation directly, but rather implicating a household-level force related to resources. The effects are meaningfully large in magnitude, similar to or larger than for other biomarkers with important income-gradients like cholesterol, % HbA1C, blood pressure, and C-reactive protein (Supplement Table S7) (35,36). Although household resources and free T3 are inversely related, Figure 3 shows free T3, but not free T4 or TSH, is lower among unemployed men.…”

Section: Socio-economic Status and Employmentmentioning

confidence: 85%

Sub-clinical triiodothyronine levels predict health, demographic, and socioeconomic outcomes

Lawton

Sabatini

Hochbaum

2023

Preprint

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The Hypothalamic-Pituitary-Thyroid (HPT) axis is fundamental to human biology, exerting central control over energy expenditure, metabolic rate, and body temperature. However, the consequences of 'normal' physiologic HPT-axis variation in non-clinical populations are poorly understood. Using nationally-representative data from the 2007-2012 NHANES, we explore relationships with demographics, mortality, and socio-economic factors. We find much larger variation across age in free T3 than other HPT-axis hormones. T3 and T4 have opposite effects on mortality: free T3 is inversely related and free T4 is positively related with likelihood of death. Free T3 and household income are negatively related, particularly at lower incomes. Finally, free T3 among older adults is associated with labor both on the extensive margin (unemployment) and intensive margin (hours worked). Physiologic TSH/T4 explain only 1% of T3 variation, and neither are appreciably correlated to socio-economic outcomes. Taken together, our data suggest an unappreciated complexity and non-linearity of the HPT-axis signaling cascade broadly such that TSH and T4 may not be accurate surrogates of free T3. Furthermore, we find that sub-clinical variation in the HPT-axis effector hormone T3 is an important and overlooked factor linking socio-economic forces, human biology, and aging.

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Section: Socio-economic Status and Employmentmentioning

confidence: 85%

Sub-clinical triiodothyronine levels predict health, demographic, and socioeconomic outcomes

Lawton

Sabatini

Hochbaum

2023

Preprint

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“…In addition, when considering the inclusion of this dimension only for the national level analysis, we decided to exclude it as the information on income level was missing for a sizeable proportion of respondents (32%), which would have otherwise precluded our ability to draw any meaningful conclusions with a reasonable level of confidence. Furthermore, recent evidence has emerged showing that income may account only for a minor proportion of the socio-economic gradient in excess-weight [ 57 ].…”

Section: Discussionmentioning

confidence: 99%

Socio-economic accounting of inequalities in excess weight: a population-based analysis

2023

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Background The prevalence of excess weight has been increasing globally in the last decades, affecting disproportionally adults from low socio-economic backgrounds and putting undue pressure on health systems and societal resources. In England, tackling unfair and unjust health inequalities is at the heart of national public health policy, and a prerequisite for enabling these decision makers to set policy priorities is an understanding of the prevalence and determinants of excess weight inequalities in their local population. Methods We conducted both pooled (England) and regional-level (nine regions: North-East, North-West, Yorkshire and Humber, East Midlands, West Midlands, East of England, London, South East and South West) analyses of individual level data from a nationally representative sample of adults (N = 6,387). We used the Corrected Concentration Index (CCI) to measure absolute inequalities in excess weight across three dimensions of socio-economic deprivation: neighbourhood-level deprivation, occupational status and educational qualification. We used a Shapley decomposition method to evaluate their relative contribution to inequality. Results At a national level, all three dimensions of socio-economic deprivation were found to be positively associated with excess weight across the adult population, as measured by the CCI, with educational qualification ranking first [CCI: -0.090, p < 0.01], closely followed by neighbourhood-level deprivation [CCI: -0.050, p < 0.01]. Large variation was found between regions and genders, with inequality being either considerably higher or exclusively patterned among women. The strongest independent factor contributing to excess weight inequalities was having a long-lasting limiting illness, especially among women and towards the right tail of the excess weight spectrum. Heterogeneous patterns of contribution across the excess weight spectrum were found, however age played a dominant role toward the left tail of the distribution. Conclusions While socio-economic inequalities in excess weight exist in the English adult population, our findings underscore the importance of considering multiple dimensions of deprivation and the unique needs of different populations when developing policies to address overweight and obesity. Targeted interventions for adults with overweight and obesity with long-lasting illnesses and women can generate both short-term and long-term economic benefits, by reducing healthcare costs and increasing workforce productivity.

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“…In addition, our findings support empirically derived evidence indicating the significant impact education ( 32 ) and income ( 17 , 33 ) have on general health. However, the work of Sinha et al ( 34 ) indicates that there are potentially better indicators of poor health than income, and that tackling persistent deprivation in dimensions such as housing conditions and social isolation may be more fruitful.…”

Section: Discussionmentioning

confidence: 99%

Understanding health inequalities in Wales using the Blinder-Oaxaca decomposition method

Allen

Cotter-Roberts

Darlington

et al. 2022

Front. Public Health

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BackgroundThroughout Wales and the world, health inequality remains a problem that is interconnected with a wider and complex social, economic and environmental dynamic. Subsequently, action to tackle inequality in health needs to take place at a structural level, acknowledging the constraints affecting an individual's (or community's) capability and opportunity to enable change. While the ‘social determinants of health' is an established concept, fully understanding the composition of the health gap is dependent on capturing the relative contributions of a myriad of social, economic and environmental factors within a quantitative analysis.MethodThe decomposition analysis sought to explain the differences in the prevalence of these outcomes in groups stratified by their ability to save at least £10 a month, whether they were in material deprivation, and the presence of a limiting long-standing illness, disability of infirmity. Responses to over 4,200 questions within the National Survey for Wales (n = 46,189; 2016–17 to 2019–20) were considered for analysis. Variables were included based on (1) their alignment to a World Health Organization (WHO) health equity framework (“Health Equity Status Report initiative”) and (2) their ability to allow for stratification of the survey sample into distinct groups where considerable gaps in health outcomes existed. A pooled Blinder-Oaxaca model was used to analyse inequalities in self-reported health (fair/poor health, low mental well-being and low life satisfaction) and were stratified by the variables relating to financial security, material deprivation and disability status.ResultsThe prevalence of fair/poor health was 75% higher in those who were financially insecure and 95% higher in those who are materially deprived. Decomposition of the outcome revealed that just under half of the health gap was “explained” i.e., 45.5% when stratifying by the respondent's ability to save and 46% when stratifying by material deprivation status. Further analysis of the explained component showed that “Social/Human Capital” and “Income Security/Social Protection” determinants accounted the most for disparities observed; it also showed that “Health Services” determinants accounted the least. These findings were consistent across the majority of scenarios modeled.ConclusionThe analysis not only quantified the significant health gaps that existed in the years leading up to the COVID-19 pandemic but it has also shown what determinants of health were most influential. Understanding the factors most closely associated with disparities in health is key in identifying policy levers to reduce health inequalities and improve the health and well-being across populations.

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Do socioeconomic health gradients persist over time and beyond income? A distributional analysis using UK biomarker data

Cited by 9 publications

References 70 publications

Sub-clinical triiodothyronine levels predict health, demographic, and socioeconomic outcomes

Sub-clinical triiodothyronine levels predict health, demographic, and socioeconomic outcomes

Socio-economic accounting of inequalities in excess weight: a population-based analysis

Understanding health inequalities in Wales using the Blinder-Oaxaca decomposition method

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