Using biomarkers to predict healthcare costs: Evidence from a UK household panel

We investigate the extent to which healthcare service utilisation and costs can be predicted from biomarkers, using the UK Understanding Society panel. We use a sample of 2,314 adults who reported no history of diagnosed long-lasting health conditions at baseline (2010/11), when biomarkers were collected. Five years later, their GP, outpatient (OP) and inpatient (IP) utilisation was observed. We develop an econometric technique for count data observed within ranges and a method of combining administrative reference cost data with the survey data without exact individual-level matching. Our composite biomarker index (allostatic load) is a powerful predictor of costs: for those with a baseline allostatic load of at least one standard deviation (1-s.d.) above mean, a 1-s.d. reduction reduces GP, OP and IP costs by around 18%

Alternative measures to BMI:Exploring income-related inequalities in adiposity in Great Britain

Socio-economic inequalities in adiposity are of particular interest themselves but also because they may be associated with inequalities in overall health status. Using cross-sectional representative data from Great Britain (1/2010-3/2012) for 13,138 adults (5652 males and 7486 females) over age 20, we aimed to explore the presence of income-related inequalities in alternative adiposity measures by gender and to identify the underlying factors contributing to these inequalities. For this reason, we employed concentration indexes and regression-based decomposition techniques. To control for non-homogeneity in body composition, we employed a variety of adiposity measures including body fat (absolute and percentage) and central adiposity (waist circumference) in addition to the conventional body mass index (BMI). The body fat measures allowed us to distinguish between the fat- and lean-mass components of BMI. We found that the absence of income-related obesity inequalities for males in the existing literature may be attributed to their focus on BMI-based measures. Pro-rich inequalities were evident for the fat-mass and central adiposity measures for males, while this was not the case for BMI. Irrespective of the adiposity measure applied, pro-rich inequalities were evident for females. The decomposition analysis showed that these inequalities were mainly attributable to subjective financial well-being measures (perceptions of financial strain and material deprivation) and education, with the relative contribution of the former being more evident in females. Our findings have important implications for the measurement of socio-economic inequalities in adiposity and indicate that central adiposity and body composition measures should be included health policy agendas. Psycho-social mechanisms, linked to subjective financial well-being, and education -rather than income itself-are more relevant for tackling inequalities

Biomarkers as precursors of disability

Some social surveys now collect physical measurements and markers derived from biological samples, in addition to self-reported health assessments. This information is expensive to collect; its value in medical epidemiology has been clearly established, but its potential contribution to social science research is less certain. We focused on disability, which results from biological processes but is defined in terms of its implications for social functioning and wellbeing. Using data from waves 2 and 3 of the UK Understanding Society panel survey as our baseline, we estimated predictive models for disability 2-4 years ahead, using a wide range of biomarkers in addition to self-assessed health (SAH) and other socio-economic covariates. We found a quantitatively and statistically significant predictive role for a large set of nurse-collected and blood-based biomarkers, over and above the strong predictive power of self-assessed health. We also applied a latent variable model accounting for the longitudinal nature of observed disability outcomes and measurement error in in SAH and biomarkers. Although SAH performed well as a summary measure, it has shortcomings as a leading indicator of disability, since we found it to be biased in the sense of over- or under-sensitivity to certain biological pathways

Ex ante inequality of opportunity in health, decomposition and distributional analysis of biomarkers

We use a set of biomarkers to measure inequality of opportunity (IOp) in the risk of major chronic conditions in the UK. Applying a direct ex ante IOp approach, we find that inequalities in biomarkers attributed to circumstances account for a non-trivial part of the total variation. For example, observed circumstances account for 20 % of the total inequalities in our composite measure of multi-system health risk, allostatic load. We propose an extension to the decomposition of ex ante IOp to complement the mean-based approach, analysing the contribution of circumstances across the quantiles of the biomarker distributions. Shapley decompositions show that, for most of the biomarkers, the percentage contribution of socioeconomic circumstances (education and childhood socioeconomic status), relative to differences attributable to age and gender, increase towards the right tail of the biomarker distribution, where health risks are more pronounced

Model-based recursive partitioning to estimate unfair health inequalities in the United Kingdom Household Longitudinal Study

We measure unfair health inequality in the UK using a novel data- driven empirical approach. We explain health variability as the result of circumstances beyond individual control and health-related behaviours. We do this using model-based recursive partitioning, a supervised machine learning algorithm. Unlike usual tree-based algorithms, model-based recursive partitioning does identify social groups with different expected levels of health but also unveils the heterogeneity of the relationship linking behaviors and health outcomes across groups. The empirical application is conducted using the UK Household Longitudinal Study. We show that unfair inequality is a substantial fraction of the total explained health variability. This finding holds no matter which exact definition of fairness is adopted: using both the fairness gap and direct unfairness measures, each evaluated at different reference values for circumstances or effort

Weather, mental health and mobility during the first wave of the Covid-19 pandemic

During the first UK wave of the COVID-19 outbreak, the first lockdown was announced on March 23, 2020, with a final easing of the restrictions on July 4, 2020. Among the most important public health costs of lockdown restrictions are the potential adverse effects on mental health and physical activity. Using data from the UK Household Longitudinal Study (UKHLS) and Google COVID-19 Mobility Reports we find evidence of reduced park mobility during the initial period of the first UK lockdown and confirm existing evidence of worsening mental health. Linkage with weather data shows that contrary to popular belief, daily or weekly weather conditions do not exacerbate the mental health consequences of the pandemic, as we found no systematic associations during the first lockdown period; on the other hand, we find systematic links between park mobility and weather over the same period

A latent class approach to inequity in health using biomarker data

We develop an empirical approach to analyse, measure and decompose Inequality of Opportunity (IOp) in health, based on a latent class model. This addresses some of the limitations that affect earlier work in this literature concerning the definition of types, such as partial observability, the ad hoc selection of circumstances, the curse of dimensionality and unobserved type-specific heterogeneity that may lead to either upwardly or downwardly biased estimates of IOp. We apply the latent class approach to measure IOp in allostatic load, a composite measure of our biomarker data. Using data from Understanding Society (UKHLS), we find that a latent class model with three latent types best fits the data and that these types differ in terms of their observed circumstances. Decomposition analysis shows that about two-thirds of the total inequality in allostatic load can be attributed to the direct and indirect contribution of circumstances

Has working-age morbidity been declining? Changes over time in survey measures of general health, chronic diseases, symptoms and biomarkers in England 1994-2014

Objectives: As life expectancy has increased in high-income countries, there has been a global debate about whether additional years of life are free from ill-health/disability. However, little attention has been given to changes over time in morbidity in the working-age population, particularly outside the US, despite its importance for health monitoring and social policy. This study therefore asks: what are the changes over time in working-age morbidity in England over two decades? Design, setting and participants: We use a high-quality annual cross-sectional survey, the Health Survey for England (‘HSE’) 1994-2014. HSE uses a random sample of the English household population, with a combined sample size of over 140,000 people. We produce a newly-harmonised version of HSE that maximises comparability over time, including new non-response weights. While HSE is used for monitoring population health, it has hitherto not used for investigating morbidity as a whole. Outcome measures: We analyse all 39 measures that are fully comparable over time – including chronic disease diagnoses, symptomatology and a number of biomarkers – adjusting for gender and age. Results: We find a mixed picture: we see improving cardiovascular and respiratory health, but deteriorations in obesity, diabetes, some biomarkers, and feelings of extreme anxiety/depression, alongside stability in moderate mental ill-health and musculoskeletal-related health. In several domains we also see stable or rising chronic disease diagnoses even where symptomatology has declined. While data limitations make it challenging to combine these measures into a single morbidity index, there is little systematic trend for declining morbidity to be seen in the measures that predict self-reported health most strongly. Conclusions: Despite considerable falls in working-age mortality – and the assumptions of many policymakers that morbidity will follow mortality – there is no systematic improvement in overall working-age morbidity in England from 1994 to 2014

Pay (for it) as you go: Prepaid energy meters and the heat-or-eat dilemma

The “heat-or-eat” dilemma, a trade-off typically between food consumption and heating, may elevate public health concerns during the 2022 energy-price crisis. Our paper contributes to the literature by exploring the role of domestic energy prepayment meters (PPMs) in the heat-or-eat dilemma, focusing on the association between PPM use and fruit and vegetable consumption. Using a representative sample of 24,811 individuals residing in Great Britain (January 2019–May 2021), we find robust evidence of lower fruit and vegetable consumption amongst individuals using PPMs, compared to those using post-payment energy bill payment methods. On average, our point estimates suggest that individuals using a PPM consume 2.7 fewer portions of fruit and vegetables per week. Our findings hold when bounding analysis is employed to account for omitted variable bias. Using a suite of IV approaches to further alleviate endogeneity concerns we found that our ordinary least squares results are consistent as opposed to IV models. Further robustness analyses highlight the deleterious impact of PPMs on people's healthy eating habits relevant to the consumption of enough fruit and vegetables. Our results suggest that targeted support for PPM users may have beneficial effects on people's fruit and vegetable consumption patterns

The determinants of body mass in Greece: Evidence from the National Health Survey

In this study we explore the determinants of body-weight in Greece utilizing information at the individual level from the National Health Survey of 2009. BMI has been treated as both, a cardinal and an ordinal measure of body-weight, while different estimation techniques were applied (OLS, ordered probit and unconditional quantile regression). In our attempt to identify the major determinants of BMI outcomes in Greece we employed a wide range of demographic, socio-economic, lifestyle, health-related and regional characteristics. The unconditional quantile estimates uncovered differences in the estimated impact of several correlates across the BMI distribution, highlighting their superiority vis-a-vis the simple mean-based linear models of BMI. Examining the entire BMI distribution and targeting specific segments of the Greek population can render public health policies against obesity more efficient and prolific