Health poverty among people with type 2 diabetes mellitus (T2DM) in Malaysia.

In the context of the escalating burden of diabetes in low and middle-income countries (LMICs), there is a pressing concern about the widening disparities in care and outcomes across socioeconomic groups. This paper estimates health poverty measures among individuals with type 2 diabetes mellitus (T2DM) in Malaysia. Using data from the National Diabetes Registry between 2009 and 2018, the study linked 932,855 people with T2DM aged 40-75 to death records. Cox proportional hazards models were used to estimate the 5-year survival probabilities for each patient, stratified by age and sex, while controlling for comorbidities and area-based indicators of socio-economic status (SES), such as district-level asset-based indices and night-time luminosity. Measures of health poverty, based on the Foster-Greer-Thorbecke (FGT) measures, were employed to capture excessive risk of premature mortality. Two poverty line thresholds were used, namely a 5% and 10% reduction in survival probability compared to age and sex-adjusted survival probability of the general population. Counterfactual simulations estimated the extent to which comorbidities contribute to health poverty. 43.5% of the sample experienced health poverty using the 5% threshold, and 8.9% were health poor using the 10% threshold. Comorbidities contribute 2.9% for males and 5.4% for females, at the 5% threshold. At the 10% threshold, they contribute 7.4% for males and 3.4% for females. If all patients lived in areas of highest night-light intensity, poverty would fall by 5.8% for males and 4.6% for females at the 5% threshold, and 4.1% for males and 0.8% for females at the 10% threshold. In Malaysia, there is a high incidence of health poverty among people with diabetes, and it is strongly associated with comorbidities and area-based measures of SES. Expanding the application of health poverty measurement, through a combination of clinical registries and open spatial data, can facilitate simulations for health poverty alleviation.

Quantifying the mental health and economic impacts of prospective Universal Basic Income schemes among young people in the UK: a microsimulation modelling study.

OBJECTIVE: Universal Basic Income (UBI)-a largely unconditional, regular payment to all adults to support basic needs-has been proposed as a policy to increase the size and security of household incomes and promote mental health. We aimed to quantify its long-term impact on mental health among young people in England. METHODS: We produced a discrete-time dynamic stochastic microsimulation that models a close-to-reality open cohort of synthetic individuals (2010-2030) based on data from Office for National Statistics and Understanding Society. Three UBI scheme scenarios were simulated: Scheme 1-Starter (per week): £41 per child; £63 per adult over 18 and under 65; £190 per adult aged 65+; Scheme 2-Intermediate (per week): £63 per child; £145 per adult under 65; £190 per adult aged 65+; Scheme 3-Minimum Income Standard level (per week): £95 per child; £230 per adult under 65; £230 per adult aged 65+. We reported cases of anxiety and depression prevented or postponed and cost savings. Estimates are rounded to the second significant digit. RESULTS: Scheme 1 could prevent or postpone 200 000 (95% uncertainty interval: 180 000 to 210 000) cases of anxiety and depression from 2010 to 2030. This would increase to 420 000(400 000 to 440 000) for Scheme 2 and 550 000(520 000 to 570 000) for Scheme 3. Assuming that 50% of the cases are diagnosed and treated, Scheme 1 could save £330 million (£280 million to £390 million) to National Health Service (NHS) and personal social services (PSS), over the same period, with Scheme 2 (£710 million (£640 million to £790 million)) or Scheme 3 (£930 million (£850 million to £1000 million)) producing more considerable savings. Overall, total cost savings (including NHS, PSS and patients' related costs) would range from £1.5 billion (£1.2 billion to £1.8 billion) for Scheme 1 to £4.2 billion (£3.7 billion to £4.6 billion) for Scheme 3. CONCLUSION: Our modelling suggests that UBI could substantially benefit young people's mental health, producing substantial health-related cost savings.

Understanding the relationship between income and mental health among 16- to 24-year-olds: Analysis of 10 waves (2009-2020) of Understanding Society to enable modelling of income interventions.

A substantial body of evidence suggests that young people, including those at the crucial transition points between 16 and 24, now face severe mental health challenges. In this article, we analyse data from 10 waves of a major UK longitudinal household cohort study, Understanding Society, to examine the relationship between income and anxiety and depression among 16- to 24-year-olds. Using random effects logistic regression (Model 1) allowing for whether the individual was depressed in the previous period as well as sex, age, ethnicity, whether the individual was born in the UK, region, rurality, highest qualification, marital status, employment status and attrition, we find a significant and inversely monotonic adjusted association between average net equivalised household income quintiles and clinical threshold levels of depressive symptoms SF-12 Mental Component Summary (MCS score ≤45.6). This means that being in a higher income group is associated with a reduced likelihood of clinically significant depressive symptoms, allowing for observable confounding variables. Using a 'within-between' model (Model 2), we find that apart from among those with the very highest incomes, increases in average net equivalised household income over the course of childhood and adolescence are significantly associated with reduced symptoms of anxiety and depression as measured by a higher SF-12 MCS score. Compared with previous reviews, the data presented here provides an estimate of the magnitude of effect that helps facilitate microsimulation modelling of impact on anxiety and depression from changes in socioeconomic circumstances. This enables a more detailed and complete understanding of the types of socioeconomic intervention that might begin to address some of the causes of youth mental health problems.