Multimorbid life expectancy across race, socio-economic status, and sex in South Africa.

Multimorbidity is increasing globally as populations age. However, it is unclear how long individuals live with multimorbidity and how it varies by social and economic factors. We investigate this in South Africa, whose apartheid history further complicates race, socio-economic, and sex inequalities. We introduce the term 'multimorbid life expectancy' (MMLE) to describe the years lived with multimorbidity. Using data from the South African National Income Dynamics Study (2008-17) and incidence-based multistate Markov modelling, we find that females experience higher MMLE than males (17.3 vs 9.8 years), and this disparity is consistent across all race and education groups. MMLE is highest among Asian/Indian people and the post-secondary educated relative to other groups and lowest among African people. These findings suggest there are associations between structural inequalities and MMLE, highlighting the need for health-system and educational policies to be implemented in a way proportional to each group's level of need.

Inequalities in disability-free and disabling multimorbid life expectancy in Costa Rica, Mexico, and the United States.

OBJECTIVES: To better understand variations in multimorbidity severity over time, we estimate disability-free and disabling multimorbid life expectancy (MMLE), comparing Costa Rica, Mexico, and the United States. We also assess MMLE inequalities by sex and education. METHODS: Data come from the Costa Rican Study on Longevity and Healthy Aging (2005-2009), the Mexican Health and Aging Study (2012-2018), and the Health and Retirement Study (2004-2018). We apply an incidence-based multistate Markov approach to estimate disability-free and disabling MMLE and stratify models by sex and education to study within-country heterogeneity. Multimorbidity is defined as a count of two or more chronic diseases. Disability is defined using limitations in activities of daily living. RESULTS: Costa Ricans have the lowest MMLE, followed by Mexicans, then individuals from the US. Individuals from the US spend about twice as long with disability-free multimorbidity compared with individuals from Costa Rica or Mexico. Females generally have longer MMLE than males, with particularly stark differences in disabling MMLE. In the US, higher education was associated with longer disability-free MMLE and shorter disabling MMLE. We identified evidence for cumulative disadvantage in Mexico and the US, where sex differences in MMLE were larger among the lower educated. DISCUSSION: Substantial sex and educational inequalities in MMLE exist within and between these countries. Estimating disability-free and disabling MMLE reveals another layer of health inequality not captured when examining disability and multimorbidity separately. MMLE is a flexible population health measure that can be used to better understand the aging process across contexts.

Estimating risk ratio from any standard epidemiological design by doubling the cases.

BACKGROUND: Despite the ease of interpretation and communication of a risk ratio (RR), and several other advantages in specific settings, the odds ratio (OR) is more commonly reported in epidemiological and clinical research. This is due to the familiarity of the logistic regression model for estimating adjusted ORs from data gathered in a cross-sectional, cohort or case-control design. The preservation of the OR (but not RR) in case-control samples has contributed to the perception that it is the only valid measure of relative risk from case-control samples. For cohort or cross-sectional data, a method known as 'doubling-the-cases' provides valid estimates of RR and an expression for a robust standard error has been derived, but is not available in statistical software packages. METHODS: In this paper, we first describe the doubling-of-cases approach in the cohort setting and then extend its application to case-control studies by incorporating sampling weights and deriving an expression for a robust standard error. The performance of the estimator is evaluated using simulated data, and its application illustrated in a study of neonatal jaundice. We provide an R package that implements the method for any standard design. RESULTS: Our work illustrates that the doubling-of-cases approach for estimating an adjusted RR from cross-sectional or cohort data can also yield valid RR estimates from case-control data. The approach is straightforward to apply, involving simple modification of the data followed by logistic regression analysis. The method performed well for case-control data from simulated cohorts with a range of prevalence rates. In the application to neonatal jaundice, the RR estimates were similar to those from relative risk regression, whereas the OR from naive logistic regression overestimated the RR despite the low prevalence of the outcome. CONCLUSIONS: By providing an R package that estimates an adjusted RR from cohort, cross-sectional or case-control studies, we have enabled the method to be easily implemented with familiar software, so that investigators are not limited to reporting an OR and can examine the RR when it is of interest.

Diabetes comorbidities in low- and middle-income countries: An umbrella review.

BACKGROUND: Diabetes mellitus, particularly type 2 diabetes, is a major public health burden globally. Diabetes is known to be associated with several comorbidities in high-income countries. However, our understanding of these associations in low- and middle-income countries (LMICs), where the epidemiological transition is leading to a growing dual burden of non-communicable and communicable disease, is less clear. We therefore conducted an umbrella review to systematically identify, appraise and synthesise reviews reporting the association between diabetes and multiple key comorbidities in LMICs. METHODS: We searched Medline, Embase, Global Health, and Global Index Medicus from inception to 14 November 2020 for systematic reviews, with or without meta-analyses, of cohort, case-control or cross-sectional studies investigating the associations between diabetes and cardiovascular disease (CVD), chronic kidney disease (CKD), depression, dengue, pneumonia, and tuberculosis within LMICs. We sought reviews of studies focused on LMICs, but also included reviews with a mixture of high-income and at least two LMIC studies, extracting data from LMIC studies only. We conducted quality assessment of identified reviews using an adapted AMSTAR 2 checklist. Where appropriate, we re-ran meta-analyses to pool LMIC study estimates and conduct subgroup analyses. RESULTS: From 11 001 articles, we identified 14 systematic reviews on the association between diabetes and CVD, CKD, depression, or tuberculosis. We did not identify any eligible systematic reviews on diabetes and pneumonia or dengue. We included 269 studies from 29 LMICs representing over 3 943 083 participants. Diabetes was positively associated with all comorbidities, with tuberculosis having the most robust evidence (16 of 26 cohort studies identified in total) and depression being the most studied (186 of 269 studies). The majority (81%) of studies included were cross-sectional. Heterogeneity was substantial for almost all secondary meta-analyses conducted, and there were too few studies for many subgroup analyses. CONCLUSIONS: Diabetes has been shown to be associated with several comorbidities in LMICs, but the nature of the associations is uncertain because of the large proportion of cross-sectional study designs. This demonstrates the need to conduct further primary research in LMICs, to improve, and address current gaps in, our understanding of diabetes comorbidities and complications in LMICs.