The economic status of older people’s households in urban and rural settings in Peru, Mexico and China: a 10/66 INDEP study cross-sectional survey

Few data are available from middle income countries regarding economic circumstances of households in which older people live. Many such settings have experienced rapid demographic, social and economic change, alongside increasing pension coverage. Population-based household surveys in rural and urban catchment areas in Peru, Mexico and China. Participating households were selected from all households with older residents. Descriptive analyses were weighted back for sampling fractions and non-response. Household income and consumption were estimated from a household key informant interview. 877 Household interviews (3177 residents). Response rate 68 %. Household income and consumption correlated plausibly with other economic wellbeing indicators. Household Incomes varied considerably within and between sites. While multigenerational households were the norm, older resident’s incomes accounted for a high proportion of household income, and older people were particularly likely to pool income. Differences in the coverage and value of pensions were a major source of variation in household income among sites. There was a small, consistent inverse association between household pension income and labour force participation of younger adult co-residents. The effect of pension income on older adults’ labour force participation was less clear-cut. Historical linkage of social protection to formal employment may have contributed to profound late-life socioeconomic inequalities. Strategies to formalise the informal economy, alongside increases in the coverage and value of non-contributory pensions and transfers would help to address this problem

Determinants of cognitive performance and decline in 20 diverse ethno-regional groups: A COSMIC collaboration cohort study.

BACKGROUND: With no effective treatments for cognitive decline or dementia, improving the evidence base for modifiable risk factors is a research priority. This study investigated associations between risk factors and late-life cognitive decline on a global scale, including comparisons between ethno-regional groups. METHODS AND FINDINGS: We harmonized longitudinal data from 20 population-based cohorts from 15 countries over 5 continents, including 48,522 individuals (58.4% women) aged 54-105 (mean = 72.7) years and without dementia at baseline. Studies had 2-15 years of follow-up. The risk factors investigated were age, sex, education, alcohol consumption, anxiety, apolipoprotein E ε4 allele (APOE*4) status, atrial fibrillation, blood pressure and pulse pressure, body mass index, cardiovascular disease, depression, diabetes, self-rated health, high cholesterol, hypertension, peripheral vascular disease, physical activity, smoking, and history of stroke. Associations with risk factors were determined for a global cognitive composite outcome (memory, language, processing speed, and executive functioning tests) and Mini-Mental State Examination score. Individual participant data meta-analyses of multivariable linear mixed model results pooled across cohorts revealed that for at least 1 cognitive outcome, age (B = -0.1, SE = 0.01), APOE*4 carriage (B = -0.31, SE = 0.11), depression (B = -0.11, SE = 0.06), diabetes (B = -0.23, SE = 0.10), current smoking (B = -0.20, SE = 0.08), and history of stroke (B = -0.22, SE = 0.09) were independently associated with poorer cognitive performance (p < 0.05 for all), and higher levels of education (B = 0.12, SE = 0.02) and vigorous physical activity (B = 0.17, SE = 0.06) were associated with better performance (p < 0.01 for both). Age (B = -0.07, SE = 0.01), APOE*4 carriage (B = -0.41, SE = 0.18), and diabetes (B = -0.18, SE = 0.10) were independently associated with faster cognitive decline (p < 0.05 for all). Different effects between Asian people and white people included stronger associations for Asian people between ever smoking and poorer cognition (group by risk factor interaction: B = -0.24, SE = 0.12), and between diabetes and cognitive decline (B = -0.66, SE = 0.27; p < 0.05 for both). Limitations of our study include a loss or distortion of risk factor data with harmonization, and not investigating factors at midlife. CONCLUSIONS: These results suggest that education, smoking, physical activity, diabetes, and stroke are all modifiable factors associated with cognitive decline. If these factors are determined to be causal, controlling them could minimize worldwide levels of cognitive decline. However, any global prevention strategy may need to consider ethno-regional differences