Summary Ageing is a dynamic process, and trends in the health status of older adults aged at least 60 years vary over time because of several factors. We examined reported trends in morbidity and ...mortality in older adults during the past two decades to identify patterns of ageing across the world. We showed some evidence for compression of morbidity (ie, a reduced amount of time spent in worse health), in four types of studies: 1) of good quality based on assessment criteria scores; 2) those in which a disability-related or impairment-related measure of morbidity was used; 3) longitudinal studies; or 4) studies undertaken in the USA and other high-income countries. Many studies, however, reported contrasting evidence (ie, for an expansion of morbidity), but with different methods, these measures are not directly comparable. Expansion of morbidity was more common when trends in chronic disease prevalence were studied. Our secondary analysis of data from longitudinal ageing surveys presents similar results. However, patterns of limitations in functioning vary substantially between countries and within countries over time, with no discernible explanation. Data from low-income countries are very sparse, and efforts to obtain information about the health of older adults in less-developed regions of the world are urgently needed. We especially need studies that focus on refining measurements of health, functioning, and disability in older people, with a core set of domains of functioning, that investigate the effects of these evolving patterns on the health-care system and their economic implications.
Using Eurobarometer data, we document large variation across European countries in education gradients in income, self-reported health, life satisfaction, obesity, smoking and drinking. While this ...variation has been documented previously, the reasons why the effect of education on income, health and health behaviors varies is not well understood. We build on previous literature documenting that cohorts graduating in bad times have lower wages and poorer health for many years after graduation, compared to those graduating in good times. We investigate whether more educated individuals suffer smaller income and health losses as a result of poor labor market conditions upon labor market entry. We confirm that a higher unemployment rate at graduation is associated with lower income, lower life satisfaction, greater obesity, more smoking and drinking later in life. Further, education plays a protective role for these outcomes, especially when unemployment rates are high: the losses associated with poor labor market outcomes are substantially lower for more educated individuals. Variation in unemployment rates upon graduation can potentially explain a large fraction of the variance in gradients across different countries.
•High unemployment rates at graduation permanently lower income and health.•Education plays a protective role when unemployment rates are high.•Overall losses are smaller for more educated individuals.•Labor market conditions partially explain the variance in education gradients.
Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians ...using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients' experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2-11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients' pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm's ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients' pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.
Using a variety of data sets from two countries, we examine possible explanations for the relationship between education and health behaviors, known as the education gradient. We show that income, ...health insurance, and family background can account for about 30 percent of the gradient. Knowledge and measures of cognitive ability explain an additional 30 percent. Social networks account for another 10 percent. Our proxies for discounting, risk aversion, or the value of future do not account for any of the education gradient, and neither do personality factors such as a sense of control of oneself or over one's life.