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Ageism, Healthy Life Expectancy and Population Ageing: How Are They Related?

Alana OfficerJotheeswaran Amuthavalli ThiyagarajanMira Leonie SchneidersPaul NashVânia de la Fuente-Núñez
Published in: International journal of environmental research and public health (2020)
Evidence shows that ageism negatively impacts the health of older adults. However, estimates of its prevalence are lacking. This study aimed to estimate the global prevalence of ageism towards older adults and to explore possible explanatory factors. Data were included from 57 countries that took part in Wave 6 of the World Values Survey. Multilevel Latent Class Analysis was performed to identify distinct classes of individuals and countries. Individuals were classified as having high, moderate or low ageist attitudes; and countries as being highly, moderately or minimally ageist, by aggregating individual responses. Individual-level (age, sex, education and wealth) and contextual-level factors (healthy life expectancy, population health status and proportion of the population aged over 60 years) were examined as potential explanatory factors in multinomial logistic regression. From the 83,034 participants included, 44%, 32% and 24% were classified as having low, moderate and high ageist attitudes, respectively. From the 57 countries, 34 were classified as moderately or highly ageist. The likelihood of an individual or a country being ageist was significantly reduced by increases in healthy life expectancy and the proportion of older people within a country. Certain personal characteristics-younger age, being male and having lower education-were significantly associated with an increased probability of an individual having high ageist attitudes. At least one in every two people included in this study had moderate or high ageist attitudes. Despite the issue's magnitude and negative health impacts, ageism remains a neglected global health issue.
Keyphrases
  • mental health
  • healthcare
  • public health
  • global health
  • physical activity
  • high intensity
  • risk factors
  • health information
  • machine learning
  • health promotion
  • climate change
  • big data