Dataset Information

Aging without driving: evidence from the Health And Retirement Study, 1993 to 2008.

ABSTRACT: This study characterized older adults who do not drive (former and never drivers) and examined how this group of elders has changed over the past 15 years. Sample included community-living adults aged 70-85 who do not drive from the 1993 Asset and Health Dynamics Among the Oldest Old Study (N = 1,979) and 2008 Health and Retirement Study (N = 1,119). Chi-square and t-tests were used to assess differences between never and former drivers and between cohorts. Logistic regression was used to examine the predictors of having never driven. The driving status among older adults has improved over the past 15 years as the proportion of never drivers declined from 11% to 2%. However, non-driving has become more concentrated among ethnic minority women, and the gaps in education and net worth between former and never drivers widened over the 15 years.

SUBMITTER: Choi M

PROVIDER: S-EPMC4029065 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Aging without driving: evidence from the Health And Retirement Study, 1993 to 2008.

Choi Moon M Mezuk Briana B

Journal of applied gerontology : the official journal of the Southern Gerontological Society 20131001 7

This study characterized older adults who do not drive (former and never drivers) and examined how this group of elders has changed over the past 15 years. Sample included community-living adults aged 70-85 who do not drive from the 1993 Asset and Health Dynamics Among the Oldest Old Study (N = 1,979) and 2008 Health and Retirement Study (N = 1,119). Chi-square and t-tests were used to assess differences between never and former drivers and between cohorts. Logistic regression was used to examin ...[more]

PMID: 24860237

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Project description:BackgroundAs our society ages, improving medical care for an older population will be crucial. Discrimination in healthcare may contribute to substandard experiences with the healthcare system, increasing the burden of poor health in older adults. Few studies have focused on the presence of healthcare discrimination and its effects on older adults.ObjectiveWe aimed to examine the relationship between healthcare discrimination and new or worsened disability.DesignThis was a longitudinal analysis of data from the nationally representative Health and Retirement Study administered in 2008 with follow-up through 2012.ParticipantsSix thousand and seventeen adults over the age of 50 years (mean age 67 years, 56.3 % female, 83.1 % white) were included in this study.Main measuresHealthcare discrimination assessed by a 2008 report of receiving poorer service or treatment than other people by doctors or hospitals (never, less than a year=infrequent; more than once a year=frequent). Outcome was self-report of new or worsened disability by 2012 (difficulty or dependence in any of six activities of daily living). We used a Cox proportional hazards model adjusting for age, race/ethnicity, gender, net worth, education, depression, high blood pressure, diabetes, cancer, lung disease, heart disease, stroke, and healthcare utilization in the past 2 years.Key resultsIn all, 12.6 % experienced discrimination infrequently and 5.9 % frequently. Almost one-third of participants (29 %) reporting frequent healthcare discrimination developed new or worsened disability over 4 years, compared to 16.8 % of those who infrequently and 14.7 % of those who never experienced healthcare discrimination (p < 0.001). In multivariate analyses, compared to no discrimination, frequent healthcare discrimination was associated with new or worsened disability over 4 years (aHR = 1.63, 95 % CI 1.16-2.27).ConclusionsOne out of five adults over the age of 50 years experiences discrimination in healthcare settings. One in 17 experience frequent healthcare discrimination, and this is associated with new or worsened disability by 4 years. Future research should focus on the mechanisms by which healthcare discrimination influences disability in older adults to promote better health outcomes for an aging population.

Project description:BackgroundAn individual's rate of aging directly influences his/her susceptibility to morbidity and mortality. Thus, quantifying aging and disentangling how various factors coalesce to produce between-person differences in the rate of aging, have important implications for potential interventions. We recently developed and validated a novel multi-system-based aging measure, Phenotypic Age (PhenoAge), which has been shown to capture mortality and morbidity risk in the full US population and diverse subpopulations. The aim of this study was to evaluate associations between PhenoAge and a comprehensive set of factors, including genetic scores, childhood and adulthood circumstances, and health behaviors, to determine the relative contributions of these factors to variance in this aging measure.Methods and findingsBased on data from 2,339 adults (aged 51+ years, mean age 69.4 years, 56% female, and 93.9% non-Hispanic white) from the US Health and Retirement Study, we calculated PhenoAge and evaluated the multivariable associations for a comprehensive set of factors using 2 innovative approaches-Shapley value decomposition (the Shapley approach hereafter) and hierarchical clustering. The Shapley approach revealed that together all 11 study domains (4 childhood and adulthood circumstances domains, 5 polygenic score [PGS] domains, and 1 behavior domain, and 1 demographic domain) accounted for 29.2% (bootstrap standard error = 0.003) of variance in PhenoAge after adjustment for chronological age. Behaviors exhibited the greatest contribution to PhenoAge (9.2%), closely followed by adulthood adversity, which was suggested to contribute 9.0% of the variance in PhenoAge. Collectively, the PGSs contributed 3.8% of the variance in PhenoAge (after accounting for chronological age). Next, using hierarchical clustering, we identified 6 distinct subpopulations based on the 4 childhood and adulthood circumstances domains. Two of these subpopulations stood out as disadvantaged, exhibiting significantly higher PhenoAges on average. Finally, we observed a significant gene-by-environment interaction between a previously validated PGS for coronary artery disease and the seemingly most disadvantaged subpopulation, suggesting a multiplicative effect of adverse life course circumstances coupled with genetic risk on phenotypic aging. The main limitations of this study were the retrospective nature of self-reported circumstances, leading to possible recall biases, and the unrepresentative racial/ethnic makeup of the population.ConclusionsIn a sample of US older adults, genetic, behavioral, and socioenvironmental circumstances during childhood and adulthood account for about 30% of differences in phenotypic aging. Our results also suggest that the detrimental effects of disadvantaged life course circumstances for health and aging may be further exacerbated among persons with genetic predisposition to coronary artery disease. Finally, our finding that behaviors had the largest contribution to PhenoAge highlights a potential policy target. Nevertheless, further validation of these findings and identification of causal links are greatly needed.

Dataset Information

Aging without driving: evidence from the Health And Retirement Study, 1993 to 2008.

Publications

Aging without driving: evidence from the Health And Retirement Study, 1993 to 2008.

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