Objective Although there is anecdotal evidence of ageism occurring at both the structural level (in which societal institutions reinforce systematic bias against older persons) and individual level (in which older persons take in the negative views of aging of their culture), previous systematic reviews have not examined how both levels simultaneously influence health. Thus, the impact of ageism may be underestimated. We hypothesized that a comprehensive systematic review would reveal that these ageism levels adversely impact the health of older persons across geography, health outcomes, and time. Method A literature search was performed using 14 databases with no restrictions on region, language, and publication type. The systematic search yielded 13,691 papers for screening, 638 for full review, and 422 studies for analyses. Sensitivity analyses that adjusted for sample size and study quality were conducted using standardized tools. The study protocol is registered (PROSPERO CRD42018090857). Results Ageism led to significantly worse health outcomes in 95.5% of the studies and 74.0% of the 1,159 ageism-health associations examined. The studies reported ageism effects in all 45 countries, 11 health domains, and 25 years studied, with the prevalence of significant findings increasing over time (p < .0001). A greater prevalence of significant ageism-health findings was found in less-developed countries than more-developed countries (p = .0002). Older persons who were less educated were particularly likely to experience adverse health effects of ageism. Evidence of ageism was found across the age, sex, and race/ethnicity of the targeters (i.e., persons perpetrating ageism).

Background and Objectives The persistent status of ageism as one of the least acknowledged forms of prejudice may be due in part to an absence of quantifying its costs in economic terms. In this study, we calculated the costs of ageism on health conditions for all persons aged 60 years or older in the United States during 1 year. Research Design and Materials The ageism predictors were discrimination aimed at older persons, negative age stereotypes, and negative self-perceptions of aging. Health care costs of ageism were computed by combining analyses of the impact of the predictors with comprehensive health care spending data in 1 year for the eight most-expensive health conditions, among all Americans aged 60 years or older. As a secondary analysis, we computed the number of these health conditions experienced due to ageism. Results It was found that the 1-year cost of ageism was $63 billion, or one of every seven dollars spent on the 8 health conditions (15.4%), after adjusting for age and sex as well as removing overlapping costs from the three predictors. Also according to our model, ageism resulted in 17.04 million cases of these health conditions. Discussion and Implications This is the first study to identify the economic cost that ageism imposes on health. The findings suggest that a reduction of ageism would not only have a monetary benefit for society, but also have a health benefit for older persons.

Background and Objectives: Falls account for the highest proportion of preventable injury among older adults. Thus, the United States' Centers for Disease Control and Prevention (CDC) developed the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) algorithm to screen for fall risk. We referred to our STEADI algorithm adaptation as "Quick-STEADI" and compared the predictive abilities of the three-level (low, moderate, and high risk) and two-level (at-risk and not at-risk) Quick-STEADI algorithms. We additionally assessed the qualitative implementation of the Quick-STEADI algorithm in clinical settings. Research Design and Methods: We followed a prospective cohort (N = 200) of adults (65+ years) in the Bassett Healthcare Network (Cooperstown, NY) for 6 months in 2019. We conducted a generalized linear mixed model, adjusting for sociodemographic variables, to determine how baseline fall risk predicted subsequent daily falls. We plotted receiver operating characteristic (ROC) curves and measured the area under the curve (AUC) to determine the predictive ability of the Quick-STEADI algorithm. We identified a participant sample (N = 8) to gauge the experience of the screening process and a screener sample (N = 3) to evaluate the screening implementation. Results: For the three-level Quick-STEADI algorithm, participants at low and moderate risk for falls had a reduced likelihood of daily falls compared to those at high risk (−1.09, p = 0.04; −0.99, p = 0.04). For the two-level Quick-STEADI algorithm, participants not at risk for falls were not associated with a reduced likelihood of daily falls compared to those at risk (−0.89, p = 0.13). The discriminatory ability of the three-level and two-level Quick-STEADI algorithm demonstrated similar predictability of daily falls, based on AUC (0.653; 0.6570). Furthermore, participants and screeners found the Quick-STEADI algorithm to be efficient and viable. Discussion and Implications: The Quick-STEADI is a suitable, alternative fall risk screening algorithm. Qualitative assessments of the Quick-STEADI algorithm Mielenz et al. Two-Level vs. Three-Level Falls Screening demonstrated feasibility in integrating a falls screening program in a clinical setting. Future research should address the validation and the implementation of the Quick-STEADI algorithm in community health settings to determine if falls screening and prevention can be streamlined in these settings. This may increase engagement in fall prevention programs and decrease overall fall risk among older adults.

The US older adult population is projected to considerably increase in the future, and continued driving mobility is important for health aspects in populations with fewer transportation alternatives. This study evaluated whether frailty is associated with low-mileage driving (<1865 miles per year) and driving cessation among older adults. Baseline demographics and health data were collected for 2990 older drivers via in-person assessments and questionnaires, with 2964 reporting baseline frailty data. Multivariable log-binomial regression models were used to evaluate the association between baseline frailty status and low-mileage driving. Multivariable Cox proportional hazards regression were used to evaluate the association between baseline frailty status and driving cessation. For every unit increase in frailty, the estimated adjusted risk of driving fewer than 1865 miles/year increased by 138% (adjusted risk ratio: 2.38, 95% CI: 1.63-3.46). Relative to older drivers who were not frail, the adjusted hazard ratios of driving cessation were 4.15 (95% CI: 1.89-9.10) for those classified as prefrail and 6.08 (95% CI: 1.36-27.26) for those classified as frail. Frailty is positively associated with low-mileage driving status and driving cessation in a dose-response fashion. Public health interventions that reduce frailty, such as physical activity, may help older drivers maintain safe and independent mobility.

Background The COVID‐19 pandemic has overrun hospital systems while exacerbating economic hardship and food insecurity on a global scale. In an effort to understand how early action to find and control the virus is associated with cumulative outcomes, we explored how country‐level testing capacity affects later COVID‐19 mortality. Methods We used the Our World in Data database to explore testing and mortality records in 27 countries from December 31, 2019, to September 30, 2020; we applied Cox proportional hazards regression to determine the relationship between early COVID‐19 testing capacity (cumulative tests per case) and later COVID‐19 mortality (time to specified mortality thresholds), adjusting for country‐level confounders, including median age, GDP, hospital bed capacity, population density, and nonpharmaceutical interventions. Results Higher early testing implementation, as indicated by more cumulative tests per case when mortality was still low, was associated with a lower risk for higher per capita deaths. A sample finding indicated that a higher cumulative number of tests administered per case at the time of six deaths per million persons was associated with a lower risk of reaching 15 deaths per million persons, after adjustment for all confounders (HR = 0.909; P = 0.0001). Conclusions Countries that developed stronger COVID‐19 testing capacity at early timepoints, as measured by tests administered per case identified, experienced a slower increase of deaths per capita. Thus, this study operationalizes the value of testing and provides empirical evidence that stronger testing capacity at early timepoints is associated with reduced mortality and improved pandemic control.