BACKGROUND/OBJECTIVES: Frailty, loneliness, and social isolation are all associated with adverse outcomes in older adults, but little is known about their combined impact on mortality. DESIGN: Prospective cohort study. SETTING: The Longitudinal Aging Study Amsterdam. PARTICIPANTS: Community-dwelling older adults aged 65 and older (n = 1,427). MEASUREMENTS: Frailty was measured with the frailty phenotype (Fried criteria). Loneliness was assessed with the De Jong Gierveld Loneliness Scale. Social isolation was operationalized using information on partner status, social support, and network size. Two categorical variables were created, for each possible combination regarding frailty and loneliness (FL) and frailty and social isolation (FS), respectively. Mortality was monitored over a period of 22 years (1995-2017). Survival curves and Cox proportional hazard models were used to study the effects of the FL and FS combinations on mortality. Analyses were adjusted for sociodemographic factors, depression, chronic diseases, and smoking. RESULTS: Frailty prevalence was 13%, and 5.9% of the sample were frail and lonely, and 6.2% frail and socially isolated. In fully adjusted models, older adults who were only frail had a higher risk of mortality compared with people without any of the conditions (hazard ratio [HR] range = 1.40-1.48; P < .01). However, the highest risk of mortality was observed in people with a combined presence of frailty and loneliness or social isolation (HR FL = 1.83; 95% confidence interval [CI] = 1.42-2.37; HR FS = 1.77; 95% CI = 1.36-2.30). Sensitivity analyses using a frailty index based on the deficit accumulation approach instead of the frailty phenotype showed similar results, confirming the robustness of our findings. CONCLUSION: Frail older adults are at increased risk of mortality, but this risk is even higher for those who are also lonely or socially isolated. To optimize well-being and health outcomes in physically frail older adults, targeted interventions focusing on both subjective and objective social vulnerability are needed.
Purpose Delay of routine medical care during the COVID-19 pandemic may have serious consequences for the health and functioning of older adults. The aim of this study was to investigate whether older adults reported cancellation or avoidance of medical care during the first months of the COVID-19 pandemic, and to explore associations with health and socio-demographic characteristics. Methods Cross-sectional data of 880 older adults aged ≥ 62 years (mean age 73.4 years, 50.3% female) were used from the COVID-19 questionnaire of the Longitudinal Aging Study Amsterdam, a cohort study among community-dwelling older adults in the Netherlands. Cancellation and avoidance of care were assessed by self-report, and covered questions on cancellation of primary care (general practitioner), cancellation of hospital outpatient care, and postponed help-seeking. Respondent characteristics included age, sex, educational level, loneliness, depression, anxiety, frailty, multimorbidity and information on quarantine. Results 35% of the sample reported cancellations due to the COVID-19 situation, either initiated by the respondent (12%) or by healthcare professionals (29%). Postponed help-seeking was reported by 8% of the sample. Multimorbidity was associated with healthcare-initiated cancellations (primary care OR = 1.92, 95% CI = 1.09–3.50; hospital OR = 1.86, 95% CI = 1.28–2.74) and respondent-initiated hospital outpatient cancellations (OR = 2.02, 95% CI = 1.04–4.12). Depressive symptoms were associated with postponed help-seeking (OR = 1.15, 95% CI = 1.06–1.24). Conclusion About one third of the study sample reported cancellation or avoidance of medical care during the first months of the pandemic, and this was more common among those with multiple chronic conditions. How this impacts outcomes in the long term should be investigated in future research.
Background Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias. Methods A Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects. Results The simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect. Conclusion In logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model.
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