Social isolation and loneliness are critical to the health of older adults, but they have not been well-described at the end of life.
To determine the prevalence and correlates of social isolation ...and loneliness among older adults in the last years of life.
Nationally representative, cross-sectional survey.
Health and Retirement Study, 2006-2016 data.
Adults age > 50 interviewed once in the last 4 years of life (n = 3613).
We defined social isolation using a 15-item scale measuring household contacts, social network interaction, and community engagement, and frequent loneliness using the 3-item UCLA Loneliness Scale. We used multivariable logistic regression to determine their adjusted prevalence by time prior-to-death and by subgroups of interest.
Approximately 19% experienced social isolation, 18% loneliness, and 5% both in the last 4 years of life (correlation = 0.11). The adjusted prevalence of social isolation was higher for individuals nearer to death (4 years: 18% vs 0-3 months: 27%, p = 0.05) and there was no significant change in loneliness (4 years: 19% vs 0-3 months: 23%, p = 0.13). Risk factors for both isolation and loneliness included (p < 0.01): low net-worth (Isolation: 34% vs 14%; Loneliness: 29% vs 13%), hearing impairment (Isolation: 26% vs 20%; Loneliness: 26% vs 17%), and difficulty preparing meals (Isolation: 27% vs 19%; Loneliness: 29% vs 15%). Factors associated with loneliness, but not social isolation, included being female, pain, incontinence, and cognitive impairment.
Social isolation and loneliness are common at the end of life, affecting 1 in 4 older adults, but few experience both. Rates were higher for older adults who were poor and experienced functional or sensory impairments. Results can inform clinical efforts to identify and address end-of-life psychosocial suffering and health policies which prioritize social needs at the end of life.
BACKGROUND
Physical frailty is a powerful tool for identifying nondisabled individuals at high risk of adverse outcomes. The extent to which cognitive impairment in those without dementia adds value ...to physical frailty in detecting high‐risk individuals remains unclear.
OBJECTIVES
To estimate the effects of combining physical frailty and cognitive impairment without dementia (CIND) on the risk of basic activities of daily living (ADL) dependence and death over 8 years.
DESIGN
Prospective cohort study.
SETTING
The Health and Retirement Study (HRS).
PARTICIPANTS
A total of 7338 community‐dwelling people, 65 years or older, without dementia and ADL dependence at baseline (2006‐2008). Follow‐up assessments occurred every 2 years until 2014.
MEASUREMENTS
The five components of the Cardiovascular Health Study defined physical frailty. A well‐validated HRS method, including verbal recall, series of subtractions, and backward count task, assessed cognition. Primary outcomes were time to ADL dependence and death. Hazard models, considering death as a competing risk, associated physical frailty and CIND with outcomes after adjusting for sociodemographics, comorbidities, depression, and smoking status.
RESULTS
The prevalence of physical frailty was 15%; CIND, 19%; and both deficits, 5%. In unadjusted and adjusted analyses, combining these factors identified older adults at an escalating risk for ADL dependence (no deficit = 14% reference group; only CIND = 26%, sub–hazard ratio sHR = 1.5, 95% confidence interval CI = 1.3–1.8; only frail = 33%, sHR = 1.7, 95% CI = 1.4–2.0; both deficits = 46%, sHR = 2.0, 95%CI = 1.6–2.6) and death (no deficit = 21%; only CIND = 41%, HR = 1.6, 95% CI = 1.4–1.9; only frail = 56%, HR = 2.2, 95% CI = 1.7–2.7; both deficits = 66%, HR = 2.6, 95% CI = 2.0–3.3) over 8‐year follow‐up. Adding the cognitive measure to models that already included physical frailty alone increased accuracy in identifying those at higher risk of ADL dependence (Harrell's concordance C, 0.74 vs 0.71; P < .001) and death (Harrell's C, 0.70 vs 0.67; P < .001).
CONCLUSION
Physical frailty and CIND are independent predictors of incident disability and death. Because together physical frailty and CIND identify vulnerable older adults better, optimal risk assessment should supplement measures of physical frailty with measures of cognitive function. J Am Geriatr Soc 67:477–483, 2019.
Objectives
To develop a prediction index for 1‐year mortality after hip fracture in older adults that includes predictors from a wide range of domains.
Design
Retrospective cohort study.
Settings
...Health and Retirement Study (HRS).
Participants
HRS participants who experienced hip fracture between 1992 and 2010 as identified according to Medicare claims data (N = 857).
Measurements
Outcome measure was death within 1 year of hip fracture. Predictor measures were participant demographic characteristics, socioeconomic status, social support, health, geriatric symptoms, and function. Variables independently associated with 1‐year mortality were identified, and best‐subsets regression was used to identify the final model. The selected variables were weighted to create a risk index. The index was internally validated using bootstrapping to estimate model optimism.
Results
Mean age at time of hip fracture was 84, and 76% of the participants were women. There were 235 deaths (27%) during the 1‐year follow up. Five predictors of mortality were included in the final model: aged 90 and older (2 points), male sex (2 points), congestive heart failure (2 points), difficulty preparing meals (2 points), and not being able to drive (1 point). The point scores of the index were associated with 1‐year mortality, with 0 points predicting 10% risk and 7 to 9 points predicting 66% risk. The c‐statistic for the final model was 0.73, with an estimated optimism penalty of 0.01, indicating very little evidence of overfitting.
Conclusion
The prognostic index combines demographic, comorbidity, and function variables and can be used to differentiate between individuals at low and high risk of 1‐year mortality after hip fracture.
Social isolation is common in the last years of life, especially among individuals with cognitive impairment (CI), but it is unknown if social isolation is related to end-of-life health care use.
We ...used nationally-representative 2006-2018 Health and Retirement Study (HRS) data linked to Medicare claims, including adults age ≥65 interviewed in the last 4 years of life (N = 2380). We used a validated social isolation scale and three social isolation subscales: (1) household contacts (marital status, household size, nearby children), (2) social network interaction (with children, family, and friends), and (3) community engagement. End-of-life health outcomes included 2+ emergency department (ED) visits in the last month of life, hospitalizations or ICU stays in the last 6 months of life, and any hospice use. CI was defined using the validated Langa-Weir methodology. We used logistic regression to test the association of each social isolation measure with each end-of-life outcome, adjusted for sociodemographic covariates, and tested for interaction terms with CI (p < 0.2).
The mean age of our sample of decedents was 81.2 (SD = 9.9), 53% were female, 8% Black, and 4% Hispanic. Overall, social isolation and the community engagement subscale were not associated with end-of-life health care use. Fewer household contacts were associated with lower hospice use (aOR = 0.74, p = 0.005). There were significant interaction terms between the social network interaction subscale and CI for ED use (p = 0.009) and hospitalizations (p = 0.04), and a trend for ICU stays (p = 0.15); individuals with both low social network interaction and CI had lower health care use across all three outcomes compared to other groups.
Individuals with fewer household contacts had lower hospice use, and cognitively impaired individuals with low social network interaction had fewer end-of-life ED visits, hospitalizations, and ICU stays. Clinicians should consider mobilizing external support services to ensure access to goal-concordant care for older adults with limited end-of-life social contact when needed.
Background
Hypertension treatment in older adults can decrease mortality, cardiovascular events, including heart failure, cognitive impairment, and stroke risk, but may also lead to harms such as ...syncope and falls. Guidelines recommend targeting preventive interventions with immediate harms and delayed benefits to patients whose life expectancy exceeds the intervention's time to benefit (TTB). Our objective was to estimate a meta‐analyzed TTB for stroke prevention after initiation of more intensive hypertension treatment in adults aged ≥65 years.
Methods
Studies were identified from two Cochrane systematic reviews and a search of MEDLINE and Google Scholar for subsequent publications until August 31, 2021. We ed data from randomized controlled trials comparing standard (untreated, placebo, or less intensive treatment) to more intensive treatment groups in older adults (mean age ≥ 65 years). We fit Weibull survival curves and used a random‐effects model to estimate the pooled annual absolute risk reduction (ARR) between control and intervention groups. We applied Markov chain Monte Carlo methods to determine the time to ARR thresholds (0.002, 0.005, and 0.01) for a first stroke.
Results
Nine trials (n = 38,779) were identified. The mean age ranged from 66 to 84 years and study follow‐up times ranged from 2.0 to 5.8 years. We determined that 1.7 (95%CI: 1.0–2.9) years were required to prevent 1 stroke for 200 persons (ARR = 0.005) receiving more intensive hypertensive treatment. Heterogeneity was found across studies, with those focusing on tighter systolic blood pressure control (SBP < 150 mmHg) showing longer TTB. For example, in the SPRINT study (baseline SBP = 140 mmHg, achieved SBP = 121 mmHg), the TTB to avoid 1 stroke for 200 patients treated was 5.9 years (95%CI: 2.2–13.0).
Conclusions
More intensive hypertension treatment in 200 older adults prevents 1 stroke after 1.7 years. Given the heterogeneity across studies, the TTB estimates from individual studies may be more relevant for clinical decision‐making than our summary estimate.
See related Editorial by Mark A. Supiano in this issue.
Objectives
To comprehensively examine factors associated with long‐term nursing home (NH) entry from 6 domains of older adult and family caregiver risk from nationally representative surveys and ...develop a prognostic model and a simple scoring system for use in risk stratification.
Design
Retrospective observational study.
Setting
National Long‐Term Care Surveys 1999 and 2004 and National Health and Aging Trends Study 2011 and linked caregiver surveys.
Participants
Community‐living older adults receiving help with self‐care disability and their primary family or unpaid caregiver (N=2,676).
Measurements
Prediction of long‐term NH entry (>100 days or ending in death) by 24 months follow up, ascertained from Minimum Data Set assessments and dates of death from Medicare enrollment files. Risk factors were measured from survey responses.
Results
In total, 16.1% of older adults entered a NH. Our final model and risk scoring system includes 7 independent risk factors: older adult age (1 point/5 years), living alone (5 points), dementia (3 points), 3 or more of 6 self‐care activities (2 points), caregiver age (45–64: 1 point, 65–74: 2 points, ≥75: 4 points), caregiver help with money management (2 points), and caregiver report of moderate (2 points) or high (4 points) strain. Using this model, participants were assigned to risk quintiles. Long‐term NH entry was 7.0% in the lowest quintile (0–6 points), 20.4% in the middle 3 quintiles (7–14 points), and 30.9% in the highest quintile (15–22 points). The model was well calibrated and demonstrated moderate discrimination (c‐statistic=0.670 in the original data, c‐statistic=0.647 in bootstrapped samples, c‐statistic=0.652 using the point‐scoring system).
Conclusion
We developed a prognostic model and simple scoring system that may be used to stratify risk of long‐term NH entry of community‐living older adults. Our model may be useful for population health and policy applications.
See related editorial by Vincent Mor et al
Guidelines recommend targeting preventive interventions toward older adults whose life expectancy is greater than the intervention's time to benefit (TTB). The TTB for statin therapy is unknown.
To ...conduct a survival meta-analysis of randomized clinical trials of statins to determine the TTB for prevention of a first major adverse cardiovascular event (MACE) in adults aged 50 to 75 years.
Studies were identified from previously published systematic reviews (Cochrane Database of Systematic Reviews and US Preventive Services Task Force) and a search of MEDLINE and Google Scholar for subsequently published studies until February 1, 2020.
Randomized clinical trials of statins for primary prevention focusing on older adults (mean age >55 years).
Two authors independently abstracted survival data for the control and intervention groups. Weibull survival curves were fit, and a random-effects model was used to estimate pooled absolute risk reductions (ARRs) between control and intervention groups each year. Markov chain Monte Carlo methods were applied to determine time to ARR thresholds.
The primary outcome was time to ARR thresholds (0.002, 0.005, and 0.010) for a first MACE, as defined by each trial. There were broad similarities in the definition of MACE across trials, with all trials including myocardial infarction and cardiovascular mortality.
Eight trials randomizing 65 383 adults (66.3% men) were identified. The mean age ranged from 55 to 69 years old and the mean length of follow-up ranged from 2 to 6 years. Only 1 of 8 studies showed that statins decreased all-cause mortality. The meta-analysis results suggested that 2.5 (95% CI, 1.7-3.4) years were needed to avoid 1 MACE for 100 patients treated with a statin. To prevent 1 MACE for 200 patients treated (ARR = 0.005), the TTB was 1.3 (95% CI, 1.0-1.7) years, whereas the TTB to avoid 1 MACE for 500 patients treated (ARR = 0.002) was 0.8 (95% CI, 0.5-1.0) years.
These findings suggest that treating 100 adults (aged 50-75 years) without known cardiovascular disease with a statin for 2.5 years prevented 1 MACE in 1 adult. Statins may help to prevent a first MACE in adults aged 50 to 75 years old if they have a life expectancy of at least 2.5 years. There is no evidence of a mortality benefit.
Abstract Background Our objective in this study was to develop a point-based tool to predict conversion from amnestic mild cognitive impairment (MCI) to probable Alzheimer's disease (AD). Methods ...Subjects were participants in the first part of the Alzheimer's Disease Neuroimaging Initiative. Cox proportional hazards models were used to identify factors associated with development of AD, and a point score was created from predictors in the final model. Results The final point score could range from 0 to 9 (mean 4.8) and included: the Functional Assessment Questionnaire (2‒3 points); magnetic resonance imaging (MRI) middle temporal cortical thinning (1 point); MRI hippocampal subcortical volume (1 point); Alzheimer's Disease Cognitive Scale—cognitive subscale (2‒3 points); and the Clock Test (1 point). Prognostic accuracy was good (Harrell's c = 0.78; 95% CI 0.75, 0.81); 3-year conversion rates were 6% (0‒3 points), 53% (4‒6 points), and 91% (7‒9 points). Conclusions A point-based risk score combining functional dependence, cerebral MRI measures, and neuropsychological test scores provided good accuracy for prediction of conversion from amnestic MCI to AD.
Background:
Older black and Latino Americans are more likely than white Americans to die in the hospital. Whether ethnic differences in expectation of death account for this disparity is unknown.
...Objectives:
To determine whether surviving family members’ expectation of death has a differential association with site of death according to race or ethnicity.
Methods:
We conducted an analysis of decedents from the Health and Retirement Study, a nationally representative study of US older adults. Telephone surveys were conducted with family members for 5979 decedents (decedents were 55% were women, 85% white, 9% black, and 6% Latino). The outcome of interest was death in the hospital; the predictor variable was race/ethnicity, and the intervening variable was expectation of death. Covariates included sociodemographics (gender, age, household net worth, educational attainment level, religion) and health factors (chronic conditions, symptoms, health-care utilization).
Results:
Decedents’ race/ethnicity was statistically related to the expectation of death and death in the hospital. When death was not expected, whites and Latinos were more likely to die in the hospital than when death was expected (49% vs 29% for whites and 55% vs 37% for Latinos; P < .001). There was no difference in site of death according to family’s expectation of death among blacks.
Conclusion:
Expectation of death did not fully account for site of death and played a greater role among whites and Latinos than among black Americans. Discussing prognosis by itself is unlikely to address ethnic disparities. Other factors appear to play an important role as well.
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