Reweighting UK Biobank corrects for pervasive selection bias due to volunteering.

BACKGROUND: Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB. METHODS: Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias. RESULTS: Volunteer bias in all associations, as naively estimated in UKB, was substantial-in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size. CONCLUSIONS: Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available.

Development and validation of a Spanish version of the Obstetric Quality of Recovery-10 item score (ObsQoR-10-Spanish).

Background: Spanish is the second most spoken language globally with around 475 million native speakers. We aimed to validate a Spanish version of the Obstetric Quality of Recovery-10 item (ObsQoR-10) patient-reported outcome measure. Methods: ObsQoR-10-Spanish was developed using EuroQoL methodology. ObsQoR-10-Spanish was assessed in 100 Spanish-speaking patients undergoing elective Caesarean or vaginal delivery. Patients <38 weeks, undergoing an intrapartum Caesarean delivery, intrauterine death, or maternal admission to the intensive care unit (ICU) were excluded. Validity was assessed by evaluating (i) convergent validity-correlation with 24-h EuroQoL and global health visual analogue scale (GHVAS) scores (0-100); (ii) discriminant validity-difference in ObsQoR-10-Spanish score for patients with GHVAS scores >70 vs <70; (iii) hypothesis testing-correlation of ObsQoR score with maternal and neonatal factors; and (iv) cross-cultural validity assessed using differential item functioning analysis. Reliability was assessed by evaluating: (i) internal consistency; (ii) split-half reliability and (iii) test-retest reliability; and (iv) floor and ceiling effects. Results: One hundred patients were approached, recruited, and completed surveys. Validity: (i) convergent validity: the ObsQoR 24-h score correlated moderately with the 24-h EuroQoL (r=-0.632) and GHVAS scores (r=0.590); (ii) discriminant validity: the ObsQoR-10-Spanish 24-h scores were higher in women who delivered vaginally compared to via Caesarean delivery, (mean [standard deviation] scores were 89 [9] vs 81 [12]; P<0.001). The 24-h ObsQoR-Spanish scores were lower in patients experiencing a poor vs a good recovery (mean [standard deviation] scores were 76 [12.3] vs 87.1 [10.6]; P=0.001); (iii) hypothesis testing: the ObsQoR-10 score correlated negatively with age (r=-0.207) and positively with 5-min (r=0.204) and 10-min (r=0.243) Apgar scores. Remaining correlations were not significant; and (iv) differential item functioning analysis suggested no potential bias among the 10 items. Reliability: (i) internal consistency was good (Cronbach alpha=0.763); (ii) split-half reliability was good (Spearman-Brown prophesy reliability estimate of 0.866); (iii) test-retest reliability was excellent with an intra-class correlation coefficient of 0.90; and (iv) floor and ceiling effects: six patients scored a maximum total ObsQoR-10 score. Conclusions: The ObsQoR-10-Spanish patient-reported outcome measure is valid, reliable, and clinically feasible, and should be considered for use in Spanish-speaking women to assess quality of inpatient postpartum recovery.

A graph theory based similarity metric enables comparison of subpopulation psychometric networks.

Network psychometrics leverages pairwise Markov random fields to depict conditional dependencies among a set of psychological variables as undirected edge-weighted graphs. Researchers often intend to compare such psychometric networks across subpopulations, and recent methodological advances provide invariance tests of differences in subpopulation networks. What remains missing, though, is an analogue to an effect size measure that quantifies differences in psychometric networks. We address this gap by complementing recent advances for investigating whether psychometric networks differ with an intuitive similarity measure quantifying the extent to which networks differ. To this end, we build on graph-theoretic approaches and propose a similarity measure based on the Frobenius norm of differences in psychometric networks' weighted adjacency matrices. To assess this measure's utility for quantifying differences between psychometric networks, we study how it captures differences in subpopulation network models implied by both latent variable models and Gaussian graphical models. We show that a wide array of network differences translates intuitively into the proposed measure, while the same does not hold true for customary correlation-based comparisons. In a simulation study on finite-sample behavior, we show that the proposed measure yields trustworthy results when population networks differ and sample sizes are sufficiently large, but fails to identify exact similarity when population networks are the same. From these results, we derive a strong recommendation to only use the measure as a complement to a significant test for network similarity. We illustrate potential insights from quantifying psychometric network similarities through cross-country comparisons of human values networks. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Predicting pediatric healthcare provider use of virtual reality using a technology acceptance model.

Objective: The primary aim of this study was to apply a novel technology acceptance model (TAM) for virtual reality (VR) in healthcare. The secondary aim was to assess reliability of this model to evaluate factors that predict the intentions of pediatric health providers' use of VR as an anxiolytic for hospitalized pediatric patients. Materials and Methods: Healthcare providers that interacted with pediatric patients participated in a VR experience available as anxiolysis for minor procedures and then completed a survey evaluating attitudes, behaviors, and technology factors that influence adoption of new technologies. Results: Reliability for all domain measurements were good, and all confirmatory factor analysis models demonstrated good fit. Usefulness, ease of use, curiosity, and enjoyment of the VR experience all strongly predict intention to use and purchase VR technologies. Age of providers, past use, and cost of technology did not influence future purchase or use, suggesting that VR technologies may be broadly adopted in the pediatric healthcare setting. Discussion: Previous VR-TAM models in non-healthcare consumers formulated that age, past use, price willing to pay, and curiosity impacted perceived ease of use. This study established that age, past use, and cost may not influence use in healthcare. Future studies should be directed at evaluating the social influences and facilitating conditions within healthcare that play a larger influence on technology adoption. Conclusion: The VR-TAM model demonstrated validity and reliability for predicting intent to use VR in a pediatric hospital.

The distribution of child physicians and early academic achievement.

OBJECTIVE: To describe the distribution of pediatricians and family physicians (child physicians) across school districts and examine the association between physician supply and third-grade test scores. DATA SOURCES AND STUDY SETTING: Data come from the January 2020 American Medical Association Physician Masterfile, the 2009-2013 and 2014-2018 waves of American Community Survey 5-Year Data, and the Stanford Education Data Archive (SEDA), which uses test scores from all U.S. public schools. We use covariate data provided by SEDA to describe student populations. STUDY DESIGN: This descriptive analysis constructs a physician-to-child-population ratio for every school district in the country and describes the child population served by the current distribution of physicians. We fit a set of multivariable regression models to estimate the associations between district test score outcomes and district physician supply. Our model includes state fixed effects to control for unobservable state-level factors, as well as a covariate vector of sociodemographic characteristics. DATA COLLECTION: Public data from three sources were matched by district ID. PRINCIPAL FINDINGS: Physicians are highly unequally distributed across districts: nearly 3640 (29.6%) of 12,297 districts have no child physician, which includes 49% of rural districts. Rural children of color in particular have very little access to pediatric care, and this inequality is more extreme when looking exclusively at pediatricians. Districts that have higher child physician supplies tend to have higher academic test scores in early education, independent of community socioeconomic status and racial/ethnic composition. While the national data show this positive relationship (0.012 SD, 95% CI, 0.0103-0.0127), it is most pronounced for districts in the bottom tertile of physician supply (0.163 SD, 95% CI, 0.108-0.219). CONCLUSIONS: Our study demonstrates a highly unequal distribution of child physicians in the U.S., and that children with less access to physicians have lower academic performance in early education.

The Mode Effect of Web-Based Surveying on the 2018 U.S. Health and Retirement Study Measure of Cognitive Functioning.

OBJECTIVES: Measuring cognition in an aging populabtion is a public health priority. A move towards survey measurement via the web (as opposed to phone or in-person) is cost-effective but challenging as it may induce bias in cognitive measures. We examine this possibility using an experiment embedded in the 2018 wave of data collection for the U.S. Health and Retirement Study (HRS). METHODS: We utilize techniques from multiple group item response theory to assess the effect of survey mode on performance on the HRS cognitive measure. We also study the problem of attrition by attempting to predict dropout and via approaches meant to minimize bias in subsequent inferences due to attrition. RESULTS: We find evidence of an increase in scores for HRS respondents who are randomly assigned to the web-based mode of data collection in 2018. Web-based respondents score higher in 2018 than experimentally matched phone-based respondents, and they show much larger gains relative to 2016 performance and subsequently larger declines in 2020. The differential in favor of web-based responding is observed across all items, but is most pronounced for the Serial 7 task and numeracy items. Due to the relative ease of the web-based mode, we suggest a cutscore of 12 being used to indicate CIND (cognitively impaired but not demented) status when using the web-based version rather than 11. DISCUSSION: The difference in mode may be nonignorable for many uses of the HRS cognitive measure. In particular, it may require reconsideration of some cutscore-based approaches to identify impairment.

Ubiquitous bias and false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties.

Studies of interaction effects are of great interest because they identify crucial interplay between predictors in explaining outcomes. Previous work has considered several potential sources of statistical bias and substantive misinterpretation in the study of interactions, but less attention has been devoted to the role of the outcome variable in such research. Here, we consider bias and false discovery associated with estimates of interaction parameters as a function of the distributional and metric properties of the outcome variable. We begin by illustrating that, for a variety of noncontinuously distributed outcomes (i.e., binary and count outcomes), attempts to use the linear model for recovery leads to catastrophic levels of bias and false discovery. Next, focusing on transformations of normally distributed variables (i.e., censoring and noninterval scaling), we show that linear models again produce spurious interaction effects. We provide explanations offering geometric and algebraic intuition as to why interactions are a challenge for these incorrectly specified models. In light of these findings, we make two specific recommendations. First, a careful consideration of the outcome's distributional properties should be a standard component of interaction studies. Second, researchers should approach research focusing on interactions with heightened levels of scrutiny. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Estimating effects of parents' cognitive and non-cognitive skills on offspring education using polygenic scores.

Understanding how parents' cognitive and non-cognitive skills influence offspring education is essential for educational, family and economic policy. We use genetics (GWAS-by-subtraction) to assess a latent, broad non-cognitive skills dimension. To index parental effects controlling for genetic transmission, we estimate indirect parental genetic effects of polygenic scores on childhood and adulthood educational outcomes, using siblings (N = 47,459), adoptees (N = 6407), and parent-offspring trios (N = 2534) in three UK and Dutch cohorts. We find that parental cognitive and non-cognitive skills affect offspring education through their environment: on average across cohorts and designs, indirect genetic effects explain 36-40% of population polygenic score associations. However, indirect genetic effects are lower for achievement in the Dutch cohort, and for the adoption design. We identify potential causes of higher sibling- and trio-based estimates: prenatal indirect genetic effects, population stratification, and assortative mating. Our phenotype-agnostic, genetically sensitive approach has established overall environmental effects of parents' skills, facilitating future mechanistic work.

Social mobility and biological aging among older adults in the United States.

Lower socioeconomic status is associated with faster biological aging, the gradual and progressive decline in system integrity that accumulates with advancing age. Efforts to promote upward social mobility may, therefore, extend healthy lifespan. However, recent studies suggest that upward mobility may also have biological costs related to the stresses of crossing social boundaries. We tested associations of life-course social mobility with biological aging using data from participants in the 2016 Health and Retirement Study (HRS) Venous Blood Study who provided blood-chemistry (n = 9,255) and/or DNA methylation (DNAm) data (n = 3,976). We quantified social mobility from childhood to later-life using data on childhood family characteristics, educational attainment, and wealth accumulation. We quantified biological aging using 3 DNAm "clocks" and 3 blood-chemistry algorithms. We observed substantial social mobility among study participants. Those who achieved upward mobility exhibited less-advanced and slower biological aging. Associations of upward mobility with less-advanced and slower aging were consistent for blood-chemistry and DNAm measures of biological aging, and were similar for men and women and for Black and White Americans (Pearson-r effect-sizes ∼0.2 for blood-chemistry measures and the DNAm GrimAge clock and DunedinPoAm pace-of-aging measures; effect-sizes were smaller for the DNAm PhenoAge clock). Analysis restricted to educational mobility suggested differential effects by racial identity; mediating links between educational mobility and healthy aging may be disrupted by structural racism. In contrast, mobility producing accumulation of wealth appeared to benefit White and Black Americans equally, suggesting economic intervention to reduce wealth inequality may have potential to heal disparities in healthy aging.

Positive associations between cannabis and alcohol use polygenic risk scores and phenotypic opioid misuse among African-Americans.

BACKGROUND: This study examined whether polygenic risk scores (PRS) for lifetime cannabis and alcohol use were associated with misusing opioids, and whether sex differences existed in these relations in an urban, African-American sample. METHODS: Data were drawn from three cohorts of participants (N = 1,103; 45% male) who were recruited in first grade as part of a series of elementary school-based, universal preventive intervention trials conducted in a Mid-Atlantic region of the U.S. In young adulthood, participants provided a DNA sample and reported on whether they had used heroin or misused prescription opioids in their lifetime. Three substance use PRS were computed based on prior GWAS: lifetime cannabis use from Pasman et al. (2018), heavy drinking indexed via maximum number of drinks from Gelernter et al. (2019), and alcohol consumption from Kranzler et al. (2019). RESULTS: Higher PRS for lifetime cannabis use, greater heavy drinking, and greater alcohol consumption were associated with heightened risk for misusing opioids among the whole sample. Significant sex by PRS interactions were also observed such that higher PRS for heavy drinking and alcohol consumption were associated with a greater likelihood of opioid misuse among males, but not females. CONCLUSION: Our findings further elucidate the genetic contributions to misusing opioids by showing that the genetics of cannabis and alcohol consumption are associated with lifetime opioid misuse among young adults, though replication of our findings is needed.

Predictive Fit Metrics for Item Response Models.

The fit of an item response model is typically conceptualized as whether a given model could have generated the data. In this study, for an alternative view of fit, "predictive fit," based on the model's ability to predict new data is advocated. The authors define two prediction tasks: "missing responses prediction"-where the goal is to predict an in-sample person's response to an in-sample item-and "missing persons prediction"-where the goal is to predict an out-of-sample person's string of responses. Based on these prediction tasks, two predictive fit metrics are derived for item response models that assess how well an estimated item response model fits the data-generating model. These metrics are based on long-run out-of-sample predictive performance (i.e., if the data-generating model produced infinite amounts of data, what is the quality of a "model's predictions on average?"). Simulation studies are conducted to identify the prediction-maximizing model across a variety of conditions. For example, defining prediction in terms of missing responses, greater average person ability, and greater item discrimination are all associated with the 3PL model producing relatively worse predictions, and thus lead to greater minimum sample sizes for the 3PL model. In each simulation, the prediction-maximizing model to the model selected by Akaike's information criterion, Bayesian information criterion (BIC), and likelihood ratio tests are compared. It is found that performance of these methods depends on the prediction task of interest. In general, likelihood ratio tests often select overly flexible models, while BIC selects overly parsimonious models. The authors use Programme for International Student Assessment data to demonstrate how to use cross-validation to directly estimate the predictive fit metrics in practice. The implications for item response model selection in operational settings are discussed.

Estimating heritability of height without zygosity information for twins under five years in low- and middle-income countries: An application of normal finite mixture distribution models.

Twin studies are widely used to estimate heritability of traits and typically rely on knowing the zygosity of twin pairs in order to determine variation attributable to genetics. Most twin studies are conducted in high resource settings. Large scale household survey data, such as the Demographic and Health Surveys, collect various biomarkers for children under five years old in low- and middle-income countries. These data include twins but no information on zygosity. We applied mixture models to obtain heritability estimates without knowing zygosity of twins, using 249 Demographic and Health Surveys from 79 low- and middle-income countries (14,524 twin pairs). We focused on height of children, adjusted for age and sex, but also provided estimates for other biomarkers available in the data. We estimated that the heritability of height in our sample was 46%.

The Physiologic and Emotional Effects of 360-Degree Video Simulation on Head-Mounted Display Versus In-Person Simulation: A Noninferiority, Randomized Controlled Trial.

INTRODUCTION: A key simulation component is its capability to elicit physiological changes, improving recall. The primary aim was to determine whether parasympathetic responses to head-mounted display simulations (HMDs) were noninferior to in-person simulations. The secondary aims explored sympathetic and affective responses and learning effectiveness. METHODS: The authors conducted a noninferiority trial. Hospital providers who did not use chronotropic medications, have motion sickness, or have seizures were included. The authors randomized participants to in-person or HMD simulation. Biometric sensors collected respiratory sinus arrhythmia and skin conductance levels to measure parasympathetic and sympathetic states at baseline, during, and after the simulation. Affect was measured using a schedule. The authors measured 3-month recall of learning points and used split-plot analysis of variance and Mann-Whitney U tests to analyze. RESULTS: One hundred fifteen participants qualified, and the authors analyzed 56 in each group. Both groups experienced a significant change in mean respiratory sinus arrhythmia from baseline to during and from during to afterward. The difference of change between the groups from baseline to during was 0.134 (95% confidence interval = 0.142 to 0.410, P = 0.339). The difference of change from during the simulation to after was -0.060 (95% confidence interval = -0.337 to 0.217, P = 0.670). Noninferiority was not established for either period. Sympathetic arousal did not occur in either group. Noninferiority was not established for the changes in affect that were demonstrated. The mean scores of teaching effectiveness and achievement scores were not different. CONCLUSIONS: Although a parasympathetic and affective response to the video simulation on an HMD did occur, it was not discernibly noninferior to in-person in this study.

Innovative methods to analyse the impact of gender norms on adolescent health using global health survey data.

BACKGROUND: Understanding how gender norms affect health is an important entry point into designing programs and policies to change norms and improve gender equality and health. However, it is rare for global health datasets to include questions on gender norms, especially questions that go beyond measuring gender-related attitudes, thus limiting gender analysis. METHODS: We developed five case studies using health survey data from six countries to demonstrate approaches to defining and operationalising proxy measures and analytic approaches to investigating how gender norms can affect health. Key findings, strengths and limitations of our norms proxies and methodological choices are summarised. FINDINGS: Case studies revealed links between gender norms and multiple adolescent health outcomes. Proxys for norms were derived from data on attitudes, beliefs, and behaviours, as well as differences between attitudes and behaviours. Data were cross-sectional, longitudinal, census- and social network-based. Analytic methods were diverse. We found that gender norms affect: 1) Intimate partner violence in Nigeria; 2) Unhealthy weight control behaviours in Brazil and South Africa; 3) HIV status in Zambia; 4) Health and social mobility in the US; and 5) Childbirth in Honduras. INTERPRETATION: Researchers can use existing global health survey data to examine pathways through which gender norms affect health by generating proxies for gender norms. While direct measures of gender norms can greatly improve the understanding of how gender affects health, proxy measures for norms can be designed for the specific health-related outcome and normative context, for instance by either aggregating behaviours or attitudes or quantifying the difference (dissonance) between them. These norm proxies enable evaluations of the influence of gender norms on health and insights into possible reference groups and sanctions for non-compliers, thus informing programmes and policies to shape norms and improve health.

Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction.

Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, [Formula: see text], for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.

Essay content and style are strongly related to household income and SAT scores: Evidence from 60,000 undergraduate applications.

There is substantial evidence of the relationship between household income and achievement on the standardized tests often required for college admissions, yet little comparable inquiry considers the essays typically required of applicants to selective U.S. colleges and universities. We used a corpus of 240,000 admission essays submitted by 60,000 applicants to the University of California in November 2016 to measure relationships between the content of admission essays, self-reported household income, and SAT scores. We quantified essay content using correlated topic modeling and essay style using Linguistic Inquiry and Word Count. We found that essay content and style had stronger correlations to self-reported household income than did SAT scores and that essays explained much of the variance in SAT scores. This analysis shows that essays encode similar information as the SAT and suggests that college admission protocols should attend to how social class is encoded in non-numerical components of applications.

Customizing CAT Administration of the PROMIS Misuse of Prescription Pain Medication Item Bank for Patients with Chronic Pain.

OBJECTIVE: The 22-item PROMIS®-Rx Pain Medication Misuse item bank (Bank-22) imposes a high response burden. This study aimed to characterize the performance of the Bank-22 in a computer adaptive testing (CAT) setting based on varied stopping rules. METHODS: The 22 items were administered to 288 patients. We performed a CAT simulation using default stopping rules (CATPROMIS). In 5 other simulations, a "best health" response rule was added to decrease response burden. This rule stopped CAT administration when a participant selected "never" to a specified number of initial Bank-22 items (2-6 in this study, designated CATAlt2-Alt6). The Bank-22 and 7-item short form (SF-7) scores were compared to scores based on CATPROMIS, and the 5 CAT variations. RESULTS: Bank-22 scores correlated highly with the SF-7 and CATPROMIS, Alt5, Alt6 scores (r=0.87-0.95) and moderately with CATAlt2- Alt4 scores (r=0.63-0.74). In all CAT conditions, the greatest differences with Bank-22 scores were at the lower end of misuse T-scores. The smallest differences with Bank-22 and CATPROMIS scores were observed with CATAlt5 and CATAlt6. Compared to the SF-7, CATAlt5 and CATAlt6 reduced overall response burden by about 42%. Finally, the correlations between PROMIS-Rx Misuse and Anxiety T-scores remained relatively unchanged across the conditions (r=0.31-0.43, Ps < .001). CONCLUSIONS: Applying a stopping rule based on number of initial "best health" responses reduced response burden for respondents with lower levels of misuse. The tradeoff was less measurement precision for those individuals, which could be an acceptable tradeoff when the chief concern is in discriminating higher levels of misuse.

Associations of Loneliness and Social Isolation With Health Span and Life Span in the U.S. Health and Retirement Study.

BACKGROUND: Loneliness and social isolation are emerging public health challenges for aging populations. METHODS: We followed N = 11 302 U.S. Health and Retirement Study participants aged 50-95 from 2006 to 2014 to measure persistence of experiences of loneliness and exposure to social isolation. We tested associations of longitudinal loneliness and social isolation phenotypes with disability, morbidity, mortality, and biological aging through 2018. RESULTS: During follow-up, 18% of older adults met criteria for loneliness, with 6% meeting criteria at 2 or more follow-up assessments. For social isolation, these fractions were 21% and 8%. Health and Retirement Study participants who experienced loneliness and were exposed to social isolation were at increased risk for disease, disability, and mortality. Those experiencing persistent loneliness were at a 57% increased hazard of mortality compared to those who never experienced loneliness. For social isolation, the increase was 28%. Effect sizes were somewhat larger for counts of prevalent activity limitations and somewhat smaller for counts of prevalent chronic diseases. Covariate adjustment for socioeconomic and psychological risks attenuated but did not fully explain associations. Older adults who experienced loneliness and were exposed to social isolation also exhibited physiological indications of advanced biological aging (Cohen's d for persistent loneliness and social isolation = 0.26 and 0.21, respectively). For loneliness, but not social isolation, persistence was associated with increased risk. CONCLUSIONS: Deficits in social connectedness prevalent in a national sample of U.S. older adults were associated with morbidity, disability, and mortality and with more advanced biological aging. Bolstering social connectedness to interrupt experiences of loneliness may promote healthy aging.