United we thrive: friendship and subsequent physical, behavioural and psychosocial health in older adults (an outcome-wide longitudinal approach).

AIMS: Three factors converge to underscore the heightened importance of evaluating the potential health/well-being effects of friendships in older adulthood. First, policymakers, scientists, and the public alike are recognizing the importance of social relationships for health/well-being and creating national policies to promote social connection. Second, many populations are rapidly aging throughout the world. Third, we currently face what some call a 'friendship recession'. Although, growing research documents associations between friendship with better health and well-being, friendship can also have a 'dark side' and can potentially promote negative outcomes. To better capture friendship's potential heterogeneous effects, we took an outcome-wide analytic approach. METHODS: We analysed data from 12,998 participants in the Health and Retirement Study (HRS) - a prospective and nationally representative cohort of U.S. adults aged >50, and, evaluated if increases in friendship strength (between t0; 2006/2008 and t1; 2010/2012) were associated with better health/well-being across 35 outcomes (in t2; 2014/2016). To assess friendship strength, we leveraged all available friendship items in HRS and created a composite 'friendship score' that assessed the following three domains: (1) friendship network size, (2) friendship network contact frequency and (3) friendship network quality. RESULTS: Stronger friendships were associated with better outcomes on some indicators of physical health (e.g. reduced risk of mortality), health behaviours (e.g. increased physical activity) and nearly all psychosocial indicators (e.g. higher positive affect and mastery, as well as lower negative affect and risk of depression). Friendship was also associated with increased likelihood of smoking and heavy drinking (although the latter association with heavy drinking did not reach conventional levels of statistical significance). CONCLUSIONS: Our findings indicate that stronger friendships can have a dual impact on health and well-being. While stronger friendships appear to mainly promote a range of health and well-being outcomes, stronger friendships might also promote negative outcomes. Additional research is needed, and any future friendship interventions and policies that aim to enhance outcomes should focus on how to amplify positive outcomes while mitigating harmful ones.

The road to postpandemic recovery in the USA: a repeated cross-sectional survey of multidimensional well-being over two years.

OBJECTIVES: Examine differences in multidimensional well-being from before (January 2020) to three timepoints during the COVID-19 pandemic (June 2020, January 2021, January 2022). STUDY DESIGN: Repeated cross-sectional design. METHODS: Nationally representative cross-sectional cohorts of US adults completed the Secure Flourish Index before (January 2020 cohort: N = 1010) and during the COVID-19 pandemic (June 2020 cohort: N = 3020; January 2021 cohort: N = 3366; January 2022 cohort: N = 2598). We estimated differences in indicators, domains, and composite well-being between the January 2020 cohort and each of the subsequent cohorts. We also explored whether changes in well-being between January 2020 and January 2022 varied based on age, gender, and race/ethnicity. RESULTS: Initial declines in well-being observed by June 2020 were largely followed by a return to prepandemic levels in January 2022, with some exceptions. Notably, general declines in mental health have persisted through to January 2022. On the other hand, there was evidence of general improvements in character & virtue that exceeded prepandemic levels in January 2022. Young adults and racial/ethnic minorities reported lower financial & material stability in January 2022 compared to before the COVID-19 pandemic. CONCLUSIONS: Although there are promising signs that the well-being of US adults has mostly recovered to prepandemic levels, a coordinated response is urgently needed to support population mental health and the financial security of vulnerable groups. As society continues the journey toward postpandemic recovery, continued tracking of multidimensional well-being will be important for making informed decisions about public health priorities.

Instrumental variables as bias amplifiers with general outcome and confounding.

Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. This belief has had a huge impact on practical causal inference, suggesting that we should adjust for all pretreatment covariates. However, when there is unmeasured confounding between the treatment and outcome, estimators adjusting for some pretreatment covariate might have greater bias than estimators without adjusting for this covariate. This kind of covariate is called a bias amplifier, and includes instrumental variables that are independent of the confounder, and affect the outcome only through the treatment. Previously, theoretical results for this phenomenon have been established only for linear models. We fill in this gap in the literature by providing a general theory, showing that this phenomenon happens under a wide class of models satisfying certain monotonicity assumptions. We further show that when the treatment follows an additive or multiplicative model conditional on the instrumental variable and the confounder, these monotonicity assumptions can be interpreted as the signs of the arrows of the causal diagrams.

Quantile causal mediation analysis allowing longitudinal data.

Mediation analysis has mostly been conducted with mean regression models. With this approach modeling means, formulae for direct and indirect effects are based on changes in means, which may not capture effects that occur in units at the tails of mediator and outcome distributions. Individuals with extreme values of medical endpoints are often more susceptible to disease and can be missed if one investigates mean changes only. We derive the controlled direct and indirect effects of an exposure along percentiles of the mediator and outcome using quantile regression models and a causal framework. The quantile regression models can accommodate an exposure-mediator interaction and random intercepts to allow for longitudinal mediator and outcome. Because DNA methylation acts as a complex "switch" to control gene expression and fibrinogen is a cardiovascular factor, individuals with extreme levels of these markers may be more susceptible to air pollution. We therefore apply this methodology to environmental data to estimate the effect of air pollution, as measured by particle number, on fibrinogen levels through a change in interferon-gamma (IFN-γ) methylation. We estimate the controlled direct effect of air pollution on the qth percentile of fibrinogen and its indirect effect through a change in the pth percentile of IFN-γ methylation. We found evidence of a direct effect of particle number on the upper tail of the fibrinogen distribution. We observed a suggestive indirect effect of particle number on the upper tail of the fibrinogen distribution through a change in the lower percentiles of the IFN-γ methylation distribution.

Neurodevelopmental outcomes in children in relation to placental abruption.

OBJECTIVE: Placental abruption has a profound impact on perinatal mortality, but implications for neurodevelopment during childhood remain unknown. We examined the association between abruption and neurodevelopment at 8 months and 4 and 7 years and evaluated the extent to which these associations were mediated through preterm delivery. DESIGN: Secondary analysis of a multicenter prospective cohort study. SETTING: Multicenter US National Collaborative Perinatal Project (1959-76). POPULATION: Women that delivered singleton live births. METHODS: Analyses of IQ scores were based on marginal structural models (MSM) to account for losses to follow-up. We also carried out a causal mediation analysis to evaluate if the association between abruption and cognitive deficits was mediated through preterm delivery, and performed a sensitivity analysis for unobserved confounding. MAIN OUTCOME MEASURES: We evaluated cognitive development based on the Bayley scale at 8 months (Mental and Motor Scores), and intelligent quotient (IQ) based on the Stanford-Binet scale at 4 years and the Wechsler Intelligence Scale for Children at 7 years. RESULTS: The confounder and selection-bias adjusted risk ratio (RR) of abnormal 8-month Motor and Mental assessments were 2.35 (95%CI 1.39, 3.98) and 2.03 (95%CI 1.13, 3.64), respectively, in relation to abruption. The associations at 4 years were attenuated and resolved at 7 years. The proportion of children with abruption-associated neurological deficits mediated through preterm delivery ranged from 27 to 75%. Following adjustment for unobserved confounding the proportion mediated through preterm delivery was attenuated. CONCLUSION: The effect of abruption on neurodevelopmental outcomes appears restricted to an effect that is largely mediated through preterm delivery. TWEETABLE ABSTRACT: Increased risk of cognitive deficits in relation to abruption appears to be mediated through preterm delivery.

Causal mediation analysis for longitudinal data with exogenous exposure.

Mediation analysis is a valuable approach to examine pathways in epidemiological research. Prospective cohort studies are often conducted to study biological mechanisms and often collect longitudinal measurements on each participant. Mediation formulae for longitudinal data have been developed. Here, we formalize the natural direct and indirect effects using a causal framework with potential outcomes that allows for an interaction between the exposure and the mediator. To allow different types of longitudinal measures of the mediator and outcome, we assume two generalized mixed-effects models for both the mediator and the outcome. The model for the mediator has subject-specific random intercepts and random exposure slopes for each cluster, and the outcome model has random intercepts and random slopes for the exposure, the mediator, and their interaction. We also expand our approach to settings with multiple mediators and derive the mediated effects, jointly through all mediators. Our method requires the absence of time-varying confounding with respect to the exposure and the mediator. This assumption is achieved in settings with exogenous exposure and mediator, especially when exposure and mediator are not affected by variables measured at earlier time points. We apply the methodology to data from the Normative Aging Study and estimate the direct and indirect effects, via DNA methylation, of air pollution, and temperature on intercellular adhesion molecule 1 (ICAM-1) protein levels. Our results suggest that air pollution and temperature have a direct effect on ICAM-1 protein levels (i.e. not through a change in ICAM-1 DNA methylation) and that temperature has an indirect effect via a change in ICAM-1 DNA methylation.

Mediation Analysis with Multiple Mediators.

Recent advances in the causal inference literature on mediation have extended traditional approaches to direct and indirect effects to settings that allow for interactions and non-linearities. In this paper, these approaches from causal inference are further extended to settings in which multiple mediators may be of interest. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects through other pathways. The approaches proposed here accommodate exposure-mediator interactions and, to a certain extent, mediator-mediator interactions as well. The methods handle binary or continuous mediators and binary, continuous or count outcomes. When the mediators affect one another, the strategy of trying to assess direct and indirect effects one mediator at a time will in general fail; the approach given in this paper can still be used. A characterization is moreover given as to when the sum of the mediated effects for multiple mediators considered separately will be equal to the mediated effect of all of the mediators considered jointly. The approach proposed in this paper is robust to unmeasured common causes of two or more mediators.

Direct and indirect effects for neighborhood-based clustered and longitudinal data.

Definitions of direct and indirect effects are given for settings in which individuals are clustered in groups or neighborhoods and in which treatments are administered at the group level. A particular intervention may affect individual outcomes both through its effect on the individual and by changing the group or neighborhood itself. Identification conditions are given for controlled direct effects and for natural direct and indirect effects. The interpretation of these identification conditions are discussed within the context of neighborhood research and multilevel modeling. Interventions at a single point in time and time-varying interventions are both considered. The definition of direct and indirect effects requires certain stability or no-interference conditions; some discussion is given as to how these no-interference conditions can be relaxed.

Maternal-infant separation at birth among substance using pregnant women: implications for attachment.

The potential for substance use to interfere with initial maternal-infant contact and attachment was examined. Data were collected from confidential interviews with pregnant women and reviews of the medical charts of these women and their infants. Substance users (N = 60) were found to experience significantly (p = .03) more maternal-infant separation at birth than nonusers (N = 22), most often due to health concerns for mother and/or infant. Substance using mothers scored negatively more frequently than nonusing mothers on variables of attachment. Reasons for these differences are explored and some strategies for dealing with this population in the hospital milieu are suggested.

Delayed discharges for medical and surgical patients in an acute care hospital.

A study of social work-coordinated discharges from medical and surgical units at a 387-bed tertiary care hospital in Seattle, Washington examines social, medical and organizational factors associated with overstays. Social and medical variables included an absence of social supports, multiple diagnoses, and patient/family decision-making problems. Though organizational factors within and outside the hospital were identified, the most frequent systems delay was due to lack of post-hospital beds.