Associations between neighborhood characteristics and child well-being before and during the COVID-19 pandemic: A repeated cross-sectional study in the Environmental influences on Child Health Outcomes (ECHO) program.

The corona virus disease (COVID-19) pandemic disrupted daily life worldwide, and its impact on child well-being remains a major concern. Neighborhood characteristics affect child well-being, but how these associations were affected by the pandemic is not well understood. We analyzed data from 1039 children enrolled in the Environmental influences on Child Health Outcomes Program whose well-being was assessed using the Patient-Reported Outcomes Measurement Information System Global Health questionnaire and linked these data to American Community Survey (ACS) data to evaluate the impacts of neighborhood characteristics on child well-being before and during the pandemic. We estimated the associations between more than 400 ACS variables and child well-being t-scores stratified by race/ethnicity (non-Hispanic white vs. all other races and ethnicities) and the timing of outcome data assessment (pre-vs. during the pandemic). Network graphs were used to visualize the associations between ACS variables and child well-being t-scores. The number of ACS variables associated with well-being t-scores decreased during the pandemic period. Comparing non-Hispanic white with other racial/ethnic groups during the pandemic, different ACS variables were associated with child well-being. Multiple ACS variables representing census tract-level housing conditions and neighborhood racial composition were associated with lower well-being t-scores among non-Hispanic white children during the pandemic, while higher percentage of Hispanic residents and higher percentage of adults working as essential workers in census tracts were associated with lower well-being t-scores among non-white children during the same study period. Our study provides insights into the associations between neighborhood characteristics and child well-being, and how the COVID-19 pandemic affected this relationship.

Identifying Predictors of Initial Surgical Failure during Minimally Invasive Endoscopic Intracerebral Hemorrhage Evacuation.

Background and Purpose: Intracerebral hemorrhage (ICH) is a common and severe disease with high rates of morbidity and mortality; however, minimally invasive surgical (MIS) hematoma evacuation represents a promising avenue for treatment. In February of 2019, the MISTIE III study found that stereotactic thrombolysis with catheter drainage did not benefit patients with supratentorial spontaneous ICH but that a clinical benefit may be present when no more than 15 mL of hematoma remains at the end of treatment. Intraoperative CT (iCT) imaging has the ability to assess whether or not this surgical goal has been met in real time, allowing for operations to add additional CT-informed 'evacuation periods' (EPs) to achieve the surgical goal. Here, we report on the frequency and predictors of initial surgical failure on at least one iCT requiring additional EPs in a large cohort of patients undergoing endoscopic minimally invasive ICH evacuation with the SCUBA technique. Methods: All patients who underwent minimally invasive endoscopic evacuation of supratentorial spontaneous ICH in a major health system between December 2015 and October 2018 were included in this study. Patient demographics, clinical and radiographic features, procedural details, and outcomes were analyzed retrospectively from a prospectively collected database. Procedures were characterized as initially successful when the first iCT demonstrated that surgical success had been achieved and initially unsuccessful when the surgical goal was not achieved, and additional EPs were performed. The surgical goal was prospectively identified in December of 2015 as leaving no more than 20% of the preoperative hematoma volume at the end of the procedure. Descriptive statistics and regression analyses were performed to identify predictors of initial failure and secondary rescue. Results: Patients (100) underwent minimally invasive endoscopic ICH evacuation in the angiography suite during the study time period. In 14 cases, the surgical goal was not met on the first iCT and multiple Eps were performed; in 10 cases the surgical goal was not met, and no additional EPs were performed. In 14 cases, the surgical goal was never achieved. When additional EPs were performed, a rescue rate of 71.4% (10/14) was seen, bringing the total percentage of cases meeting the surgical goal to 86% across the entire cohort. Cases in which the surgical goal was not achieved were significantly associated with older patients (68 years vs. 60 years; p = 0.0197) and higher rates of intraventricular hemorrhage (34.2% vs. 70.8%; p = 0.0021). Cases in which the surgical goal was rescued from initial failure had similar levels of IVH, suggesting that these additional complexities can be overcome with the use of additional iCT-informed EPs. Conclusions: Initial and ultimate surgical failure occurs in a small percentage of patients undergoing minimally invasive endoscopic ICH evacuation. The use of intraoperative imaging provides an opportunity to evaluate whether or not the surgical goal has been achieved, and to continue the procedure if the surgeon feels that more evacuation is achievable. Now that level-one evidence exists to target a surgical evacuation goal during minimally invasive ICH evacuation, intraoperative imaging, such as iCT, plays an important role in aiding the surgical team to achieve the surgical goal.

Prenatal exposure to metal mixtures and childhood temporal processing in the PROGRESS Birth Cohort Study: Modification by childhood obesity.

Children are frequently exposed to various biological trace metals, some essential for their development, while others can be potent neurotoxicants. Furthermore, the inflammatory and metabolic conditions associated with obesity may interact with and amplify the impact of metal exposure on neurodevelopment. However, few studies have assessed the potential modification effect of body mass index (BMI). As a result, we investigated the role of child BMI phenotype on the relationship between prenatal exposure to metal mixtures and temporal processing. Leveraging the PROGRESS birth cohort in Mexico City, children (N = 563) aged 6-9 years completed a Temporal Response Differentiation (TRD) task where they had to hold a lever down for 10-14 s. Blood and urinary metal (As, Pb, Cd, and Mn) measurements were collected from mothers in the 2nd and 3rd trimesters. Child BMI z-scores were dichotomized to normal (between -2 and +0.99) and high (≥1.00). Covariate-adjusted weighted quantile sum (WQS) regression models were used to estimate and examine the combined effect of metal biomarkers (i.e., blood and urine) on TRD measures. Effect modification by the child's BMI was evaluated using 2-way interaction terms. Children with a high BMI and greater exposure to the metal mixture during prenatal development exhibited significant temporal processing deficits compared to children with a normal BMI. Notably, children with increased exposure to the metal mixture and higher BMI had a decrease in the percent of tasks completed (ß = -10.13; 95 % CI: -19.84, -0.42), number of average holds (ß = -2.15; 95 % CI: -3.88, -0.41), longer latency (ß = 0.78; 95 % CI: 0.13, 1.44), and greater variability in the standard deviation of the total hold time (ß = 2.08; 95 % CI: 0.34, 3.82) compared to normal BMI children. These findings implicate that high BMI may amplify the effect of metals on children's temporal processing. Understanding the relationship between metal exposures, temporal processing, and childhood obesity can provide valuable insights for developing targeted environmental interventions.

Estimating effects of longitudinal and cumulative exposure to PFAS mixtures on early adolescent body composition.

Few methods have been used to characterize repeatedly measured biomarkers of chemical mixtures. We applied latent profile analysis (LPA) to serum concentrations of four perfluoroalkyl and polyfluoroalkyl substances (PFAS) at four timepoints from gestation to age 12 years. We evaluated the relations between profiles and z-scores of height, body mass index, fat mass index, and lean body mass index at age 12 years (n = 218). We compared LPA findings with an alternative approach for cumulative PFAS mixtures using g-computation to estimate the effect of simultaneously increasing the area under the curve (AUC) for all PFAS. We identified two profiles: a higher (35% of sample) and lower PFAS profile (relative to each other), based on their average PFAS concentrations at all timepoints. The higher PFAS profile had generally lower z-scores for all outcomes, with somewhat larger effects for males, though all CIs crossed the null. For example, the higher PFAS profile was associated with a -0.50 (95% CI: -1.07, 0.08) lower BMI z-score among males but not females (0.04; 95% CI: -0.45, 0.54). We observed similar patterns with AUCs. We found that higher childhood PFAS profile and higher cumulative PFAS mixtures may be associated with altered growth in early adolescence.

The U.S. PFAS exposure burden calculator for 2017-2018: Application to the HOME Study, with comparison of epidemiological findings from NHANES.

BACKGROUND: The 2017-2018 U.S. PFAS exposure burden calculator was designed to provide a summary exposure score for per- and polyfluoroalkyl substances (PFAS) mixtures using targeted PFAS analyte data. Its aim was to place PFAS burden score estimates onto a common scale based on nationally representative U.S. reference ranges from 2017 to 2018, enabling comparisons of overall PFAS burden scores across studies even if they did not measure the same set of PFAS analytes. OBJECTIVE: To use the U.S. PFAS exposure burden calculator for comparing the same mixture of PFAS compounds in similarly aged adolescents and their associations with cardiometabolic outcomes in the HOME Study and NHANES between 2015 and 2018. METHODS: We applied the PFAS burden calculator to 8 PFAS analytes measured in the serum of adolescents from the HOME Study (Cincinnati, Ohio; age range 11-14 years; years: 2016-2019; n = 207) and NHANES (US; age range 12-14 years; years 2015-2018; n = 245). We used the non-parametric Mann-Whitney U test and chi-squared test to compare the two study samples. In both studies, we examined associations of PFAS burden scores with the same cardiometabolic outcomes, adjusted for the same core set of covariates using regression analyses. We conducted sensitivity analyses to verify robustness of exposure-outcome associations, by accounting for measurement error of PFAS burden scores. RESULTS: PFAS burden scores were significantly different (p = 0.004) between the HOME Study (median: 0.00, interquartile range - 0.37, 0.34) and the NHANES samples (median: 0.04, IQR -0.11, 0.54), while no significant difference was found for PFAS summed concentrations (p = 0.661). In the HOME Study, an interquartile (IQR) increase in PFAS burden score was associated with higher total cholesterol [7.0 mg/dL, 95% CI: 0.6, 13.4]; HDL [2.8 mg/dL, 95% CI: 0.4, 5.2]; LDL [5.9 mg/dL, 95% CI: 0.5, 11.3], insulin [0.1 log(mIU/L), 95% CI: 0.01, 0.2], and HOMA-IR [0.1, 95% CI: 0.01, 0.2]. In NHANES, an IQR increase in PFAS burden score was associated with higher diastolic blood pressure [2.4 mmHg, 95% CI: 0.4, 4.4] but not with other outcomes. Sensitivity analyses in the HOME Study and NHANES were consistent with the main findings. CONCLUSIONS: Performance of the U.S. PFAS exposure burden calculator was similar in a local versus national sample of adolescents, and may be a useful tool for the assessment of PFAS mixtures across studies.

Racial and Ethnic Inequities in Development of Type 2 Diabetes After Gestational Diabetes Mellitus.

OBJECTIVE: To estimate racial and ethnic disparities in type 2 diabetes mellitus after gestational diabetes mellitus (GDM) and to investigate baseline pregnancy clinical and social or structural characteristics as mediators. METHODS: We conducted a retrospective cohort of individuals with GDM using linked 2009-2011 New York City birth and hospital data and 2009-2017 New York City A1c Registry data. We ascertained GDM and pregnancy characteristics from birth and hospital records. We classified type 2 diabetes as two hemoglobin A 1c test results of 6.5% or higher. We grouped pregnancy characteristics into clinical (body mass index [BMI], chronic hypertension, gestational hypertension, preeclampsia, preterm delivery, caesarean, breastfeeding, macrosomia, shoulder dystocia) and social or structural (education, Medicaid insurance, prenatal care, and WIC [Special Supplemental Nutrition Program for Women, Infants, and Children] participation). We used Cox proportional hazards models to estimate associations between race and ethnicity and 8-year type 2 diabetes incidence, and we tested mediation of pregnancy characteristics, additionally adjusting for age and nativity (U.S.-born vs foreign-born). RESULTS: The analytic data set included 22,338 patients with GDM. The 8-year type 2 diabetes incidence was 11.7% overall and 18.5% in Black, 16.8% in South and Southeast Asian, 14.6% in Hispanic, 5.5% in East and Central Asian, and 5.4% in White individuals with adjusted hazard ratios of 4.0 (95% CI 2.4-3.9), 2.9 (95% CI 2.4-3.3), 3.3 (95% CI 2.7-4.2), and 1.0 (95% CI 0.9-1.4) for each group compared with White individuals. Clinical and social or structural pregnancy characteristics explained 9.3% and 23.8% of Black, 31.2% and 24.7% of Hispanic, and 7.6% and 16.3% of South and Southeast Asian compared with White disparities. Associations between education, Medicaid insurance, WIC participation, and BMI and type 2 diabetes incidence were more pronounced among White than Black, Hispanic, and South and Southeast Asian individuals. CONCLUSION: Population-based racial and ethnic inequities are substantial in type 2 diabetes after GDM. Characteristics at the time of delivery partially explain disparities, creating an opportunity to intervene on life-course cardiometabolic inequities, whereas weak associations of common social or structural measures and BMI in Black, Hispanic and South and Southeast Asian individuals demonstrate the need for greater understanding of how structural racism influences postpartum cardiometabolic risk in these groups.

Toward Advancing Precision Environmental Health: Developing a Customized Exposure Burden Score to PFAS Mixtures to Enable Equitable Comparisons Across Population Subgroups, Using Mixture Item Response Theory.

Quantifying a person's cumulative exposure burden to per- and polyfluoroalkyl substances (PFAS) mixtures is important for risk assessment, biomonitoring, and reporting of results to participants. However, different people may be exposed to different sets of PFASs due to heterogeneity in the exposure sources and patterns. Applying a single measurement model for the entire population (e.g., by summing concentrations of all PFAS analytes) assumes that each PFAS analyte is equally informative to PFAS exposure burden for all individuals. This assumption may not hold if PFAS exposure sources systematically differ within the population. However, the sociodemographic, dietary, and behavioral characteristics that underlie systematic exposure differences may not be known, or may be due to a combination of these factors. Therefore, we used mixture item response theory, an unsupervised psychometrics and data science method, to develop a customized PFAS exposure burden scoring algorithm. This scoring algorithm ensures that PFAS burden scores can be equitably compared across population subgroups. We applied our methods to PFAS biomonitoring data from the United States National Health and Nutrition Examination Survey (2013-2018). Using mixture item response theory, we found that participants with higher household incomes had higher PFAS burden scores. Asian Americans had significantly higher PFAS burden compared with non-Hispanic Whites and other race/ethnicity groups. However, some disparities were masked when using summed PFAS concentrations as the exposure metric. This work demonstrates that our summary PFAS burden metric, accounting for sources of exposure variation, may be a more fair and informative estimate of PFAS exposure.

Predictors and Trends in First-Trimester Hemoglobin A1c Screening in New York City, 2009 to 2017.

Glycated hemoglobin is an adjunct tool in early pregnancy to assess glycemic control. We examined trends and maternal predictors for those who had A1c screening in early pregnancy using hospital discharge and vital registry data between 2009 and 2017 linked with the New York City A1C Registry (N = 798,312). First-trimester A1c screening increased from 2.3% in 2009 to 7.7% in 2017. The likelihood of screening became less targeted to high-risk patients over time, with a decrease in mean A1c values from 5.8% (95% confidence interval [CI]: 5.8, 5.9) to 5.3 (95% CI: 5.3, 5.4). The prevalence of gestational diabetes mellitus increased while testing became less discriminate for those with high-risk factors, including pregestational type 2 diabetes, chronic hypertension, obesity, age over 40 years, as well as Asian or Black non-Hispanic race/ethnicity. KEY POINTS: · First-trimester A1c screening increased from 2.3% in 2009 to 7.7% in 2017 in New York City.. · The likelihood of screening became less targeted to high-risk patients over time.. · The prevalence of gestational diabetes mellitus increased, while testing became less discriminate..

Sex-specific association between prenatal manganese exposure and working memory in school-aged children in Mexico city: An exploratory multi-media approach.

It remains unclear whether manganese (Mn) exposure affects working memory (WM) in a sexually dimorphic manner. Further, no gold standard media exists to measure Mn, suggesting a combined blood and urinary Mn index may better capture the totality of exposure. We investigated the modification effect of child sex on the influence of prenatal Mn exposure on WM in school-age children, exploring two methodological frameworks to integrate exposure estimates across multiple exposure biomarkers. Leveraging the PROGRESS birth cohort in Mexico City, children (N = 559) ages 6-8 completed the between errors and strategy measures of the CANTAB Spatial Working Memory (SWM) task. Mn levels were assayed in blood and urine of mothers during the 2nd and 3rd trimesters and in umbilical cord blood from mothers and children at delivery. Weighted quantile sum regression estimated the association of a multi-media biomarker (MMB) mixture with SWM. We applied a confirmatory factor analysis to similarly quantify a latent blood Mn burden index. We then used an adjusted linear regression to estimate the Mn burden index with SWM measures. Interaction terms were used to estimate the modification effect by child sex for all models. Results showed that the between-errors-specific MMB mixture (i.e., this model demonstrates the impact of the MMB mixture on the between-error scores.) was associated (ß = 6.50, 95% CI: 0.91, 12.08) with fewer between errors for boys and more between errors for girls. The strategy-specific MMB mixture (i.e., this model demonstrates the impact of the MMB mixture on the strategy scores) was associated (ß = -1.36, 95% CI: 2.55, - 0.18) with less efficient strategy performance for boys and more efficient strategy performance for girls. A higher Mn burden index was associated (ß = 0.86, 95% CI: 0.00, 1.72) with more between errors in the overall sample. The vulnerability to prenatal Mn biomarkers on SWM differs in the directionality by child sex. An MMB mixture and composite index of body burden are stronger predictors than a single biomarker for Mn exposure on WM performance.

Influence of Gestational Diabetes Mellitus on Diabetes Risk and Glycemic Control in a Retrospective Population-Based Cohort.

OBJECTIVE: Racial/ethnic-specific estimates of the influence of gestational diabetes mellitus (GDM) on type 2 diabetes remain underexplored in large population-based cohorts. We estimated racial/ethnic differences in the influence of GDM on diabetes risk and glycemic control in a multiethnic, population-based cohort of postpartum women. RESEARCH DESIGN AND METHODS: Hospital discharge and vital registry data for New York City (NYC) births between 2009 and 2011 were linked with NYC A1C Registry data between 2009 and 2017. Women with baseline diabetes (n = 2,810) were excluded for a final birth cohort of 336,276. GDM on time to diabetes onset (two A1C tests of ≥6.5% from 12 weeks postpartum onward) or glucose control (first test of A1C <7.0% following diagnosis) was assessed using Cox regression with a time-varying exposure. Models were adjusted for sociodemographic and clinical factors and stratified by race/ethnicity. RESULTS: The cumulative incidence for diabetes was 11.8% and 0.6% among women with and without GDM, respectively. The adjusted hazard ratio (aHR) of GDM status on diabetes risk was 11.5 (95% CI 10.8, 12.3) overall, with slight differences by race/ethnicity. GDM was associated with a lower likelihood of glycemic control (aHR 0.85; 95% CI 0.79, 0.92), with the largest negative influence among Black (aHR 0.77; 95% CI 0.68, 0.88) and Hispanic (aHR 0.84; 95% CI 0.74, 0.95) women. Adjustment for screening bias and loss to follow-up modestly attenuated racial/ethnic differences in diabetes risk but had little influence on glycemic control. CONCLUSIONS: Understanding racial/ethnic differences in the influence of GDM on diabetes progression is critical to disrupt life course cardiometabolic disparities.

Phthalate mixtures and insulin resistance: an item response theory approach to quantify exposure burden to phthalate mixtures.

BACKGROUND: Molar sums are often used to quantify total phthalate exposure, but they do not capture patterns of exposure to multiple phthalates. OBJECTIVE: To introduce an exposure burden score method for quantifying exposure to phthalate metabolites and examine the association between phthalate burden scores and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). METHODS: We applied item response theory (IRT) to data from 3474 adults aged 20-60 years in the 2013-2018 National Health and Examination Survey (NHANES) to quantify latent phthalate exposure burden from 12 phthalate metabolites. We compared model fits of three IRT models that used different a priori groupings (general phthalate burden; low molecular weight (LMW) and high molecular weight (HMW) burdens; and LMW, HMW and DEHP burden), and used the best fitting model to estimate phthalate exposure burden scores. Regression models assessed the covariate-adjusted association between phthalate burden scores and HOMA-IR. We compared findings to those using molar sums. In secondary analyses, we examined how the IRT model could be used for data harmonization when a subset of participants are missing some phthalate metabolites, and accounted for measurement error of the phthalate burden scores in estimating associations with HOMA-IR through a resampling approach using plausible value imputation. RESULTS: A three correlated factors model (LMW, HMW and DEHP burdens) provided the best fit. One interquartile range (IQR) increase in DEHP burden score was associated with 0.094 (95% CI: 0.022, 0.164, p = 0.010) increase in log HOMA-IR, co-adjusted for LMW and HMW burden scores. Findings were consistent when using log molar sums. Associations of phthalate burden and insulin resistance were also consistent when participants were simulated to be missing some phthalate metabolites, and when we accounted for measurement error in estimating burden scores. CONCLUSION: Both phthalate molar sums and burden scores are sensitive to associations with insulin resistance. Phthalate burden scores may be useful for data harmonization.

Developing an Exposure Burden Score for Chemical Mixtures Using Item Response Theory, with Applications to PFAS Mixtures.

BACKGROUND: There are few existing methods to quantify total exposure burden to chemical mixtures, independent of a health outcome. A summary metric could be advantageous for use in biomonitoring, risk assessment, health risk calculators, and mediation models. OBJECTIVE: We developed a novel exposure burden score method for chemical mixtures, applied it to estimate exposure burden to per- and polyfluoroalkyl substances (PFAS) mixtures, and estimated associations of PFAS burden scores with cardio-metabolic outcomes in the general U.S. METHODS: We applied item response theory (IRT) to biomonitoring data from 1,915 children and adults 12-80 years of age in the 2017-2018 National Health and Examination Survey to quantify a latent PFAS burden score, using serum concentrations of eight measured PFAS biomarkers, each considered an "item." The premise of IRT is that through using both information about a participant's concentration of an individual PFAS biomarker, as well as their exposure patterns for the PFAS mixture, we can estimate the participant's latent PFAS exposure burden, independent of a health outcome. We used linear regression to estimate associations of the PFAS burden score with cardio-metabolic outcomes and compared our findings to results using summed PFAS concentrations as the exposure metric. RESULTS: PFAS burden scores and summed PFAS concentrations had moderate-high correlation (ρ=0.75). Isomers of PFOS [n-perfluorooctane sulfonic acid (n-PFOS) and perfluoromethylheptane sulfonic acid isomers (Sm-PFOS)] were the most informative to the PFAS burden scores. PFAS burden scores and summed PFAS concentrations were both significantly associated with cardio-metabolic outcomes, but associations were generally closer to the null for summed PFAS concentrations vs. the PFAS burden score. Adjusted associations (95% CIs) with total cholesterol (in milligrams per deciliter) were 8.6 (95% CI: 5.2, 11.9) and 2.4 (95% CI: 0.5, 4.2) per interquartile range increase in the PFAS burden score and summed concentrations, respectively. Sensitivity analyses showed similar associations with cardio-metabolic outcomes when only a subset of PFAS biomarkers was used to estimate PFAS burden. In a validation study, associations between PFAS burden scores and cholesterol were consistent with primary analyses but null when using summed PFAS concentrations. DISCUSSION: IRT offers a straightforward way to include exposure biomarkers with low detection frequencies and can reduce exposure measurement error. Further, IRT enables comparisons of exposure burden to chemical mixtures across studies even if they did not measure the exact same set of chemicals, which supports harmonization across studies and consortia. We provide an accompanying PFAS burden calculator (https://pfasburden.shinyapps.io/app_pfas_burden/), enabling researchers to calculate PFAS burden scores based on U.S. population exposure reference ranges. https://doi.org/10.1289/EHP10125.

Prenatal exposure to PM2.5 and childhood cognition: Accounting for between-site heterogeneity in a pooled analysis of ECHO cohorts in the Northeastern United States.

BACKGROUND: Emerging studies have investigated the adverse health effects of PM2.5 using data from multiple cohorts, and results often are not generalizable across cohorts. We aimed to assess associations between prenatal PM2.5 and childhood cognition in two U.S. cohorts while accounting for between-site heterogeneity. METHODS: Analyses included 348 mother-child dyads enrolled in the dual site (New York City and Boston) PRogramming of Intergenerational Stress Mechanisms (PRISM) cohort and in the First Thousand Days of Life (FTDL) study (Northern Virginia) participating in the Environmental influences on Child Health Outcomes (ECHO) national consortium. Residential prenatal PM2.5 exposure was estimated using a validated satellite-based model and childhood cognition was measured using the NIH Toolbox Cognition Battery at three to eight years of age. We used a log-linear model applied to contingency tables formed by cross-classifying covariates by site to examine between-site heterogeneity using 3rd trimester PM2.5 exposure, age-corrected cognition scores, and covariates potentially causing heterogeneities. Multivariable linear regression models informed by the combinability analysis were used to estimate the coefficients and 95% confidence intervals (CIs) for the association between 3rd trimester PM2.5 exposure and age-corrected cognition scores (mean = 100, SD = 15). RESULTS: The log-linear model indicated that inter-study associations were similar between PRISM-NYC and FTDL, which were different from those in PRISM-Boston. Accordingly, we combined the data of PRISM-NYC and FTDL cohorts. We observed associations between 3rd trimester PM2.5 and cognition scores, findings were varying by site, childsex, and test. For example, a 1 µg/m3 increase of 3rd trimester PM2.5 was associated with -4.35 (95% CI = -8.73, -0.25) mean early childhood cognition scores in females in PRISM-Boston. In the pooled NYC + FTDL site, the association between PM2.5 and childhood cognition may be modified by maternal education and urbanicity. CONCLUSIONS: We found associations between prenatal PM2.5 and impaired childhood cognition. Since multi-site analyses are increasingly conducted, our findings suggest the needed awareness of between-site heterogeneity.

Prospective assessment of dementia on transitions in homeboundness using multistate Markov models.

BACKGROUND: One in 20 older adults in the United States is homebound and rarely/never leaves home. Although being homebound decreases the quality of the lived experience of individuals with serious illnesses, little is known about the frequency or likelihood of transitions in or out of homebound status. The objective of this study was to characterize the probability of transitions to and from homebound status among older adults and examine the relationship between dementia status and homebound transitions. METHODS: Using 2011-2018 data from the National Health and Aging Trends Study (NHATS), a nationally representative longitudinal study of aging in the United States, we identified 6375 community-dwelling Medicare beneficiaries. Homebound status (independent, semi-homebound (leaving home but with difficulty or help), homebound (rarely or never leaving home), nursing home resident, dead) was assessed annually via self-report. Transition probabilities across states were assessed using a multistate Markov model. RESULTS: Less than half of homebound individuals remain homebound (probability = 41.5% [95% CI: 39.2%, 43.5%]) after 1 year. One out of four dies (24% [22.3%, 26.0%]) and there is a low probability (3.2% [2.5%, 4.1%]) of transition to a nursing home. Dementia status was associated with increased risk of progression from independence to homebound status (HR: 1.83 [1.01, 3.34]). Dementia was consistently associated with increased probabilities of transitions to death including a two-fold increased hazards of progression from homebound to death (HR: 2.18 [1.69, 2.81]). Homebound individuals with dementia have a 34.2% [25.8%, 48.1%] probability of death in 5 years, compared with 17.4% [13.7%, 24.3%] among those without dementia. DISCUSSION: Dementia is associated with greater risk of transitioning across homebound states. There is a greater need to support home-based care for patients with dementia, especially as the ongoing COVID pandemic has raised concerns about the need to invest in alternative models to nursing home care.

The dynamics of being homebound over time: A prospective study of Medicare beneficiaries, 2012-2018.

BACKGROUND/OBJECTIVES: Approximately 2 million people, or 6% of older adults in the United States, are homebound. In cross-sectional studies, homebound older adults have high levels of morbidity and mortality, but there is little evidence of longitudinal outcomes after becoming homebound. The aim of this research is to prospectively assess over 6 years the dynamics of homebound status, ongoing community residence, and death in a population of community-dwelling older adults who are newly homebound. DESIGN/SETTING: Prospective cohort study using 2011-2018 data from the National Health and Aging Trends Study (NHATS), an annual, nationally-representative longitudinal study of aging in the United States. PARTICIPANTS: Two hundred and sixty seven newly homebound older adults in 2012. MEASUREMENTS: Homebound status was defined via self-report as living in the community but rarely/never leaving home in the prior month. Semi-homebound was defined as leaving the house only with difficulty or help. RESULTS: One year after becoming newly homebound, 33.1% remained homebound, 22.8% were completely independent, 23.8% were semi-homebound, 2.2% were in a nursing home, and 18.0% died. Homebound status is highly dynamic; 6 years after becoming homebound, 13.5% remained homebound and 65.0% had died. Recovering from being homebound at 1 year was associated with younger age and lower baseline rates of receiving help with activities of daily living, in particular, with bathing. CONCLUSION: Homebound status is a dynamic state. Even if transient, becoming homebound is strongly associated with functional decline and death. Identifying newly homebound older adults and developing interventions to mitigate associated negative consequences needs to be prioritized.

Evaluating the Impact of New York's Executive Order on Face Mask Use on COVID-19 Cases and Mortality: a Comparative Interrupted Times Series Study.

BACKGROUND: On April 17, 2020, the State of New York (NY) implemented an Executive Order that requires all people in NY to wear a face mask or covering in public settings where social distancing cannot be maintained. Although the Centers for Disease Control and Prevention recommended face mask use by the general public, there is a lack of evidence on the effect of face mask policies on the spread of COVID-19 at the state level. OBJECTIVE: To assess the impact of the Executive Order on face mask use on COVID-19 cases and mortality in NY. DESIGN: A comparative interrupted time series analysis was used to assess the impact of the Executive Order in NY with Massachusetts (MA) as a comparison state. PARTICIPANTS: We analyzed data on COVID-19 in NY and MA from March 25 to May 6, 2020. INTERVENTION: The Executive Order on face mask use in NY. MAIN MEASURES: Daily numbers of COVID-19 confirmed cases and deaths. KEY RESULTS: The average daily number of confirmed cases in NY decreased from 8549 to 5085 after the Executive Order took effect, with a trend change of 341 (95% CI, 187-496) cases per day. The average daily number of deaths decreased from 521 to 384 during the same two time periods, with a trend change of 52 (95% CI, 44-60) deaths per day. Compared to MA, the decreasing trend in NY was significantly greater for both daily numbers of confirmed cases (P = 0.003) and deaths (P < 0.001). CONCLUSIONS: The Executive Order on face mask use in NY led to a significant decrease in both daily numbers of COVID-19 confirmed cases and deaths. Findings from this study provide important evidence to support state-level policies that require face mask use by the general public.

Allostatic load scoring using item response theory.

Allostatic load is commonly operationalized using a sum-score of high-risk biomarkers. However, this method implies that biomarkers contribute equally to allostatic load, as each is given equal weight. Our goal in this methodological paper is to evaluate this, and complementarily, to identify biomarkers that are most informative and least informative for developing an allostatic load index. Item response theory models provide an alternate approach to calculating the allostatic load score, by treating individual biomarkers (e.g. "items") as indicators of a latent allostatic load construct. Item response theory scores account for the data-driven discriminating power of each biomarker, and an individual's pattern of biomarker responses. To demonstrate feasibility of this approach, we used data from the 2015-2016 National Health Examination and Nutrition Survey (NHANES; N â€‹= â€‹3751), with twelve allostatic load biomarkers representing immune response, metabolic function and cardiovascular health. Item response theory models revealed that body-mass-index and C-reactive protein were the most informative biomarkers for allostatic load. Both higher allostatic load sum-score and allostatic load item response theory score were associated with lower socio-economic status (p â€‹= â€‹0.008; p<0.001, respectively). Further, both formulations of allostatic load were positively associated with a nine-item depression screener (p<0.001 for both), but only the item response theory score was also positively associated with the impact of depressive symptoms on daily life (p â€‹= â€‹0.045). Item response theory scores may be more finely tuned to tease out effects, compared to sum-scores, and also provide more flexibility when there are missing biomarker measurements. Supplemental R code for our approach are included.

Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients.

There is growing concern that the social and physical distancing measures implemented in response to the Covid-19 pandemic may negatively impact health in other areas, via both decreased physical activity and increased social isolation. Here, we investigated whether increased engagement with digital social tools may help mitigate effects of enforced isolation on physical activity and mood, in a naturalistic study of at-risk individuals. Passively sensed smartphone app use and actigraphy data were collected from a group of psychiatric outpatients before and during imposition of strict Covid-19 lockdown measures. Data were analysed using Gaussian graphical models: a form of network analysis which gives insight into the predictive relationships between measures across timepoints. Within-individuals, we found evidence of a positive predictive path between digital social engagement, general smartphone use, and physical activity-selectively under lockdown conditions (N = 127 individual users, M = 6201 daily observations). Further, we observed a positive relationship between social media use and total daily steps across individuals during (but not prior to) lockdown. Although there are important limitations on the validity of drawing causal conclusions from observational data, a plausible explanation for our findings is that, during lockdown, individuals use their smartphones to access social support, which may help guard against negative effects of in-person social deprivation and other pandemic-related stress. Importantly, passive monitoring of smartphone app usage is low burden and non-intrusive. Given appropriate consent, this could help identify people who are failing to engage in usual patterns of digital social interaction, providing a route to early intervention.