We present a novel approach that integrates the application of advanced bioinformatics and biostatistics methods to delineate potential placental pathways through which trace metal exposures impact fetal growth.
The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.
IMPORTANCE: A large and growing population of older adults with multimorbidity, cognitive impairment, and functional disability live in the community, but many never or rarely leave their homes. Being homebound is associated with decreased access to medical services, poor health outcomes, and increased mortality. Yet, it is unknown what factors, in particular socioeconomic factors, are associated with new onset of homebound status. OBJECTIVE: To evaluate the association between income and risk of becoming homebound. DESIGN: Observational cohort study using 2011 to 2018 data from the National Health and Aging Trends Study, a nationally representative sample of Medicare beneficiaries aged 65 years and older. SETTING: Population-based study in the United States. PARTICIPANTS: A total of 7,042 initially nonhomebound community-dwelling older adults. EXPOSURE: Total annual household income at baseline (in 2011) measured via self-report. OUTCOME: Annual measure of homebound status, defined as leaving home an average of 1 d/wk or less. RESULTS: Over 7 years, 15.81% of older adults in the lowest income quartile (≤$15,003) became homebound, compared with only 4.64% of those in the highest income quartile (>$60,000). In a competing risks analysis accounting for risks of death and nursing home admission, and adjusted for clinical and demographic characteristics, those in the lowest income quartile had a substantially higher subhazard of becoming homebound than those in the highest income quartile (1.65; 95% confidence interval = 1.20-2.29). Moreover, we see evidence of a gradient in risk of homebound status by income quartile. CONCLUSION AND RELEVANCE: Our work demonstrates that financial resources shape the risk of becoming homebound, which is associated with negative health consequences. In the context of existing income disparities, more support is needed to assist older adults with limited financial resources who wish to remain in the community.
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.
Background: Preterm infants face unique stress states in early life. Early-life stress has been associated with changes in cortisol reactivity and behavioral abnormalities later in childhood in non-preterm populations. The neonatal infant stressor scale (NISS) has been used to estimate infant stress in the neonatal intensive care unit (NICU), but has not been biomarker-validated. The relationship between NISS scores and salivary cortisol is unknown. Objective: To test the association between NISS scores and salivary cortisol in the NICU Hospital Exposures and Long-Term Health (NICU-HEALTH) preterm birth cohort. Methods: 386 salivary cortisol specimens were collected from 125 NICU-HEALTH participants during the NICU hospitalization. NISS scores were calculated to represent the infant's experience in the 6 hours prior to specimen collection. Adjusted mixed-effect regression models were used to assess the association between each NISS score and salivary cortisol. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.