Comparing traditional and causal inference methodologies for evaluating impacts of long-term air pollution exposure on hospitalization with Alzheimer's disease and related dementias.

Alzheimer's disease and related dementias (ADRD) present a growing public health burden in the United States. One actionable risk factor for ADRD is air pollution: multiple studies have found associations between air pollution and exacerbation of ADRD. Our study builds on previous studies by applying modern statistical causal inference methodologies-generalized propensity score (GPS) weighting and matching-on a large, longitudinal dataset. We follow 50 million Medicare enrollees to investigate impacts of three air pollutants-fine particular matter (PM${}_{2.5}$), nitrogen dioxide (NO${}_2$), and summer ozone (O${}_3$)-on elderly patients' rate of first hospitalization with ADRD diagnosis. Similar to previous studies using traditional statistical models, our results found increased hospitalization risks due to increased PM${}_{2.5}$ and NO${}_2$ exposure, with less conclusive results for O${}_3$. In particular, our GPS weighting analysis finds IQR increases in PM${}_{2.5}$, NO${}_2$, or O${}_3$ exposure results in hazard ratios of 1.108 (95% CI: 1.097-1.119), 1.058 (1.049-1.067), or 1.045 (1.036-1.054), respectively. GPS matching results are similar for PM${}_{2.5}$ and NO${}_2$ with attenuated effects for O${}_3$. Our results strengthen arguments that long-term PM${}_{2.5}$ and NO${}_2$ exposure increases risk of hospitalization with ADRD diagnosis. Additionally, we highlight strengths and limitations of causal inference methodologies in observational studies with continuous treatments. Keywords: Alzheimer's disease and related dementias, air pollution, Medicare, causal inference, generalized propensity score.

Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution.

Children's health studies support an association between maternal environmental exposures and children's birth outcomes. A common goal is to identify critical windows of susceptibility-periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different levels of individual, family, and neighborhood characteristics. Using an administrative Colorado birth cohort we estimate the individualized relationship between weekly exposures to fine particulate matter (PM 2.5) during gestation and birth weight. To achieve this goal, we propose a statistical learning method combining distributed lag models and Bayesian additive regression trees to estimate critical windows at the individual level and identify characteristics that induce heterogeneity from a high-dimensional set of potential modifying factors. We find evidence of heterogeneity in the PM 2.5 -birth weight relationship, with some mother-child dyads showing a 3 times larger decrease in birth weight for an IQR increase in exposure (5.9 to 8.5 PM 2.5 µg/m3) compared to the population average. Specifically, we find increased vulnerabilitity for non-Hispanic mothers who are either younger, have higher body mass index or lower educational attainment. Our case study is the first precision health study of critical windows.

Associations of street-view greenspace with Parkinson's disease hospitalizations in an open cohort of elderly US Medicare beneficiaries.

INTRODUCTION: Protective associations of greenspace with Parkinson's disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). METHODS: We created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007-2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. RESULTS: There were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. CONCLUSION: Increasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

Applying a multistate survival model to explore the role of fine particles in promoting frailty in the Medicare cohort.

Fine particle pollution is a well-established risk to human health. Observational epidemiology generally treats events as though they are independent of one another and so do not examine the role air pollution may play in promoting the progression of disease. Multistate survival models account for the complex pathway of disease to death. We employ a multistate survival model to characterize the role of chronic exposure to PM2.5 in affecting the rate at which Medicare beneficiaries transition to first hospitalization for cardiovascular disease and then subsequently death. We use an open cohort of Medicare beneficiaries and PM2.5 concentrations estimated with photochemical model predictions, satellite-based observations, land-use data, and meteorological variables. The multistate model included three transitions: (1) entry to cardiovascular hospital admission; (2) entry to death; and (3) cardiovascular hospital admission to death. The transition intensity was modeled using a Cox proportional hazards model. For a 1 µg/m3 increase in annual mean PM2.5, we estimate a nationally pooled hazard ratio of 1.022 (95% confidence interval [CI] = 1.018, 1.025) for the transition from entry to first cardiovascular hospital admission; 1.054 (95% CI = 1.039, 1.068) for the transition from entry to death; 1.036 (95% CI = 1.027, 1.044) for the transition from first cardiovascular hospital admission to death. The hazard ratios exhibited some heterogeneity within each of nine climatological regions and for each of the three transitions. We find evidence for the role of PM in both promoting chronic illness and increasing the subsequent risk of death.

Radiological findings in the proximal sesamoid bones of yearling and 2-year-old Thoroughbred sales horses: Prevalence, progression and associations with racing performance.

BACKGROUND: Radiological findings in the proximal sesamoid bones (sesamoids) are a persistent source of controversy at Thoroughbred sales, due to inconsistent classification and conflicting assignment of potential clinical importance. OBJECTIVES: To define the prevalence of sesamoid findings on sales repository radiographs in yearling and 2-year-old Thoroughbreds and to analyse associations with racing performance. To track the changes in sesamoid findings between 1 and 2 years of age in horses that present for sale at both ages. STUDY DESIGN: Prospective cohort study using an enrolled sample. METHODS: Horses were enrolled from a 2016 yearling sale and five 2017 2-year-old sales with consignor permission. Radiological findings relating to sesamoid vascular channel appearance, abaxial contour changes and sesamoid fragments were examined. Associations between sesamoid findings and racing performance from 2 to 4 years of age were examined using multivariate regression analyses. Clinical follow-up was sought to ascertain why horses that did not race never started. RESULTS: A total of 2508 yearlings and 436 2-year-olds were included for evaluation. Interobserver agreement using the new grading system was substantial. Yearling findings associated with a significantly reduced probability of starting a race were: Grade 3 vascular channels in forelimb sesamoids (0.52, P < 0.001, 95% confidence interval (CI): 0.37-0.67), abaxial new bone in forelimb sesamoids (0.62, P = 0.01, 95% CI: 0.49-0.73), apical or abaxial fragments in forelimb sesamoids (0.55, P = 0.005, 95% CI: 0.37-0.72). For affected horses that did race, Grade 3 vascular channels in forelimb sesamoids were associated with fewer race starts (9.9 starts, P = 0.03, 95% CI: 8.0-12.2) and Grade 3 vascular channels in hindlimb sesamoids were associated with a delayed start to racing careers (54 days, P = 0.01, 95% CI: 20-89). Abaxial new bone in forelimb sesamoids was associated with a 54% reduction in total earnings (P = 0.003, 95% CI: 24-72) and a 46% reduction in earnings per start (P = 0.002, 95% CI: 21-64). Abaxial concavity occurred predominantly in yearling medial forelimb sesamoids, had no impact on racing performance and mostly resolved by 2-year-old sale. MAIN LIMITATIONS: These findings are applicable to horses presented for sale at public auction and may underestimate the prevalence of severe lesions in non-sales horses. CONCLUSIONS: Grade 3 vascular channels, forelimb sesamoid abaxial new bone and forelimb sesamoid fragments are important findings in sales repository radiology. The new grading scale assigns a numerical grade for vascular channel appearance that matches the number of enlarged vascular channels evident in a given sesamoid. Abaxial contour changes, when present in sesamoids that are Grade 0 for enlarged vascular channels, are noted separately as either abaxial new bone or abaxial concavity. Fragments are also noted and interpreted separately.

Rural-urban differences in health care unaffordability.

PURPOSE: To compare health care unaffordability in rural versus urban contexts while also examining the impact of sociodemographic/socioeconomic variables on this relationship. METHODS: We examined survey responses from the 2019-2021 National Health Interview Survey (n = 90,761). We conducted chi-squared tests comparing urban and rural subsamples and multivariable logistic regression analyses examining the associations between rurality and 3 measures of health care unaffordability while also including interactions between rurality and individual characteristics of respondents. FINDINGS: In bivariate analyses, compared to their urban counterparts, rural residents were more likely to report problems paying medical bills (15.0% vs 11.5%, P <.001) and being unable to pay medical bills (9.3% vs 7.1%, P < .001). In fully adjusted multivariable regression analyses, rural residents were significantly less likely than their urban counterparts to report being worried about paying medical bills (AOR: .915, CI: .871-.961, P < .001). We found significant interactions between rural residency and insurance type, age, income to poverty ratio, and race/ethnicity for the outcome of problems paying medical bills; and significant interactions between rural residency and income to poverty ratio and race and ethnicity for the outcome of being unable to pay medical bills. CONCLUSION: Rural residents report higher rates of 2 measures of health care unaffordability as compared to their urban counterparts. In multivariable logistic models, rural residency is not associated with higher rates of health care unaffordability; however, significant interactions exist between rural residency and individual variables demonstrating the heterogenous experiences of health care unaffordability based on these intersectional identities.

Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States.

Imposing stricter regulations for PM2.5 has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM2.5. However, many studies reporting a relationship between COVID-19 outcomes and PM2.5 have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM2.5 concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM2.5 and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1 µg/m3 in long-term PM2.5 exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual's odds of COVID-19 mortality. Code to reproduce our study is publicly available at https://github.com/NSAPH/PM_COVID_ecoinference. The results confirm previous evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.

Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs.

Maternal exposure to environmental chemicals during pregnancy can alter birth and children's health outcomes. Research seeks to identify critical windows, time periods when exposures can change future health outcomes, and estimate the exposure-response relationship. Existing statistical approaches focus on estimation of the association between maternal exposure to a single environmental chemical observed at high temporal resolution (e.g., weekly throughout pregnancy) and children's health outcomes. Extending to multiple chemicals observed at high temporal resolution poses a dimensionality problem and statistical methods are lacking. We propose a regression tree-based model for mixtures of exposures observed at high temporal resolution. The proposed approach uses an additive ensemble of tree pairs that defines structured main effects and interactions between time-resolved predictors and performs variable selection to select out of the model predictors not correlated with the outcome. In simulation, we show that the tree-based approach performs better than existing methods for a single exposure and can accurately estimate critical windows in the exposure-response relation for mixtures. We apply our method to estimate the relationship between five exposures measured weekly throughout pregnancy and birth weight in a Denver, Colorado, birth cohort. We identified critical windows during which fine particulate matter, sulfur dioxide, and temperature are negatively associated with birth weight and an interaction between fine particulate matter and temperature. Software is made available in the R package dlmtree.

Time-lagged relationships between a decade of air pollution exposure and first hospitalization with Alzheimer's disease and related dementias.

Alzheimer's disease and related dementias (ADRD) poses substantial health challenges among an aging population. One of the primary challenges in studying ADRD is that biological processes underlying these ailments begin decades prior to diagnosis. Previous studies indicate a relationship between ADRD and air pollution exposure to both fine particulate matter (PM2.5) and nitrogen dioxide (NO2) but are limited in their interpretation because they consider exposure measurements at a single time point. Our retrospective cohort study considered 27 + million Medicare enrollees in the United States followed up to 17 years and matched with highly accurate annual air pollution exposure measurements for PM2.5, NO2, and summer ozone. We applied distributed lag models and estimated the lagged associations between air pollution and odds of first hospitalization with ADRD. We found significantly increased odds due to overall PM2.5 and NO2 exposure and time-lagged exposure 10 and 8 years prior to admission, respectively. Furthermore, we found the connection between air pollution exposure and increased odds of first hospitalization with ADRD exists at air pollution levels below current National Ambient Air Quality Standards set by the US Environmental Protection Agency, with the steepest increase in odds occurring at low concentrations of PM2.5. Our findings are the first to show that air pollution exposures from as many as 10 years prior to the admission are related to increased odds of hospitalizations with ADRD. As there are no clear treatments available for ADRD, identifying modifiable risk factors such as air pollution exposure may make significant contributions towards prevention or delayed disease progression.

Treed distributed lag nonlinear models.

In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_{2.5}$ and birth weight in a Colorado, USA birth cohort.

Developmental plasticity of physiology and morphology in diet-restricted European shag nestlings (Phalacrocorax aristotelis).

Growing animals may exhibit developmental plasticity as an adaptation to variability in the environmental conditions during development. We examined physiological and morphological responses to short-term food shortage of 12-16-day-old European shag nestlings kept under laboratory conditions. After 4 days on a weight maintenance diet, the resting metabolic rate (RMR) of diet-restricted nestlings was 36.5% lower compared with control fed nestlings, after controlling for body mass. This response was accompanied by a reduction in body temperature (T(b)) and by reductions in the size of several visceral organs, muscles and lipid stores, while the overall structural growth was maintained almost in line with the age-specific growth rate of controls. Hence, the pattern of energy allocation reflected a very high priority to structural growth at the expense of visceral organs, lipid deposits and muscles. The reduced T(b) and size of the liver served as important physiological processes behind the observed reductions in RMR. We discuss the possible adaptive significance of this differential developmental plasticity during temporal food shortage. This is the first study of avian developmental plasticity to report substantial energy saving in combination with a high structural growth rate.