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Climate change is already harming public health through warmer, more erratic weather and many downstream consequences. Research can support climate change adaptation by characterizing climate-related exposures, identifying vulnerable populations, and identifying effective interventions. Furthermore, the main source of greenhouse gas emissions, fossil fuel combustion, also produces air pollutants such as fine particulate matter (PM2.5) that directly harm human health. In this dissertation my colleagues and I have examined the effects of short-term temperature exposure on myocardial infarction and stroke to inform adaption (Chapters 1 to 3) and leveraged multiple exposure models to estimate annual PM2.5 concentrations and quantify uncertainty (Chapter 4). To examine the effects of short-term temperature exposure, we conducted case-crossover analyses using an administrative dataset of hospitalizations in New York State. For PM2.5 prediction and uncertainty characterization, we applied a flexible ensemble approach to leverage seven already-developed PM2.5 models. Overall, we found that warmer hourly temperatures and greater daily temperature variability increased the risk of ischemic events, and lower hourly temperatures increased the risk of hemorrhagic stroke. The ensemble model showed high predictive accuracy, demonstrating the strength of this approach, and we observed greatest uncertainty in the Pacific Northwest and southeast coast. The methods applied in this dissertation can be applied to other exposures and in different settings to further quantify the risks of climate impacts and improve air pollution assessment. Future research should examine the joint impacts of multiple weather factors, strategies to protect people in group housing from extreme weather, measurements and models to reduce uncertainty of air pollution exposures, and propagate exposure uncertainty into health models. However, the need for further research should not delay climate action today.

Natural hazard-induced disasters have become a priority concern for international humanitarian child protection actors due to the increase in their intensity, frequency, and the risks they pose for children worldwide. In responding to natural hazard-induced disasters, international humanitarian child protection actors are giving increasing attention to the role of the family in promoting children's well-being. The current interventions implemented to support families in these crisis settings are limited by the lack of grounded understanding of what family is in different contexts, and an understanding of how families adapt to contextual stressors. Using a combination of a literature review, meta-ethnography and critical reflection on how to strengthen humanitarian practice, this dissertation examines how families respond, adapt and can be better supported in natural disaster settings. Key findings are that families draw upon cultural strategies of adaptation to navigate changes in their environment and support their children’s well-being. These strategies are often disrupted by external humanitarian responders, however, hindering the ability of families to adapt and causing unintended harm. Current family-level interventions are limited by their reliance on Northern conceptions of the nuclear family, their failure to recognize the rich diversity of family arrangements and supports that exist, and their predominant focus on dynamics within the family unit, often neglecting to fully consider the interactions families have with their social and physical surroundings. This dissertation provides evidence for why and how current interventions to strengthen families in humanitarian contexts should be realigned in a manner that gives greater attention to families’ adaptation strategies, including cultural and relational aspects, builds on local strengths and avoids causing unintended harm.

Real-world evidence (RWE) plays a crucial role in understanding the impact of medical interventions and uncovering disparities in clinical practice. However, confounding bias, especially unmeasured confounding, poses challenges to inferring causal relationships from observational data, such as estimating treatment effects and treatment responses. Various methods have been developed to reduce confounding bias, including methods specific for detecting and adjusting for unmeasured confounding. However, these methods typically rely on assumptions that are either untestable or too strong to hold in practice. Some methods also require domain knowledge that is rarely available in medicine. Despite recent advances in method development, the challenge of unmeasured confounding in observational studies persists. This dissertation provides insights in adjusting for unmeasured confounding by exploiting correlations within electronic health records (EHRs). In Aim 1, we demonstrate a novel use of probabilistic model for inferring unmeasured confounders from drug co-prescription pattern. In Aim 2, we provide theoretical justifications and empirical evidence that adjusting for all (pre-treatment) covariates without explicitly selecting for confounders, as implemented in the large-scale propensity score (LSPS) method, offers a more robust approach to mitigating unmeasured confounding. In Aim 3, we shift focus to the problem of evaluating fairness of treatment allocation in clinical practice from a causal perspective. We develop a causal fairness algorithm for assessing treatment allocation. By applying this fairness analysis method to a cohort of patients with coronary artery disease from EHR data, we uncover disparities in treatment allocation based on gender and race, highlighting the importance of addressing fairness concerns in clinical practice. Furthermore, we demonstrate that social determinants of health, variables that are often unavailable in EHR databases and are potential unmeasured confounders, do not significantly impact the estimation of treatment responses when conditioned on clinical features from EHR data, shedding light on the intricate relationship between EHR features and social determinants of health. Collectively, this dissertation contributes valuable insights into addressing unmeasured confounding in the context of evidence generation from EHRs. These findings have significant implications for improving the reliability of observational studies and promoting equitable healthcare practices.

Problem to be addressed: Stillbirth is a major public health problem. The stillbirth burden is on a par with newborn deaths. The stillbirth rate measures not only a substantial portion of the global and national burden of mortality, but also equity and quality of care for women’s and children’s health. Reducing the numbers of these deaths requires an understanding of why they occur, yet approximately one-third of stillbirths are unexplained, even in settings with high-quality autopsy and placental examination, while deaths considered to be explained are usually ascribed to single, proximal causes. An important limiting factor for efforts to reduce the large and inequitable stillbirth burden has been insufficient research into conditions that could inform prevention strategies and reduce inequity.1 2 Substantial evidence exists for associations between structural racism, maternal stress, and adverse pregnancy outcomes, yet research focusing on stillbirth is sparse, particularly at the ends of the causal spectrum—macro-level structural conditions and mechanisms. Several studies have called for research on possible biological mechanisms by which racism, racism-related stress, and stillbirth may be associated, including epigenetic mechanisms.3-6 The most recent review of causes of racial disparities in stillbirth rates in the U.S. recommended that researchers take a multi-domain approach, considering not just individual-level risk factors, which have been relatively well-studied, but also upstream factors such as institutional racism, and biological mechanisms such as epigenetic modification. The objective of this dissertation was to explore evidence that could help to explain persistent racial disparities in stillbirth. The specific aims were: 1. To review the literature on racial disparity in stillbirth rates; 2. To assess whether structural racism can help to explain racial disparity in stillbirth rates in New York City; and 3. To assess whether maternal stress is associated with stillbirth, whether stress is associated with methylation of stress-related genes, whether methylation is associated with stillbirth, and whether there is evidence that methylation of stress-related genes mediates associations between stress and stillbirth. Materials and methods used: For Aim 1, we carried out a scoping review of the literature in five databases (PubMed, Scopus, Cinahl, Embase, PsycInfo) to identify all reports including stillbirth rates stratified by race in the U.S., mapping exposures and effect modifiers (“domains of analysis”) and authors’ comments on racial disparity in stillbirths (“domains of explanation”) into one of eight domains (race, genetic, fetal, maternal, family, community, healthcare system, and structural). We defined Stillbirth Disparity Ratios (SDRs) as the ratio of the stillbirth rate in a racial/ethnic minority group to the stillbirth rate in white individuals. Selected SDRs were extracted from each report, as were all SDRs for Black/white comparisons. For Aim 2, we modelled associations between four measures of structural racism and stillbirth in all non-Hispanic (NH) Black and white singleton births in New York City between 2009 and 2018. Exposures were four Public Use Microdata Area (PUMA)-level measures of structural racism (Indices of Dissimilarity, Isolation, and Concentration at the Extremes (ICE), and an Educational Inequity Ratio) constructed from U.S. Census American Community Survey data. Using multilevel logistic regression, we first tested for interaction between race and structural racism in relation to stillbirth. For structural racism measures that interacted with race, we estimated odds ratios for stillbirth separately in 221,925 NH Black and 325,058 NH white births. Race-specific models were further stratified by maternal age. For Aim 3, we assessed associations between maternal stressors and stillbirth in 183 non-anomalous full-term singleton births (63 stillbirths and 120 livebirths) from the U.S. Stillbirth Collaborative Research Network. Measuring maternal stress with two hypothesized stressors, an Index of Significant Life Events and an Index of Disadvantage, we assessed associations between maternal stressors and stillbirth in our sample, and then whether maternal stressors and stillbirth were associated with differential methylation of 1,191 CpGs on five stress-related genes (BDNF, FKBP5, HSD11B2, IGF2, and NR3C1). Finally, we assessed whether methylation mediates associations between stressors and stillbirth. Conclusions reached: For Aim 1, we found 95 reports presenting stillbirth rates stratified by race/ethnicity in the U.S. We found evidence of increased risk of stillbirth in Black as compared to white births in the majority of the 83 reports with the necessary data. Among the 1143 Black-white SDRs that we extracted, the median SDR was 1.67, with 74% of SDRs showing evidence of disparity. Family and community factors, healthcare system factors, and structural factors were commonly used as domains of explanation (20-38% of reports), but rarely (family/community, structural, 4-5%) or never (healthcare system) used in analysis. The most commonly used domains of analysis—fetal and maternal factors including gestational age, maternal age, education, and prenatal care—do not appear able to explain the observed racial disparities. Gaps in the literature include a paucity of studies examining the possible role of health system, community, and structural factors in Black-white disparity in stillbirth rates, and limited data on other types of racial disparities in stillbirth rates, including Hispanic and Native American births. For Aim 2, we found that structural racism as measured by ICE and Isolation was associated with stillbirth in NH Black but not NH white mothers. This would seem consistent with our hypothesis that structural racism may help to explain racial disparity in stillbirth rates; however, the associations we observed were not in the expected direction. Specifically, NH Black mothers living in PUMAs with a high concentration of privilege had 90% greater odds of stillbirth in comparison to those living in PUMAs with a high concentration of disadvantage (ICE quintile 5 vs 1), and NH Black mothers living in PUMAs that were the most isolated had 40% lower odds of stillbirth in comparison to those living in PUMAs that were the least isolated (Isolation tertile 3 vs 1). We suggest that while the measures we used (ICE and Isolation) do help to explain the Black-white disparity in stillbirth rates, our results raise questions about the way these measures operationalize structural racism, meriting further investigation. For Aim 3, we found that having two or more vs no items in the Index of Disadvantage (“Disadvantage”) was associated with more than fourfold greater odds of stillbirth (95% CI 1.58, 12.93). We found no association between the Index of Significant Life Events and stillbirth. We found that 32 out of 1,191 CpGs on five stress-related genes were differentially methylated with respect to stillbirth, and six CpGs were differentially methylated with respect to Disadvantage. Methylation at two CpGs on IGF2 and one on HSD11B2 (cg02097792, cg12283393, and cg19413291, respectively) mediated the association between Disadvantage and stillbirth. Research on causes is a critical component of stillbirth prevention and reducing the inequitable distribution of this public health burden. Limited understanding of causes at both “ends of the spectrum”, from upstream distal factors to mechanisms, has likely contributed to slow progress on prevention.7 8 This dissertation contributes to science and public health by providing researchers with data to support new lines of inquiry, e.g., into associations between structural racism and stillbirth, and for methylation as a mechanism of effect, that should help to improve our understanding of causes. Our research may also support health policy makers who now have additional data to illustrate the adverse health outcomes of structural racism in the U.S. Finally, it may help the parents and other family members of stillborn babies who continually seek to understand “why”.

The problem that this study addressed was the lack of a survey tool to adequately assess the impact of practicing capoeira, including for People of Color who are male adults attending undergraduate and graduate universities. Thus, the purpose of the study was to evaluate the value and utility of a new survey designed to enhance research and scholarship on capoeira, while seeking to identify significant predictors of high ratings for having experienced beneficial impacts from capoeira. A small sample permitted only a pilot study that was largely exploratory in nature with suggestive findings. The sample of undergraduate and graduate students was 100% male (N=59) with a mean age of 25; and 47.5% identified as Black, 22% as Asian American, 27.1% as White, and 8.5% identifying as Latinx. Findings showed men reported closest to good physical health and between fair and good mental/emotional health. The mean number of years engaged in capoeira was two years, while playing between three times per month and once per week. Some 78% played capoeira at moderate intensity with 17% reporting high or very high intensity. The sample indicated experiencing a mean of 4 out of the 5 elements of the graduation (batizado) experience. They indicated a moderately high level for experiencing elements of classic capoeira with the highest rated being showcasing trickery without inflicting harm. Common experiences during the capoeira included feelings of transcendence, feeling accepted by others, and encouragement to develop one’s own identity. They indicated a high level of beneficial impacts from capoeira with the top ranked being a more positive attitude and a greater sense of control over one’s life. Noteworthy findings indicated men of color had significantly higher beneficial impacts from capoeira than White men. Both the Pearson correlations and regression found higher beneficial impacts were associated with higher exposure to the basic elements of capoeira, higher exposure during capoeira play to commonly reported experiences, and higher race-related stress due to being a Person of Color. The benefits found for men of color and those experiencing higher race-related stress support university investments, so men of color have access to community-based capoeira.

Note:

The severity of flood events is predicted to increase as a consequence of climate change and may lead to a higher burden of waterborne infectious diseases in the United States. Contaminated floodwater transports bacterial, protozoal, and viral pathogens that typically cause moderate intestinal or respiratory disease, but can also lead to more serious disseminated infections among immunocompromised, young, and older people. Hydroclimatology and drinking water infrastructure influence the transmission of disease, but their roles are not well-understood and may vary by pathogen-type or geographic region. Specific outbreaks of waterborne disease have been attributed to major floods and cases have been positively associated with some meteorological variables, but the association between infections and flooding has not been systematically examined. In this dissertation, we examine the association between seasonal and extreme floods and parasitic and bacterial infections using multiple flood-indicator variables and exposure definitions. In Chapter 2, we use multimodel inference and generalized linear mixed models to determine the effect of seasonal meteorology on hospitalizations across the US. We found that hospitalization rates were generally higher in rural areas and in places that relied on groundwater for drinking water sources. Soil moisture, precipitation, and runoff were associated with significant increases in hospitalizations for Legionnaires' disease, Cryptosporidiosis, and Campylobacteriosis, respectively. In Chapter 3, we use 23 years of weekly case data to examine the effect of cyclonic storms on six waterborne infections in a conditional quasi-Poisson statistical model. Storm exposure was defined separately for distinct storm hazards, namely wind speed and cumulative rainfall, and effects were examined over 3 weeks post-storm. We found that exposure to storm-related rainfall was associated with immediate and lagged increases in cases. In Chapter 4, we use a nonparametric bootstrap to examine the effect of anomalous meteorological conditions, i.e. extremes unrelated to cyclonic storms, on Legionnaires' disease hospitalizations. We also assess the effect of exposure to specific cyclonic storms in a GLMM framework and compare these approaches. Extreme precipitation and months with cyclonic storms were positively associated with Legionnaires' disease hospitalizations. Determining the effect of flooding on Legionnaires' disease is particularly important as it causes severe illness and has steadily increased in incidence for 20 years. An objective of this dissertation was to develop a framework for examining flood-disease dynamics in the context of hydrometeorological and infrastructure-related factors that may influence transmission. We demonstrated that drinking water source, rurality, and geography may play an important role in these dynamics; the analyses also underscored, however, the urgent need for more extensive epidemiological surveillance and water quality data. Climate change will likely place a considerable strain on aging water infrastructure in the US. A nuanced understanding of flood-disease dynamics is central to mitigating these effects.

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