arXiv subjects

Rachel C. Nethery

Publications and source records attributed to Rachel C. Nethery.

22 records · Page 2Linked to original sources

arXiv2020

Integrated causal-predictive machine learning models for tropical cyclone epidemiology

Strategic preparedness has been shown to reduce the adverse health impacts of hurricanes and tropical storms, referred to collectively as tropical cyclones (TCs), but its protective impact could be enhanced by a more comprehensive and rigorous characterization of TC epidemiology. To generate the insights and tools necessary for high-precision TC preparedness, we develop and apply a novel Bayesian machine learning approach that standardizes estimation of historic TC health impacts, discovers common patterns and sources of heterogeneity in those health impacts, and enables identification of communities at highest health risk for future TCs. The model integrates (1) a causal inference component to quantify the immediate health impacts of recent historic TCs at high spatial resolution and (2) a predictive component that captures how TC meteorological features and socioeconomic/demographic characteristics of impacted communities are associated with health impacts. We apply it to a rich data platform containing detailed historic TC exposure information and Medicare claims data. The health outcomes used in our analyses are all-cause mortality and cardiovascular- and respiratory-related hospitalizations. We report a high degree of heterogeneity in the acute health impacts of historic TCs at both the TC level and the community level, with substantial increases in respiratory hospitalizations, on average, during a two-week period surrounding TCs. TC sustained windspeeds are found to be the primary driver of increased mortality and respiratory risk. Our modeling approach has broader utility for predicting the health impacts of many types of extreme climate events.

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arXiv2019

Causal inference and machine learning approaches for evaluation of the health impacts of large-scale air quality regulations

We develop a causal inference approach to estimate the number of adverse health events prevented by large-scale air quality regulations via changes in exposure to multiple pollutants. This approach is motivated by regulations that impact pollution levels in all areas within their purview. We introduce a causal estimand called the Total Events Avoided (TEA) by the regulation, defined as the difference in the expected number of health events under the no-regulation pollution exposures and the observed number of health events under the with-regulation pollution exposures. We propose a matching method and a machine learning method that leverage high-resolution, population-level pollution and health data to estimate the TEA. Our approach improves upon traditional methods for regulation health impact analyses by clarifying the causal identifying assumptions, utilizing population-level data, minimizing parametric assumptions, and considering the impacts of multiple pollutants simultaneously. To reduce model-dependence, the TEA estimate captures health impacts only for units in the data whose anticipated no-regulation features are within the support of the observed with-regulation data, thereby providing a conservative but data-driven assessment to complement traditional parametric approaches. We apply these methods to investigate the health impacts of the 1990 Clean Air Act Amendments in the US Medicare population.

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arXiv2018

A causal inference framework for cancer cluster investigations using publicly available data

Often, a community becomes alarmed when high rates of cancer are noticed, and residents suspect that the cancer cases could be caused by a known source of hazard. In response, the CDC recommends that departments of health perform a standardized incidence ratio (SIR) analysis to determine whether the observed cancer incidence is higher than expected. This approach has several limitations that are well documented in the literature. In this paper we propose a novel causal inference approach to cancer cluster investigations, rooted in the potential outcomes framework. Assuming that a source of hazard representing a potential cause of increased cancer rates in the community is identified a priori, we introduce a new estimand called the causal SIR (cSIR). The cSIR is a ratio defined as the expected cancer incidence in the exposed population divided by the expected cancer incidence under the (counterfactual) scenario of no exposure. To estimate the cSIR we need to overcome two main challenges: 1) identify unexposed populations that are as similar as possible to the exposed one to inform estimation under the counterfactual scenario of no exposure, and 2) make inference on cancer incidence in these unexposed populations using publicly available data that are often available at a much higher level of spatial aggregation than what is desired. We overcome the first challenge by relying on matching. We overcome the second challenge by developing a Bayesian hierarchical model that borrows information from other sources to impute cancer incidence at the desired finer level of spatial aggregation. We apply our proposed approach to determine whether trichloroethylene vapor exposure has caused increased cancer incidence in Endicott, NY.

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arXiv2018

Estimating Population Average Causal Effects in the Presence of Non-Overlap: The Effect of Natural Gas Compressor Station Exposure on Cancer Mortality

Most causal inference studies rely on the assumption of overlap to estimate population or sample average causal effects. When data exhibit non-overlap, estimation of these estimands requires reliance on model specifications, due to poor data support. All existing methods to address non-overlap, such as trimming or down-weighting data in regions of poor support, change the estimand. In environmental health research, where study results are often intended to influence policy, changes in the estimand can diminish the study's impact, because estimates may not be representative of effects in the population of interest to policymakers. Researchers may be willing to make additional, minimal modeling assumptions in order to preserve the ability to estimate population average causal effects. We seek to make two contributions on this topic. First, we propose a flexible, data-driven definition of propensity score overlap and non-overlap regions. Second, we develop a novel Bayesian framework to estimate population average causal effects with minor model dependence and appropriately large uncertainties in the presence of non-overlap. In this approach, the tasks of estimating causal effects in the overlap and non-overlap regions are delegated to two distinct models, suited to the degree of data support in each region. Tree ensembles are used to non-parametrically estimate individual causal effects in the overlap region, where the data can speak for themselves. In the non-overlap region, where insufficient data support means reliance on model specification is necessary, individual causal effects are estimated by extrapolating trends from the overlap region via a spline model. The promising performance of our method is demonstrated in simulations. Finally, we utilize our method to perform a novel investigation of the causal effect of natural gas compressor station exposure on cancer outcomes.

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