E-cigarettes are generally thought of as a safer smoking alternative to traditional cigarettes. However, little is known about the effects of e-cigarette liquids (e-liquids) on the lung. Since over ...7,000 unique flavors have been identified for purchase in the United States, our goal was to conduct a screen that would test whether different flavored e-liquids exhibited different toxicant profiles. We tested the effects of 13 different flavored e-liquids with nicotine and propylene glycol/vegetable glycerin (PG/VG) serving as controls on a lung epithelial cell line (CALU3). Using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay as an indicator of cell proliferation/viability, we demonstrated a dose-dependent decrease of MTT metabolism by all flavors tested. However, a group of four flavors consistently showed significantly greater toxicity compared with the PG/VG control, indicating the potential for some flavors to elicit more harmful effects than others. We also tested the aerosolized "vapor" from select e-liquids on cells and found similar dose-dependent trends, suggesting that direct e-liquid exposures are a justifiable first-pass screening approach for determining relative e-liquid toxicity. We then identified individual chemical constituents for all 13 flavors using gas chromatography-mass spectrometry. These data revealed that beyond nicotine and PG/VG, the 13 flavored e-liquids have diverse chemical constituents. Since all of the flavors exhibited some degree of toxicity and a diverse array of chemical constituents with little inhalation toxicity available, we conclude that flavored e-liquids should be extensively tested on a case-by-case basis to determine the potential for toxicity in the lung and elsewhere.
Black Americans are exposed to higher annual levels of air pollution containing fine particulate matter (particles with an aerodynamic diameter of ≤2.5 μm PM
) than White Americans and may be more ...susceptible to its health effects. Low-income Americans may also be more susceptible to PM
pollution than high-income Americans. Because information is lacking on exposure-response curves for PM
exposure and mortality among marginalized subpopulations categorized according to both race and socioeconomic position, the Environmental Protection Agency lacks important evidence to inform its regulatory rulemaking for PM
standards.
We analyzed 623 million person-years of Medicare data from 73 million persons 65 years of age or older from 2000 through 2016 to estimate associations between annual PM
exposure and mortality in subpopulations defined simultaneously by racial identity (Black vs. White) and income level (Medicaid eligible vs. ineligible).
Lower PM
exposure was associated with lower mortality in the full population, but marginalized subpopulations appeared to benefit more as PM
levels decreased. For example, the hazard ratio associated with decreasing PM
from 12 μg per cubic meter to 8 μg per cubic meter for the White higher-income subpopulation was 0.963 (95% confidence interval CI, 0.955 to 0.970), whereas equivalent hazard ratios for marginalized subpopulations were lower: 0.931 (95% CI, 0.909 to 0.953) for the Black higher-income subpopulation, 0.940 (95% CI, 0.931 to 0.948) for the White low-income subpopulation, and 0.939 (95% CI, 0.921 to 0.957) for the Black low-income subpopulation.
Higher-income Black persons, low-income White persons, and low-income Black persons may benefit more from lower PM
levels than higher-income White persons. These findings underscore the importance of considering racial identity and income together when assessing health inequities. (Funded by the National Institutes of Health and the Alfred P. Sloan Foundation.).
Hurricanes and other tropical cyclones have devastating effects on society. Previous case studies have quantified their impact on some health outcomes for particular tropical cyclones, but a ...comprehensive assessment over longer periods is currently missing. Here, we used data on 70 million Medicare hospitalizations and tropical cyclone exposures over 16 years (1999-2014). We formulated a conditional quasi-Poisson model to examine how tropical cyclone exposure (days greater than Beaufort scale gale-force wind speed; ≥34 knots) affect hospitalizations for 13 mutually-exclusive, clinically-meaningful causes. We found that tropical cyclone exposure was associated with average increases in hospitalizations from several causes over the week following exposure, including respiratory diseases (14.2%; 95% confidence interval CI: 10.9-17.9%); infectious and parasitic diseases (4.3%; 95%CI: 1.2-8.1%); and injuries (8.7%; 95%CI: 6.0-11.8%). Average decadal tropical cyclone exposure in all impacted counties would be associated with an estimated 16,772 (95%CI: 8,265-25,278) additional hospitalizations. Our findings demonstrate the need for targeted preparedness strategies for hospital personnel before, during, and after tropical cyclones.
To investigate the impact of the US Voting Rights Act (VRA) of 1965 on Black and Black versus White infant deaths in Jim Crow states.
Using data from 1959 to 1980 and 2017 to 2021, we applied ...difference-in-differences methods to quantify differential pre-post VRA changes in infant deaths in VRA-exposed versus unexposed counties, controlling for population size and social, economic, and health system characteristics. VRA-exposed counties, identified by Section 4, were subject to government interventions to remove existing racist voter suppression policies.
Black infant deaths in VRA-exposed counties decreased by an average of 11.4 (95% confidence interval CI = 1.7, 21.0) additional deaths beyond the decrease experienced by unexposed counties between the pre-VRA period (1959-1965) and the post-VRA period (1966-1970). This translates to 6703 (95% CI = 999.6, 12 348) or 17.5% (95% CI = 3.1%, 28.1%) fewer deaths than would have been experienced in the absence of the VRA. The equivalent differential changes were not significant among the White or total population.
Passage of the VRA led to pronounced reductions in Black infant deaths in Southern counties subject to government intervention because these counties had particularly egregious voter suppression practices. (
2024;114(3):300-308. https://doi.org/10.2105/AJPH.2023.307518).
There is growing concern that phthalate exposures may have an impact on child neurodevelopment. Prenatal exposure to phthalates has been linked with externalizing behaviors and executive functioning ...defects suggestive of an attention-deficit hyperactivity disorder (ADHD) phenotype.
We undertook an investigation into whether prenatal exposure to phthalates was associated with clinically confirmed ADHD in a population-based nested case-control study of the Norwegian Mother and Child Cohort (MoBa) between the years 2003 and 2008.
Phthalate metabolites were measured in maternal urine collected at midpregnancy. Cases of ADHD (
=297) were obtained through linkage between MoBa and the Norwegian National Patient Registry. A random sample of controls (
=553) from the MoBa population was obtained.
In multivariable adjusted coexposure models, the sum of di-2-ethylhexyl phthalate metabolites (∑DEHP) was associated with a monotonically increasing risk of ADHD. Children of mothers in the highest quintile of ∑DEHP had almost three times the odds of an ADHD diagnosis as those in the lowest OR=2.99 (95% CI: 1.47, 5.49). When ∑DEHP was modeled as a log-linear (natural log) term, for each log-unit increase in exposure, the odds of ADHD increased by 47% OR=1.47 (95% CI: 1.09, 1.94). We detected no significant modification by sex or mediation by prenatal maternal thyroid function or by preterm delivery.
In this population-based case-control study of clinical ADHD, maternal urinary concentrations of DEHP were monotonically associated with increased risk of ADHD. Additional research is needed to evaluate potential mechanisms linking phthalates to ADHD. https://doi.org/10.1289/EHP2358.
Most causal inference studies rely on the assumption of overlap to estimate population or sample average causal effects. When data suffer from non-overlap, estimation of these estimands requires ...reliance on model specifications due to poor data support. All existing methods to address nonoverlap, such as trimming or down-weighting data in regions of poor data support, change the estimand so that inference cannot be made on the sample or the underlying population. In environmental health research settings where study results are often intended to influence policy, population-level inference may be critical and changes in the estimand can diminish the impact of the study results, 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 and causal effect heterogeneity. 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 nonparametrically 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. Code and data to implement the method and reproduce all simulations and analyses is available on Github (https://github.com/rachelnethery/overlap).
We aimed to evaluate the impact of the EPA's Mobile Source Air Toxics rules (MSAT), which targeted benzene emissions, on childhood and young adult leukemia and lymphoma incidence in Alaska.
MSAT was ...implemented in 2011 and produced a dramatic decline in ambient benzene in Alaska. Due to previous benzene-related regulations enacted in the continental United States, MSAT had relatively modest impacts in other states. This created quasi-experimental conditions leveraged in this study. Using 2-year state-level incidence rates of childhood and young adult leukemia and lymphoma for each US state 2001-2018, we examined MSAT-attributable changes in incidence by applying a difference-in-differences approach.
We found evidence of a substantial reduction associated with MSAT in incidence of childhood and young adult lymphoma (-1.23 -1.84, -0.62 cases per 100,000), but not in leukemia (-0.13 -0.77, 0.51 cases per 100,000).
Our findings are consistent with the hypothesis that MSAT, which reduced benzene levels in Alaska, led to a decline in lymphoma incidence in children and young adults.
We develop a causal inference approach to estimate the number of adverse health events that were prevented due to changes in exposure to multiple pollutants attributable to a large-scale air quality ...intervention/regulation, with a focus on the 1990 Clean Air Act Amendments (CAAA). We introduce a causal estimand called the Total Events Avoided (TEA) by the regulation, defined as the difference in the number of health events expected under the no-regulation pollution exposures and the number observed with-regulation. We propose matching and machine learning methods that leverage population-level pollution and health data to estimate the TEA. Our approach improves upon traditional methods for regulation health impact analyses by formalizing causal identifying assumptions, utilizing population-level data, minimizing parametric assumptions, and collectively analyzing multiple pollutants. To reduce model-dependence, our approach estimates cumulative health impacts in the subset of regions with projected no-regulation features lying within the support of the observed with-regulation data, thereby providing a conservative but data-driven assessment to complement traditional parametric approaches. We analyze the health impacts of the CAAA in the US Medicare population in the year 2000, and our estimates suggest that large numbers of cardiovascular and dementia-related hospitalizations were avoided due to CAAA-attributable changes in pollution exposure.
U.S. Environmental Protection Agency (EPA) air quality (AQ) monitors, the “gold standard” for measuring air pollutants, are sparsely positioned across the U.S. Low-cost sensors (LCS) are increasingly ...being used by the public to fill in the gaps in AQ monitoring; however, LCS are not as accurate as EPA monitors. In this work, we investigate factors impacting the differences between an individual’s true (unobserved) exposure to air pollution and the exposure reported by their nearest AQ instrument (which could be either an LCS or an EPA monitor). We use simulations based on California data to explore different combinations of hypothetical LCS placement strategies (e.g., at schools or near major roads), for different numbers of LCS, with varying plausible amounts of LCS device measurement errors. We illustrate how real-time AQ reporting could be improved (or, in some cases, worsened) by using LCS, both for the population overall and for marginalized communities specifically. This work has implications for the integration of LCS into real-time AQ reporting platforms.
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