Air pollution exposure disparities across US population and income groups.

Air pollution contributes to the global burden of disease, with ambient exposure to fine particulate matter of diameters smaller than 2.5 µm (PM2.5) being identified as the fifth-ranking risk factor for mortality globally1. Racial/ethnic minorities and lower-income groups in the USA are at a higher risk of death from exposure to PM2.5 than are other population/income groups2-5. Moreover, disparities in exposure to air pollution among population and income groups are known to exist6-17. Here we develop a data platform that links demographic data (from the US Census Bureau and American Community Survey) and PM2.5 data18 across the USA. We analyse the data at the tabulation area level of US zip codes (N is approximately 32,000) between 2000 and 2016. We show that areas with higher-than-average white and Native American populations have been consistently exposed to average PM2.5 levels that are lower than areas with higher-than-average Black, Asian and Hispanic or Latino populations. Moreover, areas with low-income populations have been consistently exposed to higher average PM2.5 levels than areas with high-income groups for the years 2004-2016. Furthermore, disparities in exposure relative to safety standards set by the US Environmental Protection Agency19 and the World Health Organization20 have been increasing over time. Our findings suggest that more-targeted PM2.5 reductions are necessary to provide all people with a similar degree of protection from environmental hazards. Our study is observational and cannot provide insight into the drivers of the identified disparities.

Measuring long-term exposure to wildfire PM2.5 in California: Time-varying inequities in environmental burden.

Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM2.5) concentrations differ from relatively smoothly varying total PM2.5. Thus, we introduced a conceptual model for computing long-term wildfire PM2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM2.5 exposure based on intermittent and extreme daily wildfire PM2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM2.5 < 5 µg/m3; 2) days with non-zero wildfire PM2.5; 3) mean wildfire PM2.5 during peak exposure week; 4) smoke waves (≥2 consecutive days with <15 µg/m3 wildfire PM2.5); and 5) mean annual wildfire PM2.5 concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM2.5 metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM2.5 exposure for several racial/ethnic groups.

Air Pollution and Mortality at the Intersection of Race and Social Class.

BACKGROUND: Black Americans are exposed to higher annual levels of air pollution containing fine particulate matter (particles with an aerodynamic diameter of ≤2.5 µm [PM2.5]) than White Americans and may be more susceptible to its health effects. Low-income Americans may also be more susceptible to PM2.5 pollution than high-income Americans. Because information is lacking on exposure-response curves for PM2.5 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 PM2.5 standards. METHODS: 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 PM2.5 exposure and mortality in subpopulations defined simultaneously by racial identity (Black vs. White) and income level (Medicaid eligible vs. ineligible). RESULTS: Lower PM2.5 exposure was associated with lower mortality in the full population, but marginalized subpopulations appeared to benefit more as PM2.5 levels decreased. For example, the hazard ratio associated with decreasing PM2.5 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. CONCLUSIONS: Higher-income Black persons, low-income White persons, and low-income Black persons may benefit more from lower PM2.5 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.).

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.

Assessing the causal effects of a stochastic intervention in time series data: are heat alerts effective in preventing deaths and hospitalizations?

The methodological development of this article is motivated by the need to address the following scientific question: does the issuance of heat alerts prevent adverse health effects? Our goal is to address this question within a causal inference framework in the context of time series data. A key challenge is that causal inference methods require the overlap assumption to hold: each unit (i.e., a day) must have a positive probability of receiving the treatment (i.e., issuing a heat alert on that day). In our motivating example, the overlap assumption is often violated: the probability of issuing a heat alert on a cooler day is near zero. To overcome this challenge, we propose a stochastic intervention for time series data which is implemented via an incremental time-varying propensity score (ItvPS). The ItvPS intervention is executed by multiplying the probability of issuing a heat alert on day $t$-conditional on past information up to day $t$-by an odds ratio $\delta_t$. First, we introduce a new class of causal estimands, which relies on the ItvPS intervention. We provide theoretical results to show that these causal estimands can be identified and estimated under a weaker version of the overlap assumption. Second, we propose nonparametric estimators based on the ItvPS and derive an upper bound for the variances of these estimators. Third, we extend this framework to multisite time series using a spatial meta-analysis approach. Fourth, we show that the proposed estimators perform well in terms of bias and root mean squared error via simulations. Finally, we apply our proposed approach to estimate the causal effects of increasing the probability of issuing heat alerts on each warm-season day in reducing deaths and hospitalizations among Medicare enrollees in 2837 US counties.

Excess Fatal Overdoses in the United States During the COVID-19 Pandemic by Geography and Substance Type: March 2020-August 2021.

Objectives. To assess heterogeneity in pandemic-period excess fatal overdoses in the United States, by location (state, county) and substance type. Methods. We used seasonal autoregressive integrated moving average (SARIMA) models to estimate counterfactual death counts in the scenario that no pandemic had occurred. Such estimates were subtracted from actual death counts to assess the magnitude of pandemic-period excess mortality between March 2020 and August 2021. Results. Nationwide, we estimated 25 668 (95% prediction interval [PI] = 2811, 48 524) excess overdose deaths. Specifically, 17 of 47 states and 197 of 592 counties analyzed had statistically significant excess overdose-related mortality. West Virginia, Louisiana, Tennessee, Kentucky, and New Mexico had the highest rates (20-37 per 100 000). Nationally, there were 5.7 (95% PI = 1.0, 10.4), 3.1 (95% PI = 2.1, 4.2), and 1.4 (95% PI = 0.5, 2.4) excess deaths per 100 000 involving synthetic opioids, psychostimulants, and alcohol, respectively. Conclusions. The steep increase in overdose-related mortality affected primarily the southern and western United States. We identified synthetic opioids and psychostimulants as the main contributors. Public Health Implications. Characterizing overdose-related excess mortality across locations and substance types is critical for optimal allocation of public health resources. (Am J Public Health. 2024;114(6):599-609. https://doi.org/10.2105/AJPH.2024.307618).

Differential recall bias in estimating treatment effects in observational studies.

Observational studies are frequently used to estimate the effect of an exposure or treatment on an outcome. To obtain an unbiased estimate of the treatment effect, it is crucial to measure the exposure accurately. A common type of exposure misclassification is recall bias, which occurs in retrospective cohort studies when study subjects may inaccurately recall their past exposure. Particularly challenging is differential recall bias in the context of self-reported binary exposures, where the bias may be directional rather than random and its extent varies according to the outcomes experienced. This paper makes several contributions: (1) it establishes bounds for the average treatment effect even when a validation study is not available; (2) it proposes multiple estimation methods across various strategies predicated on different assumptions; and (3) it suggests a sensitivity analysis technique to assess the robustness of the causal conclusion, incorporating insights from prior research. The effectiveness of these methods is demonstrated through simulation studies that explore various model misspecification scenarios. These approaches are then applied to investigate the effect of childhood physical abuse on mental health in adulthood.

Confounder-dependent Bayesian mixture model: Characterizing heterogeneity of causal effects in air pollution epidemiology.

Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (pm2.5) increases mortality rate. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial role in understanding vulnerability to air pollution. To inform policy, it is necessary to identify groups of the population that are more or less vulnerable to air pollution. In causal inference literature, the group average treatment effect (GATE) is a distinctive facet of the conditional average treatment effect. This widely employed metric serves to characterize the heterogeneity of a treatment effect based on some population characteristics. In this paper, we introduce a novel Confounder-Dependent Bayesian Mixture Model (CDBMM) to characterize causal effect heterogeneity. More specifically, our method leverages the flexibility of the dependent Dirichlet process to model the distribution of the potential outcomes conditionally to the covariates and the treatment levels, thus enabling us to: (i) identify heterogeneous and mutually exclusive population groups defined by similar GATEs in a data-driven way, and (ii) estimate and characterize the causal effects within each of the identified groups. Through simulations, we demonstrate the effectiveness of our method in uncovering key insights about treatment effects heterogeneity. We apply our method to claims data from Medicare enrollees in Texas. We found six mutually exclusive groups where the causal effects of pm2.5 on mortality rate are heterogeneous.

A Literature Review of the Effects of Air Pollution on COVID-19 Health Outcomes Worldwide: Statistical Challenges and Data Visualization.

Several peer-reviewed papers and reviews have examined the relationship between exposure to air pollution and COVID-19 spread and severity. However, many of the existing reviews on this topic do not extensively present the statistical challenges associated with this field, do not provide comprehensive guidelines for future researchers, and review only the results of a relatively small number of papers. We reviewed 139 papers, 127 of which reported a statistically significant positive association between air pollution and adverse COVID-19 health outcomes. Here, we summarize the evidence, describe the statistical challenges, and make recommendations for future research. To summarize the 139 papers with data from geographical locations around the world, we also present anopen-source data visualization tool that summarizes these studies and allows the research community to contribute evidence as new research papers are published.

Effects of low-level air pollution exposures on hospital admission for myocardial infarction using multiple causal models.

Myocardial infarctions have been associated with PM2.5, and more recently with NO2 and O3, however counterfactual designs have been lacking and argument continues over the extent of confounding control. Here we introduce a doubly robust, counterfactual-based approach that deals with nonlinearity and interactions in associations between confounders and both outcome and exposure, as well as a double negative controls approach that capture omitted confounders. We used data from over 4 million admissions for myocardial infarction in the US Medicare population between 2000 and 2016 and linked them by ZIP code of residence to high resolution predictions of annual PM2.5, NO2, and O3. We computed the counts of admissions for each ZIP code-year. In the doubly robust approach, we divided each pollutant into deciles, and for each decile, we fitted a gradient boosting machine model to estimate the effects of covariates, including the co-pollutants, on the counts. We used these models to predict, for all ZIP code-years, the expected counts had everyone be exposed in that decile. We also estimated the probability of being in that decile given all covariates, again with a gradient boosting machine, and used inverse probability weights to compute the weighted average rate of MI admission in each decile. In the negative control approach, for each pollutant, we fitted a quasi-Poisson model to estimate the exposure effect, adjusting for covariates including the co-pollutants, and negative exposure and outcome controls to control for unmeasured confounding. Each 1-µg/m3 increase in annual PM2.5 increased the admission for MI by 1.37 cases per 10,000 person-years (95% CI: 1.20, 1.54) in the doubly robust approach, and by 0.69 cases (95% CI 0.60, 0.78) using the negative control approach. Elevated risks were seen even below annual PM2.5 level of 8 µg/m3. Results for NO2 and O3 were inconsistent.

Effects of long-term average temperature on cardiovascular disease hospitalizations in an American elderly population.

BACKGROUND: Short-term exposure to high or low temperatures is associated with increased mortality and morbidity. Less is known about effects of long-term exposure to high or low temperatures. Prolonged exposure to high or low temperatures might contribute to pathophysiological mechanisms, thereby influencing the development of diseases. Our aim was to evaluate associations of long-term temperature exposure with cardiovascular disease (CVD) hospitalizations. METHODS: We constructed an open cohort consisting of all fee-for-service Medicare beneficiaries, aged ≥65, living in the contiguous US from 2000 through 2016 (∼61.6 million individuals). We used data from the 4 km Gridded Surface Meteorological dataset to assess the summer (June-August) and winter (December-February) average daily maximum temperature for each year for each zip code. Cox-equivalent Poisson models were used to estimate associations with first CVD hospitalization, after adjustment for potential confounders. We performed stratified analyses to assess potential effect modification by sex, age, race, Medicaid eligibility and relative humidity. RESULTS: Higher summer average and lower winter average temperatures were associated with an increased risk of CVD hospitalization. We found a HR of 1.068 (95% CI: 1.063, 1.074) per IQR increase (5.2 °C) for summer average temperature and a HR of 1.022 (95% CI: 1.017, 1.028) per IQR decrease (11.7 °C) for winter average temperature. Positive associations of higher summer average temperatures were strongest for individuals aged <75 years, Medicaid eligible, and White individuals. Positive associations of lower winter average temperatures were strongest for individuals aged <75 years and Black individuals, and individuals living in low relative humidity areas. CONCLUSIONS: Living in areas with high summer average temperatures or low winter average temperatures could increase the risk of CVD hospitalizations. The magnitude of the associations of summer and winter average temperatures differs by demographics and relative humidity levels.

Air Pollutants and Asthma Hospitalization in the Medicaid Population.

Rationale: Risk of asthma hospitalization and its disparities associated with air pollutant exposures are less clear within socioeconomically disadvantaged populations, particularly at low degrees of exposure. Objectives: To assess effects of short-term exposures to fine particulate matter (particulate matter with an aerodynamic diameter of ⩽2.5 µm [PM2.5]), warm-season ozone (O3), and nitrogen dioxide (NO2) on risk of asthma hospitalization among national Medicaid beneficiaries, the most disadvantaged population in the United States, and to test whether any subpopulations were at higher risk. Methods: We constructed a time-stratified case-crossover dataset among 1,627,002 hospitalizations during 2000-2012 and estimated risk of asthma hospitalization associated with short-term PM2.5, O3, and NO2 exposures. We then restricted the analysis to hospitalizations with degrees of exposure below increasingly stringent thresholds. Furthermore, we tested effect modifications by individual- and community-level characteristics. Measurements and Main Results: Each 1-µg/m3 increase in PM2.5, 1-ppb increase in O3, and 1-ppb increase in NO2 was associated with 0.31% (95% confidence interval [CI], 0.24-0.37%), 0.10% (95% CI, 0.05 - 0.15%), and 0.28% (95% CI, 0.24 - 0.32%) increase in risk of asthma hospitalization, respectively. Low-level PM2.5 and NO2 exposures were associated with higher risk. Furthermore, beneficiaries with only one asthma hospitalization during the study period or in communities with lower population density, higher average body mass index, longer distance to the nearest hospital, or greater neighborhood deprivation experienced higher risk. Conclusions: Short-term air pollutant exposures increased risk of asthma hospitalization among Medicaid beneficiaries, even at concentrations well below national standards. The subgroup differences suggested individual and contextual factors contributed to asthma disparities under effects of air pollutant exposures.

Long-Term Association of Air Pollution and Hospital Admissions Among Medicare Participants Using a Doubly Robust Additive Model.

BACKGROUND: Studies examining the nonfatal health outcomes of exposure to air pollution have been limited by the number of pollutants studied and focus on short-term exposures. METHODS: We examined the relationship between long-term exposure to fine particulate matter with an aerodynamic diameter <2.5 micrometers (PM2.5), NO2, and tropospheric ozone and hospital admissions for 4 cardiovascular and respiratory outcomes (myocardial infarction, ischemic stroke, atrial fibrillation and flutter, and pneumonia) among the Medicare population of the United States. We used a doubly robust method for our statistical analysis, which relies on both inverse probability weighting and adjustment in the outcome model to account for confounding. The results from this regression are on an additive scale. We further looked at this relationship at lower pollutant concentrations, which are consistent with typical exposure levels in the United States, and among potentially susceptible subgroups. RESULTS: Long-term exposure to fine PM2.5 was associated with an increased risk of all outcomes with the highest effect seen for stroke with a 0.0091% (95% CI, 0.0086-0.0097) increase in the risk of stroke for each 1-µg/m3 increase in annual levels. This translated to 2536 (95% CI, 2383-2691) cases of hospital admissions with ischemic stroke per year, which can be attributed to each 1-unit increase in fine particulate matter levels among the study population. NO2 was associated with an increase in the risk of admission with stroke by 0.00059% (95% CI, 0.00039-0.00075) and atrial fibrillation by 0.00129% (95% CI, 0.00114-0.00148) per ppb and tropospheric ozone was associated with an increase in the risk of admission with pneumonia by 0.00413% (95% CI, 0.00376-0.00447) per parts per billion. At lower concentrations, all pollutants were consistently associated with an increased risk for all our studied outcomes. CONCLUSIONS: Long-term exposure to air pollutants poses a significant risk to cardiovascular and respiratory health among the elderly population in the United States, with the greatest increase in the association per unit of exposure occurring at lower concentrations.

A Bayesian Multi-Outcome Analysis of Fine Particulate Matter and Cardiorespiratory Hospitalizations.

BACKGROUND: Short-term fine particulate matter (PM2.5) exposure is positively associated with acute cardiovascular and respiratory events. Understanding whether this association varies across specific cardiovascular and respiratory conditions has important biologic, clinical, and public health implications. METHODS: We conducted a time-stratified case-crossover study of hospitalizations from 2000 through 2014 among United States Medicare beneficiaries aged 65+. The outcomes were hospitalizations with any of 57 cardiovascular and 32 respiratory discharge diagnoses. We estimated associations with two-day moving average PM2.5 as a piecewise linear term with a knot at PM2.5 = 25 g/m3. We used Multi-Outcome Regression with Tree-structured Shrinkage (MOReTreeS) to identify de novo groups of related diseases such that PM2.5 associations are: (1) similar within outcome groups; but (2) different between outcome groups. We adjusted for temperature, humidity, and individual-level characteristics. We introduce an R package, moretrees. RESULTS: Our dataset included 16,007,293 cardiovascular and 8,690,837 respiratory hospitalizations. Of 57 cardiovascular diseases, 51 were grouped and positively associated with PM2.5. We observed a stronger positive association for heart failure, which formed a separate group. We observed negative associations for groups containing the outcomes other aneurysm and intracranial hemorrhage. Of 32 respiratory outcomes, 31 were grouped and were positively associated with PM2.5. Influenza formed a separate group with a negative association. CONCLUSIONS: We used a new statistical approach, MOReTreeS, to uncover variation in the association between short-term PM2.5 exposure and hospitalizations for cardiovascular and respiratory causes controlling for patient characteristics, time trends, and environmental confounders.

Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties.

We introduce a framework for estimating causal effects of binary and continuous treatments in high dimensions. We show how posterior distributions of treatment and outcome models can be used together with doubly robust estimators. We propose an approach to uncertainty quantification for the doubly robust estimator, which utilizes posterior distributions of model parameters and (1) results in good frequentist properties in small samples, (2) is based on a single run of a Markov chain Monte Carlo (MCMC) algorithm, and (3) improves over frequentist measures of uncertainty which rely on asymptotic properties. We consider a flexible framework for modeling the treatment and outcome processes within the Bayesian paradigm that reduces model dependence, accommodates nonlinearity, and achieves dimension reduction of the covariate space. We illustrate the ability of the proposed approach to flexibly estimate causal effects in high dimensions and appropriately quantify uncertainty. We show that our proposed variance estimation strategy is consistent when both models are correctly specified, and we see empirically that it performs well in finite samples and under model misspecification. Finally, we estimate the effect of continuous environmental exposures on cholesterol and triglyceride levels.

Negative-Binomial and quasi-poisson regressions between COVID-19, mobility and environment in São Paulo, Brazil.

Brazil, the country most impacted by the coronavirus disease 2019 (COVID-19) on the southern hemisphere, use intensive care admissions per day, mobility and other indices to monitor quarantines and prevent the transmissions of SARS-CoV-2. In this study we quantified the associations between residential mobility index (RMI), air pollution, meteorology, and daily cases and deaths of COVID-19 in São Paulo, Brazil. We applied a semiparametric generalized additive model (GAM) to estimate: 1) the association between RMI and COVID-19, accounting for ambient particulate matter (PM2.5), ozone (O3), relative humidity, temperature and delayed exposure between 4 and 21 days, and 2) the association between COVID-19 and exposure to for ambient particulate matter (PM2.5), ozone (O3), accounting for relative humidity, temperature and mobility. We found that an RMI of 45.28% results in 1212 cases (95% CI: 1189 to 1235) and 44 deaths (95% CI: 40 to 47). Increasing the isolation from 45.28% to 50% would avoid 438 cases and 21 deaths. Also, we found that an increment of 10 µg⋅m-³ of PM2.5 results in a risk of 1.140 (95% CI: 1.021 to 1.274) for cases and 1.086 (95% CI: 1.008 to 1.170) for deaths, while O3 produces a relative risk of 1.075 (95% CI: 1.006 to 1.150) for cases and 1.063 (95% CI: 1.006 to 1.124) for deaths, respectively. We compared our results with observations and literature review, finding well agreement. Policymakers can use such mobility indices as tools to control social distance activities. Spatial distancing is an important factor to control COVID-19, however, measuring face-mask usage would enhance the understanding the pandemic dynamic. Small increments of air pollution result in an increased number of COVID-19 cases and deaths.

Do temporal trends of associations between short-term exposure to fine particulate matter (PM2.5) and risk of hospitalizations differ by sub-populations and urbanicity-a study of 968 U.S. counties and the Medicare population.

While associations between short-term exposure to fine particulate matter (PM2.5) and risk of hospitalization are well documented and evidence suggests that such associations change over time, it is unclear whether these temporal changes exist in understudied less-urban areas or differ by sub-population. We analyzed daily time-series data of 968 continental U.S. counties for 2000-2016, with cause-specific hospitalization from Medicare claims and population-weighted PM2.5 concentrations originally estimated at 1km × 1 km from a hybrid model. Circulatory and respiratory hospitalizations were categorized based on primary diagnosis codes at discharge. Using modified Bayesian hierarchical modelling, we evaluated the temporal trend in association between PM2.5 and hospitalizations and whether disparities in this trend exist across individual-level characteristics (e.g., sex, age, race, and Medicaid eligibility as a proxy for socio-economic status) and urbanicity. Urbanicity was categorized into three levels by county-specific percentage of urban population based on urban rural delineation from the U.S. Census. In this cohort with understudied less-urban areas without regulatory monitors, we still found positive association between circulatory and respiratory hospitalization and short-term exposure to PM2.5, with higher effect estimates towards the end of study period. Consistent with current literature, we identified significant disparity in associations by race, socioeconomic status and urbanicity. We found that the percentage change in circulatory hospitalization rate per 10 µg/m3 increase in PM2.5 was higher in the 2008-2016 time period compared to the 2000-2007 period by 0.33% (95% posterior credible interval 0.22, 0.44%), 0.52% (0.33, 0.69%), and 0.67% (0.53, 0.83%) for low, medium and high tertiles of urban areas, respectively. We also observed significant differences in temporal trends of associations across socioeconomic status, sex, and age, indicating a possible widening in disparity of PM2.5-related health burden. This study raises the importance of considering environmental justice issues in PM2.5-related health impacts with respect to how associations may change over time.

The role of outdoor and indoor air quality in the spread of SARS-CoV-2: Overview and recommendations by the research group on COVID-19 and particulate matter (RESCOP commission).

There are important questions surrounding the potential contribution of outdoor and indoor air quality in the transmission of SARS-CoV-2 and perpetuation of COVID-19 epidemic waves. Environmental health may be a critical component of COVID-19 prevention. The public health community and health agencies should consider the evolving evidence in their recommendations and statements, and work to issue occupational guidelines. Evidence coming from the current epidemiological and experimental research is expected to add knowledge about virus diffusion, COVID-19 severity in most polluted areas, inter-personal distance requirements and need for wearing face masks in indoor or outdoor environments. The COVID-19 pandemic has highlighted the need for maintaining particulate matter concentrations at low levels for multiple health-related reasons, which may also include the spread of SARS-CoV-2. Indoor environments represent even a more crucial challenge to cope with, as it is easier for the SARS-COV2 to spread, remain vital and infect other subjects in closed spaces in the presence of already infected asymptomatic or mildly symptomatic people. The potential merits of preventive measures, such as CO2 monitoring associated with natural or controlled mechanical ventilation and air purification, for schools, indoor public places (restaurants, offices, hotels, museums, theatres/cinemas etc.) and transportations need to be carefully considered. Hospital settings and nursing/retirement homes as well as emergency rooms, infectious diseases divisions and ambulances represent higher risk indoor environments and may require additional monitoring and specific decontamination strategies based on mechanical ventilation or air purification.

Role of vitamin K2 in bone-vascular crosstalk.

Vitamin K (VK) is a fat-soluble vitamin that is indispensable for the activation of vitamin K-dependent proteins (VKDPs). It has been shown to play an important role in the proper calcium deposit at the bone level, hindering that on the vascular walls. The deficiency of this vitamin in European populations is frequent and unknown. It is related to several factors, poor dietary intake, altered intestinal absorption or altered production by bacteria, indicating possible dysbiosis. For Vitamin K2 (VK2), there is currently no official reference daily intake (RDI). However, the effects of VK2 on the improvement of health in cardiovascular diseases, on bone metabolism, on chronic kidney diseases have been the subject of research in recent decades. The microbiota in the gastrointestinal tract plays an important role: Bacteroides are primarily capable of synthetizing very long chain forms of menaquinones and, in addition to the bacteria present in the intestinal flora, VK2 is also produced by bacteria used in food fermentation processes. This review provides an update on the current literature regarding the origin of VK2 and its implications in what is called the "calcium paradox", namely the lack of calcium in the bone and its storage in the wall of the vessel.

Association of Tropical Cyclones With County-Level Mortality in the US.

Importance: Tropical cyclones have a devastating effect on society, but a comprehensive assessment of their association with cause-specific mortality over multiple years of study is lacking. Objective: To comprehensively evaluate the association of county-level tropical cyclone exposure and death rates from various causes in the US. Design, Setting, and Participants: A retrospective observational study using a Bayesian conditional quasi-Poisson model to examine how tropical cyclones were associated with monthly death rates. Data from 33.6 million deaths in the US were collected from the National Center for Health Statistics over 31 years (1988-2018), including residents of the 1206 counties in the US that experienced at least 1 tropical cyclone during the study period. Exposures: Tropical cyclone days per county-month, defined as number of days in a month with a sustained maximal wind speed 34 knots or greater. Main Outcomes and Measures: Monthly cause-specific county-level death rates by 6 underlying causes of death: cancers, cardiovascular diseases, infectious and parasitic diseases, injuries, neuropsychiatric conditions, and respiratory diseases. The model yielded information about the association between each additional cyclone day per month and monthly county-level mortality compared with the same county-month in different years, up to 6 months after tropical cyclones, and how these estimated associations varied by age, sex, and social vulnerability. The unit of analysis was county-month. Results: There were 33 619 393 deaths in total (16 691 681 females and 16 927 712 males; 8 587 033 aged 0-64 years and 25 032 360 aged 65 years or older) from the 6 causes recorded in 1206 US counties. There was a median of 2 tropical cyclone days experienced in total in included US counties. Each additional cyclone day was associated with increased death rates in the month following the cyclone for injuries (3.7% [95% credible interval {CrI}, 2.5%-4.9%]; 2.0 [95% CrI, 1.3-2.7] additional deaths per 1 000 000 for 2018 monthly age-standardized median rate [DPM]; 54.3 to 56.3 DPM), infectious and parasitic diseases (1.8% [95% CrI, 0.1%-3.6%]; 0.2 [95% CrI, 0.0-0.4] additional DPM; 11.7 to 11.9 DPM), respiratory diseases (1.3% [95% CrI, 0.2%-2.4%]; 0.6 [95% CrI, 0.1-1.1] additional DPM; 44.9 to 45.5 DPM), cardiovascular diseases (1.2% [95% CrI, 0.6%-1.7%]; 1.5 [95% CrI, 0.8-2.2] additional DPM; 129.6 to 131.1 DPM), neuropsychiatric conditions (1.2% [95% CrI, 0.1%-2.4%]; 0.6 [95% CrI, 0.1-1.2] additional DPM; 52.1 to 52.7 DPM), with no change for cancers (-0.3% [95% CrI, -0.9% to 0.3%]; -0.3 [95% CrI, -0.9 to 0.3] additional DPM; 100.4 to 100.1 DPM). Conclusions and Relevance: Among US counties that experienced at least 1 tropical cyclone from 1988-2018, each additional cyclone day per month was associated with modestly higher death rates in the months following the cyclone for several causes of death, including injuries, infectious and parasitic diseases, cardiovascular diseases, neuropsychiatric conditions, and respiratory diseases.