Air pollution and the sequelae of COVID-19 patients: A multistate analysis.

IMPORTANCE: Recent evidence links air pollution to the severity COVID-19 symptoms and to death from the disease. To date, however, few studies have assessed whether air pollution affects the sequelae to more severe states or recovery from COVID-19 in a cohort with individual data. OBJECTIVE: To assess how air pollution affects the transition to more severe COVID-19 states or to recovery from COVID-19 infection in a cohort with detailed patient information. DESIGN AND OUTCOMES: We used a cohort design that followed patients admitted to hospital in the Kaiser Permanente Southern California (KPSC) Health System, which has 4.7 million members with characteristics similar to the general population. Enrollment began on 06/01/2020 and ran until 01/30/2021 for all patients admitted to hospital while ill with COVID-19. All possible states of sequelae were considered, including deterioration to intensive care, to death, discharge to recovery, or discharge to death. Transition risks were estimated with a multistate model. We assessed exposure using chemical transport model that predicted ambient concentrations of nitrogen dioxide, ozone, and fine particulate matter (PM2.5) at a 1 km scale. RESULTS: Each increase in PM2.5 concentration equivalent to the interquartile range was associated with increased risk of deterioration to intensive care (HR of 1.16; 95% CI: 1.12-1.20) and deterioration to death (HR of 1.11; 95% CI: 1.04-1.17). Results for ozone were consistent with PM2.5 effects, but ozone also affected the transition from recovery to death: HR of 1.24 (95% CI: 1.01-1.51). NO2 had weaker effects but displayed some elevated risks. CONCLUSIONS: PM2.5 and ozone were significantly associated with transitions to more severe states while in hospital and to death after discharge from hospital. Reducing air pollution could therefore lead to improved prognosis for COVID-19 patients and a sustainable means of reducing the health impacts of coronaviruses now and in the future.

Air pollution and meteorology as risk factors for COVID-19 death in a cohort from Southern California.

BACKGROUND: Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models. METHODS: We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients. FINDINGS: We found significant associations between COVID-19 death and several air pollution exposures, including: PM2.5 mass, PM0.1 mass, PM2.5 nitrates, PM2.5 elemental carbon, PM2.5 on-road diesel, and PM2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM2.5 mass and PM2.5 nitrate to HR âˆ¼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects. INTERPRETATION: Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.

BAYESIAN HIERARCHICAL MODELING AND ANALYSIS FOR ACTIGRAPH DATA FROM WEARABLE DEVICES.

The majority of Americans fail to achieve recommended levels of physical activity, which leads to numerous preventable health problems such as diabetes, hypertension, and heart diseases. This has generated substantial interest in monitoring human activity to gear interventions toward environmental features that may relate to higher physical activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraph units) continuously record the activity levels of a subject, producing massive amounts of high-resolution measurements. Analyzing actigraph data needs to account for spatial and temporal information on trajectories or paths traversed by subjects wearing such devices. Inferential objectives include estimating a subject's physical activity levels along a given trajectory; identifying trajectories that are more likely to produce higher levels of physical activity for a given subject; and predicting expected levels of physical activity in any proposed new trajectory for a given set of health attributes. Here, we devise a Bayesian hierarchical modeling framework for spatial-temporal actigraphy data to deliver fully model-based inference on trajectories while accounting for subject-level health attributes and spatial-temporal dependencies. We undertake a comprehensive analysis of an original dataset from the Physical Activity through Sustainable Transport Approaches in Los Angeles (PASTA-LA) study to ascertain spatial zones and trajectories exhibiting significantly higher levels of physical activity while accounting for various sources of heterogeneity.

Opportunistic Environmental Sensing with Smartphones: a Critical Review of Current Literature and Applications.

PURPOSE OF REVIEW: This review sought to summarize recent literature and applications of passive, or opportunistic, mobile sensing in the fields of exposure science in built environment settings; highlight innovative opportunistic sensing systems; and analyze their functionality, significant features, and limitations. RECENT FINDINGS: Fifty-two papers related to opportunistic environmental sensing from 2009 or later were related to this review, of which 27 were included. An array of applications have emerged in environmental monitoring, employing anywhere from one to six of the phone's on-board sensors. The viability of an application is determined by several key factors: the number and quality of sensors on-board the smartphone; power and processing demand; algorithm complexity; data security; mobile network coverage; reliance on external data sources; minimum number of users required; and degree of user burden when using the application. Some factors are universal, while others are more context-specific. Future research should assess sensing applications based on these factors.

Advancing Alternative Analysis: Integration of Decision Science.

BACKGROUND: Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals. OBJECTIVES: We assessed whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics. METHODS: A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and were prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings. RESULTS: We concluded that the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients and would also advance the science of decision analysis. CONCLUSIONS: We advance four recommendations: a) engaging the systematic development and evaluation of decision approaches and tools; b) using case studies to advance the integration of decision analysis into alternatives analysis; c) supporting transdisciplinary research; and d) supporting education and outreach efforts. https://doi.org/10.1289/EHP483.

CicLAvia: Evaluation of participation, physical activity and cost of an open streets event in Los Angeles.

Physical activity is beneficial for health, but there are limited opportunities in urban areas to safely access public streets for traffic-free cycling, skating or walking. Ciclovías are open streets programs that close major roads to motor vehicles so they can be exclusively used by bicyclists and pedestrians. We estimated participation in one Los Angeles Ciclovía event (CicLAvia) using intercept surveys and 14 surveillance cameras which were placed along the 6-mile route in April 2014. We also applied estimates of the distance and speed traveled from the use of GPS data acquired from subsequent CicLAvia events. CicLAvia attracted between 37,700 and 53,950 active participants generating 176,500 to 263,000 MET-hours of energy expenditure, at an estimated cost borne by tax dollars of $1.29 to $1.91 per MET-hour. Among participants, 37% had never previously participated in CicLAvia, but 40% of individuals said that if they were not at CicLAvia they would have been physically active elsewhere and 45% would have been sedentary. Given its large reach, it makes sense to increase the frequency of Ciclovías to occur more than a few times a year to promote population health.

Air quality impacts of a CicLAvia event in Downtown Los Angeles, CA.

CicLAvia in Los Angeles, CA is the open streets program that closes streets to motorized vehicles and invites people to walk, run, play or ride their bicycles on these streets, allowing them to experience the city in a new way and get exercise at the same time. Since the events reduce the motorized traffic flow, which is a significant source of air pollution, on the streets, it is reasonable to hypothesize that the CicLAvia events can reduce the concentrations of traffic-emitted air pollutants during the road closure. This study is the first experiment to test this hypothesis. The on-road and community-wide ultrafine particle (UFP) and PM2.5 were measured on the Event-Sunday (October 5th, 2014) and the Pre- and Post- Sundays (September 28(th) and October 12(th), 2014). Data analysis results showed the on-road UFP and PM2.5 reduction was 21% and 49%, respectively, and the community-wide PM2.5 reduction was 12%.