Developing consistent data and methods to measure the public health impacts of ambient air quality for Environmental Public Health Tracking: progress to date and future directions

The Disproportionate Impact of COVID-19 among Undocumented Immigrants and Racial Minorities in the US

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has had an unprecedented effect, especially among under-resourced minority communities. Surveillance of those at high risk is critical for preventing and controlling the pandemic. We must better understand the relationships between COVID-19-related cases or deaths and characteristics in our most vulnerable population that put them at risk to target COVID-19 prevention and management efforts. Population characteristics strongly related to United States (US) county-level data on COVID-19 cases and deaths during all stages of the pandemic were identified from the onset of the epidemic and included county-level socio-demographic and comorbidities data, as well as daily meteorological modeled observation data from the North American Regional Reanalysis (NARR), and the NARR high spatial resolution model to assess the environment. Advanced machine learning (ML) approaches were used to identify outbreaks (geographic clusters of COVID-19) and included spatiotemporal risk factors and COVID-19 vaccination efforts, especially among vulnerable and underserved communities. COVID-19 outcomes were found to be negatively associated with the number of people vaccinated and positively associated with age, the prevalence of cardiovascular disease, diabetes, and the minority population. There was also a strong positive correlation between unauthorized immigrants and the prevalence of COVID-19 cases and deaths. Meteorological variables were also investigated, but correlations with COVID-19 were relatively weak. Our findings suggest that COVID-19 has had a disproportionate impact across the US population among vulnerable and minority communities. Findings also emphasize the importance of vaccinations and tailored public health initiatives (e.g., mask mandates, vaccination) to reduce the spread of COVID-19 and the number of COVID-19 related deaths across all populations.

Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland

The fine particulate matter baseline (PMB), which includes PM_2.5 monitor readings fused with Community Multiscale Air Quality (CMAQ) model predictions, using the Hierarchical Bayesian Model (HBM), is less accurate in rural areas without monitors. To address this issue, an upgraded HBM was used to form four experimental aerosol optical depth (AOD)-PM_2.5 concentration surfaces. A case-crossover design and conditional logistic regression evaluated the contribution of the AOD-PM_2.5 surfaces and PMB to four respiratory-cardiovascular hospital events in all 99 12 km² CMAQ grids, and in grids with and without ambient air monitors. For all four health outcomes, only two AOD-PM_2.5 surfaces, one not kriged (PMC) and the other kriged (PMCK), had significantly higher Odds Ratios (ORs) on lag days 0, 1, and 01 than PMB in all grids, and in grids without monitors. In grids with monitors, emergency department (ED) asthma PMCK on lag days 0, 1 and 01 and inpatient (IP) heart failure (HF) PMCK ORs on lag days 01 were significantly higher than PMB ORs. Warm season ORs were significantly higher than cold season ORs. Independent confirmation of these results should include AOD-PM_2.5 concentration surfaces with greater temporal-spatial resolution, now easily available from geostationary satellites, such as GOES-16 and GOES-17.

Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models

Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project

OBJECTIVE

Small area data are key to better understanding the complex relationships between environmental health, health outcomes, and risk factors at a local level. In 2014, the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program (Tracking Program) conducted the Sub-County Data Pilot Project with grantees to consider integration of sub-county data into the National Environmental Public Health Tracking Network (Tracking Network).

DESIGN

The Tracking Program and grantees developed sub-county-level data for several data sets during this pilot project, working to standardize processes for submitting data and creating required geographies. Grantees documented challenges they encountered during the pilot project and documented decisions.

RESULTS

This article covers the challenges revealed during the project. It includes insights into geocoding, aggregation, population estimates, and data stability and provides recommendations for moving forward.

CONCLUSION

National standards for generating, analyzing, and sharing sub-county data should be established to build a system of sub-county data that allow for comparison of outcomes, geographies, and time. Increasing the availability and accessibility of small area data will not only enhance the Tracking Network's capabilities but also contribute to an improved understanding of environmental health and informed decision making at a local level.

Assessing the impact of fine particulate matter (PM2.5) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates

An enhanced research paradigm is presented to address the spatial and temporal gaps in fine particulate matter (PM_2.5) measurements and generate realistic and representative concentration fields for use in epidemiological studies of human exposure to ambient air particulate concentrations. The general approach for research designed to analyze health impacts of exposure to PM_2.5 is to use concentration data from the nearest ground-based air quality monitor(s), which typically have missing data on the temporal and spatial scales due to filter sampling schedules and monitor placement, respectively. To circumvent these data gaps, this research project uses a Hierarchical Bayesian Model (HBM) to generate estimates of PM_2.5 in areas with and without air quality monitors by combining PM_2.5 concentrations measured by monitors, PM_2.5 concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM_2.5 concentrations. This methodology represents a substantial step forward in the approach for developing representative PM_2.5 concentration datasets to correlate with inpatient hospitalizations and emergency room visits data for asthma and inpatient hospitalizations for myocardial infarction (MI) and heart failure (HF) using case-crossover analysis. There were two key objective of this current study. First was to show that the inputs to the HBM could be expanded to include AOD data in addition to data from PM_2.5 monitors and predictions from CMAQ. The second objective was to determine if inclusion of AOD surfaces in HBM model algorithms results in PM_2.5 air pollutant concentration surfaces which more accurately predict hospital admittance and emergency room visits for MI, asthma, and HF. This study focuses on the New York City, NY metropolitan and surrounding areas during the 2004-2006 time period, in order to compare the health outcome impacts with those from previous studies and focus on any benefits derived from the changes in the HBM model surfaces. Consistent with previous studies, the results show high PM_2.5 exposure is associated with increased risk of asthma, myocardial infarction and heart failure. The estimates derived from concentration surfaces that incorporate AOD had a similar model fit and estimate of risk as compared to those derived from combining monitor and CMAQ data alone. Thus, this study demonstrates that estimates of PM_2.5 concentrations from satellite data can be used to supplement PM_2.5 monitor data in the estimates of risk associated with three common health outcomes. Results from this study were inconclusive regarding the potential benefits derived from adding AOD data to the HBM, as the addition of the satellite data did not significantly increase model performance. However, this study was limited to one metropolitan area over a short two-year time period. The use of next-generation, high temporal and spatial resolution satellite AOD data from geostationary and polar-orbiting satellites is expected to improve predictions in epidemiological studies in areas with fewer pollutant monitors or over wider geographic areas.

Short-term effects of ambient air pollutants on asthma-related emergency department visits in Indianapolis, Indiana, 2007-2011

OBJECTIVE

We estimate the short-term associations between daily changes in ambient air pollutants and daily asthma-related emergency department (ED) visits in Indianapolis, IN.

METHODS

We identified asthma-related ED visits among Indianapolis residents aged ≥5 years. We used Poisson regression in a time-series framework to estimate the increased risk for asthma-related ED visits from exposure to ambient SO2, PM2.5 and ozone during the warm season (April-September) and SO2 and PM2.5 during the cold (October-March) season, from 2007 to 2011. Our models controlled for measured confounders, including weather and respiratory infections, as well as unmeasured confounders using a natural cubic spline to account for long-term seasonal trends.

RESULTS

During 2007-2011 in Indianapolis, 165,056 asthma-related ED visits occurred. We found statistically significant positive associations (p < 0.05) between ambient air pollutants and ED visits during the warm season for persons aged 5-44 years. Interquartile range increases in daily ozone concentrations with same day, 2-day lagged, and 3-day moving average were associated with increased risks for ED visits of 3.2% (95% CI: 0.2%, 6.3%), 4.4% (0.1%, 8.9%) and 4.8% (0.2%, 9.6%), respectively. Interquartile range increases in 3-day moving averages for SO2 were associated with an increased risk of 3.3% (95% CI: 0.2%, 6.5%). We identified statistically significant associations (p < 0.05) between increased SO2 and PM2.5 levels and decreased ED visits among some age groups, primarily during the cold season, and no significant positive associations between changes in PM2.5 concentration and asthma-related ED visits.

CONCLUSIONS

During the warm season, increases in ozone and SO2 concentrations were associated with increased asthma morbidity in children and young adults in Indianapolis. These results will enable reliable estimation of the health impacts of increases in these pollutants on asthma-related ED visits in Indianapolis and similar communities.

A decade of environmental public health tracking (2002-2012): progress and challenges

BACKGROUND

The creation of the Centers for Disease Control and Prevention Environmental Public Health Tracking Program spawned an invigorating and challenging approach toward implementing the nation's first population-based, environmental disease tracking surveillance system. More than 10 years have passed since its creation and an abundance of peer-reviewed articles have been published spanning a broad variety of public health topics related primarily to the goal of reducing diseases of environmental origin.

OBJECTIVE

To evaluate peer-reviewed literature related to Environmental Public Health Tracking during 2002-2012, recognize major milestones and challenges, and offer recommendations.

DESIGN

A narrative overview was conducted using titles and abstracts of peer-reviewed articles, key word searches, and science-based search engine databases.

MAIN OUTCOMES

Eighty published articles related to "health tracking" were identified and categorized according to 4 crossed-central themes. The Science and Research theme accounted for the majority of published articles, followed by Policy and Practice, Collaborations Among Health and Environmental Programs, and Network Development.

CONCLUSIONS

Overall, progress was reported in the areas of data linkage, data sharing, surveillance methods, and network development. Ongoing challenges included formulating better ways to establish the connections between health and the environment, such as using biomonitoring, public water systems, and private well water data. Recommendations for future efforts include use of data to inform policy and practice and use of electronic health records data for environmental health surveillance.

Engaging academia to advance the science and practice of environmental public health tracking

Public health agencies at the federal, state, and local level are responsible for implementing actions and policies that address health problems related to environmental hazards. These actions and policies can be informed by integrating or linking data on health, exposure, hazards, and population. The mission of the Centers for Disease Control and Prevention׳s National Environmental Public Health Tracking Program (Tracking Program) is to provide information from a nationwide network of integrated health, environmental hazard, and exposure data that drives actions to improve the health of communities. The Tracking Program and federal, state, and local partners collect, integrate, analyze, and disseminate data and information to inform environmental public health actions. However, many challenges exist regarding the availability and quality of data, the application of appropriate methods and tools to link data, and the state of the science needed to link and analyze health and environmental data. The Tracking Program has collaborated with academia to address key challenges in these areas. The collaboration has improved our understanding of the uses and limitations of available data and methods, expanded the use of existing data and methods, and increased our knowledge about the connections between health and environment. Valuable working relationships have been forged in this process, and together we have identified opportunities and improvements for future collaborations to further advance the science and practice of environmental public health tracking.

Estimation of age-related vulnerability to air pollution: assessment of respiratory health at local scale

This paper demonstrates association of short-term variation in pollution and health outcomes within the same geographical area for a typical urban setting in the northern part of the UK from time series analysis. It utilises publicly available datasets for regulated air pollutants (PM₁₀, NO₂, SO₂, CO and O₃), meteorology and respiratory hospital admissions (and mortality) between April 2002 and December 2005 to estimate the respiratory health effect of pollution exposure, mainly in the elderly. Our results show that PM₁₀ and O₃ are positively associated with respiratory hospital admissions in the elderly, specifically in the age group 70-79. CO effects seem to be concentrated on the most elderly age group (80+) whereas NO₂ seems to have the opposite age-related effect, with lower effects on the more elderly.

Summary of the workshop on methodologies for environmental public health tracking of air pollution effects

The US Centers for Disease Control and Prevention established the Environmental Public Health Tracking (EPHT) program to support state and local projects that characterize the impact of the environment on health. The projects involve compiling, linking, analyzing, and disseminating environmental and health surveillance information, thereby engaging stakeholders and guiding actions to improve public health. One of the EPHT objectives is to track the public health impact of ambient air pollution with analyses that are timely and relevant to state and local stakeholders. To address methodological issues relevant to this objective, in January 2008, government officials and researchers from the USA, Canada, and Europe gathered in Baltimore, Maryland for a 2-day workshop. Using commissioned papers and presentations on key methodological issues as well as examples of previous air pollution impact assessments, work group discussions produced a set of consensus recommendations for the EPHT program. These recommendations noted the need for data that will encourage local stakeholders to support continued progress in air pollution control. The limitations of using only local data for analyses were also noted. To improve local estimates of air pollution health impacts, methods were recommended that "borrow strength" from other evidence. An incremental approach to implementing such methods was recommended. The importance and difficulty of communicating uncertainties in local health impact assessments was emphasized, as was the need for coordination among different agencies conducting health impact assessments.