Statistical issues in health impact assessment at the state and local levels

Estimating Intra-Urban Inequities in PM2.5-Attributable Health Impacts: A Case Study for Washington, DC

Air pollution levels are uneven within cities, contributing to persistent health disparities between neighborhoods and population sub-groups. Highly spatially resolved information on pollution levels and disease rates is necessary to characterize inequities in air pollution exposure and related health risks. We leverage recent advances in deriving surface pollution levels from satellite remote sensing and granular data in disease rates for one city, Washington, DC, to assess intra-urban heterogeneity in fine particulate matter (PM2.5)- attributable mortality and morbidity. We estimate PM2.5-attributable cases of all-cause mortality, chronic obstructive pulmonary disease, ischemic heart disease, lung cancer, stroke, and asthma emergency department (ED) visits using epidemiologically derived health impact functions. Data inputs include satellite-derived annual mean surface PM2.5 concentrations; age-resolved population estimates; and statistical neighborhood-, zip code- and ward-scale disease counts. We find that PM2.5 concentrations and associated health burdens have decreased in DC between 2000 and 2018, from approximately 240 to 120 cause-specific deaths and from 40 to 30 asthma ED visits per year (between 2014 and 2018). However, remaining PM2.5-attributable health risks are unevenly and inequitably distributed across the District. Higher PM2.5-attributable disease burdens were found in neighborhoods with larger proportions of people of color, lower household income, and lower educational attainment. Our study adds to the growing body of literature documenting the inequity in air pollution exposure levels and pollution health risks between population sub-groups, and highlights the need for both high-resolution disease rates and concentration estimates for understanding intra-urban disparities in air pollution-related health risks.

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM_2.5 and O₃ exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO₂ exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO₂ exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health

Epidemiological studies have been critical for estimating associations between exposure to ambient particulate matter (PM) air pollution and adverse health outcomes. Because total PM mass is a temporally and spatially varying mixture of constituents with different physical and chemical properties, recent epidemiological studies have focused on PM constituents. Most studies have estimated associations between PM constituents and health using the same statistical methods as in studies of PM mass. However, these approaches may not be sufficient to address challenges specific to studies of PM constituents, namely assigning exposure, disentangling health effects, and handling measurement error. We reviewed large, population-based epidemiological studies of PM constituents and health and describe the statistical methods typically applied to address these challenges. Development of statistical methods that simultaneously address multiple challenges, for example, both disentangling health effects and handling measurement error, could improve estimation of associations between PM constituents and adverse health outcomes.

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.

The public health benefits of reducing fine particulate matter through conversion to cleaner heating fuels in New York City

In recent years, both New York State and City issued regulations to reduce emissions from burning heating oil. To assess the benefits of these programs in New York City, where the density of emissions and vulnerable populations vary greatly, we simulated the air quality benefits of scenarios reflecting no action, partial, and complete phase-out of high-sulfur heating fuels using the Community MultiScale Air Quality (CMAQ) model conducted at a high spatial resolution (1 km). We evaluated the premature mortality and morbidity benefits of the scenarios within 42 city neighborhoods and computed benefits by neighborhood poverty status. The complete phase-out scenario reduces annual average fine particulate matter (PM2.5) by an estimated 0.71 μg/m(3) city-wide (average of 1 km estimates, 10-90th percentile: 0.1-1.6 μg/m(3)), avoiding an estimated 290 premature deaths, 180 hospital admissions for respiratory and cardiovascular disease, and 550 emergency department visits for asthma each year. The largest improvements were seen in areas of highest building and population density and the majority of benefits have occurred through the partial phase out of high-sulfur heating fuel already achieved. While emissions reductions were greatest in low-poverty neighborhoods, health benefits are estimated to be greatest in high-poverty neighborhoods due to higher baseline morbidity and mortality rates.

Quantifying uncertainty in health impact assessment: a case-study example on indoor housing ventilation

Quantitative health impact assessment (HIA) is increasingly being used to assess the health impacts attributable to an environmental policy or intervention. As a consequence, there is a need to assess uncertainties in the assessments because of the uncertainty in the HIA models. In this paper, a framework is developed to quantify the uncertainty in the health impacts of environmental interventions and is applied to evaluate the impacts of poor housing ventilation. The paper describes the development of the framework through three steps: (i) selecting the relevant exposure metric and quantifying the evidence of potential health effects of the exposure; (ii) estimating the size of the population affected by the exposure and selecting the associated outcome measure; (iii) quantifying the health impact and its uncertainty. The framework introduces a novel application for the propagation of uncertainty in HIA, based on fuzzy set theory. Fuzzy sets are used to propagate parametric uncertainty in a non-probabilistic space and are applied to calculate the uncertainty in the morbidity burdens associated with three indoor ventilation exposure scenarios: poor, fair and adequate. The case-study example demonstrates how the framework can be used in practice, to quantify the uncertainty in health impact assessment where there is insufficient information to carry out a probabilistic uncertainty analysis.

PM2.5 and ozone health impacts and disparities in New York City: sensitivity to spatial and temporal resolution

Air quality health impact assessment (HIA) synthesizes information about air pollution exposures, health effects, and population vulnerability for regulatory decision-making and public engagement. HIAs often use annual average county or regional data to estimate health outcome incidence rates that vary substantially by season and at the subcounty level. Using New York City as an example, we assessed the sensitivity of estimated citywide morbidity and mortality attributable to ambient fine particulate matter (PM_2.5) and ozone to the geographic (county vs. neighborhood) and temporal (seasonal vs. annual average) resolution of health incidence data. We also used the neighborhood-level analysis to assess variation in estimated air pollution impacts by neighborhood poverty concentration. Estimated citywide health impacts attributable to PM_2.5 and ozone were relatively insensitive to the geographic resolution of health incidence data. However, the neighborhood-level analysis demonstrated increasing impacts with greater neighborhood poverty levels, particularly for PM_2.5-attributable asthma emergency department visits, which were 4.5 times greater in high compared to low-poverty neighborhoods. PM_2.5-attributable health impacts were similar using seasonal and annual average incidence rates. Citywide ozone-attributable asthma morbidity was estimated to be 15 % lower when calculated from seasonal, compared to annual average incidence rates, as asthma morbidity rates are lower during the summer ozone season than the annual average rate. Within the ozone season, 57 % of estimated ozone-attributable emergency department for asthma in children occurred in the April-June period when average baseline incidence rates are higher than in the July-September period when ozone concentrations are higher. These analyses underscore the importance of utilizing spatially and temporally resolved data in local air quality impact assessments to characterize the overall city burden and identify areas of high vulnerability.

Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation

Air quality modeling could potentially improve exposure estimates for use in epidemiological studies. We investigated this application of air quality modeling by estimating location-specific (point) and spatially-aggregated (county level) exposure concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM(2.5)) and ozone (O(3)) for the eastern U.S. in 2002 using the Community Multi-scale Air Quality (CMAQ) modeling system and a traditional approach using ambient monitors. The monitoring approach produced estimates for 370 and 454 counties for PM(2.5) and O(3), respectively. Modeled estimates included 1861 counties, covering 50% more population. The population uncovered by monitors differed from those near monitors (e.g., urbanicity, race, education, age, unemployment, income, modeled pollutant levels). CMAQ overestimated O(3) (annual normalized mean bias=4.30%), while modeled PM(2.5) had an annual normalized mean bias of -2.09%, although bias varied seasonally, from 32% in November to -27% in July. Epidemiology may benefit from air quality modeling, with improved spatial and temporal resolution and the ability to study populations far from monitors that may differ from those near monitors. However, model performance varied by measure of performance, season, and location. Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors.

Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure

Exposure to high levels of air pollution during the pregnancy is associated with increased probability of preterm birth (PTB), a major cause of infant morbidity and mortality. New statistical methodology is required to specifically determine when a particular pollutant impacts the PTB outcome, to determine the role of different pollutants, and to characterize the spatial variability in these results. We develop a new Bayesian spatial model for PTB which identifies susceptible windows throughout the pregnancy jointly for multiple pollutants (PM(2.5) , ozone) while allowing these windows to vary continuously across space and time. We geo-code vital record birth data from Texas (2002-2004) and link them with standard pollution monitoring data and a newly introduced EPA product of calibrated air pollution model output. We apply the fully spatial model to a region of 13 counties in eastern Texas consisting of highly urban as well as rural areas. Our results indicate significant signal in the first two trimesters of pregnancy with different pollutants leading to different critical windows. Introducing the spatial aspect uncovers critical windows previously unidentified when space is ignored. A proper inference procedure is introduced to correctly analyze these windows.

Risk factor frequency for learning disabilities in low socioeconomic level preschool children in Mexico city

The objective was to identify the frequency of risk factors for Learning Disabilities (LD) in low socioeconomic level children in Mexico City. We studied children by means of: Wechsler, Bender-Gestalt, and Human drawing tests. Average age of male subjects was 5.6±0.9 years, while that of the female group was 5.4±0.5 years. In male subjects, average Total intelligence quotient (T-IQ) score was 98±12.2 while, in the female group, this was 99±12.2. On the Bender-Gestalt test, male subjects had a mental and visual-motor average age of <1 year under chronological age. Female subjects had a mental and visual-motor age 8-7 months under the norm. On the Human drawing test, in male and female subjects, the most frequent at-risk features comprised: self-isolation in 25% of subjects, shyness in 22.4%, and poor internal controls in 22%. In conclusion, we found a high at-risk factor frequency for LD in children of low socioeconomic strata. We highlight the importance of screening children before they attain school age.

Impact of climate change on ambient ozone level and mortality in southeastern United States

There is a growing interest in quantifying the health impacts of climate change. This paper examines the risks of future ozone levels on non-accidental mortality across 19 urban communities in Southeastern United States. We present a modeling framework that integrates data from climate model outputs, historical meteorology and ozone observations, and a health surveillance database. We first modeled present-day relationships between observed maximum daily 8-hour average ozone concentrations and meteorology measured during the year 2000. Future ozone concentrations for the period 2041 to 2050 were then projected using calibrated climate model output data from the North American Regional Climate Change Assessment Program. Daily community-level mortality counts for the period 1987 to 2000 were obtained from the National Mortality, Morbidity and Air Pollution Study. Controlling for temperature, dew-point temperature, and seasonality, relative risks associated with short-term exposure to ambient ozone during the summer months were estimated using a multi-site time series design. We estimated an increase of 0.43 ppb (95% PI: 0.14-0.75) in average ozone concentration during the 2040's compared to 2000 due to climate change alone. This corresponds to a 0.01% increase in mortality rate and 45.2 (95% PI: 3.26-87.1) premature deaths in the study communities attributable to the increase in future ozone level.

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.