Real-time indoor PM2.5 monitoring in an urban cohort: Implications for exposure disparities and source control

Home indoor air quality and cognitive function over one year for people working remotely during COVID-19

The coronavirus disease 2019 (COVID-19) pandemic triggered an increase in remote work-from-home for office workers. Given that many homes now function as offices despite not being designed to support office work, it is critical to research the impact of indoor air quality (IAQ) in homes on the cognitive performance of people working from home. In this study, we followed 206 office workers across the U.S. over one year under remote or hybrid-remote settings during 2021-2022. Participants placed two real-time, consumer-grade indoor environmental monitors in their home workstation area and bedroom. Using a custom smartphone application geofenced to their residential address, participants responded to surveys and periodic cognitive function tests, including the Stroop color-word interference test, Arithmetic two-digit addition/subtraction test, and Compound Remote Associates Task (cRAT). Exposures assessed included carbon dioxide (CO2) and thermal conditions (indoor heat index: a combination of temperature and relative humidity) averaged over 30 minutes prior to each cognitive test. In fully adjusted longitudinal mixed models (n≤121), we found that indoor thermal conditions at home were associated with cognitive function outcomes non-linearly (p<0.05), with poorer cognitive performance on the Stroop test and poorer creative problem-solving on the cRAT when conditions were either too warm or too cool. Most indoor CO2 levels were <640 ppm, but there was still a slight association between higher CO2 and poorer cognitive performance on Stroop (p=0.09). Our findings highlight the need to enhance home indoor environmental quality for optimal cognitive function during remote work, with benefits for both employees and employers.

Disparities in joint exposure to environmental and social stressors in urban households in Greater Boston

OBJECTIVES

Understanding how environmental and social stressors cluster is critical to explaining and addressing health disparities. It remains unclear how these stressors cluster at fine spatial resolution in low to medium-income, urban households. We explored patterns of environmental and social exposures at the household-level and potential predictors of these joint exposures in two environmental justice communities in the Greater Boston area.

METHODS

We recruited 150 households in Chelsea, MA and the Dorchester neighborhood of Boston, MA, between 2016 and 2019 and collected data on two domains: environmental and social stressor. For each domain, we fit Latent Class Analysis (LCA) models to exposure data to assess intra-domain variability, and cross-classified the resultant classes to identify joint exposure profiles. We compared differences in the distribution of these profiles by participants' demographic and household characteristics using χ2, Fisher's exact, Analysis of Variance, and Kruskal-Wallis tests.

RESULTS

We identified two latent classes in each domain: High environmental (n = 90; 60.4%), Low environmental (n = 59; 39.6%), High Social (n = 31; 20.8%), and Low Social (n = 118; 79.2%). Cross-classification yielded four joint exposure profiles: Both Low (n = 46, 30.9%); Both High (n = 18, 12.1%); High environmental-Low Social (n = 72, 48.3%); and Low environmental-High Social (n = 13, 8.7%). Significant group differences were found by housing type (e.g., single-family vs. multi-family) (Fisher's exact p = 0.0016), housing tenure (p = 0.0007), and study site (p < 0.0001). We also observed differences by race/ethnicity, income, and education: households that were Hispanic/Latinx, below the poverty level, and with lower education were more likely to be in the Both High group.

CONCLUSIONS

Our analyses confirmed that environmental and social stressors cluster in socially disadvantaged households. Housing type, housing tenure, and location of the residence were also strong predictors of cluster membership, with renter and multi-family residents at risk of high exposures to environmental and social stressors.

Methods in Public Health Environmental Justice Research: a Scoping Review from 2018 to 2021

PURPOSE OF REVIEW

The volume of public health environmental justice (EJ) research produced by academic institutions increased through 2022. However, the methods used for evaluating EJ in exposure science and epidemiologic studies have not been catalogued. Here, we completed a scoping review of EJ studies published in 19 environmental science and epidemiologic journals from 2018 to 2021 to summarize research types, frameworks, and methods.

RECENT FINDINGS

We identified 402 articles that included populations with health disparities as a part of EJ research question and met other inclusion criteria. Most studies (60%) evaluated EJ questions related to socioeconomic status (SES) or race/ethnicity. EJ studies took place in 69 countries, led by the US (n = 246 [61%]). Only 50% of studies explicitly described a theoretical EJ framework in the background, methods, or discussion and just 10% explicitly stated a framework in all three sections. Among exposure studies, the most common area-level exposure was air pollution (40%), whereas chemicals predominated personal exposure studies (35%). Overall, the most common method used for exposure-only EJ analyses was main effect regression modeling (50%); for epidemiologic studies the most common method was effect modification (58%), where an analysis evaluated a health disparity variable as an effect modifier. Based on the results of this scoping review, current methods in public health EJ studies could be bolstered by integrating expertise from other fields (e.g., sociology), conducting community-based participatory research and intervention studies, and using more rigorous, theory-based, and solution-oriented statistical research methods.

Deaths Attributable to Indoor PM2.5 in Urban China When Outdoor Air Meets 2021 WHO Air Quality Guidelines

The World Health Organization reduced the recommended level of annual mean PM_2.5 concentrations to 5 μg/m³ in 2021. Previously, the guideline was geared toward ambient air pollution, and now it explicitly applies to indoor air pollution. However, the disease burden attributed to different indoor emission sources has been overlooked, particularly in urban areas. Our objective was to estimate the mortality attributable to indoor PM_2.5 in urban areas in China. Our model estimated 711 thousand (584-823) deaths and 2.75 trillion (2.26-3.19) CNY economic losses attributable to PM_2.5 in urban China in 2019, in which indoor sources contributed 394 thousand (323-457) deaths and 1.53 trillion (1.25-1.77) CNY losses. There would still be 536 thousand (427-638) PM_2.5-attributable deaths and 2.07 trillion (1.65-2.47) CNY losses each year when the outdoor PM_2.5 is 5 μg/m³, of which 485 thousand (386-578) deaths and 1.87 trillion (1.49-2.23) CNY are attributable to indoor sources. Despite cleaner outdoor air and no solid fuels being used, considerable health hazards and economic losses are attributable to indoor PM_2.5. Measures to reduce PM_2.5 exposure in humans from both indoor and outdoor sources are required to achieve a substantial reduction in deaths.

Location-specific strategies for eliminating US national racial-ethnic [Formula: see text] exposure inequality

Air pollution levels in the United States have decreased dramatically over the past decades, yet national racial-ethnic exposure disparities persist. For ambient fine particulate matter ([Formula: see text]), we investigate three emission-reduction approaches and compare their optimal ability to address two goals: 1) reduce the overall population average exposure ("overall average") and 2) reduce the difference in the average exposure for the most exposed racial-ethnic group versus for the overall population ("national inequalities"). We show that national inequalities in exposure can be eliminated with minor emission reductions (optimal: ~1% of total emissions) if they target specific locations. In contrast, achieving that outcome using existing regulatory strategies would require eliminating essentially all emissions (if targeting specific economic sectors) or is not possible (if requiring urban regions to meet concentration standards). Lastly, we do not find a trade-off between the two goals (i.e., reducing overall average and reducing national inequalities); rather, the approach that does the best for reducing national inequalities (i.e., location-specific strategies) also does as well as or better than the other two approaches (i.e., sector-specific and meeting concentration standards) for reducing overall averages. Overall, our findings suggest that incorporating location-specific emissions reductions into the US air quality regulatory framework 1) is crucial for eliminating long-standing national average exposure disparities by race-ethnicity and 2) can benefit overall average exposures as much as or more than the sector-specific and concentration-standards approaches.

Sensitivity of modeled residential fine particulate matter exposure to select building and source characteristics: A case study using public data in Boston, MA

Many techniques for estimating exposure to airborne contaminants do not account for building characteristics that can magnify contaminant contributions from indoor and outdoor sources. Building characteristics that influence exposure can be challenging to obtain at scale, but some may be incorporated into exposure assessments using public datasets. We present a methodology for using public datasets to generate housing models for a test cohort, and examined sensitivity of predicted fine particulate matter (PM2.5) exposures to selected building and source characteristics. We used addresses of a cohort of children with asthma and public tax assessor's data to guide selection of floorplans of US residences from a public database. This in turn guided generation of coupled multi-zone models (CONTAM and EnergyPlus) that estimated indoor PM2.5 exposure profiles. To examine sensitivity to model parameters, we varied building floors and floorplan, heating, ventilating and air-conditioning (HVAC) type, room or floor-level model resolution, and indoor source strength and schedule (for hypothesized gas stove cooking and tobacco smoking). Occupant time-activity and ambient pollutant levels were held constant. Our address matching methodology identified two multi-family house templates and one single-family house template that had similar characteristics to 60 % of test addresses. Exposure to infiltrated ambient PM2.5 was similar across selected building characteristics, HVAC types, and model resolutions (holding all else equal). By comparison, exposures to indoor-sourced PM2.5 were higher in the two multi-family residences than the single family residence (e.g., for cooking PM2.5 exposure, by 26 % and 47 % respectively) and were sensitive to HVAC type and model resolution. We derived the influence of building characteristics and HVAC type on PM2.5 exposure indoors using public data sources and coupled multi-zone models. With the important inclusion of individualized resident behavior data, similar housing modeling can be used to incorporate exposure variability in health studies of the indoor residential environment.

Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina

Studies increasingly use output from the Environmental Protection Agency's Fused Air Quality Surface Downscaler ("downscaler") model, which provides spatial predictions of daily concentrations of fine particulate matter (PM2.5) and ozone (O3) at the census tract level, to study the health and societal impacts of exposure to air pollution. Downscaler outputs have been used to show that lower income and higher minority neighborhoods are exposed to higher levels of PM2.5 and lower levels of O3. However, the uncertainty of the downscaler estimates remains poorly characterized, and it is not known if all subpopulations are benefiting equally from reliable predictions. We examined how the percent errors (PEs) of daily concentrations of PM2.5 and O3 between 2002 and 2016 at the 2010 census tract centroids across North Carolina were associated with measures of racial and educational isolation, neighborhood disadvantage, and urbanicity. Results suggest that there were socioeconomic and demographic disparities in surface concentrations of PM2.5 and O3, as well as their prediction uncertainties. Neighborhoods characterized by less reliable downscaler predictions (i.e., higher PEPM2.5 and PEO3) exhibited greater levels of aerial deprivation as well as educational isolation, and were often non-urban areas (i.e., suburban, or rural). Between 2002 and 2016, predicted PM2.5 and O3 levels decreased and O3 predictions became more reliable. However, the predictive uncertainty for PM2.5 has increased since 2010. Substantial spatial variability was observed in the temporal changes in the predictive uncertainties; educational isolation and neighborhood deprivation levels were associated with smaller increases in predictive uncertainty of PM2.5. In contrast, racial isolation was associated with a greater decline in the reliability of PM2.5 predictions between 2002 and 2016; it was associated with a greater improvement in the predictive reliability of O3 within the same time frame.

A process for creating data report-back tools to improve equity in environmental health

BACKGROUND

Although there is increasing interest in reporting results of environmental research efforts back to participants, evidence-based tools have not yet been applied to developed materials to ensure their accessibility in terms of literacy, numeracy, and data visualization demand. Additionally, there is not yet guidance as to how to formally assess the created materials to assure a match with the intended audience.

METHODS

Relying on formative qualitative research with participants of an indoor air quality study in Dorchester, Massachusetts, we identified means of enhancing accessibility of indoor air quality data report-back materials for participants. Participants (n = 20) engaged in semi-structured interviews in which they described challenges they encountered with scientific and medical materials and outlined written and verbal communication techniques that would help facilitate engagement with and accessibility of environmental health report-back materials. We coupled these insights from participants with best practice guidelines for written materials by operationalizing health literacy tools to produce accessible audience-informed data report-back materials.

RESULTS

The resulting data report-back materials had a 7th -grade reading level, and between a 4th -8th grade level of overall document complexity. The numeracy skills required to engage with the material were of the lowest demand, and we incorporated best practices for risk communication and facilitating understanding and actionability of the materials. Use of a rigorous assessment tool provides evidence of accessibility and appropriateness of the material for the audience.

CONCLUSIONS

We outline a process for developing and evaluating environmental health data reports that are tailored to inspire risk-reduction actions, and are demonstrably accessible in terms of their literacy, numeracy, and data visualization demand. Adapting health literacy tools to create and evaluate environmental data report-back materials is a novel and evidence-based means of ensuring their accessibility.

Development of a multidimensional housing and environmental quality index (HEQI): application to the American Housing Survey

BACKGROUND

Substandard housing conditions and hazardous indoor environmental exposures contribute to significant morbidity and mortality worldwide. Housing indices that capture the multiple dimensions of healthy housing are important for tracking conditions and identifying vulnerable households. However, most indices focus on physical deficiencies and repair costs and omit indoor environmental exposures, as few national data sources routinely collect this information.

METHODS

We developed a multidimensional Housing and Environmental Quality Index (HEQI) based on the World Health Organization's Housing and Health Guidelines and applied it to the 2019 American Housing Survey (AHS). The HEQI consisted of ten domains associated with poor health: household fuel combustion, dampness and mold, pests and allergens, lead paint risk, high indoor temperatures, low indoor temperatures, household crowding, injury hazards, inadequate water and sanitation, and ventilation. We evaluated the validity and performance of the HEQI against three housing characteristics (i.e., year built, monthly rent costs, unit satisfaction rating) and two established indices (i.e., Adequacy Index, Poor Quality Index).

RESULTS

Approximately 79% (92 million) of U.S. households reported at least one HEQI domain associated with poor health (mean per household: 1.3; range: 0,8). Prevalent domains included household fuel combustion (61.4%), dampness and mold (15.9%), inadequate water and sanitation (14.3%), and injury hazards (11.9%). Pests and allergens, low indoor temperatures, and injury hazards were consistently associated with older homes, lower rent costs, and lower unit satisfaction. Compared to established housing indices, the HEQI captured four new environmental domains which enabled the identification of 57.7 million (63%) more households with environmental risk factors like mold, cockroaches, crowding, household fuel combustion, and higher building leakage.

CONCLUSIONS

Indoor environmental exposures are prevalent in U.S. households and not well-captured by existing housing indices. The HEQI is a multidimensional tool that can be used to monitor indoor environmental exposures and housing quality trends in the U.S. Some domains, including radon, pesticides, asbestos, noise, and housing accessibility could not be assessed due to the lack of available data in the AHS. The mounting evidence linking residential environmental exposures with adverse health outcomes underscore the need for this data in the AHS and other national surveys.

Characterizing the Environmental Health Literacy and Sensemaking of Indoor Air Quality of Research Participants

This study is based on in-depth semi-structured interviews with the participants of an indoor air quality monitoring study. The purpose of the interviews was to capture participants’ perceptions of indoor air quality and engage them in a discussion of those factors that influenced their behavior. Interview study participants (n = 20) noted the importance of family health concerns and their own sensory awareness of possible contaminants. They discussed their level of personal control over their home environment as well as their access to needed resources. This study is based on grounded theory and applies interpretivist epistemological methods. Study findings offer insights into how people perceive their home environment and what influences their decision making and action. Analyses indicate that perceived agency, risk perception, access to resources, and information all influenced participants’ sense of ability to take action as well as their interest in taking action. These insights serve to challenge some of the current work in environmental health literacy which tends to focus on and measure an individual’s knowledge or skills. Our analysis suggests that consideration be given to a number of factors that include perceived agency, access to resources, and the quality of information provided.

Health outcomes in redlined versus non-redlined neighborhoods: A systematic review and meta-analysis

BACKGROUND

Redlining was a racialized zoning practice in the U.S. that blocked fair access to home loans during the 1930s, and recent research is illuminating health problems in the current residents of these historically redlined areas. However, this work has not yet been holistically summarized. Here, we present the first systematic review and meta-analysis comparing health outcomes in redlined versus non-redlined neighborhoods in U.S. cities.

METHODS

We extracted relevant articles in PubMed, Web of Science, Cochrane and Science Direct databases published from January 2010 to September 2021.

RESULTS

The search revealed 12 studies on preterm births (n = 3), gunshot-related injuries (n = 2), cancer (n = 1), asthma (n = 1), self-rated health (n = 1), multiple health outcomes (n = 2), heat-related outcomes (n = 1) and COVID-19 incidence and mortality (n = 1). A meta-analysis of three studies found the odds of having preterm birth was significantly higher (OR = 1.41, 95% CI: 1.05, 1.88; p = 0.02) among women living in redlined areas compared to those in non-redlined areas. Review of other outcomes revealed that gunshot-related injuries, asthma, heat-related outcomes, and multiple chronic conditions were worse in redlined areas, while associations with cancer varied by cancer type. In terms of cause-specific mortality, one study revealed no link between residential redlining and infant mortality rate, while one study on COVID-19 outcomes was inconclusive.

CONCLUSIONS

Overall, this review presents evidence that living in historically redlined areas is associated with increased risk of multiple serious adverse health outcomes. Further research on mechanisms, remediation, and neighborhood-level interventions is needed to strengthen the understanding of the impacts of redlining on health.

MCR: Open-Source Software to Automate Compilation of Health Study Report-Back

Sharing individualized results with health study participants, a practice we and others refer to as "report-back," ensures participant access to exposure and health information and may promote health equity. However, the practice of report-back and the content shared is often limited by the time-intensive process of personalizing reports. Software tools that automate creation of individualized reports have been built for specific studies, but are largely not open-source or broadly modifiable. We created an open-source and generalizable tool, called the Macro for the Compilation of Report-backs (MCR), to automate compilation of health study reports. We piloted MCR in two environmental exposure studies in Massachusetts, USA, and interviewed research team members (n = 7) about the impact of MCR on the report-back process. Researchers using MCR created more detailed reports than during manual report-back, including more individualized numerical, text, and graphical results. Using MCR, researchers saved time producing draft and final reports. Researchers also reported feeling more creative in the design process and more confident in report-back quality control. While MCR does not expedite the entire report-back process, we hope that this open-source tool reduces the barriers to personalizing health study reports, promotes more equitable access to individualized data, and advances self-determination among participants.