Cohort profile: The Assessing Imperial Valley Respiratory Health and the Environment (AIRE) study.

BACKGROUND: The Children's Assessing Imperial Valley Respiratory Health and the Environment (AIRE) study is a prospective cohort study of environmental influences on respiratory health in a rural, southeastern region of California (CA), which aims to longitudinally examine the contribution of a drying saline lake to adverse health impacts in children. OBJECTIVES: This cohort was established through a community-academic partnership with the goal of assessing the health effects of childhood exposures to wind-blown particulate matter (PM) and inform public health action. We hypothesize that local PM sources are related to poorer children's respiratory health. POPULATION: Elementary school children in Imperial Valley, CA. DESIGN: Prospective cohort study. METHODS: Between 2017 and 2019, we collected baseline information on 731 children, then follow-up assessments yearly or twice-yearly since 2019. Data have been collected on children's respiratory health, demographics, household characteristics, physical activity and lifestyle, via questionnaires completed by parents or primary caregivers. In-person measurements, conducted since 2019, repeatedly assessed lung function, height, weight and blood pressure. Exposure to air pollutants has been assessed by multiple methods and individually assigned to participants using residential and school addresses. Health data will be linked to ambient and local sources of PM, during and preceding the study period to understand how spatiotemporal trends in these environmental exposures may relate to respiratory health. PRELIMINARY RESULTS: Analyses of respiratory symptoms indicate a high prevalence of allergies, bronchitic symptoms and wheezing. Asthma diagnosis was reported in 24% of children at enrolment, which exceeds both CA state and US national prevalence estimates for children. CONCLUSIONS: The Children's AIRE cohort, while focused on the health impacts of the drying Salton Sea and air quality in Imperial Valley, is poised to elucidate the growing threat of drying saline lakes and wind-blown dust sources to respiratory health worldwide, as sources of wind-blown dust emerge in our changing climate.

Association between long-term exposure to particulate air pollution with SARS-CoV-2 infections and COVID-19 deaths in California, U.S.A.

Previous studies have reported associations between air pollution and COVID-19 morbidity and mortality, but most have limited their exposure assessment to a large area, have not used individual-level variables, nor studied infections. We examined 3.1 million SARS-CoV-2 infections and 49,691 COVID-19 deaths that occurred in California from February 2020 to February 2021 to evaluate risks associated with long-term neighborhood concentrations of particulate matter less than 2.5 µm in diameter (PM2.5). We obtained individual address data on SARS-CoV-2 infections and COVID-19 deaths and assigned 2000-2018 1km-1km gridded PM2.5 surfaces to census block groups. We included individual covariate data on age and sex, and census block data on race/ethnicity, air basin, Area Deprivation Index, and relevant comorbidities. Our analyses were based on generalized linear mixed models utilizing a Poisson distribution. Those living in the highest quintile of long-term PM2.5 exposure had risks of SARS-CoV-2 infections 20% higher and risks of COVID-19 mortality 51% higher, compared to those living in the lowest quintile of long-term PM2.5 exposure. Those living in the areas of highest long-term PM2.5 exposure were more likely to be Hispanic and more vulnerable, based on the Area Deprivation Index. The increased risks for SARS-CoV-2 Infections and COVID-19 mortality associated with highest long-term PM2.5 concentrations at the neighborhood-level in California were consistent with a growing body of literature from studies worldwide, and further highlight the importance of reducing levels of air pollution to protect public health.

Returning Individual Tap Water Testing Results to Research Study Participants after a Wildfire Disaster.

After the devastating wildfire that destroyed most of the town of Paradise, California in 2018, volatile organic compounds were found in water distribution pipes. Approximately 11 months after the fire, we collected tap water samples from 136 homes that were still standing and tested for over 100 chemicals. Each participant received a customized report showing the laboratory findings from their sample. Our goal was to communicate individual water results and chemical information rapidly in a way that was understandable, scientifically accurate, and useful to participants. On the basis of this process, we developed a framework to illustrate considerations and priorities that draw from best practices of previous environmental results return research and crisis communication, while also addressing challenges specific to the disaster context. We also conducted a follow-up survey on participants' perceptions of the results return process. In general, participants found the results return communications to be understandable, and they felt less worried about their drinking water quality after receiving the information. Over one-third of the participants reported taking some kind of action around their water usage habits after receiving their results. Communication with participants is a critical element of environmental disaster research, and it is important to have a strategy to communicate results that achieves the goals of timeliness, clarity, and scientific accuracy, ultimately empowering people toward actions that can reduce exposure.

Fire and Water: Assessing Drinking Water Contamination After a Major Wildfire.

We investigated patterns of volatile organic compound (VOC) contamination in drinking water systems affected by the California 2018 Camp Fire. We performed spatial analysis of over 5000 water samples collected over a 17 month period by a local water utility, sampled tap water for VOCs in approximately 10% (N = 136) of standing homes, and conducted additional nontargeted chemical analysis of 10 samples. Benzene contamination was present in 29% of service connections to destroyed structures and 2% of service connections to standing homes. A spatial pattern was apparent. Tap water in standing homes 11 months after the fire contained low concentrations of benzene in 1% of samples, but methylene chloride was present in 19% of samples, including several above regulatory limits. Elevated methylene chloride was associated with greater distance from the water meter to the tap, longer stagnation time, and the presence of a destroyed structure on the service connection; it was inversely associated with certain trihalomethanes. Nontargeted analysis identified multiple combustion byproducts in the water at 2/10 homes. Our findings support the hypothesis that pyrolysis and smoke intrusion from depressurization contributed to the benzene contamination. Further research is needed to test the hypothesis that methylene chloride may be generated from the dehalogenation of disinfection byproducts stagnating in galvanized iron pipes.

Performance of a Low-Cost Sensor Community Air Monitoring Network in Imperial County, CA.

Air monitoring networks developed by communities have potential to reduce exposures and affect environmental health policy, yet there have been few performance evaluations of networks of these sensors in the field. We developed a network of over 40 air sensors in Imperial County, CA, which is delivering real-time data to local communities on levels of particulate matter. We report here on the performance of the Network to date by comparing the low-cost sensor readings to regulatory monitors for 4 years of operation (2015-2018) on a network-wide basis. Annual mean levels of PM10 did not differ statistically from regulatory annual means, but did for PM2.5 for two out of the 4 years. R2s from ordinary least square regression results ranged from 0.16 to 0.67 for PM10, and increased each year of operation. Sensor variability was higher among the Network monitors than the regulatory monitors. The Network identified a larger number of pollution episodes and identified under-reporting by the regulatory monitors. The participatory approach of the project resulted in increased engagement from local and state agencies and increased local knowledge about air quality, data interpretation, and health impacts. Community air monitoring networks have the potential to provide real-time reliable data to local populations.

Developing Youth Environmental Health Literacy and Civic Leadership through Community Air Monitoring in Imperial County, California.

With a rapidly changing climate, new leaders must be trained to understand and act on emerging environmental threats. In California's Imperial Valley, a collaborative of community members, researchers, and scientists developed a community air monitoring network to provide local residents with better air quality information. To expand the reach of the project and to prepare the next generation of youth leaders we developed an internship program to increase environmental health literacy and civic leadership. In the 10-week program, high school students learned about air quality science, respiratory health, community air monitoring, and policies intended to improve air quality. The students learned to present this information to their peers, neighbors, family, and community leaders. The program used participatory approaches familiar to community-engaged research to center the students' experience. Surveys and interviews with the students were used to assess the program and found that the students became more familiar with air quality policies, increased their ability to use air monitoring resources, and increased their own confidence in their ability to effect change. With the growing threats related to environmental hazards, it is vital to prepare youth leaders to understand, communicate, and act.

Community-Engaged Air Monitoring to Build Resilience Near the US-Mexico Border.

Initiated in response to community concerns about high levels of air pollution and asthma, the Imperial County Community Air Monitoring Project was conducted as a collaboration between a community-based organization, a non-governmental environmental health program, and academic researchers. This community-engaged research project aimed to produce real-time, community-level air quality information through the establishment of a community air monitoring network (CAMN) of 40 low-cost particulate matter (PM) monitors in Imperial County, California. Methods used to involve the community partner organization and residents in the development, operation, and use of the CAMN included the following: (1) establishing equitable partnerships among the project collaborators; (2) forming a community steering committee to guide project activities; (3) engaging residents in data collection to determine monitor sites; (4) providing hands-on training to assemble and operate the air monitors; (5) conducting focus groups to guide display and dissemination of monitoring data; and (6) conducting trainings on community action planning. This robust community engagement in the project resulted in increased awareness, knowledge, capacity, infrastructure, and influence for the community partner organization and among community participants. Even after the conclusion of the original research grant funding for this project, the CAMN continues to be operated and sustained by the community partner, serving as a community resource used by residents, schools, researchers, and others to better understand and address air pollution and its impacts on community health, while strengthening the ability of the community to prepare for, respond to, and recover from harmful air pollution.

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA.

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.

Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes.

Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM2.5 (airborne particles with sizes less than 2.5 µm in diameter) hourly average concentration is equal to or greater than 35 µg m-3, are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region; however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations.

Residential agricultural pesticide exposures and risks of preeclampsia.

We investigated risks of preeclampsia phenotypes from potential residential pesticide exposures, including 543 individual chemicals and 69 physicochemical groupings that were applied in the San Joaquin Valley of California during the study period, 1998-2011. The study population was derived from birth certificate data linked with Office of Statewide Health Planning and Development maternal and infant hospital discharge data. The following numbers of women with preeclampsia phenotypes were identified: 1045 with superimposed (pre-existing hypertension with preeclampsia) preeclampsia (265 with gestational weeks 20-31 and 780 with gestational weeks 32-36); 3471 with severe preeclampsia (824 with gestational weeks 20-31 and 2647 with gestational weeks 32-36); and 2780 with mild preeclampsia (207 with gestational weeks 20-31 and 2573 with gestational weeks 32-36). The reference population for these groups was 197,461 women who did not have diabetes (gestational or pre-existing), did not have any hypertensive disorder, and who delivered at 37 weeks or later. The frequency of any exposure was lower or about the same in each preeclampsia case group (further delineated by gestational age), and month time period, relative to the frequency in reference population controls. Nearly all odds ratios were below 1.0 for these any vs no exposure comparisons. This study showed a general lack of increased risks between a range of agriculture pesticide exposures near women's residences and various preeclampsia phenotypes.

Combining Community Engagement and Scientific Approaches in Next-Generation Monitor Siting: The Case of the Imperial County Community Air Network.

Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach.

Residential Agricultural Pesticide Exposures and Risks of Spontaneous Preterm Birth.

BACKGROUND: Pesticides exposures are aspects of the human exposome that have not been sufficiently studied for their contribution to risk for preterm birth. We investigated risks of spontaneous preterm birth from potential residential exposures to 543 individual chemicals and 69 physicochemical groupings that were applied in the San Joaquin Valley of California during the study period, 1998-2011. METHODS: The study population was derived from birth certificate data linked with Office of Statewide Health Planning and Development maternal and infant hospital discharge data. After exclusions, the analytic study base included 197,461 term control births and 27,913 preterm case births. Preterm cases were more narrowly defined as 20-23 weeks (n = 515), 24-27 weeks (n = 1,792), 28-31 weeks (n = 3,098), or 32-36 weeks (n = 22,508). RESULTS: The frequency of any (versus none) pesticide exposure was uniformly lower in each preterm case group relative to the frequency in term controls, irrespective of gestational month of exposure. All odds ratios were below 1.0 for these any versus no exposure comparisons. The majority of odds ratios were below 1.0, many of them statistically precise, for preterm birth and exposures to specific chemical groups or chemicals. CONCLUSIONS: This study showed a general lack of increased risk of preterm birth associated with a range of agriculture pesticide exposures near women's residences.

Is in utero exposure to maternal socioeconomic disadvantage related to offspring ovarian reserve in adulthood?

BACKGROUND: Because the ovarian follicle pool is established in utero, adverse exposures during this period may be especially impactful on the size and health of the initial follicle endowment, potentially shaping trajectories of ovarian follicle loss and the eventual onset of menopause. Building on a robust literature linking socioeconomic status (SES) and menopausal timing, the current study examined adverse prenatal exposures related to maternal SES, hypothesizing that greater maternal socioeconomic disadvantage would be associated with lower ovarian reserve in the adult offspring. METHODS: In a healthy, community-based sub-sample (n = 350) of reproductive age participants in the OVA Study (2006-2011), prenatal maternal SES was examined in relation to two biomarkers of ovarian reserve, antimullerian hormone (AMH) and antral follicle count (AFC). Prenatal maternal SES was assessed indirectly using maternal addresses abstracted from participant birth certificates, geocoded, and linked to US Census-derived variables, including neighborhood-level characteristics: education (% of individuals with a HS diploma); poverty (% of families below the poverty line); unemployment (% of individuals > 16 years who are unemployed); and income (median family income). RESULTS: In separate covariate-adjusted linear regression models (following the backward elimination of main effects with P > .10), greater maternal neighborhood education was related to higher ovarian reserve as marked by higher levels of offspring AMH (beta = .142, P < .001) and AFC (beta = .092, P < .10) with models accounting for 19.6% and 21.5% of the variance in AMH and AFC, respectively. In addition, greater maternal neighborhood poverty was related to lower ovarian reserve as marked by lower offspring AMH (beta = -.144, P < .01), with the model accounting for 19.5% of the variance in AMH. CONCLUSIONS: Maternal socioeconomic disadvantage measured indirectly at the neighborhood level was associated with lower ovarian reserve among the adult offspring, independently of offspring SES and other potential confounding factors. This suggests SES-related adversity exposures may have a detrimental impact on the size or health of the initial follicle endowment, leading to accelerated follicle loss over time.

Traffic-Related Air Pollution and All-Cause Mortality during Tuberculosis Treatment in California.

BACKGROUND: Ambient air pollution and tuberculosis (TB) have an impact on public health worldwide, yet associations between the two remain uncertain. OBJECTIVE: We determined the impact of residential traffic on mortality during treatment of active TB. METHODS: From 2000-2012, we enrolled 32,875 patients in California with active TB and followed them throughout treatment. We obtained patient data from the California Tuberculosis Registry and calculated traffic volumes and traffic densities in 100- to 400-m radius buffers around residential addresses. We used Cox models to determine mortality hazard ratios, controlling for demographic, socioeconomic, and clinical potential confounders. We categorized traffic exposures as quintiles and determined trends using Wald tests. RESULTS: Participants contributed 22,576 person-years at risk. There were 2,305 deaths during treatment for a crude mortality rate of 1,021 deaths per 10,000 person-years. Traffic volumes and traffic densities in all buffers around patient residences were associated with increased mortality during TB treatment, although the findings were not statistically significant in all buffers. As the buffer size decreased, fifth-quintile mortality hazards increased, and trends across quintiles of traffic exposure became more statistically significant. Increasing quintiles of nearest-road traffic volumes in the 100-m buffer were associated with 3%, 14%, 19%, and 28% increased risk of death during TB treatment [first quintile, referent; second quintile hazard ratio (HR)=1.03 [95% confidence interval (CI): 0.86, 1.25]; third quintile HR=1.14 (95% CI: 0.95, 1.37); fourth quintile HR=1.19 (95% CI: 0.99, 1.43); fifth quintile HR=1.28 (95% CI: 1.07, 1.53), respectively; p-trend=0.002]. CONCLUSIONS: Residential proximity to road traffic volumes and traffic density were associated with increased all-cause mortality in patients undergoing treatment for active tuberculosis even after adjusting for multiple demographic, socioeconomic, and clinical factors, suggesting that TB patients are susceptible to the adverse health effects of traffic-related air pollution. https://doi.org/10.1289/EHP1699.

The Imperial County Community Air Monitoring Network: A Model for Community-based Environmental Monitoring for Public Health Action.

SUMMARY: The Imperial County Community Air Monitoring Network (the Network) is a collaborative group of community, academic, nongovernmental, and government partners designed to fill the need for more detailed data on particulate matter in an area that often exceeds air quality standards. The Network employs a community-based environmental monitoring process in which the community and researchers have specific, well-defined roles as part of an equitable partnership that also includes shared decision-making to determine study direction, plan research protocols, and conduct project activities. The Network is currently producing real-time particulate matter data from 40 low-cost sensors throughout Imperial County, one of the largest community-based air networks in the United States. Establishment of a community-led air network involves engaging community members to be citizen-scientists in the monitoring, siting, and data collection process. Attention to technical issues regarding instrument calibration and validation and electronic transfer and storage of data is also essential. Finally, continued community health improvements will be predicated on facilitating community ownership and sustainability of the network after research funds have been expended. https://doi.org/10.1289/EHP1772

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial, California, USA.

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM2.5 and PM10 (particulate matter with aerodynamic diameters <2.5 and <10 µm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R2 for converted hourly averaged Dylos mass measurements versus a PM2.5 BAM was 0.79 and that versus a PM10 BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R2 = 0.35-0.81). IMPLICATIONS: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.

Evaluating the Use of Electronic Health Records for Type 2 Diabetes Surveillance in 2 California Counties, 2010-2014.

OBJECTIVES: Electronic health records (EHRs) and electronic laboratory records (ELRs) are increasingly seen as a rich source of data for performing public health surveillance activities and monitoring community health status. Their potential for surveillance of chronic illness, however, may be underused. Our objectives were to (1) evaluate the use of EHRs and ELRs for diabetes surveillance in 2 California counties and (2) examine disparities in diabetes prevalence by geography, income, and race/ethnicity. METHODS: We obtained data on a clinical diagnosis of diabetes and hemoglobin A1c (HbA1c) test results for adult members of Kaiser Permanente Northern California living in Contra Costa County or Solano County at any time during 2010-2014. We evaluated the validity of using HbA1c test results to determine diabetes prevalence, using clinical diagnoses as a gold standard. We estimated disparities in diabetes prevalence by combining HbA1c test results with US Census data on income, race, and ethnicity. RESULTS: When compared with a clinical diagnosis of diabetes, data on a patient's 5-year maximum HbA1c value ≥6.5% yielded the best combination of sensitivity (87.4%) and specificity (99.2%). The prevalence of 5-year maximum HbA1c ≥6.5% decreased with increasing median family income and increased with greater proportions of residents who were either non-Hispanic black or Hispanic. CONCLUSIONS: Timely diabetes surveillance data from ELRs can be used to document disparities, target interventions, and evaluate changes in population health. ELR data may be easier to access than a patient's entire EHR, but outcome metric validation with diabetes diagnoses would need to be ongoing. Future research should validate ELR and EHR data across multiple providers.

Estimating the Proportion of Childhood Cancer Cases and Costs Attributable to the Environment in California.

OBJECTIVES: To estimate the proportion of cases and costs of the most common cancers among children aged 0 to 14 years (leukemia, lymphoma, and brain or central nervous system tumors) that were attributable to preventable environmental pollution in California in 2013. METHODS: We conducted a literature review to identify preventable environmental hazards associated with childhood cancer. We combined risk estimates with California-specific exposure prevalence estimates to calculate hazard-specific environmental attributable fractions (EAFs). We combined hazard-specific EAFs to estimate EAFs for each cancer and calculated an overall EAF. Estimated economic costs included annual (indirect and direct medical) and lifetime costs. RESULTS: Hazards associated with childhood cancer risks included tobacco smoke, residential exposures, and parental occupational exposures. Estimated EAFs for leukemia, lymphoma, and brain or central nervous system cancer were 21.3% (range = 11.7%-30.9%), 16.1% (range = 15.0%-17.2%), and 2.0% (range = 1.7%-2.2%), respectively. The combined EAF was 15.1% (range = 9.4%-20.7%), representing $18.6 million (range = $11.6 to $25.5 million) in annual costs and $31 million in lifetime costs. CONCLUSIONS: Reducing environmental hazards and exposures in California could substantially reduce the human burden of childhood cancer and result in significant annual and lifetime savings.