Co-kriging with a low-cost sensor network to estimate spatial variation of brake and tire-wear metals and oxidative stress potential in Southern California.

Due to regulations and technological advancements reducing tailpipe emissions, an increasing proportion of emissions arise from brake and tire wear particulate matter (PM). PM from these non-tailpipe sources contains heavy metals capable of generating oxidative stress in the lung. Although important, these particles remain understudied because the high cost of actively collecting filter samples. Improvements in electrical engineering, internet connectivity, and an increased public concern over air pollution have led to a proliferation of dense low-cost air sensor networks such as the PurpleAir monitors, which primarily measure unspeciated fine particulate matter (PM2.5). In this study, we model the concentrations of Ba, Zn, black carbon, reactive oxygen species concentration in the epithelial lining fluid, dithiothreitol (DTT) loss, and OH formation. We use a co-kriging approach, incorporating data from the PurpleAir network as a secondary predictor variable and a land-use regression (LUR) as an external drift. For most pollutant species, co-kriging models produced more accurate predictions than an LUR model, which did not incorporate data from the PurpleAir monitors. This finding suggests that low-cost sensors can enhance predictions of pollutants that are costly to measure extensively in the field.

The association between natural environments and childhood mental health and development: A systematic review and assessment of different exposure measurements.

BACKGROUND: Several studies have assessed the relationship between exposure to natural environments (NEs) and childhood mental health and development. In most cases, a positive association has been found, but results are inconsistent, and the strength of association is unclear. This inconsistency may reflect the heterogeneity in measurements used to assess NE. OBJECTIVES: This systematic review aims to identify the most common NE metrics used in childhood mental health and development research. Our second aim is to identify the metrics that are most consistently associated with health and assess the relative strength of association depending on type of NE exposure measurement, in terms of metric used (i.e., measurement technique, such as remote sensing), but also rate (i.e., spatial and temporal exposure). METHODS: We used the PRISMA protocol to identify eligible studies, following a set of pre-defined inclusion criteria based on the PECOS strategy. A number of keywords were used for retrieving relevant articles from Medline, Embase, PsychINFO, and Web of Science databases between January 2000-November 2020. From these, we extracted data on type of NE measurement and relative association to a number of indicators of childhood mental health and development. We conducted a systematic assessment of quality and risk of bias in the included articles to evaluate the level of evidence. Case studies and qualitative studies were excluded. RESULTS: After screening of title (283 studies included), abstract, and full article, 45 studies were included in our review. A majority of which were conducted in North America and Europe (n = 36; 80%). The majority of studies used land use or land covers (LULC, n = 24; 35%) to determine exposures to NEs. Other metrics included the normalized difference vegetation index (NDVI), expert measures (e.g., surveys of data collection done by experts), surveys (e.g., self-reported assessments), and use of NE (e.g., measures of a participant's use of NE such as through GPS tracts or parent reports). Rate was most commonly determined by buffer zones around residential addresses or postal codes. The most consistent association to health outcomes was found for buffers of 100 m, 250 m, 500 m, and within polygons boundaries (e.g., census tracts). Six health categories, academic achievement, prevalence of doctor diagnosed disorders, emotional and behavioral functioning, well-being, social functioning, and cognitive skills, were created post hoc. We found sufficient evidence between NDVI (Landsat) and emotional and behavioral well-being. Additionally, we found limited evidence between LULC datasets and academic achievement; use of NE, parent/guardian reported greenness, and expert measures of greenness and emotional and behavioral functioning; and use of NE and social functioning. DISCUSSION: This review demonstrates that several NE measurements must be evaluated further before sufficient evidence for a potential association between distinct NE exposure metrics and childhood mental health and development can be established. Further, we suggest increased coordination between research efforts, for example, by replication of studies and comparing different NE measurements systematically, so that effect sizes can be confirmed for various health outcomes. Finally, we recommend implementing research designs that assess underlying pathways of nature-health relations and utilize measurement techniques that adequately assess exposure, access, use, and perception of NEs in order to contribute to a better understanding of health impacts of surrounding natural environments.

Predicting differential improvements in annual pollutant concentrations and exposures for regulatory policy assessment.

Over the past decade, researchers and policy-makers have become increasingly interested in regulatory and policy interventions to reduce air pollution concentrations and improve human health. Studies have typically relied on relatively sparse environmental monitoring data that lack the spatial resolution to assess small-area improvements in air quality and health. Few studies have integrated multiple types of measures of an air pollutant into one single modeling framework that combines spatially- and temporally-rich monitoring data. In this paper, we investigated the differential effects of California emissions reduction plan on reducing air pollution between those living in the goods movement corridors (GMC) that are within 500 m of major highways that serve as truck routes to those farther away or adjacent to routes that prohibit trucks. A mixed effects Deletion/Substitution/Addition (D/S/A) machine learning algorithm was developed to model annual pollutant concentrations of nitrogen dioxide (NO2) by taking repeated measures into consideration and by integrating multiple types of NO2 measurements, including those through government regulatory and research-oriented saturation monitoring into a single modeling framework. Difference-in-difference analysis was conducted to identify whether those living in GMC demonstrated statistically larger reductions in air pollution exposure. The mixed effects D/S/A machine learning modeling result indicated that GMC had 2 ppb greater reductions in NO2 concentrations from pre- to post-policy period than far away areas. The difference-in-difference analysis demonstrated that the subjects living in GMC experienced statistically significant greater reductions in NO2 exposure than those living in the far away areas. This study contributes to scientific knowledge by providing empirical evidence that improvements in air quality via the emissions reductions plan policies impacted traffic-related air pollutant concentrations and associated exposures most among low-income Californians with chronic conditions living in GMC. The identified differences in pollutant reductions across different location domains may be applicable to other states or other countries if similar policies are enacted.

Short-term impact of PM2.5 on contemporaneous asthma medication use: Behavior and the value of pollution reductions.

Asthma ranks among the most costly of chronic diseases, accounting for over $50 billion annually in direct medical expenditures in the United States. At the same time, evidence has accumulated that fine particulate matter pollution can exacerbate asthma symptoms and generate substantial economic costs. To measure these costs, we use a unique nationwide panel dataset tracking asthmatic individuals' use of rescue medication and their exposure to PM2.5 (particulate matter with an aerodynamic diameter of <2.5 µm) concentration between 2012 and 2017, to estimate the causal relationship between pollution and inhaler use. Our sample consists of individuals using an asthma digital health platform, which relies on a wireless sensor to track the place and time of inhaler use events, as well as regular nonevent location and time indicators. These data provide an accurate measurement of inhaler use and allow spatially and temporally resolute assignment of pollution exposure. Using a high-frequency research design and individual fixed effects, we find that a 1 µg/m3 (12%) increase in weekly exposure to PM2.5 increases weekly inhaler use by 0.82%. We also show that there is seasonal, regional, and income-based heterogeneity in this response. Using our response prediction, and an estimate from the literature on the willingness to pay to avoid asthma symptoms, we show that a nationwide 1 µg/m3 reduction in particulate matter concentration would generate nearly $350 million annually in economic benefits.

Digital Health Intervention for Asthma: Patient-Reported Value and Usability.

BACKGROUND: Although digital health tools are increasingly recognized as effective in improving clinical outcomes such as asthma control and medication adherence, few studies have assessed patient experiences and perception of value. OBJECTIVE: The aim of this study was to evaluate patient satisfaction, perception of usability and value, and desire to continue after 12 months of using a digital health intervention to support asthma management. METHODS: Participants were enrolled in a randomized controlled study evaluating the impact of a digital health platform for asthma management. Participants used electronic inhaler sensors to track medication use and accessed their information in a digital health platform. Electronic surveys were administered to intervention arm participants aged 12 years and older after 12 months of use. The survey assessed asthma control, patient satisfaction with the sensor device, and perception of the usability and value of the digital health platform through closed-ended and open-ended questions. Logistic regression models were used to assess the impact of participants' characteristics on survey completion, satisfaction, and perception of value. RESULTS: Of the 207 intervention arm participants aged 12 years and older, 89 submitted survey responses (42.9% response rate). Of these 89 participants, 70 reported being very satisfied (79%, 70/89) or somewhat satisfied (20%, 18/89) with the inhaler sensor device. Moreover, 93% (83/89) expressed satisfaction with the reports, and 90% (80/89) found the information from the reports useful for learning about their asthma. In addition, 72% (64/89) of the participants reported that they were interested in continuing to use the sensor and platform beyond the study. There were no significant differences in satisfaction with the device or the platform across participants' characteristics, including device type, age, sex, insurance type, asthma control, or syncing history; however, participants with smartphones and longer participation were more likely to take the survey. CONCLUSIONS: Electronic sensors and a digital health platform were well received by participants who reported satisfaction and perceived value. These results were consistent across multiple participants' characteristics. These findings can add to a limited literature to keep improving digital health interventions and ensure the meaningful and enduring impact on patient outcomes.

Assessment of Diastolic Function in Single-Ventricle Patients After the Fontan Procedure.

BACKGROUND: Patients with functional single ventricles after the Fontan procedure have abnormal cardiac mechanics. The aims of this study were to determine factors that influence diastolic function and to describe associations of diastolic function with current clinical status. METHODS: Echocardiograms were obtained as part of the Pediatric Heart Network Fontan Cross-Sectional Study. Diastolic function grade (DFG) was assessed as normal (grade 0), impaired relaxation (grade 1), pseudonymization (grade 2), or restrictive (grade 3). Studies were also classified dichotomously (restrictive pattern present or absent). Relationships between DFG and pre-Fontan variables (e.g., ventricular morphology, age at Fontan, history of volume-unloading surgery) and current status (e.g., systolic function, valvar regurgitation, exercise performance) were explored. RESULTS: DFG was calculable in 326 of 546 subjects (60%) (mean age, 11.7 ± 3.3 years). Overall, 32% of patients had grade 0, 9% grade 1, 37% grade 2, and 22% grade 3 diastolic function. Although there was no association between ventricular morphology and DFG, there was an association between ventricular morphology and E', which was lowest in those with right ventricular morphology (P < .001); this association remained significant when using Z scores adjusted for age (P < .001). DFG was associated with achieving maximal effort on exercise testing (P = .004); the majority (64%) of those not achieving maximal effort had DFG 2 or 3. No additional significant associations of DFG with laboratory or clinical measures were identified. CONCLUSIONS: Assessment of diastolic function by current algorithms results in a high percentage of patients with abnormal DFG, but few clinically or statistically significant associations were found. This may imply a lack of impact of abnormal diastolic function on clinical outcomes in this cohort, or it may indicate that the methodology may not be applicable to pediatric patients with functional single ventricles.

Integrating smart-phone based momentary location tracking with fixed site air quality monitoring for personal exposure assessment.

Epidemiological studies investigating relationships between environmental exposures from air pollution and health typically use residential addresses as a single point for exposure, while environmental exposures in transit, at work, school or other locations are largely ignored. Personal exposure monitors measure individuals' exposures over time; however, current personal monitors are intrusive and cannot be operated at a large scale over an extended period of time (e.g., for a continuous three months) and can be very costly. In addition, spatial locations typically cannot be identified when only personal monitors are used. In this paper, we piloted a study that applied momentary location tracking services supplied by smart phones to identify an individual's location in space-time for three consecutive months (April 28 to July 28, 2013) using available Wi-Fi networks. Individual exposures in space-time to the traffic-related pollutants Nitrogen Oxides (NOX) were estimated by superimposing an annual mean NOX concentration surface modeled using the Land Use Regression (LUR) modeling technique. Individual's exposures were assigned to stationary (including home, work and other stationary locations) and in-transit (including commute and other travel) locations. For the individual, whose home/work addresses were known and the commute route was fixed, it was found that 95.3% of the time, the individual could be accurately identified in space-time. The ambient concentration estimated at the home location was 21.01 ppb. When indoor/outdoor infiltration, indoor sources of air pollution and time spent outdoors were taken into consideration, the individual's cumulative exposures were 28.59 ppb and 96.49 ppb, assuming a respective indoor/outdoor ratio of 1.33 and 5.00. Integrating momentary location tracking services with fixed-site field monitoring, plus indoor-outdoor air exchange calibration, makes exposure assessment of a very large population over an extended time period feasible.

Urban greenness and physical activity in a national survey of Canadians.

Recent research suggests that urban greenness has important beneficial effects on health. While some epidemiological studies have investigated associations between measures of greenness and participation in physical activities, the findings have been inconsistent and several have relied on self-reported rather than objectively determined measures of greenness. Further, there have been few national studies. Our aim was to characterize associations between residential measures of greenness and physical activity within a nationally representative sample of Canadians. Moreover, we sought to determine to what extent these associations were modified by age, sex and income. Our analyses are based on data collected from participants of the 2001 Canadian Community Health Survey. Analyses were restricted to 69,910 individuals who were: 20 years of age and older, lived in urban areas, and for whom a residential measure of greenness could be assigned. The Normalised Difference Vegetation Index (NDVI), which is an objectively defined measure of greenness based on satellite imaging, was assigned to the residential addresses of the participants. Our NDVI values were based on 30 m and 500 m buffers from the centroid of the provided postal codes. Logistic regression was used to estimate odds ratios (ORs) and their 95% confidence intervals to describe associations between greenness and several measures of physical activity. The ORs were adjusted for age, sex, smoking status, marital status, and income. We found that participants who resided in the highest quartile of greenness, based on a 500 m buffer, were more likely to participate in leisure-time physical activity (adjusted OR=1.34, 95% CI=1.25-1.44) when compared to those in the lowest quartile. Positive associations were observed between greenness and physical activity in all income groupings. A key finding was the observation of a stronger association between greenness and measures of physical activity among younger adults, especially women. Our findings have important public health implications as they suggest that access to urban green environments contributes to increased participation in leisure-time physical activity which has demonstrable health benefits.

Inequalities in cumulative environmental burdens among three urbanized counties in California.

Low-income communities and communities of color often suffer from multiple environmental hazards that pose risks to their health. Here we extended a cumulative environmental hazard inequality index (CEHII) - developed to assess inequalities in air pollution hazards - to compare the inequality among three urban counties in California: Alameda, San Diego, and Los Angeles. We included a metric for heat stress to the analysis because exposure to excessively hot weather is increasingly recognized as a threat to human health and well-being. We determined if inequalities from heat stress differed between the three regions and if this added factor modified the metric for inequality from cumulative exposure to air pollution. This analysis indicated that of the three air pollutants considered, diesel particulate matter had the greatest inequality, followed by nitrogen dioxide (NO(2)) and fine particulate matter (PM(2.5)). As measured by our index, the inequalities from cumulative exposure to air pollution were greater than those of single pollutants. Inequalities were significantly different among single air pollutant hazards within each region and between regions; however, inequalities from the cumulative burdens did not differ significantly between any two regions. Modeled absolute and relative heat stress inequalities were small except for relative heat stress in San Diego which had the second highest inequality. Our analysis, techniques, and results provide useful insights for policy makers to assess inequalities between regions and address factors that contribute to overall environmental inequality within each region.

Green space, health inequality and pregnancy.

Green spaces have been suggested to improve physical and mental health and well-being by increasing physical activity, reducing air pollution, noise, and ambient temperature, increasing social contacts and relieving psychophysiological stress. Although these mechanisms also suggest potential beneficial effects of green spaces on pregnancy outcomes, to our knowledge there is no available epidemiological evidence on this impact. We investigated the effects of surrounding greenness and proximity to major green spaces on birth weight and gestational age at delivery and described the effect of socioeconomic position (SEP) on these relationships. This study was based on a cohort of births (N=8246) that occurred in a major university hospital in Barcelona, Spain, during 2001-2005. We determined surrounding greenness from satellite retrievals as the average of Normalized Difference Vegetation Index (NDVI) in a buffer of 100 m around each maternal place of residence. To address proximity to major green spaces, a binary variable was used to indicate whether maternal residential address is situated within a buffer of 500 m from boundaries of a major green space. For each indicator of green exposure, linear regression models were constructed to estimate change in outcomes adjusted for relevant covariates including individual and area level SEP. None of the indicators of green exposure was associated with birth weight and gestational age. After assessing effect modification based on the level of maternal education, we detected an increase in birth weight (grams) among the lowest education level group (N=164) who had higher surrounding NDVI (Regression coefficient (95% confidence interval (CI) of 436.3 (43.1, 829.5)) or lived close to a major green space (Regression coefficient (95% CI)) of 189.8 (23.9, 355.7)). Our findings suggest a beneficial effect of exposure to green spaces on birth weight only in the lowest SEP group.

Does exposure to air pollution in urban parks have socioeconomic, racial or ethnic gradients?

Little is known about the levels of air pollution at public parks where regular exercise takes place or in park-adjacent neighborhoods where people have easy access to parks. In this study we investigated the ambient concentrations of criteria pollutants nitrogen dioxide (NO(2)), fine particulate (PM(2.5)) and ozone (O(3)) at public parks and in park-adjacent neighborhoods for metropolitan Los Angeles. Socioeconomic and racial-ethnic inequalities in exposure to the three criteria pollutants were also investigated using multiple linear regression models. In addition, differences in inhalation doses from breathing the three +criteria pollutants were investigated for the top and bottom quartile racial composition in the parks and neighborhoods. Our research showed that although public parks had on average the lowest pollutant concentrations of NO(2) and PM(2.5), they had relatively high O(3) concentrations. Park-adjacent neighborhoods, by contrast, had the highest NO(2) and PM(2.5) concentrations, but the lowest O(3) concentrations. Higher exposures to NO(2) and PM(2.5) were systematically identified for the lower socioeconomic position or higher minority population neighborhoods. For children and adolescents aged 6-15 engaging in high and moderate intensity activities in and around public parks, those from the top quartile of primarily Hispanic neighborhoods had much higher (63%) inhaled doses of NO(2) compared to the bottom quartile counterpart. PM(2.5) showed a similar but less pronounced pattern of inhalation doses. Evidence of socioeconomic and racial-ethnic gradients was found in air pollution exposure and inhalation doses in and around the urban parks in Los Angeles. This suggests that patterns of exposure inequality found in other environmental justice research are present in exposures in and around urban parks.

An index for assessing demographic inequalities in cumulative environmental hazards with application to Los Angeles, California.

Researchers in environmental justice contend that low-income communities and communities of color face greater impacts from environmental hazards. This is also of concern for policy makers. In this context, our paper has two principal objectives. First, we propose a method for creating an index capable of summarizing racial-ethnic and socioeconomic inequalities from the impact of cumulative environmental hazards. Second, we apply the index to Los Angeles County to illustrate the potential applications and complexities of its implementation. Individual environmental inequality indices are calculated based on unequal shares of environmental hazards for racial-ethnic groups and socioeconomic positions. The illustrated hazards include ambient concentrations of particulate matter, nitrogen dioxide, and estimates of cancer risk associated with modeled estimates for diesel particulate matter. The cumulative environmental hazard inequality index (CEHII) then combines individual environmental hazards, using either a multiplicative or an additive model. Significant but modest inequalities exist for both individual and cumulative environmental hazards in Los Angeles. The highest level of inequality among racial-ethnic and socioeconomic groups occurs when a multiplicative model is used to estimate cumulative hazard. The CEHII provides a generalized framework that incorporates environmental hazards and socioeconomic characteristics to assess inequalities in cumulative environmental risks.

Multi-level modelling in health research: a caution and rejoinder on temporally mismatched data.

In a review of the multilevel modelling literature (MLM) we find that data on individuals and their social environment contexts (neighbourhoods, municipalities) are often drawn from different years/time periods. This temporal mismatch has scarcely attracted any attention though it can significantly influence modelling results and interpretation. We demonstrate the influence of temporal mismatch first by outlining the degree of neighbourhood mobility in large metropolitan areas in Britain, Canada and the United States and second with a brief MLM example. We conclude that researchers ought to provide more study context when such mismatch is unavoidable.