Communities catalyzing change with data to mitigate an invisible menace, traffic-related air pollution.

OBJECTIVES: To identify strategies and tactics communities use to translate research into environmental health action. METHODS: We employed a qualitative case study design to explore public health action conducted by residents, organizers, and public health planners in two Massachusetts communities as part of a community based participatory (CBPR) research study. Data sources included key informant interviews (n = 24), reports and direct observation of research and community meetings (n = 10) and project meeting minutes from 2016-2021. Data were coded deductively drawing on the community organizing and implementation frameworks. RESULTS: In Boston Chinatown, partners drew broad participation from community-based organizations, residents, and municipal leaders, which resulted in air pollution mitigation efforts being embedded in the master planning process. In Somerville, partners focused on change at multiple levels, developer behavior, and separate from the funded research, local legislative efforts, and litigation. CONCLUSIONS: CBPR affords communities the ability to environmental health efforts in a way that is locally meaningful, leveraging their respective strengths. External facilitation can support the continuity and sustainment of community led CBPR efforts.

Exposure to ultrafine particles and cognitive decline among older people in the United States.

BACKGROUND: Some studies suggest that ambient particulate air pollution is associated with cognitive decline. However, the findings are mixed, and there is no relevant research examining the influences of ultrafine particles (UFP), which may have more toxicity than larger particles. We therefore conducted this study to investigate whether residential UFP exposure is associated with cognitive decline using data from the Alzheimer's Disease Research Centers in the United States. METHODS: This is a longitudinal study of participants who were aged 65 years and older and had normal cognitive status at baseline. Residential UFP exposure, expressed as particle number concentrations (PNC), was assessed in 2016-2017 using a nationwide land use regression model, and was assigned to each participant using their 3-digit residential ZIP codes. Cognitive functions including memory, attention, language, executive function, and global function were assessed annually using 15 neuropsychological tests from March 2015 to February 2022. Linear mixed-effects models were used to examine the associations after adjustment for covariates including baseline age, sex, APOE ε4 status, race, education, smoking status, history of diabetes, quartiles of neighborhood median household income, and interaction terms of follow-up time with each covariate. RESULTS: This study included 5646 participants (mean age 76 years, 65% female). On average, each participant had 4 annual visits. When PNC was treated as a continuous variable, there were no statistically or clinically significant changes in annual decline of each cognitive function in relation to an interquartile range elevation in PNC (4026 particles/cm3). Similarly, when PNC was treated as a categorical variable including five exposure groups, there were no linear exposure-response trends in annual decline of each cognitive function across the five exposure groups. CONCLUSIONS: This study found no meaningful associations between residential UFP exposure and cognitive decline in global and domain-specific functions. There is a need for further research that assigns UFP exposure at a finer geographic scale.

Ultrafine Particle Number Concentration Model for Estimating Retrospective and Prospective Long-Term Ambient Exposures in Urban Neighborhoods.

Short-term exposure to ultrafine particles (UFP; <100 nm in diameter), which are present at high concentrations near busy roadways, is associated with markers of cardiovascular and respiratory disease risk. To date, few long-term studies (months to years) have been conducted due to the challenges of long-term exposure assignment. To address this, we modified hybrid land-use regression models of particle number concentrations (PNCs; a proxy for UFP) for two study areas in Boston (MA) by replacing the measured PNC term with an hourly model and adjusting for overprediction. The hourly PNC models used covariates for meteorology, traffic, and sulfur dioxide concentrations (a marker of secondary particle formation). We compared model performance against long-term PNC data collected continuously from 9 years before and up to 3 years after the model-development period. Model predictions captured the major temporal variations in the data and model performance remained relatively stable retrospectively and prospectively. The Pearson correlation of modeled versus measured hourly log-transformed PNC at a long-term monitoring site for 9 years prior was 0.74. Our results demonstrate that highly resolved spatial-temporal PNC models are capable of estimating ambient concentrations retrospectively and prospectively with generally good accuracy, giving us confidence in using these models in epidemiological studies.

Traffic-related particulate matter affects behavior, inflammation, and neural integrity in a developmental rodent model.

Recent studies indicate that exposure to airborne particulate matter (PM) is associated with cognitive delay, depression, anxiety, autism, and neurodegenerative diseases; however, the role of PM in the etiology of these outcomes is not well-understood. Therefore, there is a need for controlled animal studies to better elucidate the causes and mechanisms by which PM impacts these health outcomes. We assessed the effects of gestational and early life exposure to traffic-related PM on social- and anxiety-related behaviors, cognition, inflammatory markers, and neural integrity in juvenile male rats. Gestating and lactating rats were exposed to PM from a Boston (MA, USA) traffic tunnel for 5 h/day, 5 days/week for 6 weeks (3 weeks gestation, 3 weeks lactation). The target exposure concentration for the fine fraction of nebulized PM, measured as PM2.5, was 200 µg/m3. To assess anxiety and cognitive function, F1 male juveniles underwent elevated platform, cricket predation, nest building, social behavior and marble burying tests at 32-60 days of age. Upon completion of behavioral testing, multiple cytokines and growth factors were measured in these animals and their brains were analyzed with diffusion tensor MRI to assess neural integrity. PM exposure had no effect on litter size or weight, or offspring growth; however, F1 litters developmentally exposed to PM exhibited significantly increased anxiety (p = 0.04), decreased cognition reflected in poorer nest-organization (p = 0.04), and decreased social play and allogrooming (p = 0.003). MRI analysis of ex vivo brains revealed decreased structural integrity of neural tissues in the anterior cingulate and hippocampus in F1 juveniles exposed to PM (p < 0.01, p = 0.03, respectively). F1 juvenile males exposed to PM also exhibited significantly decreased plasma levels of both IL-18 (p = 0.03) and VEGF (p = 0.04), and these changes were inversely correlated with anxiety-related behavior. Chronic exposure of rat dams and their offspring to traffic-related PM during gestation and lactation decreases social behavior, increases anxiety, impairs cognition, decreases levels of inflammatory and growth factors (which are correlated with behavioral changes), and disrupts neural integrity in the juvenile male offspring. Our findings add evidence that exposure to traffic-related air pollution during gestation and lactation is involved in the etiology of autism spectrum disorder and other disorders which include social and cognitive deficits and/or increased anxiety.

Two communities, one highway and the fight for clean air: the role of political history in shaping community engagement and environmental health research translation.

BACKGROUND: This paper explores strategies to engage community stakeholders in efforts to address the effects of traffic-related air pollution (TRAP). Communities of color and low-income communities are disproportionately impacted by environmental threats including emissions generated by major roadways. METHODS: Qualitative instrumental case study design was employed to examine how community-level factors in two Massachusetts communities, the City of Somerville and Boston's Chinatown neighborhood, influence the translation of research into practice to address TRAP exposure. Guided by the Interactive Systems Framework (ISF), we drew on three data sources: key informant interviews, observations and document reviews. Thematic analysis was used. RESULTS: Findings indicate political history plays a significant role in shaping community action. In Somerville, community organizers worked with city and state officials, and embraced community development strategies to engage residents. In contrast, Chinatown community activists focused on immediate resident concerns including housing and resident displacement resulting in more opposition to local municipal leadership. CONCLUSIONS: The ISF was helpful in informing the team's thinking related to systems and structures needed to translate research to practice. However, although municipal stakeholders are increasingly sympathetic to and aware of the health impacts of TRAP, there was not a local legislative or regulatory precedent on how to move some of the proposed TRAP-related policies into practice. As such, we found that pairing the ISF with a community organizing framework may serve as a useful approach for examining the dynamic relationship between science, community engagement and environmental research translation. Social workers and public health professionals can advance TRAP exposure mitigation by exploring the political and social context of communities and working to bridge research and community action.

Urban environment as an independent predictor of insulin resistance in a South Asian population.

BACKGROUND: Developing countries, such as India, are experiencing rapid urbanization, which may have a major impact on the environment: including worsening air and water quality, noise and the problems of waste disposal. We used health data from an ongoing cohort study based in southern India to examine the relationship between the urban environment and homeostasis model assessment of insulin resistance (HOMA-IR). METHODS: We utilized three metrics of urbanization: distance from urban center; population density in the India Census; and satellite-based land cover. Restricted to participants without diabetes (N = 6350); we built logistic regression models adjusted for traditional risk factors to test the association between urban environment and HOMA-IR. RESULTS: In adjusted models, residing within 0-20 km of the urban center was associated with an odds ratio for HOMA-IR of 1.79 (95% CI 1.39, 2.29) for females and 2.30 (95% CI 1.64, 3.22) for males compared to residing in the furthest 61-80 km distance group. Similar statistically significant results were identified using the other metrics. CONCLUSIONS: We identified associations between urban environment and HOMA-IR in a cohort of adults. These associations were robust using various metrics of urbanization and adjustment for individual predictors. Our results are of public health concern due to the global movement of large numbers of people from rural to urban areas and the already large burden of diabetes.

Visualizing Air Pollution: Communication of Environmental Health Information in a Chinese Immigrant Community.

This study developed and evaluated a visual approach to promoting environmental health literacy about highway pollution. The Interactive Map of Chinatown Traffic Pollution was the centerpiece of a communication approach designed to make complex scientific information about traffic-related air pollution comprehensible to Chinese immigrants with limited English proficiency. The map enabled visualization of the spatial distribution of ultrafine particles (less than 100 nanometers in diameter), a toxic and invisible form of air pollution, in Boston Chinatown. A university-community partnership enabled design of intergenerational training sessions aimed toward empowering community members to take health-promoting actions that reduce exposure to ultrafine particulate pollution. A mixed methods approach was taken to evaluation. Nine high school youth learned to use the map and then tutored adults recruited from English as a Second Language (ESL) classes and from a community workshop. Seventy-three of these adults completed a pre-post survey measuring change in three domains: pollution knowledge, attitudes toward environmental issues, and self-efficacy in using maps. Adult participants demonstrated statistically significant improvements in all three domains (Wilcoxon signed-rank test, all p < 0.01). Seventeen adults and nine youth participated in interviews. Interview participants reported adjusting daily routines to reduce exposure to pollution.

Combining Measurements from Mobile Monitoring and a Reference Site To Develop Models of Ambient Ultrafine Particle Number Concentration at Residences.

Significant spatial and temporal variation in ultrafine particle (UFP; <100 nm in diameter) concentrations creates challenges in developing predictive models for epidemiological investigations. We compared the performance of land-use regression models built by combining mobile and stationary measurements (hybrid model) with a regression model built using mobile measurements only (mobile model) in Chelsea and Boston, MA (USA). In each study area, particle number concentration (PNC; a proxy for UFP) was measured at a stationary reference site and with a mobile laboratory driven along a fixed route during an ∼1-year monitoring period. In comparing PNC measured at 20 residences and PNC estimates from hybrid and mobile models, the hybrid model showed higher Pearson correlations of natural log-transformed PNC ( r = 0.73 vs 0.51 in Chelsea; r = 0.74 vs 0.47 in Boston) and lower root-mean-square error in Chelsea (0.61 vs 0.72) but no benefit in Boston (0.72 vs 0.71). All models overpredicted log-transformed PNC by 3-6% at residences, yet the hybrid model reduced the standard deviation of the residuals by 15% in Chelsea and 31% in Boston with better tracking of overnight decreases in PNC. Overall, the hybrid model considerably outperformed the mobile model and could offer reduced exposure error for UFP epidemiology.

Short-and medium-term associations of particle number concentration with cardiovascular markers in a Puerto Rican cohort.

Air pollution has been linked to adverse cardiovascular outcomes; however, susceptibility may vary by population. Puerto Rican adults living in the US may be a susceptible group due to a high rate of adverse cardiovascular events. We evaluated the effect of changes in ambient particle number concentration (PNC, a measure of ultrafine particles) and effects on biomarkers of cardiovascular risk in the Boston Puerto Rican Health Study (BPRHS), a longitudinal cohort (n = 1499). Ambient PNC was measured at a fixed site between 2004 and 2013 and daily mean concentrations were used to construct PNC metrics, including lags of 0, 1 and 2 days and moving averages (MAs) of 3, 7 and 28 days. We examined the association of each metric with C-reactive protein (CRP) and blood pressure. Each model included subject-specific random intercepts to account for multiple measurements. An interquartile range (IQR) increase in PNC was associated with CRP for all metrics, notably a 3-day increase in PNC was associated with a 7.1% (95% CI: 2.0%, 12.2%) increase in CRP. Significant associations with CRP were seen in women, but not men; with current and former (but not non-) smokers; participants younger (but not older) than 65 y; those without diabetes (but not with), and those with (but not without), hypertension. Our study extends knowledge about the health effects of air pollution to a vulnerable population that has been understudied.

Longitudinal associations of long-term exposure to ultrafine particles with blood pressure and systemic inflammation in Puerto Rican adults.

BACKGROUND: Few longitudinal studies have examined the association between ultrafine particulate matter (UFP, particles < 0.1 µm aerodynamic diameter) exposure and cardiovascular disease (CVD) risk factors. We used data from 791 adults participating in the longitudinal Boston Puerto Rican Health Study (Massachusetts, USA) between 2004 and 2015 to assess whether UFP exposure was associated with blood pressure and high sensitivity C-reactive protein (hsCRP, a biomarker of systemic inflammation). METHODS: Residential annual average UFP exposure (measured as particle number concentration, PNC) was assigned using a model accounting for spatial and temporal trends. We also adjusted PNC values for participants' inhalation rate to obtain the particle inhalation rate (PIR) as a secondary exposure measure. Multilevel linear models with a random intercept for each participant were used to examine the association of UFP with blood pressure and hsCRP. RESULTS: Overall, in adjusted models, an inter-quartile range increase in PNC was associated with increased hsCRP (ß = 6.8; 95% CI = - 0.3, 14.0%) but not with increased systolic blood pressure (ß = 0.96; 95% CI = - 0.33, 2.25 mmHg), pulse pressure (ß = 0.70; 95% CI = - 0.27, 1.67 mmHg), or diastolic blood pressure (ß = 0.55; 95% CI = - 0.20, 1.30 mmHg). There were generally stronger positive associations among women and never smokers. Among men, there were inverse associations of PNC with systolic blood pressure and pulse pressure. In contrast to the primary findings, an inter-quartile range increase in the PIR was positively associated with systolic blood pressure (ß = 1.03; 95% CI = 0.00, 2.06 mmHg) and diastolic blood pressure (ß = 1.01; 95% CI = 0.36, 1.66 mmHg), but not with pulse pressure or hsCRP. CONCLUSIONS: We observed that exposure to PNC was associated with increases in measures of CVD risk markers, especially among certain sub-populations. The exploratory PIR exposure metric should be further developed.

Comparisons of Traffic-Related Ultrafine Particle Number Concentrations Measured in Two Urban Areas by Central, Residential, and Mobile Monitoring.

Traffic-related ultrafine particles (UFP; <100 nanometers diameter) are ubiquitous in urban air. While studies have shown that UFP are toxic, epidemiological evidence of health effects, which is needed to inform risk assessment at the population scale, is limited due to challenges of accurately estimating UFP exposures. Epidemiologic studies often use empirical models to estimate UFP exposures; however, the monitoring strategies upon which the models are based have varied between studies. Our study compares particle number concentrations (PNC; a proxy for UFP) measured by three different monitoring approaches (central-site, short-term residential-site, and mobile on-road monitoring) in two study areas in metropolitan Boston (MA, USA). Our objectives were to quantify ambient PNC differences between the three monitoring platforms, compare the temporal patterns and the spatial heterogeneity of PNC between the monitoring platforms, and identify factors that affect correlations across the platforms. We collected >12,000 hours of measurements at the central sites, 1,000 hours of measurements at each of 20 residential sites in the two study areas, and >120 hours of mobile measurements over the course of ~1 year in each study area. Our results show differences between the monitoring strategies: mean one-minute PNC on-roads were higher (64,000 and 32,000 particles/cm3 in Boston and Chelsea, respectively) compared to central-site measurements (23,000 and 19,000 particles/cm3) and both were higher than at residences (14,000 and 15,000 particles/cm3). Temporal correlations and spatial heterogeneity also differed between the platforms. Temporal correlations were generally highest between central and residential sites, and lowest between central-site and on-road measurements. We observed the greatest spatial heterogeneity across monitoring platforms during the morning rush hours (06:00-09:00) and the lowest during the overnight hours (18:00-06:00). Longer averaging times (days and hours vs. minutes) increased temporal correlations (Pearson correlations were 0.69 and 0.60 vs. 0.39 in Boston; 0.71 and 0.61 vs. 0.45 in Chelsea) and reduced spatial heterogeneity (coefficients of divergence were 0.24 and 0.29 vs. 0.33 in Boston; 0.20 and 0.27 vs. 0.31 in Chelsea). Our results suggest that combining stationary and mobile monitoring may lead to improved characterization of UFP in urban areas and thereby lead to improved exposure assignment for epidemiology studies.

Lessons from in-home air filtration intervention trials to reduce urban ultrafine particle number concentrations.

BACKGROUND: Exposure to airborne ultrafine particle (UFP; <100 nm in aerodynamic diameter) is an emerging public health problem. Nevertheless, the benefit of using high efficiency particulate arrestance (HEPA) filtration to reduce UFP concentrations in homes is not yet clear. METHODS: We conducted a randomized crossover study of HEPA filtration without a washout period in 23 homes of low-income Puerto Ricans in Boston and Chelsea, MA (USA). Most participants were female, older adults who were overweight or obese. Particle number concentrations (PNC, a proxy for UFP) were measured indoors and outdoors at each home continuously for six weeks. Homes received both HEPA filtration and sham filtration for three weeks each in random order. RESULTS: Median PNC under HEPA filtration was 50-85% lower compared to sham filtration in most homes, but we found no benefit in terms of reduced inflammation; associations between hsCRP, IL-6, or TNFRII in blood samples and indoor PNC were inverse and not statistically significant. CONCLUSIONS: Limitations to our study design likely contributed to our findings. Limitations included carry-over effects, a population that may have been relatively unresponsive to UFP, reduction in PNC even during sham filtration that limited differences between HEPA and sham filtration, window opening by participants, and lack of fine-grained (room-specific) participant time-activity information. Our approach was similar to other recent HEPA intervention studies of particulate matter exposure and cardiovascular risk, suggesting that there is a need for better study designs.

Transferability and generalizability of regression models of ultrafine particles in urban neighborhoods in the Boston area.

Land use regression (LUR) models have been used to assess air pollutant exposure, but limited evidence exists on whether location-specific LUR models are applicable to other locations (transferability) or general models are applicable to smaller areas (generalizability). We tested transferability and generalizability of spatial-temporal LUR models of hourly particle number concentration (PNC) for Boston-area (MA, U.S.A.) urban neighborhoods near Interstate 93. Four neighborhood-specific regression models and one Boston-area model were developed from mobile monitoring measurements (34-46 days/neighborhood over one year each). Transferability was tested by applying each neighborhood-specific model to the other neighborhoods; generalizability was tested by applying the Boston-area model to each neighborhood. Both the transferability and generalizability of models were tested with and without neighborhood-specific calibration. Important PNC predictors (adjusted-R(2) = 0.24-0.43) included wind speed and direction, temperature, highway traffic volume, and distance from the highway edge. Direct model transferability was poor (R(2) < 0.17). Locally-calibrated transferred models (R(2) = 0.19-0.40) and the Boston-area model (adjusted-R(2) = 0.26, range: 0.13-0.30) performed similarly to neighborhood-specific models; however, some coefficients of locally calibrated transferred models were uninterpretable. Our results show that transferability of neighborhood-specific LUR models of hourly PNC was limited, but that a general model performed acceptably in multiple areas when calibrated with local data.

Gender, Ethnicity and Environmental Risk Perception Revisited: The Importance of Residential Location.

Studies in the U.S. have found that white men are less concerned about pollution than are women or people of other ethnicity. These studies have not assessed respondents' proximity to localized sources of pollution. Our objective was to assess lay perceptions of risk from air pollution in an ethnically diverse sample in which proximity to a major perceptible source of pollution is known. Cross sectional interview study of combined area probability and convenience sample of individuals 40 and older in the Boston area, selected according to proximity to high traffic controlled access highways. Of 697 respondents 46% were white, 37% Asian (mostly Chinese), 6.3% African-American, 6.3% Latino, and 7.6% other ethnicity. While white respondents, and particularly white men, were less concerned about air pollution than others, this effect disappeared when controlling for distance from the highway. White men were slightly less supportive than others of government policy to control pollution. The "white male" effect may in part be accounted for by the greater likelihood of minority respondents to live near perceptible localized sources of pollution.

An hourly regression model for ultrafine particles in a near-highway urban area.

Estimating ultrafine particle number concentrations (PNC) near highways for exposure assessment in chronic health studies requires models capable of capturing PNC spatial and temporal variations over the course of a full year. The objectives of this work were to describe the relationship between near-highway PNC and potential predictors, and to build and validate hourly log-linear regression models. PNC was measured near Interstate 93 (I-93) in Somerville, MA using a mobile monitoring platform driven for 234 h on 43 days between August 2009 and September 2010. Compared to urban background, PNC levels were consistently elevated within 100-200 m of I-93, with gradients impacted by meteorological and traffic conditions. Temporal and spatial variables including wind speed and direction, temperature, highway traffic, and distance to I-93 and major roads contributed significantly to the full regression model. Cross-validated model R(2) values ranged from 0.38 to 0.47, with higher values achieved (0.43 to 0.53) when short-duration PNC spikes were removed. The model predicts highest PNC near major roads and on cold days with low wind speeds. The model allows estimation of hourly ambient PNC at 20-m resolution in a near-highway neighborhood.

Spatial and temporal differences in traffic-related air pollution in three urban neighborhoods near an interstate highway.

Relatively few studies have characterized differences in intra- and inter-neighborhood traffic-related air pollutant (TRAP) concentrations and distance-decay gradients in along an urban highway for the purposes of exposure assessment. The goal of this work was to determine the extent to which intra- and inter-neighborhood differences in TRAP concentrations can be explained by traffic and meteorology in three pairs of neighborhoods along Interstate 93 (I-93) in the metropolitan Boston area (USA). We measured distance-decay gradients of seven TRAPs (PNC, pPAH, NO, NOX, BC, CO, PM2.5) in near-highway (<400 m) and background areas (>1 km) in Somerville, Dorchester/South Boston, Chinatown and Malden to determine whether (1) spatial patterns in concentrations and inter-pollutant correlations differ between neighborhoods, and (2) variation within and between neighborhoods can be explained by traffic and meteorology. The neighborhoods ranged in area from 0.5 to 2.3 km2. Mobile monitoring was performed over the course of one year in each pair of neighborhoods (one pair of neighborhoods per year in three successive years; 35-47 days of monitoring in each neighborhood). Pollutant levels generally increased with highway proximity, consistent with I-93 being a major source of TRAP; however, the slope and extent of the distance-decay gradients varied by neighborhood as well as by pollutant, season and time of day. Correlations among pollutants differed between neighborhoods (e.g., ρ = 0.35-0.80 between PNC and NOX and ρ = 0.11-0.60 between PNC and BC) and were generally lower in Dorchester/South Boston than in the other neighborhoods. We found that the generalizability of near-road gradients and near-highway/urban background contrasts was limited for near-highway neighborhoods in a metropolitan area with substantial local street traffic. Our findings illustrate the importance of measuring gradients of multiple pollutants under different ambient conditions in individual near-highway neighborhoods for health studies involving inter-neighborhood comparisons.

Evidence for the healthy immigrant effect in older Chinese immigrants: a cross-sectional study.

BACKGROUND: Previous work has found that first-generation immigrants to developed nations tend to have better health than individuals born in the host country. We examined the evidence for the healthy immigrant effect and convergence of health status between Chinese immigrants (n = 147) and U.S. born whites (n = 167) participating in the cross-sectional Community Assessment of Freeway Exposure and Health study and residing in the same neighborhoods. METHODS: We used bivariate and multivariate models to compare disease prevalence and clinical biomarkers. RESULTS: Despite an older average age and lower socioeconomic status, Chinese immigrants were less likely to have asthma (OR = 0.20, 95% CI = 0.09-0.48) or cardiovascular disease (OR = 0.44, 95% CI = 0.20-0.94), had lower body mass index (BMI), lower inflammation biomarker levels, lower average sex-adjusted low-density lipoprotein (LDL) cholesterol, and higher average sex-adjusted high-density lipoprotein (HDL) cholesterol. However, there was no significant difference in the prevalence of diabetes or hypertension. Duration of time in the U.S. was related to cardiovascular disease and asthma but was not associated with diabetes, hypertension, BMI, HDL cholesterol, LDL cholesterol, socioeconomic status, or health behaviors. CONCLUSIONS: The lower CVD and asthma prevalence among the Chinese immigrants may be partially attributed to healthier diets, more physical activity, lower BMI, and less exposure to cigarette smoke. First generation immigrant status may be protective even after about two decades.

Characterizing the low wage immigrant workforce: a comparative analysis of the health disparities among selected occupations in Somerville, Massachusetts.

BACKGROUND: This study estimates job-related risks among common low wage occupations (cleaning, construction, food service, cashier/baggers, and factory workers) held by predominantly Haitian, El Salvadorian, and Brazilian immigrants living or working in Somerville, Massachusetts. METHODS: A community-based cross-sectional survey on immigrant occupational health was conducted between 2006 and 2009 and logistic regression was used to assess the job-related risks among the most common low wage occupations. RESULTS: Construction workers reported significantly higher health risks, and lower access to occupational health services than the other occupations. Compared to cashier/baggers, the reference population in this study, cleaners reported significantly lower access to health and safety and work training and no knowledge of workers' compensation. Factory workers reported significantly lower work training compared to cashier/baggers. Food service workers reported the least access to doctors compared to the other occupations. CONCLUSION: We found significant variability in risks among different low wage immigrant occupations. The type of occupation independently contributed to varying levels of risks among these jobs. We believe our findings to be conservative and recommend additional inquiry aimed at assuring the representativeness of our findings.

The Exposure Peaks of Traffic-Related Ultrafine Particles Associated with Inflammatory Biomarkers and Blood Lipid Profiles.

In this article, we explored the effects of ultrafine particle (UFP) peak exposure on inflammatory biomarkers and blood lipids using two novel metrics-the intensity of peaks and the frequency of peaks. We used data previously collected by the Community Assessment of Freeway Exposure and Health project from participants in the Greater Boston Area. The UFP exposure data were time-activity-adjusted hourly average concentration, estimated using land use regression models based on mobile-monitored ambient concentrations. The outcome data included C-reactive protein, interleukin-6 (IL-6), tumor necrosis factor-alpha receptor 2 (TNF-RII), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides and total cholesterol. For each health indicator, multivariate regression models were used to assess their associations with UFP peaks (N = 364-411). After adjusting for age, sex, body mass index, smoking status and education level, an increase in UFP peak exposure was significantly (p < 0.05) associated with an increase in TNF-RII and a decrease in HDL and triglycerides. Increases in UFP peaks were also significantly associated with increased IL-6 and decreased total cholesterol, while the same associations were not significant when annual average exposure was used. Our work suggests that analysis using peak exposure metrics could reveal more details about the effect of environmental exposures than the annual average metric.

Positional error and time-activity patterns in near-highway proximity studies: an exposure misclassification analysis.

BACKGROUND: The growing interest in research on the health effects of near-highway air pollutants requires an assessment of potential sources of error in exposure assignment techniques that rely on residential proximity to roadways. METHODS: We compared the amount of positional error in the geocoding process for three different data sources (parcels, TIGER and StreetMap USA) to a "gold standard" residential geocoding process that used ortho-photos, large multi-building parcel layouts or large multi-unit building floor plans. The potential effect of positional error for each geocoding method was assessed as part of a proximity to highway epidemiological study in the Boston area, using all participants with complete address information (N = 703). Hourly time-activity data for the most recent workday/weekday and non-workday/weekend were collected to examine time spent in five different micro-environments (inside of home, outside of home, school/work, travel on highway, and other). Analysis included examination of whether time-activity patterns were differentially distributed either by proximity to highway or across demographic groups. RESULTS: Median positional error was significantly higher in street network geocoding (StreetMap USA = 23 m; TIGER = 22 m) than parcel geocoding (8 m). When restricted to multi-building parcels and large multi-unit building parcels, all three geocoding methods had substantial positional error (parcels = 24 m; StreetMap USA = 28 m; TIGER = 37 m). Street network geocoding also differentially introduced greater amounts of positional error in the proximity to highway study in the 0-50 m proximity category. Time spent inside home on workdays/weekdays differed significantly by demographic variables (age, employment status, educational attainment, income and race). Time-activity patterns were also significantly different when stratified by proximity to highway, with those participants residing in the 0-50 m proximity category reporting significantly more time in the school/work micro-environment on workdays/weekdays than all other distance groups. CONCLUSIONS: These findings indicate the potential for both differential and non-differential exposure misclassification due to geocoding error and time-activity patterns in studies of highway proximity. We also propose a multi-stage manual correction process to minimize positional error. Additional research is needed in other populations and geographic settings.