Interdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes.
Jesus Serrano-Lomelin, Charlene C. Nielsen, M. Shazan M. Jabbar, Osnat Wine, Colin Bellinger, Paul J. Villeneuvee, Dave Stieb, Nancy Aelicks, Khalid Aziz, Irena Buka, Sue Chandra, Susan Crawford, Paul Demers, Anders C. Erickson, Perry Hystad, Manoj Kumar, Erica Phipps, Prakesh S. Shah, YanYuan, Osmar R. Zaiane, Alvaro R. Osornio-Vargas.
Environment International Volume 131, October 2019, https://doi.org/10.1016/j.envint.2019.104972
Abstract
Background
Adverse birth outcomes (ABO) such as prematurity and small for gestational age confer a high risk of mortality and morbidity. ABO have been linked to air pollution; however, relationships with mixtures of industrial emissions are poorly understood. The exploration of relationships between ABO and mixtures is complex when hundreds of chemicals are analyzed simultaneously, requiring the use of novel approaches.
Objective
We aimed to generate robust hypotheses spatially linking mixtures and the occurrence of ABO using a spatial data mining algorithm and subsequent geographical and statistical analysis. The spatial data mining approach aimed to reduce data dimensionality and efficiently identify spatial associations between multiple chemicals and ABO.
Methods
We discovered co-location patterns of mixtures and ABO in Alberta, Canada (2006–2012). An ad-hoc spatial data mining algorithm allowed the extraction of primary co-location patterns of 136 chemicals released into the air by 6279 industrial facilities (National Pollutant Release Inventory), wind-patterns from 182 stations, and 333,247 singleton live births at the maternal postal code at delivery (Alberta Perinatal Health Program), from which we identified cases of preterm birth, small for gestational age, and low birth weight at term. We selected secondary patterns using a lift ratio metric from ABO and non-ABO impacted by the same mixture. The relevance of the secondary patterns was estimated using logistic models (adjusted by socioeconomic status and ABO-related maternal factors) and a geographic-based assignment of maternal exposure to the mixtures as calculated by kernel density.
Results
From 136 chemicals and three ABO, spatial data mining identified 1700 primary patterns from which five secondary patterns of three-chemical mixtures, including particulate matter, methyl-ethyl-ketone, xylene, carbon monoxide, 2-butoxyethanol, and n-butyl alcohol, were subsequently analyzed. The significance of the associations (odds ratio > 1) between the five mixtures and ABO provided statistical support for a new set of hypotheses.
Conclusion
This study demonstrated that, in complex research settings, spatial data mining followed by pattern selection and geographic and statistical analyses can catalyze future research on associations between air pollutant mixtures and adverse birth outcomes.
August 12 | 2019
/in Paper of the Week /by Mary SpeckEvaluation of a method to indirectly adjust for unmeasured covariates in the association between fine particulate matter and mortality.
Erickson AC, Brauer M, Christidis T, Pinault L, Crouse DL, van Donkelaar A, Weichenthal S, Pappin A, Tjepkema M, Martin RV, Brook JR, Hystad P, Burnett RT.
Environ Res. 2019 Aug;175:108-116. doi: 10.1016/j.envres.2019.05.010 Epub 2019 May 11.
Abstract
BACKGROUND:
Indirect adjustment via partitioned regression is a promising technique to control for unmeasured confounding in large epidemiological studies. The method uses a representative ancillary dataset to estimate the association between variables missing in a primary dataset with the complete set of variables of the ancillary dataset to produce an adjusted risk estimate for the variable in question. The objective of this paper is threefold: 1) evaluate the method for non-linear survival models, 2) formalize an empirical process to evaluate the suitability of the required ancillary matching dataset, and 3) test modifications to the method to incorporate time-varying exposure data, and proportional weighting of datasets.
METHODS:
We used the association between fine particle air pollution (PM2.5) with mortality in the 2001 Canadian Census Health and Environment Cohort (CanCHEC, N = 2.4 million, 10-years follow-up) as our primary dataset, and the 2001 cycle of the Canadian Community Health Survey (CCHS, N = 80,630) as the ancillary matching dataset that contained confounding risk factor information not available in CanCHEC (e.g., smoking). The main evaluation process used a gold-standard approach wherein two variables (education and income) available in both datasets were excluded, indirectly adjusted for, and compared to true models with education and income included to assess the amount of bias correction. An internal validation for objective 1 used only CanCHEC data, whereas an external validation for objective 2 replaced CanCHEC with the CCHS. The two proposed modifications were applied as part of the validation tests, as well as in a final indirect adjustment of four missing risk factor variables (smoking, alcohol use, diet, and exercise) in which adjustment direction and magnitude was compared to models using an equivalent longitudinal cohort with direct adjustment for the same variables.
RESULTS:
At baseline (2001) both cohorts had very similar PM2.5 distributions across population characteristics, although levels for CCHS participants were consistently 1.8-2.0 μg/m3 lower. Applying sample-weighting largely corrected for this discrepancy. The internal validation tests showed minimal downward bias in PM2.5 mortality hazard ratios of 0.4-0.6% using a static exposure, and 1.7-3% when a time-varying exposure was used. The external validation of the CCHS as the ancillary dataset showed slight upward bias of -0.7 to -1.1% and downward bias of 1.3-2.3% using the static and time-varying approaches respectively.
CONCLUSIONS:
The CCHS was found to be fairly well representative of CanCHEC and its use in Canada for indirect adjustment is warranted. Indirect adjustment methods can be used with survival models to correct hazard ratio point estimates and standard errors in models missing key covariates when a representative matching dataset is available. The results of this formal evaluation should encourage other cohorts to assess the suitability of ancillary datasets for the application of the indirect adjustment methodology to address potential residual confounding.
August 6 | 2019
/in Paper of the Week /by Mary SpeckAcute Blood Pressure and Cardiovascular Effects of Near-Roadway Exposures with and without N95-Respirators.
Morishita M, Wang L, Speth K, Zhou N, Bard RL, Li F, Brook JR, Rajagopalan S, Brook RD.
Am J Hypertens. 2019 Jul 27. pii: hpz113. [Epub ahead of print] DOI: 10.1093/ajh/hpz113
Abstract
BACKGROUND:
The risk for cardiovascular events increases within hours of near-roadway exposures. We aimed to determine the traffic-related air pollutants (TRAP) and biological mechanisms involved and if reducing particulate matter<2.5 µm (PM2.5) inhalation is protective.
METHODS:
Fifty healthy-adults underwent multiple 2-hour near-roadway exposures (Tuesdays-Fridays) in Ann Arbor during 2 separate weeks (randomized to wear an N95-respirator during one week). Monday both weeks, participants rested 2-hours in an exam room (once wearing an N95-respirator). Brachial blood pressure, aortic hemodynamics and heart rate variability were repeatedly-measured during exposures. Endothelial function (reactive hyperemia index [RHI]) was measured post-exposures (Thursdays). Black carbon (BC), total particle count (PC), PM2.5, noise and temperature were measured throughout exposures.
RESULTS:
PM2.5 (9.3±7.7 µg/m3), BC (1.3±0.6 µg/m3), PC (8375±4930 particles/cm3) and noise (69.2±4.2 dB) were higher (p-values<0.01) and aortic hemodynamic parameters trended worse while near-roadway (p-values<0.15 versus exam room). Other outcomes were unchanged. Aortic hemodynamics trended towards improvements with N95-respirator usage while near-roadway (p-values<0.15 versus no-use), whereas other outcomes remained unaffected. Higher near-roadway PC and BC exposures were associated with increases in aortic augmentation pressures (p-values<0.05) and trends toward lower RHI (p-values<0.2). N95-respirator usage did not mitigate these adverse responses (non-significant pollutant-respirator interactions). Near-roadway outdoor-temperature and noise were also associated with cardiovascular changes.
CONCLUSIONS:
Exposure to real-world combustion-derived particulates in TRAP, even at relatively-low concentrations, acutely worsened aortic hemodynamics. Our mixed findings regarding the health benefits of wearing N95-respirators support that further studies are needed to validate if they adequately-protect against TRAP given their growing worldwide usage.
July 29 | 2019
/in Paper of the Week /by Mary SpeckParticulate matter air pollution and national and county life expectancy loss in the USA: A spatiotemporal analysis.
James E. Bennett , Helen Tamura-Wicks , Robbie M. Parks, Richard T. Burnett, C. Arden Pope III, Matthew J. Bechle, Julian D. Marshall, Goodarz Danaei, Majid Ezzati.
PLOS | Medicine Published: July 23, 2019 https://doi.org/10.1371/journal.pmed.1002856
Abstract
Background
Exposure to fine particulate matter pollution (PM2.5) is hazardous to health. Our aim was to directly estimate the health and longevity impacts of current PM2.5 concentrations and the benefits of reductions from 1999 to 2015, nationally and at county level, for the entire contemporary population of the contiguous United States.
Methods and findings
We used vital registration and population data with information on sex, age, cause of death, and county of residence. We used four Bayesian spatiotemporal models, with different adjustments for other determinants of mortality, to directly estimate mortality and life expectancy loss due to current PM2.5 pollution and the benefits of reductions since 1999, nationally and by county. The covariates included in the adjusted models were per capita income; percentage of population whose family income is below the poverty threshold, who are of Black or African American race, who have graduated from high school, who live in urban areas, and who are unemployed; cumulative smoking; and mean temperature and relative humidity. In the main model, which adjusted for these covariates and for unobserved county characteristics through the use of county-specific random intercepts, PM2.5 pollution in excess of the lowest observed concentration (2.8 μg/m3) was responsible for an estimated 15,612 deaths (95% credible interval 13,248–17,945) in females and 14,757 deaths (12,617–16,919) in males. These deaths would lower national life expectancy by an estimated 0.15 years (0.13–0.17) for women and 0.13 years (0.11–0.15) for men. The life expectancy loss due to PM2.5was largest around Los Angeles and in some southern states such as Arkansas, Oklahoma, and Alabama. At any PM2.5 concentration, life expectancy loss was, on average, larger in counties with lower income and higher poverty rate than in wealthier counties. Reductions in PM2.5 since 1999 have lowered mortality in all but 14 counties where PM2.5 increased slightly. The main limitation of our study, similar to other observational studies, is that it is not guaranteed for the observed associations to be causal. We did not have annual county-level data on other important determinants of mortality, such as healthcare access and quality and diet, but these factors were adjusted for with use of county-specific random intercepts.
Conclusions
According to our estimates, recent reductions in particulate matter pollution in the USA have resulted in public health benefits. Nonetheless, we estimate that current concentrations are associated with mortality impacts and loss of life expectancy, with larger impacts in counties with lower income and higher poverty rate.
July 22 | 2019
/in Paper of the Week /by Mary SpeckSpatial variations in ambient ultrafine particle concentrations and risk of congenital heart defects.
Lavigne E, Lima I, Hatzopoulou M, Van Ryswyk K, Decou ML, Luo W, van Donkelaar A, Martin RV, Chen H, Stieb DM, Crighton E, Gasparrini A, Elten M, Yasseen AS 3rd, Burnett RT, Walker M, Weichenthal S.
Environ Int. 2019 Jul 1;130:104953. DOI: 10.1016/j.envint.2019.104953 [Epub ahead of print]
Abstract
BACKGROUND:
Cardiovascular malformations account for nearly one-third of all congenital anomalies, making these the most common type of birth defects. Little is known regarding the influence of ambient ultrafine particles (<0.1 μm) (UFPs) on their occurrence.
OBJECTIVE:
This population-based study examined the association between prenatal exposure to UFPs and congenital heart defects (CHDs).
METHODS:
A total of 158,743 singleton live births occurring in the City of Toronto, Canada between April 1st 2006 and March 31st 2012 were identified from a birth registry. Associations between exposure to ambient UFPs between the 2nd and 8th week post conception when the foetal heart begins to form and CHDs identified at birth were estimated using random-effects logistic regression models, adjusting for personal- and neighbourhood-level covariates. We also investigated multi-pollutant models accounting for co-exposures to PM2.5, NO2 and O3.
RESULTS:
A total of 1468 CHDs were identified. In fully adjusted models, UFP exposures during weeks 2 to 8 of pregnancy were not associated with overall CHDs (Odds Ratio (OR) per interquartile (IQR) increase = 1.02, 95% CI: 0.96-1.08). When investigating subtypes of CHDs, UFP exposures were associated with ventricular septal defects (Odds Ratio (OR) per interquartile (IQR) increase = 1.13, 95% CI: 1.03-1.33), but not with atrial septal defect (Odds Ratio (OR) per interquartile (IQR) increase = 0.89, 95% CI: 0.74-1.06).
CONCLUSION:
This is the first study to evaluate the association between prenatal exposure to UFPs and the risk of CHDs. UFP exposures during a critical period of embryogenesis were associated with an increased risk of ventricular septal defect.
July 15 | 2019
/in Paper of the Week /by Mary SpeckInterdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes.
Jesus Serrano-Lomelin, Charlene C. Nielsen, M. Shazan M. Jabbar, Osnat Wine, Colin Bellinger, Paul J. Villeneuvee, Dave Stieb, Nancy Aelicks, Khalid Aziz, Irena Buka, Sue Chandra, Susan Crawford, Paul Demers, Anders C. Erickson, Perry Hystad, Manoj Kumar, Erica Phipps, Prakesh S. Shah, YanYuan, Osmar R. Zaiane, Alvaro R. Osornio-Vargas.
Environment International Volume 131, October 2019, https://doi.org/10.1016/j.envint.2019.104972
Abstract
Background
Adverse birth outcomes (ABO) such as prematurity and small for gestational age confer a high risk of mortality and morbidity. ABO have been linked to air pollution; however, relationships with mixtures of industrial emissions are poorly understood. The exploration of relationships between ABO and mixtures is complex when hundreds of chemicals are analyzed simultaneously, requiring the use of novel approaches.
Objective
We aimed to generate robust hypotheses spatially linking mixtures and the occurrence of ABO using a spatial data mining algorithm and subsequent geographical and statistical analysis. The spatial data mining approach aimed to reduce data dimensionality and efficiently identify spatial associations between multiple chemicals and ABO.
Methods
We discovered co-location patterns of mixtures and ABO in Alberta, Canada (2006–2012). An ad-hoc spatial data mining algorithm allowed the extraction of primary co-location patterns of 136 chemicals released into the air by 6279 industrial facilities (National Pollutant Release Inventory), wind-patterns from 182 stations, and 333,247 singleton live births at the maternal postal code at delivery (Alberta Perinatal Health Program), from which we identified cases of preterm birth, small for gestational age, and low birth weight at term. We selected secondary patterns using a lift ratio metric from ABO and non-ABO impacted by the same mixture. The relevance of the secondary patterns was estimated using logistic models (adjusted by socioeconomic status and ABO-related maternal factors) and a geographic-based assignment of maternal exposure to the mixtures as calculated by kernel density.
Results
From 136 chemicals and three ABO, spatial data mining identified 1700 primary patterns from which five secondary patterns of three-chemical mixtures, including particulate matter, methyl-ethyl-ketone, xylene, carbon monoxide, 2-butoxyethanol, and n-butyl alcohol, were subsequently analyzed. The significance of the associations (odds ratio > 1) between the five mixtures and ABO provided statistical support for a new set of hypotheses.
Conclusion
This study demonstrated that, in complex research settings, spatial data mining followed by pattern selection and geographic and statistical analyses can catalyze future research on associations between air pollutant mixtures and adverse birth outcomes.
July 10 | 2019
/in Paper of the Week /by Mary SpeckAir pollution, lung function and COPD: results from the population-based UK Biobank study.
Dany Doiron, Kees de Hoogh, Nicole Probst-Hensch, Isabel Fortier, Yutong Cai, Sara De Matteis, Anna L. Hansell.
European Respiratory Journal 2019; https://doi.org/10.1183/13993003.02140-2018
Abstract
Ambient air pollution increases the risk of respiratory mortality but evidence for impacts on lung function and chronic obstructive pulmonary disease (COPD) is less well established. The aim was to evaluate whether ambient air pollution is associated with lung function and COPD, and explore potential vulnerability factors.
We used UK Biobank data on 3 03 887 individuals aged 40–69 years, with complete covariate data and valid lung function measures. Cross-sectional analyses examined associations of Land Use Regression-based estimates of particulate matter (PM2.5, PM10 and PMcoarse) and nitrogen dioxide (NO2) concentrations with forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), the FEV1/FVC ratio, and COPD (FEV1/FVC <lower limit of normal). Effect modification was investigated for sex, age, obesity, smoking status, household income, asthma status, and occupations previously linked to COPD.
Higher exposures to each pollutant were significantly associated with lower lung function. A 5 µg·m−3 increase in PM2.5 concentration was associated with lower FEV1 (−83.13 mL [95%CI: −92.50, −73.75]) and FVC (−62.62 mL [95%CI: −73.91, −51.32]). COPD prevalence was associated with higher concentrations of PM2.5 (OR 1.52 [95%CI: 1.42, 1.62], per 5 µg·m−3), PM10 (OR 1.08 [95%CI: 1.00, 1.16], per 5 µg·m−3), and NO2 (OR 1.12 [95%CI: 1.10, 1.14], per 10 µg·m−3), but not with PMcoarse. Stronger lung function associations were seen for males, individuals from lower income households, and “at-risk” occupations, and higher COPD associations for obese, lower income, and non-asthmatic participants.
Ambient air pollution was associated with lower lung function and increased COPD prevalence in this large study.
July 2 | 2019
/in Paper of the Week /by Mary SpeckInteraction between neighborhood walkability and traffic-related air pollution on hypertension and diabetes: The CANHEART cohort.
Howell NA, Tu JV, Moineddin R, Chen H, Chu A, Hystad P, Booth GL.
Environ Int. 2019 Jun 19:104799. DOI: 10.1016/j.envint.2019.04.070
Abstract
BACKGROUND:
Living in unwalkable neighborhoods has been associated with heightened risk for diabetes and hypertension. However, highly walkable environments may have higher concentrations of traffic-related air pollution, which may contribute to increased cardiovascular disease risk. We therefore aimed to assess how walkability and traffic-related air pollution jointly affect risk for hypertension and diabetes.
METHODS:
We used a cross-sectional, population-based sample of individuals aged 40-74 years residing in selected large urban centres in Ontario, Canada on January 1, 2008, assembled from administrative databases. Walkability and traffic-related air pollution (NO2) were assessed using validated tools and linked to individuals based on neighborhood of residence. Logistic regression was used to estimate adjusted associations between exposures and diagnoses of hypertension or diabetes accounting for potential confounders.
RESULTS:
Overall, 2,496,458 individuals were included in our analyses. Low walkability was associated with higher odds of hypertension (lowest vs. highest quintile OR = 1.34, 95% CI: 1.32, 1.37) and diabetes (lowest vs. highest quintile OR = 1.25, 95% CI: 1.22, 1.29), while NO2exhibited similar trends (hypertension: OR = 1.09 per 10 p.p.b., 95% CI: 1.08, 1.10; diabetes: OR = 1.16, 95% CI: 1.14, 1.17). Significant interactions were identified between walkability and NO2 on risk for hypertension (p < 0.0001 and diabetes (p < 0.0001). At higher levels of pollution (40 p.p.b.), differences in the probability of hypertension (lowest vs. highest walkability quintile: 0.26 vs. 0.25) or diabetes (lowest vs. highest walkability quintile: 0.15 vs. 0.15) between highly walkable and unwalkable neighborhoods were diminished, compared to differences observed at lower levels of pollution (5 p.p.b.) (hypertension, lowest vs. highest walkability quintile: 0.21 vs. 0.13; diabetes, lowest vs. highest walkability quintile: 0.09 vs. 0.06).
CONCLUSIONS:
Walkability and traffic-related air pollution interact to jointly predict risk for hypertension and diabetes. Although walkable neighborhoods appear to have beneficial effects, they may accentuate the harmful effects of air pollution on cardiovascular risk factors.
June 24 | 2019
/in Paper of the Week /by Mary SpeckUsing maps to communicate environmental exposures and health risks: Review and best-practice recommendations.
Stieb DM, Huang A, Hocking R, Crouse DL, Osornio-Vargas AR, Villeneuve PJ.
Environ Res. 2019 May 31;176:108518. DOI:10.1016/j.envres.2019.05.049. [Epub ahead of print]
Abstract
BACKGROUND:
Graphical materials can be effective communication tools, and maps in particular are a potentially powerful means of conveying spatial information. Previous reviews have provided insights on the application of cartographic best practices, pitfalls to avoid, and considerations related to risk perception and communication, but none has reviewed primary studies of the effectiveness or utility of maps to users, nor have they addressed the issue from the perspective of health literacy, environmental health literacy, or public health ethics.
OBJECTIVES:
To systematically identify and review the literature pertaining to evaluation of maps in general, or specific map features, as environmental exposure and health risk communication tools; to formulate best-practice recommendations; and to identify future research priorities.
METHODS:
A health science librarian searched the literature for commentaries, reviews, and primary studies. Titles, abstracts, and full-text papers were screened for inclusion, and details of methods and results were extracted from 4 reviews and commentaries and 18 primary studies. This was supplemented by one additional review and 13 additional primary studies pertaining to use of maps for communication about wildfires and floods. One additional paper was identified by reviewing reference lists of all relevant papers.
RESULTS: and Discussion:
While there are significant gaps in the evidence, we formulated best practice recommendations highlighting the perspectives of health literacy and environmental health literacy. Key recommendations include: understanding the map developer’s societal role and mental model underlying map design; defining, understanding and iteratively engaging with map users; informing map design using key theoretical constructs; accounting for factors affecting risk perception; adhering to risk communication principles and cartographic best practices; and considering environmental justice and public health ethics implications. Recommendations for future research are also provided.
CIHR Data Analysis Grants | June 26th | 2019
/in Events, News, Webinars /by Eleanor SettonThe Canadian Institutes for Health Research has announced a new Operating Grant Competition for data analysis using existing databases and cohorts. The intent of this funding opportunity is to highlight and encourage the use of previously funded cohort, administrative, and survey data. There will be three funding streams; one stream in cancer prevention and control, another in reproductive, maternal, child, and youth health, as well as a stream in healthy cities intervention research.
CANUE hosted a webinar on June 26th (9 am pacific | 12 noon eastern) for researchers who would like more detailed information on our data holdings, partnerships with health data holders, and an opportunity to ask questions directly to the CANUE team.
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June 18 | 2019
/in Paper of the Week /by Mary SpeckAccelerometer and GPS Data to Analyze Built Environments and Physical Activity.
Tamura K, Wilson JS, Goldfeld K, Puett RC, Klenosky DB, Harper WA, Troped PJ.
Res Q Exerc Sport. 2019 Jun 14:1-8. DOI: 10.1080/02701367.2019.1609649. [Epub ahead of print]
Abstract
Purpose: Most built environment studies have quantified characteristics of the areas around participants’ homes. However, the environmental exposures for physical activity (PA) are spatially dynamic rather than static. Thus, merged accelerometer and global positioning system (GPS) data were utilized to estimate associations between the built environment and PA among adults. Methods: Participants (N = 142) were recruited on trails in Massachusetts and wore an accelerometer and GPS unit for 1-4 days. Two binary outcomes were created: moderate-to-vigorous PA (MVPA vs. light PA-to-sedentary); and light-to-vigorous PA (LVPA vs. sedentary). Five built environment variables were created within 50-meter buffers around GPS points: population density, street density, land use mix (LUM), greenness, and walkability index. Generalized linear mixed models were fit to examine associations between environmental variables and both outcomes, adjusting for demographic covariates. Results: Overall, in the fully adjusted models, greenness was positively associated with MVPA and LVPA (odds ratios [ORs] = 1.15, 95% confidence interval [CI] = 1.03, 1.30 and 1.25, 95% CI = 1.12, 1.41, respectively). In contrast, street density and LUM were negatively associated with MVPA (ORs = 0.69, 95% CI = 0.67, 0.71 and 0.87, 95% CI = 0.78, 0.97, respectively) and LVPA (ORs = 0.79, 95% CI = 0.77, 0.81 and 0.81, 95% CI = 0.74, 0.90, respectively). Negative associations of population density and walkability with both outcomes reached statistical significance, yet the effect sizes were small. Conclusions: Concurrent monitoring of activity with accelerometers and GPS units allowed us to investigate relationships between objectively measured built environment around GPS points and minute-by-minute PA. Negative relationships between street density and LUM and PA contrast evidence from most built environment studies in adults. However, direct comparisons should be made with caution since most previous studies have focused on spatially fixed buffers around home locations, rather than the precise locations where PA occurs.