Socioeconomic status (SES) is an important determinant of health and potential modifier of the effects of environmental contaminants. There has been a lack of comprehensive indices for measuring overall SES in Canada. Here, a more comprehensive SES index is developed aiming to support future studies exploring health outcomes related to environmental pollution in Canada. SES variables (n = 22, Census Canada 2006) were selected based on: cultural identities, housing characteristics, variables identified in Canadian environmental injustice studies and a previous deprivation index (Pampalon index). Principal component analysis with a single varimax rotation (factor loadings ≥ │60│) was performed on SES variables for 52974 census dissemination areas (DA). The final index was created by averaging the factor scores per DA according to the three components retained. The index was validated by examining its association with preterm birth (gestational age < 37 weeks), term low birth weight (LBW, <2500 g), small for gestational age (SGA, <10 percentile of birth weight for gestational age) and PM2.5 (particulate matter ≤ 2.5 μm) exposures in Edmonton, Alberta (1999–2008). Index values exhibited a relatively normal distribution (median = 0.11, mean = 0.0, SD = 0.58) across Canada. Values in Alberta tended to be higher than in Newfoundland and Labrador, Northwest Territories and Nunavut (Pearson chi-square p < 0.001 across provinces). Lower quintiles of our index and the Pampalon’s index confirmed know associations with a higher prevalence of LBW, SGA, preterm birth and PM2.5 exposure. Results with our index exhibited greater statistical significance and a more consistent gradient of PM2.5 levels and prevalence of pregnancy outcomes. Our index reflects more dimensions of SES than an earlier index and it performed superiorly in capturing gradients in prevalence of pregnancy outcomes. It can be used for future research involving environmental pollution and health in Canada.

Air pollution in developing countries is a growing concern. It is associated with urbanization and social and economic structures. The understanding of how social factors can influence the perception and the potential impact of air pollution have not been addressed sufficiently. This paper addresses the social vulnerability and exposure to PM₁₀ association and its influence on the air quality perception of residents in Mexicali, a Mexico–US border city. This study used individual variables and population census data, as well as statistical and spatial analyses. A cluster of socially vulnerable populations with high exposure to coarse particulate matter (PM₁₀) was found in the city’s peripheral areas. The spatial distribution of the local perception of air quality varied by the exposure zones of the estimated PM₁₀ concentrations. Respondents living in very high exposure areas perceive air quality as “poor,” contrarily to a worse perception in areas of intermediate and lower exposure to PM₁₀. Proximity to stationary sources of pollution was associated with a poor perception of air quality. Results also indicate that low household income and poor air quality perceived at the place of residence negatively influences the perceived changes in the air quality over time. The knowledge of chronic health effects related to air pollution was scarce in the sampled population, especially in the areas with very high exposure and high social vulnerability. These findings can serve as a support in local air quality management.

Cytomegalovirus (CMV) infects 40–70% of women, but infection has been reported in >95% of breast cancer patients. We investigated the consequences of these observations by infecting mice with mCMV or a negative control medium for 4 days, 11 days or 10 weeks to establish active, intermediate or latent infections, respectively. Syngeneic 4T1 or E0771 breast cancer cells were then injected into a mammary fat pad of BALB/c or C57BL/6 mice, respectively. Infection did not affect tumor growth in these conditions, but latently infected BALB/c mice developed more lung metastases. The latent mCMV infection of MMTV-PyVT mice, which develop spontaneous breast tumors, also did not affect the number or sizes of breast tumors. However, there were more tumors that were multilobed with greater blood content, which had enhanced vasculature and decreased collagen content. Most significantly, mCMV infection also increased the number and size of lung metastases, which showed a higher cell proliferation. Viral DNA was detected in breast tumors and lung nodules although viral mRNA was not. These novel results have important clinical implications since an increased metastasis is prognostic of decreased survival. This work provides evidence that treating or preventing HCMV infections may increase the life expectancy of breast cancer patients by decreasing metastasis.

Objective: Using a summary measure of health inequalities, this study evaluated the distribution of adverse birth outcomes (ABO) and related maternal risk factors across area-level socioeconomic status (SES) gradients in urban and rural Alberta, Canada. Design: Cross-sectional study using a validated perinatal clinical registry and an area-level SES. Setting: The study was conducted in Alberta, Canada. Data about ABO and related maternal risk factors were obtained from the Alberta Perinatal Health Program between 2006 and 2012. An area-level SES index derived from census data (2006) was linked to the postal code at delivery. Participants: Women (n=3 30 957) having singleton live births with gestational age ≥22 weeks. Primary and secondary outcome measures: We estimated concentration indexes to assess inequalities across SES gradients in both rural and urban areas (CIdx _R and CIdx _U , respectively) for spontaneous preterm birth (PTB), small for gestational age (SGA), large for gestational age (LGA), gestational hypertension, gestational diabetes, smoking and substance use during pregnancy and pre-pregnancy weight >91 kg. Results: The highest health inequalities disfavouring low SES groups were identified for substance abuse and smoking in rural areas (CIdx _R −0.38 and −0.23, respectively). Medium inequalities were identified for LGA (CIdx _R −0.08), pre-pregnancy weight >91 kg (CIdx _R −0.07), substance use (CIdx _U −0.15), smoking (CIdx _U −0.14), gestational diabetes (CIdx _U −0.10) and SGA (CIdx _U −0.07). Low inequalities were identified for PTB (CIdx _R −0.05; CIdx _U −0.05) and gestational diabetes (CIdx _R −0.04). Inequalities disfavouring high SES groups were identified for gestational hypertension (CIdx _R +0.04), SGA (CIdx _R +0.03) and LGA (CIdx _U +0.03). Conclusions: ABO and related maternal risk factors were unequally distributed across the socioeconomic gradient in urban–rural settings, with the greatest concentrations in lower SES groups of rural areas. Future research is needed on underlying mechanisms driving SES gradients in perinatal health across the rural–urban spectrum.

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. 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. 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. 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. 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.