Constrained groupwise additive index models.

In environmental epidemiology, there is wide interest in creating and using comprehensive indices that can summarize information from different environmental exposures while retaining strong predictive power on a target health outcome. In this context, the present article proposes a model called the constrained groupwise additive index model (CGAIM) to create easy-to-interpret indices predictive of a response variable, from a potentially large list of variables. The CGAIM considers groups of predictors that naturally belong together to yield meaningful indices. It also allows the addition of linear constraints on both the index weights and the form of their relationship with the response variable to represent prior assumptions or operational requirements. We propose an efficient algorithm to estimate the CGAIM, along with index selection and inference procedures. A simulation study shows that the proposed algorithm has good estimation performances, with low bias and variance and is applicable in complex situations with many correlated predictors. It also demonstrates important sensitivity and specificity in index selection, but non-negligible coverage error on constructed confidence intervals. The CGAIM is then illustrated in the construction of heat indices in a health warning system context. We believe the CGAIM could become useful in a wide variety of situations, such as warning systems establishment, and multipollutant or exposome studies.

Estimating the heat-related mortality and morbidity burden in the province of Quebec, Canada.

BACKGROUND: As climate change increases the frequency and intensity of extreme heat events, there is an urgent need to quantify the heat-related health burden. However, most past studies have focussed on a single health outcome (mainly mortality) or on specific heatwaves, thus providing limited knowledge of the total pressure heat exerts on health services. OBJECTIVES: This study aims to quantify the heat-related mortality and morbidity burden for five different health outcomes including all-cause mortality, hospitalizations, emergency department (ED) visits, ambulance transports and calls to a health hotline, using the province of Quebec (Canada) as a case study. METHODS: A two-step statistical analysis was employed to estimate regional heat-health relationships using Distributed Lag Non-Linear Models (DLNM) and pooled estimates using a multivariate meta-regression. Heat burden was quantified by attributable fraction (AF) and attributable number (AN) for two temperature ranges: all heat (above the minimum mortality/morbidity temperature) and extreme heat (above the 95th percentile of temperature). RESULTS: Higher temperatures were associated with greater risk ratios for all health outcomes studied, but at different levels. Significant AF ranging from 2 to 3% for the all heat effect and 0.4-1.0% for extreme heat were found for all health outcomes, except for hospitalizations that had an AF of 0.1% for both heat exposures. The estimated burden of all heat (and extreme heat) every summer across the province was 470 (200) deaths, 225 (170) hospitalizations, 36 000 (6 200) ED visits, 7 200 (1 500) ambulance transports and 15 000 (3 300) calls to a health hotline, all figures significant. DISCUSSION: This new knowledge on the total heat load will help public health authorities to target appropriate actions to reduce its burden now and in the future. The proposed state-of-the-art framework can easily be applied to other regions also experiencing the adverse effects of extreme heat.

The relationship between hot temperatures and hospital admissions for psychosis in adults diagnosed with schizophrenia: A case-crossover study in Quebec, Canada.

INTRODUCTION: Some studies have found hot temperatures to be associated with exacerbations of schizophrenia, namely psychoses. As climate changes faster in Northern countries, our understanding of the association between temperature and hospital admissions (HA) for psychosis needs to be deepened. OBJECTIVES: 1) Among adults diagnosed with schizophrenia, measure the relationship between mean temperatures and HAs for psychosis during summer. 2) Determine the influence of individual and ecological characteristics on this relationship. METHODS: A cohort of adults diagnosed with schizophrenia (n = 30,649) was assembled using Quebec's Integrated Chronic Disease Surveillance System (QICDSS). The follow-up spanned summers from 2001 to 2019, using hospital data from the QICDSS and meteorological data from the National Aeronautics and Space Administration's (NASA) Daymet database. In four geographic regions of the province of Quebec, a conditional logistic regression was used for the case-crossover analysis of the relationship between mean temperatures (at lags up to 6 days) and HAs for psychosis using a distributed lag non-linear model (DLNM). The analyses were adjusted for relative humidity, stratified according to individual (age, sex, and comorbidities) and ecological (material and social deprivation index and exposure to green space) factors, and then pooled through a meta-regression. RESULTS: The statistical analyses revealed a statistically significant increase in HAs three days (lag 3) after elevated mean temperatures corresponding to the 90th percentile relative to a minimum morbidity temperature (MMT) (OR 1.040; 95% CI 1.008-1.074), while the cumulative effect over six days was not statistically significant (OR 1.052; 95% IC 0.993-1.114). Stratified analyses revealed non statistically significant gradients of increasing HAs relative to increasing material deprivation and decreasing green space levels. CONCLUSIONS: The statistical analyses conducted in this project showed the pattern of admissions for psychosis after hot days. This finding could be useful to better plan health services in a rapidly changing climate.

A heat-health watch and warning system with extended season and evolving thresholds.

BACKGROUND: Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere. METHODS: The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality. RESULTS: We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season. CONCLUSIONS: This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July-August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.

Barriers to the use of toxicogenomics data in human health risk assessment: A survey of Canadian risk assessors.

Regulatory agencies worldwide need to modernize human health risk assessment (HHRA) to meet challenges of the 21st century. Toxicogenomics is at the core of this improvement. Today, however, the use of toxicogenomics data in HHRA is very limited. The purpose of this survey was to identify barriers to the application of toxicogenomics data in HHRA by human health risk assessors. An online survey targeting Canadian risk assessors gathered information on their knowledge and perception of toxicogenomics, their current and future inclusion of toxicogenomics data in HHRA, and barriers to the use of such data. Twenty-nine (29) participants completed a questionnaire after 2 months of solicitation. The results show that the application of toxicogenomics data in Canada is marginal, with 85% of respondents reporting that they never or rarely used such data. Knowledge of toxicogenomics by Canadian risk assessors is also limited: about two-thirds of respondents (68%) were not at all or only slightly familiar with the concept. Lack of guidelines for toxicogenomics data interpretation, data quality assessment and on their use in HHRA, were found to be major barriers. In conclusion, there is a need for interventions aimed at facilitating the use of toxicogenomics data in HHRA, when available.

Women exposure during pregnancy to haloacetaldehydes and haloacetonitriles in drinking water and risk of small-for-gestational-age neonate.

BACKGROUND: Past studies have examined the effects of maternal exposure to water chlorination disinfection by-products (DBPs), such as trihalomethanes (THMs) and haloacetic acids (HAAs) during pregnancy. However, no human-based study has yet evaluated the effect of emerging DBPs, such as haloacetaldehydes (HAs) and haloacetonitriles (HANs) on small-for-gestational-age (SGA) status in newborns. OBJECTIVE: This study aims to assess the association between maternal multiroute exposure to HAs and HANs during the third trimester of pregnancy and SGA status at birth, among neonates delivered by women residing in the Quebec City area (Province of Quebec, Canada). We also evaluated the interaction between exposure to these emerging unregulated by-products and regulated DBPs also found in drinking water (THMs and HAAs), for which a positive association with adverse reproductive outcomes has been suggested in previous studies. METHODS: We conducted a population-based case-control study in the Quebec City area. SGA newborns (n=330) were compared to 1100 controls, with matching based on calendar week of birth. HA and HAN concentrations in drinking water at participant's tap were estimated using spatio-temporal strategy based on bimonthly measurements carried out at several locations in the participant's distribution system. A computer-assisted telephone interview was completed to collect information on individual habits of water consumption and water related activities in order to determine individual multiroute exposure. This enabled us to estimate the dose of HAs and HANs absorbed daily by each participant. Associations between total HA, HAN concentrations in drinking water and SGA were analyzed. Associations between the daily-absorbed doses of these emerging DBPs and SGA were also analyzed. Odds ratios (ORs) comparing the 4th quartile of exposure to the reference group (the first three quartiles) were obtained by means of conditional logistic regression, and controlling for potential confounders. RESULTS: Globally, no evidence of increased risk of SGA was found with total HA and HAN concentrations in tap water when participants in the 4th quartile of exposure were compared to the first three quartiles (OR=1.0; 95% CI [0.7-1.5] and OR=0.8; 95% CI [0.6-1.2], respectively). Similarly, no association was found with the daily-absorbed doses of total HAs or HANs (OR=0.9; 95% CI [0.6-1.3] and OR=1.1; 95% CI [0.7-1.6], respectively). However, a small non statistically significant association was found between the dose of brominated HA and SGA (OR=1.4; 95% CI [0.9-2.1]). Also, in spite of the lack of interaction between other DBP classes, an unexpected negative interaction was observed between concentration of chloral hydrate (CH) (which represents the main HA species), and regulated DBPs (P=0.006). CONCLUSION: In this population, exposure to low levels of HAs and HANs during the third trimester of pregnancy through drinking water was not associated to SGA status in newborns. Nonetheless, more research is needed to clarify possible effect of brominated compounds and interaction between different DBPs.

Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning.

Extreme heat events have significant health impacts that need to be adequately quantified in the context of climate change. Traditionally, heat-health association methods have relied on statistical models using a single air temperature index, without considering other heat-related variables that may influence the relationship and their potentially complex interactions. This study aims to introduce and compare different machine learning (ML) models, which naturally consider interactions between predictors and non-linearities, to re-examine the importance of temperature, weather and air pollution predictors in modeling the heat-mortality relationship. ML approaches based on tree ensembles and neural networks, as well as non-linear statistical models, were used to model the heat-mortality relationship in the two most populated metropolitan areas of the province of Quebec, Canada. The models were calibrated using a comprehensive database of heat-related predictors including various lagged temperature indices, temperature variations, meteorological and air pollution variables. Performance was evaluated based on out-of-sample summer mortality predictions. For the two studied regions, models relying only on lagged temperature indices performed better, or equally well, than models considering more heat-related predictors such as temperature variations, weather and air pollution variables. The temperature index with the best performance differed by region, but both mean temperature and humidex were among the best indices. In terms of modeling approaches, non-linear statistical models were as competent as more advanced ML models for predicting out-of-sample summer mortality. This research validated the current use of non-linear statistical models with the appropriate lagged temperature index to model the heat-mortality relationship. Although ML models have not improved the performance of all-cause mortality modeling, these approaches should continue to be explored, particularly for other health effects that may be more directly linked to heat exposure and, in the future, when more data become available.

Machine and deep learning for modelling heat-health relationships.

Extreme heat events pose a significant threat to population health that is amplified by climate change. Traditionally, statistical models have been used to model heat-health relationships, but they do not consider potential interactions between temperature-related and air pollution predictors. Artificial intelligence (AI) methods, which have gained popularity for health applications in recent years, can account for these complex and non-linear interactions, but have been underutilized in modelling heat-related health impacts. In this paper, six machine and deep learning models were considered to model the heat-mortality relationship in Montreal (Canada) and compared to three statistical models commonly used in the field. Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Single- and Multi-Layer Perceptrons (SLP and MLP), Long Short-Term Memory (LSTM), Generalized Linear and Additive Models (GLM and GAM), and Distributed Lag Non-Linear Model (DLNM) were employed. Heat exposure was characterized by air temperature, relative humidity and wind speed, while air pollution was also included in the models using five pollutants. The results confirmed that air temperature at lags of up to 3 days was the most important variable for the heat-mortality relationship in all models. NO2 concentration and relative humidity (at lags 1 to 3 days) were also particularly important. Ensemble tree-based methods (GBM and RF) outperformed other approaches to model daily mortality during summer months based on three performance criteria. However, a partial validation during two recent major heatwaves highlighted that non-linear statistical models (GAM and DLNM) and simpler decision tree may more closely reproduce the spike of mortality observed during such events. Hence, both machine learning and statistical models are relevant for modelling heat-health relationships depending on the end user goal. Such extensive comparative analysis should be extended to other health outcomes and regions.

Heat-related mortality prediction using low-frequency climate oscillation indices: Case studies of the cities of Montréal and Québec, Canada.

Heat-related mortality is an increasingly important public health burden that is expected to worsen with climate change. In addition to long-term trends, there are also interannual variations in heat-related mortality that are of interest for efficient planning of health services. Large-scale climate patterns have an important influence on summer weather and therefore constitute important tools to understand and predict the variations in heat-related mortality. Methods: In this article, we propose to model summer heat-related mortality using seven climate indices through a two-stage analysis using data covering the period 1981-2018 in two metropolitan areas of the province of Québec (Canada): Montréal and Québec. In the first stage, heat attributable fractions are estimated through a time series regression design and distributed lag nonlinear specification. We consider different definitions of heat. In the second stage, estimated attributable fractions are predicted using climate index curves through a functional linear regression model. Results: Results indicate that the Atlantic Multidecadal Oscillation is the best predictor of heat-related mortality in both Montréal and Québec and that it can predict up to 20% of the interannual variability. Conclusion: We found evidence that one climate index is predictive of summer heat-related mortality. More research is needed with longer time series and in different spatial contexts. The proposed analysis and the results may nonetheless help public health authorities plan for future mortality related to summer heat.

A cold-health watch and warning system, applied to the province of Quebec (Canada).

CONTEXT: A number of studies have shown that cold has an important impact on human health. However, almost no studies focused on cold warning systems to prevent those health effects. For Nordic regions, like the province of Quebec in Canada, winter is long and usually very cold with an observed increase in mortality and hospitalizations throughout the season. However, there is no existing system specifically designed to follow in real-time this mortality increase throughout the season and to alert public health authorities prior to cold waves. OBJECTIVE: The aim is to establish a watch and warning system specifically for health impacts of cold, applied to different climatic regions of the province of Quebec. METHODOLOGY: A methodology previously used to establish the health-heat warning system in Quebec is adapted to cold. The approach identifies cold weather indicators and establishes thresholds related to extreme over-mortality or over-hospitalization events in the province of Quebec, Canada. RESULTS AND CONCLUSION: The final health-related thresholds proposed are between (-15 °C, -23 °C) and (-20 °C, -29 °C) according to the climatic region for excesses of mortality, and between (-13 °C, -23 °C) and (-17 °C, -30 °C) for excesses of hospitalization. These results suggest that the system model has a high sensitivity and an acceptable number of false alarms. This could lead to the establishment of a cold-health watch and warning system with valid indicators and thresholds for each climatic region of Quebec. It can be seen as a complementary system to the existing one for heat warnings, in order to help the public health authorities to be well prepared during an extreme cold event.

Short-term Forecasting of Daily Abundance of West Nile Virus Vectors Culex pipiens-restuans (Diptera: Culicidae) and Aedes vexans Based on Weather Conditions in Southern Québec (Canada).

Since 2002, human cases of West Nile virus (WNV) have occurred every year in southern Canada, but WNV risk remains challenging to predict. Here, we explored the ability of weather-based forecasting models to predict the seasonal abundance of two WNV vector species (Culex pipiens-restuans and Aedes vexans) in Québec, Canada, and explored the importance of accounting for larvicide use and local habitat (forest park vs residential garden). A gamma-generalized linear model predicting mosquito abundance was developed based on an approach previously used in Ontario combining temperature and precipitation during the days preceding mosquito captures. This model was calibrated and validated for each species with independent entomological datasets from the Montréal region collected in 2013 and 2014. Culex pipiens-restuans abundance was associated with mean degree days (dd; >9°C) over the 22 d before mosquito capture and with mean precipitation over the 71 d before capture; Ae. vexans abundance with the mean dd (>12°C) over the 24 d before capture and mean precipitation over the 30 d before capture. These results are consistent with temperature effects on immature development rates and adult activity, and effects of precipitation on the abundance and suitability of breeding sites. Taking into account larvicide use and habitat significantly improved the predictions. This study provides evidence that weather conditions can yield robust short-term predictions of the regional daily mosquito abundance, particularly when accounting for local variation in habitat or mosquito control efforts, and may provide real-time indicators of WNV or other mosquito-borne disease risks during the summer.

Toward an Improved Air Pollution Warning System in Quebec.

The nature of pollutants involved in smog episodes can vary significantly in various cities and contexts and will impact local populations differently due to actual exposure and pre-existing sensitivities for cardiovascular or respiratory diseases. While regulated standards and guidance remain important, it is relevant for cities to have local warning systems related to air pollution. The present paper proposes indicators and thresholds for an air pollution warning system in the metropolitan areas of Montreal and Quebec City (Canada). It takes into account past and current local health impacts to launch its public health warnings for short-term episodes. This warning system considers fine particulate matter (PM2.5) as well as the combined oxidant capacity of ozone and nitrogen dioxide (Ox) as environmental exposures. The methodology used to determine indicators and thresholds consists in identifying extreme excess mortality episodes in the data and then choosing the indicators and thresholds to optimize the detection of these episodes. The thresholds found for the summer were 31 µg/m3 for PM2.5 and 43 ppb for Ox in Montreal, and 32 µg/m3 and 23 ppb in Quebec City. In winter, thresholds found were 25 µg/m3 and 26 ppb in Montreal, and 33 µg/m3 and 21 ppb in Quebec City. These results are in line with different guidelines existing concerning air quality, but more adapted to the cities examined. In addition, a sensitivity analysis is conducted which suggests that Ox is more determinant than PM2.5 in detecting excess mortality episodes.

An environmentally relevant mixture of organochlorines, their metabolites and effects on preimplantation development of porcine embryos.

Environmental exposure of human populations to organochlorines is still widespread despite several international regulations banning or restricting their use. This study tested the hypothesis that an environmentally relevant complex mixture of organochlorines comprising polychlorinated biphenyls (PCBs), technical chlordane, dichlorodiphenyldichloroethylene and 12 other components is toxic for porcine embryos (at relative concentrations of 1-10000-fold the environmental organochlorine levels of contamination or 4.2 microg/l total PCBs). We also tested the embryotoxicity of a metabolised organochlorine mixture (relative concentrations of 0.9, 1.8, 2.7, 3.6 and 4.5 microg/l hydroxy-PCBs (OH-PCBs)) obtained by extracting plasma samples from sows treated with the native mixture. Embryos produced in vitro were exposed to either the organochlorine mixture or the metabolised extract for 9 days. The organochlorine mixture reduced embryonic development at the 10000x concentration (relative concentration of 42 mg/l PCBs; p=0.05). The organochlorine mixture also reduced the mean number of blastomeres per expanded blastocyst in a dose-dependent manner (p=0.038) but did not induce blastomere apoptosis (p>0.05). In contrast, the metabolised extract did not affect development or blastomere number at the concentrations tested, although the highest level of this mixture (4.5 microg/l OH-PCBs) was still very low (i.e. similar to the 1x concentration of the organochlorine mixture, which also did not alter embryo parameters). These data lead to the conclusion that while high concentrations of the native organochlorine mixture are toxic for porcine embryos, concentrations of either the native or the metabolised mixture that bear some relevance to exposure of human populations in the Arctic were without observable effect.

Availability, Quality, and Relevance of Toxicogenomics Data for Human Health Risk Assessment: A Scoping Review of the Literature on Trihalomethanes.

Human health risk assessment (HHRA) must be adapted to the challenges of the 21st century, and the use of toxicogenomics data in HHRA is among the changes that regulatory agencies worldwide are trying to implement. However, the use of toxicogenomics data in HHRA is still limited. The purpose of this study was to explore the availability, quality, and relevance to HHRA of toxicogenomics publications as potential barriers to their use in HHRA. We conducted a scoping review of available toxicogenomics literature, using trihalomethanes as a case study. Four bibliographic databases (including the Comparative Toxicogenomics Database) were assessed. An evaluation table was developed to characterize quality and relevance of studies included on the basis of criteria proposed in the literature. Studies were selected and analyzed by 2 independent reviewers. Only 9 studies, published between 1997 and 2015, were included in the analysis. Based on the selected criteria, critical methodological details were often missing; in fact, only 3 out of 9 studies were considered to be of adequate quality for HHRA. No studies met >3 (out of 7) criteria of relevance to HHRA (eg, adequate number of doses and sample size). This first scoping review of toxicogenomics publications on trihalomethanes shows that low availability, quality, and relevance to HHRA of toxicogenomics publications presents potential barriers to their use in HHRA. Improved reporting of methodological details and study design is needed in the future so that toxicogenomics studies can be appropriately assessed regarding their quality and value for HHRA.

Effect of an environmentally relevant metabolized organochlorine mixture on porcine cumulus-oocyte complexes.

Organochlorine compounds and their metabolites bioaccumulate and have been detected in follicular and genital tract fluids of humans and animals. This study was designed to investigate the effect of a metabolised organochlorine mixture, extracted from plasma of sows treated with an environmentally relevant organochlorine mixture in the course of a previous study, on porcine cumulus-oocyte complexes (COCs) in vitro. The major component of the metabolised mixture is 2,2-bis(p-chlorophenyl)-1,1-dichloroethylene (p,p'-DDE) at 15.1 mg/l, which accounts for 40.7% of the total extract. Polychlorinated biphenyls (PCBs) account for 30.8% of the extract and hydroxylated PCB metabolites (OH-PCBs) for 11.8%. Exposure of COCs to the metabolised mixture induced a decrease of apoptotic cumulus cells at low concentrations and an increase at higher concentrations following a U-shaped curve (p=0.0106), with the intermediate treatment (3.6 microg/l OH-PCBs) significantly reducing apoptosis compared to the extraction control (p=0.05). However, the metabolised mixture did not affect cumulus expansion, oocyte maturation, penetration, development to blastocyst, or the number of cells per blastocyst. This study also indicates that organochlorine metabolites similar in concentrations to levels found in Arctic populations can affect growing cumulus-oocyte complexes without inducing an overt toxicological response.

Mechanisms Involved in Porcine Early Embryo Survival following Ethanol Exposure.

Alcohol consumption during pregnancy is still a cause of preventable birth defects and developmental disabilities. However, little is known about the impact of ethanol on preimplantation embryos and the molecular mechanisms involved. We aimed to determine the toxicogenomic impacts and the mechanisms involved in preimplantation embryonic survival following 0.2% ethanol exposure in porcine embryos. Gene expression changes were measured with a porcine embryo specific microarray and confirmed by RT-qPCR. When compared with control, ethanol exposure led to a 43% decrease in blastocyst rate and activated pathways associated with oxidative stress and nervous system damage, such as TP53 and TGF. Moreover, we observed a mitochondrial dysfunction in the exposed embryos as revealed by the decrease in Mitotracker Red fluorescence intensity (25 and 41% in 4-cell embryos and blastocysts, respectively) and a modification in the expression of GABRB3, APP, CLU, and MIOX genes. We therefore present evidence of neuronal-like adverse effects on undifferentiated cells suggesting that fetal alcohol spectrum disorder could have its origin as early as in the first week postfertilization.

Integrated Management of Residential Indoor Air Quality: A Call for Stakeholders in a Changing Climate.

A paradigm change in the management of environmental health issues has been observed in recent years: instead of managing specific risks individually, a holistic vision of environmental problems would assure sustainable solutions. However, concrete actions that could help translate these recommendations into interventions are lacking. This review presents the relevance of using an integrated indoor air quality management approach to ensure occupant health and comfort. At the nexus of three basic concepts (reducing contaminants at the source, improving ventilation, and, when relevant, purifying the indoor air), this approach can help maintain and improve indoor air quality and limit exposure to several contaminants. Its application is particularly relevant in a climate change context since the evolving outdoor conditions have to be taken into account during building construction and renovation. The measures presented through this approach target public health players, building managers, owners, occupants, and professionals involved in building design, construction, renovation, and maintenance. The findings of this review will help the various stakeholders initiate a strategic reflection on the importance of indoor air quality and climate change issues for existing and future buildings. Several new avenues and recommendations are presented to set the path for future research activities.

Criteria for the prioritization of public health interventions for climate-sensitive vector-borne diseases in Quebec.

Prioritizing resources for optimal responses to an ever growing list of existing and emerging infectious diseases represents an important challenge to public health. In the context of climate change, there is increasing anticipated variability in the occurrence of infectious diseases, notably climate-sensitive vector-borne diseases. An essential step in prioritizing efforts is to identify what considerations and concerns to take into account to guide decisions and thus set disease priorities. This study was designed to perform a comprehensive review of criteria for vector-borne disease prioritization, assess their applicability in a context of climate change with a diverse cross-section of stakeholders in order to produce a baseline list of considerations to use in this decision-making context. Differences in stakeholder choices were examined with regards to prioritization of these criteria for research, surveillance and disease prevention and control objectives. A preliminary list of criteria was identified following a review of the literature. Discussions with stakeholders were held to consolidate and validate this list of criteria and examine their effects on disease prioritization. After this validation phase, a total of 21 criteria were retained. A pilot vector-borne disease prioritization exercise was conducted using PROMETHEE to examine the effects of the retained criteria on prioritization in different intervention domains. Overall, concerns expressed by stakeholders for prioritization were well aligned with categories of criteria identified in previous prioritization studies. Weighting by category was consistent between stakeholders overall, though some significant differences were found between public health and non-public health stakeholders. From this exercise, a general model for climate-sensitive vector-borne disease prioritization has been developed that can be used as a starting point for further public health prioritization exercises relating to research, surveillance, and prevention and control interventions in a context of climate change. Multi-stakeholder engagement in prioritization can help broaden the range of criteria taken into account, offer opportunities for early identification of potential challenges and may facilitate acceptability of any resulting decisions.

Low concentrations of bromodichloromethane induce a toxicogenomic response in porcine embryos in vitro.

Bromodichloromethane (BDCM) is one of the trihalomethanes present in chlorinated water. Humans are thus daily exposed. Previous contradictory results failed to clearly establish the adverse effects of low concentrations of BDCM. By using the porcine preimplantation embryo as a sensitive model, we showed that exposure to low concentrations of BDCM (10 and 100ppb) during the first week of embryo development induced adverse effect on the blastocyst rate and alteration of the estradiol pathway. Our results also suggest that blastocysts exposed to BDCM present transcriptomic and epigenomic adaptive modifications compatible with the cardiac anomalies observed by previous studies of newborns exposed to BDCM during gestation. Thus, phenotypic observations and toxicogenomic adaptations of embryo to low concentration of BDCM provide insights for BDCM risk assessment. Indeed, our results support the use of sensitive toxicogenomic models using environmentally relevant concentrations to which humans are exposed in order to conduct the risk assessment.

Disinfection by-products exposure and intra-uterine growth restriction: Do genetic polymorphisms of CYP2E1or deletion of GSTM1 or GSTT1 modify the association?

BACKGROUND: Exposure to disinfection by-products (DBPs) during pregnancy was associated with reduced foetal growth. Genetic susceptibility might play a role, especially for genes encoding for the Cytochrome P450 (CYP2E1) and Glutathione S-Transferase (GST) enzymes, involved in metabolism and activation of DBPs. Few epidemiological studies evaluated these gene-environment interactions and their results were never replicated. OBJECTIVE: This study aims to examine interactions between trihalomethanes (THM) or haloacetic acids (HAA) exposure and genetic polymorphisms on small for gestational age (SGA) neonates by investigating single nucleotide polymorphisms (SNPs) in CYP2E1 gene and GSTM1 and GSTT1 deletions in mothers-children pairs. METHODS: A population-based case-control study of 1549 mothers and 1455 children was conducted on SGA and THM/HAA exposure. DNA was extracted from blood or saliva cells. Targeted SNPs and deletions were genotyped. Statistical interaction between SNPs/deletions and THMs or HAAs in utero exposure with regard to SGA occurrence was evaluated by unconditional logistic regression with control of potential confounders. RESULTS: Previously reported positive modification of the effect of THM uterine exposure by mothers or newborns CYP2E1 rs3813867 C allele or GSTM1 deletion was not replicated. However interactions with CYP2E1 rs117618383 and rs2515641 were observed but were not statistically significant after correction for multiple testing. CONCLUSIONS: Previous positive interactions between THMs exposure and CYP2E1 and GSTM1 were not replicated but interactions with other CYP2E1 polymorphisms are reported.