Differential Mortality Risks Associated With PM2.5 Components: A Multi-Country, Multi-City Study.

BACKGROUND: The association between fine particulate matter (PM2.5) and mortality widely differs between as well as within countries. Differences in PM2.5 composition can play a role in modifying the effect estimates, but there is little evidence about which components have higher impacts on mortality. METHODS: We applied a 2-stage analysis on data collected from 210 locations in 16 countries. In the first stage, we estimated location-specific relative risks (RR) for mortality associated with daily total PM2.5 through time series regression analysis. We then pooled these estimates in a meta-regression model that included city-specific logratio-transformed proportions of seven PM2.5 components as well as meta-predictors derived from city-specific socio-economic and environmental indicators. RESULTS: We found associations between RR and several PM2.5 components. Increasing the ammonium (NH4+) proportion from 1% to 22%, while keeping a relative average proportion of other components, increased the RR from 1.0063 (95% confidence interval [95% CI] = 1.0030, 1.0097) to 1.0102 (95% CI = 1.0070, 1.0135). Conversely, an increase in nitrate (NO3-) from 1% to 71% resulted in a reduced RR, from 1.0100 (95% CI = 1.0067, 1.0133) to 1.0037 (95% CI = 0.9998, 1.0077). Differences in composition explained a substantial part of the heterogeneity in PM2.5 risk. CONCLUSIONS: These findings contribute to the identification of more hazardous emission sources. Further work is needed to understand the health impacts of PM2.5 components and sources given the overlapping sources and correlations among many components.

Evaluation of the ERA5 reanalysis-based Universal Thermal Climate Index on mortality data in Europe.

Air temperature has been the most commonly used exposure metric in assessing relationships between thermal stress and mortality. Lack of the high-quality meteorological station data necessary to adequately characterize the thermal environment has been one of the main limitations for the use of more complex thermal indices. Global climate reanalyses may provide an ideal platform to overcome this limitation and define complex heat and cold stress conditions anywhere in the world. In this study, we explored the potential of the Universal Thermal Climate Index (UTCI) based on ERA5 - the latest global climate reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) - as a health-related tool. Employing a novel ERA5-based thermal comfort dataset ERA5-HEAT, we investigated the relationships between the UTCI and daily mortality data in 21 cities across 9 European countries. We used distributed lag nonlinear models to assess exposure-response relationships between mortality and thermal conditions in individual cities. We then employed meta-regression models to pool the results for each city into four groups according to climate zone. To evaluate the performance of ERA5-based UTCI, we compared its effects on mortality with those for the station-based UTCI data. In order to assess the additional effect of the UTCI, the performance of ERA5-and station-based air temperature (T) was evaluated. Whilst generally similar heat- and cold-effects were observed for the ERA5-and station-based data in most locations, the important role of wind in the UTCI appeared in the results. The largest difference between any two datasets was found in the Southern European group of cities, where the relative risk of mortality at the 1st percentile of daily mean temperature distribution (1.29 and 1.30 according to the ERA5 vs station data, respectively) considerably exceeded the one for the daily mean UTCI (1.19 vs 1.22). These differences were mainly due to the effect of wind in the cold tail of the UTCI distribution. The comparison of exposure-response relationships between ERA5-and station-based data shows that ERA5-based UTCI may be a useful tool for definition of life-threatening thermal conditions in locations where high-quality station data are not available.

High resolution mapping of nitrogen dioxide and particulate matter in Great Britain (2003-2021) with multi-stage data reconstruction and ensemble machine learning methods.

In this contribution, we applied a multi-stage machine learning (ML) framework to map daily values of nitrogen dioxide (NO2) and particulate matter (PM10 and PM2.5) at a 1 km2 resolution over Great Britain for the period 2003-2021. The process combined ground monitoring observations, satellite-derived products, climate reanalyses and chemical transport model datasets, and traffic and land-use data. Each feature was harmonized to 1 km resolution and extracted at monitoring sites. Models used single and ensemble-based algorithms featuring random forests (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), as well as lasso and ridge regression. The various stages focused on augmenting PM2.5 using co-occurring PM10 values, gap-filling aerosol optical depth and columnar NO2 data obtained from satellite instruments, and finally the training of an ensemble model and the prediction of daily values across the whole geographical domain (2003-2021). Results show a good ensemble model performance, calculated through a ten-fold monitor-based cross-validation procedure, with an average R2 of 0.690 (range 0.611-0.792) for NO2, 0.704 (0.609-0.786) for PM10, and 0.802 (0.746-0.888) for PM2.5. Reconstructed pollution levels decreased markedly within the study period, with a stronger reduction in the latter eight years. The pollutants exhibited different spatial patterns, while NO2 rose in close proximity to high-traffic areas, PM demonstrated variation at a larger scale. The resulting 1 km2 spatially resolved daily datasets allow for linkage with health data across Great Britain over nearly two decades, thus contributing to extensive, extended, and detailed research on the long-and short-term health effects of air pollution.

Reconstructing individual-level exposures in cohort analyses of environmental risks: an example with the UK Biobank.

Recent developments in linkage procedures and exposure modelling offer great prospects for cohort analyses on the health risks of environmental factors. However, assigning individual-level exposures to large population-based cohorts poses methodological and practical problems. In this contribution, we illustrate a linkage framework to reconstruct environmental exposures for individual-level epidemiological analyses, discussing methodological and practical issues such as residential mobility and privacy concerns. The framework outlined here requires the availability of individual residential histories with related time periods, as well as high-resolution spatio-temporal maps of environmental exposures. The linkage process is carried out in three steps: (1) spatial alignment of the exposure maps and residential locations to extract address-specific exposure series; (2) reconstruction of individual-level exposure histories accounting for residential changes during the follow-up; (3) flexible definition of exposure summaries consistent with alternative research questions and epidemiological designs. The procedure is exemplified by the linkage and processing of daily averages of air pollution for the UK Biobank cohort using gridded spatio-temporal maps across Great Britain. This results in the extraction of exposure summaries suitable for epidemiological analyses of both short and long-term risk associations and, in general, for the investigation of temporal dependencies. The linkage framework presented here is generally applicable to multiple environmental stressors and can be extended beyond the reconstruction of residential exposures. IMPACT: This contribution describes a linkage framework to assign individual-level environmental exposures to population-based cohorts using high-resolution spatio-temporal exposure. The framework can be used to address current limitations of exposure assessment for the analysis of health risks associated with environmental stressors. The linkage of detailed exposure information at the individual level offers the opportunity to define flexible exposure summaries tailored to specific study designs and research questions. The application of the framework is exemplified by the linkage of fine particulate matter (PM2.5) exposures to the UK Biobank cohort.

Prenatal exposure to ambient air pollution and subsequent risk of lower respiratory tract infections in childhood and adolescence: A systematic review.

BACKGROUND: Pregnancy represents a critical window of vulnerability to the harmful effects of air pollution on health. However, long-term consequences such as risk of having lower respiratory tract infections (LRTIs) are less explored. This systematic review aims to synthesize previous research on prenatal exposure to ambient (outdoor) air pollution and LRTIs in childhood and adolescence. METHODS: We systematically searched Embase, MEDLINE, Web of Science Core Collection, CINAHL, and Global Health up to May 17, 2024. We included peer-reviewed publications of studies which investigated the association between prenatal exposure to ambient air pollution and LRTIs up to the age of 19. We excluded conference abstracts, study protocols, review articles, and grey literature. Screening and data extraction was conducted by two reviewers independently. We used the Office of Health Assessment and Translation tool to assess risk of bias and conducted a narrative synthesis. RESULTS: The search yielded 6056 records, of which 16 publications describing 12 research studies were eligible for the synthesis. All studies were conducted in high- or upper-middle-income countries in Europe or Asia. Half (6) of the studies focused on LRTIs occurring within the first three years of life, and the others also included LRTIs in older children (up to age 14). Air pollutants investigated included nitrogen dioxide, sulphur dioxide, particulate matter (PM2.5: diameter ≤2.5 µm and PM10: diameter ≤10 µm), carbon monoxide, ozone, and benzene. Findings on a potential association between prenatal ambient air pollution exposure and LRTIs were inconclusive, without a clear and consistent direction. There was some suggestion of a positive association with prenatal PM2.5 exposure. The small number of studies identified, their poor geographical representation, and their methodological limitations including concerns for risk of bias preclude more definitive conclusions. CONCLUSION: The available published evidence is insufficient to establish whether prenatal exposure to ambient air pollution increases risk of LRTIs in children and adolescents. With many populations exposed to high levels of air pollution, there is an urgent need for research in more diverse settings, more transparent reporting of methods, and exploring how, when, and for whom prenatal exposure to ambient air pollution leads to the greatest health risks. PROSPERO REGISTRATION NUMBER: CRD42023407689.

A reproducible ensemble machine learning approach to forecast dengue outbreaks.

Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model's qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health.

Improving communicative access and patient experience in acute stroke care: An implementation journey.

INTRODUCTION: Patient experience for people with aphasia/families in acute care is frequently reported as negative, with communication barriers contributing to adverse events and significant long-term physical and psychosocial sequelae. Although the effectiveness of providing supported communication training and resources for health care providers in the stroke system is well documented, there is less evidence of implementation strategies for sustainable system change. This paper describes an implementation process targeting two specific areas: 1) improving Stroke Team communication with patients with aphasia, and 2) helping the Stroke Team provide support to families. The project aimed for practical sustainable solutions with potential contribution toward the development of an implementation practice model adaptable for other acute stroke contexts. METHODS: The project was designed to create a communicatively accessible acute care hospital unit for people with aphasia. The process involved a collaboration between a Stroke Team covering two units/wards led by nurse managers (19 participants), and a community-based Aphasia Team with expertise in Supported Conversation for Adults with Aphasia (SCA™) - an evidence-based method to reduce language barriers and increase communicative access for people with aphasia. Development was loosely guided by the integrated knowledge translation (iKT) model, and information regarding the implementation process was gathered in developmental fashion over several years. OUTCOMES: Examples of outcomes related to the two target areas include provision of accessible information about aphasia to patients as well as development of two new products - a short virtual SCA™ eLearning module relevant to acute care, and a pamphlet for families on how to keep conversation alive. Potential strategies for sustaining a focus on aphasia and communicative access emerged as part of the implementation process. CONCLUSIONS: This implementation journey allowed for a deeper understanding of the competing demands of the acute care context and highlighted the need for further work on sustainability of communicative access interventions for stroke patients with aphasia and their families.

Optimal heat stress metric for modelling heat-related mortality varies from country to country.

Combined heat and humidity is frequently described as the main driver of human heat-related mortality, more so than dry-bulb temperature alone. While based on physiological thinking, this assumption has not been robustly supported by epidemiological evidence. By performing the first systematic comparison of eight heat stress metrics (i.e., temperature combined with humidity and other climate variables) with warm-season mortality, in 604 locations over 39 countries, we find that the optimal metric for modelling mortality varies from country to country. Temperature metrics with no or little humidity modification associates best with mortality in ~40% of the studied countries. Apparent temperature (combined temperature, humidity and wind speed) dominates in another 40% of countries. There is no obvious climate grouping in these results. We recommend, where possible, that researchers use the optimal metric for each country. However, dry-bulb temperature performs similarly to humidity-based heat stress metrics in estimating heat-related mortality in present-day climate.

Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe.

BACKGROUND: Heat and cold are established environmental risk factors for human health. However, mapping the related health burden is a difficult task due to the complexity of the associations and the differences in vulnerability and demographic distributions. In this study, we did a comprehensive mortality impact assessment due to heat and cold in European urban areas, considering geographical differences and age-specific risks. METHODS: We included urban areas across Europe between Jan 1, 2000, and Dec 12, 2019, using the Urban Audit dataset of Eurostat and adults aged 20 years and older living in these areas. Data were extracted from Eurostat, the Multi-country Multi-city Collaborative Research Network, Moderate Resolution Imaging Spectroradiometer, and Copernicus. We applied a three-stage method to estimate risks of temperature continuously across the age and space dimensions, identifying patterns of vulnerability on the basis of city-specific characteristics and demographic structures. These risks were used to derive minimum mortality temperatures and related percentiles and raw and standardised excess mortality rates for heat and cold aggregated at various geographical levels. FINDINGS: Across the 854 urban areas in Europe, we estimated an annual excess of 203 620 (empirical 95% CI 180 882-224 613) deaths attributed to cold and 20 173 (17 261-22 934) attributed to heat. These corresponded to age-standardised rates of 129 (empirical 95% CI 114-142) and 13 (11-14) deaths per 100 000 person-years. Results differed across Europe and age groups, with the highest effects in eastern European cities for both cold and heat. INTERPRETATION: Maps of mortality risks and excess deaths indicate geographical differences, such as a north-south gradient and increased vulnerability in eastern Europe, as well as local variations due to urban characteristics. The modelling framework and results are crucial for the design of national and local health and climate policies and for projecting the effects of cold and heat under future climatic and socioeconomic scenarios. FUNDING: Medical Research Council of UK, the Natural Environment Research Council UK, the EU's Horizon 2020, and the EU's Joint Research Center.

The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis.

BACKGROUND AND AIM: The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally. METHODS: We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone. RESULTS: We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m3 compared to 11.0 g/m3) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries. CONCLUSION: Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 transmission.

Small-area assessment of temperature-related mortality risks in England and Wales: a case time series analysis.

BACKGROUND: Epidemiological literature on the health risks associated with non-optimal temperature has mostly reported average estimates across large areas or specific population groups. However, the heterogeneous distribution of drivers of vulnerability can result in local differences in health risks associated with heat and cold. We aimed to analyse the association between ambient air temperature and all-cause mortality across England and Wales and characterise small scale patterns in temperature-related mortality risks and impacts. METHODS: We performed a country-wide small-area analysis using data on all-cause mortality and air temperature for 34 753 lower super output areas (LSOAs) within 348 local authority districts (LADs) across England and Wales between Jan 1, 2000, and Dec 31, 2019. We first performed a case time series analysis of LSOA-specific and age-specific mortality series matched with 1 × 1 km gridded temperature data using distributed lag non-linear models, and then a repeated-measure multivariate meta-regression to pool LAD-specific estimates using area-level climatological, socioeconomic, and topographical predictors. FINDINGS: The final analysis included 10 716 879 deaths from all causes. The small-area assessment estimated that each year in England and Wales, there was on average 791 excess deaths (empirical 95% CI 611-957) attributable to heat and 60 573 (55 796-65 145) attributable to cold, corresponding to standardised excess mortality rates of 1·57 deaths (empirical 95% CI 1·21-1·90) per 100 000 person-years for heat and 122·34 deaths (112·90-131·52) per 100 000 person-years for cold. The risks increased with age and were highly heterogeneous geographically, with the minimum mortality temperature ranging from 14·9°C to 22·6°C. Heat-related mortality was higher in urban areas, whereas cold-related mortality showed a more nuanced geographical pattern and increased risk in areas with greater socioeconomic deprivation. INTERPRETATION: This study provides a comprehensive assessment of excess mortality related to non-optimal outdoor temperature, with several risk indicators reported by age and multiple geographical levels. The analysis provides detailed risk maps that are useful for designing effective public health and climate policies at both local and national levels. FUNDING: Medical Research Council, Natural Environment Research Council, EU Horizon 2020 Programme, National Institute of Health Research.

Differential impact of government lockdown policies on reducing air pollution levels and related mortality in Europe.

Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe.

Comparison of weather station and climate reanalysis data for modelling temperature-related mortality.

Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk.

A Comparative Analysis of the Temperature-Mortality Risks Using Different Weather Datasets Across Heterogeneous Regions.

New gridded climate datasets (GCDs) on spatially resolved modeled weather data have recently been released to explore the impacts of climate change. GCDs have been suggested as potential alternatives to weather station data in epidemiological assessments on health impacts of temperature and climate change. These can be particularly useful for assessment in regions that have remained understudied due to limited or low quality weather station data. However to date, no study has critically evaluated the application of GCDs of variable spatial resolution in temperature-mortality assessments across regions of different orography, climate, and size. Here we explored the performance of population-weighted daily mean temperature data from the global ERA5 reanalysis dataset in the 10 regions in the United Kingdom and the 26 cantons in Switzerland, combined with two local high-resolution GCDs (HadUK-grid UKPOC-9 and MeteoSwiss-grid-product, respectively) and compared these to weather station data and unweighted homologous series. We applied quasi-Poisson time series regression with distributed lag nonlinear models to obtain the GCD- and region-specific temperature-mortality associations and calculated the corresponding cold- and heat-related excess mortality. Although the five exposure datasets yielded different average area-level temperature estimates, these deviations did not result in substantial variations in the temperature-mortality association or impacts. Moreover, local population-weighted GCDs showed better overall performance, suggesting that they could be excellent alternatives to help advance knowledge on climate change impacts in remote regions with large climate and population distribution variability, which has remained largely unexplored in present literature due to the lack of reliable exposure data.

Mortality Risk from Respiratory Diseases Due to Non-Optimal Temperature among Brazilian Elderlies.

Over the past decade, Brazil has experienced and continues to be impacted by extreme climate events. This study aims to evaluate the association between daily average temperature and mortality from respiratory disease among Brazilian elderlies. A daily time-series study between 2000 and 2017 in 27 Brazilian cities was conducted. Data outcomes were daily counts of deaths due to respiratory diseases in the elderly aged 60 or more. The exposure variable was the daily mean temperature from Copernicus ERA5-Land reanalysis. The association was estimated from a two-stage time series analysis method. We also calculated deaths attributable to heat and cold. The pooled exposure-response curve presented a J-shaped format. The exposure to extreme heat increased the risk of mortality by 27% (95% CI: 15-39%), while the exposure to extreme cold increased the risk of mortality by 16% (95% CI: 8-24%). The heterogeneity between cities was explained by city-specific mean temperature and temperature range. The fractions of deaths attributable to cold and heat were 4.7% (95% CI: 2.94-6.17%) and 2.8% (95% CI: 1.45-3.95%), respectively. Our results show a significant impact of non-optimal temperature on the respiratory health of elderlies living in Brazil. It may support proactive action implementation in cities that have critical temperature variations.

A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries.

There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission.

A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain.

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008-2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5.

Family perspectives from three aphasia centers in Ontario, Canada.

Speech-language pathologists and social workers at the three Aphasia Centres in Ontario, Canada, work in partnership with aphasic adults and their families to increase communicative access to participation in various aspects of social and community life. The delivery of optimal service in this context requires an expansion of the traditional role played by speech-language pathologists in the field of aphasia. Illustrative ideas, activities, and programs developed by the three centers are described with emphasis on the benefits of a professional partnership between the professions of speech-language pathology and social work.