A space-time multivariate Bayesian model to analyse road traffic accidents by severity

The paper investigates the dependences between levels of severity of road traffic accidents, accounting at the same time for spatial and temporal correlations. The study analyses road traffic accidents data at ward level in England over the period 2005–2013. We include in our model multivariate spatially structured and unstructured effects to capture the dependences between severities, within a Bayesian hierarchical formulation. We also include a temporal component to capture the time effects and we carry out an extensive model comparison. The results show important associations in both spatially structured and unstructured effects between severities, and a downward temporal trend is observed for low and high levels of severity. Maps of posterior accident rates indicate elevated risk within big cities for accidents of low severity and in suburban areas in the north and on the southern coast of England for accidents of high severity. The posterior probability of extreme rates is used to suggest the presence of hot spots in a public health perspective.Areti Boulieri acknowledges support from the National Institute for Health Research and the Medical Research Council Doctoral Training Partnership. Marta Blangiardo acknowledges support from the National Institute for Health Research and the Medical Research Council–Public Health England Centre for Environment and Health. Silvia Liverani acknowledges support from the Leverhulme Trust (grant ECF-2011-576)

Quantification of annual settlement growth in rural mining areas using machine learning

Studies on annual settlement growth have mainly focused on larger cities or incorporated data rarely available in, or applicable to, sparsely populated areas in sub-Saharan Africa, such as aerial photography or night-time light data. The aim of the present study is to quantify settlement growth in rural communities in Burkina Faso affected by industrial mining, which often experience substantial in-migration. A multi-annual training dataset was created using historic Google Earth imagery. Support vector machine classifiers were fitted on Landsat scenes to produce annual land use classification maps. Post-classification steps included visual quality assessments, majority voting of scenes of the same year and temporal consistency correction. Overall accuracy in the four studied scenes ranged between 58.5% and 95.1%. Arid conditions and limited availability of Google Earth imagery negatively affected classification accuracy. Humid study sites, where training data could be generated in proximity to the areas of interest, showed the highest classification accuracies. Overall, by relying solely on freely and globally available imagery, the proposed methodology is a promising approach for tracking fast-paced population dynamics in rural areas where population data is scarce. With the growing availability of longitudinal high-resolution imagery, including data from the Sentinel satellites, the potential applications of the methodology presented will further increase in the futur

A review of ambient air pollution exposure assessment methods in determining childhood respiratory health effects in children under five

Various epidemiological studies have reported on air pollution exposure-related lung function decline and respiratory health effects in children. Children have increased susceptibility to ambient air pollutants as physiological and structural changes of the lung are still occurring within the first five years of life after birth. This review examines applications in air pollution exposure assessment methods when evaluating lung function and respiratory health concentration-response effects in young children, while considering the effects of critical windows of exposure. We identified 13 studies that used various methods of exposure assessment in assessing respiratory health outcomes (presence of lower respiratory tract infections, respiratory symptoms, wheezing and asthma) in children under five. The methods applied included personal monitoring (n = 1), proximity-based methods (n = 3), inverse distance weighting (n = 2), geographic weighted regression (n = 1), dispersion modeling (n = 1), satellite-based methods (n = 2) and land use regression modeling (n = 5). These studies assessed exposure and outcomes at different "windows of susceptibility": antenatally/specific trimesters (n = 8), infancy (n = 5) and early childhood (n = 6). In most studies, the reported measures of air pollutants were noted to be below the prescribed limits, though for some, a cause-effect association was observed. It was also noted that there was very little variation in estimates between time points or trimesters of exposure, likely attributed to limitations in the selected exposure assessment method. Moderate to high correlations between trimesters were reported for most studies. © 2022 by the authors

The air and viruses we breathe: assessing the effect the PM2.5 air pollutant has on the burden of COVID-19

Evidence suggests an association between air pollutant exposure and worse outcomes for respiratory viral diseases, like COVID-19. However, does breathing polluted air over many years affect the susceptibility to SARS-CoV-2 infection or severity of COVID-19 disease, and how intense are these effects? As climate change intensifies, air pollutant levels may rise, which might further affect the burden of respiratory viral diseases. We assessed the effect of increasing exposure to PM2.5 (particulate matter ≤ 2.5 microns in diameter) on SARS-CoV-2 susceptibility or COVID-19 severity and projected the impact on infections and hospitalisations over two years. Simulations in a hypothetical, representative population show that if exposure affects severity, then hospital admissions are projected to increase by 5-10% for a one-unit exposure increase. However, if exposure affects susceptibility, then infections would increase with the potential for onward transmission and hospital admissions could increase by over 60%. Implications of this study highlight the importance of considering this potential additional health and health system burden as part of strategic planning to mitigate and respond to changing air pollution levels. It is also important to better understand at which point PM2.5 exposure affects SARS-CoV-2 infection through to COVID-19 disease progression, to enable improved protection and better support of those most vulnerabl

Air pollution, lung function and COPD: results from the population-based UK Biobank study

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 303 887 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 (particles with a 50% cut-off aerodynamic diameter of 2.5 and 10 µm: PM2.5 and PM10, respectively; and coarse particles with diameter between 2.5 μm and 10 μm: 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/FV

Housing conditions and respiratory health in children in mining communities: an analysis of data from 27 countries in sub-Saharan Africa

Background Poor housing conditions, such as poor building materials and weak structures as well as high levels of indoor air pollution, are important risk factors for a broad range of diseases, including acute respiratory infections (ARI). In mining areas, research on the determinants of respiratory health predominantly focuses on exposures to outdoor air pollutants deriving from mining operations. However, mining projects also influence the socioeconomic status of households, which, in turn, affect housing quality and individual behaviors and, thus, housing quality and levels of indoor air pollution. In this study, we aimed to determine how proximity to an industrial mining project impacts housing quality, sources of indoor air pollution, and prevalence of ARI. Methods We merged data from 131 Demographic and Health Surveys (DHS) with georeferenced data on mining projects in sub-Saharan Africa (SSA) to determine associations between housing quality, indoor air pollution sources, and child respiratory health. Spatial differences in selected indicators were explored using descriptive cross-sectional analyses. Furthermore, we applied a quasi-experimental difference-in-differences (DiD) approach using generalized linear mixed-effects models to compare temporal changes in household and child health indicators at different operational phases of mining projects and as a function of distance to mines. Results For cross-sectional analyses, data of 183,466 households and 141,384 children from 27 countries in SSA were used, while 41,648 households and 34,406 children from 23 SSA countries were included in the DiD analyses. The increase in the share of houses being built from finished building materials after mine opening was more than 4-fold higher (odds ratio (OR): 4.32, 95% confidence interval (CI): 2.98-6.24) in close proximity to mining sites (i.e., ≤ 10 km) compared to areas further away (i.e., 10-50 km). However, these benefits were not equally distributed across socioeconomic strata, with considerably weaker effects observed among poorer households. Increases in indoor tobacco smoking rates in close proximity to operating mines were twice as high as in comparison areas (OR: 2.06, 95% CI: 1.15-3.68). The cross-sectional analyses revealed that traditional cooking fuels (e.g., charcoal, dung, and wood) were less frequently used (OR: 0.27, 95% CI: 0.23-0.31) in areas located in close proximity to mines than in comparison areas. Overall, no statistically significant association between mining operations and the prevalence of symptoms related to ARI in children under the age of 5 years was observed (OR: 0.78, 95% CI: 0.29-2.07). Conclusions Mines impact known risk factors for ARI through diverse pathways. The absence of significant changes in ARI symptoms among children is likely the result of counteracting effects between improvements in housing infrastructure and increased exposures to air pollutants from outdoor sources and tobacco smoking. For mining projects to unfold their full potential for community development, we recommend that impact assessments move beyond the mere appraisal of mining-related pollution emissions and try to include a more comprehensive set of pathways through which mines can affect ARI in exposed communities

Nutrition interventions for healthy ageing across the lifespan:a conference report

Thanks to advances in modern medicine over the past century, the world's population has experienced a marked increase in longevity. However, disparities exist that lead to groups with both shorter lifespan and significantly diminished health, especially in the aged. Unequal access to proper nutrition, healthcare services, and information to make informed health and nutrition decisions all contribute to these concerns. This in turn has hastened the ageing process in some and adversely affected others' ability to age healthfully. Many in developing as well as developed societies are plagued with the dichotomy of simultaneous calorie excess and nutrient inadequacy. This has resulted in mental and physical deterioration, increased non-communicable disease rates, lost productivity and quality of life, and increased medical costs. While adequate nutrition is fundamental to good health, it remains unclear what impact various dietary interventions may have on improving healthspan and quality of life with age. With a rapidly ageing global population, there is an urgent need for innovative approaches to health promotion as individual's age. Successful research, education, and interventions should include the development of both qualitative and quantitative biomarkers and other tools which can measure improvements in physiological integrity throughout life. Data-driven health policy shifts should be aimed at reducing the socio-economic inequalities that lead to premature ageing. A framework for progress has been proposed and published by the World Health Organization in its Global Strategy and Action Plan on Ageing and Health. This symposium focused on the impact of nutrition on this framework, stressing the need to better understand an individual's balance of intrinsic capacity and functional abilities at various life stages, and the impact this balance has on their mental and physical health in the environments they inhabit

Explorative assessment of the temperature-mortality association to support health-based heat-warning thresholds: a national case-crossover study in Switzerland

Defining health-based thresholds for effective heat warnings is crucial for climate change adaptation strategies. Translating the non-linear function between heat and health effects into an effective threshold for heat warnings to protect the population is a challenge. We present a systematic analysis of heat indicators in relation to mortality. We applied distributed lag non-linear models in an individual-level case-crossover design to assess the effects of heat on mortality in Switzerland during the warm season from 2003 to 2016 for three temperature metrics (daily mean, maximum, and minimum temperature), and various threshold temperatures and heatwave definitions. Individual death records with information on residential address from the Swiss National Cohort were linked to high-resolution temperature estimates from 100 m resolution maps. Moderate (90th percentile) to extreme thresholds (99.5th percentile) of the three temperature metrics implied a significant increase in mortality (5 to 38%) in respect of the median warm-season temperature. Effects of the threshold temperatures on mortality were similar across the seven major regions in Switzerland. Heatwave duration did not modify the effect when considering delayed effects up to 7 days. This nationally representative study, accounting for small-scale exposure variability, suggests that the national heat-warning system should focus on heatwave intensity rather than duration. While a different heat-warning indicator may be appropriate in other countries, our evaluation framework is transferable to any country

Residential radon - Comparative analysis of exposure models in Switzerland.

Residential radon exposure is a major public health issue in Switzerland due to the known association between inhaled radon progeny and lung cancer. To confirm recent findings of an association with skin cancer mortality, an updated national radon model is needed. The aim of this study was to derive the best possible residential radon prediction model for subsequent epidemiological analyses. Two different radon prediction models were developed (linear regression model vs. random forest) using ca. 80,000 measurements in the Swiss Radon Database (1994-2017). A range of geographic predictors and building specific predictors were considered in the 3-D models (x,y, floor of dwelling). A five-fold modelling strategy was used to evaluate the robustness of each approach, with models developed (80% measurement locations) and validated (20%) using standard diagnostics. Random forest consistently outperformed the linear regression model, with higher Spearman's rank correlation (51% vs. 36%), validation coefficient of determination (R <sup>2</sup> 31% vs. 15%), lower root mean square error (RMSE) and lower fractional bias. Applied to the population of 5.4 million adults in 2000, the random forest resulted in an arithmetic mean (standard deviation) of 75.5 (31.7) Bq/m <sup>3</sup> , and indicated a respective 16.1% and 0.1% adults with predicted radon concentrations exceeding the World Health Organization (100 Bq/m <sup>3</sup> ) and Swiss (300 Bq/m <sup>3</sup> ) reference values