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There is highly suggestive evidence for an effect of air pollution exposure on dementia-related outcomes, but evidence is not yet present to clearly pinpoint which pollutants are the probable causal agents. The aims of this study was to assess the longitudinal association between exposures of fine ambient particulate matter (PM2.5) from residential wood burning, and vehicle exhaust, with dementia. We used data from the Betula study, a longitudinal study of dementia in Umeå, Northern Sweden. The study size was 1 806 and the participants were followed from study entry (1993-1995) to 2010. Modelled levels of source-specific fine particulate matter at the residential address were combined with information on wood stoves or wood boilers, and with validated data on dementia diagnosis and individual-level characteristics from the Betula study. Cox proportional hazards models were used to estimate Hazard Ratios (HRs) and their 95% CIs for dementia incidence (vascular dementia and Alzheimer's disease), adjusted for individual-level characteristics. The emission of PM2.5 from local residential wood burning was associated with dementia incidence with a hazard ratio of 1.55 for a 1 μg/m3 increase in PM2.5 (95% Confidence Interval (CI): 1.00-2.41, p-value 0.05). Study participants with an address in an area with the highest quartile of PM2.5 from residential wood burning and who also had a wood-burning stove were more likely to develop dementia than those in the lower three quartiles without a wood-burning stove with hazard ratios of 1.74 (CI: 1.10-2.75, p-value 0.018). Particulate matter from traffic exhaust seemed to be associated with dementia incidence with hazard ratios of 1.66, (CI: 1.16-2.39), p-value 0.006, and 1.41 (CI: 0.97-2.23), p-value 0.07, in the third and fourth quartiles, respectively. If the associations we observed are causal, then air pollution from residential wood burning, and air pollution from traffic, might be independent important risk factors for dementia.

Background: Although serious health effects associated with paniculate matter (PM) with aerodynamic diameter ≤ 10 μm (PM) and ≤ 2.5 μm (PM₂.₅; fine fraction) are documented in many studies, the effects of coarse PM (PM₂.₅-₁₀) are still under debate. Objective: In this study, we estimated the effects of short-term exposure of PM₂.₅-₁₀ on daily mortality in Stockholm, Sweden. Method: We collected data on daily mortality for the years 2000 through 2008. Concentrations of PM₁₀ , PM₂.₅, ozone, and carbon monoxide were measured simultaneously in central Stockholm. We used additive Poisson regression models to examine the association between daily mortality and PM₂.₅-₁₀ on the day of death and the day before. Effect estimates were adjusted for other pollutants (two-pollutant models) during different seasons. Results: We estimated a 1.68% increase [95% confidence interval (CI): 0.20%, 3.15%] in daily mortality per 10-μg/m³ increase in PM₂.₅-₁₀ (single-pollutant model). The association with PM₂.₅-₁₀ was stronger for November through May, when road dust is most important (1.69% increase; 95% CI: 0.21%, 3.17%), compared with the rest of the year (1.31% increase; 95% CI: -2.08%, 4.70%), although the difference was not statistically significant. When adjusted for other pollutants, particularly PM₂.₅, the effect estimates per 10 μg/m³ for PM₂.₅-₁₀ decreased slightly but were still higher than corresponding effect estimates for PM₂.₅· Conclusions: Our analysis shows an increase in daily mortality associated with elevated urban background levels of PM₂.₅-₁₀· Regulation of PM₂.₅-₁₀ should be considered, along with actions to specifically reduce PM₂.₅-₁₀ emissions, especially road dust suspension, in cities.

The river Göta Älv is a source of freshwater for 0.7 million swedes. The river is subject to contamination from sewer systems discharge and runoff from agricultural lands. Climate models projects an increase in precipitation and heavy rainfall in this region. This study aimed to determine how daily rainfall causes variation in indicators of pathogen loads, to increase knowledge of variations in river water quality and discuss implications for risk management. Data covering 7 years of daily monitoring of river water turbidity and concentrations of E. coli, Clostridium and coliforms were obtained, and their short-term variations in relation with precipitation were analyzed with time series regression and non-linear distributed lag models. We studied how precipitation effects varied with season and compared different weather stations for predictive ability. Generally, the lowest raw water quality occurs 2 days after rainfall, with poor raw water quality continuing for several more days. A rainfall event of >15 mm/24-h (local 95 percentile) was associated with a three-fold higher concentration of E. coli and 30% higher turbidity levels (lag 2). Rainfall was associated with exponential increases in concentrations of indicator bacteria while the effect on turbidity attenuated with very heavy rainfall. Clear associations were also observed between consecutive days of wet weather and decreased water quality. The precipitation effect on increased levels of indicator bacteria was significant in all seasons. Rainfall elevates microbial risks year-round in this river and freshwater source and acts as the main driver of varying water quality. Heavy rainfall appears to be a better predictor of fecal pollution than water turbidity. An increase of wet weather and extreme events with climate change will lower river water quality even more, indicating greater challenges for drinking water producers, and suggesting better control of sources of pollution.

BACKGROUND : The systematic evaluation of the results of time-series studies of air pollution is challenged by differences in model specification and publication bias. METHODS : We evaluated the associations of inhalable particulate matter (PM) with an aerodynamic diameter of 10 μm or less (PM10) and fine PM with an aerodynamic diameter of 2.5 μm or less (PM2.5) with daily all-cause, cardiovascular, and respiratory mortality across multiple countries or regions. Daily data on mortality and air pollution were collected from 652 cities in 24 countries or regions. We used overdispersed generalized additive models with random-effects meta-analysis to investigate the associations. Two-pollutant models were fitted to test the robustness of the associations. Concentration–response curves from each city were pooled to allow global estimates to be derived. RESULTS : On average, an increase of 10 μg per cubic meter in the 2-day moving average of PM10 concentration, which represents the average over the current and previous day, was associated with increases of 0.44% (95% confidence interval [CI], 0.39 to 0.50) in daily all-cause mortality, 0.36% (95% CI, 0.30 to 0.43) in daily cardiovascular mortality, and 0.47% (95% CI, 0.35 to 0.58) in daily respiratory mortality. The corresponding increases in daily mortality for the same change in PM2.5 concentration were 0.68% (95% CI, 0.59 to 0.77), 0.55% (95% CI, 0.45 to 0.66), and 0.74% (95% CI, 0.53 to 0.95). These associations remained significant after adjustment for gaseous pollutants. Associations were stronger in locations with lower annual mean PM concentrations and higher annual mean temperatures. The pooled concentration–response curves showed a consistent increase in daily mortality with increasing PM concentration, with steeper slopes at lower PM concentrations. CONCLUSIONS : Our data show independent associations between short-term exposure to PM10 and PM2.5 and daily all-cause, cardiovascular, and respiratory mortality in more than 600 cities across the globe. These data reinforce the evidence of a link between mortality and PM concentration established in regional and local studies.

Although studies have provided estimates of premature deaths attributable to either heat or cold in selected countries, none has so far offered a systematic assessment across the whole temperature range in populations exposed to different climates. We aimed to quantify the total mortality burden attributable to non-optimum ambient temperature, and the relative contributions from heat and cold and from moderate and extreme temperatures. We collected data for 384 locations in Australia, Brazil, Canada, China, Italy, Japan, South Korea, Spain, Sweden, Taiwan, Thailand, UK, and USA. We fitted a standard time-series Poisson model for each location, controlling for trends and day of the week. We estimated temperature–mortality associations with a distributed lag non-linear model with 21 days of lag, and then pooled them in a multivariate metaregression that included country indicators and temperature average and range. We calculated attributable deaths for heat and cold, defined as temperatures above and below the optimum temperature, which corresponded to the point of minimum mortality, and for moderate and extreme temperatures, defined using cutoffs at the 2·5th and 97·5th temperature percentiles. We analysed 74 225 200 deaths in various periods between 1985 and 2012. In total, 7·71% (95% empirical CI 7·43–7·91) of mortality was attributable to non-optimum temperature in the selected countries within the study period, with substantial differences between countries, ranging from 3·37% (3·06 to 3·63) in Thailand to 11·00% (9·29 to 12·47) in China. The temperature percentile of minimum mortality varied from roughly the 60th percentile in tropical areas to about the 80–90th percentile in temperate regions. More temperature-attributable deaths were caused by cold (7·29%, 7·02–7·49) than by heat (0·42%, 0·39–0·44). Extreme cold and hot temperatures were responsible for 0·86% (0·84–0·87) of total mortality. Most of the temperature-related mortality burden was attributable to the contribution of cold. The effect of days of extreme temperature was substantially less than that attributable to milder but non-optimum weather. This evidence has important implications for the planning of public-health interventions to minimise the health consequences of adverse temperatures, and for predictions of future effect in climate-change scenarios. UK Medical Research Council.

Heatwaves are a critical public health problem. There will be an increase in the frequency and severity of heatwaves under changing climate. However, evidence about the impacts of climate change on heatwave-related mortality at a global scale is limited. We collected historical daily time series of mean temperature and mortality for all causes or nonexternal causes, in periods ranging from January 1, 1984, to December 31, 2015, in 412 communities within 20 countries/regions. We estimated heatwave-mortality associations through a two-stage time series design. Current and future daily mean temperature series were projected under four scenarios of greenhouse gas emissions from 1971-2099, with five general circulation models. We projected excess mortality in relation to heatwaves in the future under each scenario of greenhouse gas emissions, with two assumptions for adaptation (no adaptation and hypothetical adaptation) and three scenarios of population change (high variant, median variant, and low variant). Results show that, if there is no adaptation, heatwave-related excess mortality is expected to increase the most in tropical and subtropical countries/regions (close to the equator), while European countries and the United States will have smaller percent increases in heatwave-related excess mortality. The higher the population variant and the greenhouse gas emissions, the higher the increase of heatwave-related excess mortality in the future. The changes in 2031-2080 compared with 1971-2020 range from approximately 2,000% in Colombia to 150% in Moldova under the highest emission scenario and high-variant population scenario, without any adaptation. If we considered hypothetical adaptation to future climate, under high-variant population scenario and all scenarios of greenhouse gas emissions, the heatwave-related excess mortality is expected to still increase across all the countries/regions except Moldova and Japan. However, the increase would be much smaller than the no adaptation scenario. The simple assumptions with respect to adaptation as follows: no adaptation and hypothetical adaptation results in some uncertainties of projections. This study provides a comprehensive characterisation of future heatwave-related excess mortality across various regions and under alternative scenarios of greenhouse gas emissions, different assumptions of adaptation, and different scenarios of population change. The projections can help decision makers in planning adaptation and mitigation strategies for climate change.

Identifying the risk factors for morbidity and mortality effects pre-, during and post-flood may aid the appropriate targeting of flood-related adverse health prevention strategies. We conducted a systematic PubMed search to identify studies examining risk factors for health effects of precipitation-related floods, among Organisation for Economic Co-Operation and Development (OECD) member countries. Research identifying flood-related morbidity and mortality risk factors is limited and primarily examines demographic characteristics such as age and gender. During floods, females, elderly and children appear to be at greater risk of psychological and physical health effects, while males between 10 to 29 years may be at greater risk of mortality. Post-flood, those over 65 years and males are at increased risk of physical health effects, while females appear at greater risk of psychological health effects. Other risk factors include previous flood experiences, greater flood depth or flood trauma, existing illnesses, medication interruption, and low education or socio-economic status. Tailoring messages to high-risk groups may increase their effectiveness. Target populations differ for morbidity and mortality effects, and differ pre-, during, and post-flood. Additional research is required to identify the risk factors associated with pre- and post-flood mortality and post-flood morbidity, preferably using prospective cohort studies.

The mortality impacts of hot and cold temperatures have been thoroughly documented, with most locations reporting a U-shaped relationship with a minimum mortality temperature (MMT) at which mortality is lowest. How MMT may have evolved over previous decades as the global mean surface temperature has increased has not been thoroughly explored. We used observations of daily mean temperatures to investigate whether MMT changed in Stockholm, Sweden, from the beginning of the 20th century until 2009. Daily mortality and temperature data for the period 1901-2009 in Stockholm, Sweden, were used to model the temperature-mortality relationship. We estimated MMT using distributed lag nonlinear Poisson regression models considering lags up to 21 days of daily mean temperature as the exposure variable. To avoid large influences on the MMT from intra- and interannual climatic variability, we estimated MMT based on 30-year periods. Furthermore, we investigated whether there were trends in the absolute value of the MMT and in the relative value of the MMT (the corresponding percentile of the same-day temperature distribution) over the study period. Our findings suggest that both the absolute MMT and the relative MMT increased in Stockholm, Sweden, over the course of the 20th century. The increase in the MMT over the course of the 20th century suggests autonomous adaptation within the context of the large epidemiological, demographical, and societal changes that occurred. Whether the rate of increase will be sustained with climate change is an open question. Oudin Åström D, Tornevi A, Ebi KL, Rocklöv J, Forsberg B. 2016. Evolution of minimum mortality temperature in Stockholm, Sweden, 1901-2009. Environ Health Perspect 124:740-744; http://dx.doi.org/10.1289/ehp.1509692.

Residential wood combustion (RWC) is one of the largest sources of fine particles (PM2.5) in the Nordic cities. The current study aims to calculate the related health effects in four studied city areas in Sweden, Finland, Norway, and Denmark.

Ambient air pollution is suspected to cause lung cancer. We aimed to assess the association between long-term exposure to ambient air pollution and lung cancer incidence in European populations. This prospective analysis of data obtained by the European Study of Cohorts for Air Pollution Effects used data from 17 cohort studies based in nine European countries. Baseline addresses were geocoded and we assessed air pollution by land-use regression models for particulate matter (PM) with diameter of less than 10 μm (PM10), less than 2·5 μm (PM2·5), and between 2·5 and 10 μm (PMcoarse), soot (PM2·5absorbance), nitrogen oxides, and two traffic indicators. We used Cox regression models with adjustment for potential confounders for cohort-specific analyses and random effects models for meta-analyses. The 312 944 cohort members contributed 4 013 131 person-years at risk. During follow-up (mean 12·8 years), 2095 incident lung cancer cases were diagnosed. The meta-analyses showed a statistically significant association between risk for lung cancer and PM10 (hazard ratio [HR] 1·22 [95% CI 1·03–1·45] per 10 μg/m3). For PM2·5 the HR was 1·18 (0·96–1·46) per 5 μg/m3. The same increments of PM10 and PM2·5 were associated with HRs for adenocarcinomas of the lung of 1·51 (1·10–2·08) and 1·55 (1·05–2·29), respectively. An increase in road traffic of 4000 vehicle-km per day within 100 m of the residence was associated with an HR for lung cancer of 1·09 (0·99–1·21). The results showed no association between lung cancer and nitrogen oxides concentration (HR 1·01 [0·95–1·07] per 20 μg/m3) or traffic intensity on the nearest street (HR 1·00 [0·97–1·04] per 5000 vehicles per day). Particulate matter air pollution contributes to lung cancer incidence in Europe. European Community's Seventh Framework Programme.