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OBJECTIVES: To estimate the exposure-response function associating polycyclic aromatic hydrocarbon (PAH) exposure and lung cancer, with consideration of smoking. METHODS: Mortality, occupational exposure and smoking histories were ascertained for a cohort of 16 431 persons (15 703 men and 728 women) who had worked in one of four aluminium smelters in Quebec from 1950 to 1999. A variety of exposure-response functions were fitted to the cohort data using generalised relative risk models. RESULTS: In 677 lung cancer cases there was a clear trend of increasing risk with increasing cumulative exposure to PAH measured as benzo(a)pyrene (BaP). A linear model predicted a relative risk of 1.35 (95% CI 1.22 to 1.51) at 100 mug/m(-3) BaP years, but there was a significant departure from linearity in the direction of decreasing slope with increasing exposures. Among the models tried, the best fitting were a two-knot cubic spline and a power curve (RR = (1+bx)(p)), the latter predicting a relative risk of 2.68 at 100 mug/m(-3) BaP years. Additive models and multiplicative models for combining risks from occupational PAH and smoking fitted almost equally well, with a slight advantage to the additive. CONCLUSION: Despite the large cohort with long follow-up, the shape of the exposure-response function and the mode of combination of risks due to occupational PAH and smoking remains uncertain. If a linear exposure-response function is assumed, the estimated slope is broadly in line with the estimate from a previous follow-up of the same cohort, and somewhat higher than the average found in a recent meta-analysis of lung cancer studies. [ABSTRACT FROM AUTHOR]

Learn how to safeguard and streamline the complicated immunization process. [ABSTRACT FROM AUTHOR]

We analyzed data from a survey of occupational and other factors in pregnancy to assess the effects of cigarette, alcohol, and coffee consumption on pregnancy outcome. The risk of low birth weight for gestational age was found to increase substantially with smoking. Occasional consumers of alcohol had a slightly reduced risk relative to total abstainers. In more frequent drinkers, there was a small increase in risk. Risk increased slightly with coffee consumption. [ABSTRACT FROM AUTHOR]

This Series has examined the health implications of policies aimed at tackling climate change. Assessments of mitigation strategies in four domains-household energy, transport, food and agriculture, and electricity generation-suggest an important message: that actions to reduce greenhouse-gas emissions often, although not always, entail net benefits for health. In some cases, the potential benefits seem to be substantial. This evidence provides an additional and immediate rationale for reductions in greenhouse-gas emissions beyond that of climate change mitigation alone. Climate change is an increasing and evolving threat to the health of populations worldwide. At the same time, major public health burdens remain in many regions. Climate change therefore adds further urgency to the task of addressing international health priorities, such as the UN Millennium Development Goals. Recognition that mitigation strategies can have substantial benefits for both health and climate protection offers the possibility of policy choices that are potentially both more cost effective and socially attractive than are those that address these priorities independently. [ABSTRACT FROM AUTHOR]

Background: Regression analyses of time series of disease counts on environmental determinants are a prominent component of environmental epidemiology. For planning such studies, it can be useful to predict the precision of estimated coefficients and power to detect associations of given magnitude. Existing generic approaches for this have been found somewhat complex to apply and do not easily extend to multiple series studies analysed in two stages. We have sought a simpler approximate approach which can easily extend to multiple series and give insight into factors determining precision., Methods: We derive approximate expressions for precision and hence power in single and multiple time series studies of counts from basic statistical theory, compare the precision predicted by these with that estimated by analysis in real data from 51 cities of varying size, and illustrate the use of these estimators in a realistic planning scenario., Results: In single series studies with Poisson outcome distribution, precision and power depend only on the usable variation of exposure (i.e. that conditional on covariates) and the total number of disease events, regardless of how many days those are spread over. In multiple time series (eg multi-city) studies focusing on the meta-analytic mean coefficient, the usable exposure variation and the total number of events (in all series) are again the sole determinants if there is no between-series heterogeneity or within-series overdispersion. With heterogeneity, its extent and the number of series becomes important. For all but the crudest approximation the estimates of standard errors were on average within + 20% of those estimated in full analysis of actual data., Conclusions: Predicting precision in coefficients from a planned time series study is possible simply and given limited information. The total number of disease events and usable exposure variation are the dominant factors when overdispersion and between-series heterogeneity are low.

Although diurnal temperature range (DTR) is a key index of climate change, few studies have reported the health burden of DTR and its temporal changes at a multi-country scale. Therefore, we assessed the attributable risk fraction of DTR on mortality and its temporal variations in a multi-country data set. We collected time-series data covering mortality and weather variables from 308 cities in 10 countries from 1972 to 2013. The temporal change in DTR-related mortality was estimated for each city with a time-varying distributed lag model. Estimates for each city were pooled using a multivariate meta-analysis. The results showed that the attributable fraction of total mortality to DTR was 2.5% (95% eCI: 2.3-2.7%) over the entire study period. In all countries, the attributable fraction increased from 2.4% (2.1-2.7%) to 2.7% (2.4-2.9%) between the first and last study years. This study found that DTR has significantly contributed to mortality in all the countries studied, and this attributable fraction has significantly increased over time in the USA, the UK, Spain, and South Korea. Therefore, because the health burden of DTR is not likely to reduce in the near future, countermeasures are needed to alleviate its impact on human health., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Background: Few studies have examined variation in the associations between heat waves and mortality in an international context., Objectives: We aimed to systematically examine the impacts of heat waves on mortality with lag effects internationally., Methods: We collected daily data of temperature and mortality from 400 communities in 18 countries/regions and defined 12 types of heat waves by combining community-specific daily mean temperature ≥90th, 92.5th, 95th, and 97.5th percentiles of temperature with duration ≥2, 3, and 4 d. We used time-series analyses to estimate the community-specific heat wave-mortality relation over lags of 0-10 d. Then, we applied meta-analysis to pool heat wave effects at the country level for cumulative and lag effects for each type of heat wave definition., Results: Heat waves of all definitions had significant cumulative associations with mortality in all countries, but varied by community. The higher the temperature threshold used to define heat waves, the higher heat wave associations on mortality. However, heat wave duration did not modify the impacts. The association between heat waves and mortality appeared acutely and lasted for 3 and 4 d. Heat waves had higher associations with mortality in moderate cold and moderate hot areas than cold and hot areas. There were no added effects of heat waves on mortality in all countries/regions, except for Brazil, Moldova, and Taiwan. Heat waves defined by daily mean and maximum temperatures produced similar heat wave-mortality associations, but not daily minimum temperature., Conclusions: Results indicate that high temperatures create a substantial health burden, and effects of high temperatures over consecutive days are similar to what would be experienced if high temperature days occurred independently. People living in moderate cold and moderate hot areas are more sensitive to heat waves than those living in cold and hot areas. Daily mean and maximum temperatures had similar ability to define heat waves rather than minimum temperature. https://doi.org/10.1289/EHP1026.

Background: The evidence and method are limited for the associations between mortality and temperature variability (TV) within or between days., Objectives: We developed a novel method to calculate TV and investigated TV-mortality associations using a large multicountry data set., Methods: We collected daily data for temperature and mortality from 372 locations in 12 countries/regions (Australia, Brazil, Canada, China, Japan, Moldova, South Korea, Spain, Taiwan, Thailand, the United Kingdom, and the United States). We calculated TV from the standard deviation of the minimum and maximum temperatures during the exposure days. Two-stage analyses were used to assess the relationship between TV and mortality. In the first stage, a Poisson regression model allowing over-dispersion was used to estimate the community-specific TV-mortality relationship, after controlling for potential confounders. In the second stage, a meta-analysis was used to pool the effect estimates within each country., Results: There was a significant association between TV and mortality in all countries, even after controlling for the effects of daily mean temperature. In stratified analyses, TV was still significantly associated with mortality in cold, hot, and moderate seasons. Mortality risks related to TV were higher in hot areas than in cold areas when using short TV exposures (0-1 days), whereas TV-related mortality risks were higher in moderate areas than in cold and hot areas when using longer TV exposures (0-7 days)., Conclusions: The results indicate that more attention should be paid to unstable weather conditions in order to protect health. These findings may have implications for developing public health policies to manage health risks of climate change., Citation: Guo Y, Gasparrini A, Armstrong BG, Tawatsupa B, Tobias A, Lavigne E, Coelho MS, Pan X, Kim H, Hashizume M, Honda Y, Guo YL, Wu CF, Zanobetti A, Schwartz JD, Bell ML, Overcenco A, Punnasiri K, Li S, Tian L, Saldiva P, Williams G, Tong S. 2016. Temperature variability and mortality: a multi-country study. Environ Health Perspect 124:1554-1559; http://dx.doi.org/10.1289/EHP149., Competing Interests: The authors declare they have no actual or potential competing financial interests.

Objectives: To investigate associations between daily concentrations of air pollution and myocardial infarction (MI), ST-elevation MI (STEMI) and non-ST-elevation MI (NSTEMI)., Methods: Modelled daily ground-level gaseous, total and speciated particulate pollutant concentrations and ground-level daily mean temperature, all at 5 km×5 km horizontal resolution, were linked to 202 550 STEMI and 322 198 NSTEMI events recorded on the England and Wales Myocardial Ischaemia National Audit Project (MINAP) database. The study period was 2003-2010. A case-crossover design was used, stratified by year, month and day of the week. Data were analysed using conditional logistic regression, with pollutants modelled as unconstrained distributed lags 0-2 days. Results are presented as percentage change in risk per 10 µg/m(3) increase in the pollutant relevant metric, having adjusted for daily mean temperature, public holidays, weekly influenza consultation rates and a sine-cosine annual cycle., Results: There was no evidence of an association between MI or STEMI and any of O3, NO2, PM2.5, PM10 or selected PM2.5 components (sulfate and elemental carbon). For NSTEMI, there was a positive association with daily maximum 1-hour NO2 (0.27% (95% CI 0.01% to 0.54%)), which persisted following adjustment for O3 and adjustment for PM2.5. The association appeared to be confined to the midland and southern regions of England and Wales., Conclusions: The study found no evidence of an association between the modelled pollutants (including components) investigated and STEMI but did find some evidence of a positive association between NO2 and NSTEMI. Confirmation of this association in other studies is required.

Background: Investigators have examined whether heat mortality risk is increased in neighborhoods subject to the urban heat island (UHI) effect but have not identified degrees of difference in susceptibility to heat and cold between cool and hot areas, which we call acclimatization to the UHI., Objectives: We developed methods to examine and quantify the degree of acclimatization to heat- and cold-related mortality in relation to UHI anomalies and applied these methods to London, UK., Methods: Case-crossover analyses were undertaken on 1993-2006 mortality data from London UHI decile groups defined by anomalies from the London average of modeled air temperature at a 1-km grid resolution. We estimated how UHI anomalies modified excess mortality on cold and hot days for London overall and displaced a fixed-shape temperature-mortality function ("shifted spline" model). We also compared the observed associations with those expected under no or full acclimatization to the UHI., Results: The relative risk of death on hot versus normal days differed very little across UHI decile groups. A 1°C UHI anomaly multiplied the risk of heat death by 1.004 (95% CI: 0.950, 1.061) (interaction rate ratio) compared with the expected value of 1.070 (1.057, 1.082) if there were no acclimatization. The corresponding UHI interaction for cold was 1.020 (0.979, 1.063) versus 1.030 (1.026, 1.034) (actual versus expected under no acclimatization, respectively). Fitted splines for heat shifted little across UHI decile groups, again suggesting acclimatization. For cold, the splines shifted somewhat in the direction of no acclimatization, but did not exclude acclimatization., Conclusions: We have proposed two analytical methods for estimating the degree of acclimatization to the heat- and cold-related mortality burdens associated with UHIs. The results for London suggest relatively complete acclimatization to the UHI effect on summer heat-related mortality, but less clear evidence for cold-related mortality., Citation: Milojevic A, Armstrong BG, Gasparrini A, Bohnenstengel SI, Barratt B, Wilkinson P. 2016. Methods to estimate acclimatization to urban heat island effects on heat- and cold-related mortality. Environ Health Perspect 124:1016-1022; http://dx.doi.org/10.1289/ehp.1510109.

Few studies have examined the variation in mortality risk associated with heat during the summer. Here, we apply flexible statistical models to investigate the issue by using a large multicountry data set. We collected daily time-series data of temperature and mortality from 305 locations in 9 countries, in the period 1985-2012. We first estimated the heat-mortality relationship in each location with time-varying distributed lag non-linear models, using a bivariate spline to model the exposure-lag-response over lag 0-10. Estimates were then pooled by country through multivariate meta-analysis. Results provide strong evidence of a reduction in risk over the season. Relative risks for the 99th percentile versus the minimum mortality temperature were in the range of 1.15-2.03 in early summer. In late summer, the excess was substantially reduced or abated, with relative risks in the range of 0.97-1.41 and indications of wider comfort ranges and higher minimum mortality temperatures. The attenuation is mainly due to shorter lag periods in late summer. In conclusion, this multicountry analysis suggests a reduction of heat-related mortality risk over the summer, which can be attributed to several factors, such as true acclimatization, adaptive behaviors, or harvesting effects. These findings may have implications on public health policies and climate change health impact projections., (© The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Background: Recent investigations have reported a decline in the heat-related mortality risk during the last decades. However, these studies are frequently based on modeling approaches that do not fully characterize the complex temperature-mortality relationship, and are limited to single cities or countries., Objectives: We assessed the temporal variation in heat-mortality associations in a multi-country data set using flexible modelling techniques., Methods: We collected data for 272 locations in Australia, Canada, Japan, South Korea, Spain, the United Kingdom, and the United States, with a total 20,203,690 deaths occurring in summer months between 1985 and 2012. The analysis was based on two-stage time-series models. The temporal variation in heat-mortality relationships was estimated in each location with time-varying distributed lag nonlinear models, expressed through an interaction between the transformed temperature variables and time. The estimates were pooled by country through multivariate meta-analysis., Results: Mortality risk due to heat appeared to decrease over time in several countries, with relative risks associated to high temperatures significantly lower in 2006 compared with 1993 in the United States, Japan, and Spain, and a nonsignificant decrease in Canada. Temporal changes are difficult to assess in Australia and South Korea due to low statistical power, and we found little evidence of variation in the United Kingdom. In the United States, the risk seems to be completely abated in 2006 for summer temperatures below their 99th percentile, but some significant excess persists for higher temperatures in all the countries., Conclusions: We estimated a statistically significant decrease in the relative risk for heat-related mortality in 2006 compared with 1993 in the majority of countries included in the analysis., Citation: Gasparrini A, Guo Y, Hashizume M, Kinney PL, Petkova EP, Lavigne E, Zanobetti A, Schwartz JD, Tobias A, Leone M, Tong S, Honda Y, Kim H, Armstrong BG. 2015. Temporal variation in heat-mortality associations: a multicountry study. Environ Health Perspect 123:1200-1207; http://dx.doi.org/10.1289/ehp.1409070.

Background: The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case-control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters., Methods: The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages., Results: By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression., Conclusions: Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.

Background: Estimates of the effectiveness of influenza vaccines in older adults may be biased because of difficulties identifying and adjusting for confounders of the vaccine-outcome association. We estimated vaccine effectiveness for prevention of serious influenza complications among older persons by using methods to account for underlying differences in risk for these complications., Methods: We conducted a retrospective cohort study among Ontario residents aged ≥ 65 years from September 1993 through September 2008. We linked weekly vaccination, hospitalization, and death records for 1.4 million community-dwelling persons aged ≥ 65 years. Vaccine effectiveness was estimated by comparing ratios of outcome rates during weeks of high versus low influenza activity (defined by viral surveillance data) among vaccinated and unvaccinated subjects by using log-linear regression models that accounted for temperature and time trends with natural spline functions. Effectiveness was estimated for three influenza-associated outcomes: all-cause deaths, deaths occurring within 30 days of pneumonia/influenza hospitalizations, and pneumonia/influenza hospitalizations., Results: During weeks when 5% of respiratory specimens tested positive for influenza A, vaccine effectiveness among persons aged ≥ 65 years was 22% (95% confidence interval [CI], -6%-42%) for all influenza-associated deaths, 25% (95% CI, 13%-37%) for deaths occurring within 30 days after an influenza-associated pneumonia/influenza hospitalization, and 19% (95% CI, 4%-31%) for influenza-associated pneumonia/influenza hospitalizations. Because small proportions of deaths, deaths after pneumonia/influenza hospitalizations, and pneumonia/influenza hospitalizations were associated with influenza virus circulation, we estimated that vaccination prevented 1.6%, 4.8%, and 4.1% of these outcomes, respectively., Conclusions: By using confounding-reducing techniques with 15 years of provincial-level data including vaccination and health outcomes, we estimated that influenza vaccination prevented ~4% of influenza-associated hospitalizations and deaths occurring after hospitalizations among older adults in Ontario.

Background: Short-term exposure to ozone has been associated with increased daily mortality. The shape of the concentration-response relationship-and, in particular, if there is a threshold-is critical for estimating public health impacts., Objective: We investigated the concentration-response relationship between daily ozone and mortality in five urban and five rural areas in the United Kingdom from 1993 to 2006., Methods: We used Poisson regression, controlling for seasonality, temperature, and influenza, to investigate associations between daily maximum 8-hr ozone and daily all-cause mortality, assuming linear, linear-threshold, and spline models for all-year and season-specific periods. We examined sensitivity to adjustment for particles (urban areas only) and alternative temperature metrics., Results: In all-year analyses, we found clear evidence for a threshold in the concentration-response relationship between ozone and all-cause mortality in London at 65 µg/m3 [95% confidence interval (CI): 58, 83] but little evidence of a threshold in other urban or rural areas. Combined linear effect estimates for all-cause mortality were comparable for urban and rural areas: 0.48% (95% CI: 0.35, 0.60) and 0.58% (95% CI: 0.36, 0.81) per 10-µg/m3 increase in ozone concentrations, respectively. Seasonal analyses suggested thresholds in both urban and rural areas for effects of ozone during summer months., Conclusions: Our results suggest that health impacts should be estimated across the whole ambient range of ozone using both threshold and nonthreshold models, and models stratified by season. Evidence of a threshold effect in London but not in other study areas requires further investigation. The public health impacts of exposure to ozone in rural areas should not be overlooked.

Background: It is well known that high ambient temperatures are associated with increased mortality, even in temperate climates, but some important details are unclear. In particular, how heat-mortality associations (for example, slopes and thresholds) vary by climate has previously been considered only qualitatively., Methods: An ecological time-series regression analysis of daily counts of all-cause mortality and ambient temperature in summers between 1993 and 2006 in the 10 government regions was carried out, focusing on all-cause mortality and 2-day mean temperature (lags 0 and 1)., Results: All regions showed evidence of increased risk on the hottest days, but the specifics, in particular the threshold temperature at which adverse effects started, varied. Thresholds were at about the same centile temperatures (the 93rd, year-round) in all regions-hotter climates had higher threshold temperatures. Mean supra-threshold slope was 2.1%/°C (95% CI 1.6 to 2.6), but regions with higher summer temperatures showed greater slopes, a pattern well characterised by a linear model with mean summer temperature. These climate-based linear-threshold models capture most, but not all, the association; there was evidence for some non-linearity above thresholds, with slope increasing at highest temperatures., Conclusion: Effects of high daily summer temperatures on mortality in English regions are quite well approximated by threshold-linear models that can be predicted from the region's climate (93rd centile and mean summer temperature). It remains to be seen whether similar relationships fit other countries and climates or change over time, such as with climate change.

Objectives: We compared the ability of several heat-health warning systems to predict days of heat-associated mortality using common data sets., Methods: Heat-health warning systems initiate emergency public health interventions once forecasts have identified weather conditions to breach predetermined trigger levels. We examined 4 commonly used trigger-setting approaches: (1) synoptic classification, (2) epidemiologic assessment of the temperature-mortality relationship, (3) temperature-humidity index, and (4) physiologic classification. We applied each approach in Chicago, Illinois; London, United Kingdom; Madrid, Spain; and Montreal, Canada, to identify days expected to be associated with the highest heat-related mortality., Results: We found little agreement across the approaches in which days were identified as most dangerous. In general, days identified by temperature-mortality assessment were associated with the highest excess mortality., Conclusions: Triggering of alert days and ultimately the initiation of emergency responses by a heat-health warning system varies significantly across approaches adopted to establish triggers.

In this report, the third in this Series on health and climate change, we assess the changes in particle air pollution emissions and consequent effects on health that are likely to result from greenhouse-gas mitigation measures in the electricity generation sector in the European Union (EU), China, and India. We model the effect in 2030 of policies that aim to reduce total carbon dioxide (CO(2)) emissions by 50% by 2050 globally compared with the effect of emissions in 1990. We use three models: the POLES model, which identifies the distribution of production modes that give the desired CO(2) reductions and associated costs; the GAINS model, which estimates fine particulate matter with aerodynamic diameter 2.5 microm or less (PM(2.5)) concentrations; and a model to estimate the effect of PM(2.5) on mortality on the basis of the WHO's Comparative Risk Assessment methods. Changes in modes of production of electricity to reduce CO(2) emissions would, in all regions, reduce PM(2.5) and deaths caused by it, with the greatest effect in India and the smallest in the EU. Health benefits greatly offset costs of greenhouse-gas mitigation, especially in India where pollution is high and costs of mitigation are low. Our estimates are approximations but suggest clear health gains (co-benefits) through decarbonising electricity production, and provide additional information about the extent of such gains.

Energy used in dwellings is an important target for actions to avert climate change. Properly designed and implemented, such actions could have major co-benefits for public health. To investigate, we examined the effect of hypothetical strategies to improve energy efficiency in UK housing stock and to introduce 150 million low-emission household cookstoves in India. Methods similar to those of WHO's Comparative Risk Assessment exercise were applied to assess the effect on health that changes in the indoor environment could have. For UK housing, the magnitude and even direction of the changes in health depended on details of the intervention, but interventions were generally beneficial for health. For a strategy of combined fabric, ventilation, fuel switching, and behavioural changes, we estimated 850 fewer disability-adjusted life-years (DALYs), and a saving of 0.6 megatonnes of carbon dioxide (CO(2)), per million population in 1 year (on the basis of calculations comparing the health of the 2010 population with and without the specified outcome measures). The cookstove programme in India showed substantial benefits for acute lower respiratory infection in children, chronic obstructive pulmonary disease, and ischaemic heart disease. Calculated on a similar basis to the UK case study, the avoided burden of these outcomes was estimated to be 12 500 fewer DALYs and a saving of 0.1-0.2 megatonnes CO(2)-equivalent per million population in 1 year, mostly in short-lived greenhouse pollutants. Household energy interventions have potential for important co-benefits in pursuit of health and climate goals.

We used Comparative Risk Assessment methods to estimate the health effects of alternative urban land transport scenarios for two settings-London, UK, and Delhi, India. For each setting, we compared a business-as-usual 2030 projection (without policies for reduction of greenhouse gases) with alternative scenarios-lower-carbon-emission motor vehicles, increased active travel, and a combination of the two. We developed separate models that linked transport scenarios with physical activity, air pollution, and risk of road traffic injury. In both cities, we noted that reduction in carbon dioxide emissions through an increase in active travel and less use of motor vehicles had larger health benefits per million population (7332 disability-adjusted life-years [DALYs] in London, and 12 516 in Delhi in 1 year) than from the increased use of lower-emission motor vehicles (160 DALYs in London, and 1696 in Delhi). However, combination of active travel and lower-emission motor vehicles would give the largest benefits (7439 DALYs in London, 12 995 in Delhi), notably from a reduction in the number of years of life lost from ischaemic heart disease (10-19% in London, 11-25% in Delhi). Although uncertainties remain, climate change mitigation in transport should benefit public health substantially. Policies to increase the acceptability, appeal, and safety of active urban travel, and discourage travel in private motor vehicles would provide larger health benefits than would policies that focus solely on lower-emission motor vehicles.

Background/objective: Many occupational exposures causing disease cannot feasibly be eliminated entirely, but policies that reduce the exposures may be under consideration. This paper sets out to clarify how to estimate the reduction in occupational disease following a reduction in exposure, and shows a real-data illustration for doing this., Methods: Modest extensions of standard expressions for attributable fractions permit estimation of fractions by which cases would be reduced by policies that do not eliminate exposure but change exposure distributions. However, this requires information on the exposure-response relation and on distribution of exposures., Results: From hypothetical scenarios and a real example this paper explores how attributable cases are distributed by exposure level and, in particular, the proportion by which attributable cancers are reduced by eliminating exposures above a limit (the classic occupational limit regulation). It shows how this depends on the shape of the exposure-response relation and to some extent the shape of the exposure distribution, as well as on the proportion exposed above the limit. For linear no-threshold relations and left-skewed exposure distributions, the majority of the burden may be in a large number of people experiencing small relative risks, and thus may not be tackled by a strategy to reduce exposures above a certain limit., Conclusion: With appropriate data, estimating the disease burden in terms of the distribution of exposure is straightforward and can help to clarify the likely outcome of an intervention.

Background: Environmental pollution as a cause of congenital anomalies is sometimes suspected because of clustering of anomalies in areas of higher exposure. This highlights questions around spatial heterogeneity (clustering) in congenital anomaly rates. If spatial variation is endemic, then any one specific cluster is less remarkable, though the presence of uncontrolled geographically clustered risk factors is suggested. If rates are relatively homogeneous across space other than around specific hazards, then evidence for these hazards causing the clusters is strengthened. We sought to estimate the extent of spatial heterogeneity in congenital anomaly rates in the United Kingdom., Methods: The study population covered about one million births from five registers in Britain from 1991-1999. We estimated heterogeneity across four geographical levels: register area, hospital catchment, electoral ward, and enumeration district, using a negative binomial regression model. We also sought clusters using a circular scan statistic., Results: Congenital anomaly rates clearly varied across register areas and hospital catchments (p < 0.001), but not below this level (p > 0.2). Adjusting for socioeconomic deprivation and maternal age made little difference to the extent of geographical variation for most congenital anomaly subtypes. The two most significant circular clusters (of four ano-rectal atresias and six congenital heart diseases) contained two or more siblings., Conclusion: The variation in rates between registers and hospital catchment area may have resulted in part from differences in case ascertainment, and this should be taken into account in geographical epidemiological studies of environmental exposures. The absence of evidence for variation below this level should be interpreted cautiously in view of the low power of general heterogeneity tests. Nevertheless, the data suggest that strong localised clusters in congenital anomalies are uncommon, so clusters around specific putative environmental hazards are remarkable when observed. Negative binomial models applied at successive hierarchical levels provide an approach of intermediate complexity to characterising geographical heterogeneity.

Background: Mortality increases with hot weather, although the extent to which lives are shortened is rarely quantified. We compare the extent to which short-term mortality displacement can explain heat deaths in Delhi, São Paulo, and London given contrasting demographic and health profiles., Methods: We examined time-series of daily mortality data in relation to daily ambient temperature using Poisson models and adjusting for season, relative humidity, rainfall, particulate air pollution, day of the week, and public holidays. We used unconstrained distributed lag models to identify the extent to which heat-related excesses were followed by deficits (mortality displacement)., Results: For each city, an increase in all-cause mortality was observed with same-day (lag 0) and previous day (lag 1) temperatures greater than a threshold of 20 degrees C. At lag 0, the excess risk was greatest in Delhi and smallest in London. In Delhi, an excess was apparent up to 3 weeks after exposure, after which a deficit was observed that offset just part of the overall excess. In London, the heat excess persisted only 2 days and was followed by deficits, such that the sum of effects was 0 by day 11. The pattern in São Paulo was intermediate between these. The risk summed over the course of 28 days was 2.4% (95% confidence interval = 0.1 to 4.7%) per degree greater than the heat threshold in Delhi, 0.8% (-0.4 to 2.1%) in São Paulo and -1.6% (-3.4 to 0.3%) in London. Excess risks were sustained up to 4 weeks for respiratory deaths in São Paulo and London and for children in Delhi., Conclusions: Heat-related short-term mortality displacement was high in London but less in Delhi, where infectious and childhood mortality still predominate.

Objective: To estimate the protection against death provided by vaccination against influenza., Design: Prospective cohort follow up supplemented by weekly national counts of influenza confirmed in the community., Setting: Primary care., Participants: 24,535 patients aged over 75 years from 73 general practices in Great Britain., Main Outcome Measure: Death., Results: In unvaccinated members of the cohort daily all cause mortality was strongly associated with an index of influenza circulating in the population (mortality ratio 1.16, 95% confidence interval 1.04 to 1.29 at 90th centile of circulating influenza). The association was strongest for respiratory deaths but was also present for cardiovascular deaths. In contrast, in vaccinated people mortality from any cause was not associated with circulating influenza. The difference in patterns between vaccinated and unvaccinated people could not easily be due to chance (P = 0.02, all causes)., Conclusions: This study, using a novel and robust approach to control for confounding, provides robust evidence of a protective effect on mortality of vaccination against influenza.

Objectives: To describe past practice in meta-analyses found in occupational epidemiology, identifying the major issues that should be considered by researchers planning a meta-analysis in this setting., Methods: An electronic search of relevant online databases was undertaken. Papers were included in the review if they contained a statistical synthesis of risks in an occupational health setting., Results: Sixty reports of meta-analyses were identified, mostly in cancer. The number of meta-analyses has increased consistently over the last 20 years. A majority of studies focused on a mean overall effect, although more than half of them also investigated heterogeneity of results. Both fixed effect and random effects meta-analysis models were employed, the former more often, and in eight studies used despite a statistically significant test for heterogeneity. A large proportion of the meta-analyses included different effect measures in the statistical synthesis, for example, including standardized mortality ratios (SMRs) and standardized incidence ratios. Most meta-analyses limited to a single type of effect measure focused on SMRs. The vast majority of meta-analyses combined all studies regardless of variation in the extent of information on exposures., Conclusions: Meta-analyses in occupational epidemiology should properly explore and incorporate heterogeneity among studies. The meta-SMR is an important construct in this field, evidenced by a large proportion of cohort studies in the meta-analyses we identified. Controversy remains over the definition and validity of the meta-SMR. In addition, several other issues, notably dealing with heterogeneity in exposure, warrant further consideration.

Background: The effects of air pollution or weather on mortality may be stronger in susceptible groups. Conventional investigation of such effect modification through interaction terms in time-series regression analysis depends on hard-to-verify modeling assumptions, and can be computationally unwieldy. As an alternative, we investigate the use of case-only approaches originally proposed for studying gene-environment interactions., Methods: We consider an investigation of whether persons of low socio-economic status (SES) are more susceptible to the effect of high outside temperatures on mortality. If low SES persons are more prevalent among deaths on hot days than on days with more moderate temperatures, then this suggests the group is more susceptible. Extending the case-only theory developed for gene-environment interactions allows this to be described more quantitatively., Results: Conventionally based analysis estimated that mortality in the Sao Paulo rose by 2.3% (SE 0.3%) for each degree of increase in outside temperature above 20 degrees C. This effect was greater by 1.11% (SE 0.72) in the lowest compared with highest quartile of SES. Case-only analysis estimated the difference in effect to be 1.14% (SE 0.72)., Conclusion: The simplicity and reduced assumptions of the case-only approach provide an advantage over conventional analysis, although the approach gives information only on modification, not main effects.

Although it is well known that both cigarette smoke and microscopic airborne asbestos fibres can cause lung cancer, evidence as to how these two agents combine is nebulous. Many workers have believed in the multiplicative theory, whereby asbestos increases the risk in proportion to the risk from other causes. However, evidence against this theory is mounting: a recent review concluded that the multiplicative hypothesis was untenable, and that the relative risk of lung cancer from asbestos exposure was about twice as high in non-smokers as in smokers, a finding largely independent of type of asbestos fibre. The criteria for entry to the current study were met by 7279 men in the 1891-1920 birth cohort of Quebec chrysotile miners and millers. The data consisted of date of birth, place of employment, smoking habit, asbestos exposure accumulated to age 55 and, for those 5527 who died between 1950 and June 1992, date and cause of death; 533 of the deaths were from lung cancer. For the principal analyses, ex-smokers were excluded from the study cohort, which comprised 5888 men, of whom 473 died of lung cancer. The conventional form of analysis is simply of the double dichotomy: non-smokers of cigarettes, 'unexposed' and exposed; all others, 'unexposed' and exposed. The respective standardized lung cancer mortality ratios (SMRs) were 0.29 and 0.62; and 1.37 and 1.72. Thus, the differences in relative risk, due to exposure, were closely similar, 0.33 and 0.35. On the other hand, the effects of asbestos measured by the corresponding ratios, 2.12 and 1.25, did differ, being 1.7 times as high in non-smokers as in others. The principal analysis was much more penetrating: the method was to fit models to a 'disaggregated' 6 x 10 array, by smoking habit (excluding ex-smokers) and asbestos exposure, of lung cancer SMRs. Both linear and log-linear models were fitted: the former included the additive and linear-multiplicative; the latter embraced the more conventional multiplicative form. The additive model fitted much the best. The fit of each multiplicative model was improved by the introduction of an interaction term that implied a less than multiplicative relationship. Thus smoking and exposure to chrysotile appear to have acted independently in causing lung cancer, with 10 cigarettes a day having an effect roughly equivalent to exposure amounting to 700 million particles per cubic foot x years. The refutation of the multiplicative hypothesis in these data reinforces its inapplicability in general; but the additive hypothesis is not generally applicable either. Indeed, there seems to be no good reason to believe that interactions conform to any simple theory. The implications are important.

Objectives: Our study aimed to determine the lung tissue concentration of asbestos and other mineral fibres by type and length in persons with mesothelioma aged 50 yr or less at time of diagnosis, compared to controls of similar age and geographical region. In this age group it was thought that most, but not all, work-related exposures would have been since 1970, when the importation of crocidolite, but not amosite, was virtually eliminated., Methods: Eligible cases were sought from recent reports by chest physicians to the SWORD occupational disease surveillance scheme. Lung tissue samples were obtained at autopsy from 69 male and four female cases, and mineral fibres identified, sized and counted by electron microscopy. Fibre concentrations per microg dry tissue were compared with similar estimates from a control series of autopsies of sudden or accidental deaths. Unadjusted, and adjusted odds ratios calculated by logistic regression, assessed relative risk in relation to fibre type, length and concentration., Results: Unadjusted and adjusted odds ratios increased steadily with concentration of crocidolite, amosite, tremolite and all amphiboles combined. There was also some increase with chrysotile, but well short of statistical significance. Incremental risk examined in a linear model was as highly significant for all amphiboles together as individually. Short, medium and long amphibole fibres were all associated with increased risk in relation to length. Mullite and iron fibres were significant predictors of mesothelioma when considered without adjustment for confounding by amphiboles, but, after adjustment, were weak and far from statistically significant., Conclusions: In this young age group, amosite and crocidolite fibres could account for about 80% of cases of mesothelioma, and tremolite for some 7%. The contribution of chrysotile, because of low biopersistence, cannot be reliably assessed at autopsy, but to the extent that tremolite is a valid marker, our results suggest that it was small. The steep linear trend in odds ratio shown by amphiboles combined indicates that their effects may be additive, with increased risk from the lowest detectable fibre level. Non-asbestos mineral fibres probably made no contribution to this disease. Contrary to expectation, however, some 90% of cases were in men who had started work before 1970; this was so whether or not amosite or crocidolite was found in lung tissue.

Study of the health effects of magnetic fields often depends on identifying determinants and hence indicators of personal exposure. This study identified determinants of children's exposure to magnetic fields and constructed a prediction model for them. For 632 children participating in a case-control study of childhood leukemia, we made direct measures of exposure over 48 h using a portable device, together with observations on candidate determinants. A child's age and sex, the proportion of time spent in the home, and their parents' education or income were very weak predictors of (logged) mean 48 h magnetic field (R(2) < 1%). More important were province (R(2) = 8.0%) and type of residence (R(2) = 11.3%). Low temperatures at the time of measurement were associated with high fields (about 20% increase for each 10 degrees C below 14, R(2) = 4.9%). Several visible attributes of wiring around residences predicted exposure, mostly captured in the Wertheimer-Leeper wire code (R(2) = 13.5%). Stationary 24 h measurement in the bedroom (R(2) = 63.3%) and spot measurements outside the house (R(2) = 40.7%) predicted personal exposures best. Adding other minor predictors increased only slightly variance explained by 24 h stationary (R(2) = 66.2%) and spot (R(2) = 46.8%) measurements. Without spot or stationary measurements, the best model was much less powerful (R(2) = 29.0%). We conclude that spot measurements outside the residence provide a moderately effective basis for estimating exposure for children living there, but do not perform as well as 24 h stationary measurements in the child's bedroom. Although several other easily-observed variables were associated with personal exposure, they were weak determinants, either individually or in combination., (Copyright 2001 Wiley-Liss, Inc.)

Objectives: Many studies of air pollution and health are carried out over several geographical areas, and sometimes over several countries. This paper explores three approaches to analysis in such studies: a non hierarchical model, a two-stage analysis, and multilevel modelling. Illustrations are given using a preliminary subset of data from the CESAR study., Design: The Central European Study on Air pollution and Respiratory Health (CESAR) was conducted in 25 areas within six Central European countries, enrolling 20,271 schoolchildren. Pollution averages were calculated for each area. Associations between pollution and health outcomes were estimated under different models., Main Results: A regression analysis of log FVC (forced vital capacity) on PM10, ignoring the geographical hierarchy, estimated a significant mean drop in FVC (adjusted for confounders) of 2.2% (95% CI 0.5% to 1.3%), p=0.007, from the area with the lowest PM10 to that with the highest. A multilevel model (mlm), using data for all children, but with random effects at area and country level, estimated a drop of 2.8% (-0.6% to 6.1%), p=0.110. A two-stage analysis (mean log FVC, adjusted for confounders, was estimated for each area using regression, and these means then regressed on PM10) estimated a drop of 2.6% (-0.5% to 5.5%), p=0.101. Simulation exercises showed the non hierarchical method to be very inadequate in the context of the CESAR study, with only half of all 95% confidence intervals for the estimated PM10 slope containing the true value (i.e., that used to create the simulated data). The two-stage and multilevel modelling methods gave results which were substantially better, though both underperformed slightly. All three methods appeared to give unbiased slope estimates., Conclusions: Acknowledgement of hierarchical structures is essential in statistical inference--standard errors can be substantially incorrect when they are ignored. Multilevel, random-effects models correctly address hierarchical structures, though having few units at higher levels can cause problems in convergence, especially where complex modelling is required. Two-stage analyses, acknowledging hierarchy, provide simple alternatives to random-effects models.

In a case-control study of childhood leukemia in relation to exposure to power-frequency electric and magnetic fields (EMF), 399 children resident in five Canadian provinces who were diagnosed at ages 0-14 years between 1990 and 1994 (June 1995 in British Columbia and Quebec) were enrolled, along with 399 controls. Exposure assessment included 48-hour personal EMF measurement, wire coding and magnetic field measurements for subjects' residences from conception to diagnosis/reference date, and a 24-hour magnetic field bedroom measurement. Personal magnetic fields were not related to risk of leukemia (adjusted odds ratio (OR) = 0.95, p for trend = 0.73) or acute lymphatic leukemia (OR = 0.93, p for trend = 0.64). There were no clear associations with predicted magnetic field exposure 2 years before the diagnosis/reference date or over the subject's lifetime or with personal electric field exposure. A statistically nonsignificant elevated risk of acute lymphatic leukemia was observed with very high wiring configurations among residences of subjects 2 years before the diagnosis/reference date (OR = 1.72 compared with underground wiring, 95% confidence interval 0.54-5.45). These results provide little support for a relation between power-frequency EMF exposure and risk of childhood leukemia.

Objective: To investigate the geographical variation and clustering of congenital anophthalmia and microphthalmia in England, in response to media reports of clusters., Design: Comparison of pattern of residence at birth of cases of anophthalmia and microphthalmia in England in 1988-94, notified to a special register, with pattern of residence of all births. Three groups studied included all cases, all severe cases, and all severe cases of unknown aetiology., Outcome Measures: Prevalence rates of anophthalmia and microphthalmia by region and district, and by ward population density and socioeconomic deprivation index of enumeration district grouped into fifths. Clustering expressed as the tendency for the three nearest neighbours of a case to be more likely to be cases than expected by chance, or for there to be more cases within circles of fixed radius of a case than expected by chance., Results: The overall prevalence of anophthalmia and microphthalmia was 1.0 per 10 000 births. Regional and district variation in prevalence did not reach statistical significance. Prevalence was higher in rural than urban areas: the relative risk in the group of wards of lowest population density compared with the most densely populated group was 1.79 (95% confidence interval 1.15 to 2.81) for all cases and 2.37 (1.38 to 4. 08) for severe cases. There was no evidence of a trend in risk with socioeconomic deprivation. There was very little evidence of localised clustering., Conclusions: There is very little evidence to support the presence of strongly localised environmental exposures causing clusters of children to be born with anophthalmia or microphthalmia. The excess risk in rural areas requires further investigation.

Random error (misclassification) in exposure measurements usually biases a relative risk, regression coefficient, or other effect measure towards the null value (no association). The most important exception is Berkson type error, which causes little or no bias. Berkson type error arises, in particular, due to use of group average exposure in place of individual values. Random error in exposure measurements, Berkson or otherwise, reduces the power of a study, making it more likely that real associations are not detected. Random error in confounding variables compromises the control of their effect, leaving residual confounding. Random error in a variable that modifies the effect of exposure on health--for example, an indicator of susceptibility--tends to diminish the observed modification of effect, but error in the exposure can create a supurious appearance of modification. Methods are available to correct for bias (but not generally power loss) due to measurement error, if information on the magnitude and type of error is available. These methods can be complicated to use, however, and should be used cautiously as "correction" can magnify confounding if it is present.

Objectives: Past exposures of electric utility workers to extremely low-frequency (ELF) magnetic (B) and electric fields (E) in Quebec were estimated., Methods: The current intensities were measured and durations of exposures determined for tasks or work locations in 14 job categories. Past task or location intensities were extrapolated from the present on the basis of interviews with long-term workers and utility personnel. Past task or location durations were estimated for the long-term workers. Time-weighted average (TWA) exposures for past periods were reconstructed for jobs from the intensity and duration estimates., Results: Magnetic fields were estimated to have increased the most over time for substation and distribution-line jobs. Magnetic field exposures for jobs in the generation and transmission of electricity were estimated to have increased very little. For substation jobs, the ratios of magnetic fields in 1945 to those in 1990 ranged from 0.42 to 0.69; the corresponding figures for distribution-line jobs ranged from 0.36 to 0.94. For electric fields in substations, the estimated increase over time was less than for magnetic fields, the 1945:1990 ratios ranging from 0.59 to 0.88. For the distribution line jobs, the 1945:1990 ratios for electric fields were less than 1.0 in 4 cases (0.6 to 0.89), more than 1.0 in 3 others (1.13 to 2.01) and unchanged in 1., Conclusions: Reconstruction of TWA exposures allowed changes in the intensity and the duration of exposures to be considered separately. Documentation of the intensity and duration of exposures for different tasks allows exposure reconstruction for jobs that have ceased to exist. The method is applicable elsewhere if exposure-monitoring records allow the level and duration of exposures for tasks or locations to be calculated and if estimates of past durations and intensities of exposures can be reliably obtained.

Objectives: The purpose of this study was to estimate exposure to extremely low frequency (ELF) magnetic and electric fields in the Québec electrical utility Hydro-Quebec., Methods: Personal exposures to ELF magnetic and electric fields were measured for workers randomly selected from 32 job categories at Hydro-Québec. Weekly arithmetic and geometric means, and other indices of exposure were estimated from 465 worker-weeks of data., Results: By job category, the arithmetic means of the ELF magnetic field ranged from 0.09 to 2.36 microT. Those of the ELF electric field exposures ranged from 2.5 to 400 V.m-1. Within each field, correlations of either the arithmetic or geometric means with alternative indices, including an index of the time rate of change, were generally high (r > or = 0.8). Exceptions were the 20th percentile of the electric means and the proportion of time above 12.4 and 100 microT. The day-to-day variation of exposure was greater than the variation between workers. The median between-day and between-worker components of variance (as geometric standard deviations) by job category were 2.13 and 1.71 for magnetic fields (2.24 and 1.81 for electric fields)., Conclusions: Substation workers, hydroelectric generating station operators, and cable splicers showed the highest arithmetic means for 60-Hz magnetic fields above 1 microT. For 60-Hz electric fields, forestry workers, equipment electricians in 735 kV substations, and distribution linemen (contact method) had arithmetic mean exposures greater than 100 W.m-1. Of the total variance in the logarithms of the weekly magnetic and electric field means, job category explained 49.6% and 59.5%, respectively.

We consider an epidemiologic study with a fixed budget, in which resources may be put into increasing sample size or into improving accuracy of exposure assessments. To maximize study power (efficiency), improving accuracy is preferable if and only if the proportional increase in the square of the validity coefficient is more than the proportional increase in total study costs per subject that is required to achieve it. (The validity coefficient is the correlation between the true exposure and the approximate assessment in the study base.) This is most likely to be so if the cost of exposure measurement remains a small proportion of the overall costs per subject. The design with maximum power will not generally have minimum bias in measure of effect, so that alternative optimality criteria are required if this bias is important.

Objectives: To investigate how closely the variables of exposures to magnetic fields based on the last job held in an electrical utility in Québec, Canada, compared with those based on the workers' entire employment history with the company., Methods: In large cohort studies, the last job held is often used to assign exposure to the study subjects. Exposure was assigned in this way for a mortality study of a cohort of electrical utility workers in Québec. For the present study, a sample of the cohort was used to compare the exposure estimates obtained from the last job with those obtained from full work histories., Results: The correlations between indices based on the last job and on all jobs varied between 0.75 and 0.78. The mean was slightly lower when only the last job was used. The last job was particularly good in identifying the most highly exposed people (for the exposure cut off point of 90th percentile for the last job and for all jobs, sensitivity = 0.69, specificity = 0.97, kappa = 0.66). The results suggest that although not all workers starting in highly exposed jobs stayed in them, it seemed that the workers who ended their working life in highly exposed jobs had stayed in these jobs throughout their working life., Conclusion: The results indicated some (but not catastrophic) loss of information when estimates of exposure were based on the last job only.

Objectives: This case cohort study examines whether there is an association between exposure to electric and magnetic fields and suicide in a population of 21,744 male electrical utility workers from the Canadian Province of Québec., Methods: 49 deaths from suicide were identified between 1970 and 1988 and a subcohort was selected comprising a 1% random sample from this cohort as a basis for risk estimation. Cumulative and current exposures to electric fields, magnetic fields, and pulsed electromagnetic fields (as recorded by the POSITRON meter) were estimated for the subcohort and cases through a job exposure matrix. Two versions of each of these six indices were calculated, one based on the arithmetic mean (AM), and one on the geometric mean (GM) of field strengths., Results: For cumulative exposure, rate ratios (RR) for all three fields showed mostly small non-significant increases in the medium and high exposure groups. The most increased risk was found in the medium exposure group for the GM of the electric field (RR = 2.76, 95% CI 1.15-6.62). The results did not differ after adjustment for socioeconomic state, alcohol use, marital state, and mental disorders. There was a little evidence for an association of risk with exposure immediately before the suicide., Conclusion: Some evidence for an association between suicide and cumulative exposure to the GM of the electric fields was found. This specific index was not initially identified as the most relevant index, but rather emerged afterwards as showing the most positive association with suicide among the 10 indices studied. Thus the evidence from this study for a causal association between exposure to electric fields and suicide is weak. Small sample size (deaths from suicide) and inability to control for all potential confounding factors were the main limitations of this study.