Ben Armstrong

Public health benefits of strategies to reduce greenhouse-gas emissions: urban land transport.

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.

Distributed lag non-linear models

Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross-basis’, a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright

International study of temperature, heat and urban mortality: the ‘ISOTHURM’ project

BACKGROUND This study describes heat- and cold-related mortality in 12 urban populations in low- and middle-income countries, thereby extending knowledge of how diverse populations, in non-OECD countries, respond to temperature extremes. METHODS The cities were: Delhi, Monterrey, Mexico City, Chiang Mai, Bangkok, Salvador, São Paulo, Santiago, Cape Town, Ljubljana, Bucharest and Sofia. For each city, daily mortality was examined in relation to ambient temperature using autoregressive Poisson models (2- to 5-year series) adjusted for season, relative humidity, air pollution, day of week and public holidays. RESULTS Most cities showed a U-shaped temperature-mortality relationship, with clear evidence of increasing death rates at colder temperatures in all cities except Ljubljana, Salvador and Delhi and with increasing heat in all cities except Chiang Mai and Cape Town. Estimates of the temperature threshold below which cold-related mortality began to increase ranged from 15 degrees C to 29 degrees C; the threshold for heat-related deaths ranged from 16 degrees C to 31 degrees C. Heat thresholds were generally higher in cities with warmer climates, while cold thresholds were unrelated to climate. CONCLUSIONS Urban populations, in diverse geographic settings, experience increases in mortality due to both high and low temperatures. The effects of heat and cold vary depending on climate and non-climate factors such as the population disease profile and age structure. Although such populations will undergo some adaptation to increasing temperatures, many are likely to have substantial vulnerability to climate change. Additional research is needed to elucidate vulnerability within populations.

Mortality risk attributable to high and low ambient temperature: a multicountry observational study.

Summary Background 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. Methods 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. Findings 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. Interpretation 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. Funding UK Medical Research Council.

Long-term effects of traffic-related air pollution on mortality in a Dutch cohort (NLCS-AIR study)

Background Several studies have found an effect on mortality of between-city contrasts in long-term exposure to air pollution. The effect of within-city contrasts is still poorly understood. Objectives We studied the association between long-term exposure to traffic-related air pollution and mortality in a Dutch cohort. Methods We used data from an ongoing cohort study on diet and cancer with 120,852 subjects who were followed from 1987 to 1996. Exposure to black smoke (BS), nitrogen dioxide, sulfur dioxide, and particulate matter ≤mu;M2.5), as well as various exposure variables related to traffic, were estimated at the home address. We conducted Cox analyses in the full cohort adjusting for age, sex, smoking, and area-level socioeconomic status. Results Traffic intensity on the nearest road was independently associated with mortality. Relative risks (95% confidence intervals) for a 10-μg/m3 increase in BS concentrations (difference between 5th and 95th percentile) were 1.05 (1.00–1.11) for natural cause, 1.04 (0.95–1.13) for cardiovascular, 1.22 (0.99–1.50) for respiratory, 1.03 (0.88–1.20) for lung cancer, and 1.04 (0.97–1.12) for mortality other than cardiovascular, respiratory, or lung cancer. Results were similar for NO2 and PM2.5, but no associations were found for SO2. Conclusions Traffic-related air pollution and several traffic exposure variables were associated with mortality in the full cohort. Relative risks were generally small. Associations between natural-cause and respiratory mortality were statistically significant for NO2 and BS. These results add to the evidence that long-term exposure to ambient air pollution is associated with increased mortality.

Public health benefits of strategies to reduce greenhouse-gas emissions: overview and implications for policy makers

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.

Lung cancer risk after exposure to polycyclic aromatic hydrocarbons: A review and meta-analysis

Typical polycyclic aromatic hydrocarbon mixtures are established lung carcinogens, but the quantitative exposure–response relationship is less clear. To clarify this relationship we conducted a review and meta-analysis of published reports of occupational epidemiologic studies. Thirty-nine cohorts were included. The average estimated unit relative risk (URR) at 100 μg/m3 years benzo[a]pyrene was 1.20 [95% confidence interval (CI), 1.11–1.29] and was not sensitive to particular studies or analytic methods. However, the URR varied by industry. The estimated means in coke ovens, gasworks, and aluminum production works were similar (1.15–1.17). Average URRs in other industries were higher but imprecisely estimated, with those for asphalt (17.5; CI, 4.21–72.78) and chimney sweeps (16.2; CI, 1.64–160.7) significantly higher than the three above. There was no statistically significant variation of URRs within industry or in relation to study design (including whether adjusted for smoking), or source of exposure information. Limited information on total dust exposure did not suggest that dust exposure was an important confounder or modified the effect. These results provide a more secure basis for risk assessment than was previously available.

Reducing and meta-analysing estimates from distributed lag non-linear models

BackgroundThe two-stage time series design represents a powerful analytical tool in environmental epidemiology. Recently, models for both stages have been extended with the development of distributed lag non-linear models (DLNMs), a methodology for investigating simultaneously non-linear and lagged relationships, and multivariate meta-analysis, a methodology to pool estimates of multi-parameter associations. However, the application of both methods in two-stage analyses is prevented by the high-dimensional definition of DLNMs.MethodsIn this contribution we propose a method to synthesize DLNMs to simpler summaries, expressed by a reduced set of parameters of one-dimensional functions, which are compatible with current multivariate meta-analytical techniques. The methodology and modelling framework are implemented in R through the packages dlnm and mvmeta.ResultsAs an illustrative application, the method is adopted for the two-stage time series analysis of temperature-mortality associations using data from 10 regions in England and Wales. R code and data are available as supplementary online material.Discussion and ConclusionsThe methodology proposed here extends the use of DLNMs in two-stage analyses, obtaining meta-analytical estimates of easily interpretable summaries from complex non-linear and delayed associations. The approach relaxes the assumptions and avoids simplifications required by simpler modelling approaches.

Impact of high temperatures on mortality: is there an added heat wave effect?

Background: Mortality during sustained periods of hot weather is generally regarded as being in excess of what would be predicted from smooth temperature-mortality gradients estimated using standard time-series regression models. However, the evidence for an effect of continuous days of exceptional heat (“heat wave effect”) is indirect. In addition, because some interventions may be triggered only during forecasted heat waves, it would be helpful to know what fraction of all heat-related deaths falls during these specific periods and what fraction occurs throughout the remainder of the summer. Methods: Extended time-series data sets of daily mortality counts in 3 major European cities (London, 28 years of data; Budapest, 31 years; Milan, 18 years) were examined in relation to hot weather using a generalized estimating equations approach. We modeled temperature and specific heat wave terms using a variety of specifications. Results: With a linear effect of same-day temperature above an identified threshold, an additional “heat wave” effect of 5.5% was observed in London (95% confidence interval = 2.2 to 8.9), 9.3% in Budapest (5.8 to 13.0), and 15.2% in Milan (5.7 to 22.5). Heat wave effects were reduced slightly when we relaxed the linear assumption and these effects were reduced substantially when temperature was modeled as an average value of lags 0 to 2 days. In London, fewer than half of all heat-related deaths could be attributed to identified heat wave periods. In Milan and Budapest, the fraction was less than one fifth. Conclusions: Heat wave effects were apparent in simple time-series models but were reduced in multilag nonlinear models and small when compared with the overall summertime mortality burden of heat. Reduction of the overall heat burden requires preventive measures in addition to those that target warnings and responses uniquely to heat waves.

Mortality displacement of heat-related deaths : a comparison of Delhi, Sao Paulo, and London

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°C. At lag 0, the excess risk wasgreatest 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.