Shakoor Hajat

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.

Heat‐related and cold‐related deaths in England and Wales: who is at risk?

Background: Despite the high burden from exposure to both hot and cold weather each year in England and Wales, there has been relatively little investigation on who is most at risk, resulting in uncertainties in informing government interventions. Objective: To determine the subgroups of the population that are most vulnerable to heat-related and cold-related mortality. Methods: Ecological time-series study of daily mortality in all regions of England and Wales between 1993 and 2003, with postcode linkage of individual deaths to a UK database of all care and nursing homes, and 2001 UK census small-area indicators. Results: A risk of mortality was observed for both heat and cold exposure in all regions, with the strongest heat effects in London and strongest cold effects in the Eastern region. For all regions, a mean relative risk of 1.03 (95% confidence interval (CI) 1.02 to 1.03) was estimated per degree increase above the heat threshold, defined as the 95th centile of the temperature distribution in each region, and 1.06 (95% CI 1.05 to 1.06) per degree decrease below the cold threshold (set at the 5th centile). Elderly people, particularly those in nursing and care homes, were most vulnerable. The greatest risk of heat mortality was observed for respiratory and external causes, and in women, which remained after control for age. Vulnerability to either heat or cold was not modified by deprivation, except in rural populations where cold effects were slightly stronger in more deprived areas. Conclusions: Interventions to reduce vulnerability to both hot and cold weather should target all elderly people. Specific interventions should also be developed for people in nursing and care homes as heat illness is easily preventable.

El Niño and health

El Niño Southern Oscillation (ENSO) is a climate event that originates in the Pacific Ocean but has wide-ranging consequences for weather around the world, and is especially associated with droughts and floods. The irregular occurrence of El Niño and La Niña events has implications for public health. On a global scale, the human effect of natural disasters increases during El Niño. The effect of ENSO on cholera risk in Bangladesh, and malaria epidemics in parts of South Asia and South America has been well established. The strongest evidence for an association between ENSO and disease is provided by time-series analysis with data series that include more than one event. Evidence for ENSOs effect on other mosquito-borne and rodent-borne diseases is weaker than that for malaria and cholera. Health planners are used to dealing with spatial risk concepts but have little experience with temporal risk management. ENSO and seasonal climate forecasts might offer the opportunity to target scarce resources for epidemic control and disaster preparedness.

Impact of hot temperatures on death in London: a time series approach

Study objective: This study investigated the relation between heat and mortality in London to determine the temperature threshold at which death rates increase and to quantify the effect of extreme temperatures on mortality. Design: Daily data on all cause mortality and temperature were obtained for a 21 year period and the relation between them investigated both graphically and by using non-parametric time series methods of analysis. Setting: Greater London. Participants: Daily mortality counts in Greater London between January 1976 and December 1996. Main results: A plot of the basic mortality-temperature relation suggested that a rise in heat related deaths began at about 19°C. Average temperatures above the 97th centile value of 21.5°C (excluding those days from a 15 day “heatwave” period in 1976) resulted in an increase in deaths of 3.34% (95% CI 2.47% to 4.23%) for every one degree increase in average temperature above this value. It was found that the 1976 heatwave resulted in a particularly large number of deaths in comparison with other hot periods. Conclusions: These results suggest that heat related deaths in London may begin at relatively low temperatures. Hot days occurring in the early part of any year may have a larger effect than those occurring later on; and analysis of separate heatwave periods suggest that episodes of long duration and of highest temperature have the largest mortality effect.

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.

Contrasting patterns of mortality and hospital admissions during hot weather and heat waves in Greater London, UK

Background: Epidemiological research has shown that mortality increases during hot weather and heat waves, but little is known about the effect on non-fatal outcomes in the UK. Aims and Methods: The effects of hot weather and heat waves on emergency hospital admissions were investigated in Greater London, UK, for a range of causes and age groups. Time series analyses were conducted of daily emergency hospital admissions, 1 April 1994 to 31 March 2000, using autoregressive Poisson models with adjustment for long term trend, season, day of week, public holidays, the Christmas period, influenza, relative humidity, air pollution (ozone, PM10), and overdispersion. The effects of heat were modelled using the average of the daily mean temperature over the index and previous two days. Results: There was no clear evidence of a relation between total emergency hospital admissions and high ambient temperatures, although there was evidence for heat related increases in emergency admissions for respiratory and renal disease, in children under 5, and for respiratory disease in the 75+ age group. During the heat wave of 29 July to 3 August 1995, hospital admissions showed a small non-significant increase: 2.6% (95% CI −2.2 to 7.6), while daily mortality rose by 10.8% (95% CI 2.8 to 19.3) after adjusting for time varying confounders. Conclusions: The impact of hot weather on mortality is not paralleled by similar magnitude increases in hospital admissions in the UK, which supports the hypothesis that many heat related deaths occur in people before they come to medical attention. This has evident implications for public health, and merits further enquiry.

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.

The effect of temperature on food poisoning: a time-series analysis of salmonellosis in ten European countries

We investigated the relationship between environmental temperature and reported Salmonella infections in 10 European populations. Poisson regression adapted for time-series data was used to estimate the percentage change in the number of cases associated with a 1 degree C increase in average temperature above an identified threshold value. We found, on average, a linear association between temperature and the number of reported cases of salmonellosis above a threshold of 6 degrees C. The relationships were very similar in The Netherlands, England and Wales, Switzerland, Spain and the Czech Republic. The greatest effect was apparent for temperature 1 week before the onset of illness. The strongest associations were observed in adults in the 15-64 years age group and infection with Salmonella Enteritidis (a serotype of Salmonella). Our findings indicate that higher temperatures around the time of consumption are important and reinforce the need for further education on food-handling behaviour.

Heat-related mortality: a review and exploration of heterogeneity

Although rapid response capacity has been instituted in many cities following recent catastrophic heatwave events, the recognition that theoretically preventable heat-related deaths may occur throughout the summer has provoked much less response. This essay reviews published estimates of the general summertime temperature–mortality relationship characterised in different settings around the world. A random-effects meta-regression is applied to the estimates in relation to a number of standardised city-level characteristics of demography, economy and climate. Heat thresholds were generally higher in communities closer to the equator, suggesting some population adaptation. In almost half of the locations, the risk of mortality increased by between 1% and 3% per 1°C change in high temperature. Increasing population density, decreasing city gross domestic product and increasing percentage of people aged 65 or more were all independently associated with an increase in the heat slope. Improved care of older people, residential architecture and urban planning measures to reduce high temperatures in densely populated areas are likely to play a key role alongside targeted heat-health warning systems in reducing future heat burdens.

Time series regression studies in environmental epidemiology

Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed (‘lagged’) associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model.