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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.

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.

Background: We investigated the impact of environmental temperature on mortality in São Paulo, Brazil, and examined differences in the temperature-mortality relationship with respect to cause, age group, and socioeconomic position (SEP)., Methods: Generalized additive Poisson regression models adjusted for non-temperature related seasonal factors (including air pollution) were used to analyse daily mortality counts for selected causes from 1991 to 1994. Individuals were classified by the aggregate SEP of their area of residency. These were analysed as potential modifiers of the temperature-mortality relationship., Results: Among the elderly we observed a 2.6% increase in all-cause mortality per degree increase in temperature above 20 degrees C, and a 5.5% increase per degree drop in temperature below 20 degrees C, after adjustment for confounding. Relationships were similar in children, but somewhat weaker in adults. Cold effects were present for deaths due to cardiovascular disease (CVD), respiratory, and other causes, with effects being greatest in the respiratory group. Heat effects were not found for CVD deaths in adults, but otherwise varied little by cause of mortality. There was little evidence for a modification of the mortality effects of cold or heat by SEP., Conclusions: These findings show that the U-shaped pattern of the temperature-mortality relationship found in cooler northern countries occurs also in a sub-tropical city. In addition, the relative effects of temperature were similar in each socioeconomic grouping.