Temporal variations in the triggering of myocardial infarction by air temperature in Augsburg, Germany, 1987–2014

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. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-122) contains supplementary material, which is available to authorized users.

Heat is recognized as one of the deadliest weather-related phenomena. Although the impact of high temperatures on mortality has been a subject of extensive research, few previous studies have assessed the impact of population adaptation to heat. We examined adaptation patterns by analyzing daily temperature and mortality data spanning more than a century in New York City. Using a distributed-lag nonlinear model, we analyzed the heat-mortality relation in adults age 15 years or older in New York City during 2 periods: 1900-1948 and 1973-2006, to quantify population adaptation to high temperatures over time. During the first half of the century, the decade-specific relative risk of mortality at 29°C vs. 22°C ranged from 1.30 (95% confidence interval [CI]= 1.25-1.36) in the 1910s to 1.43 (1.37-1.49) in the 1900s. Since the 1970s, however, there was a gradual and substantial decline in the relative risk, from 1.26 (1.22-1.29) in the 1970s to 1.09 (1.05-1.12) in the 2000s. Age-specific analyses indicated a greater risk for people age 65 years and older in the first part of the century, but there was less evidence for enhanced risk among this older age group in more recent decades. The excess mortality with high temperatures observed between 1900 and 1948 was substantially reduced between 1973 and 2006, indicating population adaption to heat in recent decades. These findings may have implications for projecting future impacts of climate change on mortality.

Air temperature changes have been associated with cardiovascular mortality and morbidity. The objective of this study was to examine in detail the registry-based myocardial infarction (MI) rates and coronary deaths in relation to air temperature in the area of Augsburg, Germany. Between 1995 and 2004, the Monitoring Trends and Determinants on Cardiovascular Diseases/Cooperative Health Research in the Region of Augsburg (MONICA/KORA) registry recorded 9801 cases of MI and coronary deaths. Over the same period, meteorological parameters and air pollutant concentrations were measured in the study region. Poisson regression analyses adjusting for time trend, relative humidity, season, and calendar effects were used to estimate immediate, delayed, and cumulative temperature effects on the occurrence of MIs. The daily rates of total MI, nonfatal and fatal events, and incident and recurrent events were analyzed. For the total MI cases, a 10 degrees C decrease in 5-day average temperature was associated with a relative risk of 1.10 (95% confidence interval, 1.04 to 1.15). The effect of temperature on the occurrence of nonfatal events showed a delayed pattern, whereas the association with fatal MI was more immediate. No association could be observed for recurrent events. The effects of temperature decreases on total MI cases were more pronounced in years with higher average temperatures and were visible in summer. We observed an inverse relationship between temperature and MI occurrence not only during winter but also during summer. Thus, our results suggest not a pure "cold effect" but an influence of unusual temperature decreases.