The association between ambient temperature variability and myocardial infarction in a New York-State-based case-crossover study: An examination of different variability metrics

Abstract

Background: Short-term temperature variability has been consistently associated with mortality, with limited evidence for cardiovascular outcomes. Previous studies have used multiple metrics to measure temperature variability; however, those metrics do not capture hour-to-hour changes in temperature.

Objectives: We assessed the correlation between sub-daily temperature-change-over-time metrics and previously-used metrics, and estimated associations with myocardial infarction (MI) hospitalizations.

Methods: Hour-to-hour change-over-time was measured via three metrics: 24-hr mean absolute hourly first difference, 24-hr maximum absolute hourly first difference, and 24-hr mean hourly first difference. We first assessed the Spearman correlations between these metrics and four previously-used metrics (24-hr standard deviation of hourly temperature, 24-hr diurnal temperature range, 48-hr standard deviation of daily minimal and maximal temperatures, and 48-hr difference of daily mean temperature), using hourly data from the North America Land Data Assimilation System-2 Model. Subsequently, we estimated the association between these metrics and primary MI hospitalization in adult residents of New York State for 2000-2015 using a time-stratified case-crossover design.

Results: The hour-to-hour change-over-time metrics were correlated, but not synonymous, with previously-used metrics. We observed 809,259 MI, 45% of which were among females and the mean (standard deviation) age was 70 (15). An increase from mean to 90th percentile in mean absolute first difference of temperature was associated with a 2.04% (95% Confidence Interval [CI]: 1.30-2.78%) increase in MI rate. An increase from mean to 90th percentile in mean first difference also yielded a positive association (1.86%; 95%CI: 1.09-2.64%). We observed smaller- or similar-in-magnitude positive associations for previously-used metrics.

Discussion: First, short-term hour-to-hour temperature change was positively associated with MI risk. Second, all other variability metrics yielded positive associations with MI, with varying magnitude. In future research on temperature variability, researchers should define their research question, including which aspects of variability they intend to measure, and apply the appropriate metric.

Alternative: All metrics of temperature variability, including short-term hour-to-hour temperature changes, were positively associated with MI risk, though the magnitude of effect estimates varied by metric.

Keywords: Case-crossover; Exposure assessment; Myocardial infarction; Temperature variability.

Figure 1:

Correlation of Temperature Variability Metrics

Figure 2:. Exposure-Response Curves for Main Temperature Variability Metrics

Panel A illustrates the association for a decrease or increase in MeanAbsFDT relative to the average MeanAbsFDT and Panel B illustrates the association for MeanFDT. Shaded areas represent 95% confidence intervals. Panels C and D are smoothed density plots of the two metrics. To visually focus on more typical values of variability, only variability values between the 2.5^th and 97.5^th percentiles are presented in this Figure. Exposure-response curves across the full exposure range are presented in eFigure 3.

Figure 3:. Association Between Temperature Variability Metrics and MI

Percent increase in hourly MI rate for a change in temperature variability for each metric from mean variability to (A) the 10^th percentile of variability and (B) the 90^th percentile. Error bars represent 95% confidence intervals.

Figure 4.. Summery of Effect Estimates Using Alternative Exposure Windows

Percent increase in hourly MI rate for a change in temperature variability for sensitivity analysis from mean variability to the 90^th percentile. Error bars represent 95% confidence intervals.