Seasonal variation in mortality and the role of temperature: a multi-country multi-city study

Abstract

Background Although seasonal variations in mortality have been recognized for millennia, the role of temperature remains unclear. We aimed to assess seasonal variation in mortality and to examine the contribution of temperature. Methods We compiled daily data on all-cause, cardiovascular and respiratory mortality, temperature and indicators on location-specific characteristics from 719 locations in tropical, dry, temperate and continental climate zones. We fitted time-series regression models to estimate the amplitude of seasonal variation in mortality on a daily basis, defined as the peak-to-trough ratio (PTR) of maximum mortality estimates to minimum mortality estimates at day of year. Meta-analysis was used to summarize location-specific estimates for each climate zone. We estimated the PTR with and without temperature adjustment, with the differences representing the seasonal effect attributable to temperature. We also evaluated the effect of location-specific characteristics on the PTR across locations by using meta-regression models. Results Seasonality estimates and responses to temperature adjustment varied across locations. The unadjusted PTR for all-cause mortality was 1.05 [95% confidence interval (CI): 1.00–1.11] in the tropical zone and 1.23 (95% CI: 1.20–1.25) in the temperate zone; adjusting for temperature reduced the estimates to 1.02 (95% CI: 0.95–1.09) and 1.10 (95% CI: 1.07–1.12), respectively. Furthermore, the unadjusted PTR was positively associated with average mean temperature. Conclusions This study suggests that seasonality of mortality is importantly driven by temperature, most evidently in temperate/continental climate zones, and that warmer locations show stronger seasonal variations in mortality, which is related to a stronger effect of temperature.

Assessing seasonality and the role of its potential drivers in environmental epidemiology: a tutorial

“…9 Recently, several studies have applied a cyclic spline function to model seasonality on a daily basis. 1,5 A cyclic spline function is a smoothing method to estimate periodic variation such as daily or annual pattern of time-series observations. Basically, it is a periodic piece-wise cubic function with continuity up to the second derivative, so the function of day-of-year changes continuously at the end of the year.…”

“…Basically, it is a periodic piece-wise cubic function with continuity up to the second derivative, so the function of day-of-year changes continuously at the end of the year. 13 Here, we illustrate the modelling approach using a time-series regression with a cyclic spline function to assess seasonality by following our previous work 5 :…”

“…The amplitude of seasonality can be measured as the ratio of the maximum mortality estimate at peak day to the minimum mortality estimate at trough day (i.e. peak-to-trough ratio, PTR), 5 and its 95% CI can be obtained from the variance matrix of the estimated coefficients from the cyclic spline function. 12 However, the PTR is not sensitive to seasonality shape and, more importantly, offers limited information on the impact of seasonality on mortality.…”

“…Since the modelling process involves many decisions, multiple sensitivity analyses are recommended to check the robustness of the main conclusions. 1,10,12 A series of sensitivity analyses on df of the cyclic spline and temperature adjustment has been conducted in our previous study 5 where the example dataset was included. In this tutorial, we compared the cyclic spline and a cosinor function (Supplementary Figure S1, available as Supplementary data at IJE online).…”

“…Seasonal variation in health outcomes (hereafter, seasonality) has been largely recognized. [1][2][3][4][5][6][7][8] Seasonality is widely caused by various environmental factors, such as weather and air pollution, and others such as holidays. Several methods have been used to describe and assess the seasonality in epidemiological studies, including statistical tests and graphical methods.…”