Periods of low and high ambient temperatures have been found associated with high mortality in a wide range of climates and countries.1–6 However, most previous studies have examined temperature effects only by community or country.2,4,7 Although some evidence of adaptation to local climates is clear from studies within large countries5,7 and the limited number of international studies,8–10 there are no studies with a wide range of globally diverse communities and climates. The different analytic approaches adopted in studies on single countries or regions, in particular considering different lag periods, makes it difficult to assess how associations differ across climates and societies. In addition, some studies examined the effects of only high temperatures or only cold temperatures, which makes it difficult to define whether people have the ability to adapt to their local climates.11,12 Considering the global ambient temperature changes that are expected in the context of climate change, an international perspective on the temperature health effects carries with it important public health implications.
This study aims to examine how temperature-mortality relations estimated using consistent methods vary across a wide range of communities in 12 countries/regions.
In this study, we obtained daily data on nonaccidental mortality and weather conditions in 306 communities from 12 countries/regions: Australia (3 cities during 1988–2008), Brazil (18 cities during 1997–2011), Thailand (62 provinces during 1999–2008), China (6 cities during 2002–2011), Taiwan (3 cities during 1994–2007), South Korea (7 cities during 1992–2010), Japan (7 cities during 1972–2009), Italy (10 cities during 1987–2010), Spain (51 cities during 1990–2010), United Kingdom (10 regions during 1993–2006), United States (108 cities during 1987–2000), and Canada (21 cities during 1986–2009) (see eFigure 1 for location and eTable 1 for community-specific information, https://links.lww.com/EDE/A819). Weather data included daily minimum, mean and maximum temperatures, and relative humidity. We used mean temperature to assess the effects of temperature on mortality, as it represents the exposure throughout the entire day and night and can be easily interpreted for decision-making purposes. The details for data collection are described in the supplemental material (eAppendix, https://links.lww.com/EDE/A819). This study was approved by the Behavioural and Social Sciences Ethical Review Committee, University of Queensland.
The temperature-mortality association was investigated with a 2-stage analysis using time series data from the 306 communities in the 12 countries/regions. In the first stage, we applied a time series model to each community data to estimate the city-specific temperature-mortality relation, allowing for nonlinearity and delayed effects. These estimated relations were then pooled in the second stage at country level with a multivariate meta-analysis. This approach has been illustrated in previous publications.13,14
Although the temperature-mortality association in individual cities is naturally considered with temperature on a degrees scale, this makes for difficulties when combining curves across cities with nonoverlapping temperature ranges (eTable 1, https://links.lww.com/EDE/A819). Also, because several studies suggested the adaptation of populations to their own climate,5,9 we hypothesized that health effects might be more consistent in terms of temperature percentiles than in the absolute scale of temperature.7 Therefore, we developed an approach by defining the temperature-mortality relation on a relative scale, following methods previously described.13 Specifically, we standardized the community-specific absolute temperatures to community-specific percentiles. The results are expressed in terms of temperature percentiles, which correspond to different community-specific absolute temperatures. If curves on this scale are similar across communities, this implies that relative risks across percentiles are similar. Conversely, if curves on the original degree scale are similar, on the relative scale they would differ across communities with different climates.
First Stage of Analysis
In the first stage, for each community, we used a regression model to obtain community-specific estimates assuming a quasi-Poisson distribution allowing for overdispersed death counts, which follows a standard analytical approach for time series environmental health data.15 The community-specific Poisson time series model is given as the following:
Yt ~ Poisson (μt),
where Yt is the observed daily death count on day t; α is the intercept; Tt,l is a matrix of variables obtained by the transformation of standardized temperature, β is vector of coefficients for Tt,l, and l is the lag days; NS(time, df) is natural cubic spline of time, and df is degree of freedom per year for time, which was used to control for long-term trend and seasonality; 10 df per year for time was used to control seasonality and long-term trend, with the exception of Thailand where, because there were fewer cases per day, we used 7 df per year to avoid possible over control; DOWt is a categorical variable for day of the week, and λ is vector of coefficients.
For each community, we modelled the nonlinear and delayed effect of temperatures using the term βTt,l which is parameterized using a cross-basis function expressing a distributed lag nonlinear model.16 In this study, a flexible cross-basis was defined by a natural cubic spline for the space of temperature, and a natural cubic spline with intercept for the space of lags, with the maximum lag up to 21 days. We placed 3 internal knots at equally spaced temperature percentiles (25th, 50th, and 75th) and 2 internal knots at equally spaced log-values of lag (approximately 1.4 and 5.5 days), respectively, plus intercept. The spline for temperature was centered at the 75th percentile, representing the average point of minimum mortality in preliminary analyses. These choices defined spline basis with 4 degrees of freedom for temperature and 4 degrees of freedom for lag. The choice of 21 days for the lag period was motivated by previous studies showing that effects of cold temperature appeared only after some delay and lasted for several days, whereas effects of hot temperatures were more acute and possibly affected by mortality displacement.2,14
Reduction of Distributed Lag Nonlinear Models
The 16 community-specific parameters of the cross-basis function expressed the nonlinear and delayed temperature-mortality association in each community. The association was then reduced to 3 summaries expressing the overall cumulative exposure-response relation and the lag-response relations specific to the 1st and 99th percentiles, compared with the percentile corresponding to the minimum-mortality temperature. The 2 last summaries represent the lag pattern of cold and hot temperatures, respectively. The reduction was performed for each summary by computing transformed parameters gamma γ for the unidimensional natural cubic splines for the space of temperature or lag, accordingly, from the original parameters beta β of the cross-basis above. This method has been previously described.14
Second Stage of Analysis
At the second stage, a multivariate meta-analysis was used to pool the 3 sets of community-specific parameters gamma γ obtained from the reduction of the first-stage model.14 The multivariate meta-analyses were fitted using a random effects model by maximum likelihood and was applied in each country, obtaining national pooled estimates. Heterogeneity was assessed through a multivariate extension of the I2 index, which quantifies the percentage of variability due to true differences across cities.
Estimating Minimum-mortality Temperature
Many individual cities had very imprecisely estimated temperature at which mortality was the lowest (“minimum-mortality temperature”), which would lead to problems if using these as baselines for estimating heat and cold relative risks. However, multivariate meta-analysis showed that most variation in temperature-mortality associations was explained by country (I2 = 52.7% reduced to 28.2%) when temperature was expressed on a percentile scale. We therefore used the country average minimum-mortality temperature percentile as baseline for calculation of heat and cold relative risks for all cities in that country.
Summary of the Results
We plotted the estimated pooled overall cumulative exposure-response relation at the national level. To represent the lag pattern of cold and hot temperatures on mortality, we also plotted the estimated pooled lag-response relation for cold temperature (1st versus minimum-mortality temperature) and hot temperature (99th versus minimum-mortality temperature).
To obtain an easily interpretable estimate of the effects of cold and hot temperatures on mortality, we also calculated the overall cumulative relative risks of death associated with cold temperature (1st percentile) and with hot temperature (99th percentile), both relative to the minimum-mortality temperature. These effect estimates were computed from the nonlinear exposure-response curves; thus, they reflected a portion of the true temperature-mortality association.7 To obtain a comparison with previously published studies,7 we also calculated the overall cumulative relative risks of death associated (1) with cold temperature (1st percentile of temperature) compared with the 10th percentile of temperature; and (2) with hot temperature (99th percentile of temperature) relative to 90th percentile of temperature.
We also plotted the associations of average temperature and latitude, with the minimum-mortality temperature in 12 countries/regions, to understand whether the minimum-mortality temperatures varied by country climate and latitude.
Sensitivity analyses were performed on the parameters for the community-specific model to test the robustness of our results. We changed lag days to 28 days to examine whether using 21 lag days was enough to capture the temperature effects on mortality. We modified the degrees of freedom for temperature (3–6 df). We included relative humidity into the analyses. We included air pollutants (PM10, SO2, and NO2) in the analyses using China data.
The residuals were examined to evaluate the adequacy of the community-specific models. R software (version 3.0.1, R Development Core Team 2009) was used to do data analysis. The “dlnm” package was used to create the distributed lag nonlinear model16 and the “mvmeta” package to fit the multivariate meta-analyses.13
Table 1 shows the summary of the study period, death count, and mean temperature in the 12 countries/regions. This study included 306 communities. The study period covered 1972 to 2011. The total death counts were over 38 million. Thailand had the hottest climate pattern, whereas Canada had the coldest one. A summary for daily deaths and temperature in 306 communities, ordered by latitude within each country is presented in eTable 1 (https://links.lww.com/EDE/A819). The average deaths and temperatures varied greatly by community, consistently with the range of different climates.
The pooled relations between temperature and mortality were nonlinear at the national level, with minimum-mortality temperature close to 75th percentile of temperature in all countries/regions (Figure 1). Both cold and hot temperatures were significantly associated with the increased risk of mortality in all countries/regions.
The relative risks of deaths associated with cold (1st percentile versus minimum-mortality temperature) and hot (99th percentile versus minimum-mortality temperature) temperatures differed by community and country (Figure 2; see eTable 2 for the values of relative risks, https://links.lww.com/EDE/A819).
The minimum-mortality temperatures were higher in countries with high temperature or in countries close to equator (Figure 3). But the minimum-mortality temperatures distributed around 75th percentile of temperature in all countries, ranging from 66th (Taiwan) to 80th (UK) percentiles (Table 2). The multivariate I2 statistic suggested that 52.7% of the variation in temperature-mortality curves is attributable to true heterogeneity among communities. The estimate decreased to 28.2% when allowing for country effects.
In general, the effects of cold temperature (1st percentile versus the minimum-mortality temperature) were delayed by about 2 days and lasted for at least 10 days at the national level (Figure 4), with some evidence of longer lags in Taiwan, Italy, Spain, and to some extent the United Kingdom.
The effects of hot temperature (99th percentile versus the minimum-mortality temperature) appeared immediately and generally lasted only 3 or 4 days (Figure 5), though again in Italy and Spain risks persisted longer. There was a period of relative risk below 1.0 at longer lags, consistent with mortality displacement after exposure to hot temperatures in UK and South Korea, and to a lesser extent in Canada and Japan, which was not found in other countries.
Table 2 shows the pooled overall cumulative relative risks of cold and hot effects on mortality over lags of 0–21 days in the 12 countries/regions. In summary, effect estimates for cold effects using the 1st percentile versus the minimum-mortality temperature were higher than hot effects using the 99th percentile versus the minimum-mortality temperature in all the countries/regions. In general, people living in Taiwan, Italy, and Spain were more sensitive to both cold and hot temperatures compared with people in other countries.
For comparability with some other publications,7 we also calculated relative risks for 1st versus 10th and 99th versus 90th percentiles of temperature as alternative indices of cold and heat risks. These estimates were lower, as expected. Results for each community were shown in eTable 2 (https://links.lww.com/EDE/A819).
Sensitivity analyses showed that the results were broadly similar when we used 28 lag days (eFigure 2, https://links.lww.com/EDE/A819), changed the degrees of freedom for temperature (3–6 df) (eFigure 3, https://links.lww.com/EDE/A819), or included relative humidity in the analyses (eFigure 4, https://links.lww.com/EDE/A819). When we included selected air pollutants (PM10, SO2, and NO2), the temperature effects on mortality were changed only very slightly (eFigure 5, https://links.lww.com/EDE/A819).
We have examined temperature-mortality associations using consistent methods for a much wider range of communities than has previously been investigated in a single study. In total, we studied 306 communities across 12 countries/regions, including countries from both developing and developed regions with various climate patterns (ie tropical, subtropical, and temperate). We found evidence that in all countries/regions both cold and hot temperatures were associated with the increased risks of deaths. The effects of cold temperatures appeared after a couple of days but lasted at least 10 days, whereas the effects of hot temperatures appeared immediately and lasted usually only 3 or 4 days. Despite widely ranging climates, the minimum-mortality temperatures were close to the 75th percentile of temperature in all 12 countries/regions, suggesting that people have adapted to some extent to their local climates.
Some of our findings, such as the broadly U-shaped temperature-mortality associations, have been strongly indicated by the ensemble of previous national or regional studies.4,5,7 However, our use of consistent methods across a wide range of communities removes doubt as to whether comparisons may be confused by differences in methods. Previous studies have used a wide variety of daily temperature indices, lags, mathematical forms for the association and methods to control for time-varying confounders, each of which can change the estimates of temperature-mortality associations.
The consistency of the minimum-mortality temperature around the 75th percentile across such a range of climates and levels of development is remarkable, although similar results were reported previously in national or regional studies.1,5,17 This finding is consistent with minimum-mortality temperatures in communities with colder climates being lower than in communities with warmer climates, with mortality increasing as temperature becomes unusual for the community. This suggests that, over the long-term, people partially adapt to their local climates via a range of physiological, behavioral, and technological adaptations.18
That the minimum-mortality temperatures are close to the 75th percentile of temperature across varying climates suggests a degree of long-term adaptation, but from this it cannot be inferred that such adaptation would occur over a few decades, such as following climate change. Also, the fact that minimum-mortality temperatures are higher in warmer climates does not imply complete adaptation, as the degree of risk associated with hot and cold might still vary with climate. For these reasons, we believe that it would be premature to use these results to make assumptions about adaptation to—and hence impact of—future climate change. We believe that our results can inform investigations that do so, but these would have to consider other factors as well.19 Based on our large data set, we plan to explore the potential impacts of future climate change on mortality in future studies, following pioneering more local estimates.20–22 Whatever the impact of future climate change, we found that both cold and hot temperatures increase the risk of mortality in all countries/regions. Thus, effective interventions to reduce vulnerability to both hot and cold temperatures would benefit population health.
Our finding that the minimum-mortality temperature was consistently around the 75th percentile suggests this as the most suitable reference temperature for both heat and cold summary relative risks, as it will more fully reflect the relations between cold/hot temperatures and mortality— compared with alternatives such as the common 1st versus 10th and 99th versus 90th percentile contrasts. By this measure, cold effects (1st versus minimum-mortality temperature) were higher than hot effects (99th versus minimum-mortality temperature) in all the countries/regions. Relative risks using the 1st versus 10th and 99th versus 90th percentile contrasts were substantially lower, especially for cold, and underestimate both cold and hot effects. Our results estimated by using 1st versus 10th and 99th versus 90th percentile were comparable with previous studies conducted in United States.7
There were substantial variations in the extent of risk elevation at the 1st (cold) and 99th (heat) percentiles relative to the minimum-mortality temperature—about equally between and within countries/regions. While it is the objective of this article to describe, rather than fully explore, reasons for this variation (eg climatic or societal), some between-country patterns are noteworthy. Communities in Taiwan, Italy, and Spain had higher temperature-related (cold and heat) mortality risks than those in other countries. This finding is not consistent with our prior hypothesis that developing countries (Brazil, Thailand, and China) would be more sensitive to temperatures than wealthy countries, but it would be premature to consider this as strong evidence against that hypothesis. The variation in the impact of temperatures on mortality might be modified by climatic factors or by socioeconomic, demographic, and infrastructure factors that are unrelated to whether a country is developing or developed.23 For example, air conditioning is protective to heat-related deaths,24 while poor heating and insulation affect cold-related deaths, especially in countries with a mild winter climate.25
We found that cold-related mortality was delayed while hot-related mortality was acute at the national level. The effects of cold temperatures typically lasted for 10 days compared with about 3 days for hot temperatures. Similar patterns of time lag have been observed in previous studies,7,14,26 but our observations across such a wide range of countries using the same methods is new. Our findings confirm that only timely preventive measures are helpful to reduce the health effects of hot temperatures, while several days’ protection should be implemented to reduce the health impacts of cold temperatures.
We used flexible analytic techniques—in particular, multivariate meta-analysis with distributed lag nonlinear model—to estimate and pool the nonlinear and delayed relation between temperature and mortality. These fairly new but established approaches offer flexibility for the assessment of the temperature-mortality relation, without making strong prior assumptions about the exposure-mortality shape or lag structure. Most previous studies used only conventional linear exposure-responses and univariate meta-analysis that describe the relation less completely or ignore correlated variables in the meta-analysis.27
This study has some limitations. The data for the United States covered only the period of 1987–2000, which is earlier than other countries. This might have some impact on the comparisons, as the impacts of temperature on mortality have decreased in the United States over recent years.28 However, this issue is unlikely to have a major influence on our overall conclusions. As in other similar time series studies, we used the data on temperature from fixed sites rather than individual exposure, which will create measurement error in exposure to some extent. However, these measurement errors are likely to be random, which would usually result in an underestimation of the relative risks.29 Air pollutants were not adjusted for in this study, because the data were not available for some countries. That air pollutants might be on the causal path between temperature and health can also complicate interpretation of adjusted models. However, the sensitivity analysis suggests that results were changed only slightly when we put air pollutants into the models. The study was restricted in various ways to avoid cluttering the paper with many results. For this reason, we investigated only nonaccidental mortality and did not formally explore reasons for variation on temperature-mortality associations between communities.
In conclusion, our results provide strong evidence that both cold and hot temperatures increase the risk of mortality in different countries in different climatic zones. The temperature at which mortality was lowest was close to the 75th percentile of temperature in all countries/regions, suggesting at least partial adaptation to local climate. However, the degree of risk associated with both cold and heat differed by community and country. In all countries/regions, the effects of cold temperatures appeared after a couple of days but lasted at least 10 days, whereas the effects of hot temperatures appeared immediately and lasted usually only 3 or 4 days.
We thank relevant institutes/agencies who provided data on mortality and weather conditions. We thank Adrian Barnett (Queensland University of Technology, Australia) for his insightful comments.
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