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Ambient Temperature and Cardiorespiratory Morbidity: A Systematic Review and Meta-analysis

Turner, Lyle R.a; Barnett, Adrian G.a; Connell, Desb; Tong, Shilua,c

doi: 10.1097/EDE.0b013e3182572795
Global Warming

Background: The effect of extreme temperature has become an increasing public health concern. Evaluating the impact of ambient temperature on morbidity has received less attention than its impact on mortality.

Methods: We performed a systematic literature review and extracted quantitative estimates of the effects of hot temperatures on cardiorespiratory morbidity. There were too few studies on effects of cold temperatures to warrant a summary. Pooled estimates of effects of heat were calculated using a Bayesian hierarchical approach that allowed multiple results to be included from the same study, particularly results at different latitudes and with varying lagged effects.

Results: Twenty-one studies were included in the final meta-analysis. The pooled results suggest an increase of 3.2% (95% posterior interval = −3.2% to 10.1%) in respiratory morbidity with 1°C increase on hot days. No apparent association was observed for cardiovascular morbidity (−0.5% [−3.0% to 2.1%]). The length of lags had inconsistent effects on the risk of respiratory and cardiovascular morbidity, whereas latitude had little effect on either.

Conclusions: The effects of temperature on cardiorespiratory morbidity seemed to be smaller and more variable than previous findings related to mortality.

Supplemental Digital Content is available in the text.

From the aSchool of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia; bSchool of Environment, Griffith University, Brisbane, Australia; and cSchool of Public Health, Anhui Medical University, Hefei, Anhui, China.

Submitted 29 June 2011; accepted 7 February 2012; posted 23 April 2012.

Supported partly by funds from the Australian Research Council (DP1095752) (to S.T. and D.C.); and by NHMRC Research Fellowship (#553043) (to S.T.). The authors reported no other financial interests related to this research.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article ( This content is not peer-reviewed or copy-edited; it is the sole responsibility of the author.

Correspondence: Shilu Tong, School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Victoria Park Road, Kelvin Grove, QLD 4059, Australia. E-mail:

Systemic environmental changes and their effect on human health have become an increasing public concern.1 3 Temperature-related health effects have received much attention,4 6 particularly because projected climate change scenarios point to increasing and more variable temperatures throughout the world.7 With increasing ambient temperatures, heat effects are of particular importance from a public health perspective.8,9

Measuring the effects of temperature on human health is useful for several reasons. First, it can improve the understanding of how temperature affects morbidity and mortality in various populations, which may help to predict how climate change will influence human health. Second, it may contribute to public health interventions targeting vulnerable subgroups.10 Finally, it may guide strategies for reducing the social and economic burden associated with major chronic conditions such as cardiovascular (CV) and respiratory diseases.11

Most research has concentrated on the relation between temperature and mortality.12 18 In terms of the effects of temperature on morbidity, 3 main outcomes have been examined: total hospital admissions; hospitalizations for respiratory disease19 21; and CV disease, which includes myocardial infarction (MI), acute coronary syndrome, and stroke.19,22 29 Attention has been given to methodological issues such as distributed lagged effects and harvesting, particularly when examining the effects of sustained periods of extreme weather in time-series studies.

Distributed lagged effects (including both short-term and cumulative lagged effects) have been examined in a number of studies.30 32 Short-term lags are particularly important for heat-related effects.33 Mortality displacement or harvesting16,34 occurs when the deaths of frail persons are brought forward by extreme temperatures, leading to a compensating decrease in effect estimates at longer lag periods.

Nonlinear exposure–response relationships have also been reported,35 37 below and above which the effect on health outcomes increases.18 This common U- or V-shaped relationship is often modeled by a piecewise function, which for the former is specified by a “comfort zone” of no effect between hot and cold thresholds. Other nonlinear splines using more complex bases have also been used to describe the exposure–response relationship.35 However, in some studies, the nonlinear effect of temperature has been found to be weak or even nonexistent, leading to the use of a linear model across all temperatures.22,38,39 These modeling differences might be explained in part by the temperature range of the particular studies, and also by population acclimatization. For this reason, acclimatization has often been incorporated into studies of multiple locations, most commonly by including the latitude of the population as a proxy for climate.18,32,40

Compared with studies examining temperature effects on mortality, less attention has been paid to the effects of temperature on morbidity.41 46 Among a relatively small number of temperature–morbidity studies, most have focused on the effects of hot temperatures. Therefore, our study aims to identify and quantify the relationship between hot temperatures and morbidity through a systematic review and meta-analysis.

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Data Extraction

We used a systematic search to identify all relevant studies investigating the association between temperature and morbidity. The search used the databases: PubMed, Web of Science, Science Direct, and Scopus and was conducted during October 2010 and January 2012 with no limitations on search criteria. The specific search terms used for each database differed slightly (eAppendix, Additional articles were obtained by manually scanning the reference lists of each publication.

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Filtering Procedure

The title and abstract were first used to filter studies not related to the research question. The remaining results were merged into an EndNote library and duplicates were removed. Studies that contained no quantitative results were removed next. Studies related to periods of extreme temperature such as heat waves were also excluded because of the different methods used to examine heat waves.

The full texts of the remaining studies were then thoroughly reviewed against selection criteria. Study designs were required to be time-series, case–control, or case–crossover. To examine the short-term effects of changes in temperature on morbidity, each study had to contain an outcome measure related to hospitalization for either all causes, CV diseases (including MI or stroke) or respiratory diseases. As the exposure–response relationship was of interest, the outcome measure was the change in the number of hospitalizations for a unit change in temperature, reported over a daily timescale. We excluded studies reporting only nonlinear temperature–morbidity curves, because, while their effect values could be estimated from the plots, it was not possible to derive associated standard errors (which are necessary for inclusion in the meta-analysis). All temperature measures were allowed, following recent evidence that the magnitude of temperature effects on mortality does not vary substantially, with the exposure measure used.17,47,48 Effect estimates had to be presented as a Poisson or negative binomial regression coefficient, percentage change, relative risk, or odds ratio.

We collected all effect size results with confidence intervals or standard errors, the number of lagged days used, and the study latitude, along with an associated threshold temperature when reported.

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Statistical Methods

Effect estimates were converted to a relative risk (RR) reflecting a change in hospitalizations due to 1°C increase in temperature. For some studies, this was the increase in temperature above a threshold. Standard errors for relative risks were derived from associated confidence intervals. All results were converted to a log scale for meta-analysis.

We combined studies in the meta-analysis using a random effects model to incorporate heterogeneity both within and between studies. Because a number of studies reported estimates for various lags and latitudes, we used a two-stage Bayesian hierarchical model.49 The hierarchical modeling approach assumes in the first stage that individual results (Yij) in each study are distributed around a study-level effect mean θi. In the second stage, the study means are distributed around an overall effect mean θ, with the model producing estimates for the pooled mean effects at both study and overall levels. The model took the following form:

i=1, … Nj, j=1, … M, where σij 2, фi 2, and τ2 are the result, study, and between-study variances, respectively, calculated over Nj results taken from M studies.

To model both lagged effects and absolute latitude of the population, the effect-specific mean δij was related to each study mean θi, lag (Lagij), and latitude (Latij) via the following regression equation:

the unit of the lag term being days, whereas absolute latitude was standardized to a 5-degree increase. The pooled study-mean effect sizes θi corresponded to the baseline state of 0 days lag and the mean latitude of the included studies. The latitude effect β1 was assumed to be linear, whereas the lag effect β0 was specified in 2 forms using linear and polynomial expressions, both based on the distributed lag model approach.50,51 These 2 specifications for the lag effect were compared in separate analyses using the deviance information criterion. We found that a polynomial model for the lag effect did not perform better; therefore, a linear term was used.

We implemented the meta-analyses using WinBUGS.52 Sampling used a burn-in of 20,000 Markov chain Monte Carlo iterations followed by a sample of 80,000 iterations. All pooled results for the study effect estimates (θi) were transformed to percent changes for presentation, and estimates for lag and latitude were converted to represent the percentage change in relative risk due to a 1-day or 5-degree increase, respectively. For comparison, the analyses were also rerun, replacing each location's latitude with average summer temperature.

Separate analyses were performed on studies related to respiratory and CV morbidities. We hypothesized that studies assuming a linear temperature relationship over all data would underestimate a heat effect due to the mixing of data from hot and cold periods. To account for this, analyses were also conducted on those studies that used either a nonlinear temperature relationship or restricted analyses to warm seasons only to specifically examine a heat effect. This was achieved by removing studies assuming a linear temperature relationship across all temperatures. Sufficient numbers of results were obtained to allow for further analyses of results related to stroke, acute coronary syndrome/MI, and asthma.

Sensitivity analyses were performed on subgroups based on temperature measures, age, and study design. As hot temperatures have been observed to have an immediate and short-term effect on respiratory and CV diseases, these sensitivity analyses were performed on same-day effect results only. The I 2 statistic53 was used to examine heterogeneity among studies, where increasing values (from 0% to 100%) denote increasing heterogeneity.

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In all, 2527 articles were identified by the systematic search. Through an examination of titles, abstracts, and full text, 2489 of these studies were excluded. The results of the search strategy are shown in Figure 1.

Figure 1

Figure 1

Of the remaining 38 studies, 4 reported population-standardized relative risks.33,54 56 Five studies provided effect estimates that were based on grouped temperature exposure levels rather than a unit change in temperature,36,57 60 and one study aggregated daily data over multiple days.31 Seven studies reported only correlations of hospital admissions with temperature.61 67 The remaining 21 studies were included in the meta-analysis. Table 1 shows descriptive information on these studies.

Table 1

Table 1

Among the included studies, 12 provided effect estimates for respiratory morbidity, 17 provided results for CV morbidity, and some studies examined both. Respiratory studies included total respiratory admissions and admissions for asthma, whereas CV morbidity included total CV admissions, and admissions for acute coronary syndrome, MI, and stroke. Populations were from climate zones ranging from temperate to tropical (eFigure, All age groups were examined, including the young75 and elderly.32,77

Only 3 studies specifically examined cold effects on morbidity28,68,83; therefore, our meta-analyses examined heat effects only. The majority of studies applied a time-series design using either generalized linear models (GLM) or generalized additive models (GAM), although some studies used a case–crossover design.68,81 83 Confounding variables were considered, such as air pollution, humidity or atmospheric pressure, and season.73,74,76 The most commonly used temperature definitions were daily mean and maximum temperatures, although minimum and apparent temperatures were also used. Lagged effects were considered in most studies and ranged from 1 to 28 days. One study found that effects weakened considerably for lags longer than 13 days.84

Several approaches were used to model the relationship between temperature and morbidity. Eleven studies examined heat effects using either a nonlinear relationship incorporating a particular threshold or a linear relationship over summer data only.28,68,81 83 For those specifying a threshold, values were identified using various techniques.70,79,84 In the absence of a derived threshold, several studies used a specific percentile of temperature to test for the presence of a heat effect.73,77,80 From those studies where explicit threshold values were provided, temperatures associated with heat effect estimates ranged from 19.3°C (minimum temperature) to 41.5°C (maximum temperature).

Eleven studies assumed a linear temperature effect over all temperatures. These included 3 studies75,76,78 that did not consider a nonlinear temperature effect, along with 7 studies38,39,69,71,72,74,84 that tested for nonlinear effects but found none that were statistically significant. One study32 applied a nonlinear effect model but reported linear effect estimates. These studies proposed several reasons for the lack of nonlinearity, including the weak effect of hot temperatures observed on MI or stroke, the temperate environments in which the studies were based, and population adaptation. The results extracted from each study are presented in Table 2.

Table 2

Table 2

Figure 2 shows the meta-analysis results for studies of respiratory morbidity. The pooled effect estimate for all studies was a 2.0% increase (95% posterior interval = −1.4% to 5.5%) in respiratory morbidity for a 1°C increase in temperature. After removing 4 studies that assumed a linear relationship between temperature and respiratory morbidity, the pooled effect estimate increased to 3.2% (−3.2% to 10.1%).

Figure 2

Figure 2

No temperature effect on CV morbidities was observed when all studies were included (−0.1% [−1.8% to 1.6%]) (Fig. 3) or after removing 8 studies that assumed a linear relationship between temperature and CV morbidity (−0.5% [−3.0% to 2.1%]).

Figure 3

Figure 3

The effect of latitude and lag varied somewhat across both morbidity subgroups (Table 3). The risk of respiratory morbidity decreased with increasing lag (−0.47 [−1.71 to 0.78]), whereas risk of CV morbidity increased (0.29 [−0.46 to 1.04]). An increase of 5 degrees in mean latitude for each group had little effect on the risk of either respiratory morbidity (0.03 [−2.19 to 2.31]) or CV morbidity (0.13 [−0.91 to 1.17]). When the analysis was performed using average summer temperature in place of latitude for each study, average summer temperature had little effect on respiratory morbidity (0.16 [−0.76 to 1.08]) or CV (−0.03 [−0.54 to 0.48]) morbidity.

Table 3

Table 3

Subgroup analyses were performed for stroke, acute coronary syndrome/MI, and asthma (Fig. 4). The pooled results for stroke (−1.0% [−11.3% to 10.5%]) and acute coronary syndrome/MI (1.0% [−7.0% to 9.7%]) were similar to those for CV morbidity as a whole. No effect was observed for asthma (0.3% [−11.8% to 14.1%]), for which 4 studies were available.

Figure 4

Figure 4

Table 4 shows pooled results for subgroups in the sensitivity analyses. The findings for same-day heat effect for respiratory morbidity (3.3% [−2.7% to 9.6%]) and CV morbidity (−0.3% [−2.8% to 2.4%]) did not differ substantially from the pooled results incorporating lagged effects. An analysis of only those studies that used mean and maximum temperatures resulted in a slight increase in respiratory morbidity risk (4.4% [−3.1% to 12.5%]). Respiratory risk was higher after removing studies with a case–crossover study design (5.1% [−5.9% to 17.3%]) and lower after excluding studies of the elderly (1.3% [−2.7% to 5.5%]).

Table 4

Table 4

For both respiratory and CV morbidity, I 2 values were mostly on the order of 88% to 100%, indicating large between-study heterogeneity and supporting the use of random effects models.

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This meta-analysis assesses available literature reporting quantitative estimates of ambient temperature effects on morbidity. The analysis found inconsistencies in the pooled effect estimates of temperature on cardiorespiratory morbidity. Respiratory hospitalizations were weakly associated with a 1°C increase in ambient temperature. There was no apparent association found for CV morbidity, with a mean percent change of close to zero.

The weaker association of heat with CV morbidity than with respiratory morbidities is consistent with previous findings. Although a heat effect has been reported for respiratory hospital admissions,70,85 several studies22,24,39,86 have suggested weak or absent associations of hot temperatures with CV morbidity, whereas cold temperatures are more strongly related to CV morbidity. Cold effects are often due to potential complications associated with decreased CV performance and the effects of respiratory infections, which are more common in winter; this combination of medical conditions is less prevalent in the warmer months of the year. One large study80 from the European Union noted stronger temperature effects on respiratory than on CV morbidities, suggesting that an increase in out-of-hospital deaths before medical treatment for acute CV events might explain this difference. The observation that vulnerable people die instead of being admitted to hospital has been made elsewhere45 and is a potential explanation for the smaller morbidity effect of heat observed in this study compared with previously reported mortality effects.6,87 Previous research88 has shown little evidence of a specific heat effect for MI, which may also explain the weak CV results.

Heat effects are generally immediate and short-term.33,89 Our results for respiratory morbidity support these observations, with the lag coefficient for the heat effect showing a reduced effect as lag increased. Lag coefficients increased for CV morbidity; however, only short lags were included in the meta-analysis, and therefore extrapolation to longer lags may be problematic.

An increase in latitude had little effect on respiratory or CV morbidity, although the direction of effect for CV morbidity was consistent with findings elsewhere18,35,90; that is, the association with heat increases at higher latitudes (colder climates). The adaptive capability of specific populations has been cited as a primary reason as to why people in colder climates are more affected by warmer temperatures. In general, such populations are less acclimatized to high temperatures, live in houses that are unsuitable in dealing with hot weather, and lack adaptive methods such as air conditioning.

The variability among study results, along with heterogeneity observed in the sensitivity analyses, may be related to a number of factors. Although the location of the studies would be expected to contribute to this heterogeneity, the fact that most studies were performed in temperate regions, and that the effect of latitude was unimportant, supports pooling of the studies. Other factors, including different study periods, demographics of each population, and socioeconomic conditions, may also contribute to heterogeneity. For example, such differences can be seen in studies that take account of adaptive factors such as air-conditioning usage.82 Differences in both the design and modeling methods applied in each study, including choice of confounding variables, may also have contributed to between-study heterogeneity.

The review has a number of strengths. First, it is the first time that a meta-analysis approach has been used to assess the available literature related to the effects of heat on morbidity. Second, through the implementation of a Bayesian hierarchical model, multiple results from individual studies could be included in the same meta-analysis. Additionally, the modeling approach provided a convenient method to directly incorporate and assess effects of lags and latitude on the pooled estimates. Finally, the extensive nature of the search strategy covered multiple literature sources and so reduced potential publication bias. Although unpublished negative results would not be included in this study, the overall null results indicate that publication bias is not a major issue.

The study also has some limitations. First, the results are based on a small number of studies, particularly once separated according to type of morbidity. The studies cover limited geographical areas and therefore a limited range of climatic conditions. Caution should be exercised when generalizing the pooled results. Second, the meta-analysis excluded studies reporting only nonlinear splines, as there was no way to estimate the standard error of the heat slope; however, the number of such studies was small. Third, the lack of cold-effect studies and the difficulty assessing both cold and heat effects simultaneously meant that consideration of the complete temperature effect curve36,59 was not possible. Important variables such as the use of air conditioning and socioeconomic and demographic factors were also lacking.16,82 Finally, given that the included studies generally did not report for lags longer than 4 days, it is difficult to interpret the estimates of delayed effects for longer lag periods. This probably had minimal impact, however, given the short-term, immediate nature of heat effects that were observed.

The focus here was on hospitalization. Future studies might consider analysis of general practitioner consultations,91 particularly as a means to develop strategies for the early detection of temperature-related morbidity. Studies of this type might also help to detect health effects of temperature that were not sufficiently serious to cause hospitalization, but still cause important morbidity. Extended periods of heat were not examined here. Heat waves may play a stronger role in heat-related health effects than extremes in ambient temperature.85,92,93

This study examined the effect of temperature on morbidity using a meta-analysis that incorporated a Bayesian hierarchical modeling approach. We found a potential heat effect on respiratory morbidity, but with no apparent effect on CV morbidity. Lagged effects differed in direction between respiratory and CV morbidity. This study adds to the current research on temperature effects on cardiorespiratory morbidity, particularly in relation to potential differences in effect between respiratory and CV morbidity. Such effects need to be more thoroughly understood, to ensure effective public health strategies minimize and prevent heat-related morbidity.

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We thank Kerrie Mengersen and Weiwei Yu for their advice during the preparation of this manuscript.

© 2012 Lippincott Williams & Wilkins, Inc.