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Ambient temperature and morbidity: a review of epidemiological evidence.

OBJECTIVE: In this paper, we review rhe epidemiological evidence on the relationship between ambient temperature and morbidity. We assessed the methodological issues in previous studies and proposed future research directions.

DATA SOURCES AND Data EXTRACTION: We searched the PubMed database for epidemiological studies on ambient temperature and morbidity of noncommunicabie diseases published in refereed English journals before 30 June 2010. Forty relevant studies were identified. Of these, 24 examined the relationship between ambient temperature and morbidity, 15 investigated the short-term effects of heat wave on morbidity, and 1 assessed both temperature and heat wave effects.

Data SYNTHESIS: Descriptive and time-series studies were the two main research designs used to investigate the temperature-morbidity relationship. Measurements of temperature exposure and health outcomes used in these studies differed widely. The majority of studies reported a significant relationship between ambient temperature and total or cause-specific morbidities. However, there were some inconsistencies in the direction and magnitude of nonlinear lag effects. The lag effect of hot temperature on morbidity was shorter (several days) compared with that of cold temperature (up to a few weeks). The temperature--morbidity relationship may be confounded or modified by sociodemographic factors and air pollution.

CONCLUSIONS: There is a significant short-term effect of ambient temperature on total and cause-specific morbidities. However, further research is needed to determine an appropriate temperature measure, consider a diverse range of morbidities, and to use consistent methodology to make different studies more comparable.

Key WORDS: climate change, heat wave, hospital admission, morbidity, review, temperature. Environ Health Perspect 120:19-28 (2012). [Online 8 August 2011]

It is widely accepted that climate change is occurring and that it is caused mainly by increased emissions of anthropogenic greenhouse gases, particularly over the last few decades [Intergovernmental Panel on Climate Change (TPCC) 2007a]. Global mean temperature increased by 0.07[degrees]C per decade between 1906 and 2005, compared with 0.13[degrees]C per decade from 1956 to 2005 (IPCC 2007b). Not only has the average global surface temperature increased, but the frequency and intensity of temperature extremes have also changed [IPCC 2007a; World Health Organization (WHO) 2008]. Heat wave episodes have been associated with significant health impacts, for example, in 1995 in Chicago, Illinois (Semenza et al. 1999), in 2003 in Europe (Cerutti ct al. 2006; Johnson et al. 2005; Larrieu et al. 2008; Mastrangelo et al. 2007; Oberlin et al. 2010), in 2006 in California (Knowlton et al. 2009), and in 2009 in southeastern Australia (National Climate Centre 2009). In addition, episodes of extreme cold (cold spells) are a concern in high-latitude regions (Pattenden et al. 2003) such as Russia (Revich and Shaposhnikov 2008), the Czech Republic (Kysely et al. 2009), and the Netherlands (Huynen et al. 2001).

The effect of ambient temperature on morbidity is a significant public health issue. Every year, a large number of hospitalizations are associated with exposure to extreme ambient temperatures, especially during heat waves and cold spells (Juopperi et al. 2002; Michelozzi et al. 2009; Schwartz ct al. 2004; Semenza et al. 1999). For example, during the 1995 Chicago heat wave, it was estimated that there were 1,072 (11%) excess hospital admissions among all age groups, including 838 (35%) among those 65 years of age and older, with dehydration, heat stroke, and heat exhaustion as the main causes (Semenza et al. 1999). Actual numbers of morbidities may be greater than reported, because heat-or cold-related conditions may be listed as secondary diagnoses, and many studies have often considered primary diagnoses only (Kilbourne 1999; Semenza'et al. 1999). Both heat- and cold-related morbidities occur more frequently among che elderly, as they are more vulnerable to temperature changes (Johnson et al. 2005; Knowlton et al. 2009; Kovats et al. 2004; Panagiotakos et al. 2004). In addition, urban residents may be exposed to higher temperatures than residents of surrounding suburban and rural areas because of the "heat island effect" resulting from high thermal absorption by dark paved surfaces and buildings, heat emitted from vehicles and air conditioners, lack of vegetation and trees, and poor ventilation (Barry and Chorley 2003; Hajat and Kosatsky 2009; O'Neill and Ebi 2009). Because of the urban heat island effect, people in urban areas are usually at an increased risk of morbidity from ambient heat exposure (O'Neill and Ebi 2009). The morbidity effect of temperature is likely to become more severe as the number of elderly people increases (from 737 million persons > 60 years old in 2009 to 2 billion by 2050 globally) and the proportion of urban residents increases (by approximately 18% over the next 40 years) and because climate change will continue for at least the next several decades, even under the most optimistic scenarios [IPCC 2007a; United Nations Department of Economic and Social Affairs (UNDESA) 2010a, 2010b].

In this paper, we assess the current epidemiological evidence concerning the effects of temperature on morbidity, identify knowledge gaps in this field, and make recommendations for future research directions.


The PubMed electronic database was used to retrieve published studies examining the relationship between ambient temperature and morbidity of noncommunicable diseases (we excluded communicable diseases such as vector-borne diseases, as the research designs and analysis methods differ between communicable and noncommunicable diseases). Our primary search used the following U.S. National Library of Medicine Medical Subject Headings (McSH terms) and key words: weather, climate, temperature, morbidity, hospitalization, emergency medical services, family practice, primary health care, heat wave, cold surge, and cold spell. All subterms were included, and we limited the search to original epidemiological studies published in English before 30 June 2010.

To examine the relationship between ambient temperature and morbidity, all relevant studies were included in this review. Eligibility included any epidemiological studies that used original data and appropriate effect estimates [e.g., regression coefficient, relative risk (RR), odds ratio (OR), percent change in morbidity, and morbidity or excess morbidity after heat waves]; where ambient temperature or a composite temperature measure was a main exposure of interest; and where the outcome measure included a noncommunicablc disease (e.g., cardiovascular, cerebrovascular, or respiratory diseases). Titles and abstracts were screened for relevance, and full texts were then obtained for further assessment if papers met the inclusion criteria. We also inspected the reference list of each article to check if any studies were missed from the primary electronic search.


A total of 614 articles were identified from the PubMed (National Library of Medicine 2010) database, and 76 initially met the eligibility criteria for full-text inspection after reading the abstracts (Figure 1). We excluded 41 articles because 3 had no original data, 27 assessed only the effect of season or broad weather conditions, and 11 did not report appropriate effect estimates. Five studies were added after manually inspecting the reference lists of all relevant articles. Finally, 40 articles were included in the review. Of these, 24 examined the relationship between general ambient temperature and morbidity, 15 investigated short-term effects of heat waves on morbidity, and 1 assessed both general ambient temperature-related and heat wave-related health effects.

Methodological Considerations

Study designs and statistical approaches. A variety of study designs were used to assess the health effects of heat waves and cold spells and to characterize the association berween temperature and morbidity. Most studies employed either a descriptive or time-series study design. Statistical methods varied with study design.

Descriptive studies. Simple comparisons were applied in the analysis of health effects of isolated heat waves in seven studies (Cerutti et al. 2006; Ellis et al. 1980; Johnson et al. 2005; Jones et al. 1982; Knowlton et al. 2009; Rydman et al. 1999; Scmenza et al. 1999) in addition to studies where risk factors and illnesses studied during heat waves and cold spells were often characterized in details. To assess effects of heat waves on morbidity, most of the studies estimated an excess proportion by comparing observed versus expected morbidity. Manv methods were used to calculate expected morbidity, which largely depended on the chosen baseline. Usually, expected hospital admissions were based on the average number of admissions during comparison days or weeks, for example, the days prior to or after a heat wave, or the same time period in previous years without heat waves (Huynen et al. 2001; Johnson et al. 2005; Semenza et al. 1999; Yang et al. 2009). Although such comparative analyses can provide useful insights into the short-term response of the population to a heat wave or cold spell event, they may underestimate or overestimate effects because of the use of an inappropriate baseline, potential morbidity displacement, and lack of control for confounding factors (e.g., air pollution).

Time-series studies. Time-series studies have been widely used to examine short-term effects of temperature on morbidity (Kovats et al. 2004; Linares and Diaz 2008; Michelozzi et al. 2009; Schwartz et al. 2004). Morbidity counts or rates were usually used as che outcome measures, whereas tempera-cure measurements at corresponding intervals were employed as exposure indicators. Time-series analysis using daily data was commonly applied, but weekly or monthly data were used in some studies, which may make it difficult to detect acute temperature effects on morbidity (Roger and Francesca 2008; Touloumi et al. 2004). Effects were often estimated as the percent change in morbidity per unit increase (or decrease) in temperature (e.g., one or several degrees Centigrade or interquartile range change) (Ebi et al. 2004; Green ct al. 2009; Koken et al. 2003; Lin et al. 2009). In this design, confounding is limited to time-varying factors such as air pollution, influenza epidemics, season, holiday (e.g., Christmas, New Year), and the day of the week (which could be taken into account in multivariable models).

In general, both hot and cold extremes of temperature have an adverse effect on health, which suggests a potential nonlinearity of the temperature effect. Thus, Poisson regression through generalized additive models (GAM) was widely used to assess the temperature-morbidity relationship after adjustment for long-term effects, seasonality, and other seasonally varying factors (Barnett et al. 2005; Ren et al. 2006; Schwartz ec al. 2004). Alternatively, analyses were stratified by summer/winter or warm/cold periods to remove seasonal patterns and simplify analyses (Lin et al. 2009; Michelozzi et al. 2009; Piver et al. 1999; Wang et al. 2009; Ye et al. 2001). Appropriate temperature thresholds were selected based on model fit (Kovats et al. 2004) or selected cutoff (e.g., percentiles or absolute values of the temperature distribution) (Michelozzi et al. 2009), which facilitated the analvsis of health effects of temperature extremes.

Exposure measurements. Mean daily temperature (Kovats et al. 2004; Liang et al. 2008; Schwartz et al. 2004) was a simple and common temperature indicator. Minimum (Ebi et al. 2004; Linares and Diaz 2008) and maximum temperatures (Linares and Diaz 2008; Wang et al. 2009) were also used in many studies. Diurnal temperature range was reported to be a risk factor for patients suffering from cardiovascular and respiratory diseases (Liang et al. 2008, 2009). Other studies used biometeorological indices such as apparent temperature (Green et al. 2009; Michelozzi et al. 2009) and Humidex (Mastrangelo et al. 2007). These perceived indices combine air temperature and humidity and are considered to be better measures of the effect of temperature on the human body than is temperature alone. However, no single temperature measure was reported to be superior to the others to predict the mortality (Barnett et al. 2010).

In examining the effect of heat waves (and cold spells), the first thing to be considered is the definition of the exposure, which may vary with geographic location and climatic condition because the sensitivity of populations to heat stress varies geographically (Hansen et al. 2008a; Knowlton et al. 2009; Kovats et al. 2004; Revich and Shaposhnikov 2008; Robinson 2001). As heat effects in one area may not be applicable to another area, mukicity studies were recently conducted to assess general heat effects (Anderson and Bell 2009; Green et al. 2009; Michelozzi et al. 2009). Besides heat wave intensity, heat wave duration is also an important risk factor in estimating the health effect of heat episodes (Mastrangelo et al. 2007). Vulnerability to heat stress depends on many factors, such as age, preexisting diseases, environmental humidity, and adaptative response (Bouchama and Knochel 2002; Cui et al. 2005; Parsons 2003). A long heat wave could lead to accumulated heat stress on the body when heat produced and obtained from the environment overwhelms the heat loss by thermoregulation. Over consecutive hot days without cooler nights, individuals may suffer from thermoregulatory failure, increasing the risk of illnesses (Bouchama and Knochel 2002; Parsons 2003). There is also evidence that the effect of extreme cold might increase with increasing duration, as low temperature can lead to cardiovascular stress by increasing platelet counts, red cells, blood viscosity, plasma cholesterol, fibrinogen, and blood pressure and increase susceptibility to pulmonary diseases by causing bronchoconstriction (Hong et al. 2003; Huynen et al. 2001; Keatinge et al. 1984; Mercer 2003).

Outcome measurements. Although admissions for some heat-rekted conditions such as heat stroke, heat exhaustion, fluid and electrolyte abnormalities, and acute renal failure were higher during heat waves (Hansen et al. 2008b; Knowlton et al. 2009; Semenza et al. 1999), actual numbers were assumed to be underestimated, as many cases were likely to be coded cardiovascular or respiratory diseases in primary diagnoses. As a result, some researchers recommend that primary and secondary discharge diagnoses be considered together to reduce misclassification of heat-related diseases (Kilbourne 1999; Semenza et al. 1999). The common causes of morbidity evaluated in previous studies included total cardiovascular and respiratory diseases (Lin et al. 2009; Linares and Diaz 2008; Michelozzi et al. 2009; Ren et al. 2006) and specific diseases such as stroke (Kyobutungi et al. 2005; Ohshige et al. 2006; Wang et al. 2009), acute myocardial infarction (Chang et al. 2004; Ebi et al. 2004; Schwartz et al. 2004; Ye et al. 2001), and acute coronary syndrome (ACS; Liang et al. 2008; Panagiotakos et al. 2004).

Some direct cold injuries occur during winter, such as frostbite and hypothermia (Hassi et al. 2005; Juopperi et al. 2002). Ischemic stoke (Hong et al. 2003), coronary events (Barnett et al. 2005), and cardiovascular and respiratory diseases (Hajat et al. 2004; Hajat and Haines 2002) were reported in the studies of cold temperature morbidity. No study has investigated the morbidity after a cold spell, whereas only a few studies examined cardiovascular and respiratory mortality of extreme cold temperatures (Huynen et al. 2001; Kysely ct al. 2009; Revich and Shaposhnikov 2008).

Major Findings

A number of studies examined die relationship between ambient temperature and morbidity. These studies identified the general risks of temperature as well as temperature extremes in multiple areas over time, using different research designs. Table 1 summarizes the findings of ambient temperature--morbidity studies, whereas Table 2 summarizes the findings of hem wave studies.

Threshold effects of temperature. A nonlinear relationship between temperature and morbidity was evident across different studies that illustrated U-, V-, or J-shaped patterns (Kovats et al. 2004; Liang et al. 2008; Lin et al. 2009; Linares and Diaz 2008), with the minimum morbidity at a certain temperature or temperature range (threshold temperature) and increased morbidity below and above the threshold. However, few studies identified clear threshold temperatures based on model fit (Kovats et al. 2004; Lin et al. 2009).

There is some evidence that both hot and cold threshold temperature for morbidity vaiy by location. For example, in a study in New York City, hospital admissions for respiratory diseases increased at temperatures > 28.9[degrees]C (Lin et al. 2009). However, the threshold temperature of respiratory hospital admissions in London, United Kingdom, was lower (23[degrees]C) (Kovats et al. 2004), as the cooler summers resulted in lower acclimatization to high temperature. The cold threshold temperature also differed for each region in Quebec, Canada, in winter (Bayentin et al. 2010).

Different thresholds have also been identified for different diseases. A large increase in emergency hospital admissions was observed for respiratory diseases at temperatures > 23[degrees]C in Greater London, whereas admissions for renal diseases increased above a lower temperature of 18[degrees]C (Kovats et al. 2004).

Magnitude of the effects of temperature and heat wave. Consistent with expectations that the relation between temperature and morbidity will follow a V- or J-shaped curve, a study in Taiwan reported that emergency room admissions for acute ACS were lowest for temperatures of 27-29[degrees]C. Compared with this baseline range, ACS admissions were 28.4% higher for average daily temperatures in the range of 17-27[degrees]C (with a slight increase > 29[degrees]C) and 53.9% higher for temperatures < 17[degrees]C (Liang et al. 2008). To fully assess the shape of the association between temperature and morbidity, it is necessary to evaluate associations across the entire temperature range throughout a year. Studies focused on associations during hot or cold seasons only usually show a linear association of temperature with morbidity. For example, Lin et al. (2009) reported increased counts of cardiovascular [3.6%; 95% confidence interval (CI): 0.3, 6.9J and respiratory diseases (2.7%; 95% CI: 1.3, 4.2) with a 1[degrees]C increase in temperature during the summer in New York City, whereas a study in Brisbane reported a decreased risk of emergency admissions for primary intracerebral hemorrhage with a 1[degrees]C increase in minimum temperature (RR = 0.95; 95% CI: 0.91, 0.98) during the winter (Wang et al. 2009). In contrast, a study of 12 European cities revealed that the association between temperature and cardiovascular and cerebrovascular hospital admissions tended to be negatively linear but did not reach statistical significance during hot seasons (Michelozzi et al. 2009). However, some studies that evaluated associations over the entire year also reported evidence of linear versus J-or V-shaped associations (Panagiotakos et al. 2004; Schwartz et al. 2004). For example, in 12 U.S. cities, average temperature was positively related to hospital admissions for heart diseases among adults [greater than or equal to] 65 years old (Schwartz et al. 2004). Cardiovascular, respiratory, and cerebrovascular diseases comprise many subtypes that might react to temperature in different ways (Dawson et al. 2008; Lin et al. 2009; Wang et al. 2009; Ye et al. 2001). For example, hemorrhage stroke and ischemic stroke hospital admissions, both of which would be classified as cerebrovascular diseases, showed opposite relationships to temperature increases in California (Green et al. 2009). Additionally, an interquartile range increase in maximum temperature during hot seasons in Denver, Colorado, was associated with a 12.5% and 28.3% decrease in risk of hospitalization for coronary atherosclerosis and pulmonary heart disease, respectively, compared with a 17.5% increase for acute myocardial infarction among the elderly (Koken et al. 2003). These results suggested that patients with chronic rather than acute cardiovascular conditions might avoid outdoor exposures during unfavorable weather, resulting in a null or negative association. Moreover, if appointments for mild diseases are postponed or cancelled during extremely hot or cold periods, the effect of temperature on morbidity might be underestimated.

Despite evidence of variation among specific diseases, increased overall morbidity has been consistently associated with heat waves. For example, during a Chicago, Illinois, heat wave in 1995, there were 838 (35%) more hospital admissions of the elderly ([greater than or equal to] 65 years old) compared with the average number of admissions during comparable weeks (Semenza et al. 1999). A total of 16,166 (3%) excess emergency department visits and 1,182 (1%) excess hospitalizations occurred in California during the 2006 heat wave (Knowlton et al. 2009). In England, the 2003 heat wave caused an excess of 1% total emergency hospital admissions (Johnson et al. 2005). In a study in Adelaide, Australia, Nitschke et al. (2007) reported a 4% and 7% increase in total ambulance cransporr and hospital admissions during heat waves, respectively, compared with non-heat wave periods.
Table 1. Characteristics of the ambient temperature-morbidity
studies (n = 25).

Study            Location and time   exposure

Studies of both hot and cold exposure

Ebi et al. 2004   Three U.S           Minimum and
                 regions; 1983-1997      maximum
                 and 1
                 January-June 1998   temperature

Schwartz el al.  Twelve U.S.         Daily mean
2004             1986-1994           temperature

Bayentin et al.    Quebec. Canada; 1   Mean
                 April               temperature
2010             1989-31 March 2006

Qhshige et al.     Yokohama, Japan:    Mean
2006             1992-2003

Liang et al.      Taichung, Taiwan;   Mean
2008             1 January 2000-     DTR
                 31 March 2003

Liang et al.      Taichung, Taiwan;   Mean
2009             2001-2002           DTR

Ren et al. 2006   Brisbane.           Minimum
                 1996-2001           temperature

Wang et al.      Brisbane,           Minimum and
2009             summer and winter,  maximum
                 1996-2005           Temperature

Rothwell et al.   Oxfordshire,        Mean
                 United              temperature
1996             Kingdom; 1980s

Panagiotakos    Athens, Greece;     Daily mean and
Et al 2004        2001-August 7002    minimum and maximum
                                     temperature, THI

Kyobutungi       Heidelberg.         Maximum
et al. 2005       August              temperature and
                 2000                24-hr
                                     in maximum

et al.            Scotland; 1 May     Mean
                 1990-               and
2008             22 June 2005        minimum and
                                     mean temperature
                                     change over the
                                     preceding 24 and
                                     48 hr

Chang et al.     Seventeen           Monthly mean
2004             worldwide           temperature
                 Africa, Asia,
                 Europe, and
                 Latin America),
                 1989-January 1995

Hot exposure only

Koken et al,     Denver, CO, United   Maximum
20Q3             July-August,         temperature

Green et al.      Nine U.S.           Mean apparent
2009             counties; May-       temperature

Lin et al. 2009  New York, United     Mean
                 States;              temperature,
                 summer, 1991-2004    mean apparent
                                      3-day moving
                                      average of

Piver et al.      Tokyo, Japan; July  Daily maximum
1999             August 1980-1995     temperature

Ye et al. 2001    Tokyo, Japan; July  Daily maximum
                 August 1980-1995     temperature

Kovats et al.      Greater London,    Three-day
2004             Kingdom; 1 April     moving average
                 1994-31 March 2000   temperature

and              Madrid, Spain;      variable Maximum
                 May-                and
Diaz 2008        September,          minimum
Michelozzi       Twelve European     Maximum
etal.2009        April-September,    apparent
                 city > 3 years      temperature
Cold exposure

Hong et al.        Incheon, Korea;   Daily average
2003             1998-2000           temperature,
                                     3-hr average

Hajat and        London. United      Mean temperature

Haines 2002      January 1992-
                 September 1995

Hajat et al.      United Kingdom;     Mean
2004             1992-2001

Barnett et al.   Twenty-four         Mean
                 populations         temperature
2005             worldwide,
                                     Research design and

Study            Outcome             statistical analysis
Studies of both

Ebi et al. 2004   Hospitalizations    Time-series; Poisson
                 for AMI, angina     regression, GEE
                 pectoris. CHF,

Schwartz el al.  Urgent hospital     Time-series; Poisson
2004             admissions for      regression.
                 heart disease and   distributed lag
                 MI, > 65 years      models

Bayentin et al.    Hospitalization     Time-series; GAM
2010               for IHD

Qhshige et al.     Stroke incidence    Time-series; Poisson
2006             < m emergency        Tegression, ordinary
                 transport events,   least squares
                 > 50 years of age   regression

Liang et al.      Emergency room      Time-series; Poisson
2008             admissions for       regression

Liang et al.      Emergency room      Time-series; Poisson
2009             admissions for       regression

Ren et al. 2006   Hospital            Time-series; Poisson
                 and emergency       GAM, nonparametric
                 visits for CVD      bivaiator
                 and RD              response model.

Wang et al.      Emergency           Time-series; GEE
2009             admissions for PIH
                 and IS

Rothwell et al.   First ever in a     Chi-square
1996             lifetime stroke
Pa nag iota kos  Nonfatal ACS in     Time-series; GAM
etal 2004        emergency units

Kyobutungi       IS incidence        Case-crossover,
et al. 2005                           conditional logistic
                                     Research design and

Study Dawson     Outcome Hospital    statistical analysis
et al.            admissions          Time-series; negative
2008             for acute stroke    binomial regression,
                                     Poisson regression
Chang et al.     Monthly number of   Time-series; negative
2004             newly diagnosed     binomial regression
                 cases of VTE,
                 stroke, or AMI,
                 women 15-49
                 years of age

Hot exposure only

Koken et al,     Hospital            Time-series; Poisson
2003             for CVD,> 65 years  regression, GLM,
                 of age              GEE

Green et al.      Hospital            Case-crossover;
2009             for CVD. RD.        conditional logistic
                 diabetes.           regression, meta-
                 dehydration, heat   analysis
                 stroke, to fest
                 diseases, and ARF

Lin et al. 2009  Hospital            Time-series; GAM,
                 for CVD and RD      linear-threshold

Piver et al.      Emergency           Time-series; GLM,
1999             cases for heat      GEE

Ye et al. 2001    Hospital emergency  Time-series; GLM,
                 transports for CVD  GEE
                 and RD> 65 years
                 of age

Kovats et al.    Emergency           Time-series;
2004             admissions for      autoregressive
                 CVD, RD. CD, renal  Poisson regression,
                 disease, ARF,       hockey-stick model
                 calculus of the
                 kidney and ureter
                                     Research design and

Study Linares    Outcome Emergency   statistical analysis
and              hospital            Time-series; ARIMA
Diaz 2008        admissions for all
                 causes, RD, and

Michelozzi       Hospital admission  Time-series; GEE,
Et al.2009        for CVD, CD.        random effect
                 And RD               meta-analysis
Cold exposure

Hong et al        IS onset            Case-crossover;
2003                                 conditional logistic

Hajat and        3P consultation     [toe-series; GAM
Haines 2002      for RD and CVD.
                 adults > 65 years
                 of age

Hajat et al.      GP consultations    rime-series; GLM
2004             RD, adults > 65
                 years of age

Barnett et al.   Daily records of    rime-series;
2005             coronary events.    distributed lag
                 persons 35-64       model, hierarchical
                 years of age        meta-regression;
                                     logistic model,
                                     hierarchical model

Study            Key findings                     Comments
Studies of both

Ebi et al. 2004   Temperature changes              Normal weather
                 (3[degrees]C increase in         periods and
                 maximum temperature or           El Nino events were
                 3[degrees]C decrease             analyzed
                 in minimum temperature)          separately and
                 increased                        combined; no air
                 hospitalizations for residents   pollution was
                 > 70 years                       controlled for
                 of age by 6-13% in San
                 Francisco and by
                 6-18% in Sacramento; small
                 changes in
                 Los Angeles
                 Association varied by region,
                 age, and sex
                 Lag: 7 days

Schwartz el al.  Positive linear relation for     Systematically
                 all heart diseases               examined
2004                                              temperature and
                                                  morbidity in
                 RR- 1.15(0 96.1.37) increased    several U.S. cities
                 risk of 80[degrees]F             with various
                 (compared with 0[degrees]F)      climates; air
                                                  pollution was not
                 Harvesting effect iwithin 10     controlled lor as
                 days) in hot                     confounder
                 temperatures but not in cold
                 Similar but smaller effects of
                 for Ml admissions
                 Lag; 0,1 day

Bayentin et al.   V- or U-shaped curves            No air pollution
                                                  was controlled
2010             Threshold different for each     only description of
                 region and for                   deprivation
                 both sexes                       indexes presented,
                 Lag duration dependent on the    incorporated it
                 region                           into the model
                 High admissions observed
                 earlier among
                 adults in the > 65 year age
                 group; high
                 excess risks associated with
                 high smoking
                 prevalence and high deprivation
                 (material or social)

Qhshige et al.   Significant negative effect of   Ranges rather than
                 mean                             actual values
2006             temperature on the stoke         of temperature,
                 incidence of the                 humidity and
                 emergency transport events       barometric pressure
                                                  were used;
                                                  no air pollution
                                                  was controlled for

Liang et al.      28.4% increase risk for          Only one hospital
                 17-27[degrees]C and 53.9%        was included
2008             for < 17[degrees]C (reference
                 27-29[degrees]C of mean
                 34.4% increase risk for >
                 9.6[degrees]C (reference
                 <5.8[degrees]C of DTR)

Liang et al.      RR = 1.2 for 22.95               Only one hospital
                 -26.58[degrees]C and RR = 1.5    was included
2009             for < 22.95[degrees]C
                 (reference 29.42[degrees]C of
                 RR = 1.14 for > 9.6[degrees]C
                 (reference < G.6[degrees]C
                 of DTR)

Ren et al. 2006   PM10 modified the effects of    First to examine
                 temperature                      the PMm
                 on respiratory and               modification of the
                 cardiovascular hospital          association
                 admissions with enhanced         between temperature
                 adverse effects                  and health
                 at high level, but no clear      outcomes
                 evidence for
                 emergency visits
                 Lag: 0-2 days
Wang et al.      Different response of PIH and    First to examine
                 IS to                            the impact of
2009             temperature variation by         temperature
                 season                           variation on
                 [degrees]C increase in minimum   types of stroke
                 and maximum                      morbidity in a
                 temperature 15% (5-26%) and      subtropical city
                 (2-22%) for PIH among adults <
                 65 years
                 of age in summer; in winter,
                 1[degrees]C decrease
                 in minimum and maximum
                 temperature 6%
                 (2-10%) and 7% (4-11 %) for PIH
                 those ? 65 years of age
Rothwell et al.   No significant seasonal          Community study
                 variation was                    rather than
1996             reported. The incidence of       hospital-based
                 primary                          study was
                 intracerebral hemorrhage was     conducted to avoid
                 increased                        selection bias;
                 at low temperature, but not for  the incidence of
                 ischemic                         first ever in a
                 stroke or subarachnoid           lifetime stroke was
                 hemorrhage                       collected; no
                                                  contounders were
                                                  controlled for

Panagiotakos     Negative correlation between     No air pollution
                 hospital                         was controlled for
et al 2004        admissions for ACS and daily
                 1"C decrease in mean
                 temperature was
                 associated with a 5.0%
                 increase in hospital admissions
                 for ACS;
                 similar results for minimum and
                 temperatures and for THI.
                 association for females and the

Kyobutungi       No risk associated with ambient  Used both absolute
                 maximum                          temperature
et al. 2005       temperature and its 24-hr        and temperature
                 difference                       difference in
                                                  one day; no air
                                                  pollution was
                                                  controlled for

Study Dawson     Key findings 1[degrees]C         Comments No air
et al.            increase in mean temperature     pollution was
                 during the                       controlled for
2008             preceding 24 hrZ.1% (0.7-3.5%)
                 in ischemic stroke admissions

Chang et al.     Significant negative             Monthly mean values
                 associations with                were used;
2004             temperature for stroke and AMI,  no air pollution
                 but not                          was controlled for
                 for VTE
                 5[degrees]C increase in mean
                 temperature IRR = 0.93
                 (0.89, 0.97) for stroke and IRR
                 = 0.88 (0.80,
                 0.97) for AMI
                 Lag: within 1 month
                 No modification of age and high
                 blood pressure
Hot exposure only

Koken et al,     PC increase 17.5% (2.9 to        Only July and
                 34.3%), 13.2%                    August were
20Q3             (2.9-24.4%), -12.5% (-18.9 to    included
                 -5.5%), and
                 -28.3% (-38.4 to -16.5%) for
                 AMI, CHF,
                 coronary atherosclerosis, and
                 heart disease, respectively
                 Lag: 0,1 day
                 Male had higher numbers of
                 admissions than female

Green et al.      Per 10[degrees]F increase        GIS methods were
                 apparent temperature.            used to improve
2009             2.0% (0.7-3.2%) excess risk in   exposure
                 RD,                              assessment
                 3.7% pneumonia, 3.1% diabetes,
                 dehydration, 7.4% ARF, 404.0%
                 stroke, and -10.4% in
                 hemorrhagic stroke
                 Lag: 0
                 Effect differed by age, little
                 evidence of
                 effect modification of sex,
                 ethnicity, PM25,
                 ozone, and nonlinearity

Lin et al. 2009  1[degrees]C increase above mean  One city was
                 temperature                      included; first to
                 threshold 2.7% (1.25-4.16%) for  examine the
                 RD on the                        independent and
                 same day and 3.6% (0.32-6.94%)   joint effects of
                 for CD                           temperature and
                 on lag-3 day                     humidity; conducted
                 1[degrees]C increase above mean  analyses based on
                 apparent                         family income
                 temperature threshold 2.1%
                 and 1.4% (0.4-2.4%) for RD on
                 the same
                 day and 1 day later, 2.5%, 2.1
                 %, and 3.6%
                 at 1,2, and 3 days later,
                 for CD
                 Lag: 0-3 days
                 Positive interaction between
                 temperature (> 29.4[degrees]C)
                 and humidity
                 Greater increases of CVD and RD
                 in Hispanic persons, the
                 elderly, and
                 tow-income persons; sex and
                 disease type
                 interacted with temperature

Piver et al.      Daily maximum temperature        Only July and
                 associated with                  August were
1999             heal stroke                      included
                 Greater number of beat stroke
                 transport cases for males than
                 for females;
                 smallest risk for females 0-14
                 years of
                 age and the greatest risk for
                 males > 65
                 years of age

Ye et al. 2001    Except hypertension and          Only July and
                 pneumonia, daily                 August were
                 maximum temperature not          included. Several
                 associated' with                  specific
                 hospital emergency transport     diseases were
                 1 [degrees]C increase 3.8%
                 (2.0-5.0%) increase in
                 pneumonia and 1.4% (0.4-2.0%)
                 in hypertension

Kovatset al.      No relation between total        Contrasting
                 emergency                        patterns of
2004             hospital admissions and high     and hospital
                 temperature;                     admissions during
                 1[degrees]C above threshold      hot weather
                 5.44% (1.92-9.09%)
                 for RD, 1.30% (0.27-2.35%) for
                 disease, 0.24% (0.02-0.46%)
                 children < 5 years of age, and
                 (4.44-17.67%) for RD for adults
                 in the
                 > 75 age group

Study Linares    Key findings V-shaped            Comments Data from
and              relationship                     one hospital were
Diaz 2008        1[degrees]C increase above
                 maximum temperature
                 threshold 36[degrees]C 4.6%
                 (0.9-8.4%) for all
                 causes in all age groups (lag
                 0), 17.9%
                 (9.5-26.0%) for all causes
                 among adults in
                 the > 75 year age group (lag
                 1), and 27.5%
                 (13.3-41.4%) for RD among
                 adults in the
                 > 75 year age group (lag 0); no
                 between heat (> 36CIC) and
                 admissions tor
                 CVD in all the age groups
                 Lag: 0.1

Michelozzi       No or tendentious negative       First attempt to
                 relationship                     evaluate the
et al. 2009       between temperature and CVD and  effect of
                 CD;                              temperature on
                 1[degrees]C increase above       morbidity outcomes
                 threshold 14.5%                  using a
                 (1.9-7.3%) in Mediterranean and  standardized
                 13.1%                            methodology in a
                 (0.8-5.5%) in North-Continental  multicenter
                 region                           European study
                 among adults in the > 75 year
                 age group
                 for RD, almost twice that for
                 all ages
                 Lag: 0-3 days

Cold exposure

Honget al.        IS onset was associated with     Used bidirectional
                 decrease                         control selection
2003             in temperature. One              scheme; assessed
                 interquartile range              lag structure
                 decrease in temperature          in hours
                 (17.4[degrees]C) OR = 2.9
                 (1.5-5.3) for IS on lag 1
                 Lag: 1 day, 24-54 h
                 Stronger effects in winter and
                 for women.
                 adults > 65 years of age,
                 persons, and those with
                 hypertension or

Hajat and        Vt decrease <5[degrees]C, 10.5%  Primary care data
                 (7.6-13.4%)                      could be
Haines 2002      increase in RD and 12.4%         influenced by
                 (0.7-25.4%)                      patient behaviors
                 in asthma; no relationship       and service
                 between cold                     availability (i.e.,
                 temperature and GP for CVD       time when a patient
                                                  can be seen
                                                  by a general
                                                  to convenient
                 Lag: 6-15 days

Hajat et al.      Linear association between low   Primary care data
                 temperature                      were used
2004             and an increase in RD in all 16
                 1[degrees]C decrease <
                 5[degrees]C, biggest effect
                 (13.6-24.7%) increase in
                 Norwich for
                 lower respiratory tract
                 infections; weaker
                 relationships for upper
                 respiratory tract
                 infections consultation
                 Lag: 0-20 days
                 Larqer effects in the north
                 than in the south
Barnett et al.   Daily rates of coronary events   Air pollutants and
                 negatively                       respiratory
2005             correlated with the average      infections were not
                 temperature                      controlled tor
                 Lao' 0-3 davs
                 Coronary event rates increased
                 mare in
                 populations living in warm
                 climates than in
                 cold climates
                 Greater increase for women than
                 for men
                 with the odds 1.07 (1.03,1.11)

Abbreviations: ACS, acute coronary syndrome; AMI, acute myocardial
infarction; ARF, acute renal failure; ARIMA, autoregressive integrated
moving average model; CD, cerebrovascular diseases; CHF, congestive
heart failure; COPD, chronic obstructive pulmonary disease; CVD,
cardiovascular diseases; DTR, diurnal temperature range; GAM,
generalized additive models; GEE, generalized estimating equations;
GIS, geographic information system; GLM, generalized linear models;
GP, general practitioner; IHD, ischemic heart disease; IRR,
incidence rate ratio; IS, ischemic stroke; OR, odds ratio; Ml,
myocardial infarction; PIH, primary intracerebral hemorrhage;
[PM sub 10], particulate matter < 10 pm in aerodynamic diameter;
RD, respiratory diseases; RR, relative risk; THI, thermo-hydrological
index; VTE, venous thromboembolism.

Lag structure of temperature. Some studies explored temporal patterns (lag structure) of the association between exposure to temperature over previous days and health risk on a particular day. Various lag days were reported for the association of temperature with morbidity, ranging from the same day (Green et al. 2009) to 1 month (Chang et al. 2004), with shorter lags during warmer seasons and longer lags during cooler seasons (Barnett et al. 2005; Hajat and Haines 2002). In a study of 12 U.S. cities, Schwartz et al. (2004) also reported that associations with hot temperatures were more immediate than with cold temperatures. Most recent studies have reported short-term effects of high temperature on the same day and the 3 days after heat exposure (Green et al. 2009; Koken et al. 2003; Lin et al. 2009). For example, Lin et al. (2009) observed that the greatest number of hospital admissions for respiratory and cardiovascular diseases was 0-1 days and 1-3 days after increased temperatures (Lin et al. 2009). Seven-day lag was used to evaluate the effect of temperature on hospital admissions for several specific cardiovascular diseases (Ebi et al. 2004). One-month lag has also been reported by a study that evaluated average temperatures over a monthly period across several whole years (Chang et al. 2004), but it was not clear whether the effects would have been more immediate if daily data had been evaluated. Hajat and Haines (2002) found a strong association between consultations for respiratory disease and mean temperature < 5[degrees]C over a 10-day period (i.e., 6-15 days before the consultation) in London, which implied a later and longer lag for cold temperatures than hot ones.

Harvesting effects of temperature. Evidence of a harvesting effect (e.g., mortality displacement) has been documented by studies of heat-related mortality (Braga et al. 2002; Muggeo and Hajat 2009) that showed an immediate increase in mortality followed by reduced mortality among susceptible people, consistent with a temporal advance in deaths that would have occurred later in time in the absence of exposure to heat or cold. However, the impact of harvesting on morbidity has not been fully investigated, and short-, intermediate-, and long-term effects should be examined to determine the impact of harvesting. Schwartz et al. (2004) reported evidence of a short-term advance in emergency hospital admissions for heart diseases and myocardial infarction among people [greater than or equal to] 65 years of age within a few days after high-temperature exposure, with a positive association on the day of admission followed by a period of lower-than-average admissions, returning to the baseline after a week. No evidence of a harvesting effect was observed for cold weather in this study (Schwartz et al. 2004). No other temperature-morbidity studies have formally investigated the harvesting issue.
Table 2. Characteristics of the heatwave-morbidity studies (n = 16).

Study           Location and   Main          Outcome
                time           temperature

Ellis et al.      Birmingham,    2-week heat   Mortality and
1980             United         wave          morbidity
                 Kingdom; 24    with the
                 June-          reference
                 8 July 1976    period
                               before and
                               after the
                               heat wave.
                               same days in
Applegate et al  Memphis, TN,   and 975)      Heat-related
                                Heat wave     emergency
1981             United                       room visits,
                 States;                      hospital
                 25 June-20                   admissions, and
                 1980                         deaths
Jones et al.     St Louis, MO,  Heat wave     Total hospital
                 and            with the
1982             Kansas,        same periods  admissions.
                 United         in
                 States; June   1979 and      emergency room
                 and            1978
                 July 1980                    visits, and
                                              deaths from
                                              all causes

Faunt et al.     Adelaide,      10-day heat   Emergency
                 Australia;     wave          department
1995             February                     presentations

Rydmanet al.      Chicago, IL,   Heat wave     Emergency
                 United         with the      department
1999             States; 6-19   same period   visits
                 July           in 1994

Semenza et al.    Chicago, IL,   Heat-wave     Excess hospital
                 United         week with
1999             States; 13-19  four          admissions
                 July           non-heat
                 1995           comparison

Kovatset al.      Greater        Heat wave     Excess emergency
2004              United                       hospital
                  Kingdom;                     admissions
                  29 July-3

Johnson et al.    England;       10-day heat   Excess mortality
                 4-13           wave          and
2005             August 2003    period        emergency
                                compared      hospital
                                with the      admissions
                                same time

Cerutti et al.     Ticino,        Three heat    Excess mortality
                 Switzerland;   waves         and
2006             2003           compared      emergency
                                with          ambulance
                                previous      service
                                years         intervention

Mastrangelo      Veneto         Five          Daily count of
                 Region,        consecutive   hospital
                 Italy;         heat
et al. 2007       1 June-31      waves         admission by
                 August                       cause
                 2002-2003                    among people >
                                               years of age

Nitschke et a.    Adelaide,      Thirty-one    Daily ambulance
                  Australia;     heat
2007              July           waves         transports,
                  1993-June      compared      hospital
                  2006           with          admissions, and
                                 wave periods  mortality
                                spring and    *

Hansen et al.     Adelaide,      Heat waves.   Daily counts of
2008a            1 July 1993-   daily         admissions and
                                maximum       MBDs
                 30 June 2006   temperature

Hansen et al.     Adelaide,      Heat waves    Daily hospital
2008b            1995-2006                    admissions for
                                              disease. ARF.
                                              renal dialysis

Larrieu et al.   France; 2003   2003 heat     Fett morbidity,
                                wave          objective
20Q8                                          morbidity of

Knowlton et al .  California.    Heat wave     Excess
                                with the      hospitalizations
2009             United         reference     and emergency
                 States;        period
                 15 July--1     (8-14 July.   department
                 August         12-22         visits
                 2006           August
Oborlin et al.   Toulouse,      Heat wave     Emergency
                France;                      department
2010            1-31 August                  admissions of
                2003                         patients
                                             > 65 years of

Study           Research design  Key findings        Comments
                and statistical

Ellis et al.     Descriptive      Daily deaths        One single heat
1980            study            increased           wave
                                 during heat wave.
                                 No increase of new  was studied.
                                 claims for          Four
                                 sickness benefit
                                 working people.     different types
                                 More hospital       of
                                 admissions during
                                 heat wave than for  morbidity were
                                 the same period in  used.
                                 1975 or 1974.
                                 Modest increase in
                                 the episodes of
                                 sickness in two
                                 large general
Applegate et al.  Descriptive      Heat-related        A survey of
                study            emergency room      elderly
                                 visits, hospital
1981                             admissions, and     persons
                                 deaths rose         receiving
                                 markedly during
                                 wave. The most      home health care
                                 severe effects      was
                                 were seen among
                                 elderly, poor,      conducted during
                                 black, inner-city   the

Jones et al.     Descriptive     Heat wave  .        Hospital
                study            Deaths, hospital    records,
                                 admissions, and
                                 emergency room
1982                             visits from all     medical
                                 causes increased    examiners'
                                 during heat wave
                                 1980 compared with  records, and
                                 1979 and 1978 in    death
                                 St Louis and
                                 Kansas. Higher      certificates were
                                 heat stroke rates   used
                                 were found among
                                 the elderly, the    to identify
                                 poor, and           cases.

Faunt et al.     Retrospective    Ninety-four         One single heat
                                 patients had        wave
                                 illness; of
1995            survey;          78% had heat        was studied.
                descriptive      exhaustion, 85%     Only
                                 were > 60 years
                analysis         age, 20% came from  four hospitals
                                 institutional       were
                                 care, 48% lived
                                 alone, and 30% had  included.
                                 poor mobility.
                                 Severity was
                                 related to

Rydman t al.    Descriptive      There were 2,448    One single heat
                study;           excess morbidity    wave
                                 cases. Heat
1999            chi-square,      morbidity           was studied.
                Mest,            increased 5 davs
                                 before the first
                linear           related death. The
                regression       most frequent
                                 diagnoses were
                                 hyperthermia, heat
                                 exhaustion, and
                                 heat stroke.
                                 morbidity was
                                 found in age
                                 groups, cornorbid
                                 primary diseases,
                                 and disposition.

Semenza et al.   Descriptive      1,072 (11 %) more   Different
                study            hospitalizations    spectrum of
                                 and 838 (35%)
1999                             among patients >    illnesses
                                 65 years of         between
                                 age--most of
                                 these were due to   primary and all
                                 dehydration, heat
                                 stroke, heat
                                 r                   discharge
                                 and ARF. There was
                                 of underlying       during the heat
                                 CVDs, diabetes,     wave.
                                 renal diseases,
                                 nervous system

Kovats et al.     Time-series;     Hospital            Contract between
                                 admissions showed
                                 a small
2004            autoregressive   increase of 2.6%    hospital
                                 (95% CI:            admissions
                                 -2.2,7.6), whereas
                Poisson          mortality rose by   and mortality.
                regression,      10.8% (95% CI:

Johnson et al.   Descriptive      There were 2,091    The increases of
                study            excess deaths
                                 (17%). People s
2005                             years of age were   emergency
                                 at the greatest     hospital
                                 risk. An excess
                                 only 1 % in total   admissions were
                                 emergency hospital
                                 admissions was
                                 found. with         not

Ceruttiet al.    Descriptive      The 2003 mortality  Daily rates were
                study            in the population   used
                                 was not
2006                             significantly       rather than raw
                                 different from
                                 previous years
                                 except for
                                 the first heat      numbers of deaths
                                 wave. The number    or
                                 of ambulance
                                 interventions was   interventions.
                                 larger than during
                                 the previous

Mastrangelo     Ecologic study;  Heat wave           Heat wave
                GEE              duration, not       duration,
                                 increased the risk
et al. 2007                       hospital            intensity, and
                                 admissions for      timing
                                 heart diseases and
                                 RD 16%
                                 p< 0.0001) and 5%  were considered.
                                 (p< 0.0001),
                                 each additional
                                 day of heat wave
                                 duration. At least
                                 consecutive hot,
                                 humid days were
                                 required to
                                 a major increase
                                 in hospital
                                 admissions peaked
                                 equally at the
                                 first and last
                                 wave in 2003.

Nitschke et a.  Case-series      Total ambulance     Three kinds of
                study;           transport and       health
                                 total hospital
2007            Poisson          admissions          end points were
                regression,      increased by 4%     used.
                                 (95% CI: 1, 7)
                                 and 7%
                negative         (95% CI: -1,16),
                binomial         respectively.
                                 Admissions for
                regression       health, renal
                                 diseases and IHD
                                 among people
                                 years of age
                                 increased by 7%
                                 (95% CI: 1.13).
                                 (95% CI; 3, 25),
                                 and 8% (95% CI:
                                 Mortality did not

Hansen et al.    Time-series;     Hospital            First to
                Poisson          admissions          characterize
                                 increased by 7.3%
                                 during heat
2008a           regression,      waves. Above a      specific
                hockey-          threshold of        disorders
                                 there was a
                stick            positive            that contributed
                regression       association         to
                                 between ambient
                                 and hospital        increased
                                 admissions for      psychiatric
                                 MBDs. MBDs
                                 increased during    morbidity and
                                 heat waves in the   mortality
                                                     during heat

Hansen et al.    Time-series;     Admissions for      First
                Poisson          renal disease and   investigated the
                                 ARF increased
2008b           regression       heat waves, with    association
                                 IRR = 1.10 (95%     between
                                 CI: 1.00, 1.21)
                                 and IRR == 1.26     high temperature
                                 (95% CI:            and
                                 Hospitalizations    renal morbidity
                                 for dialysis        in a
                                 showed no
                                 Pre-existing        temperate
                                 diabetes did not    Australian
                                 increase the risk
                                 of renal
                                 admission.          region.

Larrieu et al.  Cross-sectional  During the heat     It was an
                                 wave, 8.8% of the   exploratory
                                 subjects felt a
20Q8            study;           deterioration of    study using a
                chi-square,      heath, and 7.8%
                                 declared an
                f-test,          morbid outcome.     questionnaire to
                logistic         Many factors were
                                 associated with
                regression       morbidity.          collect data

Knowlton et al.  Descriptive      16.166 excess       Principal and
                study            emergency           the
                                 department visits
                                 and 1,182
2009                             excess              first nine
                                 hospitalizations.   secondary
                                 department visits
                                 (RR = 6.30. 95%     diagnoses were
                                 CI: 5.67. 7.01)
                                 (RR = 10.15, 95%    included. Used
                                 CI: 7.79,13.43)
                                 for heat-related
                                 causes increased.   both emergency
                                 There were
                                 for ARF, CVD,       department visits
                                 diabetes,           and
                                 imbalance, and
                                 nephritis. The      hospitalization.
                                 heat wave impact
                                 on morbidity
                                 across regions,
                                 and age groups.
                                 Children (0-4
                                 years of age) and
                                 the elderly (> 65
                                 of age) were at
                                 greatest risk.

Oborlin et al.   Retrospective    Forty-two (5.5%)    Double-checked
                study;           patients had        medical
                                 illness. They
2010            descriptive      were more likely    record to
                analysis         to live in          ascertain
                                 institutional care
                                 than at home and    heat-related
                                 had longer length   illness.
                                 of stay and
                                 death rate than

Abbreviations: ARF, acute renal failure; CVD. cardiovascular diseases;
GEE, generalized estimating equations; IRR, incidence rate ratio;
MBDs, mental and behavioral disorders; RD, respiratory diseases.

Confounding and modification of the temperature--morbidity relationship. Some sociodemographic factors might confound and modify the temperature-morbidity relationship. Children and the elderly are usually susceptible to heat- or cold-related health risks. Although there was evidence for heat-related increases in emergency admissions for children < 5 years of age (Kovats et al. 2004), more studies reported the highest-risk age groups to be those > 65 years (Hong et al. 2003; Knowlton et al. 2009; Semenza et al. 1999) or 75 years of age (Johnson et al. 2005; Kovats et al. 2004; Lin et al. 2009). Women have been reported to have a greater risk for coronary events, ACS, and ischemic stroke in cold periods than do men (Barnett et al. 2005; Hong et al. 2003; Panagiotakos et al. 2004). However, emergency transport cases for heat stroke, cardiac insufficiency, hypertension, myocardial infarction, asthma, chronic bronchitis, and pneumonia were greater for males than for females during the summer in Tokyo (Piver et al. 1999; Ye et al. 2001). Lin et al (2009) reported a higher risk of being admitted to hospital for respiratoy diseases during the summer in New York for people of Hispanic ethnicity than for those of non-Hispanic ethnicity (6.1% vs. 1.7%), whereas no effect modification by race/ethnicity (e.g., white, black, Hispanic, Asian) or sex was found in the association between mean apparent temperature and hospital admissions for cardiovascular and respiratory diseases in California (Green et al. 2009).

In many locations, concentrations of air pollutants are associated with meteorological conditions. For example, there is usually a higher ozone concentration in summer, as it is a secondary pollutant caused by the reaction of volatile organic compounds, carbon monoxide, and nitrogen dioxide in the presence of sunlight, whereas particulate matter < 10 [micro]m in aerodynamic diameter ([PM sub 10]) peaks during the winter in many places because of the combustion of coal and/or wood for heating. These pollutants are often controlled for when considering the effect of ambient temperature on morbidity (Kovats et al. 2004; Liang et al. 2008; Linares and Diaz 2008; Michelozzi et al. 2009). However, few studies have explored whether exposure to air pollution modifies associations between temperature and morbidity. Ren et al. (2006) reported that [PM sub 10] significantly modified the relationship between daily minimum temperature and hospital admissions for cardiovascular and respiratory diseases in Brisbane, Australia, with stronger estimated effects of temperature at higher levels of [PM sub 10]. In a multicity European study, ozone did not appear to modify or confound associations between hot temperature and hospital admissions for cardiovascular, cerebrovascular, and respiratory diseases (Michelozzi et al. 2009).

Conclusions and Recommendations

We identified 40 relevant studies, most conducted in the United States and Europe during the last decade. Some descriptive studies provided early evidence of heat-related morbidity in specific cities during a single heat wave, and research has expanded recently to address the temperature--morbidity relationship in larger and more diverse populations in multiple areas. Although the case-crossover approach has seldom been used (Green et al. 2009; Hong et al. 2003; Kyobutungi et al. 2005), it is expected to be increasingly applied because of its ability to effectively control for individual-level confounding.

A number of well-controlled studies showed that ambient temperature was significantly associated with total and cause-specific morbidities, in which most reported heat effects with only a few reporting cold effects. Several studies found U- or V-shaped exposure--response relationships, with morbidity increasing at both ends of the temperature scale. The majority of studies reported detrimental effects of heat on the same day or up to the following 3 days, and longer cold effects up to a 2- to 3-week lag, with no substantial effects after more than 1 month.

A number of reasons may explain the heterogeneity of results across these studies. First, previous studies covered a wide range of populations in various geographical locations. Besides different demographic characteristics, some domestic and local adaptation factors could influence the direction and magnitude of the effects of ambient temperature on nonfatal health outcomes. For example, Ostro et al. (2010) estimated that the use of air conditioning could significantly reduce the effects of temperature on hospitalizations for multiple diseases, with 0.76% absolute reduction in excess risk of cardiovascular disease for every 10% increase in air conditioner ownership. Second, many temperature indicators have been used to define exposure, including minimum, mean, maximum temperature, diurnal temperature range, apparent temperature, and Humidex. However, which temperature measure is better to predict morbidity remains to be determined. Third, studies have evaluated different measures of morbidity, including general practitioner visits (Hajat et al. 2004; Hajat and Haines 2002), emergency department visits or admissions (Liang et al. 2009; Wang et al. 2009), and hospitalizations (Lin et al. 2009; Michelozzi et al. 2009). 'They are not mutually exclusive (e.g., a patient visiting an emergency department could be subsequently admitted to the hospiraJ). Emergency is typically considered to be less severe and more acute than hospitalization, which implies that it can catch the effect of temperature change at the early stage. It has been suggested that studies including emergency department visits may yield more valuable information for describing the epidemiology of temperature-related morbidity than a hospitalization-only study (Knowlton et al. 2009). Finally, there were also many methodological differences across studies, including statistical models, study population characteristics (e.g., age and sex), use of lag days (e.g., a single lag and multiple lag), and potential confounders considered.

The IPCC has projected that global mean surface temperature will increase by 1.8-4.0[degrees]C (best estimate) by 2100 relative to 1980-1999 (IPCC 2007a). Therefore, efforts to understand how climate change will affect health are urgently needed. Further studies are warranted to determine appropriate measures of exposure for morbidity research; to estimate nonlinear delayed temperature effects; to investigate the threshold temperatures in specific locations; and to understand the relative importance and interactive effects of air pollutants and temperature on morbidity, especially in areas with high air pollution. More multicity studies with consistent methodology should be conducted to make it easy to compare and interpret the temperature effects on morbidity across cities. There is also a need to consider more than one type of morbidity and to track cases from one health service to another by linking medical records. Such studies will provide valuable information for designing and implementing intervention strategies to alleviate the public health impacts of climate change.


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Xiaofang Ye, (1) Rodney Wolff, (2) Weiwei Yu, (1) Pavla Vaneckova, (1) Xiaochuan Pan. (3) and Shilu Tong (1)

(1)School of Public Health and Institute of Health and Biomedical Innovation, and (2) Mathematical Sciences Discipline, Faculty of Science and Technology, Queensland University of Technology, Brisbane, Queensland, Australia; (3)department of Occupational and Environmental Health, Peking University School of Public Health, Beijing, China

Address correspondence to S. Tong, School of Public Health, Queensland University of Technology, Victoria Park Rd., Kelvin Grove, QLD 4059, Australia. Telephone: 61 07 3138 9745. Fax: 61 07 3138 3369. E-mail:

We thank L. Turner at Queensland University of Technology for his useful comments on the early version of the manuscript.

X.Y. was funded by a Queensland University of Technology Fee Waiver scholarship. S.T. was supported by National Health and Medical Research Council Research Fellowship (553043).

The authors declare they have no actual or potential competing financial interests.

Received 10 November 2010; accepted 8 August 2011.
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Title Annotation:Review
Author:Xiaofang; Ye; Wolff, Rodney; Yu, Weiwei; Vaneckova, Pavla; Pan, Xiaochuan; Tong, Shilu
Publication:Environmental Health Perspectives
Article Type:Report
Geographic Code:8AUST
Date:Jan 1, 2012
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