Ambient temperature and morbidity: a review of epidemiological evidence.
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). http://dx.doi.org/10.1289/ehp.1003198 [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.
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).
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). Main temperature Study Location and time exposure variable Studies of both hot and cold exposure Ebi et al. 2004 Three U.S Minimum and California regions; 1983-1997 maximum and 1 January-June 1998 temperature Schwartz el al. Twelve U.S. Daily mean cities: 2004 1986-1994 temperature Bayentin et al. Quebec. Canada; 1 Mean April temperature 2010 1989-31 March 2006 Qhshige et al. Yokohama, Japan: Mean temperature 2006 1992-2003 Liang et al. Taichung, Taiwan; Mean temperature. 2008 1 January 2000- DTR 31 March 2003 Liang et al. Taichung, Taiwan; Mean temperature. 2009 2001-2002 DTR Ren et al. 2006 Brisbane. Minimum Australia; 1996-2001 temperature Wang et al. Brisbane, Minimum and Australia, 2009 summer and winter, maximum 1996-2005 Temperature Rothwell et al. Oxfordshire, Mean United temperature 1996 Kingdom; 1980s Panagiotakos Athens, Greece; Daily mean and January Et al 2004 2001-August 7002 minimum and maximum temperature, THI Kyobutungi Heidelberg. Maximum Germany; et al. 2005 August temperature and 1998-January 2000 24-hr difference in maximum temperature Main temperature Dawson et al. Scotland; 1 May Mean 1990- and 2008 22 June 2005 minimum and maximum temperature. mean temperature change over the preceding 24 and 48 hr Chang et al. Seventeen Monthly mean countries 2004 worldwide temperature (including Africa, Asia, Europe, and Latin America), February 1989-January 1995 Hot exposure only Koken et al, Denver, CO, United Maximum States, 20Q3 July-August, temperature 1993-1997 Green et al. Nine U.S. Mean apparent California 2009 counties; May- temperature September, 1999-2005 Lin et al. 2009 New York, United Mean States; temperature, summer, 1991-2004 mean apparent temperature, 3-day moving average of apparent temperature Piver et al. Tokyo, Japan; July Daily maximum and 1999 August 1980-1995 temperature Ye et al. 2001 Tokyo, Japan; July Daily maximum and August 1980-1995 temperature Kovats et al. Greater London, Three-day United 2004 Kingdom; 1 April moving average 1994-31 March 2000 temperature Linares and Madrid, Spain; variable Maximum May- and Diaz 2008 September, minimum 1995-2000 temperatures Michelozzi Twelve European Maximum cities; etal.2009 April-September, apparent each city > 3 years temperature during 1990-2001 Cold exposure only Hong et al. Incheon, Korea; Daily average 2003 1998-2000 temperature, 3-hr average temperature Hajat and London. United Mean temperature Kingdom; Haines 2002 January 1992- September 1995 Hajat et al. United Kingdom; Mean temperature 2004 1992-2001 Barnett et al. Twenty-four Mean populations temperature 2005 worldwide, 1980-1995 Research design and Study Outcome statistical analysis Studies of both 1 Ebi et al. 2004 Hospitalizations Time-series; Poisson for AMI, angina regression, GEE pectoris. CHF, stroke 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 ACS Liang et al. Emergency room Time-series; Poisson 2009 admissions for regression C0PD Ren et al. 2006 Hospital Time-series; Poisson admissions and emergency GAM, nonparametric visits for CVD bivaiator and RD response model. nonstratification 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 the etal 2004 emergency units Kyobutungi IS incidence Case-crossover, et al. 2005 conditional logistic regression 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 admissions 2003 for CVD,> 65 years regression, GLM, of age GEE Green et al. Hospital Case-crossover; admissions 2009 for CVD. RD. conditional logistic diabetes. regression, meta- dehydration, heat analysis stroke, to fest infectious diseases, and ARF Lin et al. 2009 Hospital Time-series; GAM, admissions for CVD and RD linear-threshold model Piver et al. Emergency Time-series; GLM, transport 1999 cases for heat GEE stroke 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; hospital 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 CVD Michelozzi Hospital admission Time-series; GEE, Et al.2009 for CVD, CD. random effect And RD meta-analysis Cold exposure only Hong et al IS onset Case-crossover; 2003 conditional logistic regression 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 for 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, Bayesian hierarchical model Study Key findings Comments Studies of both 1 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 weather Similar but smaller effects of temperature for Ml admissions Lag; 0,1 day Bayentin et al. V- or U-shaped curves No air pollution was controlled for; 2010 Threshold different for each only description of region and for deprivation both sexes indexes presented, rather 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 indexes (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 temperature) 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 mean temperature) 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 different [degrees]C increase in minimum types of stroke and maximum morbidity in a temperature 15% (5-26%) and subtropical city 12% (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 among 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 temperature 1"C decrease in mean temperature was associated with a 5.0% (4.6-5.4%) increase in hospital admissions for ACS; similar results for minimum and maximum temperatures and for THI. Stronger association for females and the elderly 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%) increase 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 pulmonary heart disease, respectively Lag: 0,1 day Male had higher numbers of hospital 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, 10.8% dehydration, 7.4% ARF, 404.0% heat 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 stratified 1[degrees]C increase above mean analyses based on apparent family income temperature threshold 2.1% (1.1-3.1%) 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, respectively, for CD Lag: 0-3 days Positive interaction between high temperature (> 29.4[degrees]C) and humidity Greater increases of CVD and RD admissions 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 emergency 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 considered 1 [degrees]C increase 3.8% (2.0-5.0%) increase in pneumonia and 1.4% (0.4-2.0%) decrease in hypertension Lag;0 Kovatset al. No relation between total Contrasting emergency patterns of mortality 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 renal disease, 0.24% (0.02-0.46%) for children < 5 years of age, and 10.86% (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 used 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 relationship 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 several 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 only 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, nonobese persons, and those with hypertension or hypercholesterolemia 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., the temperature and GP for CVD time when a patient can be seen by a general practitioner; access to convenient medical facilities) 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 locations 1[degrees]C decrease < 5[degrees]C, biggest effect 19.0% (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 exposure variable Ellis et al. Birmingham, 2-week heat Mortality and 1980 United wave morbidity Kingdom; 24 with the June- reference 8 July 1976 period (2-week periods before and after the heat wave. same days in 1974 Applegate et al Memphis, TN, and 975) Heat-related Heat wave emergency 1981 United room visits, States; hospital 25 June-20 admissions, and July 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 1993 Rydmanet al. Chicago, IL, Heat wave Emergency United with the department 1999 States; 6-19 same period visits July in 1994 1995 Semenza et al. Chicago, IL, Heat-wave Excess hospital United week with 1999 States; 13-19 four admissions July non-heat wave 1995 comparison weeks Kovatset al. Greater Heat wave Excess emergency London, 2004 United hospital Kingdom; admissions 29 July-3 August 1995 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 in 1998-2002 Cerutti et al. Ticino, Three heat Excess mortality Switzerland; waves and 2006 2003 compared emergency with ambulance previous service years intervention (2000-2002) 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 > 74 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 non-heat wave periods mortality during spring and * summer Hansen et al. Adelaide, Heat waves. Daily counts of Australia; 2008a 1 July 1993- daily admissions and maximum MBDs 30 June 2006 temperature Hansen et al. Adelaide, Heat waves Daily hospital Australia; 2008b 1995-2006 admissions for renal disease. ARF. and renal dialysis Larrieu et al. France; 2003 2003 heat Fett morbidity, wave objective 20Q8 morbidity of elderly people 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 2006) Oborlin et al. Toulouse, Heat wave Emergency France; department 2010 1-31 August admissions of 2003 patients > 65 years of age Study Research design Key findings Comments and statistical analysis Ellis et al. Descriptive Daily deaths One single heat 1980 study increased wave significantly during heat wave. No increase of new was studied. claims for Four sickness benefit among 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 practices. Applegate et al. Descriptive Heat-related A survey of study emergency room elderly visits, hospital 1981 admissions, and persons deaths rose receiving markedly during heat wave. The most home health care severe effects was were seen among elderly, poor, conducted during black, inner-city the residents. 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 in 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. nonwhites. Faunt et al. Retrospective Ninety-four One single heat patients had wave heat-related illness; of these. 1995 survey; 78% had heat was studied. descriptive exhaustion, 85% Only were > 60 years of analysis age, 20% came from four hospitals institutional were care, 48% lived alone, and 30% had included. poor mobility. Severity was related to preexisting conditions. 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 heat- linear related death. The regression most frequent heat-related diagnoses were hyperthermia, heat exhaustion, and heat stroke. Different 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 diagnoses xhaustion, and ARF. There was significant excess of underlying during the heat CVDs, diabetes, wave. renal diseases, and nervous system disorders. Kovats et al. Time-series; Hospital Contract between admissions showed a small nonsignificant 2004 autoregressive increase of 2.6% hospital (95% CI: admissions -2.2,7.6), whereas daily Poisson mortality rose by and mortality. regression, 10.8% (95% CI: 2.8,19.3). hockey-stick model Johnson et al. Descriptive There were 2,091 The increases of study excess deaths (17%). People s 75 2005 years of age were emergency at the greatest hospital risk. An excess of only 1 % in total admissions were emergency hospital admissions was found. with not mortality. 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 service interventions was interventions. larger than during the previous years. Mastrangelo Ecologic study; Heat wave Heat wave GEE duration, not duration, intensity, increased the risk of et al. 2007 hospital intensity, and admissions for timing heart diseases and RD 16% p< 0.0001) and 5% were considered. (p< 0.0001), respectively, with each additional day of heat wave duration. At least 4 consecutive hot, humid days were required to observe a major increase in hospital admissions. Hospital admissions peaked equally at the first and last heat 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 mental regression health, renal diseases and IHD among people 65-74 years of age increased by 7% (95% CI: 1.13). 13% (95% CI; 3, 25), and 8% (95% CI: 1,15). respectively. Mortality did not increase. 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 26.7[degrees]C, there was a stick positive that contributed regression association to between ambient temperature and hospital increased admissions for psychiatric MBDs. MBDs mortalities increased during morbidity and heat waves in the mortality elderly during heat waves. Hansen et al. Time-series; Admissions for First Poisson renal disease and investigated the ARF increased during 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 1.04.1.52). respectively. Hospitalizations renal morbidity for dialysis in a showed no increase. 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 objective f-test, morbid outcome. questionnaire to logistic Many factors were associated with regression morbidity. collect data from subjects. Knowlton et al. Descriptive 16.166 excess Principal and study emergency the department visits and 1,182 2009 excess first nine hospitalizations. secondary Emergency department visits (RR = 6.30. 95% diagnoses were CI: 5.67. 7.01) and hospitalizations (RR = 10.15, 95% included. Used CI: 7.79,13.43) for heat-related causes increased. both emergency There were significant increases for ARF, CVD, department visits diabetes, and electrolyte imbalance, and nephritis. The hospitalization. heat wave impact on morbidity varied across regions, race/ethnicity, and age groups. Children (0-4 years of age) and the elderly (> 65 years of age) were at greatest risk. Oborlin et al. Retrospective Forty-two (5.5%) Double-checked study; patients had medical heat-related illness. They 2010 descriptive were more likely record to analysis to live in ascertain institutional care rather than at home and heat-related had longer length illness. of stay and higher death rate than non-heat-related illness. 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: firstname.lastname@example.org
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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|Author:||Xiaofang; Ye; Wolff, Rodney; Yu, Weiwei; Vaneckova, Pavla; Pan, Xiaochuan; Tong, Shilu|
|Publication:||Environmental Health Perspectives|
|Date:||Jan 1, 2012|
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