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). 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.
Methods
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.
Results
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).
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.
References
Anderson BG, Bell ML 2009. Weather-related mortality: how heat, cold, and heat waves affect mortality in the United States. Epidemiology 20:205-213.
Applegate WB, Runyan JW, Jr, Brasfield L, Williams ML, Konigsberg C, Fouche C. 1981. Analysis of the 1980 heat wave in Memphis. J Am Geriatr Soc 29:337-342.
Barnett AG, Dobson AJ, McElduff P, Salomaa V, Kuulasmaa K, Sans S. 2005. Cold periods and coronary events: an analysis of populations worldwide. J Epidemiol Community Health 59:551-557.
Barnett AG, Tong S, Clements AC. 2010. What measure of temperature is the best predictor of mortality? Environ Res 110:604-611.
Barry RG, Chorley RJ. 2003. Atmosphere, Weather and Climate. 8th ed. NewYork: Methuen & Co. Ltd.
Bayentin L, El Adlouni S, Ouarda TB, Gosselin P, Doyon B, Chebana F. 2010. Spatial variability of climate effects on ischemic heart disease hospitalization rates for the period 1989-2006 in Quebec, Canada. Int J Health Geogr 9:5; doi:10.1186/1476-072X-9-5 [Online 8 February 2010].
Bouchama A, Knochel JP. 2002. Heat stroke. N Engl J Med 346:1978-1988.
Braga AL, Zanobetti A, Schwartz J. 2002. The effect of weather on respiratory and cardiovascular deaths in 12 U.S. cities. Environ Health Perspect 110:859-863.
Cerutti B, Tereanu C, Domenighetti G, Cantoni E, Gaia M, Bolgiani I, et al. 2006. Temperature related mortality and ambulance service interventions during the heat waves of 2003 in Ticino (Switzerland), Soz Praventivmed 51:185-193.
Chang CL, Shipley M, Marmot M, Poulter N. 2004. Lower ambient temperature was associated with an increased risk of hospitalization for stroke and acute myocardial infarction in young women. J Clin Epidemiol 57:749-757.
Cui J, Arbab-Zadeh A, Prasad A, Durand S. Levine BD, Crandall CG. 2005. Effects of heat stress on thermoregulatory responses in congestive heart failure patients. Circulation 112:2286-2292.
Dawson J, Weir C, Wright F, Bryden C, Aslanyan S, Lees K, et al. 2008. Associations between meteorological variables and acute stroke hospital admissions in the west of Scotland. Acta Neurol Scand 117:85-89.
Ebi KL, Exuzides KA, Lau E, Kelsn M, Barnston A. 2004. Weather changes associated with hospitalizations for cardiovascular diseases and stroke in California, 1983-1998. Int J Biometeorol 49:48-58.
Ellis FP, Prince HP, Lovatt G, Whittington RM. 1980. Mortality and morbidity in Birmingham during the 1976 heatwave. Q J Med 49:1-8.
Faunt JD, Wilkinson TJ, Aplin P, Henschke P, Webb M, Penhall RK. 1995. The effete in the heat: heat-related hospital presentations during a ten day heat wave. Aust NZJ Med 25:117-121.
Green RS, Basu R, Malig B, Broadwin R, Kim JJr Ostro B. 2009. The effect of temperature on hospital admissions in nine California counties. Int J Public Health 55:113-121.
Hajat S, Bird W, Haines A. 2004. Cold weather and GP consultations for respiratory conditions by elderly people in 16 locations in the U.K. Eur J Epidemiol 19:959-968.
Hajat S, Haines A. 2002. Associations of cold temperatures with GP consultations for respiratory and cardiovascular disease amongst the elderly in London. Int J Epidemiol 31:825-830.
Hajat S, Kosatsky T, 200$. Heat-reiated mortality; a review and exploration of heterogeneity. J Epidemiol Community Health; d o i: 10.1136/jec h.2009.087999 [Online 19 August 2009].
Hansen AL, Bi P, Nitschke M, Ryan P, Pisaniello D, Tucker G. 2008a. The effect of heat waves on mental health in a temperate Australian city. Environ Health Perspect 116:1369-1375.
Hansen AL, Bi P, Ryan P, Nitschke M, Pisaniello D, Tucker G. 2008b. The effect of heat waves on hospital admissions for renal disease in a temperate city of Australia. Int J Epidemiol 37:1359-1365.
Hassi J, Rytkonen M, Kotaniemi J, Rintamaki H. 2005. Impacts of cold climate on human heat balance, performance and health in circumpolar areas. Int J Circumpolar Health 64:459-467.
Hong YC, Rha JH, Lee JT, Ha EH, Kwon HJ, Kim H. 2003. Ischemic stroke associated with decrease in temperature. Epidemiology 14:473-478.
Huynen MM, Martens P, Schram D, Weijenberg MP, Kunst AE. 2001. The impact of heat waves and cold spells on mortality rates in the Dutch population. Environ Health Perspect 109:463-470.
IPCC (Intergovernmental Panel on Climate Change). 2007a. Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva: lntergovernmental Panel on Climate Change.
IPCC (Intergovernmental Panel on Climate Change). 2007b. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.
Johnson H, Kovats RS, McGregor G, Stedman J, Gibbs M, Walton H, et al. 2005. The impact of the 2003 heat wave on mortality and hospital admissions in England. Health Stat Q 25:6-11.
Jones TS, Liang AP, Kilbourne EM, Griffin MR, Patriarca PA, Wassilak SG, et al. 1982. Morbidity and mortality associated with the July 1980 heat wave in St Louis and Kansas City, M0. JAMA 247:3327-3331.
Juopperi K, Hassi J, Ervasti 0, Drebs A, Nayha S. 2002. Incidence of frostbite and ambient temperature in Finland, 1986-1995. A national study based on hospital admissions. Int J Circumpolar Health 61:352-362.
Keatinge WR, Coleshaw SR, Cotter F, Mattock M, Murphy M, Chelliah R. 1984. Increases in platelet and red cell counts, blood viscosity, and arterial pressure during mild surface cooling: factors in mortality from coronary and cerebral thrombosis in winter. Br Med J (Clin Res Ed) 289:1405-1408.
Kilbourne EM. 1999. The spectrum of illness during heatwaves. Am J Prev Med 16:359-360.
Knowlton K, Rotkin-Ellman M, King G, Margolis HG, Smith D, Solomon G, et al. 2009. The 2006 California heat wave: impacts on hospitalizations and emergency department visits. Environ Health Perspect 117:61-67.
Koken PJ, Piver WT, Ye F, Elixhauser A, Olsen LM, Portier CJ. 2003. Temperature, air pollution, and hospitalization for cardiovascular diseases among elderly people in Denver. Environ Health Perspect 111:1312-1317.
Kovats RS, Hajat S, Wilkinson P. 2004. Contrasting patterns of mortality and hospital admissions during hot weather and heatwaves in Greater London, U.K. Occup Environ Med 61:893-898.
Kyobutungi C, Grau A, Stieglbauer G, Becher H. 2005. Absolute temperature, temperature changes and stroke risk: a case-crossover study. Eur J Epidemiol 20:693-698.
Kysely J, Pokorna L, Kyncl J, Kriz B. 2009. Excess cardiovascular mortality associated with cold spells in the Czech Republic. BMC Public Health 9:19; doi:10.1186/1471-2458-9-19 [Online 15 January 2009].
Larrieu S, Carcaillon L, Lefranc A, Helmer C, Dartigues J-F, Tavernier B, et al. 2008. Factors associated with morbidity during the 2003 heat wave in two population-based cohorts of elderly subjects: PAQUID and Three City. Eur J Epidemiol 23:295-302.
Liang WM, Liu WP, Chou SY, Kuo HW. 2008. Ambient temperature and emergency room admissions for acute coronary syndrome in Taiwan. Int J Biometeorol 52:223-229.
Liang WM, Liu WP, Kuo HW. 2009. Diurnal temperature range and emergency room admissions for chronic obstructive pulmonary disease in Taiwan. Int J Biometeorol 53:17-23.
Lin S, Luo M, Walker RJ, Liu X, Hwang SA, Chinery R. 2009. Extreme high temperatures and hospital admissions for respiratory and cardiovascular diseases. Epidemiology 20:738-746.
Linares C, Diaz J. 2008. Impact of high temperatures on hospital admissions: comparative analysis with previous studies about mortality (Madrid). Eur J Public Health 18:317-322.
Mastrangelo G, Fedeli U, Visentin C, Milan G, Fadda E, Spolaore P. 2007. Pattern and determinants of hospitalization during heat waves: an ecoiogic study. BMC Public Health 7:200; doi:10.1186/1471-2458-7-200 [Online 9 August 2007J.
Mercer JB. 2003. Cold--an underrated risk factor for health. Environ Res 92:8-13.
Michelozzi P, Accetta G, De Sario M, D'lppoliti D, Marino C, Baccini M, et al. 2009. High temperature and hospitalizations for cardiovascular and respiratory causes in 12 European cities. Am J Respir Crit Care Med 179:383-389.
Muggeo VM, Hajat S. 2009. Modelling the nonlinear multiple-lag effects of ambient temperature on mortality in Santiago and Palermo; a constrained segmented distributed lag approach. Occup Environ Med 66:584-591.
National Climate Centre. 2009. The exceptional January-February 2009 heatwave in southeastern Australia, Bureau of Meteorology, Special Climate Statement 17. Melbourne,
Australia: Bureau of Meteorology. National Library of Medicine. 2010. PubMed. Available: http://www.ncbi.nlm.nih.gov/pubmed/ [accessed 8 July 2010].
Nitschke M, Tucker GR, Bi P. 2007. Morbidity and mortality during heatwaves in metropolitan Adelaide. Med J Aust 187:662-665.
Oberlin M, Tubery M, Cances-Lauwers V, Ecoiffier M, Lauque D. 2010. Heat-related illnesses during the 2003 heat wave in an emergency service. Emerg Med J 27:297-299.
Ohshige K, Hori Y, Tochikubo 0, Sugiyama M. 2006. Influence of weather on emergency transport events coded as stroke: population-based study in Japan. Int J Biometeorol 50:305-311.
O'Neill MS, Ebi KL 2009. Temperature extremes and health: impacts of climate variability and change in the United States. J Occup Environ Med 51:13-25.
Ostro B, Rauch S, Green R, Malig B, Basu R. 2010. The effects of temperature and use of air conditioning on hospitalizations. Am J Epidemiol 172:1053-1061.
Panagiotakos OB, Chrysohoou C, Pitsavos C, Nastos P, Anadiotis A, Tentolouris C, et al. 2004. Climatofogical variations in daily hospital admissions for acute coronary syndromes. Int J Cardiol 94:229-233.
Parsons K. 2003. Human Thermal Environments: The Effects of Hot, Moderate and Cold Environments on Human Health, Comfort and Performance. London and New York: Taylor & Francis.
Pattenden Sf Nikiforov B, Armstrong BG. 2003. Mortality and temperature in Sofia and London. J Epidemiol Community Health 57:628-633.
Piver WT, Ando M, Ye F, Portier CJ. 1999. Temperature and air pollution as risk factors for heat stroke in Tokyo, July and August 1980-1995. Environ Health Perspect 107:911-916.
Ren C, Williams GM, Tong S. 2006. Does particulate matter modify the association between temperature and cardiorespiratory diseases? Environ Health Perspect 114:1690 1696.
Revich B, Shaposhnikov D. 2008. Excess mortality during heat waves and cold spells in Moscow, Russia. Occup Environ Med 65:691-696.
Robinson PJ. 2001. On the definition of a heat wave. J Appl Meteoroi 40:762-775.
Roger P, Francesca D. 2008. Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health. NewYork: Springer.
Rydman RJ, Rumoro DP. Silva JC, Hogan TM, Kampe LM. 1999. The rate and risk of heat-related illness in hospital emergency departments during the 1995 Chicago heal disaster. J Med Syst 23:41-56.
Schwartz J, Samet JM, Patz JA. 2004. Hospital admissions for heart disease: the effects of temperature and humidity. Epidemiology 15:755-761.
Semenza JC, McCullough JE, Flanders WD, McGeehin MA,
Lumpkin JR. 1999. Excess hospital admissions during the July 1995 heatwave in Chicago. Am J Prev Med 16:269-277.
Touloumi G, Atkinson R, Tertre AL, Samoli E, Schwartz J, Schindler C, et al. 2004. Analysis of health outcome time series data in epidemiological studies. Environmetrics 15:101-117.
UNDESA (United Nations Department of Economic and Social Affairs). 2010a. World Population Ageing. New York: United Nations Department of Economic and Social Affairs.
UNDESA (United Nations Department of Economic and Social Affairs). 2010b. World Urbanization Prospects: The 2009 Revision. New York: United Nations Department of Economic and Social Affairs Population Division.
Wang XY, Barnett AG, Hu W, Tong S. 2009. Temperature variation and emergency hospital admissions for stroke in Brisbane, Australia, 1996-2005. Int J Biometeorol 53:535-541.
WHO (World Health Organization). 2008. Protecting Health from Climate Change--World Health Day 2008. Geneva: World Health Organization.
Yang TC, Wu PC, Chen VY, Su HJ. 2009- Cold surge: a sudden and spatially varying threat to health? Sci Total Environ 407:3421-3424.
Ye F, Piver WT, Ando M, Portier CJ. 2001. Effects of temperature and air pollutants on cardiovascular and respiratory diseases for males and females older than 65 years of age in Tokyo, July and August 1980-1995. Environ Health Perspect 109:355-359.
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: s.tong@qut.edu.au
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.
 Printer friendly
 Cite/link
 Email
 Feedback
| |
| 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 |
| Words: | 10884 |
| Previous Article: | Risks and benefits of consumption of Great Lakes fish. |
| Next Article: | European birth cohorts for environmental health research. |
| Topics: | |