The association between mortality and ambient temperature,1,2 particularly during heat waves,3 has been well documented in the past decade over several continents. To adequately characterize health effects of heat exposure, nonfatal morbidity must also be considered because fatal events constitute only a portion of acute health effects. However, the association between temperature or heat waves and morbidity has been far less studied, with much of the evidence coming from studies of hospital visits,4–12 and much less from studies of emergency room (ER) visits.4,9,10,13 Associations between temperature or heat-wave and hospital admissions for respiratory diseases,5–7,12,13 cardiovascular diseases (CVDs), heat-related causes,9,13 diabetes and circulatory diseases10 have been reported. Many studies have assessed associations with broad categories of health outcomes such as cardiovascular or respiratory diseases, rather than specific cardiorespiratory diseases4,7,9,14 that could give insight to the mechanisms involved and appropriate interventions. It is also useful to study ER visits specifically, as they represent the more immediate, acute effects and may identify vulnerability among specific populations.15 Identifying even the basic demographic predictors of vulnerability to heat effects would help target health education and interventions toward those who are most susceptible.
We examined the association between temperature during the warm season in California and health conditions using more than 1 million ER visit records. The large number of ER visits allowed us to explore broadly defined categories of cardiovascular and respiratory disease typically studied, as well as a number of specific cardiorespiratory outcomes to help address specific hypotheses on the mechanisms of heat-related cardiorespiratory health effects. The body’s effort to thermoregulate effectively involves changes in heart rate, blood flow, and breathing rate; advanced heat stress results in inflammatory responses and coagulation abnormalities.16 To assess whether the poor management of these processes manifests clinically during heat exposure, we examined ischemic events (ie, ischemic heart disease), dysrhythmia, blood pressure-related disease (ie, hypertension), and respiratory illness (ie, pneumonia). In addition, we looked at conditions commonly associated with heat exposure, such as heat stroke and dehydration as well as intestinal infections and diabetes. We also evaluated potential effect modification by age and race/ethnicity and possible confounding by air pollutants.
METHODS
Health Outcome Data
We obtained ER visit data from two sources provided by the California Office of Statewide Health Planning and Development. Records from the California Emergency Department Data were combined with records from the California Hospital Patient Discharge Data to create a dataset of both outpatient and inpatient hospital visits from 2005 to 2008. Because we were interested only in emergency visits, we restricted Patient Discharge Data records to those hospitalizations originating with an ER visit. We assumed that, for subjects in the Patient Discharge Data database, hospitalization occurred on the same day as the visit to the ER. We defined each ER visit as a case; thus, a patient could be in the dataset more than once if the person experienced multiple visits to the ER. We restricted our analysis to the warm season of 1 May to 30 September to focus on the effects of high temperatures.
We retrieved information on each patient’s residential zip code, date of visit, principal diagnosis, and whether the patient was later admitted to a hospital. We analyzed several disease outcomes, including some that have been previously investigated for hospital visits8,11: all CVDs (International Classification of Diseases, 9th Revision [ICD-9] codes 390–459) and several CVD subgroups (acute myocardial infarction, ischemic heart disease, cardiac dysrhythmias, heart failure, hypertension, hypotension, hemorrhagic stroke, aneurysm, and ischemic stroke); all respiratory illnesses (ICD-9 codes 460–519) and several respiratory disease subgroups (pneumonia, asthma, and chronic bronchitis/emphysema); diabetes (ICD-9 code 250); dehydration (ICD-9 code 276.51); heat illness, including heat stroke (ICD-9 code 992); intestinal infectious diseases (ICD-9 codes 001-009); and acute renal failure (ICD-9 code 584). We also retrieved secondary diagnoses of diabetes to determine whether preexisting diabetes modified the effect of apparent temperature on other outcomes. Age and race/ethnicity information was also derived from this dataset.
Weather and Air Pollution Data
Two monitoring datasets, the California Irrigation Management Information System17 and the US Environmental Protection Agency,18 provided weather data for our analyses. We calculated mean apparent daily temperature in degrees Fahrenheit (°F), as previously described in Basu et al,19 to account for the effects of both temperature and relative humidity. Monitors are located throughout all 16 California climate zones, which are designated by the California Energy Commission based on weather, temperature, energy use, and other factors related to climate (eFigure, https://links.lww.com/EDE/A606).
The California Air Resources Board20 provided daily 1-hour maximum concentrations for ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2), and daily average data for particulate matter <2.5 µm in aerodynamic diameter (PM2.5). We assigned temperature and air pollution data from the closest monitor to each case, with the requirements that the temperature monitor is located within 10 km and the air pollution monitor within 20 km of the population-weighted centroid of the residential zip code of each case. Because California is such a geographically and climatically diverse state, we attempted to increase the validity of the assigned exposures by also requiring the monitors to be in the same climate zone as each case’s zip code centroid. This geographical assignment was performed using ArcGIS, version 9.221 and the Hawth’s Tools add-on.22 We restricted the analyses to climate zones with at least 20 cases in each subgroup of interest.
Study Design
We used a time-stratified case-crossover study design, a statistical technique well suited to examining short-term exposures with acute outcomes.23 The approach is a modification of the matched case-control study, where each case serves as his or her own control, so that known and unknown time-invariant confounders are inherently adjusted for by study design. We compared the temperature on the day of the ER visit (case) with up to four control periods on the same weekday within the same month and year to control for time trends and day of week. (ERs often become the first option for primary care on weekends when physicians’ offices are closed.)
In previous studies of apparent temperature and mortality19 and hospital visits,11 acute effects of same-day temperature were found to have the best model fit. Thus, our a priori focus for this analysis was the effect of same-day apparent temperature (lag0) and ER visits because ER visit effects would be expected to coincide with or precede mortality or hospital visits. We did, however, examine other single-day lags up to 6 days before the ER visit (lag1 through lag6), and cumulative effects using averages of apparent temperature for the same day and previous day (lag01), the same day and the previous 3 days (lag03), and the same day and the previous 6 days (lag06). Model fits for these various lag times were compared using the deviance.
Statistical Analysis
We first conducted univariate conditional logistic regres sions, using a continuous measure of apparent temperature as our predictor of interest, to obtain an effect estimate for each climate zone. These analyses were performed using the PHREG procedure in SAS statistical software.24 In the second stage, we created an overall effect estimate by combining the climate zone effect estimates in a random-effects meta-analysis using the rmeta package in R statistical software.25 We calculated an odds ratio (OR) and its 95% confidence interval (95% CI). The results are scaled to represent a percent excess risk in ER visits per 10°F change in apparent temperature using the calculation: (OR − 1) × 100%.
To test whether the effects of apparent temperature were greater at higher levels of exposure, we conducted analyses using separate piecewise regression models26 for each climate zone with apparent temperature at lag0 and a continuous term denoting degrees of apparent temperature above the 90th percentile (apparent temperature—90th percentile), where apparent temperatures at or below the threshold were assigned a zero value. The climate zone estimates for the additional term were combined to produce overall effect estimates using random-effects meta-analysis and assessed for significance. The following outcomes were considered because they were found to be significant in the main analyses: dysrhythmia, essential hypertension, ischemic heart disease, ischemic stroke, dehydration, primary diabetes, renal failure with secondary diabetes, and respiratory disease.
To evaluate the possible influence of confounding by air pollutants, we included each air pollutant in the model with apparent temperature. An average of same-day and previous-day levels (lag01) was used for the pollutants. Because we were interested in whether any observed association may have been modified by age group (younger than 5 years, 5–18, 19–64 [adults], and 65 and older [elderly] years) or race/ethnicity group (black, white, Hispanic, and Asian), we conducted stratified analyses for these specific demographic categories. Significant differences based on t tests between groups were identified, using the 19- to 64-year age group and whites as the reference for the age and race/ethnic group analyses, respectively. We also noted instances where the magnitude of effect in the stratified age or race/ethnic group was either twice or half the effect of the comparison group. Stratified analyses were also performed to compare estimates for coastal climate zones with those located inland.
RESULTS
Using a buffer of 10 km, we gathered data for 1,215,023 cases. Descriptive information by climate zone can be found in eTable 1 (https://links.lww.com/EDE/A606). Daily mean apparent temperature for cases during the study period ranged from an average of 56.8°F (14°C) in the northern and primarily coastal climate zone 1 to 86.2°F (30.1°C) in the more southern and arid climate zone 15. The mean absolute differences between case and control apparent temperatures ranged from 3.8°F to 7.9°F (2.1° to 4.4°C). The overall ranges of means for gaseous pollutants were as follows: O3 (31.6–81.6 ppb), CO (0.3–0.9 ppm), SO2 (0.3–4.8 ppb), and NO2 (6.0–39.7 ppb). Among the climate zones with daily PM2.5 measurements available, means ranged from 9.6 to 18.2 µg/m3.
As shown in eTable 2 (https://links.lww.com/EDE/A606), we observed no correlation or only weak correlations between apparent temperature and air pollutants, with the exception of O3 (r2 = 0.59). eTable 3 (https://links.lww.com/EDE/A606) describes climate-zone-specific study population demographics, by race/ethnic group, as well as the percentage of ER patients who were later admitted to the hospital. The study population was predominantly 19–64 years of age and white. Approximately one-third of the ER visits resulted in an admission to the same hospital. The pooled results for all lags considered are presented in Figure 1 for the association between apparent temperature and dehydration. Same-day mean apparent temperature most often produced the best model fit, and we focus on this lag when reporting the results. The climate-zone-specific estimates for dehydration are included in eTable 4 (https://links.lww.com/EDE/A606). Zone 5, a coastal region, and zone 16, a mountainous semiarid region, had the strongest effect for dehydration during the study period.
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FIGURE 1: Meta-analysis results for percent excess risk of emergency room visits per 10°F (5.6°C) change in mean same-day apparent temperature and dehydration for various lag times (as described in the “Methods”’ section).
In the Table, we summarized the percent excess risk per 10°F increase in apparent temperature for various categories: CVD and disease subgroups, respiratory diseases and subgroups, and other diseases including diabetes and some heat-related illnesses for lag0 for all 16 climate zones combined. We did not find important associations between same-day apparent temperature and all CVD combined (excess risk = 0.2% [95% CI = −0.9% to 1.3%]) or all respiratory diseases combined (−0.7% [−1.7% to 0.3%]). However, a number of specific cardiorespiratory disease visits were associated with temperature, including ischemic stroke, ischemic heart disease, cardiac dysrhythmia, hypotension, and pneumonia. Negative associations were found for high blood-pressure-related outcomes, specifically essential hypertension, hemorrhagic stroke, and aneurysm. Some cardiorespiratory outcomes exhibited negative associations at later lags. Not surprisingly, known heat-related morbidities (ie, dehydration, heat illness, and renal failure) had the strongest associations with apparent temperature, although diabetes and intestinal infections were also associated. Risks of dehydration and renal failure were similar for cases with and without a secondary indication of diabetes (not shown). We found little evidence of a difference in effect at apparent temperatures above the 90th percentile, with the exception of an increased effect on dehydration and a weaker effect on heat illness at those levels.
TABLE: Meta-analysis Results for Number of ER Visits, Percent Excess Risk per 10°F, and I2 Statistic by Diagnosis, 1 May Through 30 September 2005–2008
When we adjusted for air pollutants in separate models, most of the strong associations we observed remained stable (Fig. 2). One exception was pneumonia; when either NO2 or CO was included in the model with apparent temperature, the positive association disappeared. Adding NO2 to the model also reduced the strength of the associations for ischemic stroke and cardiac dysrhythmia by ~40%. The strength of the association for hemorrhagic stroke was reduced when either O3 or SO2 were added to the model.
FIGURE 2: Meta-analysis results for percent excess risk of emergency room visits per 10°F (5.6°C) change in mean same-day apparent temperature adjusted (adj) for gaseous pollutants.
We stratified by age group for cardiovascular and respiratory diseases, as well as for outcomes that were most clearly associated with apparent temperature in the primary analysis (Figs. 3 and 4). Compared with adults 19–64 years of age, those 65 years and older showed weaker associations between apparent temperature and ischemic heart disease (1.1% [−1.0% to 3.2%]) and hypotension (7.4% [0.7% to 14.5%]). However, a more strongly negative relationship was found for hypertension (−15.5% [−20.5% to −10.2%]), while more strongly positive associations were observed for both primary diabetes (7.0% [2.9% to 11.3%]) and heat illness (595% [424% to 821%]). For young children up to 4 years of age, associations were lower for dehydration (11.7% [5.6% to 18.1%]) and negative for any respiratory visit (−2.6% [−4.1% to −1.0%]). Children up to 4 years (7.4% [2.2% to 13%]) and 5–18 years (14.7% [16.8% to 29.4%]) were more susceptible to ER visits for intestinal infections.
The outcomes that were statistically significant in the initial analyses were investigated further by race/ethnicity (Figs. 4 and 5). Compared with whites, Hispanics showed greater associations between apparent temperature and ischemic stroke (7.2% [2.7% to 11.9%]), ischemic heart disease (5.2% [−0.1% to 10.7%]), acute renal failure (21.8% [14.6% to 29.5%]), and intestinal infections (10.4% [6.1% to 15.0%]). The estimate for blacks was notably weaker for dysrhythmia (−1.4% [−7.4% to 5.0%]). For Asians, associations were larger for dehydration (37.4% [24.9% to 51.1%]), primary diabetes (7.6% [−0.1% to 17.0%]), and hypertension (−17.8% [−25.9% to −8.8%]), but weaker for hypotension (2.8% [−13.4% to 22.0%]). The broader CVD and respiratory visit categories also showed differences in the direction of association between race/ethnicities.
We investigated the effects of temperature on nonrespiratory and non-CVD outcomes in coastal regions (4 climate zones) versus noncoastal regions (11 climate zones) (not shown). The effects of apparent temperature on ER visits for diagnoses such as dehydration, renal failure, and heat illness were more pronounced among the coastal regions. In contrast, outcomes such as diabetes showed a stronger association in noncoastal regions. These regional differences, however, were not statistically significant.
DISCUSSION
We found increased apparent temperature to be associated with several CVD diagnoses including cardiac dysrhythmia, ischemic stroke, ischemic heart disease, and hypotension. Aggregating all CVD outcomes masked these individual effects because of the negative associations for aneurysm, hemorrhagic stroke, and hypertension. This reveals the importance of understanding the specific mechanisms of heat-related morbidity and the need to focus on cause-specific diseases to better understand how heat affects health. Heat was also associated with diabetes, intestinal infectious disease, dehydration, and heat illness independent of air pollutants.
We found the elderly and racial/ethnic minorities to have greater risks for several outcomes, including ischemic stroke, ischemic heart disease, diabetes, and dehydration. The elderly, however, had lower risk for other CVD outcomes, which may be attributable to treatment and surveillance administered to the most vulnerable. For example, the elderly population is more likely to be monitored for dysrhythmia and the most severe cases would likely have pacemakers. If their illness is mechanically controlled,27 they would be less likely to need an ER visit. Younger adults, in contrast, may be less likely to be under treatment or surveillance. In addition, occupation or general activity patterns may result in more time spent outdoors by younger adults, increasing their exposure and, consequently, their risk. We also observed children to be at greater risk for gastrointestinal illnesses, but young children to be at lower risk for dehydration. This lowered risk for dehydration could be a result of increased surveillance, because young children may drink more fluids while under adult supervision, especially during periods of heat exposure. For several outcomes, we found Hispanics as well as Asians and blacks to be at higher risks. This could reflect less preventive care and greater use of the ER among racial/ethnic minorities.15
In California, studies of apparent temperature and mortality19,28 and morbidity8,11 have been recently conducted. Basu and Ostro28 reported the greatest risk of mortality among persons with CVD, particularly congestive heart failure, myocardial infarction, and ischemic heart disease. Blacks, Asians, infants, young children, and the elderly were also found to be more vulnerable. Green et al,11 however, did not find apparent temperature to be associated with hospital admissions for all CVD combined. Instead, they reported excess risk for hospitalizations from ischemic stroke, pneumonia, dehydration, renal failure, and heat illness during the warm season. Several of the same associations were found using ER visit data. However, NO2 and CO confounded the association between apparent temperature and pneumonia in our study, but neither was considered in the hospitalization study, where they may have also been confounders. Green et al11 and Ostro et al8 found positive associations between temperature and overall respiratory disease, a finding not observed in our study. However, this discrepancy could be attributable to differences in the specific respiratory diseases between ER visitors and hospital admission cases. It is possible that less serious illnesses dominated respiratory visits in our ER database, for which hospital admission followed only 20% of visits. In studies examining hospitalization, some age groups were at higher risk for specific outcomes, but no race/ethnic group was identified as having increased risk, as we found.
Previous investigators also reported stronger associations in the elderly between increased ambient temperature and CVD outcomes,29,30 such as acute myocardial infarction31–34 and congestive heart failure.31 The threshold for myocardial infarction admissions was lower in the least disadvantaged area (81°F or 27°C) than the most disadvantaged area (86°F or 30°C) in the study by Loughnan et al.34 Ischemic stroke35,36 and hemorrhagic stroke36 were also found to have higher risks with greater ambient temperature in Scotland and Australia. However, Wang et al36 found increased risk only in those younger than 65 years for both categories of stroke. Although age-specific results were not given by Dawson et al,35 the average age for stroke admission was 71 years. Some negative associations have been reported between temperature and CVD outcomes such as hypertension30 and myocardial infarction.37 There was no effect of extreme heat in the study by Wolf et al,37 but this may have been attributable to the relatively temperate climate in Germany, which is not comparable to California or southern Europe.38
Outcomes other than CVD have also been reported with high ambient temperature. Lin et al30 reported a positive association between summer heat and respiratory admissions in New York City. These investigators also observed greater risk for respiratory disease among Hispanics and the elderly. Although this study controlled for O3, other gaseous pollutants were not considered. Following the Chicago heat wave in July 1995, Semenza et al29 found an excess risk of dehydration and heat illness as primary diagnoses and renal disease and diabetes as underlying causes. Hansen et al39,40 also found an association with renal disease and mental health in Australia. Heat-related mortalities associated with mental and behavioral disorders were higher in people aged 65–74 years. The association with renal disease was stronger in women aged 50–54 years and 85+ years with comorbid diabetes. In our study, we also found associations with dehydration, heat illness, and renal disease.
To fully understand the mechanism of heat exposure and associated morbidity, more studies are needed that assess personal heat exposure.41 Physiologic efforts to thermoregulate efficiently seem to be at the root of many of the effects we observed. One of the primary responses, vasodilation, reduces blood pressure,42 which could explain the reductions in ER visits owing to hypertension and the hypertension-associated blood vessel rupture outcomes, and the increase in hypotension visits. Indeed, opposite effects in cold weather have been observed.43 Increasing the volume of blood flow to extremities involves increasing heart rate, which could also overstress the heart, leading to cardiac problems. We observed increased visits for arrhythmia, consistent with that hypothesis, but did not see an increase for heart failure, possibly owing to contrasting influence from vasodilation. We observed strong associations in independent analyses of ischemic stroke and heart disease. This might be explained by increased blood viscosity and increased cholesterol levels38 or changes in coagulation leading to increased thrombosis.44 A higher sweating threshold in vulnerable populations may also trigger these and other adverse health outcomes with heat exposure.45
Some limitations in our study may be addressed in future studies. The most severe cases would not have been captured in our study because they resulted in deaths before reaching a hospital. Our ability to analyze vulnerable subgroups was limited by the lack of information reported on the ER records. Because we did not have data on socioeconomic status, we used race/ethnic group as a surrogate. In addition, even though we were able to use a secondary diagnosis of diabetes as evidence of preexisting disease, we were unable to evaluate other preexisting conditions. Future studies may be able to use more complete data and better assess factors that best predict vulnerability.
The study design also had a few limitations. The design uses individual data for the outcome but ecologic data for exposure, resulting in a semiecologic design. Because there were 1 million study participants, it is not feasible to monitor individual persons. The 7-day interval for selecting controls allowed us to control for day of the week, but this is appropriate only for non-heat-wave periods and for heat waves that last for 6 or fewer days. With longer lag times, the referent and case periods would overlap, so the true effect would be diminished. Consequently, the study design was not ideal for investigating the possibility of a harvesting phenomenon. However, we did observe negative associations at longer lag times for some health outcomes, which suggests that the possibility of harvesting should be explored further.
Although we examined the possibility that the effect would change at apparent temperatures above the 90th percentile of each climate zone, we could have missed a more absolute threshold effect, especially if some of the cooler climate zones did not often reach such a threshold. In addition, we chose to explore effects above the 90th percentile because it offered a combination of relatively high temperatures and a sufficiently high number of cases, although deviations of the effect from linearity may have been observed at other points. Future studies should address nonlinearity issues; if there are threshold effects, this could inform public health mechanisms about when to issue heat warnings and deploy mitigation measures.
Our investigation into PM2.5 as a possible confounder was limited to seven climate zones where daily monitoring was available. However, in a statewide dataset of monitors that recorded PM2.5 every third or sixth day, we found little correlation between statewide apparent temperature and PM2.5 from the same monitor during the warm season (median monitor correlation = −0.03), making PM2.5 an unlikely confounder in this analysis. We relied on short-term 24-hour averages to characterize exposure. However, the critical exposure period could have been <24 hours. Because we had only the hospital admission date, we assumed that ER visits occurred on that date. We believe this is a valid assumption.
This study adds to the growing body of information on increased ambient temperature and morbidity and is the first to focus on ER visits in California capturing both heat-wave and non-heat-wave periods. Associations are found even during non-heat-wave periods in relatively mild climates, when temperature exposures are within the usual range of mean apparent temperatures ranging from 60°F to 85°F. In other areas of the United States, similar effect estimates for temperature and mortality were observed using parallel methods, suggesting that the results tend to be generalizable.46 During heat-wave periods, mean apparent temperatures range from 80°F to 110°F (27°C to 43°C). Therefore, heat-wave periods are likely to demonstrate much larger effects for the health outcomes studied, as was found in a study examining mortality following the 2006 heat wave in California.47 We limited exposure assignment to monitors within a 10-km buffer area and within the same climate zone. This minimizes exposure measurement error that may arise when using larger areas, such as metropolitan area or counties, which may be climatically heterogeneous. Because we did not have individual occupational history or addresses, or time-activity diaries, using residential addresses was the logical choice for assessing exposure. We grouped results by climate zones, so that we could readily make comparisons between coastal and noncoastal areas.
Global warming models continue to predict increases in mean temperature and more extreme heat events. Although we found both negative and positive associations with heat, the outcomes reduced with high temperature seemed to be either rare or less serious (Table). A thorough economics evaluation would help quantify the potential risks and benefits of increased apparent temperatures on ER visits.
FIGURE 3: Meta-analysis results for percent excess risk of emergency room visits per 10°F (5.6°C) change in mean same-day apparent temperature for selected cardiorespiratory outcomes, stratified by age. All climate zones were used.
FIGURE 4: Meta-analysis results for percent excess risk of emergency room visits per 10°F (5.6°C) change in mean same-day apparent temperature for essential hypertension and hypotension, stratified by age or race/ethnicity. Number of climate zones analyzed: for hypertension, 15 for age-stratified analysis and 9 for race/ethnicity-stratified analysis; for hypotension, 15 for age-stratified analysis and 7 for race/ethnicity analysis.
FIGURE 5: Meta-analysis results for percent excess risk of emergency room visits per 10°F (5.6°C) change in mean same-day apparent temperature for selected cardiorespiratory outcomes, stratified by race/ethnicity. Number of climate zones analyzed: 14 for cardiovascular outcomes; 10 for ischemic stroke; 11 for ischemic heart disease; 10 for dysrhythmia; and 14 for respiratory outcomes.
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ACKNOWLEDGMENT
We thank Kathleen Vork and Melanie Marty for their thorough reviews and helpful comments.
