Background: Although the association of high temperatures with mortality is well-documented, the association with morbidity has seldom been examined. We assessed the potential impact of hot weather on hospital admissions due to cardiovascular and respiratory diseases in New York City. We also explored whether the weather-disease relationship varies with socio-demographic variables.
Method: We investigated effects of temperature and humidity on health by linking the daily cardiovascular and respiratory hospitalization counts with meteorologic conditions during summer, 1991–2004. We used daily mean temperature, mean apparent temperature, and 3-day moving average of apparent temperature as the exposure indicators. Threshold effects for health risks of meteorologic conditions were assessed by log-linear threshold models, after controlling for ozone, day of week, holidays, and long-term trend. Stratified analyses were used to evaluate temperature-demographic interactions.
Results: For all 3 exposure indicators, each degree C above the threshold of the temperature-health effect curve (29°C–36°C) was associated with a 2.7%–3.1% increase in same-day hospitalizations due to respiratory diseases, and an increase of 1.4%–3.6% in lagged hospitalizations due to cardiovascular diseases. These increases for respiratory admissions were greater for Hispanic persons (6.1%/°C) and the elderly (4.7%/°C). At high temperatures, admission rates increased for chronic airway obstruction, asthma, ischemic heart disease, and cardiac dysrhythmias, but decreased for hypertension and heart failure.
Conclusions: Extreme high temperature appears to increase hospital admissions for cardiovascular and respiratory disorders in New York City. Elderly and Hispanic residents may be particularly vulnerable to the temperature effects on respiratory illnesses.
From the aNew York State Department of Health, Center for Environmental Health, Troy, New York; and bNew York State Office of the Attorney General, Albany, New York.
Submitted 15 May 2008; accepted 5 November 2008.
Supported in part by grants from the National Environmental Public Health Tracking Program, Centers for Disease Control and Prevention (5U38EH000184). The National Center for Atmospheric Research (NCAR) (see Acknowledgments) is supported by grants from the National Science Foundation.
Address for correspondence: Shao Lin, Bureau of Environmental and Occupational Epidemiology, New York State Department of Health, 547 River Street, Room 200, Troy, NY 12180. E-mail: firstname.lastname@example.org.
The relationship between high temperature events and mortality has been well documented,1–4 with evidence suggesting substantial increases in mortality from respiratory and cardiovascular disease, particularly among the elderly, frail, and young.5–8 Fewer investigations have evaluated morbidity, and most of those have studied the effects during specific heat waves such as the 1995 heat wave in Chicago, during which there were more than a thousand excess hospitalizations compared with nonheat wave weeks.9 Rydman et al10 found that the first heat-related emergency room visits occurred 5 days before the first heat-related fatality. They also found that heat-related morbidity was more closely associated than mortality with the meteorologic data.10 Several recent studies have examined morbidity effects of heat waves or temperature (entire distribution) in Europe, India, and Brazil, with inconsistent findings.11–15 Most previous studies of heat waves or temperature in relation to morbidity focused on single episodes and used short periods of data. In addition, most prior studies assessed the effect of temperature, but the independent health impacts of humidity and the interaction between temperature and humidity were rarely examined.
The purpose of the current study was to: (1) assess the potential health impacts of high temperature, humidity, and apparent temperature (which combines temperature and humidity) on hospital admissions due to respiratory and cardiovascular diseases in New York City during the period 1991–2004, while controlling for time-relevant and confounding variables; (2) identify thresholds for meteorologic conditions on hospital admissions; and (3) evaluate possible interactions between meteorologic conditions and sociodemographic factors.
Climate change estimates project an increase in mean warming of between 3°C and 5°C for the northeastern United States.16 Projections estimate that cities such as New York City are likely to experience more extreme heat events due to added contributions from the urban island heat effect, with an increase in severity and frequency of heat waves.17 New York City includes a diverse urban population, with millions of residents over 65 years old—many of whom have preexisting conditions (eg, cardiovascular disease and respiratory illness) that increase susceptibility to summer heat stress. This study assesses early indicators of heat stress by evaluating morbidity, rather than mortality, associated with multiple meteorologic variables.
Study Population and Health Outcomes
We applied time-series analysis to assess the association between selected meteorologic conditions and daily hospital admission counts. The study population included all New York City residents.
The health outcomes were hospital admissions due to respiratory diseases and cardiovascular diseases. A case was defined as a resident of New York City admitted to a hospital in the summer (June, July, and August) from 1991–2004, with a principal diagnosis of either respiratory disease or cardiovascular disease. Based on the International Classification of Diseases, 9th Revision (ICD-9 code),18 respiratory diseases included the following principal diagnoses: chronic bronchitis (491), emphysema (492), asthma (493), or chronic obstructive pulmonary disease (COPD) (496). For children ages 0 to 4 years, we also included acute bronchitis and bronchiolitis (466) and bronchitis, not specified as acute or chronic (490), because these are common respiratory illnesses among very young children, and their symptoms are difficult to distinguish from asthma. Cardiovascular diseases were defined as: chronic rheumatic heart disease (ICD-9 codes 393–396), hypertension (401–405), ischemic heart diseases (410–414), cardiac dysrhythmias (427), congestive heart failure (428), or cerebrovascular diseases (430–434,436–438).
Data Sources and Exposure Assignment
Discharge data for hospital admissions among residents of New York City from 1991 to 2004 for both respiratory and cardiovascular diseases, as described above, were obtained from the New York State Department of Health's Statewide Planning and Research Cooperative System. This is a legislatively mandated database that contains hospital discharge data for at least 95% of all acute care hospital admissions in New York State, excluding admissions to psychiatric and federal hospitals.19 The data included principal diagnoses, hospital admission date, sources of payment, date of birth, sex, race, ethnicity, and street address. The family income variable was obtained from the 1990 and 2000 US Census data at the Census block level.
Meteorologic data, including hourly observations for temperature and dew point at the National Weather Service stations in the 5 New York City boroughs for the years 1991–2004, were provided by the Data Support Section of the Computational and Information Systems Laboratory at the National Center for Atmospheric Research. Two weather stations, La Guardia (LGA) and John F. Kennedy (JFK), provided temperature and dew point data over the study period. Hourly ambient ozone (O3) data were obtained from the New York State Department of Environmental Conservation. We limited the O3 data to the hours of 10:00 am–6:00 pm, which represent the peak outdoor exposure time. Figure 1 shows that 3 exposure regions, LGA, JFK, and Staten Island, resulted from the combination of the available weather stations and ozone monitors.
To define the study population more precisely, the residential address from each hospital admission record was geocoded and assigned a latitude and longitude, using Map Marker Plus (Pitney Bowes Business Insight, Troy, NY). About 94% of residential addresses were geocoded to street level, and 5% to zip code level. Less than 1% of the addresses could not be geocoded. The map of geocoded addresses was overlaid onto the map of exposure regions, using MapInfo (Pitney Bowes Business Insight, Troy, NY). Daily hospital admission counts for respiratory and cardiovascular diseases between 1991 and 2004 were summed for each exposure region.
The meteorologic indicators in this study include daily mean temperature (T), daily mean relative humidity (RH), and daily mean apparent temperature (AT), which was examined as a single day effect as well as a cumulative effect over multiple days. Apparent temperature, an index of human discomfort due to the combined effect of heat and humidity,20 was calculated using the formula of AT(C) = −2.653 + (0.994T) + (0.0153DP2), where DP stands for dew point. Daily mean relative humidity was calculated according to the formula of RH = EXP(ln(10)((7.5DP)/(237.7 + DP) – (7.5T)/(237.7 + T))).
Extreme heat exposure is defined as temperature or apparent temperature above a threshold. The threshold represents a turning point at which the relationship between temperature and health outcomes changes. Previous studies report a U-, V-, or J-shaped relationship between temperature and health endpoints.1,2,4,21,22 In general, health effects are found to be lowest around the average temperature and higher at either temperature extreme.
We analyzed the effects of lag periods of 0, 1, 2, 3, and 4 days (ie, the number of days of extreme high temperature exposure prior to hospital admission). In addition, a 3-day moving average (lag 0–lag 2 days for respiratory diseases and lag 1–lag 3 days for cardiovascular diseases) of apparent temperature was used to estimate the cumulative effect of extreme temperature on hospital admissions.
Increased rates of respiratory hospitalizations have been found to occur immediately after holidays or weekends compared with other days.23 Additionally, changes in weather patterns, human behaviors, or environmental policies may have occurred over the 14-year study period. Therefore, holidays, day of the week, mid-term variation, and long-term trends were adjusted in the time-series analysis. Ozone level and barometric pressure were treated as confounding variables and were controlled in the model. We also controlled the impact of the 2003 blackout that occurred in the northeastern United States. Stratified analyses were conducted based on individual level data such as race, age, sex and specific disease group and census block group (neighborhood) level of family income.
The effects of copollutants were also assessed and controlled for in sensitivity analyses, using the Air Quality Index.24 This is a standard system representing the levels of multiple air pollutants including ambient particulate matter (PM10 and PM2.5), nitrogen dioxide, carbon monoxide, and sulfur dioxide. The highest value of the individual pollutants was chosen to represent the daily Air Quality Index for that region. The sensitivity analyses were limited to data from 1998–2004 for which copollutant data were available.
We assessed the short-term effects of extreme heat on respiratory and cardiovascular hospital admission counts by using generalized additive models (GAM)25 with Poisson-distributed errors and a log link function, after controlling for possible confounders. Since a U-, V-, or J-shaped relationship between temperature and health endpoints is usually found, we used a linear-threshold model to quantify the effect of high temperature.2,4,21,22,26–28 This method assumes a log-linear increase in health risk above a temperature threshold, which was determined by comparing the maximum likelihood estimates over all possible threshold values in the range of data and using the value with the lowest deviance.2,4,21,26
We used a linear term, Temp > T0, to approximate the right part of the curve (extreme heat effect)
where, T0 is the threshold point and α0 and ε are the intercept and error term, respectively. The spline curves, which were indicated by s(df), were used to model the effects of temperature lower than the threshold point (Temp < T0) and control for ozone effects (O3), long-term trends, and mid-term variations (date). To eliminate seasonal patterns of hospitalizations and fit the model, we used 10–15 df/y for respiratory diseases and 1–7 df/y for cardiovascular diseases, which are within the range used in other studies.4,21,29 Calendar effects were controlled by dummy variables for Monday to Saturday (β1 to β6), public holidays (β7), the day after holidays (β8), and the blackout event (β9, β10).
The white-noise test based on the Bartlett Kolmogorov-Smirnov statistic was used to assure the fitness of the model. We also checked the model residuals for autocorrelation and partial autocorrelation functions to rule out seasonality or other patterns.26,30 Triangular cyclic components were introduced to deal with monthly or other administrative cycles in some cases; autoregressive items were used if residuals’ autocorrelation could not be controlled by increasing the degrees of freedom. For plausible competing models, the one with the lower Akaike information criterion (AIC) was selected.29
After determining the threshold points (T0) for each region, the regional threshold values were pooled using weighted inverse variance to obtain estimates for New York City, which were then used as the cut-points to evaluate extreme heat effects. The log-scaled measurement of the relative risk (RR) for New York City was back-transformed to report the percent change in risk of hospital admission per degree Celsius (°C) increase in temperature (or apparent temperature) above the threshold. We adjusted for overdispersion when calculating the standard errors of estimates.
Table 1 provides summary statistics for meteorologic indicators, ozone concentrations, and daily hospital admissions for respiratory and cardiovascular diseases in New York City for 1991–2004. Mean temperature (both temperature and apparent temperature) and mean relative humidity in the JFK region were about 1.1°C and 7% lower, respectively, than in the Staten Island and LGA regions. Mean daily respiratory admissions were highest for the residents of the JFK region, while cardiovascular admissions were highest in the Staten Island region. Disease distributions, with proportions for each subgroup of respiratory and cardiovascular disease admissions, are summarized in Table 2. Ischemic heart disease accounted for 41% of the total cardiovascular admissions, followed by heart failure (22%), cerebrovascular diseases (15%), and cardiac dysrhythmias (13%). More than half of respiratory diseases were asthma (65%), followed by chronic bronchitis (16%). All other conditions combined comprised less than 20%.
The combined effect of temperature and relative humidity on cardiovascular and respiratory disease admissions is illustrated in Figure 2, controlling for the confounding influence of the day of the week of admission. Because there was a delayed effect of temperature and humidity on cardiovascular hospital admissions, we used a 3-day lag structure in the analysis. Relative humidity had little influence on health outcomes for temperature ranging from 12°C–28°C. However, a significant trend for positive interaction between temperature and relative humidity on both health outcomes was seen when temperature exceeded a threshold of 29.4°C (ie, hospital admission rates for days with high relative humidity were higher than for days with low relative humidity when the temperature was above 29.4°C).
Table 3 presents the percent changes in daily hospital admission per degree Celsius above the thresholds, which ranged from 28.9°C–29.4°C for temperature and 31.7°C–35.6°C for apparent temperature. These temperature thresholds were close to the 95th and 99th percentiles of our temperature distributions and are consistent with those reported in other studies.2,7,27,31 For each °C above the temperature thresholds, the risk increased 2.7% (95% CI = 1.3%–4.2%) for respiratory admission on the same day, and 3.6% (0.3%–6.9%) for cardiovascular disease admission with a 3-day delayed effect. For apparent temperature, respiratory admission on the same day increased 2.1% (1.1%–3.1%) and 1-day later increased 1.4% (0.4%–2.4%) per °C above the thresholds. We also found an increased risk for cardiovascular admission: 2.5%, 2.1%, and 3.6%, at 1, 2, and 3 days later, respectively, for each °C increase in apparent temperature above threshold.
Our sensitivity analyses indicated that further adjustment of pollutants, using the Air Quality Index, did not change the effect of high temperature on cardiorespiratory admissions. For the 3-day moving average apparent temperature, the effect (3.0%/°C) for respiratory diseases controlling for AQI was close to the effect (2.8%/°C) controlling for ozone in the time period of 1998–2004. Similarly, there was no difference for cardiovascular disease admissions (0.8%/°C vs. 0.7%/°C). Sensitivity analyses based on daily mean temperature and apparent temperature yielded similar results.
The interactions between cumulative exposure to extreme heat and demographic variables and disease type were examined using a 3-day moving average of apparent temperature. Age, Hispanic ethnicity, and disease type were found to interact with temperature. Figure 3 shows that respiratory and cardiovascular admissions increased 3.1% and 1.4%, respectively, for each °C of apparent temperature above the threshold. For respiratory diseases, people of Hispanic ethnicity had higher risks for hospital admissions than those of non-Hispanic ethnicity (6.1% vs. 1.7%). Persons who were at least age 75 years also had a higher ratio of respiratory disease admissions (4.7%) compared with younger age groups (−1%–2.5%). Extreme heat influenced admissions due to chronic airway obstruction (7.6%) and asthma (3.5%) more than those due to chronic bronchitis. For cardiovascular diseases, increased admissions occurred for those 75 years and older (3.5%) compared with younger people. Extreme temperature resulted in increased admissions for cardiac dysrhythmias (3.3%) and ischemic heart disease (2.5%), but decreased admissions for cerebrovascular disease, heart failure, and hypertension.
To control for potential secular change of heat effects,3 we included a linear term in the model for the interaction between heat effects and study year. No secular trend was found for either respiratory or cardiovascular disease admissions due to extreme heat.
The current study found that hospital admissions due to respiratory and cardiovascular diseases increased with temperature and apparent temperature above certain thresholds in New York City. Basu and Samet5 reviewed several previous studies of temperature and mortality that identified temperature thresholds by geographic location ranging from 20°C–37.8°C. Similarly, Baccini et al28 observed thresholds ranging from 23.3°C for 8 northern European cities to 29.4°C for 7 Mediterranean cities. On the other hand, previous studies focusing on seasonal variations and examining the whole temperature distribution generally have found inconsistent or even negative associations between typical temperature exposure and cardiorespiratory hospital admissions.13–15 These conflicting findings may be due to the typical U- or V-shaped relationship between temperature and health endpoints; health effects are lowest at average temperature and higher at cold or hot temperature extremes. Because of this, a simple linear model may not capture the effect of high temperature.6,31 In the current study, when temperature exceeded 28.9°C or apparent temperature exceeded 31.7°C, hospital admissions due to respiratory diseases increased 2.1%–2.7% per °C above threshold on the same day, and 1.4% per °C on the next day. Few studies have examined the effect of extreme heat on respiratory morbidity. A study conducted in London found a 5.4% increase in respiratory admissions per °C above a threshold with lags of 0–2 days.21 In contrast with our study, the threshold temperature of respiratory effects in London (23°C) was lower than in New York City (28.9°C), whereas the heat effect in London was found to be stronger (5.4% per °C) than in New York City (2.7% per °C). A possible explanation for these differences is that London's cooler summers produce lower levels of acclimatization, resulting in a lower threshold for heat effects as well as a greater impact of heat. Mastrangelo et al12 found that respiratory admissions in the elderly increased with the duration rather than the intensity of a heat wave in Italy. Another recent study in Madrid found an increased respiratory effect on both mortality and morbidity when the temperature was above a threshold (95th percentile of the maximum daily summer temperature), but the increase in hospital admissions was smaller than that found for mortality.22
We also found that hospital admissions due to cardiovascular diseases increased 2.2%–3.6% when temperature or apparent temperature exceeded its respective threshold, which is consistent with previous findings. For example, during the 1995 Chicago heat wave, there was an increase in hospital admissions for cardiovascular diseases.9 Similarly, the study by Nitschke et al32 in South Australia found an 8% increase in ischemic heart disease admissions (95% CI = 1%–15%) during heat waves. In a prospective study of temperature and humidity on hospital admissions for heart disease in 12 cities, Schwartz et al30 found a positive effect for same-day exposure. In our study, however, there was a consistent delay (1–3 days) in cardiovascular hospital admissions following heat exposure compared with the effects on respiratory hospital admissions.
Our results demonstrated a positive interactive effect between temperature and humidity on both respiratory and cardiovascular hospital admissions when temperature exceeded the threshold of 29.4°C. High relative humidity interacted with temperature to intensify the health impact of high temperature, whereas lower relative humidity moderated the effect of high temperature. Although previous studies have used composite measures such as apparent temperature9 that combine temperature and relative humidity, our study is the first to examine the independent and joint effects of temperature and humidity, as well as the ways in which changes in their relationship affect health outcomes.
We found trends of positive interaction between some demographic variables and meteorologic conditions on health outcomes. The elderly, Hispanics, and people with specific diseases such as asthma, chronic airway obstruction, ischemic heart disease, and cardiac dysrhythmias tended to have higher risks for respiratory/cardiovascular diseases. Mortality studies have found that the elderly and people of low socio-economic status were more vulnerable to heat waves or high temperature,8 but no morbidity studies have examined the interactions between temperature and sociodemographic variables. Applegate et al,33 for example, found that inner-city residents who were 60 years old and older, poor, and black were at greater risk of heat-related deaths and other causes of death. One possible reason for these findings is that the elderly have more limited capacity to acclimatize to thermal extremes because of their higher sweating thresholds,34 and have a reduced ability to respond to extreme weather events.35 Low-income and minority persons are more likely to live in urban areas, less likely to be able to afford air-conditioning systems36 or health care, and may be less likely to open windows during heat waves due to high crime rates in the area.
We found increased temperature-related risks for specific diseases such as chronic airway obstruction, asthma, ischemic heart disease, and cardiac dysrhythmias. These results are consistent with those from the 1995 Chicago heat wave9 and from a heat wave study in Adelaide, Australia.32 Heart failure and hypertension hospitalizations, on the other hand, decreased when the temperature was above the threshold. This finding may be attributed to the fact that both of these conditions are driven by blood pressure, which decreases with increasing temperature.37
This study is among the few studies examining the association between hot weather conditions and morbidity over a long time period in a large urban population. We employed a comprehensive time-series model to control for confounding effects including day-of-week, holidays, blackout events, long-term trends, ambient ozone levels and other copollutants, and socio-demographics. We examined both independent and combined effects of temperature and humidity, and found that the effect of humidity is differential at high temperatures. We also examined the effects of potential interactions between temperature and sociodemographic factors on health outcomes. Because the New York City population includes a high proportion of Hispanic and elderly people who may be vulnerable to high temperatures, our findings suggest the need to develop strategies to protect such susceptible populations during high temperature events.
The current study investigates high temperatures over a long time period (14 years) rather than during a single heat wave episode. Using this design, we were able to detect thresholds of heat effects in a temperature range slightly below that of a heat wave and with a shorter period. We also found that the health impacts were higher in the Northeastern United States than in California during the 2006 heat wave—for which no increases of either respiratory or cardiovascular admissions were found.38 The stronger heat effect at lower temperatures in our study suggests a role for acclimatization, such that residents of more temperate climates are less acclimated to heat and are therefore more susceptible to extreme heat than are residents of hotter climates. Acclimatization potentially modifies both the threshold for and the magnitude of heat effects.
Several limitations should be considered. First, the threshold points identified based on minimizing model deviance could be tailored to the data set. However, because temperature thresholds for health effects vary by geographic location and climate,5,6 a certain amount of tailoring is expected. To address the uncertainty in the threshold estimates, we reported confidence intervals.
The current study used hospital admission data as health endpoints and thus we may have captured only the most severe respiratory and cardiovascular diseases. Also, meteorologic measurements were obtained from fixed monitoring sites, assumed uniformity across regions, and did not account for variability at residential locations or in individual daily activity patterns, including exposure to indoor temperatures. In addition, there are factors for which we could not control, such as pre-existing diseases, smoking, and other behavioral risk factors, which may modify the effects of high temperature.
In conclusion, we found that high temperatures in New York City in excess of certain threshold points were associated with increases in hospital admissions for respiratory diseases on the same day and for cardiovascular disorders with a delayed effect. Heat wave warning systems and prevention plans have typically been implemented based on a temperature limit above which mortality begins to increase. Public health prevention plans must also take into account effects on morbidity and early indicators of heat stress. Future research should be carried out to confirm our findings, particularly for the elderly, Hispanics and people with pre-existing health problems.
We thank Barbara A Fletcher from the New York State Department of Health for very useful advice and help on the revision of the manuscript. Meteorologic data were provided by the Data Support Section of the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NCAR).
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