1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
2 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
3 Department of Environmental Health Science, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
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ABSTRACT |
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aging; cause of death; climate; heat; mortality; weather
Abbreviations: AIC, Akaike's Information Criterion; GAM, generalized additive model; MMT, minimum mortality temperature
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INTRODUCTION |
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Heat waves cause excess deaths, many of which are due to increased demand on the cardiovascular system required for physiologic cooling (4). Data from US cities provide evidence that overall death rates increase during heat waves (5
, 6
), particularly when the temperature rises above the local population's threshold value. However, mortality rates show a strong seasonal pattern, peaking in the winter when epidemic respiratory infections, such as influenza, are most common.
We explored the weather-mortality relation in 11 of the largest metropolitan areas of the eastern United States to further characterize the weather-mortality relation across the full range of temperatures and latitudes. A number of similar studies have been published in recent years (1, 6
9
). The present study extends these early analyses by incorporating contemporary methods of time-series analyses and by exploring city-specific factors that might explain variations in the temperature-mortality association across cities; a two-stage analytical approach was used. We developed a model of the weather-mortality relation that is potentially useful for projecting the consequences of climate-change scenarios. The model also offers a summary of these data as evidence about possible weather effects on mortality.
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MATERIALS AND METHODS |
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The mortality data were provided by the Division of Vital Statistics of the National Center for Health Statistics (Hyattsville, Maryland). These mortality data excluded persons who died in the study area but did not reside within that area and whose deaths were attributed to external causes. Information was used on underlying cause of death, coded according to the International Classification of Diseases, Ninth Revision, to classify deaths as due to cardiovascular disease (codes 390459), respiratory disease (codes 460519), and all other diseases. For selected analyses, the mortality data were stratified by age as less than 65, 6575, and more than 75 years.
Both census and weather data were used as potential predictors for modeling mortality. Data from the 1980 and 1990 Census of Population and Housing were provided by the US Census Bureau (1012
). The weather data were extracted from the National Climatic Data Center EarthInfo CD2 database (13
). These data included hourly readings for temperature and dew point reported in degrees Fahrenheit (°C = 5/9 x (°F - 32)).
As detailed further below, we estimated the weather-mortality relation by using generalized additive models (GAMs) with nonparametric smoothing functions (splines) to describe nonlinear relations (14, 15
). We used Akaike's Information Criterion (AIC), a measure of fit, to select the smoothing parameters (16
). Of the available weather variables for 19731994, we considered for each day the average temperature, dew point, nighttime (between 6 p.m. and 6 a.m.) temperature and dew point, and daytime (between 6 a.m. and 6 p.m.) temperature and dew point. In exploring the weather-mortality relation, we began with lagged predictor variables to allow for possible delayed effects of weather, starting with variables unlagged and lagged by 17 days.
Data for Philadelphia and Chicago were used for initial model development. For each city, a Poisson regression GAM was fit for mortality by using as the predictors smoothing spline functions of time, average daily temperature, and average daily dew point at several lags. For these and all other GAMs fit in the analysis, 44, 88, and 176 df were used in the smooth function of time, which is an average of 2, 6, and 8 df, respectively, per year over the 22 years of record. This approach controls for smooth seasonal variations in mortality without imposing a common seasonal pattern across all years or requiring separate models to be fit for each year. Functions of average temperature and average dew point were each given a total of 6 df.
The results of this initial analysis motivated us to construct additional variables intended to better capture the lagged effects of temperature and dew point. The new variables were constructed for both temperature and dew point: T13 is the average temperature for the 3 days preceding the day on which mortality was recorded, while T410 is the average temperature for days 410 preceding the recorded mortality. Similarly, variables D13 and D410 were constructed for dew point. Other variables were considered in the model but provided no additional understanding of the weather-mortality relation.
Our statistical analyses had two stages. In the first stage, a separate log-linear regression analysis for each city produced an estimated mortality relative risk curve as a smooth function of temperature. In the second stage, we summarized the shape of the smooth relative risk curve for each city and described how these summaries varied across cities as a function of latitude and other city-level variables. Each stage is described in more detail below.
Stage 1: city-specific log-linear regressions
Our primary goal was to characterize the shape of the mortality-temperature relation for shorter time scales while, to the extent possible, controlling for possible confounding due to longer-term trends in demographic characteristics, smoking, medical care, and seasonal health events, such as influenza epidemics. After substantial preliminary analysis in which several daily temperature variables were considered as predictors of mortality by using AIC, we focused our analysis on the daily mean values of temperature and dew point that were found to be the best or close to the best predictors for all 11 cities. On the basis of a preliminary analysis, we addressed the lagged dependence of mortality on weather by using same-day temperature T0 and dew point D0, average temperature and dew point over the preceding 3 days T13, D13, and average temperature and dew point temperature 410 days prior T410, D410. Because T0, T13, and T410 (and D0, D13, and D410) were highly correlated, and because our focus was on the shape of the temperature-mortality association and not on the lag structure, we orthogonalized the temperature predictor variables and included T0, D0; adjusted T13 (adj T13), which is T13 adjusted for T0 and D0; adjusted D13 (adj D13), which is D13 adjusted for T0 and D0; and T410 and D410 adjusted for T0, D0, T13, D13 (adj T410, adj D410). The adjusted lagged variables were taken as the residuals from regressing the lagged variable on the unlagged variables; for example, adj T13 are the residuals from regressing T13 on T0 and D0. Additionally, variables reflecting the difference between the average daytime and average nighttime temperatures were constructed at lag 0 (S0) and for the averages of lags 13 (S13) and lags 410 (S410).
Several models were fit with the new lag-combination variables. Comparing the AIC values for all models, we found that those including same-day temperature and dew point, as well as previous 3-day temperature and dew point (lag 13 variables), fit best. Because the models with same-day and 3-day lag variables also have the advantage of simplicity, the lag 410 variables were not included. The daily spread in temperature variables (S0, S13, S410) did not seem to improve the model fit and were also excluded. To avoid giving undue priority to same-day variables, we also considered models with T13, D13 and T0, D0 adjusted for T13, D13 as predictors.
In a standard log-linear regression (17), log mortality is assumed to be a linear function of temperature and other predictors. In our study, we assumed that log mortality is a smooth but not necessarily linear function. We estimated this smooth function by using a GAM (14
), which fits a cubic spline function of temperature. Instead of summarizing the temperature-mortality association with a single relative risk value for all temperatures, we obtained a relative risk estimate that was a smoothly changing function of temperature. GAMs or cubic splines are widely available in most statistical software packages (e.g., S-PLUS (18
) and SAS (19
)). The degree of smoothness of the estimated mortality-temperature relative risk curve is controlled by its number of degrees of freedom. A linear function has 1 df for its one slope; a quadratic curve has 2 df for its slope and curvature, among other variables. To allow for highly nonlinear shapes, we used 6 df to describe the association of mortality with each weather variable.
Finally, the regression of mortality on weather may also be affected by key potential effect modifiers including longer-term changes in population characteristics, health behaviors (particularly smoking), trends in medical practices or access to health care, and potential seasonal-related confounders. All but the last change slowly, but not necessarily linearly, over time. Therefore, these variables can be adjusted by including a smooth function of calendar time as a predictor. Acute events affecting health, such as influenza epidemics, occur seasonally, as does temperature variation. Since detailed data on such events were not available, they were more difficult to control for without removing some or all of the temperature effects. Thus, we relied on a smooth function of calendar time to adjust for potential seasonal confounders as best as was possible. To explore the effects of this adjustment on our results, we used 44, 88, and 176 df (average of 2, 6, and 8 df, respectively, per year) for the smooth function of calendar time.
Hence, the final log-linear model for a given city has the form:
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![]() | (1) |
Stage 2: variation in relative risk curves for temperature across cities
The goal of the second-stage analysis was to describe variation in the shape of the mortality-temperature relative risk curve across cities in relation to latitude and other city-specific characteristics, such as percentage of elderly persons and percentage of homes with heating and/or air-conditioning. We summarized the relative risk curves for mortality in relation to T0 with three variables: the temperature at which the estimated relative risk curves from the GAM achieved their minimum or minimum mortality temperature (MMT), the average slope of the estimated relative risk curves at temperatures lower than the MMT (cold slope), and the average slope of the curves at temperatures higher than the MMT (hot slope) (figure 1). The cold slopes and hot slopes were found by fitting a linear regression line through those points in the fitted relative risk curve from the GAM before and after the turning point (MMT), respectively.
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![]() | (2) |
Since our summary-statistics MMT, cold slope, and hot slope were calculated from nonparametric regressions, we used a standard statistical methodbootstrapping (21)to estimate the statistical variance
i2 for each city. Boot-strapping is a computer-intensive resampling method for estimating variances. From the original sample of N days, we randomly drew a new sample, also of size N, with replacement. By using this new sample, we refit the model and obtained a new set of estimates of the MMT, cold slope, and hot slope. This process was repeated 100 times, and
i2 was estimated by the variance among these 100 replications of the summary measure (e.g., cold slope) of interest. We estimated
2 by using the method of moments and then used iteratively reweighted least squares to estimate ß1 with weights var(Yi) = (
2 +
i2)-1, i = 1, ... 11. The city-specific predictor variables, averaged from the 1980 and 1990 US Censuses, were the fractions of persons 1) more than age 65 years, 2) more than age 65 years who were disabled, 3) more than age 25 years who did not have a high school degree, and 4) living in poverty; and the fraction of occupied homes with 5) heating and 6) air-conditioning. The heating variable included homes equipped with some form of heat source (gas, oil, electric, etc.). The air-conditioning variable included homes with central air or homes with one or more wall units. Air-conditioning data were available from the 1980 US Census only, so these percentages were used in the analysis. Similarly, the fraction of persons more than age 65 years who were disabled was recorded in the 1990 US Census only; therefore, these numbers were used (10
12
).
We first regressed each of the summary scores (MMT, cold slope, and hot slope) on each predictor alone and then on latitude with each of the city-specific variables. We used this approach to estimate the effect modification by these city-specific variables over and above the apparent effect modification due to latitude. S-PLUS statistical software (18) was used for all analyses.
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RESULTS |
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We next explored the association between weather and mortality for four different time periods19731979, 19801984, 19851989, and 19901994fitting model 1 to each period. Fits for each period were similar to those described earlier for the entire period (figure 1). Thus, it seems that over the total period of 19731994, the effects of weather on mortality were qualitatively consistent.
The association between weather and mortality from various causes was examined (results not shown), with cardiovascular disease and respiratory disease grouped together because of small numbers of deaths from respiratory diseases. As in the models for all causes of death, mortality risk for cardiovascular and respiratory disease decreased as temperature increased, although the cold-weather slopes were steeper than those for all disease types combined (figure 1). As for total mortality, the slopes of the curves before the turning point were steeper for the southern cities than those for the northern cities; after the turning point, the slopes for the northernmost cities were steeper. The Other disease category included mainly cancer deaths. For the Other category, the curves representing the effects of the temperature lag 0 variable were relatively flat compared with the curve for cardiovascular and respiratory disease.
To explore the possibility of differing weather-mortality relations for each age group, we stratified the mortality data into three different age categories: less than 65, 6575, and more than 75 years. In each age group, we observed a qualitatively similar relation between weather and mortality (results not shown), although the temperature effect was smallest for the youngest age group and largest for the group aged 75 years or more.
We summarized the relative risk curves for mortality in terms of MMT, cold slope, and hot slope (table 2). As is apparent in figure 1, the cold slope was steeper (more negative) for the southernmost cities, and the hot slopes were steeper (more positive) for the northernmost cities. The MMT increased the farther south the city's location. There was a surprisingly large difference in hot slope between Baltimore and Washington, DC, two nearby cities with almost identical weather patterns. The curve representing Charlotte never turned, making it impossible to estimate a corresponding hot slope.
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For the hot slopes, significant pairwise associations were found with percentages of persons not completing high school (%NoHS), persons living in poverty (%Poverty), and homes with air conditioners (%Air Cond). A 10 percent increase in %Poverty, the strongest predictor, was estimated to increase mortality risk by approximately 4.3 percent per 10°F increase in temperature, at temperatures higher than the MMT. Adjusting for latitude had little impact on these significant associations. For MMT, only %Air Cond showed a significant association, and this association was reduced substantially by adjusting for latitude, since this variable is closely related to latitude.
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DISCUSSION |
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Given only same-day average temperature and average dew point, temperature was the index of weather that most strongly predicted mortality (AIC comparison not shown in results). However, we found that same-day dew point provided an additional explanation for mortality. With regard to time response, daily temperature at lag 0 and the average of daily temperatures at lags 1, 2, and 3 were more strongly associated with mortality than further lagged variables were. The weather more than 4 days prior was at best weakly associated with mortality once the more recent weather was taken into account. Within the selected cities, mortality risk decreased as temperature increased from the coldest days; however, after a certain critical temperature threshold, mortality risk increased in most of the cities as temperature increased.
Although this J-shaped relation was present for most cities, there were noticeable differences in the temperature-mortality response among the cities (table 2). These results are consistent with the work of others (5, 23
). Generally, populations in warmer regions tend to be most vulnerable to cold (24
), and those residing in cold climates are most sensitive to heat (23
). In temperate regions, mortality rates are highest during the winter. When analyzing data for the Netherlands, Kunst et al. (6
) also observed the decline in mortality with temperature for low and moderate temperatures and increased mortality with temperature at high average daily temperatures, that is, the J-shaped relation. We found a strong dependence of the configuration of the temperature-mortality relation on latitude (figure 1). For the more-northern cities, the MMT was generally lower and the hot slope steeper; for the more-southern cities, the MMT was generally higher and the cold slope steeper. This pattern is consistent with more effective adaptation to colder temperatures in more-northern cities and to hotter temperatures in more-southern cities. For example, the 1980 US Census showed that the prevalence of air-conditioning ranged from 65 percent in Chicago and 35 percent in Boston to 96 percent in Miami.
We found other predictors of the shape of the mortality-temperature relation (table 3). Mortality associations with colder temperatures were larger for cities with higher proportions of elderly and smaller for cities with a higher fraction of heating systems. The elderly have long been considered physiologically susceptible to temperature extremes (4). A study of the 1980 heat wave in Texas found relative death rates to be highest among males, the elderly, Blacks, and those persons engaged in heavy activity. Compared with earlier heat waves in the same region, the number of 1980 heat-related deaths was not as high, most likely because of increased use of air-conditioning (7
). A case-control study of the 1995 Chicago heat wave concluded that select groups of people were at greater risk of death; these groups included persons with known medical problems, those confined to bed, persons who did not leave home each day, and those who lived alone or on the top floor of a building (8
).
Two indicators of socioeconomic statuspercentage of persons without a high school education and percentage of those living in povertywere associated with increased mortality effects of high temperature, even after adjustment for latitude. A higher percentage of homes with air-conditioning was associated with small, but significant effects of hot temperature on mortality. We examined four time periods: 19731979, 19801984, 19851989, and 19901994. For all periods, we were surprised to observe similar relations between the weather variables and mortality, in spite of increasing penetration of air-conditioning and rising awareness of the effect of temperature on mortality.
When the data were stratified by cause of death, the same J-shaped relation was found for the cardiovascular and respiratory disease strata but not for Other causes (mainly cancer). The greater effects of temperature on cardiorespiratory deaths are consistent with prior reports (25). Mechanisms for the effects of temperature on cardiovascular mortality have been postulated. For example, blood viscosity and cholesterol levels have been found to increase with high temperatures (26
), whereas blood pressure and fibrinogen levels increase during winter, although outdoor temperature does not seem to determine the seasonal variation in fibrinogen (27
).
Larsen (9) reported that the effects of weather on mortality are noticed immediately. Kunst et al. (6
) observed effects within a week of the weather event. Our results indicate that the effects can be observed within 3 days for a cold-weather event and within 1 day for a hot-weather event. As in our data, Machenbach et al. (28
) also found greater mortality in the winter months.
Our results were based on daily mortality counts that did not exclude deaths attributed to specific health events such as influenza (29). Because sufficient data from such events rarely exist for these cities, we addressed these potential confounding effects by using different degrees of freedom in the time component of the model; we found that the temperature-mortality relative risk curves were qualitatively similar for various functions of time, including those sufficiently flexible to capture, for example, influenza epidemics. However, because temperature varies so smoothly with season, it was not possible to set aside all of the information about season and still reliably estimate a relative risk function for temperature. Hence, residual confounding by seasonal variables may have occurred. One possibility for future consideration is seasonal migrations of elderly persons from northern to southern cities. Proper adjustment for potential seasonal time confounders remains a limitation of these types of analyses because of the lack of city-specific data.
Other studies, for example, Kunst et al. (6), have considered wind speed as well as temperature and dew point. In these studies and ours, such predictors compete for explanatory effect. Future studies might use "apparent temperature," which combines temperature, wind speed, and dew point and considers "synoptic" or air mass climate analyses (30
). Weather variability could be emphasized more. We constructed a temperature-range variable accounting for the difference between average daytime and average nighttime temperature; however, this variable did not significantly predict mortality. Perhaps daily range, minimum and maximum temperatures during winter and summer months, or other carefully constructed measures reflecting sharp temperature declines in the winter months and the absence of cooling during the summer months could help predict mortality. Nighttime minimum temperatures are anticipated to increase disproportionately according to global climate-change projections (31
.
From these analyses, it would seem that public health programs to prevent heat- and cold-related mortality would best be directed at the elderly and those persons with cardiovascular and respiratory diseases. Although persons should use caution during any extreme weather event, whether hot or cold, those in the northernmost cities are more vulnerable during a warm temperature period and those in the southernmost cities are most vulnerable during cold periods. Earlier we pointed out a large difference in the hot slope between Baltimore and Washington, DC, two cities with virtually identical weather. Identifying differences between these two cities that affect their responses to weather, whether in housing stock or in weather watch/warning protocols, might lead to better public health prevention strategies.
Substantial additional investigation of this association remains. We did not address in detail how the temperature-mortality association changes by gender, age, race, and many other factors. Future research should study an aggregate weather variable that incorporates temperature, dew point, and wind speed; control for seasonal confounders such as influenza epidemics; and further consider weather variability. For example, in temperate climates, climatologists project that a 23°C increase in average summer temperatures doubles the frequency of extreme heat waves (32). Additional investigation of the effects of air-conditioning and heating on the association between weather and mortality may also be useful, as would a nationwide study on the effects of weather on mortality in large metropolitan areas. The types of models that we developed are also applicable to estimating weather-related mortality given various future climate scenarios.
An important public health question is whether the increased mortality associated with temperature largely occurs among the very frail persons who were likely to die in the short term absent stressful temperatures. This "harvesting" can be tested by using methods developed for air pollution studies (33, 34
), for which this same issue has arisen.
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ACKNOWLEDGMENTS |
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The authors acknowledge the Health Effects Institute, an organization jointly funded by the Environmental Protection Agency (EPA Assistance Award R824835) and automotive manufacturers.
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NOTES |
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REFERENCES |
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