CLIMATE EFFECTS ON HUMAN HEALTH ( page 2 )

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C. TEMPERATURE EFFECTS

1. General Impacts

The impact of temperature on morbidity and mortality can be assessed at both the seasonal and daily level. The variability in occurrence of numerous illnesses is linked to somewhat predictable seasonal trends in temperature (Persinger, 1980), although sig nificant year-to-year differences do occur. Medical disorders such as bronchitis, peptic ulcer, adrenal ulcer, glaucoma, goiter, eczema, and herpes zoster are related to seasonal variations in temperature (Tromp, 1963). Heart failure (most often myocardia l infarction) and cerebrovascular accidents represent two general mortality categories that have been correlated many times with ambient monthly temperatures (Persinger, 1980). Complications from these disorders can be expected at higher temperatures sinc e the body responds to thermal stress by forcing blood into peripheral areas to promote heat loss through the skin. This increases central blood pressure and encourages constriction of blood vessels near the core of the body. However, increases in heart d isease are also noted at very cold temperatures as well. Strong negative correlations have been found between winter temperature and deaths in certain North American, northern Asian, and European countries (Persinger, 1980).

The degree of seasonality in the climate of a region also appears to affect mortality rates. Katayama and Momiyama-Sakamoto (1970) reported that countries with smaller seasonal temperature ranges exhibit steeper regression lines in temperature-mortality c orrelations than do countries with greater temperature ranges. Maximum death rates in warmer countries are found at below normal temperatures, and in cooler countries similar temperatures will produce no appreciable rise in mortality.

There is conflicting evidence concerning the impact of daily temperature fluctuations on human mortality. Some studies contend that mostly long-term (i.e., monthly and annual) fluctuations in temperature affect mortality (Sakamoto and Katayama, 1971) and only small, irregular aberrations can be explained by daily temperature variability (Persinger, 1980). However, Kalkstein and Davis (1985) report that daily fluctuations in temperature can increase mortality rates by up to 50% in certain cities. This has been corroborated in a detailed study of New York City mortality where large increases in total and elderly mortality occurred during the 1980 heat wave (Figure V-1).

2. Impacts of Hot Weather

a. General Relationships

Much of the temperature-mortality research has concentrated on heat and cold wave episodes. It appears that hot weather extremes have a more substantial impact than cold, and many "heat stress" indices have been developed to assess the degree of impact (Q uayle and Doehring, 1981; Kalkstein, 1982; Steadman, 1984). Driscoll (1971b) related 19 different meteorological variables with total mortality and other more specific mortality classes (cause of death, age) and identified high temperature as the most imp ortant causal mechanism in summer. Many other studies support this relationship between temperature and mortality (Ellis, 1972; Ellis et al., 1975; Oechsli and Buechley, 1970). Interestingly, a majority of studies have found that most of the excess deaths that occurred during periods of intense heat were not attributed to causes traditionally considered to be weather-related, such as heat stroke (Gover, 1938). Consequently, many researchers continue to utilize total mortality figures in their analyses, as deaths from a surprisingly large number of causes appear to escalate with increasing temperature (Applegate et al., 1981; Jones et al. 1982).

Although most researchers have preferred the use of maximum temperature as the primary predictor of mortality, others continue to utilize average daily temperature as their primary weather statistic. While Kutschenreuter (1959) found that maximum temperat ure with a 1-day lag was the single most important predictive weather/mortality variable, Rogot (1973) worked strictly with daily average temperature to evaluate cardiovascular diseases; others have even used weekly averages (Lye and Kamal, 1977; Callis a nd LeDuc, 1985). Those who use daily averages cite the importance of warm nights in contributing to mortality, something that is neglected when utilizing maximum temperatures alone (Ellis et al., 1975). However, others report that daily averages tend to m ask the effect on mortality of large daily oscillations in temperature (MacFarlane and Waller, 1976).

A number of studies compare death rates for extreme periods with those encountered during normal meteorological periods; this approach has met with some success (Oechsli and Buechley, 1970; Schuman et al., 1964; Schuman, 1972). Jones et al. (1982), in sum marizing the work of others, found that high temperature, the number of days that the temperature is elevated, high humidity, and low wind velocity are all found within the climate/mortality models of various researchers (Figures V-2< /a> and V-3). An earlier work by Schuman (1972) includes smog as a related mechanism associated with fluctuations in death rate (Figure V-4).

Rather than incorporating daily death totals, many heat wave/mortality studies have utilized weekly mortality totals compiled by the Centers for Disease Control for their primary input (Centers for Disease Control, 1984). Schuman (1972) calculated expecte d weekly death rates based on a 5-year moving mean, and periods of weekly excess mortality were isolated. Callis and LeDuc (1985) compared weekly mortality rates to weather for 10 U.S. cities and uncovered some large weather-induced fluctuations. In gener al, studies incorporating weekly data sets are less revealing than their daily counterparts, as extreme episodes are often dampened when time scales are increased.

One of the most commonly reported findings in heat wave-mortality studies involves the lag time between the temperature event and the mortality response. A lag period of one day was most often uncovered (Ellis, 1972; Ellis et. al., 1975; Ellis and Nelson, 1978); others, however, have observed a two-to three-day lag (Schuman, 1972; Oechsli and Buechley, 1970), and some have noted no lag (Kalkstein and Davis, 1985).

Temperature affects not only mortality, but also morbidity. Applegate et al. (1981) demonstrated the relationship between temperature and morbidity. In that study, as shown in Figures V-5 and V-6, he found that emergency room hospital visits and admissions appear to be correlated with the 1980 heat wave in Tennessee.

b. Responses of the Population

Kilbourne et al. (1982) conducted a case study in which a number of heat factors associated with heat stroke were identified. Factors found to be associated with an increased risk of heat stroke included alcoholism, living on higher floors of buildings, a nd the use of tranquilizers. Factors found to be associated with a decreased risk were use of air conditioning, frequent exercising, consumption of fluids, and living in a well-shaded residence. During extreme heat episodes, heat stroke risk is increased as demonstrated by the 1980 heat wave in St. Louis, which resulted in a ten-fold increase in total deaths (Figure V-7).

Most research indicates that mortality rates during extreme heat vary with age, sex, and race. Oechsli and Buechley (1970) found that mortality rates during heat waves increase with age. This is supported by the work of others (e.g., Bridger et al., 1976, Lye and Kamal, 1977; Jones et al., 1982). The elderly seem to suffer from impaired physiological responses and often are unable to increase their cardiac output sufficiently during extremely hot weather (Sprung, 1979). In addition, sweating efficiency de creases with advancing age (Crowe and Moore, 1973), and many of the medications commonly taken by the elderly have been reported to increase the risk of heat stroke (Jones et al., 1982). Certain researchers have determined slight rises in mortality rates of infants during heat waves (Bridger et al., 1976; Ellis, 1972; Foster et al. 1968), but this is not a universal finding (Schuman, 1972).

Studies relating mortality to gender also yield conflicting results. Studies in which increased mortality rates were found among females during hot weather include those of Applegate et al. (1981) and Rogot and Padgett (1976). Rotton (1983) suggests that this may be attributed to differences in dress among the sexes. Bridger et al. (1976) and Ellis (1972) found higher heat-induced mortality rates among men. Studies of the role of race have also produced conflicting res ults. Schuman (1972) found that blacks appear more susceptible to heat-related deaths in St. Louis and whites are more susceptible in New York (Table V-1). However, Ellis et al. (1975) and Bridger et al. (1976) have discovered tha t white mortality rates are higher than black's under all examined conditions. Rather than race, socioeconomic status may have an influence on weather/mortality relationships. Large numbers of deaths during heat waves are found among poor inner-city resid ents who have little access to cooler environments (Jones et al., 1982).

Initial observations of daily standardized deaths vs. maximum temperature suggest that weather has an impact on only the warmest 10-20% of the days; however, the relationship on those very warm days is impressive (see Figure V-8). During warm periods, a "threshold temperature," which is the maximum temperature above which mortality increases, can be determined. The threshold temperature can be calculated objectively by using a sums of squares technique (Kalkstein, 1986). The thres hold temperature for deaths in New York, above which mortality increases dramatically, is 92deg.F. This procedure can be repeated for winter, as discussed later in this section, where the threshold temperature represents the minimum temperature below which mortality increases.

c. Acclimatization

Several studies have evaluated acclimatization as a factor contributing to heat-related deaths. Gover (1938) reported that excess mortality during a second heat wave in any year will be slight in comparison to excess mortality during the first, even if th e second heat wave is unusually extreme. Two possible explanations for this phenomenon are provided. First, the weak and susceptible members of the population die in the early heat waves of summer, thus lowering the population of susceptible people who wo uld have died during subsequent heat waves. Second, those who survive early heat waves become physiologically acclimatized and hence deal more effectively with later heat waves (Marmor, 1975). Rotton (1983) suggests that geographical acclimatization is al so significant, and people moving from a cool to a subtropical climate will adapt rather quickly, often within two weeks. However, the population must still make behavioral and cultural adjustments (Ellis, 1972). Further support for geographical acclimati zation is provided by Kalkstein and Davis (1985), who noted that mortality increased dramatically during heat waves in northern cities but not in southern cities.

There is some research that implies that the effect of acclimatization has been overstated by many scientists. The use of the wind-chill index in winter and the temperature-humidity index in summer by many meteorologists seems to indicate that they believ e acclimatization may have minimal impact on human activities. Both indices are based on absolute values only: a temperature of 93deg.F with a humidity of 43% yields the same temperature-humidity index value whether it occurs in New Orleans or Duluth. The hot weather indices most widely-accepted by the National Weather Service are all absolute, and they include the temperature-humidity index, humiture, humidex, the discomfort index, and apparent temperature (Thom, 1959; Winterling, 1979; Steadman, 1979a; 1979b; Weiss, 1983). The only geographically relative index that has been published, the weather stress index, is only beginning to be utilized to evaluate a variety of the impacts that climate has on humans (e.g., mortality) (Kalkstein and Valimont, 1986 ).

One cultural adjustment that may have an impact on heat wave-related mortality is the use of air conditioning. Kilbourne et al. (1982), in an attempt to identify factors related to heat stroke, found a strong negative relationship between daily hours of h ome air conditioning and heat-related mortality. This finding is supported by Oechsli and Buechley (1970) in their study of heat-related deaths in Los Angeles. However, Ellis and Nelson (1978) have noted that during the past 30 years, mortality during hea t waves in New York City has not changed significantly despite the increased use of air conditioning. Analysis by Marmor (1975) supports this finding; his study covering a 22-year period implied that air conditioning may be decreasing excess mortality dur ing initial summer hot spells only.

d. Some Predictive Equations

Several general algorithms have been developed to predict mortality changes during heat waves. Buechley et al. (1972) developed the following algorithm for heat-related mortality at temperatures above 90deg.F:

TMR = cycle + 0.10e[0.2(F[1] - 90)] (1)

where TMR is the temperature-specific mortality ratio (the predicted mortality for the day divided by the average annual daily mortality), cycle is the expected mortality ratio for that day of the year (an attempt to account for the impact of seasonality on mortality), and F[1] is yesterday's temperature. Cycle is computed from several years of mortality data and varies in a sinusoidal fashion, peaking in the winter and reaching a minimum at the end of the summer. Each day has a distinctive cycle value de pending upon the mean mortality rate for that time of year. The following example represents a hypothetical calculation of TMR. Assume that the maximum temperature on a given day is 100deg.F, and the cycle is 0.95. TMR = 0.95 + 0.1e[0.2(100 - 90)], which equals 1.70. Thus the equation predicts that mortality on the day following the 100deg. maximum temperature will equal 170% of the annual mean daily mortality. Oechsli and Buechley (1970) had previously developed a related algorithm, the age- and temperat ure-specific mortality ratio model (ATMR):

ATMR = 98.806 + e[(-15.23 + .0385 Age + .1655 F)] (2)

where F is the present day's maximum temperature.

In a more recent study, Marmor (1975) attempted to develop a model that accounted for acclimatization effects. This led to his sensitivity index, which decreased as the population was exposed to more hot days during the season. Sensitivity (S[d]) equals:<> 1 / (1 + e[(Ad - 6) / 0.46)] (3)

where Ad is the total number of previous days with temperatures over 90deg.F.

This sensitivity value was added to a newer version of the TMR algorithm, producing the following:

TMR = cycle + (0.05 + 0.06 sensitivity) e[(F[1] - 90)0.2] + 0.05e[(F - 90)0.2] + 0.07 e[(f - 75)0.2] (4)

where f is the previous day's minimum temperature, F[1] is the previous day's maximum temperature, and F is the present day's maximum temperature (Marmor 1975).

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