Laurence S. Kalkstein Kathleen M. Valimont
1. Weather has a profound effect on human health and well-being. It has been demonstrated that weather is associated with changes in birth rates, and sperm counts, with outbreaks of pneumonia, influenza and bronchitis, and is related to other morbi dity effects linked to pollen concentrations and high pollution levels.
2. Large increases in mortality have occurred during previous heat and cold waves. It is estimated that 1,327 fatalities occurred in the United States as a result of the 1980 heat wave; the number occurring in Missouri alone accounted for over 25% of the total.
3. Hot weather extremes appear to have a more substantial impact on mortality than cold wave episodes. Most research indicates that mortality during extreme heat events varies with age, sex, and race. Factors associated with increased risk from hea t exposure include alcoholism, living on higher floors of buildings, and the use of tranquilizers. Factors associated with decreased risk are use of air conditioning, frequent exercising, consumption of fluids, and living in shaded residences. Acclimatiza tion may moderate the impact of successive heat waves over the short term.
4. Threshold temperatures for cities, which represent maximum and minimum temperatures associated with increases in total mortality, have been determined. These threshold temperatures vary regionally; for example, the threshold temperature for wint er mortality in mild southern cities such as Atlanta is 0deg.C and for more northerly cities, such as Philadelphia, it is -5deg.C.
5. Humidity has an important impact on mortality since it contributes to the body's ability to cool itself by evaporation of perspiration. It also has an important influence on morbidity in the winter because cold, dry air leads to excessive dehydr ation of nasal passages and the upper respiratory tract and increased chance of microbial and viral infection.
6. Precipitation in the form of rainfall and snow is also associated with changes in mortality. In New York City, upward trends in mortality were noted the day after snowfalls that had accumulated 2 inches or more. In Detroit where snow is more com mon, the snowfall accumulation exceeded 6 inches before mortality increases were noted.
7. If future global warming induced by increased concentrations of trace gases does occur, it has the potential to significantly affect human mortality. In one study, total summertime mortality in New York City is estimated to increase by over 3,20 0 deaths per year for a 7deg.F trace-gas-induced warming without acclimatization. If New Yorkers fully acclimatize, the number of additional deaths are estimated to be no different than today. It is hypothesized that, if climate warming occurs, some addit ional deaths are likely to occur because economic conditions and the basic infrastructure of the city will prohibit full acclimatization even if behavior changes.
8. Two areas of important future research include investigation of morbidity impacts and the costs to society of indirect impacts (e.g., costs associated with modifying living and working areas, decreases productivity, and other climate/stress-indu ced impacts).
There is a large body of literature devoted to the impact of variable climate on human well-being. Most of the research has been done by medical scientists, and a minor amount of the work has been performed by climatologists. This section will attempt to describe much of the relevant research that has been published to date. Topics will be subdivided on the basis of weather events, as many of the manuscripts evaluated employ a regression technique to determine the impacts of one or more climatic events on human health.
There appears to be general agreement that weather has a profound impact on human health, but scientists do not agree on the precise mechanisms involved. For example, some of the research suggests that extreme weather events appear to have the greatest in fluence on health. Driscoll (1971a) correlated daily mortality for 10 cities with weather conditions in January, April, July, and October and found that large diurnal variations in temperature, dewpoint, and pressure were associated with many high mortali ty days. In addition, hot, humid weather with concomitant high pollutant concentrations were also contributory mechanisms. Other studies do not attribute large variations in mortality to extreme events, but rather to the normal seasonal changes in weather (Persinger, 1980).
The importance of determining the role of weather in human health cannot be understated. Reports of large increases in mortality during heat and cold waves are commonplace; for example, the National Oceanic and Atmospheric Administration (NOAA) estimated that 1,327 fatalities in the United States were directly attributed to the 1980 heat wave; fatalities in Missouri alone accounted for over 25% of the total excess deaths (U.S. Department of Commerce, 1980). During a heat wave in 1963, more than 4,600 deat hs above a computed mean occurred in June and July in the eastern United States (Schuman et al., 1964). The impact of weather on human well-being goes beyond mortality; even birth rates and sperm counts appear to be affected by meteorological phenomena (C alot and Blayo, 1982; Tjoa et al., 1982; White, 1985).
This report will concentrate on the effects of weather upon human mortality. However, there are numerous other impacts of weather on the general health of the population, including morbidity, short-term changes in mood, emotional well-being, and aberratio ns from normal behavior. For example, asthma attacks, many of which occur from inhalation of airborne agents such as spores and molds, appear to be related to various meteorological variables (White, 1985). Goldstein (1980) found that clusters of attacks are preceded by the passage of a cold front followed by a high pressure system. Morbidity attributed to pneumonia, influenza, bronchitis, and probably many other illnesses is also weather-related (White, 1985).
In addition, several atmospheric phenomena that are indirectly related to weather and might have an impact on mortality (the most notable being atmospheric pollutants and pollen concentrations) are not included in this review. A partial annotated bibliogr aphy of pollen concentration is presently available (Kalkstein and Robeson, 1984), but there is little research comparing weather/pollen relationships to human health. Meteorologic conditions exert a large influence on pollution concentrations and dispers ion and they also affect the impact of pollution on mortality and morbidity. Much of the literature on this topic has already been summarized (Stern, 1977).
Probably the most intensively-studied weather element that affects human mortality is air temperature, especially the impact of summer heat. A detailed description of temperature/mortality relationships follows.
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 u> which mortality increases.
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 - 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 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:< p> 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 - 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 is the previous day's maximum temperature, and F is the present day's maximum temperature (Marmor 1975).
3. Impact of Cold Weather
a. General Relationships
Many studies have provided evidence that mortality rates increase during periods of cold weather. In general, total mortality is about 15% higher on an average winter day than on an average summer day (National Center for Health Statistics, 1978). However , increases in mortality during exceedingly cold periods are less dramatic than their hot weather counterparts (Kalkstein, 1984). The impact of cold on human well-being is highly variable. Not only is cold weather responsible for direct causes of death su ch as hypothermia, influenza, and pneumonia, it is also a factor in a number of indirect ways. Death and injury from falls, accidents, carbon monoxide poisoning, and house fires are all partially attributable to cold (U.S. Department of Commerce, 1984).
Hypothermia occurs when the core body temperature falls below 35deg.C (Centers for Disease Control, 1982). Certain sectors of the population appear more susceptible to hypothermia than others. Most victims fall in one or more of the following categories: the elderly, newborns, the unconscious, alcoholics, and people on medications (Fitzgerald and Jessop, 1982; Lewin et al., 1981; Hudson and Conn, 1974; Bristow et al., 1977; Massachusetts General Hospital, 1982). In addition, malnourishment, inadequate hou sing, and high blood ethanol levels increase the incidence of hypothermia (Centers for Disease Control, 1982).
Sex and race appear to be related to susceptibility to hypothermia. Nonwhite elderly men generally constitute the highest risk group, while white women comprise the lowest risk group (Rango, 1984; Centers for Disease Control, 1982). Women possess a higher skin temperature to core temperature gradient, suggesting that they are better able to maintain a higher body core temperature during periods of cold stress (Cunningham et al., 1978; Hardy and DuBois, 1940; Wyndham et al., 1964; Graham, 1983). Some studi es contend that the difference in the response of men and women to cold is related to the amount of subcutaneous fat within the body (Hardy and DuBois, 1940; Wyndham et al., 1964), but other studies have failed to confirm this hypothesis (Bernstein et al. , 1956; Gallow et al., 1984; Veicsteinas et al., 1982). Although women are less susceptible to hypothermia, they appear to be more susceptible to peripheral cold injuries such as frostbite (Graham and Lougheed, 1985).
Age appears to have an even greater impact upon hypothermia sensitivity than gender, and the elderly display the highest mortality rates of all groups. Vasoconstriction and shivering, two primary cold adaptive measures, appear to be reduced in many elderl y persons (Collins et al. 1977; Collins and Easton et al. 1981; Wagner et al., 1974). In addition, many of the elderly do not discriminate changes in temperature well and are thus less able to adjust to them (Collins and Exton-Smith et al., 1981).
One of the first efforts to predict the impact of a severe cold wave was published by NOAA using algorithms developed by Kalkstein. Seven cities in the eastern and southern United States exhibited significant relationships between winter weather and morta lity, and the following regression equations were developed for each:
where MORT is the daily standard deviation increase in mortality above the mean, C is a constant (different for each city), MT is daily maximum temperature, HRS is the total hours in the day with temperatures below 32deg.F, MIN is daily minimum temperatur e, MD is daily minimum dewpoint, WAM is 3AM windspeed, WPM is 3PM windspeed, and CDH is a measure of the day's coldness and is calculated as follows:Atlanta: MORT = C - .11 MT Chicago: MORT = C - .08 MT Cincinnati: MORT = C - .21 MT - .01 CDH + .13 HRS Dallas: MORT = C - .12 MT - .13 MIN - .02 CDH Detroit: MORT = C - .11 MT Oklahoma City: MORT = C - .16 MT Philadelphia: MORT = C + .09 MD + .01 CDH + .06 WAM - .08 WPM,
T represents the hourly temperature and N represents total hours in a day with temperatures below 32deg.F. A map (Figure V-9) of predicted mortality increases during the January 1985 cold wave showed potentially significant increa ses in the eastern and central United States. Data limitations have precluded these predictions from being verified to date.
It appears that adaptation to cold temperatures can occur through repeated exposures. Radomski and Boutelier (1982) noted that men who had bathed in 15deg.C water for one-half hour over nine consecutive days before a trip to the Arctic showed less signs o f cold-induced stress than non-treated men.
There appears to be a cold-adaptive mechanism influencing mortality as well. In a study comparing winter mortality rates for 13 cities in different climates around the U.S., a large differential response was noted. The southern cities seemed to exhibit th e greatest increases in mortality during cold weather, while little or no response was found in northern cities (Kalkstein, 1984). In a city such as Minneapolis, no increase in mortality was noted at temperatures down to -40deg.C, but in Atlanta, mortalit y increases were evident if the maximum temperature did not exceed 0deg.C (Kalkstein ant Davis, 1985). Of the 13 cities studied, 7 demonstrated a statistically significant relationship between winter cold and mortality. The six non-significant cities incl uded cold weather locations (Minneapolis) and mild West Coast locations where very cold weather is virtually unknown (Los Angeles and San Francisco). "Threshold temperatures," which represent temperatures below which notable increases in mortality occur, were established for the seven cities (Kalkstein and Davis, 1985). The threshold temperatures were comparatively mild for the more southerly cities (0deg.C for Atlanta; 1deg.C for Dallas) and somewhat colder for the more northerly cities (-5deg.C for Phil adelphia). This differential geographical response seems to add credence to the importance of relative, rather than absolute weather conditions.
There is evidence that a lag time of two to three days exists between the offending cold weather and the ultimate mortality response (Kalkstein, 1984). Deaths did not necessarily rise on the day of the coldest temperatures, but in many cases, the sharpest increases were noted three days after the coldest weather occurred. A similar lag time was not noted after extremely hot summer days; the impact appears more immediate in summer.
1. Effects of Humidity
Humidity has an important impact on mortality since it influences the body's ability to cool itself by means of evaporation of perspiration. In addition, humidity affects human comfort, and the perceived temperature by humans is largely dependent upon atm ospheric moisture content (Persinger, 1980).
The effects of low humidity can be especially dramatic in winter, when low moisture content induces stress upon the nasal-pharynx and trachea. When very cold, dry air passes through these organs, warming occurs and air temperatures in the pharynx can reac h 30deg.F. The ability of this warmer air to hold moisture increases dramatically, and moisture is extracted at a prodigious rate from the nasal passages and upper respiratory tract, leading to excessive dehydration of these organs (Richards and Marriott, 1974). This appears to increase the chance of microbial or viral infection since a rise in the viscosity of bronchial mucous seems to reduce the ability of the body to fight offending microorganisms that may enter the body from the atmosphere. This may e xplain why Green (1966) found negative correlations between relative humidity and winter absenteeism in a number of Canadian schools.
In the summer, high moisture content during hot periods can lessen the body's ability to evaporate perspiration, possibly leading to heat stress. Recent weather/mortality models developed for the National Oceanic and Atmospheric Administration indicate th at dewpoint temperature is directly related to mortality in several eastern cities when temperatures are very hot (Kalkstein, 1985). Another summer study indicated that mental well-being may also be influenced by summer relative humidity. Persinger (1975) found significant negative relationships between relative humidity and "mood scores," which represent a measure of happiness. Sanders and Brizzolara (1982) found relative humidity to be significantly related to a linear combination of three mood variable s (vigor: r = -.82; social affection: r = -.76; elation: r = -.56).
2. Effects of Precipitation
Most of the precipitation/mortality research to date has concentrated on the impact of snow and other forms of severe winter weather. Rogot and Padgett (1976) found cold weather and snow to be statistically related to deaths from stroke and heart attack-- a finding that has been corroborated by others. In a 1978 blizzard in Rhode Island, emergency room admissions for myocardial infarction rose markedly three days after the storm, and mortality from ischemic heart disease showed a large increase for a five- day period after the storm (Faiche and Rose, 1979). The authors attributed this rise to an increase in physical and psychological stress imposed by the storm. Glass and Zack (1979) concur, suggesting that an eight-day increase in deaths from ischemic hear t disease following a number of blizzards was most likely a function of after-storm activities (snow shoveling, car pushing, etc.). Interestingly, these particular death increases appeared unrelated to temperature. Males appear to be at higher risk during these storms, probably due to the greater likelihood that they will be performing more vigorous physical activity after the storm (Glass and Zack, 1979).
In an ongoing study on the effects of snow accumulation in five U.S. cities, Kalkstein (1986) has determined threshold values of accumulated snow above which mortality rates appear to rise. In New York, significant upward trends in mortality were noted th e day after snowfalls if two or more inches of snow had accumulated. In Detroit, where snow is more common, the snowfall accumulation exceeded six inches before mortality increases were noted. No significant relationship between snowfall accumulation and mortality was apparent in Chicago. Anderson and Rochard (1979) found increases in deaths from ischemic heart disease on, and for three days after, a four-inch or greater snowfall in Toronto. Major peaks in cardiovascular deaths in Minneapolis-St. Paul als o appeared to follow days with heavy snows, with the rise most rapid the day after the storm (Baker Blocker, 1982).
Summer rainfall appears to have a limited impact on mortality. Kalkstein (1986) has shown that a significant decline in mortality is experienced the day after summer precipitation events in all of five U.S. cities studied (New York, Philadelphia, Chicago, Atlanta, Detroit). The precipitation event itself might have an indirect impact, as the cooler temperatures coinciding with a summer rainfall provide relief from excessively warm weather. However, in certain specific cases, rainfall might induce increase s in mortality. Mack (1985) found that fatal automobile accidents increased in frequency during very light rain episodes (less than .01 inch) and heavy rainfalls (greater than 0.1 inch per hour).
Frontal passages may have a profound impact on well-being and mortality as large variations in weather conditions can occur in a very short time. Rapid changes in temperature have been shown to produce a number of physiological changes in the body. Rapid drops may affect blood pH, blood pressure, urinatian volume, and tissue permeability (Persinger, 1980). Outbreaks of epidemics may also be related to frontal passage. In his study of 59 years of data, Donle (1975) noticed sudden large increases in influen za outbreaks in Germany, Norway, and Switzerland often followed the passage of a surface trough. In general, these outbreaks occurred simultaneously with the influx of cold air over northern and western Europe (the passage of a surface wave is often follo wed by a rapid influx of cold air). The influenza outbreaks in Europe most frequently occurred between January and March, when cold air masses most commonly intruded over the area.
A number of studies have also found relationships between the numbers of reported migraine attacks and rapid changes in barometric pressure. Cull (1981) found fewer occurrences of attacks when barometric pressure was low. This was partially attributed to a decrease in sunshine during low-pressure intrusions, as solar radiation is a suspected triggering mechanism for migraine onset. However, a Canadian Climate Center study (1981) found that migraines were most likely to occur on days with falling pressure, rising humidity, high winds, and rapid temperature fluctuations.
Rosen (1979) cites some startling relationships between pressure changes and human well-being. He describes research that indicates that cancer mortality rates seem to increase during low-pressure fluctuations, and deaths from circulatory diseases seem to increase during high-pressure fluctuations. He notes that rapid pressure fluctuations may penetrate buildings and propagate wave energy from their source like ripples in a pond. Humans appear to be quite sensitive to such changes.
The reduction of solar radiation by cloud cover may also have effects on well-being. By increasing the brightness level, the autonomic nervous system is affected by constriction changes in the eye pupil. According to Persinger (1980), this increases the r ate of physical activity and leads to a general feeling of well-being. Wolfe (1981) notes that the sun's rays cause chemical changes in neurotransmitter or hormone synthesis in the brain, perhaps stimulating production of the hormone epinephrine, which st imulates the mind and body. Conversely, very low light intensities are often associated with states of relaxation, tiredness, and sleepiness.
Kalkstein (1986) estimated the potential effects of global warming on New York City. The study indicated that summer weather appears to have a significant impact on New York's present mortality rates, and a "threshold temperature" of 92deg.F was uncovered , suggesting that mortality increases quite rapidly when the maximum temperature exceeds this value. Days with low relative humidities appear to increase mortality most dramatically. Five climatic scenarios were developed to estimate New York's future wea ther assuming that warming does occur, and 'acclimatized" and "unacclimatized" mortality rates were estimated for each scenario. The unacclimatized rates were computed by utilizing New York's weather/mortality algorithm developed from the historical analy sis. Acclimatized rates were computed by selecting present-day "analog cities" which resemble New York's predicted future weather, and developing weather/mortality algorithms for them.
Results shown in Table V-2 indicate that the number of additional deaths at temperatures above the threshold could increase by over tenfold if New Yorkers do not become acclimatized to the warming. The elderly will constitute an i ncreasing proportion of these deaths. However, if full acclimatization occurs, the number of additional deaths above the threshold temperature might be no different than today. It is likely, however, that economic conditions, as well as the basic structur e of the city, will prevent full acclimatization; therefore, actual mortality may fall somewhere in between the estimated values. A similar procedure developed for winter indicated that mortality is minimally affected by severe winter weather in New York.
A preliminary precipitation/mortality analysis was also undertaken, and summer days following a precipitation event had significantly lower mortality rates than summer days without precipitation. In the winter, these results were reversed, and days follow ing rain (but not snow) had significantly higher mortality rates than non-precipitation days.
Although there is much literature concerned with the impact of weather on human mortality and well-being, it appears that the contributing researchers often disagree on the magnitude and specific nature of the impact, as well as on the role of acclimatiza tion. General areas of agreement include:
There is a great need to quantify much of the subjective and intuitive information that has been published on climate/mortality relationships. Considering the enormous amount of mortality and morbidity data presently available from the National Center for Health Statistics, the Centers for Disease Control, and other agencies, more precise weather/health relationships should be uncovered in the near future. Perhaps one of the greatest challenges and areas of future research is determining the necessary cos t to society to overcome climate stress. Changes in interior environments may be needed to overcome potential direct climate change impacts on living and working environments. Indirect impacts (e.g., the loss of productivity resulting from new climate con ditions and increased insurance costs) have not been estimated. It is these impacts indirectly associated with human health/climate stress that remain important areas of research.
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