Temperature and direct effects on population health in Brisbane, 1986-1995.

Introduction

A range of impacts on population health due to climate change have been documented, although these climate/health relationships are not very well understood (McMichael & Githeko, 2001; McMichael, Woodruff, & Whetton, 2003; Patz, Campbell-Lendrum, Holloway, & Foley, 2005). The health risks include some that are directly climate-related (deaths from heatwaves, cyclones, floods) and some that occur by way of climate-sensitive biotic systems such as vector-borne infections, food-poisoning pathogens, aeroallergen production, and waterborne diseases (Intergovernmental Panel on Climate Change [IPCC], 2001). The World Health Organization (WHO) has estimated that 160,000 deaths occur annually from climate change-related extreme weather events and several major climate-sensitive infectious diseases (Ezzati, Lopez, Rodger, Hoorn, & Murray, 2002).

As one of the most important direct health consequences of extreme weather, an excess of deaths has been observed both on extremely hot days (Dessai, 2003; Smoyer, Rianham, & Hewko, 2000; Keatinge et al., 2000; Condi et al., 2005; McGeehin & Mirabelli, 2001; Semenza et al., 1996) and on cold days (Donaldson & Ermakov, 1998; McGregor, 2005;). There is a significant increase in mortality beyond a threshold temperature point that varies by climatic region (Saez, Sunyer, Castellsague, Murillo, & Anto, 1995; Kunst, Looman, & Mackenbach, 1993; Rogot & Padgett, 2003). Temperatures that exceed the threshold will increase mortality from specific causes, in particular cardiovascular, cerebrovascular, and respiratory diseases, and among specific populations, including elderly people and those of lower socioeconomic status (Saez et al., 1995). The 2003 European heatwaves caused 35,000 excess deaths, 15,000 of which occurred in France (Grynszpan, 2003; Stott, Stone, & Allen, 2003; Le Tertre et al., 2006). In Australia, a higher level of coronary heart disease mortality was found in the winter in the state of New South Wales over the period 1979-1997 (Weerasinghe, Macintyre, & Rubin, 2002). A 1993 study in Adelaide indicated that heatwaves may disproportionately affect the elderly population and people with illness (Faunt et al., 1995). Guest and co-authors (1999) analyzed data from five Australian cities over the period 1979-1990 and found that excess deaths were attributable to temperatures over the threshold temperature of 28[degrees]C.

Studies similar to ours have been conducted in various parts of the world, including Europe, Canada, and the United States (Dessai, 2003; Smoyer et al., 2000; Keatinge et al., 2000; Condi et al., 2005; McGeehin & Mirabelli, 2001; Sermenza & Rubin, 1996; McGregor, 2005; Donald-son & Ermakov, 1998). As a subtropical city located in the Southern Hemisphere, Brisbane generally has warm, humid summers and mild winters. The mean temperature in the summer is 24.8[degrees]C (with an average January maximum temperature of 29.2[degrees]C and an average January minimum temperature of 20.4[degrees]C), and the mean temperature in the winter is 15.6[degrees]C (with an average July maximum temperature of 21.2[degrees]C and an average July minimum temperature of 10.1[degrees]C). We conducted a time series analysis to assess the relationship between weather in Brisbane and human mortality. This relationship is important because residents of Brisbane belong to a population living in a subtropical environment and are not exposed to the extremely cold temperatures described in reports from Europe and North America, where different direct health effects (e.g., mortality patterns) might result from the weather in different seasons. The objectives of our study were to identify the relationship between extreme weather and human mortality in a subtropical city and to provide suggestions for preventing deaths in similar locations. Such policy suggestions might include establishing a regional early-warning system, providing alerts for emergency services, and introducing a community care mechanism such as a neighborhood watch.

[FIGURE 1 OMITTED]

Methods

Background Information and Study Population

Situated in a subtropical climate, Brisbane is the third largest city in Australia (lying at the intersection of latitude 34[degrees]6' S and longitude 153[degrees]2' E). As the capital city of the state of Queensland, it had in 1995 a population of 1,573,304. We took all of Brisbane's residents during the study period of 1986-1995 as the study population (the denominator). We treated total deaths and deaths from cardiovascular diseases or respiratory diseases as numerators to arrive at mortality rates. The numerators and denominators were modified when the focus was on subpopulations over 65 years of age.

Data Collection

For the period 1986-1995, monthly death numbers (total deaths and deaths from cardiovascular diseases and respiratory diseases, as defined by the International Classification of Diseases [ICD-9], among the general population and those 65 years of age and older) were collected from the Australian Bureau of Statistics (ABS). The population data used for the study period were extracted from ABS as well. The annual mid-year estimated resident populations in different age groups were used. Overall monthly mortality rates and mortality rates for various causes for the period 1986-1995 were calculated. Standardized age- and sex-specific mortality rates were calculated according to a standard-population method (direct standardization method) in which mortality rates are expressed as numbers of deaths per 100,000 population. The 1991 Australian population was used as the standard population in our study because this standard is generally used by the Australian Institute of Health and Welfare (AIHW) in its mortality trend studies. AIHW is an official organization that processes vital data in Australia.

Monthly meteorological data, including monthly mean maximum and minimum temperatures, total amount of precipitation, and mean relative humidity in the morning and afternoon, were retrieved from the Australian Bureau of Meteorology.

Data Analyses

Data analyses were performed with the Statistical Package for Social Sciences (SPSS, 2001). Pearson correlation analyses were conducted for correlations between monthly mortality from various diseases and the monthly average of each weather variable. To examine any lagged effect of weather variables on deaths from various causes in different populations, we analyzed correlations between the monthly disease mortality and climatic variables in the current month, and with lags of one, two, and three months. The month that had the largest correlation coefficient was chosen as the lagged period for subsequent analysis.

To minimize the impact of seasonality and to identify the impact of weather on the mortality in different seasons, we analyzed correlations between monthly weather and monthly mortality within the summer (December, January, and February, the hottest months) and within the winter (June, July, and August, the coldest months).

Since one might expect auto-correlations among both dependent and independent variables over time, we performed autoregressive integrated moving average (ARIMA) and generalized least square (GLS) regression analyses to control for possible auto-correlations in the time-series data. A model was developed after the effect of auto-correlation had been removed by the ARIMA procedures, and we performed the GLS regression analysis to assess the independent effects of each weather variable (Box & Jenkins, 1976). The regression analyses were conducted with data from different seasons (summer and winter).

Results

1. Monthly Distribution of Mortality, 1986-1995

From All Causes

There was seasonality in the distribution of mortality from all causes in Brisbane over the study period, with more deaths occurring in winters. The death rates were around 50-80 per 100,000 in June, July, and August (and occasionally in September), while they were around 30-50 per 100,000 in the rest of the year, including the summer (Figure 1).

Among Those 65 Years of Age and Over

As in the general population, there was a clear seasonal distribution of deaths in the elderly population (65 years of age and older). In most years, more deaths also occurred in June, July, and August (and occasionally in September), with mortality varying between 400 per 100,000 and 600 per 100,000, while in other months rates were between 200 per 100,000.

Deaths from Specific Causes

Deaths from cardiovascular and respiratory diseases had clear seasonal distributions. As with the mortality distribution in the general population, more deaths from cardiovascular diseases occurred in June, July, and August than in other months in most years. Similar observations were made for deaths from respiratory diseases.

2. Correlations Between Weather Variables and Monthly Mortality, 1986-1995

Correlation analyses were conducted for monthly climatic variables and monthly mortality (in various populations and for different cause-specific diseases) within summers and winters (Table 1). In the analyses for summers, a positive correlation was found between monthly mean minimum temperatures and mortality in the elderly population (correlation coefficient = .46), indicating that a higher minimum temperature was associated with higher mortality in summer. There was also significant correlation between minimum (and maximum) temperatures and deaths from cardiovascular diseases. Both rainfall and relative humidity were inversely associated with total deaths and mortality in the elderly population. There was a weak negative correlation between rainfall and temperature, suggesting that the effect of rainfall in the subsequent regression analysis might be captured through its moderating effect on temperature.

For the winter, no correlations were found between weather variables and overall mortality or mortality among the elderly population, but for all ages, negative correlations did exist between maximum temperature and mortality from cardiovascular diseases. Minimum temperatures correlated negatively with mortality from respiratory diseases.

3. Weather and Mortality from Various Diseases, 1986-1995--Regression Analyses

Regression analyses were also undertaken for summers and winters separately (Table 2). The results indicated that in the summer, an increase of minimum temperatures brought about more deaths in the elderly population in this subtropical city. For example, a 1[degrees]C increase in monthly mean maximum summer temperatures would be expected to result in about 7 percent more total deaths among people 65 years of age and older. In the winter, a reduction of maximum or minimum temperature was associated with more deaths--maximum temperatures were potential risk factors for people dying from cardiovascular diseases, and minimum temperatures had a negative association with deaths from respiratory diseases.

Discussion

This paper examined the relationship between monthly mean weather variables and monthly mortality in various populations in Brisbane, a subtropical city in Australia, over the period 1986-1995. The results indicate that more deaths occurred in the winter than during other seasons of the year although winter in Brisbane is very mild. This finding applied both to the general population and to the elderly population, and to deaths from various causes. It is understandable that more deaths would occur in winters in cold or temperate regions, but even in a subtropical region, as indicated in this study, a decrease in temperatures (in winters) may increase human mortality.

In general, temperatures play an important role in deaths, especially for the elderly population and among people with specific diseases. Exposure to cold resulted in increases in blood pressure, blood viscosity, and heart rate (Keatinge et al., 1984), which suggests that cold-induced cardiovascular stress is quite prevalent in the general population. This effect could be worse among the elderly population and among people with specific illnesses (Brennan, Greenberg, & Miall, 1982; Stout & Crawford, 1991). For respiratory diseases, a possible mechanism is that breathing cold air leads to bronchoconstriction, which might increase susceptibility to, or worsen, pulmonary infections. It has been argued that mortality is associated with cold weather because of increased incidence of influenza and other respiratory infections (Bull, 1980), increased levels of winter smog (Sakamoto-Momiyama, 1978), or the impact of seasonality on disease distribution (Douglas, al-Sayer, & Rawles, 1991). These potential risk factors did play an important role in deaths in winters in other studies and should be analyzed carefully, together with climatic factors. A study conducted in the Netherlands has indicated that all of these confounding factors may explain approximately one-third of cold-related mortality in winters (Kunst et al., 1993). Brisbane sometimes has influenza epidemics in the winter. We were not, however, able to distinguish influenza from other respiratory diseases in the data provided and therefore had to resort to the category "all respiratory diseases" in the data analysis. That lack of specificity is a limitation of this study.

The correlation and regression analyses modeled summers and winters separately. This process revealed that mortality among the elderly population and from cardiovascular diseases increased as summer minimum temperatures increased. In addition, lower winter temperatures (maximum and minimum) resulted in higher mortality, particularly from cardiovascular and respiratory illnesses.

In the regression analysis, winter temperature had a negative association with mortality from respiratory and cardiovascular diseases. Only minimum temperatures were significant in the analysis of winter data, however. Therefore, it is important to pay attention to minimum temperatures in the winter in the prevention of deaths from respiratory and cardiovascular diseases. In our study, elderly people appeared to be disproportionately stressed by comparison by other age groups, especially in summer heat conditions. This result is consistent with those of other studies (Dessai, 2003; Smoyer et al., 2000; Keatinge et al., 2000; Condi et al., 2005; McGeehin & Mirabelli, 2001; Semenza et al., 1996; McGregor, 2005; Donaldson & Ermakov, 1998) and indicates that the elderly population needs more specific care.

Rainfall was also found to be correlated with mortality rates of cardiovascular diseases in winter; both rainfall and relative humidity were negatively correlated with total causes of death in the general and elderly populations. Neither of these weather variables were significant in the regression models, however. One explanation might be that correlations between temperatures and rainfall/relative humidity in Brisbane were weak.

A study in Missouri indicated that mortality was more closely associated with weather conditions than with levels of pollution (Kalkstein & Davis, 1989). A recent study suggested that for all non-external causes of mortality and cardiovascular mortality, PM10 air pollution (particulate matter of less than 10 microns in aerodynamic diameter) caused a statistically significant modification of the effects of temperature on respiratory and cardiovascular hospital admissions (Ren, Williams, & Tong, 2006). Therefore, a study incorporating climatic variables, air pollution, population health, and their interaction is needed; it is a shortcoming of our project that it did not consider the impact of pollution.

Recent Australian national studies have predicted increased summer deaths from heat extremes in major cities of Australia, with more deaths annually in each major city by 2020 and 2050; the heat-related mortality increases are unlikely to be significantly offset by fewer cold-related deaths (McMichael et al., 2003). Even if strong policy action is adopted, an extra 4,200 to 8,000 deaths per year are expected to occur in the elderly population by 2100 as a result of extreme hot weather; by contrast, the current heat-related deaths in this age group number 1,100 (Woodruff et al, 2005). Thus, if global warming produces increases average temperatures, the result could be a reduction in mortality in winter, but this decrease would be insufficient to offset the increase in heat-related deaths in summer (Saez et al., 1995). At any rate, our study was not able to establish a connection between increased average temperatures year round and reduced wintertime mortality. Obviously, more systematic studies are needed in this area.

Corresponding author: Peng Bi, M.Med.Sci., Ph.D., Senior Lecturer, University of Adelaide, Discipline of Public Health, Adelaide SA 5005 Australia. E-mail: [e-mail].

REFERENCES

Brennan, P.J., Greenberg, G., & Miall, W.E. (1982). Seasonal variation in arterial blood pressure. British Medical Journal, 285, 919-923.

Box, G.E.P., & Jenkins, G.W. (1976). Time-series analysis: Forecasting and control. San Francisco: Holden-Day.

Bull, G.M. (1980). The weather and deaths from pneumonia. Lancet, I, 1405-1408.

Condi, S., Meli, P., Minelli, G., Solimini, R., Toccaceli, V., Vichi, M., Beltrano, C., & Perini, L. (2005). Epidemiologic study of mortality during the 2003 summer heatwave in Italy. Environmental Research, 98, 390-399.

Dessai, S. (2003). Heat stress and mortality in Lisbon: An assessment of the potential impact of climate change. International Journal of Biometeorology, 48, 37-44

Donaldson, G., & Ermakov, S. (1998). Cold related mortality and protection against cold in Yakutsk. British Medical Journal, 317, 978-82

Douglas, A.S., al-Sayer, H., & Rawles, J.M. (1991) Seasonality of diseases in Kuwait. Lancet, II, 1393-1397.

Ezzati, M., Lopez, A.D., Rodgers, A., Hoorn, S., & Murray. C.J. (2002). Selected major risk factors and global and regional burden of diseases. Lancet, 360, 1347-1360.

Faunt, J., Wilkinson, T., Aplin, P., Henschke, P., Webb, M., & Penhall, R.K. (1995). The effects in the heat: Heat-related hospital presentations during a ten day heat wave. Australian and New Zealand Journal of Medicine, 25, 117-121.

Grynszpan, D. (2003). Lessons from the French heatwave. Lancet, 362, 1169-1170.

Guest, C., Wilson, K., Woodward, A., Hennessy, K., Kalkstein, L., & Skinner, C. (1999). Climate and mortality in Australia: Retrospective study, 1979-1990, and predicted impacts in five major cities in 2030. Climate Research, 13, 1-15

Intergovernmental Panel on Climate Change. (2001). Climate change 2001: Impacts, adaptation, and vulnerability: Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change (J.J. McCarthy, O.F. Canziani, N.A. Leary, D.J. Dokken, & K.S. White, eds.,). Cambridge, UK: Cambridge University Press. Retrieved December 4, 2007, from http://grida.no/climate/ipcc_tar/wg2/index.htm.

Kalkstein, L.S., & Davis, R.E. (1989). Weather and human mortality: An evaluation of demographic and interregional responses in the United States. Annals of the Association of American Geographers, 79, 44-64.

Keatinge, W.R., Donaldson, G., Cordioli, E., Martinelli, M., Kunst, A.E., Mackenbach, J.P., Nayha, S., & Vuori, I. (2000). Heat related mortality in warm and cold regions of Europe: Observational study. British Medical Journal, 321, 670-673.

Keatinge, W.R., Coleshaw, S.R.K., Cotter, F., Mattock, M., Murphy, M., & Chelliah, R. (1984) Increases in platelet and red cell counts, blood viscosity, and arterial pressure during mild surface cooling: Factors in mortality from coronary and cerebral thrombosis in winter. British Medical Journal, 289, 1405-1408.

Kunst, A.E., Looman, C.W.N., Mackenbach, J.P. (1993). Outdoor air temperature and mortality in Netherlands: A time-series analysis. American Journal of Epidemiology, 137, 331-341.

Le Tertre, A., Lefranc, A., & Eilstein, D., Declercq, C. Medina, S., Blanchard, M., Chardon, B., Fabre, P., Filleul, L., Jusot, J.-F, Pascal, L., Prouvost, H., Cassadou, S., & Ledrans, M. (2006). Impact of the 2003 heatwave on all-cause mortality in 9 French cities. Epidemiology, 17(1), 75-79.

McMichael, A.J., & Githeko, A. (2001). Human health. In J.J. McCarthy, O.F Canziani, & N.A. Leary (Eds.), Climate change 2001: Impacts, adaptation and vulnerability. Third Assessment Report of the Intergovernmental Panel on Climate Change (pp. 451-485). Cambridge: Cambridge University Press. Retrieved December 4, 2007, from http://grida.no/climate/ipcc_tar/wg2/347.htm.

McMichael, A., Woodruff, R., & Whetton, P. (2003). Human health and climate change in Oceania: A risk assessment, Canberra, Australia: Commonwealth Department of Health and Ageing.

McGeehin, M.A., & Mirabelli, M. (2001). The potential impacts of climate variability and changes on temperature-related morbidity and mortality in the United States. Environmental Health Perspectives, 109(Suppl. 2), 185-189.

McGregor, G. (2005). Winter North Atlantic Oscillation, temperature and ischaemic heart disease mortality in three English counties. International Journal of Biometeorology, 49,197-204.

Patz, J.A., Campbell-Lendrum, D., Holloway, T., & Foley, J.A. (2005). Impact of regional climate change on human health. Nature, 438, 310-317.

Ren, C., Williams, G.M., & Tong, S. (2006). Does particulate matter modify the association between temperature and cardiorespiratory diseases? Environmental Health Perspectives, 114, 1690-1696.

Rogot, E., & Padgett, SJ. (1976). Associations of coronary and stroke mortality with temperature and snowfall in selected areas of the United States, 1962-1966. American Journal of Epidemiology, 103, 565-575.

Saez, M., Sunyer, J., Castellsague, J., Murillo, C., & Anto, J.S. (1995). Relationship between temperature and mortality: A time series analysis approach in Barcelona. International Journal of Epidemiology, 24, 576-582.

Sakamoto-Momiyama, M. (1978). Changes in the seasonality of human mortality: A medico-geographical study. Social Science & Medicine, 12, 29-42.

Semenza, J.C., Rubin, C.H., Falter, K.H., Selanikio, J.D., Flanders., W.D., Howe, H.L., & Wilhelm, J.L. (1996). Heat-related deaths during the July 1995 heat wave in Chicago. New England Journal of Medicine, 335, 84-90.

Smoyer, K., Rianham, D., & Hewko, J. (2000). Heat-stress-related mortality in five cities in South Ontario: 1980-1996. International Journal of Biometeorology, 44, 190-197.

SPSS, Inc. (2001). Statistical package for the social sciences (Version 11.0). Chicago IL: Author.

Stott, P.A., Stone, D.A., Allen, M.R. (2004). Human contribution to the European heatwave of 2003. Nature, 432, 610-614.

Stout, R.W., & Crawford, V. (1991). Seasonal variations in fibrinogen concentrations among elderly people. Lancet, II, 9-13.

Weerasinghe, D., Macintyre, C., & Rubin, G. (2002). Seasonality of coronary artery deaths in NSW. Heart, 16, 133-139.

Woodruff, R., Hales, S., Butler, C., & McMichael, A. (2005). Climate change health impacts in Australia: Effects of dramatic C[O.sub.2] emission reductions. Report for the Australian Conservation Foundation and the Australian Medical Association. Melbourne and Canberra, Australia: Australian Conservation Foundation. Retrieved December 4, 2007, from http://www.acfonline.org.au/uploads/res/res_ama_acf_full_report.pdf.

Although most of the information presented in the Journal refers to situations within the United States, environmental health and protection know no boundaries. The Journal periodically runs International Perspectives to ensure that issues relevant to our international constituency, representing over 60 countries worldwide, are addressed. Our goal is to raise diverse issues of interest to all our readers, irrespective of origin.

Peng Bi, M.Med.Sci., Ph.D.

Kevin A. Parton, M.Sc., Ph.D.

Jian Wang, M.D., Ph.D.

Ken Donald, Ph.D., F.R.A.C.P.

TABLE 1 Significant Correlation Coefficients for Seasonal Mortality and
Weather in Brisbane, 1986-1995*

                                      Maximum
Cause of Mortality      Rainfall      Temperature   Minimum Temperature

Winter
Cardiovascular disease   .456 (1)**   -.517 (0)
Respiratory disease                                -.416(0)

Summer
Total causes            -.458 (1)
Total causes for those  -.450 (1)                   .458 (0)
  65+ years of age
Cardiovascular disease                 .495 (2)     .399 (2)

                        9 a.m. Relative
Cause of Mortality      Humidity         3 p.m. Relative Humidity

Winter
Cardiovascular disease
Respiratory disease

Summer
Total causes            -.416(1)         -.425 (1)
Total causes for those  -.428 (1)        -.430 (1)
  65+ years of age
Cardiovascular disease

* Winter = June, July, August. Summer = December, January, February.
** Number in bracket represents lagged month(s).

TABLE 2 Regression Coefficients for Seasonal Mortality and Weather in
Brisbane, 1986-1995

                                                    9 a.m.    3 p.m.
Cause of                  Maximum      Minimum      Relative  Relative
Mortality       Rainfall  Temperature  Temperature  Humidity  Humidity

Winter
Cardiovascular            -.637*
  disease
Respiratory                            _.0496**
  disease

Summer
Total causes                            .0758*
  for those
  65+ years of
  age

** p < .01