a Public Health Research Unit, 1 Lilybank Gardens, University of Glasgow, Glasgow, UK.
b Department of Geography and Topographic Science, East Quadrangle, University of Glasgow, Glasgow, UK.
c Department of General Practice, 4 Lancaster Crescent, University of Glasgow, Glasgow, UK.
Reprint requests to: Islay Gemmell, MRC Social and Public Health Sciences Unit, 4 Lilybank Gardens, Glasgow G12 8RZ, UK.
![]() |
Abstract |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Methods Lagged Poisson regression analysis of numbers of deaths and average weekly temperature with adjustment for serial autocorrelation and influenza epidemics.
Results There was significant seasonal variation in weekly death rates with a difference of about 30% between a summer trough and a winter peak. This variation was principally attributable to respiratory disease, cerebrovascular disease and coronary artery disease. Seasonal variation in mortality fell from around 38% in 1981 1983 to around 26% in 19911993. There was no clear evidence of a relationship between socioeconomic status and seasonal mortality, however the extent of the fall in seasonal variation was greater in deprived areas than in affluent areas. Overall, a 1°C decrease in mean temperature was associated with a 1% increase in deaths one week later. The lag in this relationship varied by cause of death and underlying temperature.
Conclusions Seasonal variations in mortality and the relationship between temperature and mortality are a significant public health problem in Scotland. It is likely that the strength of this relationship is a result of the population being unable to protect themselves adequately from the effects of temperature rather than the effects of temperature itself.
Keywords Seasonal, mortality, temperature, deprivation
Accepted 20 October 1999
![]() |
Introduction |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Although influenza epidemics contribute to seasonal patterns,13,14 the effect is more general with variation in other respiratory causes of death, ischaemic heart disease and cerebrovascular disease.7,3 Winter weather conditions and snow falls have been associated with increased mortality in North America,15,16,17 while in Spain18 association between periods of unusually hot weather and mortality have been demonstrated. Many studies1921 have commented on a U-shaped relationship between temperature and mortality, demonstrating an increase in mortality associated with both cold spells in winter and hot spells in summer.
This paper describes the seasonal pattern of mortality in Scotland between 1981 and 1993 with reference to how this pattern varies according to socioeconomic status before assessing the direct effect of short-term falls in temperature.
![]() |
Methods |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Weekly variation in mortality was examined for all causes of death and separately for diseases of the respiratory system (ICD-9 460519), ischaemic heart disease (IHD, ICD-9 410414), cerebrovascular disease (CVD, ICD-9 430438) and all cancers (ICD-9 140239). These groupings were preferred in order to reduce the effects of possible confounding between individual causes of death (for example, between influenza-like illnesses and other respiratory causes). Seasonal variation for deaths from cancers is small; these deaths were included to provide a comparison with groups of causes known to exhibit seasonal variation.
Commonly, seasonal patterns of mortality are described by comparing monthly numbers of deaths. This can be as the ratio of deaths in the first quarter of the year to the third quarter,2 a comparison of the number of deaths from December to March with those in the remaining 8 months of the year4,13,22 or as the amplitude of a fitted cosine curve.3,9,10 The first two methods involve predetermining where the peak in mortality is expected to occur while with the simple cosinor analysis there are often too few data points and no adjustment for the fact that the data are Poisson.
In this analysis the seasonal pattern was estimated using Poisson regression analysis on weekly data. Firstly the trend was fitted using
![]() |
![]() |
The inclusion of the offset term meant that when was computed
represents the amplitude of the seasonal curve expressed as a ratio of the mean number of weekly deaths. This can then be expressed as the percentage increase in mortality from summer to winter.
The relationship between patterns of mortality and temperature was assessed using a time series regression model. Zeger23 described an approach that enables time series methodology to be applied within the framework of a generalized linear model. This method involves modelling the weekly number of deaths in a Poisson regression analysis and assessing the significance of short-term changes in weekly average temperature. The model controlled for long-term and seasonal trends in mortality, serial auto-correlation (AR(1) model), over-dispersion and influenza epidemics. The temperature terms in the model measure the effects of short-term changes in temperature over and above average annual variation. The parameter estimates obtained from the model were expressed as the percentage change in deaths following a fall in temperature of 1°C. This change in mortality was assessed for up to 5 weeks after the fall in temperature. The modelling was carried out using Splus.
![]() |
Results |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
All these seasonal increases are significant as the 95% CI demonstrate. For deaths from all causes seasonal variation in mortality increased significantly with age. The CI for the very young age group are large mainly because of the small numbers involved.
The pattern of these changes is illustrated in Figure 1 which represents weekly deaths in circular form. In this Figure, the broken line represents the number expected if there was no seasonal variation and the solid line the average weekly number of actual deaths. The dark shaded area represents 25th and 75th percentiles and the light shaded area represents the maximum and minimum in a given week over this 13-year period. On average, the weekly number of deaths was greater than that expected from late October to mid-May with a pronounced steepening of this difference from November to January and a fairly slow return to the expected number during the later winter and early spring months. A second feature, however, is that there is much greater weekly variation (the shaded areas) in winter than in summer when the maximum and minimum range is much closer to the weekly average. The large peak in the weekly maximum for December reflects the 1989 influenza epidemic;24 other year-to-year variation in weekly winter deaths is similarly attributable to influenza epidemics.
|
|
Figure 3 shows the percentage fall in death rates by season in the three main Carstairs categories between 19811983 and 19911993. The percentage fall in death rates was lower in the deprived category compared to the affluent category in every season except winter. The reduction in the winter rate in the deprived category was almost three times that for the autumn rate, whereas in the affluent category, the winter reduction was less than twice that for the autumn weeks. Thus, between the two censuses, the seasonal pattern in the deprived category reduced at a greater rate than in the affluent category although this differential in level of reduction failed to reach statistical significance.
|
|
Figure 5 describes these relationships at different underlying temperatures. In week 0, the week that the fall in temperature occurred, mortality increased by 1.06% (95% CI : 0.10,2.03) when the underlying temperature was less than 2°C and by around 0.8% when the underlying temperature was between 2°C and 10°C. When the temperature was milder, between 10°C and 14°C the increase in mortality related to a fall in temperature was negligible. A significant fall in mortality however occurred when the temperature was hot, over 14°C. At this underlying temperature a fall of 1°C was associated with a 1% (95% CI : 0.32,1.64) fall in mortality. Lag effects were observed for all these lower temperatures but with an enhanced short-term effect when the underlying baseline temperature was less than 2°C: at these temperatures, the effect of a further 1°C fall was a 1.15% (95% CI : 0.54,1.77) increase in mortality in the succeeding week. These increases are offset by reduced mortality at these lower temperatures in week 2, the effect in later weeks is less clear. The pattern is one of a direct link at low temperatures up to one week after the fall in temperature, with a reverse effect at high temperatures during the week of the fall in temperature. A more complex pattern of association occurs at longer lag periods for temperatures when the influence of temperature per se may be mediated by other environmental variables.
|
![]() |
Discussion |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
One hypothesis to explain these seasonal variations in mortality is that they simply represent shifts in the timing of events that lead to death. The effects of season or temperature could be seen as a kind of harvesting of vulnerable members of the population. This view might be supported by the age gradients and causes of death, which demonstrate the greatest seasonal variation. However, it would require a detailed prospective study involving information about the prior health of those dying in different seasons to investigate this hypothesis further.
The fact that seasonal variation in mortality fell during the study period, that the degree of change was greater in deprived areas compared to affluent areas and that social class differences were only evident in the winter weeks would also lend support to this hypothesis. Although no significant differences were found these observations indicate that while socioeconomic status has some effect on seasonal variation the strength of this association has declined. Indeed an analysis of seasonal variation according to deprivation category failed to display a similar pattern to that for social class. Similar results were found recently for London.27
Improvements in environmental characteristics such as the quality of housing, increased use of central heating and other factors closely related to socioeconomic progress could explain this apparent fall in seasonal variation in mortality. The lack of substantial evidence of a relationship between socioeconomic status and seasonal mortality may be due to the fact that area-based measures of deprivation and individual-based social class data are not adequate proxy measures of housing conditions and fuel poverty.
The observed patterns of seasonal variation are driven by temperature. On average a fall in temperature of 1°C was associated with an increase in mortality of 1%. Many studies2830 have examined the methods by which falling temperatures can affect the physiological mechanisms of the human body. Seasonal variations have been demonstrated in several risk factors for vascular disease, high blood pressure,31 cholesterol levels32,33 and fibrinogen concentration in the blood.3436 The U-shaped relationship between temperature and mortality, with very hot temperatures also having an adverse effect on health, has also been described elsewhere.18,20,21
Clearly the relationship between temperature and mortality influences seasonal patterns. However it is the vulnerability of a population and the ability of the population to protect themselves from this relationship which may determine the degree of seasonal variation in mortality experienced. Britain is generally considered to have a mild climate and, perhaps because of the perception of climate, has less strict building legislation than countries such as Finland.37 The fact that Scotland still experiences seasonal increases in mortality of the order of 30% while other European countries experience lower rates may be to do with the susceptibility of the population to changes in temperature rather than the changes in temperature themselves. This study has also shown that while slight differences in seasonal patterns in mortality can be related to socioeconomic status the susceptibility of the population is not identified by these measures. It would be difficult experimentally to isolate and modify specific aspects of lifestyle or environment as a way of demonstrating cause and effect. However specific housing characteristics such as fuel poverty,27,38 or the presence of dampness and condensation in homes may be related to the seasonal patterns in mortality experienced in the UK. This type of information is available from sample-based house condition surveys but relating sample-based explanatory variables to population-based mortality measures could lead to spurious results. A further study using housing measures available from the census such as tenure and amenities might provide more reliable insights into the relationship. Housing improvement schemes of various kinds are now fairly widespread in Scotland. With specifically collected data it is possible to envisage more focused case-control studies of housing improvements in terms of the health outcomes they achieve.
![]() |
Acknowledgments |
---|
![]() |
References |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
2 GRO(S). Quarterly Variation in Mortality in Scotland and 12 Selected Countries, since 1977. 1987. GRO(S). Vital Events Report.
3 Douglas AS, Allan TM, Rawles JM. Composition of seasonality of disease. Scot Med J 1991;36:7682.[ISI][Medline]
4 Curwen M. Excess winter mortality: a British phenomenon? Health Trends 1991;22:16975.
5 McDowell M. Long term trends in seasonal mortality. Popul Trends 1981;26:1619.
6 Kunst AE, Looman CWN, Mackenbach JP. The decline in winter excess mortality in The Netherlands. Int J Epidemiol 1990;20:97177.[Abstract]
7 Mackenbach JP, Kunst AE, Looman CWN. Seasonal variation in mortality in The Netherlands. J Epidemiol Community Health 1992;46: 26165.[Abstract]
8 Green MS, Harari G, Kristal-Boneh E. Excess winter mortality from ischaemic heart disease and stroke during colder and warmer years in IsraelAn evaluation and review of the role of environmental temperature. Eur J Public Health 1994;4:311.
9 Douglas AS, Russell D, Allan TM. Seasonal, regional and secular variations of cardiovascular and cerebrovascular mortality in New Zealand. Aust NZ J Med 1990;20:66976.[ISI][Medline]
10 Becker S, Weng S. Seasonal patterns of deaths in Matlab, Bangladesh. Int J Epidemiol 1998;27:81423.[Abstract]
11
Seto T, Mittlemann M, Davis R, Taira D, Kawachi I. Seasonal variation in coronary artery disease mortality in Hawaii: observational study. Br Med J 1998;316:194647.
12 McKee M, Sanderson C, Chenet L, Vassin S, Shkolnikov V. Seasonal variation in mortality in Moscow. J Public Health Med 1998;20:26874.[Abstract]
13 Curwen M, Devis T. Winter mortality, temperature and influenza: has the relationship changed in recent years? Popul Trends 1988;54:1720.
14 Simonsen L, Clarke MJ, Williamson G, Stroup DF, Arden NH, Schonberger LB. The impact of influenza epidemics on mortality: introducing a severity index. Am J Public Health 1997;87:194450.[Abstract]
15 Rogot E, Padgett SJ. Associations of coronary and stroke mortality with temperature and snowfall in selected areas of the United States, 196266. Am J Epidemiol 1976;103:56575.[Abstract]
16 Anderson TW, Rochard C. Cold snaps, snowfall and sudden death from ischaemic heart disease. CMA Journal 1979;121:158083.
17 Baker-Blocker A. Winter weather and cardiovascular mortality in Minneapolis-St. Paul. Am J Public Health 1982;72.
18 Saez M, Sunyer J, Castellsague J, Murillo C, Anto JM. Relationship between weather temperature and mortality: A time series analysis approach in Barcelona. Int J Epidemiol 1995;24:57682.[Abstract]
19 Enquselassie F, Dobson AJ, Alexander HM, Steele PL. Seasons, Temperature and coronary disease. Int J Epidemiol1993;22:63236.[Abstract]
20 Heunis JC, Olivier J, Bourne DE. Short-term relationships between winter temperatures and cardiac disease mortality in Cape Town. SA Med J 1985;85:101619.
21 Pan W-H, Li L-A, Tsai M-J. Temperature extremes and mortality from coronary heart disease and cerebral infarction in elderly Chinese. Lancet 1995;345:35355.[ISI][Medline]
22 Laake K, Sverre JM. Winter excess mortality: a comparison between Norway and England plus Wales. Age Ageing 1996;25:34348.[Abstract]
23 Zeger SL. A regression model for time series of counts. Biometrika 1988;75:62129.[ISI]
24 Curwen M, Dunnell K, Ashley J. Hidden influenza deaths:19891990. Popul Trends 1990;61:3133.
25 Carstairs V, Morris R. Deprivation and Health in Scotland. Aberdeen: Aberdeen University Press, 1992.
26
McLoone P, Boddy FA. Deprivation and Mortality in Scotland:1981 and 1991. Br Med J 1994;309:146570.
27 Shah S, Peacock J. Deprivation and excess winter mortality. J Epidemiol Community Health 1999;53:499502.[Abstract]
28 Keatinge WR, Coleshaw SRK, Cotter F, Mattock M, Murphy M, Chelliah R. 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. Br Med J 1984;289: 140508.[ISI][Medline]
29 Fleming DM, Cross KW, Crombie DL, Lancashire RJ. Respiratory illness and mortality in England and Wales. Eur J Epidemiol 1993;9: 57175.[ISI][Medline]
30 Monto AS, Sook KL. The Tecumseh Study of Respiratory Illness. III Incidence and periodicity of Respiratory syncytial virus and mycoplasma pneumoniae infections. Am J Epidemiol 1971;94:290301.[ISI][Medline]
31 Brennan PJ, Greenberg G, Miall WE, Thompson SG. Seasonal variation in arterial blood pressure. Br Med J 1982;285:91923.[ISI][Medline]
32 Dobson HM, Muir MM, Hume R. The effect of ascorbic acid on the seasonal variations in serum cholesterol levels. Scot Med J 1984;29: 17682.[ISI][Medline]
33 MacRury SM, Muir M, Hume R. Seasonal and climatic variation in cholesterol and vitamin C: effect of vitamin C supplementation. Scot Med J 1992;37:4952.[ISI][Medline]
34 Stout RW, Crawford V. Seasonal variations in fibrinogen concentrations among elderly people. Lancet 1991;338:913.[ISI][Medline]
35 Woodhouse PR, Tee Khaw K, Plummer M, Foley A, Meade TW. Seasonal variations of plasma fibrinogen and factor VII activity in the elderly: winter infections and death from cardiovascular disease. Lancet 1994;343:43539.[ISI][Medline]
36 Meade TW, Brozovic M, Chakrabarti RR et al. Haemostatic function and ischaemic heart disease: Principal results of the Northwick Park Heart Study. Lancet 1986;Sept.6:53337.
37 Boardman, B. Seasonal Mortality and Cold Homes. 121. 1986. University of Warwick, Watford Printers Limited. Unhealthy Housing. Conference held at University of Warwick, 14th16th December, 1986. Conference Proceedings.
38
Watt GCM. Health implications of putting value added tax on fuel. Br Med J 1994;309:103031.