Temperature, housing, deprivation and their relationship to excess winter mortality in Great Britain, 1986–1996

Paul Aylin, Sara Morris, Jon Wakefield, Ana Grossinho, Lars Jarup and Paul Elliott

Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College School of Medicine, London W2 1PG, UK.

Dr Paul Aylin, Department of Epidemiology and Public Health, Imperial College School of Medicine, St Mary's Campus, Norfolk Place, London W2 1PG, UK. E-mail: p.aylin{at}ic.ac.uk

Abstract

Objectives To examine the associations between temperature, housing, deprivation and excess winter mortality using census variables as proxies for housing conditions.

Design Small area ecological study at electoral ward level.

Setting Great Britain between 1986 and 1996.

Participants Men and women aged 65 and over.

Main outcome measures Deaths from all causes (International Classification of Diseases, Ninth Revision [ICD-9] codes 0–999), coronary heart disease (ICD-9 410–414), stroke (ICD-9 430–438) and respiratory diseases (ICD-9 460–519). Odds of death occurring in winter period of the four months December to March compared to the rest of the year.

Results During the study period (excluding the influenza epidemic year of 1989/90), a total of 1 682 687 deaths occurred in winter and 2 825 223 deaths occurred during the rest of the year among people aged >=65 (around 30 000 excess winter deaths per year). A trend of higher excess winter mortality with age was apparent across all disease categories (P < 0.01). There was a significant association between winter mortality and temperature with a 1.5% higher odds of dying in winter for every 1°C reduction in 24-h mean winter temperature. The amount of rain, wind and hours of sunshine were inversely associated with excess winter mortality. Selected housing variables derived from the English House Condition Survey showed little agreement with census-derived variables at electoral ward level. For all-cause mortality there was little association between deprivation and excess winter mortality, although lack of central heating was associated with a higher risk of dying in winter (odds ratio [OR] = 1.016, 95% CI : 1.009–1.022).

Conclusions Excess winter mortality continues to be an important public health problem in Great Britain. There was a strong inverse association with temperature. Lack of central heating was associated with higher excess winter mortality. Further work is needed to disentangle the complex relationships between different indicators of housing quality and other measures of socioeconomic deprivation and their relationship to the high number of excess winter deaths in Great Britain.

Keywords Mortality, seasonal, epidemiology, small-area analysis

Accepted 26 January 2001

It has long been known that mortality in England and Wales is higher in the winter than in the rest of the year. In the Registrar General's Third Annual Report,1 William Farr devoted several pages to a discussion on mortality in London during the years 1838–1841. The same phenomenon still accounts for up to 40 000 extra deaths in winter2,3 in England and Wales; it also occurs to a lesser extent in other countries, particularly in Scandinavia and neighbouring parts of Western Europe.4,5

Regression analysis indicates that about 88% of the variation in the numbers of excess winter deaths per annum in England and Wales can be accounted for by three variables: mean national winter temperature, number of winter deaths registered to influenza, and time (a reduction of excess winter deaths by about 500 a year).3

Excess mortality in winter is an important public health issue in the UK, potentially amenable to prevention. This study examines the associations of temperature, deprivation and housing (based on census variables) with excess winter mortality across electoral wards in Great Britain during 1986–1996. The focus is on people aged >=65, for whom the excess risk of winter death is greatest.4

Methods

We used the Office for National Statistics6 definition for winter given as the four months from December to March. Deaths from all causes (International Classification of Diseases, Ninth Revision [ICD-9] codes 0–999), coronary heart disease (ICD-9 410–414), stroke (ICD-9 430–438), respiratory diseases (ICD-9 460–519) and influenza (ICD-9 487) were obtained for males and females aged >=65 from the national death registration database held at the Small Area Health Statistics Unit. Numbers of deaths were obtained for each electoral ward in Great Britain for each year between 1 August 1986 and 31 July 1996.

In order to examine the relationship between housing conditions and excess winter mortality in Great Britain at ward level, we required nationwide predictors of poor- or good-quality housing, with particular reference to thermal conditions. Such data are not available nationally, although the English House Condition Survey (EHCS) contains data relating to the energy and thermal properties of houses,7 based on a random survey of individual households in England, conducted every 5 years. The aim was to relate the EHCS at individual level to census variables for the corresponding electoral wards and then to use these area-level data for the main analysis.

Data for a total of 9089 households was obtained from the 1991 EHCS. The numbers of observations per variable varied from 4484 to 9089. In order to work with a small subset of the available variables, we first examined relationships within the survey. We derived 14 variables from the EHCS (Figure 1Go) for further consideration based on completeness and relevance to heating and thermal efficiency. Eleven categorical variables were taken in their original form, one was a continuous measure, which we categorized into quintiles and two were categorical, which were dichotomized. We used Goodman and Kruskal's Gamma statistic8 to measure association and incorporated this into a hierarchical agglomerative clustering algorithm9,10 in order to obtain variables measuring different aspects of housing quality. Subsequently one variable was chosen from each cluster for further analysis.



View larger version (32K):
[in this window]
[in a new window]
 
Figure 1 List of variables derived from 1991 English House Condition Survey and 1991 Census for which relationships were explored

 
Twenty-six 1991 census variables and the Carstairs11 deprivation index were obtained at electoral ward level (Figure 1Go). The Carstairs index is a score derived from four census variables (unemployment, overcrowding, social class of head of household and car ownership), standardized to have zero mean across wards in Great Britain. All values were expressed in proportions of the appropriate population or households at ward level. Each house condition survey record was assigned census values using the ward code. To examine the relationship between the chosen housing variables and the available census variables we derived a categorical variable (in quintiles) from each census variable and then calculated the gamma statistic for the chosen subgroup of housing variables against the census variables. The relationship between the variables within the two data sources was then examined. Where variables were common to both data sets, we compared them directly (e.g. central heating), otherwise we compared all possible combinations.

Interpolated seasonal meteorological data were assigned to each ward by spatial smoothing using geographical information system techniques based on daily data from the national network of 116 meteorological stations and were obtained from the meteorological office. The variables were 24-h mean temperature, daytime minimum temperature, night-time minimum temperature, number of days below 0°C based on 24-h mean, days below 0°C based on daytime minimum, days below 0°C based on night-time minimum, mean daily rainfall (mm), daily bright sunshine (h) and daily mean wind speed (knots). Smoothed contours of 24-h mean temperatures for the winter season, 1986, are shown in Figure 2Go, together with the locations of the meteorological stations. The winter season values were calculated for each year by averaging daily measurements from December until March of the next year. Wards on the periphery of Great Britain which could not be assigned a value and those where complete census data were not available were excluded. This left 9143 out of the 10 933 wards originally identified for the analysis.



View larger version (41K):
[in this window]
[in a new window]
 
Figure 2 Map of Great Britain showing 24-h mean temperature for the 1986 winter season

 
The Excess Winter Deaths Ratio was used as a simple descriptive measure, based on the Office for National Statistics6 definition given as the numbers of deaths occurring in the four months from December to March compared to the average numbers of deaths occurring in the preceding and following four months.

The relationship between the selected census variables, deprivation and age with excess winter mortality was then explored for each individual meteorological variable by assuming that the counts of winter and non-winter deaths each came from a Poisson distribution. With this assumption, conditioning on the total number of deaths, the number of winter deaths is binomially distributed, with the number of trials equal to the total number of deaths (winter and non-winter). The probability in this model represents the conditional probability of a winter death given that a death has occurred. This probability was modelled as a function of the explanatory variables using a logistic regression function. Odds ratios (OR) for an increase of one unit were calculated. Census variables (including Carstairs deprivation score) were re-scaled so that the OR compare 5th and 95th percentiles. The temperature data were standardized to have zero mean over the country over 10 years.

Results

Among the 14 housing variables considered in the EHCS (Figure 1Go), the clustering algorithm identified five groups. One representative variable from each group was selected for comparison with census variables. These were the presence of mould, the presence of rising damp, wall insulation, tenure and lack of central heating. They showed little agreement (not shown) with the 26 census-derived variables at electoral ward level. The two significant associations were between proportion of those economically active and presence of mould ({gamma} = –0.08, P = 0.002) and between proportion of residents with long term limiting illness and mould ({gamma} = 0.07, P = 0.003).12 The significance levels were not adjusted for multiple testing. These two census variables were chosen, together with tenure (the proportion of owner occupied and privately rented households) and the lack of central heating in households with one or more residents of pensionable age, common to both the census and the EHCS, although not significantly associated (p >= 0.12 and P = 0.32 respectively). We also included the Carstairs index of deprivation, which showed significant relationships with four of the five variables ({gamma} in modulus <=0.35, P < 0.001), rising damp being the exception ({gamma} = 0.02, P = 0.58). The mean, median and 5th and 95th percentiles of the five selected census variables and Carstairs deprivation index are given in Table 1Go.


View this table:
[in this window]
[in a new window]
 
Table 1 Mean and median values of the five selected census variables and Carstairs deprivation index for 1991 census wards, and their 5th and 95th percentiles
 
The coldest average winter night-time minimum temperature occurred in 1995/96 (2.7°C) and the highest occurred in 1988/89 (5.9°C) (Figure 3Go). There was high correlation between all temperature variables ranging from 0.82 to 0.96 (Table 2Go). There was only modest correlation between the temperature variables and rain and wind ranging in modulus between 0.09 (rainfall and 24-h mean temperature) and 0.29 (wind speed and number of days below 0°C based on 24-h mean). Hours of sunshine were most strongly correlated with 24-h mean temperature and number of days below 0°C based on 24-h mean (0.53 and –0.53 respectively). There was a spatial pattern in temperature variables with a tendency for lower temperatures on the East coast of Britain (Figure 2Go).



View larger version (29K):
[in this window]
[in a new window]
 
Figure 3 Mean winter temperatures and number of days below 0°C for electoral wards, 1986–1996 in Great Britain

 

View this table:
[in this window]
[in a new window]
 
Table 2 Correlation matrix of annual meteorological variables in wintera between 1986 and 1996, Great Britain
 
There was an influenza epidemic during the winter of 1989/ 90 when the excess winter mortality ratio for influenza deaths rose above 70. The ratio was also high for respiratory deaths, and, to a lesser extent, all-cause mortality (Figure 4Go). Subsequent analyses were therefore carried out excluding 1989/90 data.



View larger version (19K):
[in this window]
[in a new window]
 
Figure 4 Annual excess winter mortality ratiosa, 1986–1996 in Great Britain

a Winter defined as 1 December to 31 March.

Excess winter mortality expressed as the ratio of the number of deaths in winter to half of those in the previous and subsequent 4 months.

 
The median number of excess deaths per ward varied between 1.5 (5th and 95th percentiles of –5 and 11.5) in 1988/89 and 3.0 (–4 and 15) in 1995/96. Table 3Go shows the numbers of deaths by cause in Great Britain. Within the study period (excluding 1989/90), a total of 1 682 687 deaths occurred among people aged >=65, in the four months December to March, and 2 825 223 deaths occurred during the rest of the year. This gave an excess mortality in the winter period over 9 years of 1 682 687 – 0.5 x 2 825 223, i.e. an average of around 30 000 excess winter deaths per year. Unadjusted all-cause excess winter death ratios were identical (1.19) in both excluded and included wards. Table 3Go also shows results from the conditional logistic model for each of three age groups, adjusting for 24-h mean temperature, deprivation and the five census variables. An increasing trend with age in the odds of dying in winter was apparent across all disease categories (P < 0.01). Subsequent analyses were adjusted for age using these three groups.


View this table:
[in this window]
[in a new window]
 
Table 3 Deaths in people age >=65 by cause occurring between 1 August 1986 and the 31 July 1996 (excluding 1989/90) in Great Britain, and odds of death in wintera, by age (years)
 
Table 4Go gives the OR and 95% CI for a unit of each meteorological variable adjusted for census variables, age and deprivation. The three mean temperature variables had similar associations with the odds of dying in winter ranging from 1.015 (95% CI : 1.013–1.017) to 1.017 (95% CI : 1.015–1.019) for all-cause mortality. Thus there was a 1.5% higher odds of dying in winter for every 1°C decrease in the 24-h mean winter temperature below the 10-year national average. Similar associations across all three mean temperature measures were found for coronary heart disease and stroke. For respiratory deaths, the associations with the temperature variables were larger, ranging from 1.032 (95% CI : 1.027–1.037) to 1.041 (95% CI : 1.035–1.045).


View this table:
[in this window]
[in a new window]
 
Table 4 Odds ratios (OR) of the odds of death in wintera for a change of one unit of independently modelled meteorological variablesb adjusted for age, deprivation and the five census variables, Great Britain 1986–1996 (excluding 1989/90)
 
The three variables describing number of days with temperatures below zero were similarly related to all cause excess mortality with OR ranging from 1.001 (95% CI : 1.001–1.002) to 1.003 (95% CI : 1.002–1.003) per one day increase. There was a 0.3% higher odds of dying in winter for a one-day increase in the number of days where the mean 24 hour temperature fell below 0°C.

The amount of rain was inversely related to excess winter mortality with 0.8% lower odds of dying for all causes for every mm increase above the 10-year national average. Wind speed had a small inverse relationship with all-cause winter mortality. Hours of sunlight was also inversely related to excess winter mortality with 2.9% lower odds for every additional hour of daily sunshine. Again these relationships were more marked for respiratory deaths (Table 4Go).

Table 5Go gives OR for the census variables and Carstairs deprivation index, adjusted for 24-h mean temperature and age. Of the census variables considered, only lack of central heating showed significant associations with the odds of dying in winter, for all causes and coronary heart disease, with OR ranging from 1.016 (95% CI : 1.009–1.022) for all causes, to 1.029 (95% CI : 1.011–1.048) for stroke. There was no association between the Carstairs deprivation index, all-cause and coronary heart disease excess winter mortality, although there was borderline significant lower excess mortality with increasing deprivation for respiratory disease.


View this table:
[in this window]
[in a new window]
 
Table 5 Odds ratios (OR) of the odds of death in wintera for an increase from the 5th to 95th percentile values of census-derived variables at ward level, adjusted for 24-h mean temperature and age, Great Britain, 1986–1996 (excluding 1989/90)
 
Discussion

Excess winter mortality is still an important public health problem in Great Britain, particularly in the very old. There was no apparent continuation of the reported decrease of excess winter mortality over time,13 which has been linked to an increased use of central heating.14,15 We found an average excess winter mortality of 30 000 deaths per year in Great Britain over a 9-year period (excluding 1989/90), and for 1995/96 it was in excess of 40 000.

Our findings support previous reports of an association between seasonal temperature and excess winter mortality.2 The inverse relationship with rainfall and wind could be explained by a reduction of outdoor excursions when it is raining or windy preventing outdoor cold stress.16 We found a significant relationship between excess winter mortality and lack of central heating. While Kunst et al.17 speculated that the introduction of central heating had had minimal impact, our findings are more consistent with the Eurowinter Group.18 They reported high indices of cold-related mortality associated with low living room temperatures and limited bedroom heating, as well as among people who wore fewer clothes and were less active outdoors.

In colder climates, it has been suggested that the maintenance of warmth in houses has a protective effect on winter mortality.19,20 One possible reason for the much lower excess winter mortality in Norway compared with England and Wales4 may be that heating is usually included in the rent. Consequently, poor households should have a warm indoor environment. The presence of central heating in the UK may be a proxy for the maintenance of warmth in houses. However, due to financial constraints, some poorer households may not make use of their heating. If this were the case, the relationship between excess winter mortality and central heating found in our study might have been diluted since unheated households would have been allocated to the central heating group.

By use of the EHCS,7 we endeavoured to select a small number of census variables that appeared to be relevant to aspects of housing quality. There was, however, poor agreement between individual-level EHCS data and the area-level census data. This may have reflected to some extent the fact that wards contain around 2500 households, where as only one or two households per ward were included in the EHCS. Furthermore, wards, particularly in urban areas can be quite heterogeneous in social and housing conditions. In addition, it is possible that there may have been some bias in the selection of properties for the survey, which could affect the relationship with census data.

Within the survey, owner occupation was associated with more double-glazing, the overall temperature interviewer rating and generally better heating and thermal insulation. Better heating and insulation were also found to be associated with relative affluence and higher social class,7 although in our analysis using census-based variables, the correlation between Carstairs deprivation index and ‘no central heating' was low (–0.01), as was the correlation between Carstairs index and ‘owner occupied' (–0.03).

The lack of association between deprivation and excess winter mortality was perhaps surprising but is consistent with two recent studies carried out in Scotland and Croydon.21,22 Nor was there any association between privately rented housing, a potential indicator of poor housing, and excess winter mortality. Two issues need to be considered in the interpretation of these findings. First, the census variables may only partly reflect housing quality, which may attenuate any associations with mortality. Secondly, caution is required in making inferences from small-area ecological studies to individual or household level.23 The Carstairs deprivation index is a deprivation measure calculated from data for the whole population, whereas this study is limited to deaths in those aged >=65. However, both the Carstairs and the Townsend deprivation index show a strong relationship with mortality in the elderly.24,25 We therefore think it unlikely that an alternative measure of deprivation, would have behaved very differently than Carstairs in association with excess winter deaths. It is also possible that people living in deprived areas with poor housing were compensating by wearing warm clothes and adopting other protective behaviours.19

It is possible that the deaths we are observing in winter represent shifts in the timing of events that lead to death (‘mortality displacement'), and that the effects of temperature simply serve to bring forward a death by a short period of time. However, as our study examines seasonal shifts in mortality, this delay would have to be in the order of months rather than days to account for the annual winter excess mortality. The design of our study is such that we are unable to explore further this potential displacement effect.

The aetiology of excess winter mortality has yet to be determined. Environmental temperature has an inverse relation with blood pressure.26 A raised blood pressure in a cold environment has several adverse effects. It alters the ratio of myocardial oxygen supply to demand. It increases the ventricular wall stress, cardiac work, and oxygen requirements, and reduces mechanical efficiency which could precipitate myocardial infarction.26 Vallance et al.27 suggest that infection may partly explain the seasonal variation in death from cardiovascular disease. The mechanism they put forward is that systemic inflammatory response increases risk by raising the concentration of circulating clotting factors such as fibrinogen. Alternatively, endothelial function might be altered. Khaw et al.28 reported that in an observational study of 96 men and women living in their own homes, the observed winter rise in fibrinogen was related to a winter increase in infection. Pell et al.29 suggest that people who suffer cardio-pulmonary arrest in winter have a significantly lower likelihood of surviving, in part because of a higher frequency of associated risk factors.

Excess winter mortality continues to be an important public health problem in Great Britain. International comparisons showing much higher levels of winter mortality in the UK than other countries and that the maintenance of warmth in houses has a protective effect on winter mortality suggest that it may be amenable to intervention. There was a strong inverse association with temperature. Lack of central heating was associated with higher excess winter mortality. If this relationship were causal, and the excess deaths were not simply due to a ‘mortality displacement' effect, then there could be policy implications in providing central heating to people who were not able to afford it. However, mechanisms would also have to be in place to ensure that people were able to run and afford their central heating once installed. Further work is needed to disentangle the complex relationships between different indicators of housing quality and other measures of socioeconomic deprivation and their relationship to the high number of excess winter deaths in Great Britain.


KEY MESSAGES

  • Excess winter mortality continues to be an important public health problem in Great Britain and accounted for in excess of 40 000 extra deaths in the winter of 1995/96.
  • It occurs to a lesser extent in other countries, particularly in Scandinavia and neighbouring parts of Western Europe.
  • There is a strong inverse association with mean seasonal temperature.
  • There is little association between deprivation and excess winter mortality.
  • Lack of central heating was associated with higher excess winter mortality.
  • Further work is needed to disentangle the complex relationships between different indicators of housing quality and other measures of socioeconomic deprivation and their relationship to the high number of excess winter deaths in Great Britain.

 



View larger version (132K):
[in this window]
[in a new window]
 
Photography: Mary Shaw

 
Acknowledgments

Contributors

PA, PE and LJ were involved in the conception and management of the project. PA, SM and AG were involved in the development of the project with JW overseeing the statistics. PA prepared the first draft of this paper and SM, JW, PE, AG and LJ contributed to the final submitted version. PA is the guarantor.

The Small Area Health Statistics Unit is funded by a grant from the Department of Health, Department of the Environment, Transport and The Regions, Health and Safety Executive, National Assembly for Wales, Scottish Executive and the Northern Ireland Assembly. We thank the Office for National Statistics, the General Register Office for Scotland, the Department of the Environment, Transport and the Regions and the Met. Office for provision of and permission to use their data. This work is based on data provided with the support of the ESRC and JISC and uses census and boundary material, which are copyright of the Crown, the Post Office and the ED-LINE Consortium. Crown Copyright Data supplied by The Met. Office. This research was supported, in part, by an equipment grant from the Wellcome Trust (0455051/Z/95/Z). The views expressed in this publication are those of the authors and not necessarily of the funding departments.

References

1 General Register Office. Third Annual Report of Births, Deaths and Marriages in England. London: HMSO, 1841, pp.102–09.

2 Curwen M. Excess mortality: a British Phenomenon? Health Trends1990/91;4:169–75.

3 Curwen M, Devis T. Winter mortality, temperature and influenza: has the relationship changed in recent years? Popul Trend 1988;54:17–20.

4 Laake K, Sverre JM. Winter excess mortality: A comparison between Norway and England plus Wales. Age Ageing 1996;25:343–48.[Abstract]

5 United Nations. Demographic Yearbook 1985. New York: United Nations, 1987.

6 OPCS. Trends in Respiratory Mortality 1951–1975. London: HMSO, OPCS Series DH1 No. 7,13–22.

7 Department of Environment. English House Condition Survey 1991, Energy Report. London: HMSO, 1996.

8 Goodman LA, Kruskal WH. Measures of Association for Cross Classifications. New York: Springer Verlag, 1979 (Contains articles appearing in the Journal of the American Statistical Association in 1954, 1959, 1963, 1972).

9 Gordon AD. Classification: Methods for the Exploratory Analysis of Multivariate Data. London: Chapman and Hall, 1981.

10 Hartigan JA. Clustering Algorithms. New York: John Wiley and Sons, 1975.

11 Carstairs V, Morris R. Deprivation and Health in Scotland. Aberdeen: Aberdeen University Press, 1991.

12 Morris S, Wakefield J, Aylin P, Cockings S, Jarup L, Elliott P. Relationship Between the 1991 English House Condition Survey and 1991 Census Information. London: Small Area Health Statistics Unit, Technical Report, 1999.

13 McDowall M. Long term trends in seasonal mortality. Popul Trend 1981;26:16–19.

14 Keatinge WR, Coleshaw SRK, Holmes J. Changes in seasonal mortalities with improvement in home heating in England and Wales from 1964 and 1984. Int J Biometeorol 1989;33:71–76.[ISI][Medline]

15 Lerchl A. Changes in the seasonality of mortality in Germany from 1946 to 1995: the role of temperature. Int J Biometeorol 1998;42(2): 84–88.[ISI][Medline]

16 Donaldson GC, Ermakov P, Komarov YM, MacDonald CP, Keatinge W. Cold related mortalities and protection against cold in Yakutsk, eastern Siberia: observation and interview study. Br Med J 1998;317: 978–82.[Abstract/Free Full Text]

17 Kunst AE, Looman CWN, Mackenbach JP. The decline in winter excess mortality in the Netherlands. Int. J. Epidemiol 1990;20:971–77.

18 Eurowinter Group. Cold exposure and winter mortality from ischaemic heart disease, cerebrovascular disease, respiratory disease, and all causes in warm and cold regions of Europe. Lancet 1997;349: 1341–46.[ISI][Medline]

19 Donaldson GC, Tchernjavski VE, Ermakov SP, Keatinge WR. Winter mortality and cold stress in Yekaterinburg, Russia: interview survey. Br Med J 1998;316:514–18.[Abstract/Free Full Text]

20 Eng H, Mercer JB. Seasonal variations in mortality caused by cardiovascular diseases in Norway and Ireland. J Cardiovasc Risk 1998;5: 89–95.[Medline]

21 Gemmel I, McLoone P, Boddy FA, Dickinson G, Watt GCM. Seasonal variation in mortality in Scotland. Int J Epidemiol 2000;29: 274–79.[Abstract/Free Full Text]

22 Shah S, Peacock J. Deprivation and excess winter mortality. J Epidemiol Community Health 1999;53:499–502.[Abstract]

23 Diggle P, Elliott P. Disease risk near point sources: statistical issues for analyses using individual or spatially aggregated data. J Epidemiol Community Health 1995;49(2):S20–27.[ISI][Medline]

24 Phillimore P, Beattie A, Townsend P. Widening inequality of health in northern England, 1981–91. Br Med J 1994;308:1125–28.[Abstract/Free Full Text]

25 McLoone P, Boddy FA. Deprivation and mortality in Scotland, 1981 and 1991. Br Med J 1994;309:1465–70.[Abstract/Free Full Text]

26 Kunes J, Tremblay J, Bellavance F, Hamet P. Influence of environmental temperature on the blood pressure of hypertensive patients in Montreal. Am J Hypertens 1991;4:422–26.[ISI][Medline]

27 Vallance P, Collier J, Bhagat K. Infection, inflammation, and infarction: does acute endothelial dysfunction provide a link? Lancet 1997;349:1391–92.[ISI][Medline]

28 Khaw K, Woodhouse P. Interrelation of vitamin C, infection, haemostatic factors, and cardiovascular disease. Br Med J 1995;310: 1559–63.[Abstract/Free Full Text]

29 Pell JP, Sirel J, Marsden AK, Cobbe SM. Seasonal variation in out of hospital cardiopulmonary arrest. Heart 1999;82:680–83.[Abstract/Free Full Text]