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(Stroke. 1997;28:1367-1374.)
© 1997 American Heart Association, Inc.

Articles

Stroke Incidence and Mortality Correlated to Stroke Risk Factors in the WHO MONICA Project

An Ecological Study of 18 Populations

WHO MONICA Project¹ (prepared by Birgitta Stegmayr, PhD; Kjell Asplund, MDr; PhD, Kari Kuulasmaa, PhDr; Anna-Maija Rajakangas, MSc; Per Thorvaldsen, MD, PhD; Jaakko Tuomilehto, MD, PhD)

From the Department of Medicine, University Hospital, Umeå, Sweden (B.S., K.A.); MONICA Data Centre, Department of Epidemiology and Health Promotion, National Public Health Institute, Helsinki, Finland (K.K., A.-M.R., J.T.,); and Glostrup Population Studies, Glostrup University Hospital, Glostrup, Denmark (P.T.).

Correspondence to Dr Birgitta Stegmayr, Department of Medicine, University Hospital, S-901 85 Umeå, Sweden.

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion Appendix 1 References

 
Background The aim of the present study was to determine the extent to which the variation in conventional risk factors contributed to the variation in stroke incidence among these populations.

Methods Within the WHO MONICA Project, stroke has been recorded in 18 populations in 11 countries. In population surveys, risk factors for cardiovascular diseases have been examined in the age group 35 to 64 years. Over a 3-year period, 12 224 acute strokes were registered in men and women within the same age range.

Results The highest stroke attack rates were found in Novosibirsk in Siberia, Russia, and Finland, with a more than three-fold higher incidence than in Friuli, Italy. The mean diastolic blood pressure among the populations differed by 15 mm Hg between Novosibirsk (highest) and Denmark (lowest). In multiple regression analyses, the presence of conventional cardiovascular risk factors (smoking and elevated blood pressure) explained 21% of the variation in stroke incidence among the population in men and 42% in women. In Finland, in China, and in men in Lithuania, the stroke incidence rates were higher than expected from the population risk factor levels.

Conclusion Prevalence of smoking and elevated blood pressure explain a substantial proportion of the variation of stroke attack rates between populations. However, other risk factors for stroke that were not measured in the present study also contribute considerably to interpopulation differences in stroke rates.

Key Words: cerebrovascular disorders • risk factors • hypertension • epidemiology

	Introduction

Top Abstract Introduction Subjects and Methods Results Discussion Appendix 1 References

 
Comparisons of stroke incidence and mortality in multinational populations have shown a large geographical variation.¹ However, the study designs and methods used to assess the occurrence of stroke have varied considerably. It has therefore been suggested that the differences in mortality and incidence observed in previous studies depend in part on differences in registration procedures, case ascertainment, and diagnostic procedures.^{2 3}

In the WHO MONICA Project (Multinational MONItoring of Trends and Determinants in CArdiovascular Disease), the main aim is to relate incidence and mortality of stroke and myocardial infarction to risk factors in many populations over a 10-year period. Uniform methodology has been used in all participating populations for the event registration and for the population surveys of conventional cardiovascular risk factors such as blood pressure, serum cholesterol, smoking habits, and body mass index.⁴

Hypertension is the most important risk factor for stroke, both in men and women.^{5 6 7} An analysis of nine major prospective observational studies has shown that a prolonged difference in usual diastolic blood pressure of approximately 6 mm Hg is associated with a 36% difference in the risk of stroke.⁸ In Finland and the United States, the observed changes among population levels in diastolic blood pressure, total cholesterol, and smoking were followed by a decline in stroke incidence.^{9 10}

In a meta-analysis, the overall relative risk of stroke associated with cigarette smoking was 1.5 as compared with nonsmoking individuals.¹¹ A more recent cohort study has indicated that the relative risk for stroke may be as high as 2.6 in middle-aged women who smoke.¹²

Elevated cholesterol levels have been found to be a risk factor for stroke in a meta-analysis, with an increased pooled risk of 1.3,¹³ although a more recent and comprehensive analysis failed to show a relationship between cholesterol and stroke.¹⁴ The association between serum cholesterol and different types of stroke has been shown to be negative for cerebral hemorrhage¹⁵ and positive for cerebral infarction.^{16 17 18} In another overview of 17 cholesterol-lowering trials containing more than 36 000 individuals, no benefit of cholesterol lowering was seen for the risk of stroke,¹⁹ although recent results from the 4S²⁰ and CARE²¹ trials suggest that stroke risk is also reduced in patients with statins, at least in a certain subset of patients with cardiovascular disease.

The aim of the present ecological cross-sectional analysis was to determine whether the variation in conventional cardiovascular risk factors correlated with the variation in stroke occurrence among populations.

	Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion Appendix 1 References

 
Populations
Within the populations registering stroke in the MONICA project, three population surveys were performed in the age group 35 to 64 years at the beginning, middle (optional), and end of the 10-year-registration period. In the present study, stroke incidence and mortality were measured during the 3 years of stroke registration, which included the year of the second survey or the 3 years immediately after the survey (Table 1).

View this table: [in this window] [in a new window]   Table 1. MONICA Populations, Years of Population Surveys, and the Population Sizes (Average of the Mid-Year Population for the Year(s) of the Survey) in the Age Group 35 to 64 Years

The stroke component in the MONICA Project involved 21 populations in 11 countries in the age group 35 to 64 years. Table 1 shows the average mid-year population sizes for the 18 populations included in the present study (see below) and the years during which monitoring of stroke events was conducted.

The population data were obtained from population registers, censuses, or inter-censal estimates. The total study population was 2.9 million people. The total number of stroke events included in the analysis was 12 224.

Ascertainment and Definition of Stroke Cases
Within the MONICA Project, all acute strokes were registered in a standardized way.^{22 23} Case finding and coding procedures have been described in detail.^{22 24} Stroke was defined as "rapidly developing clinical signs of focal (or global) disturbance of cerebral function lasting more than 24 hours (unless interrupted by surgery or death) with no apparent cause other than a vascular origin."^{4 22} Subdural hemorrhage, transient ischemic attacks, traumatic intracerebral hemorrhage, and lesions caused by a brain tumor were excluded. A vascular brain lesion detected solely via computerized brain tomography (CT) scan in the absence of focal signs was not included, since the stroke diagnosis was based on clinical presentation only. Multiple strokes occurring within 28 days from onset of the first attack were considered to be the same event.

The use of CT increased rapidly in many centers during the 1980s.²⁵ Nevertheless, the use of CT scanning was too infrequent in many populations to permit meaningful comparisons of stroke subtypes between the populations. All acute strokes are included in the present analyses (subarachnoid hemorrhage, intracerebral hemorrhage, brain infarction, and unspecified stroke).

On the basis of the background information, each event was classified into one of three categories: "definite stroke," "unclassifiable stroke," or "not stroke."²² In the present report, cases classified as definite stroke have been included in the non-fatal events, whereas fatal events also included the few cases coded as unclassifiable.

The term "attack rate" includes all stroke events (first and recurrent stroke), whereas "incidence" describes first-ever stroke.⁴

To ensure uniformity in the coding of stroke events between the MONICA centers, series of test cases were distributed to all participating centers at regular intervals.²² All data submitted to the MONICA Data Centre were also checked for completeness, for logical consistency, and for possible duplicate registrations of the same event before they were entered into the stroke database.

To estimate the data quality of stroke registers, five key indicators were identified.²⁵ Only populations that met the stroke data quality standards have been included in the analyses in the present study. Two populations were excluded because of incomplete case ascertainment, and one terminated the stroke component of the MONICA Project in 1987. In Novosibirsk, the stroke mortality rate according to the routine vital statistics was much higher than observed in the MONICA register. Site visits and extended validations have failed to identify any systematic errors in the Novosibirsk MONICA stroke register, and therefore the populations are included in the present analyses.

Risk Factor Surveys
The year of survey varied between 1987 and 1990 (Table 1). The population samples were randomly selected and stratified by sex and age (35 to 44, 45 to 54, and 55 to 64 years), with at least 250 subjects in each sex and age group. Table 1 shows the participation rates in the populations (men and women together) in the age group 35 to 64 years.

Standard mercury sphygmomanometers were used for blood pressure measurements in all populations except in Sweden where blood pressure was measured with Hawksley's random zero sphygmomanometer.²⁶ Blood pressure was measured twice in all subjects after a 5-minute rest in a sitting position, and the mean value of the two measurements was used. Blood pressure was considered to be elevated when the systolic blood pressure was 160 mm Hg, or diastolic blood pressure was 95 mm Hg, or the person had been taking antihypertensive drugs during the previous 2 weeks. The quality control procedures for blood pressure data for the first MONICA survey have been described.²⁷ The data quality control was not yet finished for the two populations in Novosibirsk concerning the proportion of individuals taking antihypertensive drugs. Therefore, the data for the proportion with elevated blood pressure have been excluded in some of the analyses for the Novosibirsk populations.

Serum cholesterol levels were measured from a venous blood sample taken after at least a 4-hour fast (in populations from Italy and Germany, however, non-fasting specimens were taken). Total serum cholesterol was determined with an enzymatic method, except in four populations in which other methods were used. External quality control of cholesterol measurements was provided by the MONICA Quality Control Centre for lipid standardization in Prague, Czech Republic.²² Individuals with cholesterol levels 6.5 mmol/L were considered to have elevated serum cholesterol.

A balance scale was used to measure the weight of the subjects to the nearest 0.2 kg; the subjects wore lightweight clothes and no shoes. Height was measured without shoes to the nearest centimeter. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). The cutoff point for being considered severely overweight was defined as a BMI of 30 kg·(m²)^-1.

A smoker was defined as a person smoking one or more cigarettes per day, and a heavy smoker was one who smoked more than 20 cigarettes per day.

Statistical Analyses
Attack rates and incidence of stroke in subjects aged 35 to 64 years were age-standardized directly to the "World Standard Population."²⁸ The weights used were 6, 6, 6, 5, 4, and 4 for the age groups 35 to 39, 40 to 44, 45 to 49, 50 to 54, 55 to 59, and 60 to 64 years, respectively. The 95% confidence intervals for 35- to 64-year-old subjects were calculated using a simple normal approximation of the Poisson distribution for numbers of events within age groups.²⁹

Univariate and multiple regression analyses were performed to analyze associations between stroke attack rates and risk factors using the age-standardized population attack rates and risk factor proportions as observations.³⁰

	Results

Top Abstract Introduction Subjects and Methods Results Discussion Appendix 1 References

 
Attack Rate and Incidence of Stroke
The annual attack (all stroke events) and the incidence rates (first-ever stroke only) are shown in Fig 1. The highest rates in men were found in Kuopio, Finland, and in Novosibirsk, Russia (Fig 1). These attack rates were three times higher than the lowest ones found in Friuli and two populations in East Germany. In women, the highest attack and incidence rates were seen in Novosibirsk, where stroke events were more than three times higher than in Friuli (Fig 1). In half of the populations, the stroke incidence was twice as high in men than in women. In one of the populations in Novosibirsk, the incidence of stroke was similarly high in both women and men.

View larger version (43K): [in this window] [in a new window]   Figure 1. Attack and incidence rates in men and women aged 35 to 64 years with 95% confidence intervals.

Risk Factors in the Populations
The proportion of subjects with one or more raised cardiovascular risk factors varies considerably among the populations (Tables 2 and 3). The highest proportions of elevated blood pressure were found in Finland and Germany and the lowest in Denmark. The population mean of diastolic blood pressure showed a difference of 15 mm Hg in men and 16 mm Hg in women between the population with the highest and that with the lowest diastolic blood pressure. The highest diastolic blood pressure was seen in Novosibirsk (Table 2). In univariate analysis, the proportion of the population with elevated blood pressure correlated positively with the stroke attack rate in women (r=.56; P=.016) and in men (r=.42; P=.09), although for men the correlation was not statistically significant. When the two populations from Novosibirsk were excluded, the statistically significant correlation in women disappeared (r=.09; P=.75) and became weaker in men (r=.22; P=.41). There was no significant correlation between the population mean of systolic or diastolic blood pressure and the stroke attack rate (Table 4).

View this table: [in this window] [in a new window]   Table 2. Risk Factor Levels in Men in the Second MONICA Population Survey in the Age Group 35 to 64 Years

View this table: [in this window] [in a new window]   Table 3. Risk Factor Levels for Women in the Second MONICA Population Survey in the Age Group 35 to 64 Years

View this table: [in this window] [in a new window]   Table 4. Univariate Analyses Between Age-Standardized Stroke Attack Rate and the Levels of Cardiovascular Disease Risk Factors in Men and Women Aged 35 to 64 Years

The proportion of men who were cigarette smokers was less than 30% in only two populations (North Karelia, Finland, and Northern Sweden). In Beijing (China), Warsaw (Poland), and Novosibirsk Control (Russia) more than 50% of the men were cigarette smokers. In many of the populations with a high proportion of smoking men, relatively few women were smokers. In Novosibirsk and Kaunas, Lithuania, only 3% and 4% of women, respectively, were smokers, and in Beijing 13% of the women smoked. The Finnish populations also had relatively low proportions of women who smoked. The highest proportion of women who smoked was found in Glostrup, Denmark, where 40% smoked (Table 3). A negative correlation between smoking and attack rate was found in women (r=-.63; P=.01) but not in men (Table 4).

In China 3% of men and 4% of women had high cholesterol levels. In Novi Sad and the Nordic populations, except Gothenburg, 40% of the male populations had elevated cholesterol levels. The same pattern was seen in women, although the proportion with elevated cholesterol was somewhat lower. No statistically significant correlation was found between cholesterol levels and the stroke attack rate (Table 4).

One fourth of the men in Kaunas and almost half of the women in Kaunas and Novosibirsk were obese (BMI 30 kg·(m²)^-1. In China, only 4% of the men and 9% of the women were obese. In women there was a statistically significant correlation between BMI and the stroke attack rate (r=.50; P=.03) but not in men (r=.26; P=.3) (Table 4).

In Sweden, more than 55% men were free from hypertension, smoking, or both of these risk factors, whereas in Novosibirsk fewer than 25% were. Of the men, between 3% (Denmark) and 23% (Novosibirsk) had both risk factors. In Lithuania and Beijing approximately 70% of the women also were found to be free from either of these two risk factors. In all populations, the proportion of women with both the risk factors was low.

In the multiple regression analysis with attack rate as dependent variable and the proportion with elevated blood pressure and daily smoking as independent variables, the coefficient of determination was significant in women (r²=.42; P=.016) but not in men (r²=.21; P=.16). When excluding the Novosibirsk populations, the coefficient of determination did not change in women (r²=.42; P=.029), but it did so in men (r²=.06; P=.65). The correlations between the incidence and these two risk factors were approximately the same as for the attack rate.

In the present study we chose to use stroke attack rates in the correlation analyses because some of the populations (the Russian and the Polish ones) had a large proportion of non-fatal events in which the order was unknown. Figs 2 and 3 show the association in men and women between observed attack rates and those predicted by the regression model when the two risk factors (elevated blood pressure and daily smoking) were considered together. If the presence of these two risk factors explained the attack rates completely, all the populations would be on the line. The populations below the line had their attack rates overestimated by the model, while the populations above the line had their rates underestimated.

View larger version (17K): [in this window] [in a new window]   Figure 2. The observed attack rate in men (per 100 000) against the predicted attack rate, based on the regression model with two risk factors considered (elevated blood pressure and smoking).

View larger version (17K): [in this window] [in a new window]   Figure 3. The observed attack rate in women (per 100 000) against the predicted attack rate, based on the regression model with two risk factors considered (elevated blood pressure and smoking).

In both men and women, the populations with observed stroke attack rates higher than predicted were primarily from Finland, China, and Lithuania. In East Germany, Southern Sweden (Gothenburg), and Italy (Friuli), stroke attack rates were clearly lower than predicted by the model.

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion Appendix 1 References

 
The burden of stroke is enormous worldwide, both socially and economically.³¹ In the WHO MONICA Project, the monitoring of long-term trends in stroke incidence and mortality in defined geographic areas has been combined with measurements of the population levels of traditional cardiovascular risk factors. Official mortality statistics have commonly been used in studies comparing multinational data on stroke occurrence.^{32 33 34} The quality of official mortality data varies between countries because of different traditions and levels of reliability in assigning the cause of death and because autopsies are not always performed. Data based on the uniform registration procedures are likely to give more reliable information about the total burden of stroke among populations. Having population surveys performed in the same populations gave us the opportunity to estimate the extent to which differences in risk factor levels can explain variations in the stroke incidence and mortality rate among populations.

Large differences have been shown in both stroke incidence and mortality rate among the MONICA populations.^{35 36} In this study, we used cross-sectional population data from the MONICA Project to evaluate the association between stroke incidence and risk factors for stroke. Ecological studies may be subject to bias, so-called "ecological fallacy."³⁷ In many ecological analyses, data from studies with different designs and initiated for different purposes have been compared. In the MONICA Project, a common protocol has been used. The same basic sources have been used to identify all fatal and non-fatal stroke events in the populations studied.⁴

The data collection from all populations was standardized, the data were subjected to extensive quality assurance, and the data used in this paper have passed a retrospective quality assessment. Therefore, the risk factor data should be accurate. Several validations of the data from the first survey have also been published,^{27 38 39} and similar evaluations have been completed for the second surveys used in this analysis.

Although the study was originally designed for estimation of trends within the populations, the data also should be reasonably accurate to cross-sectional comparisons. The overall response rate in surveys was more than 70% in all but one population. Slight methodological differences between the populations may have a dilution effect on the ecological comparisons. Also, for the sake of simplicity, the standard errors were not taken into account in the analysis.⁴⁰

The highest stroke attack and incidence rates were found in Finland, in Lithuania, and in Novosibirsk, Russia. However, the variation in the proportion of individuals with different risk factors explained only a few of the differences in incidence and attack rates among the populations. In multiple regression analyses, no significant correlation of the attack rate and risk factors in men was found, but in women the variation in these risk factors explained 42% of the variation in stroke attack rate.

In an ecological study from the WHO MONICA Project, the association between the three risk factors (smoking, elevated blood pressure, and elevated cholesterol) and routinely available mortality statistics was investigated.³⁴ In the multiple regression model the percentage explained was 39% in men and 35% in women. In univariate analyses, hypertension showed a strong association with mortality, whereas cholesterol in men and cholesterol and smoking in women correlated negatively.³⁴ In a recent study comparing the MONICA populations around the Baltic Sea, we found a high correlation, in both men and women, between stroke attack rates and the proportion with elevated blood pressure in the population.³⁶

There could be several reasons why correlations between risk factors and stroke attack rates in the present analyses were weaker than in previously observed correlations between risk factors and stroke mortality³⁴ and also weaker than the correlations found when comparing populations in neighboring countries.³⁶ Stewart et al included a total of 50 populations in the analyses³⁴ ; the present analyses are based on only 18 populations and have therefore less statistical power. Non-independence between some of the populations, for example through having similar levels of possible unmeasured risk factors, could have reduced the statistical power of the study. However, as evident from Figs 2 and 3, there are no close groups of geographically or culturally similar populations. Therefore, the influence of such dependencies is probably limited in the multiple regression analyses.

The fact that elevated total cholesterol levels do not correlate with stroke attack rates has been shown by others,¹⁵ and other findings indicate that the association is different in different types of stroke.^{16 18 41} In this study we chose to exclude cholesterol from the multivariate analyses since the results did not change at all when we included the proportion with elevated cholesterol into the models. Smoking seems to be of less importance as a risk factor for stroke than for myocardial infarction. In the present ecological study there was no correlation in men and a negative univariate correlation in women between stroke attack rate and the population prevalence of smoking. In the univariate analysis, however, the strong association between blood pressure and stroke may be a confounding factor that could explain the negative correlation between smoking and stroke in women. For example, in women in Beijing the blood pressure level is high and smokers are rare, whereas in Glostrup the blood pressure level is the lowest and the proportion of smokers is the highest.

Other risk factors, which were not measured in our study, influence stroke incidence, in at least some of the populations. Diabetes, which has been shown to be a significant risk factor for stroke,^{42 43 44} has a varying prevalence between populations, but glucose tolerance testing was not available in the MONICA Project. We can only speculate about the role of genetic and other environmental factors, such as socioeconomic circumstances, diet (in particular antioxidants, salt, and saturated fat), physical activity, and climatic factors, in the variation of the stroke incidence among the populations.

Hypertension, smoking, and elevated cholesterol levels are known risk factors for stroke and other cardiovascular diseases, and alterations in these factors could vary over time in different populations. It is also unknown how long the time lag from the change in an exposure to a risk factor is required until the risk of stroke changes. This may be another reason for the lack of correlations among heterogeneous populations, and is particularly relevant when risk factor levels in the population undergo rapid changes, such as with increased smoking among women. Although hypertension is the most important risk factor for stroke from a public health point of view, improved antihypertensive treatment has been suggested to explain only between 16% and 25% of the declining mortality in stroke in the United States.⁴⁵ In Finland, the change in blood pressure is associated with the change in incidence of stroke in the community. Thus, less intensive antihypertensive drug therapy in the early 1980s was associated with leveling off of the decline in stroke mortality.^{9 46} In a recent study from the former East Germany, stroke incidence was shown to be increased,⁴⁷ and the population blood pressure levels have been increasing.⁴⁸ Within the MONICA Project there was also a large variation in the proportion of hypertensive subjects in the population surveys who were inadequately treated or not treated with drugs at all for hypertension. Further efforts to improve hypertension control are warranted in almost all the populations. This requires both more effective drug treatment and non-pharmacological measures to prevent and control high blood pressure at the population level.

	Acknowledgments

 
MONICA Centres are funded predominantly by regional and national governments, research councils, and research charities. Coordination is the responsibility of the World Health Organization (WHO), assisted by local fund raising for congresses and workshops. WHO also supports the MONICA Data Centre (MDC) in Helsinki. Not covered by this general description is the ongoing generous support of the MDC by the National Public Health Institute of Finland and a contribution to the WHO from the National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md, USA, for support of the MDC and the Quality Control Centre for Event Registration in Dundee. Likewise appreciated are grants from ASTRA Hässle AB, Sweden, Hoechst AG, Germany, Hoffmann–La Roche AG, Switzerland, and the Institute de Recherches Internationales Servier (IRIS), France, to support data analysis and preparation of publications. The study was also supported by grants from the Swedish Public Health Institute, Västerbotten County Council, the Year 1987 Stroke Fund, and the Joint Committee of the Northern Sweden Health Care Region. The authors would like to thank statistician Hanna Tolonen, MONICA Data Centre, for her valuable assistance.

	Footnotes

 
¹ See the "Appendix" for list of participants.

	Appendix 1

Top Abstract Introduction Subjects and Methods Results Discussion Appendix 1 References

 
Sites and Key Personnel of Contributing MONICA Centres
China. Beijing Heart, Lung, and Blood Vessel Research Institute, Beijing: Wu Zhausu, Wu Yingkai.

Denmark. Glostrup Population Studies, Glostrup University Hospital, Glostrup: M. Schroll, H. Kirkby, S. Henriksen, D. Jeppesen, G. Vincents, P. Thorvaldsen.

Finland. National Public Health Institute, Helsinki: J. Tuomilehto, P. Puska, J. Torppa, C. Sarti, T. Nuottimäki, V. Salomaa, M. Mähönen; Kuopio University Hospital: J. Sivenius; North Karelia Central Hospital: K. Salmi; Turku City Hospital: E.V. Narna, P. Immonen-Räihä; Loimaa District Hospital: E. Kaarsalo.

Germany. Centre for Epidemiology and Health Research, Berlin: L. Heinemann, D. Eisenblätter, W. Barth, A. Assmann, E. Classen, H. Schaedlich.

Italy. Institute of Cardiology, Regional Hospital, Udine: D. Vanuzzo, L. Pilotto, G. Cignacco, R. Marini, G. Zilio.

Lithuania. Kaunas Medical Academy, Institute of Cardiology, Kaunas: J. Bluzhas, D. Rasenyté.

Poland. National Institute of Cardiology, Department of Cardiovascular Epidemiology and Prevention, Warsaw: S.L. Rywick, M. Polakowska, G. Broda, B. Jasinski.

Russian Federation. National Research Centre of Preventive Medicine, Moscow: T. Varlamova; Institute of Internal Medicine, Academy of Medical Sciences, Novosibirsk: Y. Nikitin, V. Feigin, S. Malyutina, T. Vinogradova.

Sweden. Preventive Cardiology Unit, Östra Hospital, Göteborg: L. Wilhelmsen, P. Harmsen; Department of Medicine, Kalix Hospital, Kalix: F. Huhtasaari, V. Lundberg; Department of Medicine, University Hospital, Umeå: K. Asplund, B. Stegmayr.

Yugoslavia. Novi Sad Health Centre, Novi Sad: M. Planojevic, D. Jacovlj.

MONICA Management Centre. World Health Organization, Geneva: I. Gyarfas, I. Martin, M.-J. Watson.

MONICA Stroke Advisory Group. K. Asplund, R. Bonita, D. Eisenblätter, S. Hatano, M. Schroll, H. Tunstall-Pedoe, J. Tuomilehto, P.O. Wester, Wu Zhaosu.

MONICA Data Centre. National Health Institute, Helsinki, Finland: K. Kuulasmaa, J. Tuomilehto, A.-M. Rajakangas, E. Ruokokoski.

Received February 18, 1997; revision received April 28, 1997; accepted April 29, 1997.

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