Project e-publications, No. 22
Do trends in population levels of blood pressure and other
cardiovascular risk factors explain trends in stroke event rates? -
methodological appendix to a paper published in Stroke
Hanna Tolonen1, Kari Kuulasmaa1, Kjell
Asplund2, Markku Mähönen1 for the WHO MONICA Project3
1 Department of Epidemiology and Health Promotion,
National Public Health Institute (KTL), Helsinki, Finland
2 Department of Medicine, University Hospital, Umeå, Sweden
3 Annex: Sites and key personnel of the WHO
Correspondence to Hanna Tolonen (firstname.lastname@example.org)
© Copyright World Health Organization (WHO) and the WHO MONICA Project
investigators 2002. All rights reserved.
This document is the methodological appendix to the paper titled "Do trends
in population levels of blood pressure and other cardiovascular risk factors
explain trends in stroke rates? Comparisons of 15 populations in 9 countries
within the WHO MONICA Stroke Project." published in Stroke in 2002 .
- the derivation of the quality score which was used to weight the
populations in the regression analysis;
- a description of the stroke risk score used to summarize the combined
impact of blood pressure, smoking, cholesterol and BMI on the risk of stroke;
- results of the sensitivity analyses referred to in the parent publication.
The overall quality score, which was used for weighting the data in the
regression analyses in reference , has values between
zero and two. If the score is two, no problems were identified in the quality of
the data for a population, whereas a score of zero indicates major concern about
the data quality. The overall quality score was derived from the quality scores
of the individual data components. The values of the overall quality score and
its components are shown in Table A1. The definition
of the overall quality score is included in the following description of the
columns of Table A1:
- Population: The abbreviation of the
population name, as described in Table 1 of reference .
The lower case letter at the end of the abbreviation (a, b etc.) identifies
the exact definition of the population, and has been described in the MONICA
quality assessment reports [2-8].
- Event trends: Quality items related to
the trends in the coronary event rates.
- Dem: Square root of two times the
Summary score of quality of the population demographic data [Table
6 of reference 2]. In the cases where the
population is a combination of several reporting units, the mean of the
scores of the contributing reporting units was taken.
- Event: The Summary score for stroke
event trends quality 
- Event trends score: Geometric mean
of (Dem+0.2)/1.1 and Event. (Note: 0.2 is
added to Dem so that it cannot make the geometric mean zero
regardless of the value of Event).
- Risk factor trends: Quality items related to
the trends in the risk factors. Even though three surveys were considered for
most of the populations, the quality score is based on the initial and final
survey only. The middle survey has a relatively small influence in the
estimates of the linear trends. Furthermore, for the two populations where the
quality of the middle survey was much lower than the quality of the initial
and final survey, the middle survey was not used for estimating the trends in
the risk factors.
- Response rate: Response rates in the MONICA
surveys have been reported using two definitions, A and B, which differ in
the way of counting those people whose eligibility for the survey could
not be assessed because they could not be contacted .
For the quality score, the mean of item response rates A and B for Body
Mass Index (BMI) was used. BMI was chosen because it reflects best the
response rate for the clinical examination. The overall response rate
concerns the questionnaire data, and in some populations only a portion of
those people who were interviewed at home came to the clinical
- Ini: Response rate in the initial
- Fin: Response rate in the final
- Response rate score: 2×Square
root of [1 - (1 - Ini)×(1 - Fin) - (|Ini
- Risk factors
- Smok: This is based on
Section 6 (Recommendations for using MONICA data on smoking
Subsection 6.3 (Trend analyses) of the Quality Assessment of Data
on Smoking Behaviour . Score 0 was given where
the quality assessment report recommends that the data should not be
used for analysis of trends of daily smoking. Score 1.41 (i.e. the
square root of two) was given where there was a change in the relevant
question on smoking but there was evidence that the change did not
affect the trend estimates much. All other populations were given a
score of 2.
- Bp: Quality score for blood
pressure trends (QST) between the initial and the final surveys from
19 of the Quality Assessment of Data on Blood Pressure .
- Chol: Total cholesterol overall
summary score (TCOSS) for the initial and final surveys from
Table 16 of the Quality Assessment of Total Cholesterol
- BMI: This is derived from the
summary score for weight and height measurements for the initial and
the final surveys from
Table 10 of the Quality Assessment of Weight and Height
Measurements . The score is 0 if the summary
score for the initial or final survey was 0. Otherwise the score is
the mean of the scores for the initial and final surveys.
- Risk factors mean: The arithmetic
mean of smok, bp, chol and
- Risk factor trends score: Half of
the product of Response rate score and Risk factors
- Overall quality score: Geometric mean of
Event trends score and Risk factor trends score.
The quality scores for individual risk factors (systolic blood pressure,
daily cigarette smoking, total cholesterol and BMI) also have values between
zero and two, with value two indicating a good quality and value zero indicating
major concern about the data quality. The values of the quality score for each
risk factor and its components are shown in Table A2.
The definition of the quality scores for each risk
factor are otherwise the same as for the overall quality score, except that the
"Risk factors mean" is replaced with the quality score of
the individual risk factor in question.
Stroke risk score was derived from the follow-up of the Finnish MONICA risk
factor surveys conducted in 1982 and 1987. Subjects were 25-64 years old during
the baseline examination. The total number of subjects was 14902 (7195 men and
7707 women). They were followed-up until the end of 1995. By that time there
were a total of 553 fatal and non-fatal stroke cases (ICD = 430-439). The
follow-up procedure has been described elsewhere .
Risk factors used for the stroke risk score were systolic blood pressure
measurement (mmHg), daily smoking (0/1), total cholesterol (mmol/L) and BMI
The risk score was defined as a linear combination of the levels of the risk
factors, where the coefficients were obtained using the Cox-proportional hazards
model . The coefficients are given in Table 1.
Table 1. Coefficients for stroke risk score
|Systolic blood pressure (mmHg)
|Daily smoking (%)
|Total cholesterol (mmol/l)
In the analysis, the coefficients for systolic blood pressure and total
cholesterol of Table 1 were multiplied by 1.5 to compensate the regression
The coefficients of Table 1 are similar to coefficients obtained from other
studies. For example, in the Kaunas-Rotterdam Intervention Study 
the risk ratios for men were: 1.02 for systolic blood pressure, 2.01 for
smoking, 0.97 for total cholesterol and 1.03 for BMI.
The parent publication  refers to sensitivity analyses
which were performed to assess the robustness of the results. The results of the
sensitivity analyses are shown in Table A3a for the
simple regression analysis using systolic blood pressure as the explanatory
variable. Table A3b shows the results by other
individual risk factors (daily smoking, total cholesterol and BMI) as
explanatory variables. Table A3c shows the results
when systolic blood pressure and smoking are used as the explanatory variables
in a multiple regression analysis. The results for the simple regression
analysis using the risk score are given in Table A3d.
In each table, the analysis was repeated:
- for the full and the lagged event registration period;
- with and without quality weighting;
- with and without excluding populations with low quality;
- with and without subarachnoid haeorrhage included in the stroke events;
- using the full age range (35-64 years) and restricting the analysis to the
age group 55-64 years.
- Tolonen H, Mähönen M, Asplund K, Rastenyte D, Kuulasmaa
K, Vanuzzo D, Tuomilehto J, for the WHO MONICA Project. Do trends in
population levels of blood pressure and other cardiovascular risk factors
explain trends in stroke event rates? Comparisons of 15 populations in 9
countries within the WHO MONICA Stroke Project. Stroke 2002;33:2367-2375.
- Moltchanov V, Kuulasmaa K, Torppa J, for the WHO MONICA
Project. Quality assessment of demographic data in the WHO MONICA Project.
(April 1999). Available from: URL:http://www.thl.fi/publications/monica/demoqa/demoqa.htm,
- Mähönen M, Asplund K, Tolonen H and Kuulasmaa K, for
the WHO MONICA Project. Stroke event trend quality score for the WHO MONICA
Project. (2001). Available from: URL:http://www.thl.fi/publications/monica/strokescore/score.htm,
- Wolf H, Kuulasmaa K, Tolonen H, Ruokokoski E, for the
WHO MONICA Project. Participation rates, quality of sampling frames and
sampling fractions in the MONICA surveys. (September 1998). Available from:
- Molarius A, Kuulasmaa K, Evans A, McCrum E, Tolonen H,
for the WHO MONICA Project. Quality assessment of data on smoking behaviour in
the WHO MONICA Project. (February 1999). Available from: URL:http://www.thl.fi/publications/monica/smoking/qa30.htm,
- Kuulasmaa K, Hense HW, Tolonen H, for theWHO MONICA
Project. Quality assessment of data on blood pressure in the WHO MONICA
Project. (May 1998). Available from: URL:http://www.thl.fi/publications/monica/bp/bpqa.htm,
- Ferrario M, Kuulasmaa K, Grafnetter D, Moltchanov V,
for the WHO MONICA Project. Quality assessment of total cholesterol
measurements in the WHO MONICA Project. (April 1999). Available from: URL:http://www.thl.fi/publications/monica/tchol/tcholqa.htm,
- Molarius A, Kuulasmaa K, Sans S, for the WHO MONICA
Project. Quality assessment of weight and height measurements in the WHO
MONICA Project. (May 1998). Available from: URL:http://www.thl.fi/publications/monica/bmi/bmiqa20.htm,
- Jousilahti P, Vartiainen E, Tuomilehto J, Puska P. Sex,
age, cardiovascular risk factors, and coronary heart disease. A prospective
follow-up study of 14 786 middle-aged men and women in Finland. Circulation
- Clayton D & Hill M. Statistical Models in
Epidemiology. New York: Oxford University Press; 1993.
- Clarke R, Shipley M, Lewington S, et al.
Underestimation of risk associations due to regression dilution in long-term
follow-up of prospective studies. Am J Epidemiol 1999;150:341-353.
- MacMahon S, Peto R, Cutler J, et al. Blood pressure,
stroke and coronary heart disease, part 1: prolonged differences in blood
pressure - prospective observational studies corrected for the regression
dilution bias. Lancet 1990;355:765-774.
- Law MR, Wald NJ, Wu T, Hackshaw B, Bailey A.
Systematic underestimation of association between serum cholesterol
concentration and ischaemic heart disease in observational studies: data from
the BAPU study. BMJ 1994;308:363-366.
- Rastenyte D, Tuomilehto J, Domarkiene S, Cepaitis Z &
Reklaitiene R. Risk factors for death from stroke in middle-aged Lithuanian
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We thank Pekka Jousilahti for his help in deriving score risk score from
The 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 WHO from the National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, Maryland, USA for support of the MDC.
The completion of the MONICA Project is generously assisted through a Concerted
Action Grant from the European Community. Likewise appreciated are grants from
ASTRA Hässle AB, Sweden, Hoechst AG, Germany, Hoffmann-La Roche AG, Switzerland,
the Institut de Recherches Internationales Servier (IRIS), France, and Merck &
Co. Inc., New Jersey, USA, to support data analysis and preparation of