Psychosomatic Medicine Tips for Better Browsing
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF)
Right arrow A correction has been published
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Kivimäki, M.
Right arrow Articles by Pulkkinen, L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kivimäki, M.
Right arrow Articles by Pulkkinen, L.
Related Collections
Right arrow Blood Pressure
Right arrow Social Class
Right arrow Other Epidemiology
Psychosomatic Medicine 66:184-189 (2004)
© 2004 American Psychosomatic Society

ORIGINAL ARTICLES

Contribution of Early and Adult Factors to Socioeconomic Variation in Blood Pressure: Thirty-Four–Year Follow-up Study of School Children

Mika Kivimäki, PhD, Marja-Liisa Kinnunen, MD, Tuuli Pitkänen, MA, Jussi Vahtera, MD, PhD, Marko Elovainio, PhD and Lea Pulkkinen, PhD

From the University of Helsinki, Helsinki, Finland (M.K., M.E.); the University of Jyväskylä, Jyväskylä, Finland (M.-L.K., T.P., L.P.); and the Finnish Institute of Occupational Health, Helsinki, Finland (M.K., J.V.).

Address reprint requests to: Mika Kivimäki, Department of Psychology, Finnish Institute of Occupational Health, Topeliuksenkatu 41 aA, FIN-00250 Helsinki, Finland. Email: mika.kivimaki{at}ttl.fi


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 ACKNOWLEDGMENTS
 REFERENCES
 
OBJECTIVE: To prospectively examine the role of childhood and adulthood factors in the association between socioeconomic status (SES) and adult systolic and diastolic blood pressure (SBP, DBP).

METHODS: One hundred and five boys and 101 girls who were 8 years of age at entry into the study were observed for 34 years in the Jyväskylä Longitudinal Study of Personality and Social Development, Finland. Data were gathered on educational attainment and occupational status, as indicators of SES, and potential explanatory factors related to 0, (14), 27, 36, and 42 years of age. SBP and DBP were assessed at 15 and 42 years of age.

RESULTS: In a structural equation model adjusted for sex and childhood SBP, educational attainment was inversely associated with adult SBP (structural coefficient -0.17, p< .05). Incorporating the effects of parental SES and adult body mass index into the model attenuated this association so that it was no longer significant. Variation in birth weight, unemployment, smoking, alcohol consumption, and use of antihypertensive medication had marginal or no impact on the education–SBP association. No socioeconomic variation was found for DBP or occupational status.

CONCLUSIONS: Prospective evidence suggests a weak association between low educational attainment and development of high SBP. Parental SES and adult BMI were the key explanatory factors for this association.

Key Words: socioeconomic factors, • hypertension, • blood pressure, • life course, • body mass index, • health risk behaviors.

Abbreviations: BMI = body mass index;; BP = blood pressure;; DBP = diastolic blood pressure;; GFI = goodness-of-fit index;; JYLS = Jyväskylä Longitudinal Study of Personality and Social Development;; SBP = systolic blood pressure;; SES = socioeconomic status;; SRMR = root mean squared residual.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 ACKNOWLEDGMENTS
 REFERENCES
 
Research suggests an association between low socioeconomic status (SES) and high blood pressure (BP) although this association is not as consistent or as strong as that between SES and cardiovascular disease (1). Multiple factors may contribute to socioeconomic differences in BP. Low SES has been shown to be associated with unemployment and other adverse life conditions that may induce chronic stress (2–4), a risk factor for cardiovascular problems (5–8). Studies on smoking, alcohol consumption, and body mass index (BMI) report that lifestyles increasing the risk to high BP are more common among people with low SES (3, 9–12). In contrast, diagnostic and treatment services for high BP may be more accessible to people with high SES (13–15).

Early factors, such as birth weight and parental SES, may also be important in explaining socioeconomic differences in health (3, 16). Low birth weight and parents’ low socioeconomic position reflect increased likelihood of poor nutrition, parental smoking, and childhood illnesses, and they have been found to be associated with high BP (17–19).

Research on the relationship between SES and BP has progressed, but the evidence relies mainly on information about adult populations (20–23). The few prospective studies that have started from childhood have examined predictors of adult BP rather than explanatory factors for the SES-BP relationship in adulthood (24, 25). The ongoing Jyväskylä Longitudinal Study of Personality and Social Development (JYLS) provided an opportunity to overcome some of these limitations. The JYLS study, a 34-year follow-up of school children, focuses on potential explanatory factors for the SES-BP relationship. Thus, we examined the extent to which birth weight, parental SES, adulthood lifestyle, and BMI explained the association between SES and adult BP.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 CONCLUSIONS
 ACKNOWLEDGMENTS
 REFERENCES
 
Participants
The original sample of the JYLS study comprised 369 second-grade 8-year-old pupils from 12 normal elementary school classes in the town of Jyväskylä, Finland, in 1968 (26). BP was measured at 15 and 42 years of age. The participants were interviewed and completed questionnaires on SES and risk factors of high BP at 27, 36, and 42 years of age. We obtained complete data on SES and BP for 105 males and 101 females (56% of the surviving cohort members; 6 subjects had died). These participants formed the racially homogenous cohort of the present study. The Ethics Committee of the Central Finland Health Care District approved the JYLS study.

Socioeconomic Status and Blood Pressure in Adulthood
Information on educational attainment (1 = low, 6 = high) and occupational status (1 = blue collar, 2 = lower-grade white collar, 3 = higher-grade white collar), representing indicators of SES in adulthood, were requested from the participants in the survey at 42 years of age. BP was measured in a physical examination of the participants at 42 years of age with the use of a standard mercury sphygmomanometer (Mercurius SK Stator). A registered nurse measured BP from one hand after the participant had been seated quietly for at least 5 minutes. The first and fifth Korotkoff sounds were recorded as SBP and DBP, respectively. For each participant, 3 BP measurements were recorded during the visit. We used the average of the second and third measurements in the analysis.

Early and Adulthood Factors
Sex, birth weight, childhood SBP and DBP, and parental SES comprise the variables referred to as early factors, whereas unemployment, smoking, alcohol consumption, BMI, and antihypertensive medication are termed adulthood factors. We treated sex and childhood BP as covariates, and the other variables as potential explanatory factors for the SES-BP relationship in adulthood.

Birth weight was obtained from the records of child welfare clinics, and the participants’ SBP and DBP at 15 (± 3) years of age were furnished by school health services. These records were linked to the data using the personal identification number assigned to each Finnish citizen. Parental SES (1 = blue collar, 2 = lower-grade white collar, 3 = higher-grade white collar) was derived from responses to questions about father’s and mother’s job titles in participants’ interview at 27 years of age. Information about the parent with the higher socioeconomic status was used in the analyses. This variable strongly correlated with parental SES measured at 14 years of age (Pearson r= 0.90, p< .001, N= 96).

Using information from the participant’s interviews and surveys at 27, 36, and 42 years of age, we determined the number of years of unemployment between 27 and 42 years of age (27, 28) , smoking status at 42 years of age (0 = nonsmoker, 1 = ex-smoker, 2 = current smoker), and alcohol consumption at 42 years of age (0 = no or moderate consumption, 0 to 10,000 g of absolute alcohol per year in men and 0 to 7000 g for the women; 1 = risky consumption, 10,001 to 15,000 g for men and 7000 to 10,000 g for the women; 2 = heavy consumption, > 15,000 g for the men and > 10,000 g for the women) (29). During the medical examination at 42 years of age, physical measurements of height and weight were obtained to calculate the BMI, and the participants were asked whether they were being treated with antihypertensive medication (0 = no, 1 = yes).

Statistical Analysis
All of the study measures were treated as continuous variables. The data analysis included the following 3 parts: 1) an assessment of bivariate relationships between early factors, SES, and adult BP; 2) an assessment of bivariate relationships between adulthood factors, SES, and adult BP; and 3) tests of explanatory factors for the SES-BP relationship in adulthood.

In parts 1 and 2, bivariate relationships were studied with Pearson correlation coefficients in a sample combining the men and women. Since some variables may be gender sensitive, we also tested sex interactions in these relationships using regression models with cross-product terms, as suggested by Cohen and Cohen (30). Statistically significant sex interactions were illustrated by reporting correlations separately for the men and women.

In part 3, multiple linear regression models were used to identify early and adulthood explanatory factors for the association between indicators of SES and the development of BP. This association was first adjusted for sex and childhood BP to control for effects related to sex and base-line variation in BP. Then additional adjustments were made separately for early and adulthood factors. To evaluate the contribution of each explanatory factor, we compared the estimates of the association between SES and BP before and after adjustment for the factor. Greater attenuation in the estimate indicates greater explanatory power of the adjusted factor (31).

Using the findings of the multiple regression analyses, we formed a hypothetical model regarding the specific effects of explanatory factors on the SES-BP relationship and tested the model using structural equation modeling. Again we compared estimates of the SES-BP relationship between models without and with explanatory factors. The fit of a structural model to the data were judged acceptable if the p-value of the {chi}-square test was > 0.05, the GFI > 0.90, and the SRMR < 0.05.

All the analyses were conducted with the SAS 8.12 statistical program package (SAS Institute, Cary, NC, USA), except structural equation modeling, for which LISREL 8.54 software was used (32). Data analyses were performed using maximum sample size except in multiple regression analyses, which were based on a cohort with complete data (N= 166 after list-wise deletion) to ensure comparability between models. We replicated all the other analyses with this cohort, but the results remained very similar and thus are not reported.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 CONCLUSIONS
 ACKNOWLEDGMENTS
 REFERENCES
 
Table 1 presents sample sizes, means, and standard deviations for study variables. There were missing values for parental SES and adult smoking, but for other explanatory variables the data were (almost) complete. The level of occupational status was higher for the participants than for their parents. Mean SBP/DBP in adulthood was 136/87 mm Hg in men and 126/81 mm Hg in women.


View this table:
[in this window]
[in a new window]
  		TABLE 1. Distributions, Means and Standard Deviations (SD) of the Study Variables.
 
Early Predictors of Socioeconomic Status and Blood Pressure
Table 2 presents bivariate correlations between early factors, SES, and adult BP. In the overall data set, female sex and lower parental SES predicted lower educational attainment and lower occupational status. The test for sex interactions showed that the correlation between parental SES and adult occupational status was stronger among the men (r= 0.34, p< .01) than among the women (r= 0.15, ns)(p for sex interaction < 0.05). Birth weight was not predictive of educational attainment. However, lower birth weight correlated with lower occupational status among the women (r= 0.32, p< .01)(corresponding r= -0.08, ns, among the men; p for sex interaction < .05).


View this table:
[in this window]
[in a new window]
  		TABLE 2. Correlation Coefficients Between Early Factors, Indicators of SES, and Adult BP (n = 184 to 206)
 
Male sex, lower parental SES, and higher childhood BP predicted higher adult BP (Table 2). The correlation between childhood BP and adult BP was stronger for the women (r= 0.42, p< .001 for adult SBP and r= 0.35, p< .001 for adult DBP) than for the men (r= 0.17 and r= 0.02, ns). Birth weight did not correlate with adult BP.

Adulthood Factors, Socioeconomic Status, and Blood Pressure
Table 3 presents the correlations between adulthood factors, SES, and adult BP. Nonsmoking, lower alcohol consumption, and lower BMI were correlated with higher educational attainment and higher occupational status. The correlation between unemployment and low educational attainment was significant for the women (r= -0.34, p< .001) but not for the men (r= 0.02, ns)(p for sex interaction < 0.05). Unemployment correlated with low occupational status among both sexes.


View this table:
[in this window]
[in a new window]
  		TABLE 3. Correlation Coefficients Between Adulthood Factors, Indicators of SES, and Adult BP (n = 193 to 206)
 
Higher BMI and use of antihypertensive medication correlated with higher SBP and DBP. Higher alcohol consumption also correlated with higher DBP.

Tests of Explanatory Factors
The correlation between educational attainment and adult SBP was -0.20 (p< .01), and that between occupational status and adult SBP -0.08 (ns). The corresponding correlations for adult DBP were -0.16 (p< .05) and -0.07 (ns), respectively. No sex interactions were found for these relationships. After adjustment for sex and childhood BP, only the relationship between educational attainment and SBP remained significant. Tests of explanatory factors therefore involved this relationship.

Table 4 illustrates the relationship between education and SBP after adjustment for sex and childhood SBP, and the effect of additional adjustments for potential explanatory factors. Adjustment for parental SES and adult BMI attenuated the relationship by 36% to a nonsignificant level. Thus these factors may partially explain the SES–SBP relationship. Adjustment for other factors had little effect on this relationship.


View this table:
[in this window]
[in a new window]
  		TABLE 4. Multiple Regression Analyses of Educational Attainment on SBP
 
Tests to specify the explanatory role of parental SES and adult BMI are illustrated in Figure 1. In the structural equation models, the relationship between education and SBP was adjusted for sex and SBP in childhood. The inverse relationship between education and SBP was statistically significant before (Model 1) but not after (Model 2) the inclusion of parental SES and BMI in the equation. The attenuation of the structural estimate of the relationship was 29%, a finding in line with that of the multiple regression analyses. Lower parental SES predicted lower educational attainment, which, in turn, was associated with an increased BMI, a predictor of higher BP. The fit of the structural equation models with the data were satisfactory (Model 1: {chi}-square = 1.41, df= 1, p= .23, GFI = 1.00, SRMR = 0.03; Model 2: {chi}-square = 5.12, df= 3, p= .16, GFI = 0.99, SRMR = 0.03).



View larger version (27K):
[in this window]
[in a new window]
  		Fig. 1. Structural equation models of the relationship between educational attainment and adult systolic blood pressure (N= 184). Model 1 represents this relationship before incorporating explanatory factors. Model 2 includes explanatory factors. (Solid lines refer to significant paths (p< .05) and dashed lines refer to nonsignificant paths.)

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
CONCLUSIONS
 ACKNOWLEDGMENTS
 REFERENCES
 
It is a major public health challenge to reduce socioeconomic differences in health (33). Chronic high BP has public health relevance because of its associations with cardiovascular and renal diseases (34–37). However, relatively little has been known about the determinants of the socioeconomic differences in BP (38). This study suggests that parental SES and adult BMI are important explanatory factors for the inverse relationship between adulthood SES and BP. Our evidence on Finnish school children is based on an exceptionally long follow-up, measurement of BP both in childhood and adulthood, and characterization of adult SES using more than one indicator.

People do not randomly achieve different levels of SES. Although there was an overall increase in the occupational status of the participants when compared with that of their parents, the likelihood of high educational attainment and high occupational status was greater for those whose parents had a high SES, especially for the men. Our findings accord with previous cross-sectional and prospective evidence on intergenerational trends in social position (3, 39). Low birth weight (a correlate of low parental SES) predicted a lower level of SES in adulthood, but only among women and in relation to 1 of the 2 indicators of SES. Thus, birth weight may not be as important a determinant of SES as parental SES is.

Adult SES and lifestyle were inter-related. In line with other studies (3, 9, 10, 12), people with high occupational status and, in particular, high education attainment were less likely to smoke, drink excess alcohol, and be obese than their counterparts with low SES. Causal relationships among these factors are likely to be complex and the observed associations may partially stem from early factors, such as shared lifestyle between parents and children (40, 41).

There was a weak inverse relationship between SES and BP. Higher educational attainment was associated with lower SBP, but the associations involving occupational status or DBP did not reach statistical significance. Stronger links with lifestyle risk factors may partially explain the greater BP differences between educational levels than between occupational statuses. Education is also less open to change than other SES indicators are, and it has been shown to be a stronger correlate of atherosclerosis than occupation and income in Finnish samples (42, 43).

The weak association between SES and BP in our study is in line with the findings of prior research (38). For example, the Whitehall study found nearly threefold gradient in prevalence of coronary heart disease and stroke, but the difference in systolic BP was no more than 3 to 5 mm Hg between the highest and lowest employment grade (44, 45). In the INTERSALT study, an inverse association between years of education and BP was found for men in 28 of 47 populations and for women in 38 of the same 47 populations (21). The US HANES III study showed no association between SES and BP (46, 47).

The results of our structural equation modeling suggest that the links between parental SES, education, and adult body mass are important in understanding socioeconomic differences in BP. Lower parental SES predicted lower educational attainment, which, in turn, was associated with increased BMI, a predictor of higher BP. Previous research consistently shows a positive relationship between body weight and BP (48–51). In the cross-sectional INTERSALT study, the explanatory factors for the SES-BP relationship included both BMI and the intake of alcohol, potassium, and salt (21). Our finding that parental SES may also be an explanatory factor extends prior knowledge on the socioeconomic differences in adult BP.

In our study, there were only marginal attenuations in the education-BP relationship after adjustment for birth weight and unemployment. This finding suggests that the SES gradient in BP may not be accounted for by these factors. Use of antihypertensive medication also did not explain the relationship between SES and BP, a result corresponding with those of some earlier studies with shorter follow-up periods (13).

Limitations
Our results should be interpreted in light of some limitations of the study. The drawbacks include the relatively small and homogenous sample (all white participants) and the restricted follow-up period (ie, the participants had only reached their early 40’s and thus the effect of normal aging on BP trajectories was possibly constrained). Moreover, 44% of the original cohort was lost during the follow-up; the primary factor responsible for the sample attrition was missing BP recordings. Future research with more diverse and larger samples is needed to evaluate the generalizability of our findings and to more closely explore sex differences in explanatory factors for the SES-BP relationship.

We measured base-line BP at 15 years of age, a measurement that may reflect both genetic and environmental influences (eg, through childhood BMI). Lack of information about the reliability of the instruments used to assess base-line BP is a potential weakness of this study. However, the time 1-time 2 correlation for SBP from childhood to adulthood during the 34-year follow-up was r= 0.33; this result corresponds to earlier findings of standardized BP assessments. For example, the tracking correlation of high BP for 337 school children from East Boston, Massachusetts, was 0.55 with an 8- to 12-year measurement interval (25). Several studies suggest that ambulatory BP measures are superior to casual measures as predictors of cardiovascular morbidity and mortality (52, 53). It would be beneficial to repeat the current assessments with portable BP monitors and multiple readings to better control for "white coat hypertension" and the nuisance effect of within-person variability in BP (25).


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
ACKNOWLEDGMENTS
 REFERENCES
 
Longitudinal data from a 34-year follow-up of Finnish children suggest that factors acting across life stages contribute to socioeconomic differences in BP. Parental SES, as a predictor of educational attainment, and adult BMI were key factors in explaining the inverse SES-BP relationship. Further studies are needed to confirm the generalizability of these findings.


   ACKNOWLEDGMENTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 ACKNOWLEDGMENTS
REFERENCES
 
This research was supported by the Academy of Finland in the Finnish Center of Excellence Program, 2000–2002 (Project no. 44858). Mika Kivimäki and Jussi Vahtera were supported by grants from the Academy of Finland (Projects no. 105195 and 77560).

Received for publication June 24, 2003.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 ACKNOWLEDGMENTS
 REFERENCES
 

  1. Colhoun HM, Hemingway H, Poulter NR. Socioeconomic status and blood pressure: an overview analysis. J Hum Hypertension 1998; 12: 91–110.[CrossRef][Medline]
  2. Marmot MG, Bosma H, Hemingway H, Brunner E, Stansfeld S. Contribution of job control and other risk factors to social variations in coronary heart disease incidence. Lancet 1997; 350: 235–9.[CrossRef][Medline]
  3. Power C, Matthews S. Origins of health inequalities in a national population sample. Lancet 1997; 350: 1584–9.[CrossRef][Medline]
  4. Power C, Matthews S, Manor O. Inequalities in self-rated health: explanations from different stages of life. Lancet 1998; 351: 1009–14.[CrossRef][Medline]
  5. Schnall PL, Schwartz JE, Landsbergis PA, Warren K, Pickering TG. A longitudinal study of job strain and ambulatory blood pressure: results from a three-year follow-up. Psychosom Med 1998; 60: 697–706.[Abstract/Free Full Text]
  6. McEwen BS. Protective and damaging effects of stress mediators. N Engl J Med 1998; 338: 171–9.[Free Full Text]
  7. Levenstein S, Smith MW, Kaplan GA. Psychosocial predictors of hypertension in men and women. Arch Intern Med 2001; 161: 1341–6.[Abstract/Free Full Text]
  8. Kivimäki M, Leino-Arjas P, Luukkonen R, Riihimäki H, Vahtera J, Kirjonen J. Work stress and risk of cardiovascular mortality: Prospective cohort study of industrial employees. BMJ 2002; 325: 857–60.[Abstract/Free Full Text]
  9. Lynch JW, Kaplan GA, Salonen JT. Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse. Soc Sci Med 1997; 44: 809–19.
  10. Lantz PM, House JS, Lepkowski JM, Williams DR, Mero RP, Chen J. Socioeconomic factors, health behaviors, and mortality: results from a nationally representative prospective study of US adults. JAMA 1998; 279: 1703–8.[Abstract/Free Full Text]
  11. Dyer AR, Liu K, Walsh M, Kiefe C, Jacobs DR, Bild DE. Ten-year incidence of elevated blood pressure and its predictors: The CARDIA study. J Hum Hypertension 1999; 13: 13–21.[CrossRef][Medline]
  12. Poston WS 2nd, Foreyt JP. Obesity is an environmental issue. Atherosclerosis 1999; 146: 201–9.[CrossRef][Medline]
  13. Hypertension Detection and Follow-up Program Cooperative Group. Educational level and 5-year all-cause mortality in the hypertension detection ad follow-up program. Hypertension 1987; 9: 641–46.[Abstract/Free Full Text]
  14. Bunker JP. Medicine matters after all. J R Coll Physicians Lond 1995; 29: 105–12.[Medline]
  15. Goddard M, Smith P. Equity of access to health care. Centre for Health Economics Occasional Papers York, England: University of York 1998.
  16. Wadsworth MEJ. Health inequalities in the life course perspective. Soc Sci Med 1997; 44: 859–69.
  17. Wamala SP, Lynch JW, Kaplan GA. Women’s exposure to early and later life socioeconomic disadvantage and coronary heart disease risk. Int J Epidemiol 2001; 30: 275–84.[Abstract/Free Full Text]
  18. Matthews KA, Kiefe CI, Lewis CE, Liu K, Sidney S, Yunis C. Socioeconomic trajectories and incident hypertension in a biracial cohort of young adults. Hypertension 2002; 39: 772–76.[Abstract/Free Full Text]
  19. Lackland DT, Egan BM, Ferguson PL. Low birth weight as a risk factor for hypertension. J Clin Hypertension 2003; 5: 133–36.
  20. Elford J, Whincu P, Shaper AG. Early life experience and adult cardiovascular disease: longitudinal and case-control studies. Int J Epidemiol 1991; 20: 833–44.[Abstract/Free Full Text]
  21. Stamler R, Shipley M, Elliott P, Dyer A, Sans S, Stamler J. Higher blood pressure in adults with less education. Some explanations from INTERSALT. Hypertension 1992; 19: 237–41.[Abstract/Free Full Text]
  22. Gliksman MD, Kawaschi I, Hunter D, et al. Childhood socio-economic status and risk of cardiovascular disease in middle aged US women: a prospective study. J Epidemiol Community Health 1995; 49: 10–15.[Abstract]
  23. Marmot MG, Shipley MJ, Brunner E, Hemingway H. Relative contribution of early life and adult socioeconomic factors to adult morbidity in the Whitehall II study. J Epidemiol Community Health 2001; 55: 301–7.[Abstract/Free Full Text]
  24. Lauer RM, Clarke WR. Childhood risk factors for high adult blood pressure: the Muscatine Study. Pediatrics 1989; 79: 1–25.
  25. Gillman MW, Cook NR, Rosner B, Evans DA, Keough ME, Taylor JO, Hennekens CH. Identifying children at high risk for the development of essential hypertension. J Pediatr 1993; 122: 837–46.[Medline]
  26. Pulkkinen L. Self-control and continuity from childhood to late adolescence. In: Baltes PB, Brin Jr OG, editors. Life-span development and behavior. Orlando (FL): Academic Press; p. 43–105.
  27. Kokko K, Pulkkinen L. Aggression in childhood and long-term unemployment in adulthood: a cycle of maladaptation and some protective factors. Dev Psychol 2000; 36: 463–72.[CrossRef][Medline]
  28. Kivimäki M, Elovainio M, Kokko K, Pulkkinen L, Kortteinen M, Tuomikoski H. Hostility, unemployment and health status: Testing three theoretical models. Soc Sci Med 2003; 56: 2139–52.
  29. Sillanaukee P, Kiianmaa K, Roine R, Seppä K. Criteria for heavy alcohol consumption (in Finnish). Finnish Med J 1992; 47: 2919–21.
  30. Cohen J, Cohen P. Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale (NJ): Lawrence Erlbaum; 1983.
  31. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986; 51: 1173–82.[CrossRef][Medline]
  32. Jörenskog K, Sörbom D. LISREL 8: user’s reference guide. Chicago (IL): Scientific Software International; 1996.
  33. Adler NE, Marmot M, McEwen BS, Stewart J (eds.) Socioeconomic status and health in industrialized nations. Annals of the New York Academy of Science 896. New York: New York Academy of Sciences; 1999.
  34. Kannel WB, Belanger AJ. Epidemiology of heart failure. Am Heart J 1991; 121: 951–57.[CrossRef][Medline]
  35. Klag MJ, Whelton PK, Randall BL, Neaton JD, Brancati FL, Ford CE, Shulman NB, Stamler J. Blood pressure and end-stage renal disease in men. N Engl J Med 1996; 334: 13–18.[Abstract/Free Full Text]
  36. MacMahon S, Peto R, Cutler J, Collins R, Sorlie P, Neaton J, Abbott R, Godwin J, Dyer A, Stamler J. Blood pressure, stroke, and coronary heart disease, I: Prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. Lancet 2000; 335: 765–74.
  37. McCarron P, Smith GD, Okasha M McEwen J. Blood pressure in young adulthood and mortality form cardiovascular disease. Lancet 2000; 355: 1430–31.[CrossRef][Medline]
  38. Pickering T. Cardiovascular pathways: socioeconomic status and stress effects on hypertension and cardiovascular function. Ann NY Acad Sci 1999; 896: 262–77.[Abstract/Free Full Text]
  39. Marmot M, Shipley M, Brunner E, Hemingway H. Relative contribution of early life and adult socioeconomic factors to adult morbidity in the Whitehall II study. J Epidemiol Community Health 2001; 55: 301–7.
  40. Huurre T, Aro H, Rahkonen O. Well-being and health behaviour by parental socioeconomic status: a follow-up study of adolescents aged 16 until age 32 years. Soc Psychiatry Psychiatr Epidemiol 2003; 38: 249–55.[CrossRef][Medline]
  41. Soteriades ES, DiFranza JR. Parent’s socio-economic status, adolescents’ disposable income, and adolescents’ smoking status in Massachusetts. Am J Public Health 2003; 93: 1155–60.[Abstract/Free Full Text]
  42. Lynch J, Kaplan GA, Salonen R, Cohen RD, Salonen JT. Socioeconomic status and carotid atherosclerosis. Circulation 1995; 92: 1786–92.[Abstract/Free Full Text]
  43. Helkama K. Cultural variation in social structure (in Finnish). Sosiologia 1998; 22: 247–60.
  44. Marmot MG, Shipley MJ, Rose G. Inequalities in death-specific explanations of a general pattern? Lancet 1984; 1: 1003–6.[CrossRef][Medline]
  45. Ferrie JE, Shipley MJ, Davey Smith G, Stansfeld SA, Marmot MG. Change in health inequalities among British civil servants: the Whitehall II study. J Epidemiol Community Health 2002; 56: 922–26.[Abstract/Free Full Text]
  46. National Heart, Lung and Blood Institute. Chartbook of U.S. national data on socioeconomic status and cardiovascular disease. Unpublished manuscript reviewed by Schwartz et al. 1996.
  47. Schwartz JE, Pickering TG, Landsbergis PA. Work-related stress and blood pressure: current theoretical models and considerations from a behavioral medicine perspective. J Occup Health Psychol 1996; 1: 287–310.[CrossRef][Medline]
  48. Trials of Hypertension Prevention Collaboration Research Group. The effects of nonpharmacologic interventions on blood pressure of persons with high normal levels: results of the Trials of Hypertension Prevention Phase I. JAMA 1992; 267: 1213–20.[Abstract]
  49. Trials of Hypertension Prevention Collaboration Research Group. Effects of weight loss and sodium reduction intervention on blood pressure of hypertension incidence in overweight people with high-normal blood pressure: the Trials of Hypertension Prevention, Phase II. Arch Intern Med 1997; 157: 657–67.[Abstract]
  50. He J, Whelton PK, Appel LJ, Charleston J, Klag MJ. Long-term effects of weight loss and dietary sodium reduction on incidence of hypertension. Hypertension 2000; 35: 544–49.[Abstract/Free Full Text]
  51. Lang T, Gaudemaris R, Chatellier G, Hamici L, Diene E, for the Epidemiology Group of the Society Francaise d’Hypertension Arterielle and the IHPAF Working Physicians Group. Prevalence and therapeutic control of hypertension in 30 000 subjects in the workplace. Hypertension 2001; 38: 449–54.[Abstract/Free Full Text]
  52. Staessen JA, Thijs L, Fagard R, O’Brien ET, Clement D, de Leeuw PW, Mancia G, Nachev C, Palatini P, Parati G, Tuomilehto J, Webster J. Predicting cardiovascular risk using conventional vs ambulatory blood pressure in older patients with systolic hypertension. JAMA 1999; 282: 539–46.[Abstract/Free Full Text]
  53. Verdecchia P. Prognostic value of ambulatory blood pressure: current evidence and clinical implications. Hypertension 2000; 35: 844–51.[Abstract/Free Full Text]



This article has been cited by other articles:


Home page
 HypertensionHome page
X. Chen, Y. Wang, L. J. Appel, and J. Mi
Impacts of Measurement Protocols on Blood Pressure Tracking From Childhood Into Adulthood: A Metaregression Analysis
Hypertension, March 1, 2008; 51(3): 642 - 649.
[Abstract] [Full Text] [PDF]

Home page
 HeartHome page
M Kivimaki, G Davey Smith, M Juonala, J E Ferrie, L Keltikangas-Jarvinen, M Elovainio, L Pulkki-Raback, J Vahtera, M Leino, J S A Viikari, et al.
Socioeconomic position in childhood and adult cardiovascular risk factors, vascular structure, and function: cardiovascular risk in young Finns study
Heart, April 1, 2006; 92(4): 474 - 480.
[Abstract] [Full Text] [PDF]

Home page
 HypertensionHome page
M. Kivimaki, D. A. Lawlor, G. D. Smith, L. Keltikangas-Jarvinen, M. Elovainio, J. Vahtera, L. Pulkki-Raback, L. Taittonen, J. S.A. Viikari, and O. T. Raitakari
Early Socioeconomic Position and Blood Pressure in Childhood and Adulthood: The Cardiovascular Risk in Young Finns Study
Hypertension, January 1, 2006; 47(1): 39 - 44.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF)
Right arrow A correction has been published
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Kivimäki, M.
Right arrow Articles by Pulkkinen, L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kivimäki, M.
Right arrow Articles by Pulkkinen, L.
Related Collections
Right arrow Blood Pressure
Right arrow Social Class
Right arrow Other Epidemiology

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS