Background
The inverse relation of socioeconomic status with incident cardiovascular diseases (CVDs) has been well established. However, few data are available describing this relation among ethnically diverse women with prevalent CVD. Using education as a proxy for socioeconomic status, we examined its relation to CVD mortality among women with established CVD.
Subjects
Data from 2,157 women with CVD at baseline, who participated in nine long-term U.S. cohort studies, were pooled.
Methods
Cox regression models adjusted for history of diabetes mellitus, total cholesterol, systolic and diastolic blood pressure, body mass index, smoking, race/ethnicity, and age at baseline were used to estimate hazard ratios for CVD mortality between non-high school graduates and high school graduates.
Results
During a mean follow-up time of 11.5 years, 615 CVD deaths were observed. There was an age-dependent (p = .003) inverse association between education and CVD mortality among women with CVD. At age 60, the risk of dying due to CVD among non-high school graduates was more than twice greater than that of high school graduates (hazard ratio = 2.34; 95% CI 1.27–4.29). At age 65, the hazard ratio decreased to 1.31 (95% CI 1.00–1.71). By age 70, there was no difference in the hazard of dying between high school graduates and nongraduates (hazard ratio = 1.01; 95% CI .85–1.21).
Conclusions
Our results show that among women with CVD, educational level was a significant, and age-dependent, predictor of fatal CVD independent of other traditional risk factors. These women are an important high-risk population to target secondary prevention and educational efforts.
Introduction
Socioeconomic status (SES) has been shown to be an independent risk factor for incidence of cardiovascular disease (CVD) since 1960 (
Marmot et al 1984
; Lynch et al 1996
; Diez-Roux et al 1995
). Several studies have found an inverse association between SES and CVD mortality among middle-aged adults (Feldman et al 1989
; Mackenbach et al 2000
; Otten and Bosma 1997
; Salomaa et al 2000
; Shkolnikov et al 1998
). Education is one of the most commonly used proxies for SES (Kaplan and Keil 1993
; Kitagawa and Hauser 1973
) because educational level is simple to collect, is associated with a high response rate, is minimally affected by later health status (reverse causation), and remains relatively constant (Kaplan and Keil 1993
; Kitagawa and Hauser 1973
; Bassuk et al 2002
).Many epidemiological studies have demonstrated the inverse association of education with incident CVD mortality including coronary heart disease (CHD) deaths (
Marmot et al 1984
; Lynch et al 1996
; Diez-Roux et al 1995
; Feldman et al 1989
; Mackenbach et al 2000
; Otten and Bosma 1997
; Salomaa et al 2000
; Shkolnikov et al 1998
; Kitagawa and Hauser 1973
; Bassuk et al 2002
; Elo and Preston 1996
; Hemingway et al 2000
; Mustard et al 1997
; Wamala and Orth-Gomer 1998
; Gonzalez et al 1998
; Tenconi et al 2000
; Liu et al 1982
; Antonovsky 1967
). Gonzalez et al 1998
and Mackenbach et al 2000
showed a significant inverse association between education and CVD using internationally pooled data. The inverse association between education and CHD mortality was also shown in middle-aged Italian men (40–69 years; Tenconi et al 2000
) and whites in the United States (40–59 years; Liu et al 1982
). In addition, the protective effect of education on CVD mortality has been demonstrated in large population-based epidemiological studies (Feldman et al 1989
; Otten and Bosma 1997
). In Dutch adults (≥19 years), the protective impact of education on heart disease mortality was more apparent in middle-aged and older people (≥40 years) compared to adults in general (Otten and Bosma 1997
). The inverse association was also shown among U.S. whites aged 45–74 years with a clear dose-response (Feldman et al 1989
). Studies of the relation between education and recurrent CHD are limited.Several potential mechanisms may explain the association between education and CVD mortality. Education is inversely associated with established CVD risk factors such as blood pressure, lipid levels, and obesity (
Feldman et al 1989
; Gran 1995
; Luepker et al 1993
; Garrison et al 1993
; Cirera et al 1998
; Jacobsen and Thelle 1988
; Matthews et al 1989
; Gupta et al 1994
; Pekkanen et al 1995
). In addition, the association between education and CHD may be mediated through psychosocial factors. Psychosocial factors are differentially distributed across education (Hemingway et al 2000
; Wamala et al 1999
). Individuals with low educational levels tend to have jobs with low responsibility and high demand, which may increase stress levels. Release of stress hormones could alter hemodynamics, lipid metabolism, homeostasis, and other factors, increasing susceptibility to CVD (Feldman et al 1989
; Hemingway et al 2000
; Wamala et al 1999
). Lastly, education as a proxy for SES may be related to access to medical care (Feldman et al 1989
).Most studies of educational level and CVD have been limited to populations free of CVD at baseline (
Feldman et al 1989
; Mackenbach et al 2000
; Otten and Bosma 1997
; Salomaa et al 2000
; Shkolnikov et al 1998
; Bassuk et al 2002
). Few data have estimated this relation in women with prevalent CVD. The purpose of this study was to evaluate the relation between education and CVD mortality among ethnically diverse adult women with CVD and to determine if differences in traditional risk factors explain any difference in CVD mortality based on educational status. Prevalent CVD, in this study, was composed of a history of CHD, defined by a baseline record of angina and/or myocardial infarction (MI), and/or a history of stroke at baseline according to cohort-specific definitions and their diagnostic criteria (Appendix 1) .Appendix 1Diagnostic criteria for history of MI, angina, and stroke in each cohort
| Cohort | Previous MI | Previous Angina | Previous Stroke |
|---|---|---|---|
| ARIC | ECG evidence and medical history | Rose questionnaire | Doctor’s diagnosis, “Has a doctor ever told you had stroke?” |
| Charleston | Self-reported history and ECG evidence | Self-reported history | NA |
| Evans’ County | Review panel | Review panel | Review panel |
| Framingham | Review panel | Chest pain questionnaire and review panel | Physician or hospital record |
| NHEFS | Self reported, “Have a doctor ever told you—?” | NA | Doctor’s diagnosis, “Has a doctor told that you had condition” |
| Framingham Offspring | Same method as Framingham | Same method as Framingham | |
| Rancho Bernado | Self-reported, “Have you ever been hospitalized for a heart attack?” and review panel validated the results | NA | Self-reported, “Have you ever had a stroke?” |
| San Antonio | Self-reported, “Has a doctor ever told you—” or Minnesota-coded ECG | Rose questionnaire | Self-reported stroke |
| Tecumseh | Self-reported, “Has a doctor ever told you had a heart attack?” or diagnosis from interview data or ECG | Chest pain questionnaire and review panel | Self-reported, “Have you ever had a stroke?” |
Abbreviations: MI, myocardial infarction; ECG, electrocardiogram; NA, not applicable.
Methods
Study sample
Data were from the Women’s Pooling Project (WPP), which is the combined data from nine U.S. population-based prospective cohort studies (Atherosclerosis Risk in Communities Study [ARIC], Charleston Heart Study, Evans County Study, Framingham Heart Study, Framingham Offspring Study, National Health and Nutrition Examination Survey Epidemiologic Follow-up Study [NHEFS], Rancho Bernardo Study, San Antonio Heart Study, and Tecumseh Community Health Study). Details of sampling procedures, study designs, and methods for each cohort have been published elsewhere (
The ARIC Investigators 1989
; Jackson et al 1996
; - Jackson R.
- Chambless L.E.
- Yang K.
- Byrne T.
- Watson R.
- Folsom A.
- et al.
Differences between respondents and nonrespondents in a multicenter community-based study vary by gender ethnicity. The Atherosclerosis Risk in Communities (ARIC) Study Investigators.
Journal of Clinical Epidemiology. 1996; 49: 1441-1446
National Heart, Lung and Blood Institute 1987
; Bolye 1970
; Keil et al 1984
; Johnson et al 1986
; McDonough et al 1965
; Gordon and Shurtleff 1973
; Cohen et al 1987
; National Center for Health Statistics 1973
; Criqui et al 1978
; Hazuda et al 1986
; Epstein et al 1965
). The sample included in this analysis includes 2,157 women with CVD. Women were included in this study if at baseline they were 30 years or older with prevalent CVD and had data available on history of diabetes, blood pressure, total cholesterol, smoking status, height, and weight.Definitions of baseline variables
Educational attainment for each subject was dichotomized as high school graduate or not. This was done because high school graduate was available in all cohorts. All cohorts defined subject completion of 12 years of education as a high school graduate except for the Charleston and Evans county studies, where 11 years was required for high school graduation. History of diabetes was identified by serum glucose level or treatment in all cohorts except NHEFS and Charleston study, which used self-reported physician diagnosis, and ARIC study, which combined both criteria. Smoking was classified as current smoker or not. Nonsmokers included those who never or formerly smoked. Baseline age and race/ethnicity were assessed by cohort-specific questionnaires. Total cholesterol was measured according to cohort-specific methods. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were assessed as the mean of the last two readings unless only one reading was available, in which case the single reading was used. Body mass index (BMI) was calculated as the weight in kilograms divided by the height in meters squared.
Assessment of CVD death
CVD mortality was identified based on the International Classification of Disease (ICD) codes 401–448 (
National Center for Health Statistics 1967
; World Health Organization 1977
). Most of the studies used ICD code version 9, except the Charleston, Evans County, and Framingham studies whose baseline examination were done in the 1960s and used ICD code version 8.Statistical analysis
Baseline characteristics of high school graduates and non-high school graduates were compared using χ2 tests for categorical variables and two-sided t-tests for continuous variables. A p value <.05 was considered significant.
To examine the association between educational level and age with CVD mortality, Cox regression models using age as the time axis with left truncation at baseline age were used (
Allison 1995
). Women not recorded as dying from CVD were treated as censored on the last day recorded alive. First, Cox proportional hazards regression models were used to estimate age-specific hazard ratios (HR) of non-high school graduates to high school graduates for the midpoint (N) of 5-year intervals. Each group included all women who participated in the study during the 5-year interval age group N ± 2.5 years. Events that occurred after this interval were treated as censored at the end of the interval.Next we used nonproportional hazards regression models by introducing an interaction term between age and educational status in Cox models to account for the age-dependent effect of education on CVD mortality risk. The addition of an interaction between education and age in the model allowed us to calculate the age-specific effect of education on CVD fatality continuously. The nonproportional regression model was restricted to age groups N = 60 to N = 90 years, because of the small number of CVD deaths (<30 events) in the extreme age groups (N ≤ 55 years and N ≥ 95 years). The −2 log likelihood ratio statistic, a measure of model agreement with data, was used to identify the best-fitted nonproportional Cox regression model (
Collett 1994
). The significance of the interaction term was determined by the difference between the −2 log likelihood (which has a χ2 distribution with 1 degree of freedom) in models before and after the interaction term was added. Both baseline age-adjusted HR and adjusted HR (controlled for history of diabetes, total cholesterol, SBP, DBP, BMI, smoking and race/ethnicity, in addition to baseline age) were calculated for both proportional and nonproportional hazards regression models. To identify the significance of association, the HR and its 95% confidence interval (CI) were used. When the 95% CI did not include 1, it was considered significant.Lastly, secondary analyses were done to examine potential cohort effects on the association using stratification in the nonproportional regression model (
Collett 1994
). Also, general trends of the relationship between education and CVD mortality in women with CVD were explored in each cohort individually to identify any specific cohort influence on the overall pattern of the association. Cohorts with less than 5% of CVD deaths in the age groups between N = 60 and N = 90 years were excluded for this analysis (Charleston Heart Study, Evans County Study, Framingham Offspring, and San Antonio Heart Study) because of their small number of events. All analyses were done using SAS version 8.01 (SAS Institute Inc 1999
).Results
Table 1 describes baseline characteristics of 2,157 women with established CVD by educational status. During a mean follow-up of 11.5 ± 7.4 years (24,863 person-time years), 615 CVD deaths were observed. High school graduates, compared to non-high school graduates, were younger (57 ± 9 versus 59 ± 10 years; p < .0001), were less likely to be diabetic (12% versus 18%; p < .0001), and had lower means for traditional risk factors: total cholesterol (6.09 ± 1.27 versus 6.28 ± 1.28 mmol/L; p = .0007), SBP (135 ± 28 versus 146 ± 31 mm Hg; p < .0001), DBP (79 ± 15 versus 84 ± 16 mm Hg; p < .0001), and BMI (27 ± 6 versus 29 ± 7 kg/m2; p < .0001). Moreover, the distribution of race/ethnicity between high school graduates and nongraduates were significantly different (p < .0001); specifically, high school graduates were more likely to be white (86% versus 70%; p < .0001).
Table 1Baseline characteristics among women with cardiovascular disease
| Non-HS Graduate (n = 1027) | HS Graduate (n = 1130) | p value | |
|---|---|---|---|
| Baseline age (years) (mean ± SD) | 59 ±10 | 57 ±9 | <.0001 |
| ≥65 years, n (%) | 335 (33) | 226 (20) | <.0001 |
| Race/ethnicity | |||
| White, n (%) | 723 (70) | 971 (86) | |
| Black, n (%) | 212 (21) | 123 (10.9) | |
| Hispanic, n (%) | 89 (8.7) | 35 (3) | |
| Other, n (%) | 3 (0.3) | 1 (0.1) | <.0001 |
| Disease history | |||
| Coronary heart disease, n (%) | 804 (84) | 915 (86) | .28 |
| Stroke, n (%) | 300 (30) | 289 (26) | .037 |
| Diabetes mellitus, n (%) | 186 (18) | 136 (12) | <.0001 |
| Cholesterol (mmol/L) (mean ± SD) | 6.28 ±1.28 | 6.09 ±1.27 | .0007 |
| SBP (mm Hg) (mean ± SD) | 146 ±31 | 135 ±28 | <.0001 |
| DBP (mm Hg) (mean ± SD) | 84 ±16 | 79 ±15 | <.0001 |
| BMI (kg/m2) (mean ± SD) | 29 ±7 | 27 ±6 | <.0001 |
| Smoker, n (%) | 286 (28) | 301 (27) | .53 |
Abbreviations: HS, high school; SD, standard deviation; CHD, coronary heart disease; SBP, systolic blood pressure, DBP, diastolic blood pressure; BMI, body mass index.
a The proportion of race/ethnicity between HS graduates and non-HS graduates.
Age-specific HR from Cox proportional hazards regression models, adjusted for baseline age and traditional risk factors (total cholesterol, SBP, DBP, history of diabetes, BMI, smoking status, and race/ethnicity) in addition to baseline age, are shown in Table 2. The age-specific effect was calculated in each 5-year interval age group as an estimate for the HR of the midpoint (N) in each group. The association between education and CVD fatality was greatest in age group N = 60 in both age-adjusted (HR = 2.16; 95% CI 1.09–4.27) and fully adjusted models (HR = 2.11; 95% CI 1.03–4.33). But, it became not very different from a hazard ratio of 1 by age group N = 70.
Table 2Age-specific cardiovascular disease mortality hazard ratios of non-high school graduates to high school graduates among women with cardiovascular disease at baseline
| Age Group (N) | CVD Deaths | Number of Subjects | HR (95% CI) | HR (95% CI) |
|---|---|---|---|---|
| 45 | 2 | 263 | — | — |
| 50 | 10 | 567 | 1.04 (0.29–3.69) | 0.80 (0.21–2.99) |
| 55 | 17 | 884 | 0.81 (0.30–2.19) | 0.64 (0.23–1.75) |
| 60 | 36 | 1087 | 2.16 (1.09–4.27) | 2.11 (1.03–4.33) |
| 65 | 47 | 1146 | 1.85 (1.02–3.36) | 1.76 (0.94–3.29) |
| 70 | 86 | 1070 | 1.05 (0.69–1.61) | 0.97 (0.62–1.52) |
| 75 | 107 | 808 | 1.00 (0.68–1.47) | 0.91 (0.61–1.37) |
| 80 | 126 | 604 | 0.95 (0.67–1.35) | 0.89 (0.61–1.30) |
| 85 | 106 | 372 | 0.76 (0.52–1.12) | 0.71 (0.47–1.07) |
| 90 | 57 | 159 | 0.97 (0.57–1.65) | 0.88 (0.49–1.60) |
| 95 | 18 | 36 | 0.84 (0.33–2.14) | 0.74 (0.24–2.26) |
| 100 | 3 | 4 | — | — |
Abbreviations: CVD, cardiovascular disease; HR, hazards ratio; CI, confidence interval.
a Age group N included all women who participated in the study during the 5-year interval N ± 2.5 years. Events that occurred after the interval were treated as censored at the end of the interval.
b Baseline age-adjusted hazard ratio.
c Hazard ratio adjusted for age at baseline, history of diabetes mellitus, total cholesterol, systolic and diastolic blood pressure, body mass index, smoking status, and race/ethnicity.
We next modeled the change in association between education and CVD fatality in a continuous manner using an interaction term between education and age in a Cox nonproportional hazards model. Because few CVD deaths (<30 events) occurred in the extreme age groups (N ≤ 55 and N ≥ 95 years), the analysis was restricted to age groups N = 60 to N = 90 years. The decrease in the −2 log likelihood statistics was significant (χ2 = 9.3 with 1 degree of freedom; p = .002) when the interaction term was included. Therefore, we used the nonproportional hazards model to identify a continuous association between education and CVD fatality in women with CVD. The inverse association of education with CVD fatality in women with established CVD was summarized using both proportional and nonproportional models, adjusted for traditional risk factors in age groups N = 60 to N = 90 years in Figure 1. The nonproportional Cox regression model (solid line) with its 95% CI (dotted line) shows the continuous change of the effect of education on CVD mortality with age, while the results of the proportional model, for each 5-year age group, is represented by solid circles for each midpoint N. The inverse association of education with CVD mortality among women with CVD was age-dependent. In the continuous model, non-high school graduates had 2.34 times higher risk of dying due to CVD than high school graduates (95% CI 1.27–4.29) at age 60 years. By age 65 years the hazard ratio decreased to 1.31 (95% CI 1.00–1.71), and by age 70 years, the risk of dying due to CVD was similar for non-high school graduates and high school graduates (HR = 1.01; 95% CI .85–1.21). These results remained similar when women with angina only (n = 948) were excluded, with hazard ratios of 3.03, 1.27, and .86 for ages 60, 65, and 70, respectively (p = .0021 for the inclusion of the interaction term).
Figure 1Adjusted age-specific cardiovascular disease mortality hazard ratios of non-high school graduates to high school graduates among women with cardiovascular disease. Hazard ratios calculated from Cox regression models adjusted for baseline age, history of diabetes mellitus, body mass index, total cholesterol, systolic and diastolic blood pressure, smoking status, and race/ethnicity at baseline. Continuous line derived from nonproportional hazards model containing an interaction term between education and age. Dotted line represents the 95% confidence interval for nonproportional hazards model. Solid circles derived from proportional hazards models for the midpoint (N) in 5-year interval age groups.
The potential cohort effects on the association of education with CVD fatality among women with CVD were examined using stratification in the nonproportional Cox regression model. The inverse relationship between education and CVD mortality remained significant (p = .02). In addition, we examined the association in each cohort individually to identify any specific cohort influence on the overall trend. Each cohort had similar patterns of the inverse association (see Appendix 2 and Figure 2). The significant age-dependent inverse relationship of education with CVD fatality among women with CVD seems to be determined by the general trend of association in this study, not due to cohort effects.
Figure 2General trends of the association between educational level and cardiovascular disease mortality among women with cardiovascular disease across cohorts. Six lines including overall trend derived from cohort-specific nonproportional hazard models containing an interaction term between education and age and adjusted for baseline age, history of diabetes mellitus, body mass index, total cholesterol, systolic and diastolic blood pressure, smoking status, and race/ethnicity at baseline.
Discussion
This is one of the first studies to determine the association between education and CVD fatality among women with CVD. In this study, there was a significant interaction between education and age in predicting CVD mortality, with high school graduates showing a protective effect at age 60 years that decreased and disappeared by age 70 years. The age-dependent inverse association between education and CVD mortality persisted across race/ethnicity.
Recently, two studies showed a significant inverse association of SES with CHD mortality regardless of baseline disease status (
Salomaa et al 2000
; Hemingway et al 2000
). First, Hemingway et al 2000
identified the inverse association between occupational gradient and CHD mortality among middle-aged (40–69 years) British civil servants. This study demonstrated that the inverse association was more significant among civil servants without CHD at baseline than those with. The risk of CHD mortality in the lowest grade of occupation was increased by 72% (95% CI 1.4–2.1) compared to that in the highest among men without CHD, while the increase was 56% (95% CI 1.1–2.1) among those with CHD. In addition, Salomaa et al 2000
confirmed the finding from Hemingway et al 2000
using the data from the Finnish contribution to the World Health Organization (WHO) multinational monitoring of trends and determinants of cardiovascular disease (FINMONICA) MI registry during 1983–1992. Among middle-aged (35–64 years) Finnish adults, there was a significant inverse association between education and MI death, regardless of prevalence of MI. In contrast to Hemingway et al 2000
, the protective effect of education in this study was more significant among people with MI than without across gender. Furthermore, this study showed that the protective effect of education on MI case-fatality was much stronger in women than in men. Less-educated men had 87% higher risk for a 1-year case-fatality than high-educated men (95% CI 1.71–2.05), while the risk in less-educated women was increased by 134% compared to the risk among high-educated women (95% CI 1.88–2.92).Our data are consistent with other studies showing that the impact of education on CVD mortality is age-dependent.
Antonovsky 1967
evaluated that the impact of SES on health was most prominent in young and middle-aged adults. In addition, the impact disappeared with advancing age (aged ≥70 years), regardless of race/ethnicity. The decrease in the hazard ratio of education with advanced age may be due to the increasing relative importance of the background rate of CVD and other risk factors.Relative risk peaked at aged 30–44 years, then started declining and disappeared at age 65 years and older. Also, the inverse association has been found among the middle-aged and then disappeared after age 65 years (
Elo and Preston 1996
; Mustard et al 1997
; Kitagawa and Hauser 1973
). But a recent study showed that the protective effect of education on overall mortality persisted beyond middle-age, even though the strength of association was weaker than that in middle-aged (Bassuk et al 2002
). Community-living elderly men (aged ≥65 years) with low education (≤7 years) had 44% increased risk of dying, compared to those with the highest (≥13 years) (95% CI 1.07–1.94).Study strength and limitations
Although our dataset had several advantages, compared to previous studies, which allowed us to identify the association of education with CVD fatality among adult women with CVD and to test the significance of the interaction between education and age among them, there were also several limitations. Due to the small number of CVD deaths (<30 events) in the youngest and oldest age groups (N ≤ 55 and N ≥ 95 years), they were not included in this analysis. Measurement errors due to differences in methodologies between cohorts might have biased our results, but when data were examined within cohorts, the results were similar. Lastly, although commonly used, education may not be a good proxy for SES.
Conclusions
We found that an inverse association between educational level and CVD mortality is evident among women with prevalent CVD. Moreover, there was a significant interaction between education and age among women with CVD. The inverse association between educational level and CVD mortality was greatest in women at age 60 years, decreased with age, and disappeared by age 70 years. Considering that women have higher prevalence of CVD and CVD case-fatality than men with increasing age (≥65 years;
American Heart Association 2003
; American Heart Association, 2003. 2003 Heart and stroke statistical update. Available at http://www.americanheart.org.
Lerner and Kannel 1986
; Marrugat et al 2001
; Vaccarino et al 2000
), the significant protective effect of education from our study might give a novel strategy to reduce CVD mortality among women with CVD. Especially for middle-aged women with low education, increasing heart health education program may improve their awareness of CVD risk and then result in modifying lifestyle change by continuous education, which further leads to decrease CVD mortality gap between high school graduate and non-high school graduate among middle-aged women. More intensive preventive and educational efforts may be needed to target less-educated women across race/ethnicity by health professionals and public health policy makers to maximize survival from CVD in women. This may include an emphasis on blood pressure, blood lipid, and weight control. In addition, more studies are needed to identify this association across race/ethnicity among adult women.Acknowledgements
This study was supported by a grant from the National Heart, Lung and Blood Institute (KO8 HL 03681) and by a grant from the American Heart Association (9750703N). The authors would like to thank the steering committee members of the Women’s Pooling Project for their participation in the project, including Ralph D’Agostino, Boston University; Elizabeth Barrett-Connor, University of California San Diego; Victor Hawthorne, University of Michigan; Millicent Higgins, University of Michigan; William Kannel, Framingham Study; Julian Keil, Medical University of South Carolina; Michael Stern, University of Texas, University of Maryland School of Medicine; Susan Sutherland, Research Institute at Mission St. Joseph’s; H. Al Tyroler, University of North Carolina; and Aaron Folson, University of Minnesota.
Appendix 2.
Assessing general patterns of the association across cohorts
General trends of the inverse association between educational level and CVD fatality in women with CVD were examined to identify any specific cohort influence on the overall pattern (Figure 2). Cohorts with at least 5% of CVD deaths in the age groups N = 60 to N = 90 years (ARIC, NHEFS, Rancho Bernardo, and Tecumseh studies) were included in this analysis, except for the Framingham study, which only included N = 65 to N = 90 years because of the small number of events in the age group N = 60 years (7/247 = 2.8%). Nonproportional hazard models containing an interaction term between education and age derived from each cohort including overall trend and adjusted for baseline age, history of diabetes mellitus, body mass index, total cholesterol, systolic and diastolic blood pressure, smoking status, and race/ethnicity at baseline. All five cohorts had similar patterns of the inverse association between education and CVD fatality and the variance of curvature in each cohort might be due to their different events and sample sizes in any given age.
In conclusion, the overall nonproportional hazard model seems to well illustrate the general pattern of the relationship of education with CVD fatality among women with CVD in our study, not due to the any specific cohort influence.
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Biography
Dr. Lori Mosca is Director of Preventive Cardiology at New York-Presbyterian Hospital, Associate Professor of Medicine at Columbia University, and Principal Investigator of the NIH-sponsored Women’s Pooling Project.
Dr. Furcy Paultre is a research scientist at Columbia University and the Preventive Cardiology Program at New York-Presbyterian Hospital.
Ms. Ji Won R. Lee was a trainee in the Preventive Cardiology Program at New York-Presbyterian Hospital and a Master in Public Health Student at Columbia University at the time this research was conducted.
Article Info
Publication History
Accepted:
November 12,
2004
Received in revised form:
February 27,
2004
Received:
October 14,
2003
Identification
Copyright
© 2005 Jacobs Institute of Women’s Health. Published by Elsevier Inc. All rights reserved.
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