Vasomotor menopausal symptoms and cardiovascular disease risk in midlife: A longitudinal study

Journal Pre-proof Vasomotor menopausal symptoms and cardiovascular disease risk in midlife: a longitudinal study V. Dam, A.J. Dobson, N.C. Onland-Moret, Y.T. van der Schouw, G.D. Mishra

PII:

S0378-5122(19)30498-0

DOI:

https://doi.org/10.1016/j.maturitas.2019.12.011

Reference:

MAT 7282

To appear in:

Maturitas

Received Date:

7 May 2019

Revised Date:

19 December 2019

Accepted Date:

23 December 2019

Please cite this article as: Dam V, Dobson AJ, Onland-Moret NC, van der Schouw YT, Mishra GD, Vasomotor menopausal symptoms and cardiovascular disease risk in midlife: a longitudinal study, Maturitas (2019), doi: https://doi.org/10.1016/j.maturitas.2019.12.011

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Vasomotor menopausal symptoms and cardiovascular disease risk in midlife: a longitudinal study

Running title: VMS and CVD risk

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V Dam*a,b,c, A J Dobsona, N C Onland-Moretb, Y T van der Schouwb, G D Mishraa

University of Queensland, School of Public Health, 266 Herston Road, QLD 4006, Brisbane,

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Australia b

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Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht,

Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands

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Netherlands Heart Institute, Moreelsepark 1, 3511 EP, Utrecht, the Netherlands.

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*Corresponding author: Veerle Dam; [email protected]

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Highlights 

Studies of the association between vasomotor menopausal symptoms and the clinical outcomes of cardiovascular disease have produced contradictory results.

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We found no convincing association between vasomotor menopausal symptoms, hot flushes and night sweats, or the duration of symptoms, and cardiovascular disease, coronary heart disease and cerebrovascular disease.

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This is the largest study to date of the association of vasomotor menopausal symptoms, hot flushes and night sweats, with cardiovascular disease, coronary heart disease and cerebrovascular disease.

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This is the only study to have investigated the duration of menopausal symptoms in relation to cardiovascular disease, coronary heart disease and cerebrovascular disease.

Abstract

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Objective: To ascertain the association between vasomotor menopausal symptoms (VSM), hot flushes and night sweats, and cardiovascular disease, coronary heart disease and cerebrovascular disease.

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Study design: The study sample comprised 8,881 women (aged 45-50 years) with available hospital separation data from the 1946-51 cohort (1996-2016) of the ongoing Australian

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Longitudinal Study on Women’s Health, a national prospective cohort study.

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Main outcome measures: First fatal or non-fatal cardiovascular disease, coronary heart disease, and cerebrovascular disease events were obtained through linkage with hospital

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admission data, the National Death Index, and Medicare Benefits Schedule. Hot flushes and night sweats were assessed via questionnaires at each main survey. Additionally, we

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calculated the duration of symptoms based on whether or not women reported vasomotor menopausal symptoms in each survey.

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Results: There were 925 cardiovascular disease, 484 coronary heart disease and 154 cerebrovascular disease events. There was no consistent evidence of any association with vasomotor menopausal symptoms, hot flushes and night sweats. We did find marginally statistically significant associations between presence of night sweats and cardiovascular disease (Hazard Ratio = 1.18, 95% Confidence Interval: 1.01-1.38), and between the duration of vasomotor menopausal symptoms [years] and coronary heart disease (Hazard Ratioper year = 2

1.03, 95% Confidence Interval: 1.00-1.05). However, given the number of associations tested, these findings could very well have arisen by chance. Conclusion: In this large longitudinal study with 20 years of follow-up and clinical outcomes we did not find a convincing association between vasomotor menopausal symptoms, hot flushes, night sweats and cardiovascular disease, coronary heart disease and cerebrovascular

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disease.

Keywords: Cardiovascular Disease; Cerebrovascular Disease; Coronary Heart Disease;

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Vasomotor Menopausal Symptoms

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1. Introduction Cardiovascular disease (CVD) is the leading cause of death in women from high income countries [1]. CVD accounted for almost 30% of all deaths in Australian women in 2015 [2]. Recently, research on CVD has shifted towards a more sex-specific focus since there are marked sex-differences in the development and progression of CVD and its risk factors [3]. A recent review showed an association between hot flushes (HF) and night sweats

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(NS) during menopausal transition and CVD risk factors: higher systolic and diastolic blood pressure, higher circulating total cholesterol levels and higher Body Mass Index (BMI) compared to women without these vasomotor menopausal symptoms (VMS) [4].

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Furthermore, several studies also found associations between VMS and intermediate CVD outcomes: lower flow mediated dilation, higher aortic calcification, higher carotid intima

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media thickness and plaque [5–8], which in turn are prospectively associated with incident CVD. These results indicate that HF and NS might increase CVD risk. However, only a few

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studies investigated the association between HF, NS or VMS with hard CVD endpoints [9– 13]. These studies show contradictory results, varying between less CVD risk, more CVD

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risk and no association at all. Furthermore, most studies only study VMS as a whole and they all suggest that duration of symptoms should be further investigated in relation to CVD.

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Although an association between VMS with cardiovascular risk factors and

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intermediate endpoints seems clear, the association with clinical outcomes needs further investigation, in particular with respect to duration of menopausal symptoms and individual types of symptoms (i.e. HF and NS). Therefore, the aim of the current study was to examine the association between VMS (and HF and NS separately) and the occurrence and duration of each type of symptom with fatal and non-fatal CVD, CHD, and cerebrovascular disease.

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2. Materials and Methods 2.1 Study population Details of the Australian Longitudinal Study on Women’s Health (ALSWH) were described previously [14,15] and are available at www.alswh.org.au. In brief, ALSWH is a national prospective cohort study that recruited a nationally representative sample of over 40,000 women in 1996. Three age groups were sampled: 18-23 (1973-78 cohort), 45-50 (1946-51 cohort), and 70-75 (1921-26 cohort). All women were sampled randomly from the national

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health insurance scheme (Medicare) that includes all Australian citizens and permanent residents. Women from rural and remote areas were intentionally oversampled [14,15].

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For this study we used data from the 1946-51 cohort who have been surveyed every 2-3 years since the start of ALSWH. At baseline (1996) 13,715 women agreed to participate.

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At Survey 2 13,499 women were still eligible (i.e., they could be located, and had not withdrawn or died) and 12,338 responded (91.7%). The corresponding numbers for

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successive surveys were: at Survey 3 13,149 women were eligible and 11,226 responded (85.4%); at Survey 4 12,843 women were eligible and 10,905 responded (84.9%); at Survey 5

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12,466 women were eligible and 10,638 responded (85.3%); at Survey 6 12,063 women were eligible and 10,011 responded (83.0%); at Survey 7 11,291 women were eligible and 9,151

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(80.4%).

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responded (81.0%); and at Survey 8 (2016) 10,719 women were eligible and 8,622 responded

Of the 13,715 women at baseline, we only had hospital data for women living in the

Australia Capital Territory, New South Wales, Queensland, South Australia and Western Australia. After additionally excluding women who did not give consent for linkage, 8,881 women remained in the final analysis sample. 2.2 Ethical approval 5

All participants gave written informed consent and the study was approved by the Human Research Ethics Committees of the University of Newcastle and the University of Queensland. 2.3 Cardiovascular disease, coronary heart disease and cerebrovascular disease First fatal or non-fatal CVD, CHD and cerebrovascular disease events were obtained through linkage with hospital separations data, the National Death Index (NDI) and Medicare

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Benefits Schedule (MBS) databases. If participants withdrew from the ALSWH, linked data remained available unless the participant had opted out of linkage. Time to event was calculated from the return date of Survey 1 (baseline) until the first event, and death from any

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other cause or the end of availability of hospital data in the last known State of residence was used as the censoring date. Hospital data were available from May 2001 until March 2017 for

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New South Wales, from March 2002 until December 2011 for Queensland, from January 1970 until December 2015 for Western Australia, from July 2004 until June 2013 for

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Australian Capital Territory and from January 2002 until December 2010 for South Australia. Causes of death data were available until October 2015. If a woman had an event or if her

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observation was censored, survey data collected after this event was not used in the analysis. The hospital data were coded by the Australian version of the International

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Classification of Diseases (ICD-10-AM) for the diagnoses and the Australian Classification

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of Health Interventions (ACHI) for procedures. Western Australia used ICD-9 coding before 2000, therefore we decided to exclude the cases (n=30) that occurred prior to 2000. Causes of death were coded according to ICD-10. From the MBS database we used MBS-specific codes for the following procedures: percutaneous transluminal balloon angioplasty or bypass of coronary arteries (to define CHD events), endarterectomy of neck arteries (to define cerebrovascular disease) and procedures performed for peripheral arterial occlusive disease or

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abdominal aneurysm for CVD. All CHD and cerebrovascular disease events were also counted as CVD events. All relevant ICD, ACHI and MBS codes can be found in Appendix A. 2.4 Vasomotor symptoms of menopause Both HF and NS were assessed via a self-reported questionnaire at each ALSWH survey. Women responded to the questions: ‘In the last 12 months, have you had any of the

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following: 1) hot flushes, 2) night sweats?’ Response options were ‘never’, ‘rarely’, ‘sometimes’, or ‘often’. For the occurrence of symptoms we dichotomized the responses into ‘no’, including ‘never’ and ’rarely’, and ‘yes, including ‘sometimes’ and ‘often’.

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Additionally, we created a VMS variable, combining HF and NS, which was defined ‘yes’ when either HF or NS was ‘yes’.

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We also calculated a crude measure of duration of symptoms from the responses to

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the survey questionnaires that went out every 2-3 years. For example, the duration for HF was based on the dichotomous variables. Duration at Survey 1: if women reported presence

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of HF at Survey 1, we assumed the duration to be one year; while for women who did not report HF at Survey 1 the duration of symptoms was set to zero years. Duration at Survey 2: if women answered ‘No’ for HF at Survey 2, we defined the duration as that of Survey 1. If

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women answered ‘Yes’ at Survey 2 and duration at Survey 1 was 0 years, we defined the

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duration as being 1year. If women answered ‘Yes’ at Survey 2 and duration at Survey 1 was 1 year, we defined the duration as being 3 years (addition of 2 years, since there were 2 years between Survey 1 and Survey 2). Duration at Survey 3: if women answered ‘No’ for HF at Survey 3, we defined the duration as that of Survey 2. If women answered ‘Yes’ at Survey 3 and duration at Survey 2 was 0 years, we defined the duration as being 1 year. If women answered ‘Yes’ at Survey 3 and duration at Survey 2 was 1 year, we defined the duration as

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being 4 years (addition of 3 years, since there were 3 years between Survey 2 and Survey 3). If women answered ‘Yes’ at Survey 3 and duration at Survey 2 was 3 years, we defined the duration as being 6 years. This was continued until Survey 8 and was calculated in the same way for NS and VMS. 2.5 Covariates Education level was determined at Survey 1 and categorized as: low (no formal qualifications

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or school certificate), middle (higher school certificate, trade/apprenticeships, or certificate or diploma), or high (university degree or higher degree). Other covariates were ascertained at each ALSWH survey. BMI was defined as self-reported weight (kg) divided by the square of

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self-reported height (m2) and was categorized as underweight (<18.5 kg/m2), healthy weight (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2) or obese (≥30 kg/m2). Physical activity data

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were only consistently measured from Survey 3 onwards and categorized as nil/sedentary (<0-10 minutes/week), low (11-150 minutes/week), moderate (151-300 minutes/week), or

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high (>300 minutes/week). Smoking was categorized as never smoker, former smoker or current smoker. Alcohol consumption was categorized in line with the Australian National

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Health and Medical Research Council (NHMRC) guidelines as non-drinker, rarely drinks (alcohol consumption less than once a month), low risk (up to two drinks a day), or risky

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drinker (three or more drinks a day) [16]. Finally, the women were asked if they were

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currently using menopausal hormone therapy (MHT) in each survey. 2.6 Statistical analysis Baseline characteristics were described by percentage (number, N) or mean (standard deviation, SD) across categories of HF and NS. We used two sets of Cox regression models, stratified by State of residence (due to different follow-up times), to investigate the associations between: (i) time varying occurrence of VMS, HF, NS, and (ii) duration of these 8

symptoms, and the times to CVD, CHD and cerebrovascular disease events, or censoring. Robust standard errors were used to construct 95% confidence intervals (CI). We applied four levels of covariate adjustment: adjustment for baseline age (Model 1), further adjustment for time-varying MHT use (Model 2), further adjustment for well-known cardiovascular risk factors: education level, time-varying physical activity, time-varying smoking status and time-varying alcohol status (Model 3), and further adjusted for time-varying BMI (Model 4). As a sensitivity analysis we also investigated the association between HF and NS with four

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response categories (never [reference category], rarely, sometimes, often) with the various outcomes. These analyses were performed with R version 3.4.2[17]. The main results are based on an available case analysis including 8,881 women in model 1; this number

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decreases after additional covariates were used for further adjustment. We performed sensitivity analyses with multiple imputed data including all 8,881 women in all models.

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Methods and results for the sensitivity analyses can be found in Appendix B.

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3. Results At baseline 5622 women never had HF or NS, 1080 women only had HF, 310 only had NS and 1794 had both HF and NS (at this survey, data on VMS were missing for 75 women who provided data a later surveys). Baseline characteristics showed that women with HF had less education, were more likely to use MHT, be current smokers, risky drinkers and obese, compared to women without HF (Table 1). The same differences were found between women with and without NS. The baseline characteristics were comparable between women with HF

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or NS or without HF or NS (Table 1) and between women included and excluded from the analyses (Appendix B, Table B.1). There were 925 CVD events (cumulative incidence = 10.4%), including 484 CHD events (cumulative incidence = 5.4%) and 154 cerebrovascular

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events (cumulative incidence = 1.7%), over the 20 years of follow-up.

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3.1 Cardiovascular disease

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Overall, we did not find an association between time-varying VMS (Hazard Ratio [HR]=1.07, 95%CI: 0.92-1.24, Table 2) or HF (HR=1.06, 95%CI: 0.92-1.24, Table 2) and CVD events. For NS we only found a marginally statistically significant association with

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CVD risk (HR=1.18, 95%CI: 1.01-1.38, Table 2). Also, the duration of symptoms was not associated with CVD events as shown in Table 3 (HRper year for HF duration=1.00, 95%CI: for NS duration=1.00, 95%CI: 0.98-1.03; HRper

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0.98-1.03; HRper

year

year

for VMS

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duration=1.01, 95%CI: 0.99-1.03). Finally, the sensitivity analyses with four response categories for the symptoms showed no association with HF and CVD events and only a significant association for the ‘often’ category in the association between NS and CVD events (HR = 1.34, 95%CI: 1.08-1.67, Appendix C, Table C.1 and C.2).

3.2 Coronary heart disease

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We also did not find an association between VMS (HR=1.03, 95%CI: 0.84-1.26), HF (HR=1.06, 95%CI: 0.86-1.30) and NS (HR=1.08, 95%CI: 0.87-1.34) and CHD events (Table 4). The duration of VMS was marginally statistically significantly associated with CHD risk (HRper

year=1.03,

year=1.02,

95%CI: 1.00-1.05, Table 5). However, the duration of neither HF (HRper

95%CI: 0.99-1.06) nor NS (HRper

year=1.03,

95%CI: 0.99-1.07) were statistically

significantly associated with CHD risk (Table 5). Lastly, the sensitivity analyses of HF and NS with four response categories showed no association with CHD risk (Appendix C, Table

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C.3 and C.4).

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3.3 Cerebrovascular disease

Finally, we studied the association between VMS, HF, NS and cerebrovascular disease, but

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we did not find an association (HR for VMS=1.10, 95%CI: 0.76-1.59; HR for HF=1.05,

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95%CI: 0.72-1.54; HR for NS=1.45, 95%CI: 0.99-2.11, Table 6). In addition, duration of symptoms showed no association (HRper

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for VMS duration=1.00, 95%CI: 0.97-1.05;

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HRper year for HF duration=1.00, 95%CI: 0.94-1.06; HRper year for NS duration=1.01, 95%CI: 0.95-1.07; Table 7). Lastly, the sensitivity analyses with HF in categories showed no association with cerebrovascular disease and the analyses with NS showed only a statistically

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significant results for the ‘often’ group with a HR of 1.79 (95%CI: 1.05-3.06, Appendix C,

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Table C.5 and C.6).

3.4 Sensitivity analyses As a sensitivity analyses we also performed all analyses, in the imputed data. This yielded approximately the same results (Appendix D). 11

Discussion In this longitudinal study with 20 years of follow-up and clinical outcomes we did not find an association between VMS, HF, NS and CVD, CHD or cerebrovascular disease. We did find a 18% increased CVD risk in women with NS compared to women without NS and a 3% increased CHD risk for every additional year a women experiences VMS. However, effect sizes are small, not clinically relevant, and only marginally statistically significant. Moreover, given the number of associations tested these findings could also very well be chance

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findings.

Only a few studies investigated the association between VMS, HF and/or NS and CVD, CHD and/or cerebrovascular diseases [9–13] and one study included a meta-analysis

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[18]. These studies found very divergent results, with some studies showing decreased risks

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of CVD, CHD or stroke for women experiencing VMS [11], some increased risks [11–13], but often not statistically significant [9–12]. This may be unexpected, since studies that

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investigated the association between VMS and cardiovascular risk factors [4] or intermediate outcomes [6,7] convincingly show increased risk in women with VMS, suggesting a likely

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association with clinical endpoints. There could be several explanations for this discrepancy. For instance, the studies that investigated CVD risk factors or intermediate outcomes found

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only small effect sizes for their outcomes, which were almost always continuous. For studies with clinical outcomes, which are often dichotomous, the sample size would need to be much

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larger to have enough power to show a difference between women with and without VMS. The largest study comparable to our study in design, included 10,787 women of whom 303 developed CHD, and did not find any statistically significant associations with either HF or NS in the fully adjusted models [10]. The largest study on VMS included 60,027 women of whom 2,812 developed CVD, 1,726 developed CHD and 1226 developed stroke. In this study, in the fully adjusted models, a statistically significant association was only found 12

between ‘late VMS’ and CVD and CHD, but not for ‘early’ or ‘persistent (early and late)’ VMS [11]. However, the definitions of ‘early’, ‘late’, and ‘persistent’ VMS were based on recall of VMS between menopause onset and enrolment in the study. Enrolment in the study was after menopause, which makes it very hard to compare the findings with our results. Another explanation could be the large number of cross-sectional studies that investigated the association between VMS and cardiovascular risk factors. Since VMS have a temporary nature, it might be possible that the elevations in CVD risk factors are also

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temporary, making the association with CVD less obvious. In our study, we had repeated measurements of the VMS, which we modelled as time-varying variables, taking the

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temporary nature of VMS into account.

In addition to the inconsistent associations found in empirical studies, the

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physiological pathways are not fully understood. A possible mechanism suggested to underlie the putative association between VMS and CVD is the estrogen decline during the

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menopausal transition. Estrogen decline is often accompanied by the development of VMS and women experiencing severe VMS often use MHT to reduce the symptoms. The decline

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in estrogen is also suggested to be associated with an increased CVD risk [19,20], which might be counteracted by MHT use. However, recent meta-analyses of clinical trials with

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risk [22].

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MHT found no associations for cardiac or stroke mortality [21], or even an increased CHD

Strengths of this study are the long follow-up, the repeated measurements of a large

number of covariates and the multiple-registry approach to identify cases. However, some limitations need to be acknowledged. First, assumptions are made to calculate the duration of symptoms: if a woman had HF or NS at one survey and again on the next survey, she had a duration of two or three years, depending on the time between surveys. However, it is

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possible that women had symptoms that went away and came back again. This might have led to some misclassification, because some women might be classified with a longer duration than they really had. Therefore, the results with duration might entail an underrepresentation of the real effect. Second, for some States hospital data were not available before baseline, so we could not exclude prevalent cases. As this number would be expected to be very small since women were only 45-50 years old at baseline we do not think that this has influenced the results. Furthermore, our inclusion criteria meant that some cases

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were excluded, namely those in Western Australia before 2000 when ICD-10 was adopted (n=30) and some due to coding changes in MBS before 2000 (n=5). Third, we did not exclude women using MHT. However, women using MHT probably reflect the group with

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the most severe symptoms; therefore, excluding them might give a false representation of symptom variety. On the other hand, including them could introduce misclassification.

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Women with severe symptoms often use MHT to reduce their symptoms, which might result

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in them filling in ‘No’ on the VMS question. We tried to avoid this by asking for symptoms in the past 12 months, but still an average of 16.4% of women in each survey reported not

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4. Conclusion

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having VMS while using MHT. This could result in an underestimation of the effect size.

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In conclusion, this longitudinal study with long follow-up and clinical outcomes did not find a convincing association between VMS, HF, NS and CVD, CHD and cerebrovascular disease.

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Contributors V Dam was responsible for the study concept and design, analyzed the data and drafted the article, conducted statistical review, and critically revised the article for important intellectual content. A J Dobson was responsible for the study concept and design, conducted statistical review, and critically revised the article for important intellectual content.

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N C Onland-Moret critically revised the article for important intellectual content.

Y T van der Schouw critically revised the article for important intellectual content.

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G D Mishra was responsible for the study concept and design, conducted statistical review,

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Conflict of interest

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and critically revised the article for important intellectual content.

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The authors declare that they have no conflict of interest.

Funding

The Australian Government Department of Health provided funding. V Dam is supported by the Dutch Heart Foundation [2013T083]. G Mishra is funded by the NHMRC Principal Research Fellowship [APP1121844].

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. Ethical approval All participants gave written informed consent and the study was approved by the Human

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Research Ethics Committee of the University of Newcastle and the University of Queensland.

Provenance and peer review

Peer review was directed by Professor Margaret Rees independently of Yvonne van der

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Schouw, an author and Maturitas editor, who was blinded to the process.

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Research data (data sharing and collaboration)

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There are no linked research data sets for this paper. Data will be made available on request.

Acknowledgments

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The research on which this paper is based was conducted as part of the Australian Longitudinal Study on Women’s Health, the University of Newcastle and the University of Queensland. We are grateful to the Australian Government Department of Health for funding and to the women who provided the survey data.

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We acknowledge the Department of Health and Medicare Australia for providing the MBS data. We also acknowledge the Australian Institute of Health and Welfare (AIHW) as the integrating authority for these data. We acknowledge the assistance of the Data Linkage Unit at the Australian Institute of Health and Welfare (AIHW) for undertaking the data linkage to the National Death Index (NDI). The authors wish to thank staff at the Centre for Health Record Linkage, New South Wales Ministry Health; the Data Linkage Branch, Western Australia Department of Health;

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the Statistical Services Branch, Queensland Health; SA NT DataLink, University of South Australia; and the data custodians of the NSW Admitted Patient Data Collection, WA Hospital Morbidity Data Collection, Queensland Hospital Admitted Patients Data Collection

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and SA Public Hospital Inpatient Separations. We also acknowledge the assistance of the

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the National Death Index (NDI).

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Data Linkage Unit at the Australian Institute of Health and Welfare (AIHW) for linkage to

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Table 1. Baseline (1996) characteristics of ALSWH participants born in 1946-51, of whom hospital data was available, stratified by reporting of hot flushes and night sweats at baseline* Hot flushes

Hot flushes

Night sweats

Night sweats

Characteristics

(N) N=5938

(Y) N=2888

(N) N=6711

(Y) N=2109

Age**

47.5 (1.5)

47.8 (1.4)

47.5 (1.5)

47.9 (1.4)

MHT use (yes)

14.2% (844)

29.4% (849)

16.0% (1071)

29.2% (616)

Low

48.0% (2829)

60.1% (1718)

49.8% (3317)

58.7% (1225)

Middle

36.1% (2126)

31.5% (900)

35.2% (2346)

32.5% (678)

High

15.9% (937)

8.4% (239)

14.9% (993)

8.8% (184)

Nil/Sedentary

16.6% (798)

21.8% (478)

17.2% (930)

21.7% (343)

Low

37.9% (1824)

36.4% (797)

38.0% (2061)

35.5% (561)

Moderate

20.8% (1002)

18.4% (404)

20.6% (1114)

18.4% (291)

High

24.7% (1187)

23.6% (660)

24.2% (1313)

24.3% (328)

48.7% (1362)

55.3% (3608)

45.7% (932)

27.6% (772)

28.8% (1882)

29.1% (593)

23.6% (660)

15.8% (1034)

25.3% (516)

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Physical

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na

55.1% (3182)

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Never smoker Former smoker

29.6 % (1708)

Current smoker 15.3% (885)

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activity***

Smoking status

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Education level

Alcohol status Non-drinker

14.3% (846)

17.0% (488)

15.0% (998)

16.2% (338)

Rarely drinks

30.6% (1804)

32.2% (824)

30.8% (2058)

31.9% (665)

Low risk

50.2% (2958)

44.4% (1272)

49.5% (3304)

45.1% (922)

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drinker 4.9% (290)

6.4% (182)

4.7% (312)

7.7% (161)

Underweight

2.1% (118)

1.4% (40)

1.8% (119)

1.9% (38)

Healthy weight

54.7% (3128)

43.7% (1210)

53.0% (3428)

45.1% (911)

Overweight

27.1% (1548)

31.7% (879)

27.9% (1806)

30.6% (619)

Obese

16.2% (925)

23.2% (642)

17.2% (1110)

22.4% (453)

Risky drinker BMI

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* Numbers do not add up, since some women had missing values for HF or NS at Survey 1. **Mean (SD)

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***Based on survey 3

Table 2. Hazard Ratios for the association between time-varying vasomotor symptoms, hot

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flushes and night sweats and cardiovascular disease

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N=8,881 (events=925) VMS (Yes)

Hot flushes (Yes)

Night sweats (Yes)

Model 1*

1.14 (0.99-1.30)

1.12 (0.98-1.28)

1.24 (1.08-1.42)

Model 2**

1.13 (0.99-1.30)

1.12 (0.98-1.28)

1.24 (1.07-1.42)

Model 3***

1.08 (0.93-1.25)

1.07 (0.92-1.25)

1.21 (1.04-1.42)

1.06 (0.92-1.24)

1.18 (1.01-1.38)

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na

Model

Model 4****

1.07 (0.92-1.24)

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For each symptom the ‘No’ group is the reference group. *Model 1 adjusted for baseline age **Model 2 additionally adjusted for MHT use (time-varying) ***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status ****Model 4 additionally adjusted for time-varying BMI 27

Table 3. Hazard Ratios for the association between time-varying duration of symptoms and cardiovascular disease N=8,881 (events=925) VMS duration

HF duration

NS duration

Model 1*

1.01 (1.00-1.03)

1.00 (0.98-1.02)

1.00 (0.98-1.03)

Model 2**

1.01 (1.00-1.03)

1.00 (0.98-1.02)

1.00 (0.98-1.03)

Model 3***

1.01 (1.00-1.03)

1.00 (0.98-1.03)

1.01 (0.98-1.03)

Model 4****

1.01 (0.99-1.03)

1.00 (0.98-1.03)

*Model 1 adjusted for baseline age

1.00 (0.98-1.03)

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**Model 2 additionally adjusted for MHT use (time-varying)

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Model

smoking status, and alcohol status

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***Model 3 additionally adjusted for education level and time-varying physical activity,

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****Model 4 additionally adjusted for time-varying BMI

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Table 4. Hazard Ratios for the association between time-varying vasomotor symptoms, hot flushes and night sweats and coronary heart disease

Model

VMS (Yes)

Hot flushes (Yes)

Night sweats (Yes)

1.16 (0.97-1.39)

1.15 (0.95-1.38)

1.21 (1.00-1.47)

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Model 1*

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N=8,881 (events=484)

Model 2**

1.16 (0.96-1.39)

1.15 (0.95-1.38)

1.21 (1.00-1.47)

Model 3***

1.04 (0.85-1.27)

1.05 (0.86-1.29)

1.11 (0.89-1.37)

Model 4****

1.03 (0.84-1.26)

1.06 (0.86-1.30)

1.08 (0.87-1.34)

For each symptom the ‘No’ group is the reference group *Model 1 adjusted for baseline age 28

**Model 2 additionally adjusted for MHT use (time-varying) ***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status ****Model 4 additionally adjusted for time-varying BMI

Table 5. Hazard Ratios for the association between time-varying duration of symptoms and coronary heart disease

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N=8,881 (events=484) Model

VMS duration

HF duration

Model 1*

1.03 (1.01-1.05)

1.02 (0.99-1.06)

Model 2**

1.03 (1.01-1.05)

1.02 (0.99-1.06)

Model 3***

1.03 (1.00-1.05)

1.02 (0.99-1.06)

1.03 (0.99-1.07)

Model 4****

1.03 (1.00-1.05)

1.02 (0.99-1.06)

1.03 (0.99-1.07)

1.03 (0.99-1.06)

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1.03 (0.99-1.06)

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*Model 1 adjusted for baseline age

NS duration

**Model 2 additionally adjusted for MHT use (time-varying)

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***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status

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****Model 4 additionally adjusted for time-varying BMI

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Table 6. Hazard Ratios for the association between time-varying vasomotor symptoms and cerebrovascular disease N=8,881 (events=154) Model

VMS (Yes)

Hot flushes (Yes)

Night sweats (Yes)

Model 1*

1.02 (0.73-1.41)

1.01 (0.72-1.40)

1.31 (0.94-1.84)

Model 2**

1.01 (0.73-1.40)

1.00 (0.72-1.39)

1.30 (0.93-1.82) 29

Model 3***

1.09 (0.75-1.58)

1.05 (0.72-1.53)

1.44 (0.99-2.10)

Model 4****

1.10 (0.76-1.59)

1.05 (0.72-1.54)

1.45 (0.99-2.11)

For each symptom the ‘No’ group is the reference group *Model 1 adjusted for baseline age **Model 2 additionally adjusted for MHT use (time-varying) ***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status

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****Model 4 additionally adjusted for time-varying BMI

Table 7. Hazard Ratios for the association between time-varying duration of symptoms and

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cerebrovascular disease N=8,881 (events=154) VMS duration

HF duration

NS duration

Model 1*

0.99 (0.96-1.03)

0.98 (0.93-1.04)

0.98 (0.93-1.04)

Model 2**

0.99 (0.96-1.03)

0.98 (0.93-1.04)

0.98 (0.93-1.04)

Model 3***

1.00 (0.96-1.04)

1.00 (0.94-1.06)

1.01 (0.95-1.07)

Model 4****

1.00 (0.97-1.05)

1.00 (0.94-1.06)

1.01 (0.95-1.07)

na

lP

re

Model

ur

*Model 1 adjusted for baseline age

**Model 2 additionally adjusted for MHT use (time-varying)

Jo

***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status ****Model 4 additionally adjusted for time-varying BMI

30

oo

f

Appendix A

Outcome

Coding

CVD

ICD-10-AM

pr

Table A.1. Codes used for ascertainment of outcomes

I11, I12, I13, I12 to I27, I46.1, I46.9, I48 to I50, I51.6, I51.9, I60, I61, I62.1, I62.9, I63 to I72,

e-

I73.9, I74, I77.9

ACHI

06/07,

Pr

as for IHD and cerebrovascular disease + 32739-00, 32742-00, 33115-00, 33118-00, 35303-

35307-00/01, 35309-06/07

na l

as for IHD and cerebrovascular disease + 32712, 32739, 32742, 32748, 32751, 32754, 32757, MBS codes

32703,

32721, 32724, 32730, 32733, 32736, 32708, 32710, 32711, 32715, 32718, 32760, 32763,

Jo ur

32766, 32769

33506, 33509, 33512 , 33515, 33518, 33521, 33524, 33527, 33530, 33533, 33536, 33539, 33543, 33545, 33548, 33551, 33554, 33800, 33806, 35100, 34103, 33080, 33112, 33115, 33116, 33118, 33119, 33121,

31

ICD-10-AM

I20 to I25

ACHI

38300-00, 38303-00, 38306-00/01/02, 38497-00/01/02/03, 38500-00/01/02, 38503-00/01/02

MBS codes

38300, 38303, 38306, 38315, 38315, 38318, 38497, 38498, 38500, 38501, 38503, 38504

pr

CHD

oo

f

33151, 33154, 33157, 33160, 35303, 35306, 35307, 35309

I60 to I69

Disease

ACHI

33500-00

MBS codes

33500, 32700, 32703

Pr

e-

Cerebrovascular ICD-10-AM

Jo ur

na l

ICD-10-AM and ACHI codes are described here according to the 8th edition only. . MBS coding can be found at http://www.mbsonline.gov.au.

32

of

Appendix B

ro

Table B.1. Baseline (1996) characteristics of ALSWH participants born in 1946-51, of whom hospital data was available, stratified by

Hot

Night

flushes (N)

flushes (Y)

sweats (N)

N = 5938

N = 2888

Age**

47.5 (1.5)

MHT use (yes)

14.2%

Education level

Middle

Excluded women

Hot

Hot

Night

Night

sweats (Y)

flushes (N)

flushes (Y)

sweats (N)

sweats (Y)

N = 6711

N = 2109

N = 3113

N = 1684

N = 3473

N = 1320

47.8 (1.4)

47.5 (1.5)

47.9 (1.4)

47.5 (1.5)

47.8 (1.4)

47.5 (1.5)

47.8 (1.5)

29.4%

16.0%

29.2%

13.4%

27.5%

14.5%

28.4%

lP

(844)

(849)

(1071)

(616)

(416)

(462)

(503)

(374)

48.0%

60.1%

49.8%

58.7%

42.4%

54.3%

43.7%

54.2%

(2829)

(1718)

(3317)

(1225)

(1310)

(899)

(1505)

(703)

36.1%

31.5%

35.2%

32.5%

40.1%

35.6%

39.7%

35.4%

(2126)

(900)

(2346)

(678)

(1239)

(590)

(1368)

(460)

Jo

Low

Night

re

Hot

ur na

Included women

-p

reporting of hot flushes and night sweats at baseline*

33

15.9%

8.4%

14.9%

8.8%

17.5%

(937)

(239)

(993)

(184)

(539)

16.6%

21.8%

17.2%

(798)

(478)

(930)

37.9%

36.4%

38.0%

(1824)

(797)

20.8%

High

Smoking status

Former smoker

(570)

(135)

17.5%

19.0%

(343)

(406)

(248)

(464)

(188)

35.5%

35.5%

38.2%

36.1%

37.4%

(2061)

(561)

(857)

(469)

(956)

(370)

18.4%

20.6%

18.4%

21.6%

18.3%

21.1%

19.0%

(1002)

(404)

(1114)

(291)

(522)

(225)

(558)

(188)

24.7%

23.6%

24.2%

24.3%

26.0%

23.3%

25.4%

24.5%

(1187)

(660)

(1313)

(328)

(627)

(286)

(672)

(242)

55.1%

48.7%

55.3%

45.7%

56.0%

48.4%

56.1%

45.9%

(3182)

(1362)

(3608)

(932)

(1681)

(782)

(1874)

(585)

29.6%

27.6%

28.8%

29.1%

26.6%

29.4%

27.1%

29.1%

re

20.2%

Jo

Never smoker

(167)

16.8%

lP

Moderate

10.4%

21.7%

ur na

Low

-p

activity*** Nil/Sedentary

16.6%

ro

Physical

10.1%

of

High

34

(772)

(1882)

(593)

(799)

15.3%

23.6%

15.8%

25.3%

17.4%

(885)

(660)

(1034)

(516)

(522)

14.3%

17.0%

15.0%

(846)

(488)

(998)

30.6%

32.2%

30.8%

(1804)

(824)

Low risk

50.2%

drinker

BMI

Underweight

22.2%

16.8%

25.0%

(359)

(561)

(319)

14.7%

16.0%

(338)

(460)

(254)

(503)

(208)

31.9%

30.6%

34.5%

32.1%

31.7%

(2058)

(665)

(942)

(573)

(1100)

(413)

44.4%

49.5%

45.1%

50.0%

44.2%

49.2%

44.8%

(2958)

(1272)

(3304)

(922)

(1536)

(734)

(1689)

(584)

4.9%

6.4%

4.7%

7.7%

4.5%

6.0%

4.0%

7.6%

(290)

(182)

(312)

(161)

(137)

(100)

(138)

(99)

2.1%

1.4%

1.8%

1.9%

1.6%

1.5%

1.5%

1.9%

(118)

(40)

(119)

(38)

(49)

(24)

(49)

(24)

re

15.3%

Jo

Risky drinker

(371)

15.0%

lP

Rarely drinks

(906)

16.2%

ur na

Non-drinker

-p

Alcohol status

ro

Current smoker

(476)

of

(1708)

35

45.1%

54.4%

(3128)

(1210)

(3428)

(911)

(1634)

27.1%

31.7%

27.9%

30.6%

(1548)

(879)

(1806)

(619)

16.2%

23.2%

17.2%

(925)

(642)

(1110)

41.8%

52.2%

44.5%

(672)

(1744)

(564)

27.1%

32.7%

28.2%

31.0%

(813)

(526)

(943)

(393)

of

53.0%

22.4%

16.9%

24.1%

18.1%

22.6%

(453)

(507)

(387)

(606)

(286)

re

Obese

43.7%

ro

Overweight

54.7%

-p

Healthy weight

**Mean (SD)

Jo

ur na

***Based on survey 3

lP

* Numbers do not add up, since some women missing values for HF or NS at Survey 1.

36

Appendix C Table C.1. Hazard Ratios for the association between time-varying hot flushes and cardiovascular disease N=7112 (events=946) Never

Rarely

Sometimes

Often

Model 1*

Ref.

0.90 (0.73-1.10)

1.00 (0.84-1.18)

1.23 (1.03-1.48)

Model 2**

Ref.

0.89 (0.73-1.10)

1.00 (0.84-1.18)

1.23 (1.02-1.48)

Model 3***

Ref.

0.86 (0.69-1.08)

0.96 (0.80-1.16)

1.15 (0.93-1.41)

Model

Ref.

0.86 (0.69-1.08)

0.95 (0.79-1.15)

ro

of

Model

-p

4****

1.14 (0.93-1.41)

re

*Model 1 adjusted for baseline age

**Model 2 additionally adjusted for MHT use (time-varying)

lP

***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status

ur na

****Model 4 additionally adjusted for time-varying BMI

Table C.2. Hazard Ratios for the association between time-varying night sweats and cardiovascular disease

Jo

N=7112 (events=946) Model

Never

Rarely

Sometimes

Often

Model 1*

Ref.

0.97 (0.79-1.18)

1.12 (0.94-1.33)

1.42 (1.17-1.72)

Model 2**

Ref.

0.97 (0.79-1.18)

1.11 (0.93-1.33)

1.41 (1.16-1.72)

Model 3***

Ref.

0.91 (0.73-1.14)

1.08 (0.89-1.31)

1.37 (1.10-1.70)

37

Ref.

Model

0.91 (0.73-1.15)

1.05 (0.86-1.28)

1.34 (1.08-1.67)

4**** *Model 1 adjusted for baseline age **Model 2 additionally adjusted for MHT use (time-varying) ***Model 3 additionally adjusted for education level and time-varying physical activity,

of

smoking status, and alcohol status

ro

****Model 4 additionally adjusted for time-varying BMI

Table C.3. Hazard Ratios for the association between time-varying hot flushes and coronary

-p

heart disease

Never

Rarely

Model 1*

Ref.

0.97 (0.74-1.29)

Model 2**

Ref.

Model 3***

Ref.

4****

Often

1.10 (0.87-1.39)

1.20 (0.93-1.55)

0.97 (0.74-1.28)

1.10 (0.87-1.38)

1.20 (0.93-1.55)

0.91 (0.67-1.24)

1.01 (0.78-1.30)

1.06 (0.79-1.41)

1.01 (0.78-1.30)

1.06 (0.79-1.41)

ur na

Model

Sometimes

lP

Model

Ref.

re

N=7112 (events=502)

0.91 (0.67-1.24)

*Model 1 adjusted for baseline age

Jo

**Model 2 additionally adjusted for MHT use (time-varying) ***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status ****Model 4 additionally adjusted for time-varying BMI

38

Table C.4. Hazard Ratios for the association between time-varying night sweats and coronary heart disease N=7112 (events=502) Never

Rarely

Sometimes

Often

Model 1*

Ref.

1.03 (0.79-1.35)

1.09 (0.86-1.40)

1.44 (1.10-1.88)

Model 2**

Ref.

1.03 (0.79-1.35)

1.09 (0.86-1.40)

1.44 (1.10-1.88)

Model 3***

Ref.

0.93 (0.69-1.26)

0.99 (0.75-1.30)

1.26 (0.93-1.69)

Model

Ref.

0.94 (0.69-1.27)

0.96 (0.73-1.27)

1.23 (0.92-1.67)

ro

of

Model

4****

-p

*Model 1 adjusted for baseline age

re

**Model 2 additionally adjusted for MHT use (time-varying)

***Model 3 additionally adjusted for education level and time-varying physical activity,

lP

smoking status, and alcohol status

ur na

****Model 4 additionally adjusted for time-varying BMI

Table C.5. Hazard Ratios for the association between time-varying hot flushes and cerebrovascular disease N=7114 (events=152)

Never

Rarely

Sometimes

Often

Model 1*

Ref.

0.64 (0.38-1.09)

0.78 (0.51-1.18)

1.12 (0.72-1.76)

Model 2**

Ref.

0.64 (0.38-1.08)

0.77 (0.51-1.17)

1.11 (0.71-1.74)

Model 3***

Ref.

0.59 (0.31-1.10)

0.78 (0.48-1.25)

1.18 (0.70-1.98)

Model

Ref.

0.59 (0.32-1.11)

0.78 (0.49-1.26)

1.19 (0.71-1.99)

Jo

Model

39

4**** *Model 1 adjusted for baseline age **Model 2 additionally adjusted for MHT use (time-varying) ***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status

of

****Model 4 additionally adjusted for time-varying BMI

ro

Table C.6. Hazard Ratios for the association between time-varying night sweats and cerebrovascular disease

Never

Rarely

Model 1*

Ref.

0.92 (0.56-1.53)

Model 2**

Ref.

0.92 (0.56-1.52)

Model 3***

Ref.

Model

Ref.

Often 1.52 (0.94-2.45)

1.15 (0.76-1.74)

1.51 (0.93-2.44)

1.00 (0.56-1.77)

1.25 (0.78-1.99)

1.78 (1.04-3.03)

1.01 (0.57-1.78)

1.25 (0.79-1.99)

1.79 (1.05-3.06)

lP

1.15 (0.76-1.75)

ur na

4****

Sometimes

re

Model

-p

N=7114 (events=152)

*Model 1 adjusted for baseline age

**Model 2 additionally adjusted for MHT use (time-varying)

Jo

***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status ****Model 4 additionally adjusted for time-varying BMI

40

Appendix D Methods for multiple imputation Missing values in the survey data ranged from 0.2% to 40.5%, mainly due to Survey 3, in which questions were slightly differently asked compared to the other surveys. As a sensitivity analysis we also performed multiple imputation using the two-fold fully conditional specification algorithm (Welch et al., 2014; Welch, Bartlett and Petersen, 2014), with 10 imputations, 20

of

among-time iterations and 10 within-time iterations. We used a time window of two, so

ro

measurements recorded at t-2 until t+2 were included in the imputations for time t. If a woman was lost to follow-up, the two-fold algorithm replaces imputed values falling outside her follow-

-p

up period with missing values. In order to reach convergence in the imputation, we had to

re

dichotomize all the time-varying covariates. For physical activity we combined ‘nil/sedentary’ with ‘low’ and ‘moderate’ with ‘high’, for alcohol use we combined ‘non-drinker’ with ‘rarely

lP

drinks’ and ‘low risk’ with ‘risky drinker’, for smoking we combined ‘never’ with ‘former’ and for BMI we combined ‘underweight’ with ‘normal weight’ and ‘overweight’ with ‘obese’.

ur na

Additionally, we had to include indicator variables for ‘missing’ categories for physical activity and alcohol. These missing value indicator categories were not used in the available case analysis. Imputation was performed with Stata version 14.0 (StataCorp, 2015).

Jo

Results for multiple imputation

Table D.1. Hazard Ratio’s for the association between time-varying hot flushes and cardiovascular disease, all states combined, strata(state) as variable in model No = Never/Rarely, Yes = Sometimes/Often Model

VSM (Yes)

Hot flushes (Yes)

Night sweats (Yes)

41

Model 1*

1.09 (0.95-1.24)

1.09 (0.95-1.24)

1.19 (1.03-1.37)

Model 2**

1.09 (0.95-1.24)

1.08 (0.95-1.24)

1.19 (1.03-1.36)

Model 3*** 1.02 (0.89-1.17)

1.02 (0.89-1.17)

1.12 (0.97-1.29)

1.01 (0.89-1.16)

1.01 (0.88-1.16)

1.11 (0.97-1.28)

Model 4****

of

For each symptom the ‘No’ group is the reference group *Model 1 adjusted for age

ro

**Model 2 additionally adjusted for MHT use (time-varying)

***Model 3 additionally adjusted for education level and time-varying physical activity,

-p

smoking status, and alcohol status

re

****Model 4 additionally adjusted for time-varying BMI

lP

Table D.2. Hazard Ratios for the association between time-varying duration of symptoms and cardiovascular disease, all states combined, strata(state) as variable in model HF duration

NS duration

1.02 (1.00-1.03)

1.01 (0.98-1.03)

1.01 (0.99-1.03)

Model 2**

1.02 (1.00-1.04)

1.01 (0.98-1.03)

1.01 (0.99-1.03)

Model 3***

1.02 (1.01-1.04)

1.01 (0.99-1.04)

1.02 (0.99-1.04)

Model

1.02 (1.00-1.04)

1.01 (0.98-1.03)

1.02 (0.99-1.04)

Jo

Model 1*

VSM duration

ur na

Model

4****

*Model 1 adjusted for age (time-varying) **Model 2 additionally adjusted for MHT use (time-varying)

42

***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status ****Model 4 additionally adjusted for time-varying BMI

Table D.3. Hazard Ratios for the association between time-varying hot flushes and coronary heart disease, all states combined, strata(state) as variable in model

Hot flushes (Yes)

Model 1*

1.10 (0.91-1.33)

1.11 (0.92-1.34)

Model 2**

1.10 (0.91-1.33)

1.11 (0.92-1.34)

Model 3***

1.04 (0.86-1.25)

1.05 (0.86-1.26)

Model

1.02 (0.85-1.24)

1.03 (0.85-1.25)

1.20 (0.99-1.46) 1.21 (0.99-1.47)

re

1.14 (0.94-1.39) 1.13 (0.93-1.38)

lP

4****

Night sweats (Yes)

ro

VSM (Yes)

-p

Model

of

No = Never/Rarely, Yes = Sometimes/Often

For each symptom the ‘No’ group is the reference group

ur na

*Model 1 adjusted for age (time-varying)

**Model 2 additionally adjusted for MHT use (time-varying) ***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status

Jo

****Model 4 additionally adjusted for time-varying BMI

Table D.4. Hazard Ratios for the association between time-varying duration of symptoms and coronary heart disease, all states combined, strata(state) as variable in model Model

HF duration

NS duration

VSM duration

43

Model 1*

1.04 (1.02-1.06)

1.02 (0.99-1.06)

1.03 (0.99-1.06)

Model 2**

1.04 (1.02-1.06)

1.02 (0.99-1.06)

1.03 (0.99-1.06)

Model 3***

1.04 (1.02-1.07)

1.03 (1.00-1.07)

1.03 (1.00-1.07)

Model 4****

1.04 (1.02-1.06)

1.03 (0.99-1.06)

1.03 (1.00-1.07)

*Model 1 adjusted for age (time-varying)

of

**Model 2 additionally adjusted for MHT use (time-varying) ***Model 3 additionally adjusted for education level and time-varying physical activity,

-p

****Model 4 additionally adjusted for time-varying BMI

ro

smoking status, and alcohol status

re

Table D.5. Hazard Ratios for the association between time-varying hot flushes and cerebrovascular disease, all states combined, strata(state) as variable in model

VSM (Yes)

Model 1*

1.05 (0.74-1.49)

ur na

Model

lP

No = Never/Rarely, Yes = Sometimes/Often

Hot flushes (Yes)

Night sweats (Yes)

1.02 (0.72-1.45)

1.34 (0.95-1.90)

Model 2**

1.05 (0.74-1.48)

1.01 (0.71-1.44)

1.33 (0.94-1.89)

Model 3***

0.99 (0.70-1.41)

0.96 (0.67-1.37)

1.27 (0.90-1.81)

Model

1.00 (0.70-1.42)

0.97 (0.68-1.38)

1.28 (0.90-1.82)

Jo

4****

For each symptom the ‘No’ group is the reference group *Model 1 adjusted for age (time-varying) **Model 2 additionally adjusted for MHT use (time-varying)

44

***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status ****Model 4 additionally adjusted for time-varying BMI

Table D.6. Hazard Ratios for the association between time-varying duration of symptoms and

of

cerebrovascular disease, all states combined, strata(state) as variable in model VSM duration

HF duration

NS duration

Model 1*

1.00 (0.96-1.04)

0.99 (0.93-1.05)

0.99 (0.93-1.04)

Model 2**

1.00 (0.96-1.04)

0.99 (0.93-1.05)

Model 3***

1.01 (0.97-1.05)

1.00 (0.94-1.07)

1.00 (0.94-1.06)

Model

1.01 (0.97-1.05)

1.00 (0.94-1.07)

1.00 (0.94-1.06)

re

-p

0.98 (0.93-1.04)

lP

4**** *Model 1 adjusted for age

ro

Model

**Model 2 additionally adjusted for MHT use (time-varying)

ur na

***Model 3 additionally adjusted for education level and time-varying physical activity, smoking status, and alcohol status

Jo

****Model 4 additionally adjusted for time-varying BMI

45