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Stroke is a major cause of death and disability worldwide.1 The burden is particularly large among women, given their longer life expectancy, higher strokes rates in older age compared with men, and lower physical functioning post-stroke.2 Similarly, depression is a global health problem, with a high prevalence3 and ranked as the fourth leading cause of morbidity globally.4

Although the development of depression after stroke is well-recognized,5 the role of depression as a risk factor for stroke is less well understood. Conflicting findings from existing studies led to recent systematic review and meta-analyses of studies that prospectively examined depression and stroke risk.6,7 Although the authors concluded that depres-sion increases stroke risk, there was substantial heterogeneity between studies, which was not fully explained in subgroup analyses.7 Specifically, subgroup analyses revealed substan-tial heterogeneity between studies of women.7 This could be a result of differences in the age of study populations because meta-analysis revealed a stronger association in studies where the mean age was 2-fold increased odds of stroke (odds ratio, 2.41; 95% confidence interval, 1.783.27), which attenuated after adjusting for age, socioeconomic status, lifestyle, and physiological factors (odds ratio, 1.94; 95% confidence interval, 1.372.74). Findings were robust to sensitivity analyses addressing methodological issues, including definition of depression, antidepressant use, and missing covariate data.

ConclusionsDepression is a strong risk factor for stroke in midaged women, with the association partially explained by lifestyle and physiological factors. Further studies of midaged and older women from the same population are needed to confirm whether depression is particularly important in younger women and to inform targeted intervention approaches. (Stroke. 2013;44:1555-1560.)

Key Words: cerebrovascular disease depression mental health stroke

Depression and Risk of Stroke in Midaged WomenA Prospective Longitudinal Study

Caroline A. Jackson, PhD; Gita D. Mishra, PhD

Received February 11, 2013; final revision received March 17, 2013; accepted March 26, 2013.From the Centre for Longitudinal and Life Course Research, School of Population Health, University of Queensland, Australia.The online-only Data Supplement is available with this article at http://stroke.ahajournals.org/lookup/suppl/doi:10.1161/STROKEAHA.

113.001147/-/DC1.Correspondence to Caroline A. Jackson, PhD, School of Population Health, University of Queensland, Herston Rd, Herston QLD 4006, Australia. E-mail

caroline.jackson@uq.ed.au 2013 American Heart Association, Inc.Stroke is available at http://stroke.ahajournals.org DOI: 10.1161/STROKEAHA.113.001147

1556 Stroke June 2013

MethodsStudy SettingWe included participants from the Australian Longitudinal Study on Womens Health, a population-based study of women born in 19211926, 19461951, and 19731978. Women were randomly selected from the Medicare database, which covers all citizens and permanent residents of Australia, including refugees and immigrants. Women born in 19461951 were surveyed, using self-completed question-naires, in 1996 (survey 1, S1), 1998 (survey 2, S2), 2001 (survey 3, S3), 2004 (survey 4, S4), 2007 (survey 5, S5), and 2010 (survey 6, S6). Full details of recruitment and response rates are reported elsewhere.11

Study PopulationWe included women from the 19461951 cohort, which recruited 13 715 women at S1, 12 338 (90%) of whom returned S2. For these analyses, we followed women from S2 onward because information on depression was not collected at S1. We excluded women as indi-cated in Figure 1.

StrokeIncident stroke was determined at S3 to S6, when women were asked, In the past 3 years have you been diagnosed or treated for stroke? We identified deaths and cause of death through linkage of Australian Longitudinal Study on Womens Health to the National Death Index. Stroke deaths were determined using the following International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) codes from the principal or secondary diag-nosis fields of death certificate data: I60I60.9, I61.0I61.9, I63.0I63.9, and I64.

Measures of DepressionDepressive symptoms experienced in the past week were identified using the Center for Epidemiological Studies Depression Scale short-ened version (CESD-10).12 It is well validated and has good testretest reliability and predictive validity compared with the original 20-item format.1214 In S2 to S4, women were also asked During the past 4 weeks have you taken any medications for depression, and in S5, women were asked to list the medications they had taken in the past month. For the latter survey, we ascertained antidepressant use from the anatomic therapeutical chemical system, by identifying medications coded as N06A or N06B.

In primary analyses, we defined depression at each time point as being present if women reported taking antidepressant medication or if they scored 10 on the CESD-10 scale.

Women were also asked at each survey In the past 3 years have you been diagnosed or treated for depression? which was used in sensitivity analyses to investigate whether the definition of depression impacted the results.

Other Exposure VariablesLifestyle and physiological stroke risk factors were determined at S2 to S5. Smoking was dichotomized into current smoker or not. Body mass index was (kg/m2) calculated from self-reported weight and height. Physical activity was assessed using questions from the Australian physical activity survey15 and defined according to min-utes of moderate activity per week: nil/sedentary (300). Alcohol intake was defined in light of the Australian National Health and Medical Research Council guidelines,16 with risky drinkers (1528 drinks per week) and high-risk drinkers (>28 drinks per week) categorized ac-cordingly. For women identified as low risk by the National Health and Medical Research Council guidelines, we separately categorized those classified as low-risk drinkers from those reporting that they drink only rarely. Non-drinkers were classified separately.

At S1 and S2, women were asked whether they had ever been di-agnosed with or treated for hypertension, diabetes mellitus, or heart disease. Subsequently, they were asked whether they had been diag-nosed with these conditions in the period since the previous survey. Women were also asked whether they had undergone a hysterectomy or oophorectomy.

Relationship status at S2 was categorized into married/defactor partner, divorced/separated/widowed, and single. Socioeconomic sta-tus (SES) was measured by education level at S1 (no formal qualifica-tions; school leaving certificate; high school leaving certificate; trade or apprenticeship; certificate or diploma; and university degree) and home ownership at S2. These SES indicators were included because they were independently associated with stroke in this cohort, whereas occupation and managing on income were not. A small number of women were inconsistent in returning surveys. Where women returned S4, S5, or S6, but did not return the prior survey, we carried forward the values for exposure variables from the immediately preceding sur-vey, if returned. Therefore exposure variables for 5% of women at S3, 3.5% at S4, and 2.7% at S5 were derived from the previous survey.

Statistical AnalysisAll analyses were performed using Stata version 12.0 (StataCorp; College Station, TX.)

We compared characteristics of women according to depression status at S2 using the Pearson 2 test and Student t test. To account for multiple observations for each participant, we used generalized estimating equation regression models for binary outcome data (using an unstructured correlation structure and a logit link function). We calculated odds ratios (ORs) with 95% confidence intervals (CIs) for the relationship between depression and first-ever stroke, including depression as a time-varying covariate. Women who reported stroke did not contribute to the analyses of time periods thereafter. We calculated crude ORs before adjusting for present age and SES and then additional confounding factors. All physiological and lifestyle variables were included as time-varying covariates. Once women reported a chronic condition, they were considered to have it at each subsequent survey. Time lags were used so that exposures, including depression, were associated with stroke occurring at the subsequent survey. We determined model fit at each time point using the HosmerLemeshow goodness-of-fit test, which indicated that the model was well calibrated.

Sensitivity AnalysesWe performed sensitivity analyses, where we repeated analyses using alternate definitions of depression: CESD-10 10; doctor diagnosed depression or CESD-10 10; doctor diagnosed depression, CESD-10 10 or taking anti-depressants. We also repeated the primary analysis but excluded women taking antidepressants. Finally, a proportion of returned questionnaires (5%17% across surveys) were incomplete

12,338 womenreturned survey 2

12,191 women

10,547 women contributed to analyses

1644 women excluded: 709 (5.8%) returned no further

surveys 13 (0.1%) did return further

survey(s), but didnt completestroke question(s) at any point

235 (1.9%) had no data on depression and stroke occurrence for at least one survey period

687 (6%) did not have complete data on exposures and outcome for at least one survey period

147 with previous stroke excluded

Figure 1. Flow diagram of included and excluded women.

Jackson and Mishra Depression and Stroke Risk in Midaged Women 1557

with respect to some covariates. We therefore performed a sensitiv-ity analysis, where we repeated the primary analyses, but performed multiple imputation for all covariates with missing values.

ResultsAmong 12 338 women who returned S2, 12 191 did not have a history of previous stroke. Follow-up rates at S3, S4, S5, and S6 were 88%, 85%, 82%, and 78%. We excluded 1644 women (Figure 1). Of 12 191 women without stroke at S2, we included 10 547 (87%) in the analyses, 79% of whom returned all subsequent surveys. Almost all women (99.4%)

were nonindigenous. There were some baseline differences between included and excluded women, with the latter being less well educated, generally less healthy, and more likely to have depression (Table I in the online-only Data Supplement).

At S2, the mean age was 52.5 (1.5 SD) and prevalence of depression was 25.1%, with a similar prevalence at subse-quent surveys (Table II in the online-only Data Supplement). Depression at S2 was associated with SES, marital status, life-style behaviors, and physiological stroke risk factors (Table 1).

During follow-up, 177 first-ever strokes occurred, 5 of which were fatal, giving a stroke prevalence of 1.5%. Of these, 143 were

Table 1. Characteristics of Women According to Depression Status* at Survey 2

Characteristics No Depression (n=7787) Depression (n=2612) P Value

Demographics

Mean age (SD) 52.5 (1.5) 52.5 (1.5) 0.22

Area of residence, n=9876

Urban 2736 (35.4) 963 (37.0)

Rural 4541 (58.7) 1496 (57.5)

Remote 462 (6.0) 144 (5.5) 0.27

Education

No formal qualifications 1228 (15.8) 339 (13.0)

School certificate 1351 (17.4) 398 (15.2)

High school certificate 276 (3.5) 93 (3.6)

Trade or apprentice 1344 (17.3) 415 (15.9)

Certificate/diploma 2483 (31.9) 820 (31.4)

University degree or higher 1105 (14.2) 547 (20.9)

1558 Stroke June 2013

included in the primary analyses, and 170 were included in the sensitivity analysis after multiple imputation of missing covari-ate data. In the primary analyses, depression was associated with >2-fold greater odds of stroke (OR, 2.41; 95% CI, 1.783.27). This association attenuated but remained statistically significant on controlling for age, SES, lifestyle, and physiological stroke risk factors (OR, 1.94; 95% CI, 1.372.74; Table 2).

We found similar results in sensitivity analyses where we used alternate definitions of depression (Table 3). The associa-tion between depression and stroke also was almost identical when we repeated the analyses after multiple imputation of missing covariate data (OR, 1.98; 95% CI, 1.442.72).

DiscussionIn our study, depression was associated with a marked increased risk of incident stroke among midaged women,

even after adjusting for age, SES, lifestyle, and physiological risk factors. Our findings suggest that the association between depression and stroke risk among midaged women, in particu-lar, may be stronger than previously thought. Interestingly, the role of depression as a preventable risk factor for stroke is often overlooked, with depression notably absent from reviews and guidelines for the primary prevention of stroke.20,21 The association seems to be only partially accounted for by tra-ditional stroke risk factors, highlighting the possible role of other novel risk factors, or biological mechanisms, such as the proposed neuroendocrine17 and immunologic/inflamma-tory18,19 pathways.

Comparisons With Other StudiesOur finding that depression increases stroke risk is in keep-ing with results of most other prospective studies,6,7 although

Table 2. ORs of Incident Stroke for Depression, Lifestyle, and Physiological Factors

Risk FactorUnadjusted OR (95% CI)

Age and SES-Adjusted* OR (95% CI)

Fully Adjusted OR (95% CI)

P Value From Fully Adjusted Model

Depression 2.41 (1.783.27) 2.21 (1.623.03) 1.94 (1.372.74)

Jackson and Mishra Depression and Stroke Risk in Midaged Women 1559

the magnitude of the effect in our study population is larger than in many previous studies. However, existing studies have generally included only a broad age range or elderly people, without stratification by age. No previous study has included a female-only population as young as the Australian Longitudinal Study on Womens Health 19461951 cohort. The closest comparison is with the Nurses Health Study, which found that depression was associated with a 30% increased stroke risk. However, the mean age of this cohort was 14 years higher than in our study, and results were not reported by different age groups.10 In subgroup analyses of a recent review, the authors compared studies with a population mean age of

1560 Stroke June 2013

AcknowledgmentsDrs Jackson and Mishra designed the study. Dr Jackson performed the analyses and drafted the article. Drs Jackson and Mishra inter-preted the results, and Dr Mishra commented on the draft article.

Sources of FundingAustralian Longitudinal Study on Womens Health is funded by the Australian Commonwealth Department of Health and Ageing. The funding organizations had no role in the design and conduct of the study or in data collection, analysis, interpretation of results, and preparation of the article.

DisclosuresDrs Jackson and Mishra were supported by the Australian National Health and Medical Research Council (grant number: APP1000986).

References 1. Johnston SC, Mendis S, Mathers CD. Global variation in stroke burden

and mortality: estimates from monitoring, surveillance, and modelling. Lancet Neurol. 2009;8:345354.

2. Reeves MJ, Bushnell CD, Howard G, Gargano JW, Duncan PW, Lynch G, et al. Sex differences in stroke: epidemiology, clinical presentation, medical care, and outcomes. Lancet Neurol. 2008;7:915926.

3. Bromet E, Andrade LH, Hwang I, Sampson NA, Alonso J, de Girolamo G, et al. Cross-national epidemiology of DSM-IV major depressive epi-sode. BMC Med. 2011;9:90.

4. World Health Organisation. The global burden of disease: 2004 update. 2008

5. Hackett ML, Yapa C, Parag V, Anderson CS. Frequency of depres-sion after stroke: a systematic review of observational studies. Stroke. 2005;36:13301340.

6. Dong JY, Zhang YH, Tong J, Qin LQ. Depression and risk of stroke: a meta-analysis of prospective studies. Stroke. 2012;43:3237.

7. Pan A, Sun Q, Okereke OI, Rexrode KM, Hu FB. Depression and risk of stroke morbidity and mortality: a meta-analysis and systematic review. JAMA. 2011;306:12411249.

8. Avendano M, Kawachi I, Van Lenthe F, Boshuizen HC, Mackenbach JP, Van den Bos GA, et al. Socioeconomic status and stroke incidence in the US elderly: the role of risk factors in the EPESE study. Stroke. 2006;37:13681373.

9. Salaycik KJ, Kelly-Hayes M, Beiser A, Nguyen AH, Brady SM, Kase CS, et al. Depressive symptoms and risk of stroke: the Framingham Study. Stroke. 2007;38:1621.

10. Pan A, Okereke OI, Sun Q, Logroscino G, Manson JE, Willett WC, et al. Depression and incident stroke in women. Stroke. 2011;42:27702775.

11. Lee C, Dobson AJ, Brown WJ, Bryson L, Byles J, Warner-Smith P, et al. Cohort profile: the Australian Longitudinal Study on womens health. Int J Epidemiol. 2005;34:987991.

12. Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depres-sion in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med. 1994;10:7784.

13. Carpenter JS, Andrykowski MA, Wilson J, Hall LA, Rayens MK, Sachs B, et al. Psychometrics for two short forms of the Center for Epidemiologic Studies-Depression Scale. Issues Ment Health Nurs. 1998;19:481494.

14. Furukawa T, Anraku K, Hiroe T, Takahashi K, Kitamura T, Hirai T, et al. Screening for depression among first-visit psychiatric patients: com-parison of different scoring methods for the Center for Epidemiologic Studies Depression Scale using receiver operating characteristic analy-ses. Psychiatry Clin Neurosci. 1997;51:7178.

15. Armstrong T, Bauman A, Davies J. Physical activity patterns of aus-tralian adults: results of the 1999 National Physical Activity Survey. Canberra. 2000.

16. National Health and Medical Research Council. Australian alcohol guidelines: health risks and benefits. Canberra 2001.

17. Musselman DL, Evans DL, Nemeroff CB. The relationship of depression to cardiovascular disease: epidemiology, biology, and treatment. Arch Gen Psychiatry. 1998;55:580592.

18. Shimbo D, Chaplin W, Crossman D, Haas D, Davidson KW. Role of depression and inflammation in incident coronary heart disease events. Am J Cardiol. 2005;96:10161021.

19. Stewart JC, Rand KL, Muldoon MF, Kamarck TW. A prospective evalu-ation of the directionality of the depression-inflammation relationship. Brain Behav Immun. 2009;23:936944.

20. Goldstein LB, Bushnell CD, Adams RJ, Appel LJ, Braun LT, Chaturvedi S, et al; American Heart Association Stroke Council; Council on Cardiovascular Nursing; Council on Epidemiology and Prevention; Council for High Blood Pressure Research, Council on Peripheral Vascular Disease, and Interdisciplinary Council on Quality of Care and Outcomes Research. Guidelines for the primary prevention of stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2011;42:517584.

21. Grysiewicz RA, Thomas K, Pandey DK. Epidemiology of ischemic and hemorrhagic stroke: incidence, prevalence, mortality, and risk factors. Neurol Clin. 2008;26:871895, vii.

22. Hackam DG, Mrkobrada M. Selective serotonin reuptake inhibitors and brain hemorrhage: a meta-analysis. Neurology. 2012;79:18621865.

23. Britton A, Milne B, Butler T, Sanchez-Galvez A, Shipley M, Rudd A, et al. Validating self-reported strokes in a longitudinal UK cohort study (Whitehall II): extracting information from hospital medical records ver-sus the Hospital Episode Statistics database. BMC Med Res Methodol. 2012;12:83.

24. Engstad T, Bonaa M, Viitanen M. Validity of self-reported stroke. Stroke. 2000;31:16021607.

25. OMahony PG, Dobson R, Rodgers H, James OF, Thomson RG. Validation of a population screening questionnaire to assess prevalence of stroke. Stroke. 1995;26:13341337.

26. Simpson CF, Boyd CM, Carlson MC, Griswold ME, Guralnik JM, Fried LP. Agreement between self-report of disease diagnoses and medical record validation in disabled older women: factors that modify agree-ment. J Am Geriatr Soc. 2004;52:123127.

27. Truelsen T, Piechowski-Jwiak B, Bonita R, Mathers C, Bogousslavsky J, Boysen G. Stroke incidence and prevalence in Europe: a review of available data. Eur J Neurol. 2006;13:581598.

28. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62:593602.