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Factors influencing self-reported anxiety or depression following stroke or TIA using linked registry and hospital data

  • Tharshanah Thayabaranathan
  • Nadine E. Andrew
  • Monique F. Kilkenny
  • Rene Stolwyk
  • Amanda G. Thrift
  • Rohan Grimley
  • Trisha Johnston
  • Vijaya Sundararajan
  • Natasha A. Lannin
  • Dominique A. Cadilhac
Article

Abstract

Purpose

Approximately 30–50% of survivors experience problems with anxiety or depression post-stroke. It is important to understand the factors associated with post-stroke anxiety or depression to identify effective interventions.

Methods

Patient-level data from the Australian Stroke Clinical Registry (years 2009–2013), from participating hospitals in Queensland (n = 23), were linked with Queensland Hospital Emergency and Admission datasets. Self-reported anxiety or depression was assessed using the EQ-5D-3L, obtained at 90–180 days post-stroke. Multivariable multilevel logistic regression, with manual stepwise elimination of variables, was used to investigate the association between self-reported anxiety or depression, patient factors and acute stroke processes of care. Comorbidities, including prior mental health problems (e.g. anxiety, depression and dementia) coded in previous hospital admissions or emergency presentations using ICD-10 diagnosis codes, were identified from 5 years prior to stroke event.

Results

2853 patients were included (median age 74; 45% female; 72% stroke; 24% transient ischaemic attack). Nearly half (47%) reported some level of anxiety or depression post-stroke. The factors most strongly associated with anxiety or depression were a prior diagnosis of anxiety or depression [Adjusted Odds Ratio (aOR) 2.37, 95% confidence interval (95% CI) 1.66–3.39; p < 0.001], dementia (aOR 1.91, 95% CI 1.24–2.93; p = 0.003), being at home with support (aOR 1.41, 95% CI 1.12–1.69; p = < 0.001), and low socioeconomic advantage compared to high (aOR 1.59, 95% CI 1.21–2.10; p = 0.001). Acute stroke processes of care were not independently associated with anxiety or depression.

Conclusions

Identification of those with prior mental health problems for early intervention and support may help reduce the prevalence of post-stroke anxiety or depression.

Keywords

Anxiety Depression Stroke Registries Data linkage Quality of life Comorbidity 

Notes

Acknowledgements

We acknowledge members of the Australian Stroke Clinical Registry (AuSCR) Steering Committee, staff from the George Institute for Global Health and the Florey Institute of Neuroscience and Mental Health who manage the AuSCR (Online-Only Data Supplement). We also thank the hospital clinicians (Online-Only Data Supplement) and patients who contribute data to AuSCR. We also acknowledge the data linkage team in Queensland Health (Data Linkage Queensland) who undertook the linkage of data that this study is based on.

Funding

The Australian Stroke Clinical Registry (AuSCR) was supported by grants from the National Health and Medical Research Council (NHMRC: 1034415), Monash University, Queensland Health, Victorian Department of Health and Human Services, the Stroke Foundation, Allergan Australia, Ipsen, Boehringer Ingelheim, and consumer donations. TT is supported by the Australian Government Research Training Program Scholarship. The following authors receive Research Fellowship support from the NHMRC: NEA (1072053), MFK (1109426), AGT (1042600), NAL (1112158) and DAC (1063761 co-funded by Heart Foundation).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study as part of the registry.

Supplementary material

11136_2018_1960_MOESM1_ESM.docx (35 kb)
Supplementary material 1 (DOCX 35 KB)

References

  1. 1.
    Andrew, N. E., Kilkenny, M., Naylor, R., Purvis, T., Lalor, E., Moloczij, N., & Cadilhac, D. A. (2014). Understanding long-term unmet needs in Australian survivors of stroke. International Journal of Stroke, 9, 106–112.CrossRefPubMedGoogle Scholar
  2. 2.
    Chrichton, S. L., Bray, B. D., McKevitt, C., Rudd, A. G., & Wolfe, C. D. A. (2016). Patient outcomes up to 15 years after stroke: Survival, disability, quality of life, cognition and mental health. Journal of Neurology, Neurosurgery and Psychiatry, 87, 1091–1098.CrossRefGoogle Scholar
  3. 3.
    Broomfield, N. M., Quinn, T. J., Abdul-Rahim, A. H., Walters, M. R., & Evans, J. J. (2014). Depression and anxiety symptoms post-stroke/TIA: Prevalence and associations in cross-sectional data from a regional stroke registry. BMC Neurology, 14, 198.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Berg, A., Palomaki, H., Lehtihalmes, M., Lonnqvist, J., & Kaste, M. (2003). Poststroke depression: An 18-month follow-up. Stroke, 34, 138–143.CrossRefPubMedGoogle Scholar
  5. 5.
    Shi, Y., Yang, D., Zeng, Y., & Wu, W. (2017) Risk factors for post-stroke depression: A meta-analysis. Frontiers in Aging Neuroscience.  https://doi.org/10.3389/fnagi.2017.00218.Google Scholar
  6. 6.
    Cadilhac, D. A., Kilkenny, M. F., Longworth, M., Pollack, M. R. P., & Levi, C. R., on behalf of Greater Metropolitan Clinical T, Stroke Services New South Wales Coordinating C (2011). Metropolitan–rural divide for stroke outcomes: do stroke units make a difference? Internal Medicine Journal, 41, 321–326.CrossRefPubMedGoogle Scholar
  7. 7.
    National Stroke Foundation.(2010). Clinical guidelines for stroke management 2010. Melbourne.Google Scholar
  8. 8.
    Stroke Unit Trialists C. (2007). Organised inpatient (stroke unit) care for stroke. Cochrane Database of Systematic Reviews, 4, 4.Google Scholar
  9. 9.
    Cadilhac, D. A., Andrew, N. E., Kilkenny, M. F., Hill, K., Grabsch, B., Lannin, N. A., Thrift, A. G., Anderson, C. S., Donnan, G. A., Middleton, S., & Grimley, R. (2017). Improving quality and outcomes of stroke care in hospitals: Protocol and statistical analysis plan for the Stroke123 implementation study. International Journal of Stroke, 13, 96–106.CrossRefPubMedGoogle Scholar
  10. 10.
    Andrew, N. E., Sundararajan, V., Thrift, A. G., Kilkenny, M. F., Katzenellenbogen, J., Flack, F., Gattellari, M., Boyd, J. H., Anderson, P., Grabsch, B., et al. (2016). Addressing the challenges of cross-jurisdictional data linkage between a national clinical quality registry and government-held health data. Australian and New Zealand Journal of Public Health, 40, 436–442.CrossRefPubMedGoogle Scholar
  11. 11.
    Lannin, N. A., Anderson, C., Lim, J., Paice, K., Price, C., Faux, S., Levi, C., Donnan, G., & Cadilhac, D. (2013). Telephone follow-up was more expensive but more efficient than postal in a national stroke registry. Journal of Clinical Epidemiology, 66, 896–902.CrossRefPubMedGoogle Scholar
  12. 12.
    Preen, D. B., Holman, C. D. A. J., Spilsbury, K., Semmens, J. B., & Brameld, K. J. (2006). Length of comorbidity lookback period affected regression model performance of administrative health data. Journal of Clinical Epidemiology, 59, 940–946.CrossRefPubMedGoogle Scholar
  13. 13.
    WHO (World Health Organization) (2007). International statistical classification of diseases and related health problems. Retrieved April 16, 2017. 10th Revision, Version for 2010.Google Scholar
  14. 14.
    Cadilhac, D. A., Lannin, N. A., Anderson, C. S., Levi, C. R., Faux, S., Price, C., Middleton, S., Lim, J., Thrift, A. G., & Donnan, G. A. (2010). Protocol and pilot data for establishing the Australian Stroke Clinical Registry. International Journal of Stroke, 5, 217–226.CrossRefPubMedGoogle Scholar
  15. 15.
    Rabin, R., & de Charro, F. (2001). EQ-5D: A measure of health status from the EuroQol Group. Annals of Medicine, 33, 337–343.CrossRefPubMedGoogle Scholar
  16. 16.
    Dorman, P. J., Waddell, F., Slattery, J., Dennis, M., & Sandercock, P. (1997). Is the EuroQol a valid measure of health-related quality of life after stroke? Stroke, 28, 1876–1882.CrossRefPubMedGoogle Scholar
  17. 17.
    Hunger, M., Sabariego, C., Stollenwerk, B., Cieza, A., & Leidl, R. (2012). Validity, reliability and responsiveness of the EQ-5D in German stroke patients undergoing rehabilitation. Quality of Life Research, 21, 1205–1216.CrossRefPubMedGoogle Scholar
  18. 18.
    Konig, H. H., Bernert, S., Angermeyer, M. C., Matschinger, H., Martinez, M., Vilagut, G., Haro, J. M., de Girolamo, G., de Graaf, R., Kovess, V., & Alonso, J. (2009). Comparison of population health status in six european countries: Results of a representative survey using the EQ-5D questionnaire. Medical Care, 47, 255–261.CrossRefPubMedGoogle Scholar
  19. 19.
    McCaffrey, N., Kaambwa, B., Currow, D. C., & Ratcliffe, J. (2016). Health-related quality of life measured using the EQ-5D-5L: South Australian population norms. Health and Quality of Life Outcomes, 14, 133.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Jorgensen, T. S., Turesson, C., Kapetanovic, M., Englund, M., Turkiewicz, A., Christensen, R., Bliddal, H., Geborek, P., & Kristensen, L. E. (2017). EQ-5D utility, response and drug survival in rheumatoid arthritis patients on biologic monotherapy: A prospective observational study of patients registered in the south Swedish SSATG registry. PLoS ONE, 12, e0169946.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Christensen, M. C., Mayer, S., & Ferran, J. M. (2009). Quality of life after intracerebral hemorrhage: Results of the factor seven for acute hemorrhagic stroke (FAST) trial. Stroke, 40, 1677–1682.CrossRefPubMedGoogle Scholar
  22. 22.
    Lindgren, P., Glader, E. L., & Jonsson, B. (2008). Utility loss and indirect costs after stroke in Sweden. European Journal of Cardiovascular Prevention and Rehabilitation, 15, 230–233.CrossRefPubMedGoogle Scholar
  23. 23.
    Brooks, R. (1996). EuroQol: The current state of play. Health Policy, 37, 53–72.CrossRefPubMedGoogle Scholar
  24. 24.
    Kim, S.-K., Kim, S.-H., Jo, M.-W., & Lee, S. (2015). Estimation of minimally important differences in the EQ-5D and SF-6D indices and their utility in stroke. Health and Quality of Life Outcomes, 13, 32.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Pickard, A. S., Neary, M. P., & Cella, D. (2007). Estimation of minimally important differences in EQ-5D utility and VAS scores in cancer. Health and Quality of Life Outcomes, 5, 70.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Quan, H., Li, B., Couris, C. M., Fushimi, K., Graham, P., Hider, P., Januel, J.-M., & Sundararajan, V. (2011). Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. American Journal of Epidemiology, 173, 676–682.CrossRefPubMedGoogle Scholar
  27. 27.
    Charlson, M., Szatrowski, T. P., Peterson, J., & Gold, J. (1994). Validation of a combined comorbidity index. Journal of Clinical Epidemiology, 47, 1245–1251.CrossRefPubMedGoogle Scholar
  28. 28.
    Australian Bureau of Stastistics. (2006). Socio-economic indexes for areas (SEIFA). Canberra: Australian Bureau of Stastistics.Google Scholar
  29. 29.
    Counsell, C., Dennis, M., McDowall, M., & Warlow, C. (2002). Predicting outcome after acute and subacute stroke. Stroke, 33, 1041.CrossRefPubMedGoogle Scholar
  30. 30.
    Royston, P., Moons, K. G. M., Altman, D. G., & Vergouwe, Y. (2009). Prognosis and prognostic research: Developing a prognostic model. British Medical Journal, 338, b604.CrossRefPubMedGoogle Scholar
  31. 31.
    Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society Series B, 64, 583–639.CrossRefGoogle Scholar
  32. 32.
    Cadilhac, D. A., Pearce, D. C., Levi, C. R., & Donnan, G. A. (2008). Improvements in the quality of care and health outcomes with new stroke care units following implementation of a clinician-led, health system redesign programme in New South Wales, Australia. Quality and Safety in Health Care, 17, 329–333.CrossRefPubMedGoogle Scholar
  33. 33.
    Cadilhac, D. A., Kim, J., Lannin, N. A., Levi, C. R., Dewey, H. M., Hill, K., Faux, S., Andrew, N. E., Kilkenny, M. F., Grimley, R., et al. (2016). Better outcomes for hospitalized patients with TIA when in stroke units: An observational study. Neurology, 86, 2042–2048.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Hackett, M. L., Yapa, C., Parag, V., & Anderson, C. S. (2005). Frequency of depression after stroke: A systematic review of observational studies. Stroke, 36, 1330–1340.CrossRefPubMedGoogle Scholar
  35. 35.
    Kutlubaev, M. A., & Hackett, M. L. (2014). Part II: Predictors of depression after stroke and impact of depression on stroke outcome: An updated systematic review of observational studies. International Journal of Stroke, 9, 1026–1036.CrossRefPubMedGoogle Scholar
  36. 36.
    Hackett, M. L., & Anderson, C. S. (2005). Predictors of depression after stroke. Stroke, 36, 2296.CrossRefPubMedGoogle Scholar
  37. 37.
    Stroke Foundation. National stroke audit—rehabilitation services report 2016. Melbourne, Australia.Google Scholar
  38. 38.
    Cameron, J. I., O’Connell, C., Foley, N., Salter, K., Booth, R., Boyle, R., Cheung, D., Cooper, N., Corriveau, H., Dowlatshahi, D., et al. (2016). Canadian stroke best practice recommendations: Managing transitions of care following Stroke, Guidelines Update 2016. International Journal of Stroke, 11, 807–822.CrossRefPubMedGoogle Scholar
  39. 39.
    Eng, J. J., & Reime, B. (2014). Exercise for depressive symptoms in stroke patients: A systematic review and meta-analysis. Clinical Rehabilitation, 28, 731–739.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Thayabaranathan, T., Andrew, N. E., Immink, M. A., Hillier, S., Stevens, P., Stolwyk, R., Kilkenny, M., & Cadilhac, D. A. (2017). Determining the potential benefits of yoga in chronic stroke care: a systematic review and meta-analysis. Topics in Stroke Rehabilitation, 24, 279–287.CrossRefPubMedGoogle Scholar
  41. 41.
    Ayerbe, L., Ayis, S., Rudd, A. G., Heuschmann, P. U., & Wolfe, C. D. (2011). Natural history, predictors, and associations of depression 5 years after stroke: The South London Stroke Register. Stroke, 42, 1907–1911.CrossRefGoogle Scholar
  42. 42.
    Spuling, S. M., Wolff, J. K., & Wurm, S. (2017). Response shift in self-rated health after serious health events in old age. Social Science and Medicine, 192, 85–93.CrossRefPubMedGoogle Scholar
  43. 43.
    Kapral, M. K., Wang, H., Mamdani, M., & Tu, J. V. (2002). Effect of socioeconomic status on treatment and mortality after stroke. Stroke, 33, 268.CrossRefPubMedGoogle Scholar
  44. 44.
    Broomfield, N. M., Quinn, T. J., Abdul-Rahim, A. H., Walters, M. R., & Evans, J. J. (2014). Depression and anxiety symptoms post-stroke/TIA: Prevalence and associations in cross-sectional data from a regional stroke registry. BioMed Central Neurology, 14, 198.PubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Stroke and Ageing Research, Department of Medicine, School of Clinical Sciences at Monash HealthMonash UniversityClaytonAustralia
  2. 2.Department of Medicine, Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonAustralia
  3. 3.Stroke DivisionThe Florey Institute of Neuroscience and Mental Health, University of MelbourneHeidelbergAustralia
  4. 4.Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological SciencesMonash UniversityClaytonAustralia
  5. 5.Sunshine Coast Clinical SchoolThe University of QueenslandBirtinyaAustralia
  6. 6.Statistical Services BranchQueensland Department of HealthBrisbaneAustralia
  7. 7.St Vincent’s HospitalUniversity of MelbourneMelbourneAustralia
  8. 8.School of Allied HealthLa Trobe UniversityBundooraAustralia

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