Doctor Retention in Ireland - What it may mean for the Global Health Workforce Reform Agenda; Comment on “Doctor Retention: A Cross-sectional Study of How Ireland Has Been Losing the Battle”

Document Type : Commentary

Author

Department of Environmental Health, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa

Abstract

The study of healthcare personnel migration in Ireland reports that most medical graduates plan to leave the country’s health system. It may be possible to address this challenge by understanding and addressing the reasons why young doctors plan to leave. Future studies should contribute to the retention of early career doctors in high-income countries such as Ireland. This will help reduce the migration of doctors from low- and middle-income countries in order to address the global health workforce crisis and its impact on the attainment of universal health coverage in all health systems.

Keywords

References

  1. Damen JA, Bots ML. Prediction of cardiovascular risk: it is not only in the details. Eur J Prev Cardiol. 2019;26(17):1886-1887. doi:10.1177/2047487319863180
  2. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;353:i2416. doi:10.1136/bmj.i2416
  3. Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Springer Science & Business Media; 2008.
  4. Hajifathalian K, Ueda P, Lu Y, et al. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveys. Lancet Diabetes Endocrinol. 2015;3(5):339-355. doi:10.1016/s2213-8587(15)00081-9
  5. Eslami A, Naghibi Irvani SS, Ramezankhani A, et al. Incidence and associated risk factors for premature death in the Tehran Lipid and Glucose Study cohort, Iran. BMC Public Health. 2019;19(1):719. doi:10.1186/s12889-019-7056-y
  6. Khalili D, Hadaegh F, Soori H, Steyerberg EW, Bozorgmanesh M, Azizi F. Clinical usefulness of the Framingham cardiovascular risk profile beyond its statistical performance: the Tehran Lipid and Glucose Study. Am J Epidemiol. 2012;176(3):177-186. doi:10.1093/aje/kws204
  7. Conroy RM, Pyörälä K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987-1003. doi:10.1016/s0195-668x(03)00114-3
  8. Khalili D, Asgari S, Hadaegh F, et al. A new approach to test validity and clinical usefulness of the 2013 ACC/AHA guideline on statin therapy: a population-based study. Int J Cardiol. 2015;184:587-594. doi:10.1016/j.ijcard.2015.03.067
  9. Fahimfar N, Khalili D, Sepanlou SG, et al. Cardiovascular mortality in a Western Asian country: results from the Iran Cohort Consortium. BMJ Open. 2018;8(7):e020303. doi:10.1136/bmjopen-2017-020303
  10. Sarrafzadegan N, Hassannejad R, Marateb HR, et al. PARS risk charts: a 10-year study of risk assessment for cardiovascular diseases in Eastern Mediterranean Region. PLoS One. 2017;12(12):e0189389. doi:10.1371/journal.pone.0189389
  11. Sepanlou SG, Malekzadeh R, Poustchi H, et al. The clinical performance of an office-based risk scoring system for fatal cardiovascular diseases in North-East of Iran. PLoS One. 2015;10(5):e0126779. doi:10.1371/journal.pone.0126779
  12. Vickers AJ. Decision analysis for the evaluation of diagnostic tests, prediction models and molecular markers. Am Stat. 2008;62(4):314-320. doi:10.1198/000313008x370302
  13. Azizi F, Rahmani M, Emami H, et al. Cardiovascular risk factors in an Iranian urban population: Tehran lipid and glucose study (phase 1). Soz Praventivmed. 2002;47(6):408-426. doi:10.1007/s000380200008
  14. Sarrafzadegan N, Talaei M, Sadeghi M, et al. The Isfahan cohort study: rationale, methods and main findings. J Hum Hypertens. 2011;25(9):545-553. doi:10.1038/jhh.2010.99
  15. Pourshams A, Khademi H, Malekshah AF, et al. Cohort Profile: the Golestan Cohort Study--a prospective study of oesophageal cancer in northern Iran. Int J Epidemiol. 2010;39(1):52-59. doi:10.1093/ije/dyp161
  16. Fotouhi A, Hashemi H, Shariati M, et al. Cohort profile: Shahroud eye cohort study. Int J Epidemiol. 2013;42(5):1300-1308. doi:10.1093/ije/dys161
  17. Sepanlou SG, Barahimi H, Najafi I, et al. Prevalence and determinants of chronic kidney disease in northeast of Iran: results of the Golestan cohort study. PLoS One. 2017;12(5):e0176540. doi:10.1371/journal.pone.0176540
  18. Vickers AJ, Cronin AM, Elkin EB, Gonen M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008;8:53. doi:10.1186/1472-6947-8-53
  19. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128-138. doi:10.1097/EDE.0b013e3181c30fb2
  20. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565-574. doi:10.1177/0272989x06295361
  21. Khalili D, Hadaegh F, Steyerberg EW. Re: Clinical usefulness of the Framingham cardiovascular risk profile beyond its statistical performance: the Tehran Lipid and Glucose Study. Am J Epidemiol. 2013;177(8):865-866. doi:10.1093/aje/kwt039
  22. Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86(2):420-428. doi:10.1037//0033-2909.86.2.420
  23. Cohen J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull. 1968;70(4):213-220. doi:10.1037/h0026256
  24. Ulmer H, Kollerits B, Kelleher C, Diem G, Concin H. Predictive accuracy of the SCORE risk function for cardiovascular disease in clinical practice: a prospective evaluation of 44 649 Austrian men and women. Eur J Cardiovasc Prev Rehabil. 2005;12(5):433-441. doi:10.1097/01.hjr.0000174791.47059.80
  25. Selvarajah S, Kaur G, Haniff J, et al. Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population. Int J Cardiol. 2014;176(1):211-218. doi:10.1016/j.ijcard.2014.07.066
  26. Marques-Vidal P, Rodondi N, Bochud M, et al. Predictive accuracy and usefulness of calibration of the ESC SCORE in Switzerland. Eur J Cardiovasc Prev Rehabil. 2008;15(4):402-408. doi:10.1097/HJR.0b013e3282fb040f
  27. Jørstad HT, Colkesen EB, Minneboo M, et al. The Systematic COronary Risk Evaluation (SCORE) in a large UK population: 10-year follow-up in the EPIC-Norfolk prospective population study. Eur J Prev Cardiol. 2015;22(1):119-126. doi:10.1177/2047487313503609
  28. Jdanov DA, Deev AD, Jasilionis D, Shalnova SA, Shkolnikova MA, Shkolnikov VM. Recalibration of the SCORE risk chart for the Russian population. Eur J Epidemiol. 2014;29(9):621-628. doi:10.1007/s10654-014-9947-7
  29. Hobbs F, Piepoli M, Hoes A, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice. European Heart Journal. 2016;37(29):2315-2381. doi:10.1093/eurheartj/ehw106
International Journal of Health Policy and Management
Volume 10, Issue 10
October 2021
Pages 647-649

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History

  • Receive Date: 21 May 2020
  • Revise Date: 06 July 2020
  • Accept Date: 06 July 2020
  • First Publish Date: 01 October 2021

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