Social Psychiatry and Psychiatric Epidemiology

, Volume 54, Issue 2, pp 255–276 | Cite as

Associations between untreated depression and secondary health care utilization in patients with hypertension and/or diabetes

  • Anita Pálinkás
  • János SándorEmail author
  • Magor Papp
  • László Kőrösi
  • Zsófia Falusi
  • László Pál
  • Zsuzsanna Bélteczki
  • Zoltán Rihmer
  • Péter Döme
Original Paper



We determined the prevalence of untreated depression in patients with hypertension (HT) and/or diabetes (DM) and estimated the extra health care use and expenditures associated with this comorbidity in a rural Hungarian adult population. We also assessed the potential workload of systematic screening for depression in this patient group.


General health check database from a primary care programme containing survey data of 2027 patients with HT and/or DM was linked to the outpatient secondary care use database of National Institute of Health Insurance Fund Management. Depression was ascertained by Beck Depression Inventory score and antidepressant drug use. The association between untreated depression and secondary healthcare utilization indicated by number of visits and expenses was evaluated by multiple logistic regression analysis controlled for socioeconomic/lifestyle factors and comorbidity. The age-, sex- and education-specific observations were used to estimate the screening workload for an average general medical practice.


The frequency of untreated depression was 27.08%. The untreated severe depression (7.45%) was associated with increased number of visits (OR 1.60, 95% CI 1.11–2.31) and related expenses (OR 2.20, 95% CI 1.50–3.22) in a socioeconomic status-independent manner. To identify untreated depression cases among patients with HT and/or DM, an average GP has to screen 42 subjects a month.


It seems to be reasonable and feasible to screen for depression in patients with HT and/or DM in the primary care, in order to detect cases without treatment (which may be associated with increase of secondary care visits and expenditures) and to initiate the adequate treatment of them.


Comorbid depression Hypertension Diabetes Health care utilization Linkage study 



The authors thank all the participants who took the time to complete the questionnaires and the members of general practitioners’ cluster who participated in data collection. Furthermore, the authors thank Alexandra Balázs for support in managing the legal issues related to data access. The reported study was carried out in the framework of the “Public Health Focused Model Programme for Organising Primary Care Services Backed by a Virtual Care Service Centre” (SH/8/1). The Model Programme is funded by the Swiss Government via the Swiss Contribution Programme (SH/8/1) in agreement with the Government of Hungary. Additional source of funding was from GINOP-2.3.2-15-2016-00005 project which was co-financed by the European Union and the European Regional Development Fund.

Author contributions

Concept/design: AP, JS, MP; data collection: AP, JS, MP, LK, ZSF, LP; data analysis/interpretation: AP, JS, ZSB, PD, ZR; drafting manuscript: AP, JS, PD, ZR. All authors contributed to the writing of the final version of the paper.

Compliance with ethical standards

Ethical approval

The study protocol was approved by the Ethics Committee of the Hungarian National Scientific Council on Health (TUKEB 16676-3/2016/EKU, 0361/16). All enrolled patients signed an informed consent form contributing to the storage and analysis of their data before the data collection of health check started. Furthermore, the legal staff of the National Institute of Health Insurance Fund Management checked each signed informed consent before issuing the permission for record linkage. The health insurance numbers of the patients were handled exclusively on computers of and within the National Institute of Health Insurance Fund Management by this institution’s own staff members dedicated to handling these sensitive data as their usual job. Nobody else from the study personnel obtained permission to handle these data.

Conflict of interest

ZR has in the past received speakers’ bureau honoraria, advisory board funding or travel support from AstraZeneca, Janssen, Lundbeck, Lilly, Servier-EGIS, Richter and TEVA-Biogal. The other authors report no biomedical financial interests or potential conflicts of interest. The authors declare that the presented epidemiological research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


  1. 1.
    DALYs GBD2015, Collaborators HALE. (2016) Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388:1603–1658. CrossRefGoogle Scholar
  2. 2.
    GBD Compare | IHME Viz Hub. Accessed 9 May 2017
  3. 3.
    Soler EP, Ruiz VC (2010) Epidemiology and risk factors of cerebral ischemia and ischemic heart diseases: similarities and differences. Curr Cardiol Rev 6:138–149. CrossRefGoogle Scholar
  4. 4.
    Vigo D, Thornicroft G, Atun R (2016) Estimating the true global burden of mental illness. Lancet Psychiatry 3:171–178. CrossRefGoogle Scholar
  5. 5.
    Cohen BE, Edmondson D, Kronish IM (2015) State of the art review: depression, stress, anxiety, and cardiovascular disease. Am J Hypertens 28:1295–1302. CrossRefGoogle Scholar
  6. 6.
    Fiore V, Marci M, Poggi A et al (2015) The association between diabetes and depression: a very disabling condition. Endocrine 48:14–24. CrossRefGoogle Scholar
  7. 7.
    Nemeroff CB, Goldschmidt-Clermont PJ (2012) Heartache and heartbreak–the link between depression and cardiovascular disease. Nat Rev Cardiol 9:526–539. CrossRefGoogle Scholar
  8. 8.
    Vamos EP, Mucsi I, Keszei A et al (2009) Comorbid depression is associated with increased healthcare utilization and lost productivity in persons with diabetes: a large nationally representative Hungarian population survey. Psychosom Med 71:501–507. CrossRefGoogle Scholar
  9. 9.
    Carney RM, Freedland KE (2017) Depression and coronary heart disease. Nat Rev Cardiol 14:145–155. CrossRefGoogle Scholar
  10. 10.
    Gan Y, Gong Y, Tong X et al (2014) Depression and the risk of coronary heart disease: a meta-analysis of prospective cohort studies. BMC Psychiatry 14:371. CrossRefGoogle Scholar
  11. 11.
    Hare DL, Toukhsati SR, Johansson P, Jaarsma T (2014) Depression and cardiovascular disease: a clinical review. Eur Heart J 35:1365–1372. CrossRefGoogle Scholar
  12. 12.
    Huffman JC, Celano CM, Beach SR et al (2013) Depression and cardiac disease: epidemiology, mechanisms, and diagnosis. Cardiovasc Psychiatry Neurol 2013:695925. CrossRefGoogle Scholar
  13. 13.
    Chang C-K, Hayes RD, Perera G et al (2011) Life expectancy at birth for people with serious mental illness and other major disorders from a secondary mental health care case register in London. PloS One 6:e19590. CrossRefGoogle Scholar
  14. 14.
    Walker ER, McGee RE, Druss BG (2015) Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. JAMA Psychiatry 72:334–341. CrossRefGoogle Scholar
  15. 15.
    Carney RM, Blumenthal JA, Freedland KE et al (2004) Depression and late mortality after myocardial infarction in the enhancing recovery in coronary heart disease (ENRICHD) study. Psychosom Med 66:466–474. CrossRefGoogle Scholar
  16. 16.
    Glassman AH, Bigger JT, Gaffney M (2009) Psychiatric characteristics associated with long-term mortality among 361 patients having an acute coronary syndrome and major depression: seven-year follow-up of SADHART participants. Arch Gen Psychiatry 66:1022–1029. CrossRefGoogle Scholar
  17. 17.
    de Jonge P, Honig A, van Melle JP et al (2007) Nonresponse to treatment for depression following myocardial infarction: association with subsequent cardiac events. Am J Psychiatry 164:1371–1378. CrossRefGoogle Scholar
  18. 18.
    Lichtman JH, Bigger JTJ, Blumenthal JA et al (2008) Depression and coronary heart disease: recommendations for screening, referral, and treatment: a science advisory from the American Heart Association Prevention Committee of the Council on Cardiovascular Nursing, Council on Clinical Cardiology, Council on Epidemiology and Prevention, and Interdisciplinary Council on Quality of Care and Outcomes Research: endorsed by the American Psychiatric Association. Circulation 118:1768–1775. CrossRefGoogle Scholar
  19. 19.
    Lichtman JH, Froelicher ES, Blumenthal JA et al (2014) Depression as a risk factor for poor prognosis among patients with acute coronary syndrome: systematic review and recommendations: a scientific statement from the American Heart Association. Circulation 129:1350–1369. CrossRefGoogle Scholar
  20. 20.
    Garrofé BC, Björnberg A, Phang A, Trojcakova I (2016) Health Consumer Powerhouse Euro Heart Index 2016 Report. Accessed 12 Apr 2017
  21. 21.
    Baumeister H, Haschke A, Munzinger M et al (2015) Inpatient and outpatient costs in patients with coronary artery disease and mental disorders: a systematic review. Biopsychosoc Med 9:11. CrossRefGoogle Scholar
  22. 22.
    Egede LE (2007) Major depression in individuals with chronic medical disorders: prevalence, correlates and association with health resource utilization, lost productivity and functional disability. Gen Hosp Psychiatry 29:409–416. CrossRefGoogle Scholar
  23. 23.
    Hutter N, Schnurr A, Baumeister H (2010) Healthcare costs in patients with diabetes mellitus and comorbid mental disorders–a systematic review. Diabetologia 53:2470–2479. CrossRefGoogle Scholar
  24. 24.
    Rutledge T, Vaccarino V, Johnson BD et al (2009) Depression and cardiovascular health care costs among women with suspected myocardial ischemia: prospective results from the WISE (Women’s Ischemia Syndrome Evaluation) Study. J Am Coll Cardiol 53:176–183. CrossRefGoogle Scholar
  25. 25.
    Townsend N, Wilson L, Bhatnagar P et al (2016) Cardiovascular disease in Europe: epidemiological update 2016. Eur Heart J 37:3232–3245. CrossRefGoogle Scholar
  26. 26.
    World Health Organization (2006) Highlights on health in Hungary 2005. Accessed 12 Apr 2017
  27. 27.
    Szadoczky E, Papp Z s, Vitrai J et al (1998) The prevalence of major depressive and bipolar disorders in Hungary. Results from a national epidemiologic survey. J Affect Disord 50:153–162CrossRefGoogle Scholar
  28. 28.
    Torzsa P, Rihmer Z, Gonda X et al (2008) [Prevalence of major depression in primary care practices in Hungary]. Neuropsychopharmacol Hung Magy Pszichofarmakologiai Egyesulet Lapja Off J Hung Assoc Psychopharmacol 10:265–270Google Scholar
  29. 29.
    Van de Velde S, Bracke P, Levecque K (2010) Gender differences in depression in 23 European countries. Cross-national variation in the gender gap in depression. Soc Sci Med 1982 71:305–313. Google Scholar
  30. 30.
    Purebl G, Birkas E, Csoboth C et al (2006) The relationship of biological and psychological risk factors of cardiovascular disorders in a large-scale national representative community survey. Behav Med Wash DC 31:133–139. CrossRefGoogle Scholar
  31. 31.
    Adany R, Kosa K, Sandor J et al (2013) General practitioners’ cluster: a model to reorient primary health care to public health services. Eur J Public Health 23:529–530. CrossRefGoogle Scholar
  32. 32.
    Sandor J, Kosa K, Papp M et al (2016) Capitation-based financing hampers the provision of preventive services in primary health care. Front Public Health 4:200. CrossRefGoogle Scholar
  33. 33.
    Ádány R (2013) Operations Manual for GPs Cluster on Public Health Services in Primary Health Care. Version 5. Accessed 20 Feb 2018
  34. 34.
    Gaal P, Szigeti S, Csere M et al (2011) Hungary health system review. Health Syst Transit 13:1–266Google Scholar
  35. 35.
    Rihmer Z, Torzsa P (2016) A short method of screening for depression and suicide risk in general practitioners’ practice. Háziorv Továbbk Szle 21:584–589Google Scholar
  36. 36.
    Rózsa S, Szádóczky E, Füredi J (2001) Usefulness of the Hungarian 9-item version of Beck Depression Inventory. Psychiat Hung 16:384–402Google Scholar
  37. 37.
    Beck AT, Ward CH, Mendelson M et al (1961) An inventory for measuring depression. Arch Gen Psychiatry 4:561–571CrossRefGoogle Scholar
  38. 38.
    Rethelyi JM, Berghammer R, Kopp MS (2001) Comorbidity of pain-associated disability and depressive symptoms in connection with sociodemographic variables: results from a cross-sectional epidemiological survey in Hungary. Pain 93:115–121CrossRefGoogle Scholar
  39. 39.
    Kopp MS, Skrabski A, Szedmak S (1995) Socioeconomic factors, severity of depressive symptomatology, and sickness absence rate in the Hungarian population. J Psychosom Res 39:1019–1029CrossRefGoogle Scholar
  40. 40.
    Paulik E, Nagymajtenyi L, Easterling D, Rogers T (2011) Smoking behaviour and attitudes of Hungarian Roma and non-Roma population towards tobacco control policies. Int J Public Health 56:485–491. CrossRefGoogle Scholar
  41. 41.
    Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG (2001) AUDIT The Alcohol Use Disorders Identification Test Guidelines for Use in Primary Care. Accessed 20 Mar 2017
  42. 42.
    Eurostat (2013) European Health Interview Survey (EHIS wave 2) Methodological manual. Accessed 20 Mar 2017
  43. 43.
    Nedo E, Paulik E (2012) Association of smoking, physical activity, and dietary habits with socioeconomic variables: a cross-sectional study in adults on both sides of the Hungarian-Romanian border. BMC Public Health 12:60. CrossRefGoogle Scholar
  44. 44.
    International Diabetes Federation (2006) The IDF consensus worldwide definition of the metabolic syndrome. Accessed 20 Mar 2017
  45. 45.
    WHO Consultation on Obesity (2000) Obesity: preventing and managing the global epidemic: report of a WHO consultation. Accessed 20 Mar 2017
  46. 46.
    Széles G, Vokó Z, Jenei T et al (2003) Establishment and preliminary evaluation of the General Practitioners’ Morbidity Sentinel Stations Program in Hungary. Prevalence of hypertension, diabetes mellitus and liver cirrhosis. Orv Hetil 144:1521–1529Google Scholar
  47. 47.
    Li Z, Li Y, Chen L et al (2015) Prevalence of depression in patients with hypertension: a systematic review and meta-analysis. Medicine (Baltimore) 94:e1317. CrossRefGoogle Scholar
  48. 48.
    Anderson RJ, Freedland KE, Clouse RE, Lustman PJ (2001) The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care 24:1069–1078CrossRefGoogle Scholar
  49. 49.
    Palacios JE, Khondoker M, Achilla E et al (2016) A single, one-off measure of depression and anxiety predicts future symptoms, higher healthcare costs, and lower quality of life in coronary heart disease patients: analysis from a multi-wave, Primary Care Cohort Study. PLoS One 11:e0158163. CrossRefGoogle Scholar
  50. 50.
    Neighbors HW, Caldwell C, Williams DR et al (2007) Race, ethnicity, and the use of services for mental disorders: results from the National Survey of American Life. Arch Gen Psychiatry 64:485–494. CrossRefGoogle Scholar
  51. 51.
    Roy-Byrne PP, Joesch JM, Wang PS, Kessler RC (2009) Low socioeconomic status and mental health care use among respondents with anxiety and depression in the NCS-R. Psychiatr Serv Wash DC 60:1190–1197. CrossRefGoogle Scholar
  52. 52.
    Sripada RK, Richards SKH, Rauch SAM et al (2015) Socioeconomic Status and Mental Health Service Use Among National Guard Soldiers. Psychiatr Serv Wash DC 66:992–995. CrossRefGoogle Scholar
  53. 53.
    Edelstein B, Drozdick L, Ciliberti C (2010) Assessment of depression and bereavement in older adults. Handbook of assessment in clinical gerontology. 2nd edn. Wiley, HobokenGoogle Scholar
  54. 54.
    Davidson KW, Kupfer DJ, Bigger JT et al (2006) Assessment and treatment of depression in patients with cardiovascular disease: National Heart, Lung, and Blood Institute Working Group Report. Psychosom Med 68:645–650. CrossRefGoogle Scholar
  55. 55.
    Hirschfeld RMA (2001) The comorbidity of major depression and anxiety disorders: recognition and management in primary care. Prim Care Companion J Clin Psychiatry 3:244–254CrossRefGoogle Scholar
  56. 56.
    Keski-Rahkonen A, Mustelin L (2016) Epidemiology of eating disorders in Europe: prevalence, incidence, comorbidity, course, consequences, and risk factors. Curr Opin Psychiatry 29:340–345. CrossRefGoogle Scholar
  57. 57.
    Wong J, Motulsky A, Eguale T et al (2016) Treatment indications for antidepressants prescribed in primary care in Quebec, Canada, 2006–2015. JAMA 315:2230–2232. CrossRefGoogle Scholar
  58. 58.
    Zonda T, Bozsonyi K, Kmetty Z (2016) Ecological investigation on suicide and antidepressive medication. J Ment Health Psychosom 17:97–116Google Scholar
  59. 59.
    Jimmy B, Jose J (2011) Patient medication adherence: measures in daily practice. Oman Med J 26:155–159. CrossRefGoogle Scholar
  60. 60.
    Goldstein CM, Gathright EC, Garcia S (2017) Relationship between depression and medication adherence in cardiovascular disease: the perfect challenge for the integrated care team. Patient Prefer Adherence 11:547–559. CrossRefGoogle Scholar
  61. 61.
    Brown MT, Bussell J, Dutta S et al (2016) Medication Adherence: Truth and Consequences. Am J Med Sci 351:387–399. CrossRefGoogle Scholar
  62. 62.
    Farmer KC (1999) Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice. Clin Ther 21:1074–1090. discussion 1073.CrossRefGoogle Scholar
  63. 63.
    World Health Organization (2003) Adherence to long-term therapies: evidence for action. Accessed 15 May 2018
  64. 64.
    Cramer JA, Benedict A, Muszbek N et al (2008) The significance of compliance and persistence in the treatment of diabetes, hypertension and dyslipidaemia: a review. Int J Clin Pract 62:76–87. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Anita Pálinkás
    • 1
  • János Sándor
    • 1
    Email author
  • Magor Papp
    • 2
  • László Kőrösi
    • 3
  • Zsófia Falusi
    • 3
  • László Pál
    • 3
  • Zsuzsanna Bélteczki
    • 4
  • Zoltán Rihmer
    • 5
    • 6
  • Péter Döme
    • 5
    • 6
  1. 1.Department of Preventive Medicine, Faculty of Public HealthUniversity of DebrecenDebrecenHungary
  2. 2.National Institute of Health PromotionBudapestHungary
  3. 3.Department of FinancingNational Institute of Health Insurance Fund ManagementBudapestHungary
  4. 4.Santha Kalman Mental Health Centre and Special HospitalNagykalloHungary
  5. 5.Department of Psychiatry and Psychotherapy, Faculty of MedicineSemmelweis UniversityBudapestHungary
  6. 6.Laboratory for Suicide Research and PreventionNational Institute of Psychiatry and AddictionsBudapestHungary

Personalised recommendations