Associations between untreated depression and secondary health care utilization in patients with hypertension and/or diabetes
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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.
KeywordsComorbid 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.
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
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.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. https://doi.org/10.1016/S0140-6736(16)31460-X CrossRefGoogle Scholar
- 2.GBD Compare | IHME Viz Hub. http://vizhub.healthdata.org/gbd-compare. Accessed 9 May 2017
- 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. https://doi.org/10.1097/PSY.0b013e3181a5a7ad CrossRefGoogle Scholar
- 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. https://doi.org/10.1097/01.psy.0000133362.75075.a6 CrossRefGoogle Scholar
- 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. https://doi.org/10.1001/archgenpsychiatry.2009.121 CrossRefGoogle Scholar
- 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. https://doi.org/10.1161/CIRCULATIONAHA.108.190769 CrossRefGoogle Scholar
- 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. https://doi.org/10.1161/CIR.0000000000000019 CrossRefGoogle Scholar
- 20.Garrofé BC, Björnberg A, Phang A, Trojcakova I (2016) Health Consumer Powerhouse Euro Heart Index 2016 Report. https://healthpowerhouse.com/files/EHI_2016/EHI_2016_report.pdf. Accessed 12 Apr 2017
- 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. https://doi.org/10.1016/j.genhosppsych.2007.06.002 CrossRefGoogle Scholar
- 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. https://doi.org/10.1016/j.jacc.2008.09.032 CrossRefGoogle Scholar
- 26.World Health Organization (2006) Highlights on health in Hungary 2005. http://www.euro.who.int/__data/assets/pdf_file/0016/103219/E88736.pdf. Accessed 12 Apr 2017
- 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
- 33.Ádány R (2013) Operations Manual for GPs Cluster on Public Health Services in Primary Health Care. Version 5. http://alapellatasimodell.hu/images/mukodesi_kezikonyv/SH.8.1_operations_manual_version5.pdf. Accessed 20 Feb 2018
- 34.Gaal P, Szigeti S, Csere M et al (2011) Hungary health system review. Health Syst Transit 13:1–266Google Scholar
- 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.Rózsa S, Szádóczky E, Füredi J (2001) Usefulness of the Hungarian 9-item version of Beck Depression Inventory. Psychiat Hung 16:384–402Google Scholar
- 41.Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG (2001) AUDIT The Alcohol Use Disorders Identification Test Guidelines for Use in Primary Care. http://www.talkingalcohol.com/files/pdfs/WHO_audit.pdf. Accessed 20 Mar 2017
- 42.Eurostat (2013) European Health Interview Survey (EHIS wave 2) Methodological manual. http://ec.europa.eu/eurostat/documents/3859598/5926729/KS-RA-13-018-EN.PDF/26c7ea80-01d8-420e-bdc6-e9d5f6578e7c. Accessed 20 Mar 2017
- 44.International Diabetes Federation (2006) The IDF consensus worldwide definition of the metabolic syndrome. https://www.idf.org/e-library/consensus-statements/60-idfconsensus-worldwide-definitionof-the-metabolic-syndrome. Accessed 20 Mar 2017
- 45.WHO Consultation on Obesity (2000) Obesity: preventing and managing the global epidemic: report of a WHO consultation. http://apps.who.int/iris/handle/10665/42330. Accessed 20 Mar 2017
- 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
- 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. https://doi.org/10.1371/journal.pone.0158163 CrossRefGoogle Scholar
- 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.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. https://doi.org/10.1097/01.psy.0000233233.48738.22 CrossRefGoogle Scholar
- 58.Zonda T, Bozsonyi K, Kmetty Z (2016) Ecological investigation on suicide and antidepressive medication. J Ment Health Psychosom 17:97–116Google Scholar
- 63.World Health Organization (2003) Adherence to long-term therapies: evidence for action. http://www.who.int/chp/knowledge/publications/adherence_full_report.pdf. Accessed 15 May 2018