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Danish clinical quality databases – an important and untapped resource for clinical research

Authors Sørensen HT, Pedersen L, Jørgensen J, Ehrenstein V

Received 21 May 2016

Accepted for publication 23 May 2016

Published 25 October 2016 Volume 2016:8 Pages 425—427

DOI https://doi.org/10.2147/CLEP.S113265

Checked for plagiarism Yes


Henrik Toft Sørensen,1 Lars Pedersen,1 Jørgen Jørgensen,2 Vera Ehrenstein1

1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, 2Danish Regions, Copenhagen, Denmark

The health care systems in many countries are facing several challenges: the aging population,the need for personalized medicine, evolving treatment modalities, quality andsafety imperatives, and unsustainable costs, to mention only a few. Population aging in the face of significant pressure to contain costs is perhaps the most immediate challenge. The proportion of people aged 65 years or older in Western Europe and North America is expected to increase to 26% in 2025. Furthermore, clinical medicine in the Western world is confronting an evolving set of diseases, as smoking becomes less prevalent and obesity more common. Diagnostics and treatment of chronic disease have improved, while the threshold for initiating preventive treatment of asymptomatic conditions has been lowered. Consequently, the number of patients with multimorbidity, that is, the coexistence of several chronic diseases, will increase dramatically.1 To treat a disease or prevent its progression, patients with several chronic diseases often take multiple drugs, each with potentially severe side effects. In patients with multiple morbidities, “polypharmacy” is a challenging clinical issue, often associated with iatrogenic harm.2 A call for innovative approaches to polypharmacy has been the focus of recent editorials in high-impact medical journals.3–5 Randomized trials rarely address multimorbidity, adherence to treatments, co-intervention (polypharmacy), or their long-term risks.6 These challenges underscore the need for population-based long-term longitudinal clinical data available for clinical care and research.




Acknowledgment

Henrik Toft Sørensen and Jørgen Jørgensen are funded by the Program for Clinical Research Infrastructure (PROCRIN) established by the Lundbeck Foundation and the Novo Nordisk Foundation.

Disclosure

The authors report no conflicts of interest in this work.

References

1.

Tinetti ME, Fried TR, Boyd CM. Designing health care for the most common chronic condition – multimorbidity. JAMA. 2012;307:2493–2494.

2.

Lees J, Chan A. Polypharmacy in elderly patients with cancer: clinical implications and management. Lancet Oncol. 2011;12:1249–1257.

3.

Pronovost PJ, Goeschel CA. Time to take health delivery research seriously. JAMA. 2011;306:310–311.

4.

Salisbury C. Multimorbidity: redesigning health care for people who use it. Lancet. 2012;380:7–9.

5.

The Lancet. How to cope with an ageing population. Lancet. 2013;382:1225.

6.

Sørensen HT, Lash TL, Rothman KJ. Beyond randomized controlled trials: a critical comparison of trials with nonrandomized studies. Hepatology. 2006;44:1075–1082.

7.

Hansen AG, Looft C. Leprosy: In Its Clinical & Pathological Aspects. Bristol: John Wright & Co; 1973.

8.

Schmidt M, Pedersen L, Sørensen HT. The Danish Civil Registration System as a tool in epidemiology. Eur J Epidemiol. 2014;29:541–549.

9.

Green A. Danish clinical databases: an overview. Scand J Public Health. 2011;39(7 Suppl):68–71.

10.

Nørgaard M, Johnsen SP. How can the research potential of the clinical quality databases be maximized? The Danish experience. J Intern Med. 2016;279:132–140.

11.

Jameson JL, Longo DL. Precision medicine – personalized, problematic, and promising. N Engl J Med. 2015;372:2229–2234.

12.

Baron JA. Screening for cancer with molecular markers: progress comes with potential problems. Nat Rev Cancer. 2012;12:368–371.

13.

Chen R, Mias GI, Li-Pook-Than J, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148:1293–1307.

14.

Whirl-Carrillo M, McDonagh EM, Hebert JM, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012;92:414–417.

15.

The Danish National Biobank [homepage on the Internet]. Copenhagen: Statens Serum Institut. Available from: http://www.biobankdenmark.dk/. Accessed May 20, 2016.

16.

The Danish Cancer Biobank [homepage on the Internet]. Herlev: Herlev Hospital. Available from: http://www.cancerbiobank.dk/. Accessed May 20, 2016.

17.

Pathak J, Kho AN, Denny JC. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. J Am Med Inform Assoc. 2013;20:e206–e211.

18.

Roque FS, Jensen PB, Schmock H, et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol. 2011;7:e1002141.

19.

McGlynn EA, Asch SM, Adams J, et al. The quality of health care delivered to adults in the United States. N Engl J Med. 2003;348:2635–2645.

20.

Newman-Toker DE, Pronovost PJ. Diagnostic errors – the next frontier for patient safety. JAMA. 2009;301:1060–1062.

21.

Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ. Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010;363:2124–2134.

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