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Development of the “chronic condition measurement guide”: a new tool to measure chronic conditions in older people based on ICD-10 and ATC-codes

  • Helle Gybel Juul-LarsenEmail author
  • Line Due Christensen
  • Ove Andersen
  • Thomas Bandholm
  • Susanne Kaae
  • Janne Petersen
Research Paper
  • 37 Downloads

Key summary points

Aim

To develop a comprehensive open-source measurement guide of the most prevalent chronic conditions among persons aged 65+ based on registry data of both diagnoses and prescribed drugs [“The Chronic Condition Measurement Guide (CCMG)].

Findings

Based on the Danish population aged 65 years and older we developed the CCMG identifying 83 different chronic conditions based on registry data of both diagnoses and prescribed drugs. By applying the CCMG to a large national cohort of all Danish citizens aged 65 or above, we found that the prevalence of multimorbidity ranged from 10 to 69% using different years of history and in- or excluding information about drug prescribing.

Message

The CCMG is easily implemented using registry data and we recommend using 10 years of history and drug prescribing information.

Abstract

Purpose

The aim of the study was to develop a comprehensive open-source measurement guide of the most prevalent chronic conditions among persons aged 65+ based on registry data of both diagnoses and prescribed drugs [the chronic condition measurement guide (CCMG)]. Furthermore, to investigate proof of concept of the measurement guide, different years of history and in- and excluding data on prescribed drugs. Finally, to investigate the measurement guide with other measurement guides designed to identify chronic conditions in persons aged 65+.

Methods

The measurement guide was based on the 200 most prevalent chronic ICD10 codes in the Danish population 65+ years in 2015; the 200 most prevalent chronic ICD10 codes and causes of death in a cohort of 209,337 people who died of non-traumatic causes (January 2011–January 2016). Prescribed drugs were included in the measurement guide based on a literature review and specialist opinions.

Results

We identified 83 different chronic conditions based on 1241 unique ICD-10 codes. Multimorbidity prevalence ranged from 10% (1-year history, excluding prescribing information) to 69% (15-year history, including prescribing information). We identified 95% of the persons with multimorbidity using the 29 most prevalent chronic conditions. Inclusion of these 29 conditions affected the prevalence of multimorbidity and 1-year mortality when the CCMG was compared with other measurement guides.

Conclusion

The CCMG is easily implemented using registry data. When implementing the measurement guide 10 years of history and drug prescribing information should be used. Using the CCMG to study multimorbidity, we recommend using at least the 29 most prevalent chronic conditions.

Keywords

Chronic conditions People aged 65+ Multimorbidity Measurement 

Notes

Acknowledgements

The Authors like to thank Henrik Hedegaard Klausen MD, PhD, Beata Malmqvist MD, PhD, Ejvind Frausing MD, Ane Kathrine Skielbo MD, PhD, Thomas Huneck Haupt MD, PhD for validating the drugs used in the Chronic Condition Measurement Guide.

Compliance with ethical standards

Conflict of interest

The authors report no conflicts of interest in this work.

Ethical approval

This study has been approved by The Data Protection Agency (Project no. 704775 at Statistics Denmark). No approval from The Danish Research Ethics Committees for The Capital Region was needed since only national registry data have been used.

Informed consent

As this is a registry study based on national registers no informed consent exists.

Supplementary material

41999_2019_188_MOESM1_ESM.docx (26 kb)
Supplementary material 1 (DOCX 26 kb)
41999_2019_188_MOESM2_ESM.docx (25 kb)
Supplementary material 2 (DOCX 26 kb)

References

  1. 1.
    Nicholson K, Makovski TT, Griffith LE, Raina P, Stranges S, van den Akker M (2019) Multimorbidity and comorbidity revisited: refining the concepts for international health research. J Clin Epidemiol 105:142–146.  https://doi.org/10.1016/j.jclinepi.2018.09.008 CrossRefGoogle Scholar
  2. 2.
    van den Akker M, Buntinx F, Knottnerus JA (1996) Comorbidity or multimorbidity. Eur J Gen Pract 2:65–70.  https://doi.org/10.3109/13814789609162146 CrossRefGoogle Scholar
  3. 3.
    Marengoni A, Vetrano DL, Onder G (2019) Target population for clinical trials on multimorbidity: is disease count enough? J Am Med Dir Assoc 20:113–114.  https://doi.org/10.1016/j.jamda.2018.10.012 CrossRefGoogle Scholar
  4. 4.
    Willadsen TG, Bebe A, Køster-Rasmussen R, Jarbøl DE, Guassora AD, Waldorff FB et al (2016) The role of diseases, risk factors and symptoms in the definition of multimorbidity—a systematic review. Scand J Prim Health Care 34:112–121.  https://doi.org/10.3109/02813432.2016.1153242 CrossRefGoogle Scholar
  5. 5.
    Diederichs C, Berger K, Bartels DB (2011) The measurement of multiple chronic diseases—a systematic review on existing multimorbidity indices. J Gerontol A Biol Sci Med Sci 66:301–311.  https://doi.org/10.1093/gerona/glq208 CrossRefGoogle Scholar
  6. 6.
    Prados-Torres A, Calderón-Larrañaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M (2014) Multimorbidity patterns: a systematic review. J Clin Epidemiol 67:254–266.  https://doi.org/10.1016/j.jclinepi.2013.09.021 CrossRefGoogle Scholar
  7. 7.
    Violan C, Foguet-Boreu Q, Flores-Mateo G, Salisbury C, Blom J, Freitag M et al (2014) Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies. PLoS One 9:e102149.  https://doi.org/10.1371/journal.pone.0102149 CrossRefGoogle Scholar
  8. 8.
    Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373–383CrossRefGoogle Scholar
  9. 9.
    Elixhauser A, Steiner C, Harris DR, Coffey RM (1998) Comorbidity measures for use with administrative data. Med Care 36:8–27CrossRefGoogle Scholar
  10. 10.
    Linn BS, Linn MW, Gurel L (1968) Cumulative illness rating scale. J Am Geriatr Soc 16:622–626CrossRefGoogle Scholar
  11. 11.
    Miller MD, Paradis CF, Houck PR, Mazumdar S, Stack JA, Rifai AH et al (1992) Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale. Psychiatry Res 41:237–248.  https://doi.org/10.1016/0165-1781(92)90005-N CrossRefGoogle Scholar
  12. 12.
    Parkerson GR, Broadhead WE, Tse CK (1993) The Duke severity of illness checklist (DUSOI) for measurement of severity and comorbidity. J Clin Epidemiol 46:379–393CrossRefGoogle Scholar
  13. 13.
    Salisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA (2011) Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pract J R Coll Gen Pract 61:e12–e21.  https://doi.org/10.3399/bjgp11X548929 CrossRefGoogle Scholar
  14. 14.
    Rizzuto D, Melis RJF, Angleman S, Qiu C, Marengoni A (2017) Effect of chronic diseases and multimorbidity on survival and functioning in elderly adults. J Am Geriatr Soc 65:1056–1060.  https://doi.org/10.1111/jgs.14868 CrossRefGoogle Scholar
  15. 15.
    van den Bussche H, Koller D, Kolonko T, Hansen H, Wegscheider K, Glaeske G et al (2011) Which chronic diseases and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany. BMC Public Health 11:101.  https://doi.org/10.1186/1471-2458-11-101 CrossRefGoogle Scholar
  16. 16.
    Calderón-Larrañaga A, Vetrano DL, Onder G, Gimeno-Feliu LA, Coscollar-Santaliestra C, Carfí A et al (2017) Assessing and measuring chronic multimorbidity in the older population: a proposal for its operationalization. J Gerontol A Biol Sci Med Sci 72:1417–1423.  https://doi.org/10.1093/gerona/glw233 Google Scholar
  17. 17.
    von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP et al (2007) Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 335:806–808.  https://doi.org/10.1136/bmj.39335.541782.AD CrossRefGoogle Scholar
  18. 18.
    Pottegård A, Schmidt SAJ, Wallach-Kildemoes H, Sørensen HT, Hallas J, Schmidt M (2017) Data resource profile: the danish national prescription registry. Int J Epidemiol 46:798-798f.  https://doi.org/10.1093/ije/dyw213 Google Scholar
  19. 19.
    Lynge E, Sandegaard JL, Rebolj M (2011) The Danish national patient register. Scand J Public Health 39:30–33.  https://doi.org/10.1177/1403494811401482 CrossRefGoogle Scholar
  20. 20.
    World Health Organization (2014) International statistical classification of diseases and related health problems 10th revision. Available at: http://apps.who.int/classifications/icd10/browse/2015/en. Accessed 20 Jan 2019
  21. 21.
    WHO Collaborating Centre for Drug Statistics Methodology (2017) Guidelines for ATC classification and DDD assignment 2018. World Health Organization, Oslo, Norway. https://www.whocc.no/atc_ddd_index/. Accessed 20 Jan 2019
  22. 22.
    Norredam M, Kastrup M, Helweg-Larsen K (2011) Register-based studies on migration, ethnicity, and health. Scand J Public Health 39:201–205.  https://doi.org/10.1177/1403494810396561 CrossRefGoogle Scholar
  23. 23.
    Helweg-Larsen K (2011) The danish register of causes of death. Scand J Public Health 39:26–29.  https://doi.org/10.1177/1403494811399958 CrossRefGoogle Scholar
  24. 24.
    Pedersen CB (2011) The Danish civil registration system. Scand J Public Health 39:22–25.  https://doi.org/10.1177/1403494810387965 CrossRefGoogle Scholar
  25. 25.
    Perrin EC, Newacheck P, Pless IB, Drotar D, Gortmaker SL, Leventhal J et al (1993) Issues involved in the definition and classification of chronic health conditions. Pediatrics 91:787–793Google Scholar
  26. 26.
    Klausen HH, Petersen J, Bandholm T, Juul-Larsen HG, Tavenier J, Eugen-Olsen J et al (2017) Association between routine laboratory tests and long-term mortality among acutely admitted older medical patients: a cohort study. BMC Geriatr 17:62.  https://doi.org/10.1186/s12877-017-0434-3 CrossRefGoogle Scholar
  27. 27.
    Kuo RN, Dong Y-H, Liu J-P, Chang C-H, Shau W-Y, Lai M-S (2011) Predicting healthcare utilization using a pharmacy-based metric with the WHO’s Anatomic Therapeutic Chemical algorithm. Med Care 49:1031–1039.  https://doi.org/10.1097/MLR.0b013e31822ebe11 CrossRefGoogle Scholar
  28. 28.
    Vivas-Consuelo D, Usó-Talamantes R, Trillo-Mata JL, Caballer-Tarazona M, Barrachina-Martínez I, Buigues-Pastor L (2014) Predictability of pharmaceutical spending in primary health services using clinical risk groups. Health Policy Amst Neth 116:188–195.  https://doi.org/10.1016/j.healthpol.2014.01.012 CrossRefGoogle Scholar
  29. 29.
    Vivas D, Guadalajara N, Barrachina I, Trillo J-L, Usó R, de-la Poza E (2011) Explaining primary healthcare pharmacy expenditure using classification of medications for chronic conditions. Health Policy Amst Neth 103:9–15.  https://doi.org/10.1016/j.healthpol.2011.08.014 CrossRefGoogle Scholar
  30. 30.
    Halfon P, Eggli Y, Decollogny A, Seker E (2013) Disease identification based on ambulatory drugs dispensation and in-hospital ICD-10 diagnoses: a comparison. BMC Health Serv Res.  https://doi.org/10.1186/1472-6963-13-453 Google Scholar
  31. 31.
    Pratt NL, Kerr M, Barratt JD, Kemp-Casey A, Kalisch Ellett LM, Ramsay E et al (2018) The validity of the Rx-risk comorbidity index using medicines mapped to the anatomical therapeutic chemical (ATC) classification system. BMJ Open 8:e021122.  https://doi.org/10.1136/bmjopen-2017-021122 CrossRefGoogle Scholar
  32. 32.
    Lamers LM, van Vliet RCJA (2004) The Pharmacy-based cost group model: validating and adjusting the classification of medications for chronic conditions to the Dutch situation. Health Policy Amst Neth 68:113–121.  https://doi.org/10.1016/j.healthpol.2003.09.001 CrossRefGoogle Scholar
  33. 33.
    Huber CA, Szucs TD, Rapold R, Reich O (2013) Identifying patients with chronic conditions using pharmacy data in Switzerland: an updated mapping approach to the classification of medications. BMC Public Health 13:1030.  https://doi.org/10.1186/1471-2458-13-1030 CrossRefGoogle Scholar
  34. 34.
    Chini F, Pezzotti P, Orzella L, Borgia P, Guasticchi G (2011) Can we use the pharmacy data to estimate the prevalence of chronic conditions? A comparison of multiple data sources. BMC Public Health 11:688.  https://doi.org/10.1186/1471-2458-11-688 CrossRefGoogle Scholar
  35. 35.
    Johansen, NB, Lykke, MB, Bekker-Jeppesen, M, Buhelt, LP, Allesoe, K, Andreasen, AH, et al. [Sundhedsprofil for Region Hovedstaden og kommuner 2017—Kronisk sygdom. In English: Health profile for the Capitol Region of Denmark and municipalities 2017—Chronic disease] In Danish. Centre for Clinical Research and Prevention, University Hospital of Bispebjerg and Frederiksberg, The Capitol Region of Denmark. 2018. https://www.regionh.dk/fcfs/sundhedsfremme-og-forebyggelse/Documents/Sundhedsprofil_2017_Kronisk%20sygdom.pdf. Accessed 12 Dec 2018
  36. 36.
    Schram MT, Frijters D, van de Lisdonk EH, Ploemacher J, de Craen AJM, de Waal MWM et al (2008) Setting and registry characteristics affect the prevalence and nature of multimorbidity in the elderly. J Clin Epidemiol 61:1104–1112.  https://doi.org/10.1016/j.jclinepi.2007.11.021 CrossRefGoogle Scholar
  37. 37.
    Fortin M, Stewart M, Poitras M-E, Almirall J, Maddocks H (2012) A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Ann Fam Med 10:142–151.  https://doi.org/10.1370/afm.1337 CrossRefGoogle Scholar
  38. 38.
    Marengoni A, Rizzuto D, Wang H-X, Winblad B, Fratiglioni L (2009) Patterns of chronic multimorbidity in the elderly population. J Am Geriatr Soc 57:225–230.  https://doi.org/10.1111/j.1532-5415.2008.02109.x CrossRefGoogle Scholar
  39. 39.
    Holzer BM, Siebenhuener K, Bopp M, Minder CE (2017) Evidence-based design recommendations for prevalence studies on multimorbidity: improving comparability of estimates. Popul Health Metr 15:9.  https://doi.org/10.1186/s12963-017-0126-4 CrossRefGoogle Scholar

Copyright information

© European Geriatric Medicine Society 2019

Authors and Affiliations

  • Helle Gybel Juul-Larsen
    • 1
    • 2
    • 3
    Email author
  • Line Due Christensen
    • 1
    • 4
  • Ove Andersen
    • 1
    • 2
  • Thomas Bandholm
    • 1
    • 2
    • 3
    • 5
  • Susanne Kaae
    • 6
  • Janne Petersen
    • 1
    • 7
    • 8
  1. 1.Clinical Research Centre, Optimed, Amager and Hvidovre HospitalUniversity of CopenhagenHvidovreDenmark
  2. 2.Department of Clinical Medicine, Faculty of HealthUniversity of CopenhagenCopenhagenDenmark
  3. 3.Department of Occupational and Physical Therapy, Physical Medicine and Rehabilitation Research-Copenhagen (PMR-C), Amager and Hvidovre HospitalUniversity of CopenhagenHvidovreDenmark
  4. 4.The Capital Region Pharmacy, Copenhagen University Hospital HvidovreHvidovreDenmark
  5. 5.Department of Orthopedic Surgery, Amager and Hvidovre HospitalUniversity of CopenhagenHvidovreDenmark
  6. 6.Social and Clinical Pharmacy, Department of Pharmacy, Faculty of HealthUniversity of CopenhagenCopenhagenDenmark
  7. 7.Section of Biostatistics, Department of Public Health, Faculty of HealthUniversity of CopenhagenCopenhagenDenmark
  8. 8.Centre for Clinical Research and Prevention, Bispebjerg and Frederiksberg HospitalUniversity of CopenhagenCopenhagenDenmark

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