Abstracts
Résumé
L’utilisation des bases de données médico-administratives pour les études sur des questions de santé mentale est très fréquente compte tenu du grand nombre de personnes représentées dans ces bases de données et aussi du fait qu’elles portent sur plusieurs années. Plusieurs défis, liés par exemple à l’identification des personnes ayant une maladie d’intérêt ou exposées à un facteur de risque, sont à surmonter à travers des études de validation pour garantir une utilisation optimale de ces ressources. Par ailleurs, des limites (absence de certaines informations pertinentes) et la couverture d’une seule partie de la population par le régime public d’assurance médicaments du Québec sont à considérer dans l’interprétation et la généralisation des résultats des recherches à partir de ces bases de données.
Dans cet article, nous avons réalisé un survol de l’utilisation des bases de données médico-administratives pour des études épidémiologiques, en utilisant comme exemple le cas spécifique de la dépression. Nous avons en particulier utilisé ces bases de données pour déterminer l’incidence de la dépression parmi les personnes diabétiques du Québec. Cela a nécessité l’utilisation d’un algorithme préalablement validé (dans une autre province) que nous avons modifié pour définir et identifier les cas de dépression dans les bases de données de la Régie de l’assurance maladie du Québec (RAMQ). Nous avons observé une incidence de dépression de 9,47/1000 personnes-années sur un suivi de 8 ans. Enfin, nous avons évalué l’impact de la dépression sur l’adhésion et la persistance aux traitements antidiabétiques ainsi que les facteurs qui affectent l’utilisation des médicaments par ces patients. Nos résultats suggèrent que la dépression a un impact négatif sur l’utilisation des médicaments antidiabétiques et permettent d’identifier des pistes de solution.
Mots-clés :
- dépression,
- diabète de type 2,
- adhésion aux médicaments,
- persistance,
- algorithmes d’identification,
- bases de données administratives,
- RAMQ
Abstract
The use of medico-administrative databases for studies on mental health issues is very common because of the large number of people in these databases and the possibility to carry out long-term studies. Several challenges, such as identifying people with a disease of interest or being exposed to a risk factor, have to be overcome through validation studies to ensure an optimal use of these resources. Moreover, limits (lack of certain relevant information) and the coverage of about 40% of Quebec’s population by the public drug plan are to be considered in the interpretation and generalization of research results based on these data sources.
In the specific case of depression, we used these databases to determine the incidence of depression among diabetic individuals in Quebec. This required the use of a previously validated algorithm (validated in another province) that we modified to define and identify the cases of depression in the RAMQ databases. We observed an incidence of depression of 9.47 persons years over a follow-up of 8 years. Finally, we assessed the impact of depression on adherence and persistence with antidiabetic drugs as well as the factors that affect patients’ use of these drugs. Our results suggest that depression has a negative impact on the use of antidiabetic drugs and allow the identification of possible solutions.
Keywords:
- depression,
- type 2 diabetes,
- drug adherence,
- drug persistence,
- algorithms,
- administrative databases,
- RAMQ
Appendices
Bibliographie
- 1. Strom, B. L. et Kimmel, S. E. (2006). Textbook of Pharmacoepidemiology. John Wiley & Sons, Ltd.
- 2. Carrier J. D., Blais, L., Cohen, A. et coll. (2017). Initiating an antipsychotic drug treatment for schizophrenia : the situation in Quebec, Canada, from 1998 to 2006. Sante Ment Que., 42(1), 85-103.
- 3. Massamba, V., Vasiliadis, H. M. et Préville, M. (2017). Determinants of follow-up care associated with incident antidepressant use in older adults. BMC Res Notes, 10(1), 419.
- 4. Farand, L., Renaud, J. et Chagnon, F. (2004). Adolescent suicide in Quebec and prior utilization of medical services. Can J Public Health, 95(5), 357-360.
- 5. Tamblyn, R., Lavoie, G., Petrella, L. et Monette, J. (1995). The use of prescription claims databases in pharmacoepidemiological research : the accuracy and comprehensiveness of the prescription claims database in Quebec. J Clin Epidemiol, 48(8), 999-1009.
- 6. Wilchesky, M., Tamblyn, R. M. et Huang, A. (2004).Validation of diagnostic codes within medical services claims. J Clin Epidemiol, 57(2), 131-141.
- 7. Bellemare, S., Morin, M., Bastien, E., Girard, R., Blais, R. et Dube, S. (2004). Could we trust clinical statistics from data banks of the National Health Service (NHS) ? Ann Chir., 129(1), 11-13.
- 8. Chen, A.Y. et Colantonio, A. (2011). Defining neurotrauma in administrative data using the International Classification of Diseases Tenth Revision. Emerg Themes Epidemiol., 8(1), 4.
- 9. Gershon, A.S., Wang, C., Guan, J., Vasilevska-Ristovska, J., Cicutto, L. et To T. (2009). Identifying individuals with physician diagnosed COPD in health administrative databases. COPD, 6(5), 388-394.
- 10. Gagnon, B., Mayo, N. E., Laurin, C., Hanley, J.A. et McDonald, N. (2006). Identification in administrative databases of women dying of breast cancer. J Clin Oncol., 24(6), 856-862.
- 11. Chen, G., Khan, N., Walker, R. et Quan, H. (2010). Validating ICD coding algorithms for diabetes mellitus from administrative data. Diabetes Res Clin Pract., 89(2), 189-195.
- 12. Wyse, J. M., Joseph, L., Barkun, A. N. et Sewitch, M. J. (2011). Accuracy of administrative claims data for polypectomy. CMAJ, 183(11), E743-747.
- 13. Jean S., Candas, B., Belzile, E. et coll. (2012). Algorithms can be used to identify fragility fracture cases in physician-claims databases. Osteoporos Int., 23(2), 483-501.
- 14. Southern, D. A., Roberts, B., Edwards, A. et coll. (2010). Validity of administrative data claim-based methods for identifying individuals with diabetes at a population level. Can J Public Health, 101(1), 61-64.
- 15. Townsend, L., Walkup, J. T., Crystal, S. et Olfson, M. (2012). A systematic review of validated methods for identifying depression using administrative data. Pharmacoepidemiol Drug Saf, 21 Suppl 1, 163-173.
- 16. Katon, W. J., Richardson, L., Russo, J., Lozano, P. et McCauley, E. (2006). Quality of mental health care for youth with asthma and comorbid anxiety and depression. Med Care, 44(12), 1064-1072.
- 17. McCusker, J., Cole, M., Latimer, E. et coll. (2008). Recognition of depression in older medical inpatients discharged to ambulatory care settings : a longitudinal study. Gen Hosp Psychiatry, 30(3), 245-251.
- 18. Kahn, L. S., Fox, C. H., McIntyre, R. S., Tumiel-Berhalter, L., Berdine, D. E. et Lyle H. (2008). Assessing the prevalence of depression among individuals with diabetes in a Medicaid managed-care program. Int J Psychiatry Med., 38(1), 13-29.
- 19. Katon, W. J, Simon, G., Russo, J. et coll. (2004). Quality of depression care in a population-based sample of patients with diabetes and major depression. Med Care, 42(12), 1222-1229.
- 20. Solberg, L. I., Fischer, L. R., Rush, W.A. et Wei, F. (2003). When depression is the diagnosis, what happens to patients and are they satisfied ? Am J Manag Care, 9(2), 131-140.
- 21. Frayne, S. M., Miller, D. R., Sharkansky, E. J. et coll. (2010). Using administrative data to identify mental illness : what approach is best ? Am J Med Qual., 25(1), 42-50.
- 22. Smith, E. G., Henry, A. D., Zhang, J., Hooven. F. et Banks, S. M. (2009). Antidepressant adequacy and work status among medicaid enrollees with disabilities : a restriction-based, propensity score-adjusted analysis. Community Ment Health J., 45(5), 333-340.
- 23. Kramer, T. L., Owen, R. R., Cannon, D. et coll. (2003). How well do automated performance measures assess guideline implementation for new-onset depression in the Veterans Health Administration ? Jt Comm J Qual Saf., 29(9), 479-489.
- 24. Solberg, L. I., Engebretson, K. I., Sperl-Hillen, J. M., Hroscikoski, M. C. et O’Connor, P. J. (2006). Are claims data accurate enough to identify patients for performance measures or quality improvement ? The case of diabetes, heart disease, and depression. Am J Med Qual., 21(4), 238-245.
- 25. Spettell, C. M., Wall, T. C., Allison, J. et coll. (2003). Identifying physician-recognized depression from administrative data : consequences for quality measurement. Health Serv Res., 38(4), 1081-1102.
- 26. Noyes, K., Liu, H., Lyness, J. M. et Friedman, B. (2011). Medicare beneficiaries with depression : comparing diagnoses in claims data with the results of screening. Psychiatr Serv., 62(10), 1159-1166.
- 27. West, S. L., Richter, A., Melfi, C. A., McNutt, M., Nennstiel, M. E. et Mauskopf, J. A. (2000). Assessing the Saskatchewan database for outcomes research studies of depression and its treatment. J Clin Epidemiol., 53(8), 823-831.
- 28. Alaghehbandan, R., Macdonald, D., Barrett, B., Collins, K. et Chen, Y. (2012). Using administrative databases in the surveillance of depressive disorders – case definitions. Popul Health Manag., 15(6), 372-380.
- 29. Kahn, L. S., Fox, C. H., McIntyre, R. S., Tumiel-Berhalter, L., Berdine, D. E. et Lyle, H. (2008). Assessing the prevalence of depression among individuals with diabetes in a Medicaid managed-care program. Int J Psychiatry Med., 38(1), 13-29.
- 30. Allan, C. E., Valkanova, V. et Ebmeier, K. P. (2014). Depression in older people is underdiagnosed. Practitioner, 258(1771), 19-22, 12-13.
- 31. Préville, M., Boyer, R., Grenier, S. et coll. (2008). The epidemiology of psychiatric disorders in Quebec’s older adult population. Can J Psychiatry, 53(12), 822-832.
- 32. Kruijshaar, M. E., Barendregt, J., Vos, T., de Graaf, R., Spijker, J. et Andrews G. (2005). Lifetime prevalence estimates of major depression : An indirect estimation method and a quantification of recall bias. Eur J Epidemiol., 20(1), 103-111.
- 33. Wang, P. S., Benner, J. S., Glynn, R. J., Winkelmayer, W. C., Mogun, H. et Avorn, J. (2004). How well do patients report noncompliance with antihypertensive medications ? : a comparison of self-report versus filled prescriptions. Pharmacoepidemiol Drug Saf., 13(1), 11-19.
- 34. Lunghi, C., Moisan, J., Grégoire, J. P. et Guénette, L. (2016). Incidence of Depression and Associated Factors in Patients With Type 2 Diabetes in Quebec, Canada : A Population-Based Cohort Study. Medicine, 95(21), e3514.
- 35. de Groot, M., Anderson, R., Freedland, K. E., Clouse, R. E. et Lustman, P. J. (2001). Association of depression and diabetes complications : a meta-analysis. Psychosom Med., 63(4), 619-630.
- 36. Rotella, F. et Mannucci, E. (2013). Diabetes mellitus as a risk factor for depression. A meta-analysis of longitudinal studies. Diabetes Res Clin Pract., 99(2), 98-104.
- 37. Gonzalez, J. S., Safren, S. A., Delahanty, L. M. et coll. (2008). Symptoms of depression prospectively predict poorer self-care in patients with Type 2 diabetes. Diabet Med., 25(9), 1102-1107.
- 38. Lustman, P. J. et Clouse, R. E. (2005). Depression in diabetic patients : the relationship between mood and glycemic control. J Diabetes Complications, 19(2), 113-122.
- 39. van Dooren, F. E., Nefs, G., Schram, M. T., Verhey, F. R., Denollet, J. et Pouwer, F. (2013). Depression and risk of mortality in people with diabetes mellitus : a systematic review and meta-analysis. PLoS One, 8(3), e57058.
- 40. Alaghehbandan, R., Macdonald, D., Barrett, B., Collins, K. et Chen, Y. (2012). Using administrative databases in the surveillance of depressive disorders – case definitions. Popul Health Manag., 15(6), 372-380.
- 41. Lunghi, C., Moisan, J., Grégoire, J. P. et Guénette, L. (2013). Prevalence of Depression in Patients with Type 2 Diabetes (T2D) in Quebec. Paper presented at : Canadian Journal of Diabetes.
- 42. Messier, L., Elisha, B., Schmitz, N. et coll. (2013). Changes in depressive symptoms and changes in lifestyle-related indicators : a 1-year follow-up study among adults with type 2 diabetes in Quebec. Can J Diabetes, 37(4), 243-248.
- 43. Brown, L. C., Majumdar, S. R., Newman, S. C. et Johnson, J. A. (2006). Type 2 diabetes does not increase risk of depression. Can Med Assoc J., 175(1), 42-46.
- 44. Fisher, L., Skaff, M. M., Mullan, J. T. et coll. (2007). Clinical depression versus distress among patients with type 2 diabetes : not just a question of semantics. Diabetes Care, 30(3), 542-548.
- 45. DiMatteo, M. R., Lepper, H. S. et Croghan, T. W. (2000). Depression is a risk factor for noncompliance with medical treatment : meta-analysis of the effects of anxiety and depression on patient adherence. Arch Intern Med., 160(14), 2101-2107.
- 46. Grenard, J. L., Munjas, B. A., Adams, J. L. et coll. (2011). Depression and medication adherence in the treatment of chronic diseases in the United States : a meta-analysis. J Gen Intern Med., 26(10), 1175-1182.
- 47. Zomahoun, H. T., Moisan, J., Lauzier, S., Guillaumie, L., Grégoire, J. P. et Guénette, L. (2016). Predicting Noninsulin Antidiabetic Drug Adherence Using a Theoretical Framework Based on the Theory of Planned Behavior in Adults With Type 2 Diabetes : A Prospective Study. Medicine (Baltimore), 95(15), e2954.
- 48. Caughey, G. E., Preiss, A. K., Vitry, A. I. et coll. (2013). Does antidepressant medication use affect persistence with diabetes medicines ? Pharmacoepidemiol Drug Saf., 22(6), 615-622.
- 49. Kalsekar, I. D., Madhavan, S. S., Amonkar, M. M. et coll. (2006). Impact of depression on utilization patterns of oral hypoglycemic agents in patients newly diagnosed with type 2 diabetes mellitus : a retrospective cohort analysis. Clin Ther., 28(2), 306-318.
- 50. Wahl, C., Grégoire, J. P., Teo, K. et coll. (2005). Concordance, compliance and adherence in healthcare : closing gaps and improving outcomes. Healthc Q., 8(1), 65-70.
- 51. Cramer, J. A., Roy, A., Burrell, A. et coll. (2008). Medication compliance and persistence : terminology and definitions. Value Health, 11(1), 44-47.
- 52. Grégoire, J.-P. et Moisan J. (2016). Assessment of adherence to drug treatment in database research. Drug Utilization Research : John Wiley & Sons, Ltd ; 369-380.
- 53. Choudhry, N. K., Shrank, W. H., Levin R. L. et coll. (2009). Measuring concurrent adherence to multiple related medications. Am J Manag Care, 15(7), 457-464.
- 54. Andrade, S. E., Kahler, K. H., Frech, F. et Chan, K. A. (2006). Methods for evaluation of medication adherence and persistence using automated databases. Pharmacoepidemiol Drug Saf., 15, 565-574 ; discussion 575-567.
- 55. Suissa, S. (2008). Immortal time bias in pharmaco-epidemiology. Am J Epidemiol., 167(4), 492-499.
- 56. Levesque, L. E., Hanley, J. A., Kezouh, A. et Suissa, S. (2010). Problem of immortal time bias in cohort studies : example using statins for preventing progression of diabetes. BMJ, 340, b5087.
- 57. Grégoire, J. P., Sirois, C., Blanc, G., Poirier, P. et Moisan, J. (2010). Persistence patterns with oral antidiabetes drug treatment in newly treated patients – a population-based study. Value Health, 13(6), 820-828.
- 58. Lunghi, C., Zongo, A., Moisan, J., Grégoire, J. P. et Guénette, L. (2017). The impact of incident depression on medication adherence in patients with type 2 diabetes. Diabetes Metab., 43(6), 521-528.
- 59. Lunghi, C., Moisan, J., Grégoire, J. P. et Guénette, L. (2017). The Association between Depression and Medication Nonpersistence in New Users of Antidiabetic Drugs. Value Health, 20(6), 728-735.
- 60. Guénette, L., Moisan, J., Breton, M. C., Sirois, C. et Grégoire, J. P. (2013). Difficulty adhering to antidiabetic treatment : factors associated with persistence and compliance. Diabetes Metab., 39(3), 250-257.
- 61. Lunghi, C., Zongo, A., Moisan, J., Grégoire, J. P. et Guénette, L. (2017). Factors associated with antidiabetic medication non-adherence in patients with incident comorbid depression. J Diabetes Complications, 31(7), 1200-1206.
- 62. Régie de l’Assurance Maladie du Québec. Rapport annuel de gestion. 2013-2014. http://www.ramq.gouv.qc.ca/SiteCollectionDocuments/citoyens/fr/rapports/rappann1314.pdf
- 63. Alberta Netcare Learning Centre. http://www.health.alberta.ca/documents/HISCA-Pharmaceutical-Info.pdf Accessed 13 août 2018.
- 64. Solberg, L. I., Engebretson, K. I., Sperl-Hillen, J. M., Hroscikoski, M. C. et O’Connor P. J. (2006). Are claims data accurate enough to identify patients for performance measures or quality improvement ? The case of diabetes, heart disease, and depression. Am J Med Qual., 21(4), 238-245.