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Changing Trends in Type 2 Diabetes Mellitus Treatment Intensification, 2002-2010
Rozalina G. McCoy, MD; Yuanhui Zhang, PhD; Jeph Herrin, PhD; Brian T. Denton, PhD; Jennifer E. Mason, PhD; Victor M. Montori, MD; Steven A. Smith, MD; Nilay D. Shah, PhD
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Changing Trends in Type 2 Diabetes Mellitus Treatment Intensification, 2002-2010

Rozalina G. McCoy, MD; Yuanhui Zhang, PhD; Jeph Herrin, PhD; Brian T. Denton, PhD; Jennifer E. Mason, PhD; Victor M. Montori, MD; Steven A. Smith, MD; Nilay D. Shah, PhD
Glycemic control can lower the risk of diabetes-related complications, and delayed treatment intensification can impede optimal diabetes care.This study examines trends in hyperglycemia treatment intensification between 2002 and 2010.
There was a persistent and significant change in the class of second-line agents used to intensify glucose-lowering therapy over each cohort of new metformin starters (Table 2). Sulfonylureas and meglitinides were the most commonly used second-line agents, but their use decreased from 48.9% in the 2002-2003 cohort, to 43% in the 2004-2005 cohort, to 42.3% in the 2006-2007 cohort (P <.001). Two drug classes saw profound changes in prescription patterns: thiazolidinediones and incretins. The use of incretins rose for each successive cohort, from 4.2% in the 2002-2003 cohort, to 12.7% in the 2004-2005 cohort, to 26.7% in the 2006-2007 cohort (P <.001). Conversely, thiazolidinedione use decreased slightly between the 2002-2003 and 2004-2005 cohorts, from 43.5% to 40.9%, but then fell sharply to 26.2% in the 2006-2007 cohort (P <.001). Insulin use as a second-line agent increased from 3.2% in the 2002-2003 and 2004-2005 cohorts to 4.5% in the 2006-2007 cohort (P <.001).

Treatment Intensification

The likelihood of treatment intensification was significantly higher in each successive cohort of patients on metformin monotherapy (Table 3 and Figure). Overall, compared with the 2002-2003 cohort, the hazard ratio for intensification was 1.07 (95% CI, 1.04-1.10; P <.001) in the 2004-2005 cohort, and was 1.11 (95% CI, 1.07-1.13; P <.001) in the 2006-2007 cohort. The log-rank test showed statistically significant differences in the cumulative probability of treatment intensification between any 2 cohorts for the whole observation period (P <.001). This trend remained highly significant after adjustment for potential confounders of treatment intensification, specifically patient age, sex, race/ethnicity, education and median household income, comorbidity burden, and statin therapy.

DISCUSSION

Clinical inertia and failure to escalate glucose-lowering therapy in the management of diabetes contributes to deterioration of glycemic control13-17 and increased risk of diabetes-related complications.3-9 Population-level studies, particularly the 2003 assessment of chronic disease care in the United States, revealed deficiencies in the quality of diabetes care20; the importance of treatment intensification in achieving high-quality diabetes care was subsequently highlighted in 2006.21 Both studies were thought to have affected and improved clinical practice, and the 2-year cohorts in our study were designed to follow these important secular trends. While recent studies showed improved population-level glycemic control over time,18 less was known about concurrent trends in diabetes treatment intensification practices. This study, conducted in a large, national, and racially diverse cohort of adults with T2DM, found that the likelihood of treatment intensification rose significantly and consistently over the past decade, albeit in small increments.

Our study confirmed prior observations regarding several important confounders of treatment intensification. These include socioeconomic status (assessed in our study using median education level and household income of the region where the patient lives), age, sex, and comorbidity. Because administrative data do not include laboratory values such as A1C, we could not establish with certainty the degree to which observed differences in treatment intensification are due to differences in glycemic control versus disparities in healthcare access and disease management practices. Still, these important observations highlight the need for further study of enduring disparities in healthcare.

Consistent with studies in different populations, we found that the patients with greater disease burden were more likely to have their treatment intensified.32,33 This stands in stark contrast to current guidelines that recommend a more cautious and less intensive therapeutic approach to managing patients with multiple comorbidities.10 A possible reason for this is that patients with multiple chronic diseases have closer contact with the healthcare system, allowing more opportunities for treatment intensification. Nonetheless, whether treatment intensification in this patient population ultimately improves clinical outcomes or quality of life is uncertain.

The choice of second-line agents used in clinical practice to intensify metformin monotherapy changed significantly over the course of the study. These trends are consistent with recently published epidemiologic data about the overall use of antidiabetes drugs (not specifically as second-line agents).22,23,34 While metformin is regarded by most as the first-line agent in the treatment of T2DM due to its efficacy, safety, and affordability, there is no consensus and insufficient empirical evidence supporting the use of one second-line agent over another.10,35-37 Our study, in contrast to others, focused specifically on choice of second-line agents as add-on therapy to metformin. We found that while sulfonylureas continue to be the most common choice for second-line therapy, their use decreased as they lost a percentage of their market share to newer agents that are better marketed and potentially have a lesser side effect profile.38-41 

We also saw a dramatic decline in thiazolidinedione use, and an even greater rise in incretin use. Thiazolidinediones have become progressively unpopular since rosiglitazone was linked to increased risk of cardiovascular events, and pioglitazone to risk of fractures and bladder cancer.42-45 Incretin agents were the newest class of diabetes medications included in our study, and they carry little risk of hypoglycemia and weight gain.38,39,41 However, there is uncertainty regarding the optimal timing of their initiation, specifically whether they are better used as second- line agents when metformin is no longer sufficient or as third-line agents in addition to metformin and another glucose-lowering drug such as a sulfonylurea.10,35,46 How this change in clinical practice will influence patient outcomes remains to be seen, but emerging evidence suggests that incretins as second-line agents are not superior to sulfonylureas. Indeed, their use is associated with shorter time to serious diabetes complications, shorter time of insulin-independence, and significantly higher cost.47

Limitations

Although secondary analysis of administrative data enables real-world longitudinal assessments of disease management and progression, this methodology has important limitations that are apparent in our study. Because only data from the period of health plan enrollment are available, potential therapies and interventions prior to enrollment are unknown. Another limitation of our study is the censoring for patients who discontinued metformin, as such censoring may be informative rather than outcome-neutral. Patients who discontinue all medications may be at low risk of intensification because of excellent glycemic control, or may be nonadherent and therefore at high risk. An analysis that accounted for all competing risks, including medication changes to a variety of different regimen or no regimen, is problematic when the proportional hazards assumption is not met, as found in our study. Patients were also censored upon disenrollment from the health plan. There are several potential reasons for plan disenrollment, ranging from change of job and insurance provider to death. While it is possible that the different reasons for disenrollment may be differentially associated with likelihood of treat- ment intensification, this was controlled for by including a measure of comorbidity in our analyses.

The type of pharmacy benefit—specifically differences in coinsurance and annual caps on pharmacy benefit— may have contributed to patient adherence as well as provider treatment intensification practices. While all patients in our study had private medical insurance with a pharmacy benefit, the effect of individual plan variability on treatment intensification and second-line medication choice could not be determined. Similarly, people covered primarily by public health insurance programs are not included, such that our study population is not fully representative of the general population, with greater representation of younger individuals of higher socioeconomic status. However, these are also strengths of our study, as we focused on a population often excluded from research using public insurance or specific health system data.

CONCLUSIONS

Our study extends and reinforces earlier findings by Fu and colleagues, who showed that the likelihood of treatment intensification among patients with T2DM on metformin monotherapy increased between 1997 and 2008.48 However, the study by Fu and colleagues was restricted by its reliance on electronic health records (EHRs) to define the target population and measure treatment intensification,48 such that medication changes made outside systems using these EHRs could not be captured, and data on both treatment initiation and discontinuation may therefore be incomplete. Although the data set used by Fu and colleagues included EHRs from a large number of healthcare providers, it is unclear whether data were collected on the patient or practice level, whereby patients who changed practices may have been counted twice (if moved to another participating practice) or missed (if moved to a nonparticipating practice). Moreover, this study was conducted in the early era of EHR implementation, and patients receiving care in practices that had access to EHRs may not be representative of the general population.49 By using administrative data, we were able to ensure a more comprehensive measure of treatment intensification. Because our data set is patient- rather than provider-centered, patients were not lost if they switched providers or pharmacies. Finally, our study took into consideration confounders of treatment intensification such as race/ethnicity, education level, and socioeconomic status, which were not assessed in prior studies. In the current era of quality improvement and performance measurement, we observed a steadily increasing rate of diabetes treatment intensification. However, it remains unclear if such intensification resulted in better long-term outcomes for patients, or to what degree it increased the burden of disease, affected quality of life and patient capacity, or impeded adherence.

Author Affiliations: Division of Endocrinology, Diabetes, Metabolism, & Nutrition, Department of Medicine (RGM, VMM, SAS), and Knowledge and Evaluation Research Unit (VMM, NDS), and Division of Health Care Policy & Research, Department of Health Sciences Research (VMM, SAS, NDS), Mayo Clinic, Rochester, MN; Graduate Program in Operations Research, North Carolina State University (YZ), Raleigh, NC; Division of Cardiology, Yale School of Medicine, Yale University (JH), New Haven, CT; Health Research & Educational Trust (JH), Chicago, IL; Department of Industrial & Operations Engineering, University of Michigan (BTD), Ann Arbor, MI; Department of Public Health Sciences, University of Virginia (JEM), Charlottesville, VA.

Source of Funding: Funding for this work was provided by the National Science Foundation grant number CMMI 0969885 (BTD), Agency for Healthcare Research and Quality research grants R21HS017628 and R18HS018339 (NDS), and National Center for Advancing Translational Sciences research grant UL1RR024150 (NDS). The authors of the manuscript are responsible for the entirety of its content. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or any other funding agency.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (BTD, JEM, VMM, RGM, NDS, SAS, YZ); acquisition of data (NDS); analysis and interpretation of data (BTD, JH, JEM, VMM, RGM, NDS, SAS, YZ); drafting of the manuscript (BTD, VMM, RGM); critical revision of the manuscript for important intellectual content (JH, JEM, VMM, RGM, NDS, SAS, YZ); statistical analysis (BTD, JH, YZ); obtaining funding (BTD); administrative, technical, or logistic support (NDS); and supervision (BTD, VMM).

Address correspondence to: Nilay D. Shah, PhD, Division of Health Care Policy & Research, Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905. E-mail: shah.nilay@mayo.edu.
REFERENCES

1. Centers for Disease Control and Prevention. National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States, 2014. Atlanta, GA: US Department of Health and Human Services; 2014.

2. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care. 2013;36(4):1033-1046.

3. Riddle MC, Ambrosius WT, Brillon DJ, et al; Action to Control Cardiovascular Risk in Diabetes Investigators. Epidemiologic relationships between A1C and all-cause mortality during a median 3.4-year follow-up of glycemic treatment in the ACCORD trial. Diabetes Care. 2010;33(5):983-990.

4. Gaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med. 2008;358(6):580-591.

5. McBrien KA, Manns BJ, Chui B, et al. Health care costs in people with diabetes and their association with glycemic control and kidney function. Diabetes Care. 2013;36(5):1172-1180.

6. Zhang CY, Sun AJ, Zhang SN, et al. Effects of intensive glucose control on incidence of cardiovascular events in patients with type 2 diabetes: a meta-analysis. Ann Med. 2010;42(4):305-315.

7. Zhang P, Brown MB, Bilik D, Ackermann RT, Li R, Herman WH. Health utility scores for people with type 2 diabetes in U.S. managed care health plans: results from Translating Research Into Action for Diabetes (TRIAD). Diabetes Care. 2012;35(11):2250-2256.

8. Stratton IM, Adler AI, Neil HA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321(7258):405-412.

9. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359(15):1577-1589.

10. American Diabetes Association. Standards of medical care in diabetes—2015. Diabetes Care. 2015;38(suppl 1):S1-S90.

11. Redmon B, Caccamo D, Flavin P, et al. Diagnosis and management of type 2 diabetes mellitus in adults. Bloomington (MN): Institute for Clinical Systems Improvement (ICSI); 2014 Jul: 85.

12. Turner RC, Cull CA, Frighi V, Holman RR. Glycemic control with diet, sulfonylurea, metformin, or insulin in patients with type 2 diabetes mellitus: progressive requirement for multiple therapies (UKPDS 49). UK Prospective Diabetes Study (UKPDS 49) Group. JAMA. 1999;281(21):2005-2012.

13. Schmittdiel JA, Uratsu CS, Karter AJ, et al. Why don’t diabetes patients achieve recommended risk factor targets? poor adherence versus lack of treatment intensification. J Gen Intern Med. 2008;23(5): 588-594.

14. Sidorenkov G, Voorham J, Haaijer-Ruskamp FM, de Zeeuw D, Denig P. Association between performance measures and glycemic control among patients with diabetes in a community-wide primary care cohort. Med Care. 2013;51(2):172-179.

15. Sidorenkov G, Voorham J, de Zeeuw D, Haaijer-Ruskamp FM, Denig P. Do treatment quality indicators predict cardiovascular outcomes in patients with diabetes? PLoS One. 2013;8(10):e78821.

16. Khunti K, Wolden ML, Thorsted BL, Andersen M, Davies MJ. Clinical inertia in people with type 2 diabetes: a retrospective cohort study of more than 80,000 people. Diabetes Care. 2013;36(11):3411-3417.

17. Grant RW, Cagliero E, Dubey AK, et al. Clinical inertia in the management of type 2 diabetes metabolic risk factors. Diabet Med. 2004;21(2):150-155.

18. Stark Casagrande S, Fradkin JE, Saydah SH, Rust KF, Cowie CC. The prevalence of meeting A1C, blood pressure, and LDL goals among people with diabetes, 1988–2010. Diabetes Care. 2013;36(8):2271-2279.

19. Kerr EA, Lucatorto MA, Holleman R, Hogan MM, Klamerus ML, Hofer TP; VA Diabetes Enhancement Research Initiative (QUERI) Work- group on Clinical Action Measures. Monitoring performance for blood pressure management among patients with diabetes mellitus: too much of a good thing? Arch Intern Med. 2012;172(12):938-945.

20. 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(26):2635-2645.

21. Rodondi N, PengT, Karter AJ, et al.Therapy modifications in response to poorly controlled hypertension, dyslipidemia, and diabetes mellitus. Ann Intern Med. 2006;144(7):475-484.

22. Turner LW, Nartey D, Stafford RS, Singh S, Alexander GC. Ambulatory treatment of type 2 diabetes in the U.S., 1997-2012. Diabetes Care. 2014;37(4):985-992.

23. Hampp C, Borders-Hemphill V, Moeny DG, Wysowski DK. Use of antidiabetic drugs in the U.S., 2003-2012. Diabetes Care. 2014;37(5):1367-1374.

24. National Committee for Quality Assurance (NCQA). HEDIS 2009, vol. 2: technical update. Washington, DC: NCQA; 2008.

25. Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245-1251.

26. McEwen LN, Kim C, Karter AJ, et al. Risk factors for mortality among patients with diabetes: the Translating Research Into Action for Diabetes (TRIAD) Study. Diabetes Care. 2007;30(7):1736-1741.

27. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-619.

28. Zwiener I, Blettner M, Hommel G. Survival analysis: part 15 of a series on evaluation of scientific publications. Dtsch Arztebl Int. 2011; 108(10):163-169.

29. Gharibvand L, Jeske DR, Solomon L. Evaluation of a Hospice Care Referral Program Using Cox Proportional Hazards Model. 2008 Annual Conference Proceedings of the Western Users of SAS® Software (WUSS); 2008; Universal City, California. http://www.lexjansen.com/wuss/2008/anl/anl06.pdf. Accessed April 25, 2015.

30. Lee ET, Wang JW. Statistical Methods for Survival Data Analysis. 3rd ed. Hoboken, NJ: J. Wiley & Sons; 2003.

31. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York, NY: J. Wiley & Sons, 1987.

32. Vitry AI, Roughead EE, Preiss AK, et al. Influence of comorbidities on therapeutic progression of diabetes treatment in Australian veterans: a cohort study. PLoS One. 2010;5(11):e14024.

33. Voorham J, Haaijer-Ruskamp FM, Wolffenbuttel BH, de Zeeuw D, Stolk RP, Denig P. Differential effects of comorbidity on antihypertensive and glucose-regulating treatment in diabetes mellitus--a cohort study. PLoS One. 2012;7(6):e38707.

34. Alexander GC, Sehgal NL, Moloney RM, Stafford RS. National trends in treatment of type 2 diabetes mellitus, 1994-2007. Arch Intern Med. 2008;168(19):2088-2094.

35. Garber AJ, Abrahamson MJ, Barzilay JI, et al. AACE/ACE comprehensive diabetes management algorithm 2015. Endocr Pract. 2015; 21(4):438-447.

36. Riethof M, Flavin PL, Lindvall B, et al. Diagnosis and Management ofType 2 Diabetes Mellitus in Adults. Bloomington, MN: Institute for Clinical Systems Improvement; 2012.

37. Bennett WL, Maruthur NM, Singh S, et al. Comparative effectiveness and safety of medications for type 2 diabetes: an update including new drugs and 2-drug combinations [published correction appears in Ann Intern Med. 2011;155(1):67-68]. Ann Intern Med. 2011;154(9):602- 613. Review.

38. Bennett WL, Wilson LM, Bolen S, et al. Oral Diabetes Medications for Adults With Type 2 Diabetes: An Update. Rockville, MD: Agency for Healthcare Research and Quality (US); 2011.

39. Shyangdan DS, Royle P, Clar C, Sharma P, Waugh N, Snaith A. Glucagon-like peptide analogues for type 2 diabetes mellitus. Cochrane Database System Rev. 2011(10):CD006423.

40. Liu SC,TuYK, Chien MN, Chien KL. Effect of antidiabetic agents added to metformin on glycaemic control, hypoglycaemia and weight change in patients with type 2 diabetes: a network meta-analysis. Diabetes Obes Metab. 2012;14(9):810-820.

41. KaragiannisT, Paschos P, Paletas K, Matthews DR,Tsapas A. Dipeptidyl peptidase-4 inhibitors for treatment of type 2 diabetes mellitus in the clinical setting: systematic review and meta-analysis. BMJ. 2012; 344:e1369.

42. Niyomnaitham S, Page A, La Caze A, Whitfield K, Smith AJ. Utilisation trends of rosiglitazone and pioglitazone in Australia before and after safety warnings. BMC Health Serv Res. 2014;14:151.

43. Starner CI, Schafer JA, Heaton AH, Gleason PP. Rosiglitazone and pioglitazone utilization from January 2007 through May 2008 associated with five risk-warning events. J Manag Care Pharm. 2008;14(6): 523-531.

44. Cohen A, Rabbani A, Shah N, Alexander GC. Changes in glitazone use among office-based physicians in the U.S., 2003-2009. Diabetes Care. 2010;33(4):823-825.

45. Ruiter R, Visser LE, van Herk-Sukel MP, et al. Prescribing of rosiglitazone and pioglitazone following safety signals: analysis of trends in dispensing patterns in the Netherlands from 1998 to 2008. Drug Saf. 2012;35(6):471-480.

46. Qaseem A, Humphrey LL, Sweet DE, Starkey M, Shekelle P; Clinical Guidelines Committee of the American College of Physicians. Oral pharmacologic treatment of type 2 diabetes mellitus: a clinical practice guideline from the American College of Physicians. Ann Intern Med. 2012;156(3):218-231.

47. ZhangY, McCoy RG, Mason JE, Smith SA, Shah ND, Denton BT. Second-line agents for glycemic control for type 2 diabetes: are newer agents better? Diabetes Care. 2014;37(5):1338-1345.

48. Fu AZ, QiuY, Davies MJ, Radican L, Engel SS.Treatment intensification in patients with type 2 diabetes who failed metformin monotherapy. Diabetes Obes Metab. 2011;13(8):765-769.

49. Oderda GM, Brixner D, Lieberman M. PMC1 comparison of EMR data to US national data. Value Health. 2007;10(6):A450-A451.
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