The role of TRANSLATION SCIENCES in diabetes care

1. The role of TRANSLATION SCIENCESin diabetes care Mohammed K. Ali, MBChB, MSc, MBA Emory University, Atlanta Centers for Disease Control and Prevention, Atlanta

2. Disclosures The findings and conclusions in this talk are those of the presenter and do not necessarily represent the views of Emory University or the Centers for Disease Control and Prevention No conflict(s) of interest to declare

3. Overview: • What are Translational Sciences? • Example of Diabetes Care • Quality of care • Quality improvement • Policies • Conclusions

4. Conceptual Diagram Knowledge Translation (‘bridge’) Research Implementation TRANSLATING EVIDENCE INTO ACTION

5. Definition • Broad term; multi-dimensional • “the exchange, synthesis (assessment and review), and ethically-sound application of research knowledge to benefit the population through: • improved health outcomes, • more effective services and products, and • a strengthened health care system” (adapted from CIHR, 2004)

6. Translation Sciences • Phase 1: “bench to bedside” • Basic scientific discoveries to produce new drugs/devices/therapies and testing under controlled circumstances • Phase 2: “bedside to community” • Health services / clinicians / public health • Adoption of clinical research findings into real-life communities and practices (uncontrolled situations are pervasive) • Woolf, JAMA 2008

7. Business (pharmaceuticals, diagnostics, interventions) Public health guidelines (immunizations) Clinical guidelines (e.g., ADA Standards of Care) Policy development & modification (tobacco laws) Health-related behaviors (prevention, self-management, common knowledge) The impacts of research:

8. Epidemiology: • Distribution • Determinants Translational Sciences: Transfer scientific evidence into practice to reduce morbidity + improve QOL • Basic Sciences: • Proof of concept DISTRIBUTION PRACTICE/POLICY AVAILABILITY Diffusion of interventions Supply EVIDENCE EFFICIENCY Biggest effect on most people EFFECTIVENESS Real world settings EFFICACY Surveillance and Epidemiology Ideal settings Etiology / Burdens / Laboratory

9. So, how does one translate research into practice?

10. In order to implement…

11. Delayed Implementation “Studies suggest that it takes an average of 17 years for research evidence to reach clinical practice.” Balas, E. A., & Boren, S. A. (2000). • “These figures are almost certainly an • -underestimate of the time it takes to translate research to impacts • -overestimate of studies that survive to contribute to utilization • Even so, the largest segment of translational time in these estimates encompasses the region of dissemination and implementation.” William M.K. Trochim (Bethesda, MD . 16 March 2010)

12. Common Barriers in translation • Access to research evidence • Sheer volume of research • 1,000 articles indexed daily on medline • Internist required to read 17 articles daily • Skills to appraise, understand, and apply evidence • Content of evidence not aligned with needs [validity>applicability; adequate detail of intervention(s)] • Other priorities / tradeoffs

13. Synthesis is a key step • large volume of research data • systematic review of evidence • implementation by key stakeholders (Ohlsson, 2002)

14. Transforming Evidence into knowledge tools Strength of Evidence: Level A: Data derived from multiple, well-conducted, adequately powered RCTs or meta-analyses Level B: Data derived from single RCT or large non-randomized studies(cohort, registries, meta-analysis cohorts) Level C: Consensus opinion of experts and/or small studies, retrospective or observational studies (+/- methodological flaws, biases)

15. Diabetes Care

16. Spectrum Cardiovascular disease Amputation Death Normal Pre-diabetes Diabetes Complications/Disability Blindness Kidney disease 8-12% $11,700 $20,700 • Care Practices • Foot exams / Eye Exams • Self-monitoring blood sugar • Risk Factor Control • Smoking / BP / Cholesterol • Health Status • Functional limitations • Quality of life UnitedHealth Group Report, 2010 Desai et al J Public Health Management Practice, 2003 (suppl). S44-51

17. 0 1 2 3 4 5 6 7 8 9 DCCT: Absolute Risk of Sustained Retinopathy Progression by HbA1c and Years of Follow-up 24 Mean HbA1c = 11% 10% 9% 20 Conventional treatment 16 Rate/100 Person-Years 12 8% 8 4 7% 0 Time During Study (y) DCCT Research Group. Diabetes 1995;44:968-983.

18. Edelman, SV. Major Diabetes Trials: DCCT, UKPDS, and Kumamoto Studies. Medscape Family Medicine

19. 0 10 20 30 40 50 Risk Reduction of Diabetes-RelatedEnd Points with Tight BP Control Microvascular End Points‡ Myocardial Infarction Diabetes-related Mortality* Stroke† 21 Risk Reduction (%) 32 37 44 * Death due to MI, sudden death, stroke, peripheral vascular disease, renal disease, hyperglycemia, or hypoglycemia. † Fatal or nonfatal. ‡ Retinopathy requiring photocoagulation, vitreous hemorrhage and fatal or nonfatal renal failure. Mean BP : 144/82 mm Hg (tight BP control) vs 154/87 mm Hg (less tight BP control). UK Prospective Diabetes Study Group. BMJ. 1998;317:703-713.

20. Heart Protection Study: Lipid Mx

21. Steno-II Study: 80 patients: Intensive/comprehensive care (BP, A1c, lipids, ACE, aspirin) 80 patients: standard care • 7.8 yr follow-up: • HbA1c ↓ 0.7% • BP ↓ 11/4 mmHg • LDL ↓ 34 mg/dl • Use of Aspirin/ACEi ~ ↑20% • 53% ↓ CVD • 61% ↓ Nephropathy • 58% ↓ Retinopathy • 63% ↓ Neuropathy 13.3 yr follow-up: all-cause mortality ↓ 46% CVD events ↓ 59% CVD-mortality ↓ 57% Gaede P et al NEJM 2003 & 2008

22. Straightforward – so, how were we doing?

23. Proportion of individuals reaching target HbA1c is not improving over time NHANES (1988–1994) 60 NHANES (1999–2000) 48% 50 44% 37% 36% 40 34% 29% Individuals achieving goals (%) 30 20 7% 5% 10 0 HbA1c < 7.0% BP < 130/80 mmHg Total cholesterol < 200 mg/dL Good control* *Individuals achieving goals for HbA1c, blood pressure and total cholesterol Saydah SH, et al. JAMA 2004; 291:335–342.

24. Behavior change = difficult

25. Common Barriers

26. TRIAD study

27. TRIAD study • Reduced use of recommended meds • Reduced preventive care • Poorer risk factor control • Coverage gaps (copayments, cost-shifting) • Younger adults • Women • African-Americans • Quality of patient-provider relationships (communication, trust) • Unrecognized or untreated depression • Emphasis on reporting of processes, outcomes • Redesign benefit plans that optimize costs and care

28. Quality Improvement Interventions ↑ Processes of care ↑ Intermediate biochemical outcomes ↑ Patient Satisfaction

29. Synthesizing the Evidence • Meta-analysis † and Qualitative Review ‡ • Most strategies work (modest improvements) • Most effective = Team changes & case management * CDC. MMWR 2001 † Shojania KG. JAMA 2006; ‡ Renders et al. Diabetes Care 2001.

30. NCQA – Quality Indicators – reporting Wider use of team-based care The impacts of this:

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35. Preventive Practices Δ 99-02 to 07-10 Ali et al, NEJM 2013

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37. Also… • No “one-size-fits-all” interventions – individualize / adapt / contextualize • Other social-contextual-policy factors (outside the health system) also influence care and outcomes

38. Population (LOW) Individuals (HIGH) Cost Intensity

39. Policies that impact diabetes • Policies at workplaces e.g., wellness; checks • Legislative initiativeson access, delivery, outcomes, accountability, and costs of care. • Insurance reimbursement and benefit designs, (e.g., no co-pays for statins) • Economic incentives and disincentives, such as subsidies, taxes, and vouchers for community programs. • Screening implemented by health systems, schools, and community settings.

40. Examples

41. Example: Food labeling Elbel, Health Affairs, 2009

42. Example: Food labeling Elbel, Health Affairs, 2009

43. Efficacy vs. Effectiveness • Understanding cause  Changing Practice (process as outcomes) • Patient factors  Patient, provider, systems(largely biological) • Internal validity  Generalizability • Focus on rare  Focus is on common • Benefit as relative  Benefit as absolute • Perfect health  Optimal health for mostfor few (Narayan, 2000)

44. Metrics of interest • Effectiveness (comparative) • Processes (adherence, utilization) • Clinical endpoints • Costs (patient and system level) • Patient-centered outcomes (e.g., acceptability) • Unintended effects (e.g., disparities) (Narayan, 2000)

45. Conclusions

46. Translation– successes • Development of knowledge tools using evidence • Chronic care model and solutions to improve care • Translation– challenges • Implementation gaps still exist • Translation– Opportunities • Can really help decision making or evaluating options • Prediction: accurately identifying people with DM • Testing supportive policies/environmental changes

47. mkali@emory.edu www.cdc.gov/diabetes Thank you! Any questions?

48. Δ = difference b/w intervention & control group

49. Selecting a Combo of Strategies • Non-physician Care Coordinators • Low cost training • Build capacity in low-resource settings = new cadre of health worker • Execute algorithm-based management • Monitor patients & identify those needing attention / intensification • Encourage & motivate • Coordinate care • Decision-support Systems • Calculation of risk • Prompts and reminders • Store serial data • Multi-disciplinary Team • Guidance and support

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