Understanding Diseases and Treatments with Canadian Real-world Evidence

Transcription

1 Understanding Diseases and Treatments with Canadian Real-world Evidence Real-World Evidence for Successful Market Access WHITEPAPER REAL-WORLD EVIDENCE

2 Generating real-world evidence requires the right data, the appropriate tools and methods to structure and interrogate it, and grounded science to turn it into actionable insights and engage stakeholders appropriately. IMS Brogan enables this process by bringing together multiple data sources, an intelligent technology layer between the raw data and the ultimate analytical need, and innovative researchers and methodologies.

3 Understanding Diseases and Treatments with Canadian Real-world Evidence Patient outcomes are becoming the most meaningful currency for healthcare decision making. The role of real-world evidence (RWE) in assessing these endpoints has elevated its importance on every major life sciences company agenda, putting health economics & outcomes research (HEOR), epidemiology and drug safety at the heart of the desired transformation. DATA SOURCE: WHERE DOES THE DATA COME FROM? The IMS Brogan EMR database consists of over de-identified patients from electronic medical records entered by GPs and Specialists with adhoc access to a further 5 million patient EMRs as required. The data is mainly collected from Ontario, with some records from Quebec and Alberta. Access to additional patients is available based on study requirements. DATA COMPONENTS: WHAT IS IN THE DATA? Using IMS Evidence 360 Cohort Builder, one can observe BMI, BP, other CV risk factors, and interventions, providing additional insight into the full value of medicines. FIGURE 1: EMR DATA INCLUDES MULTIPLE PATIENT METRICS 1 Calculated using date of birth age Billing Codes 1. Age 2. Sex 2 3 Male, Female Yes, Never, Quit (+quit date) 9. Program Patient 3. Smoking Status Blood pressure, pulse, temperature, height and weight (BMI) Used to derive number of times off work Referrals to a specialist (type of specialty recorded) 8. Rx 4. Vitals 7 Integrated test name, result and range 8 Name, Rx and Refill, DIN, ATC, posology 7. Lab Results 6. Referrals 5. Sick Note 9 10 Screening, Diabetes, Womens Health OHIP, RAMQ, Private REAL-WORLD EVIDENCE 3

4 Data Validation: How useful is it? How representative is the data? BACKGROUND Observational data derived from clinical practice is becoming increasingly important to answer questions that cannot be addressed in Randomized Clinical Trials. There is a need in Canada for more comprehensive data which includes confounders such as smoking status and body weight. The objective of this study was to validate data from a primary care Electronic Medical Record (EMR) system. METHODS We analyzed consistency, completeness and comprehensiveness of de-identified patient data from 816 Primary Health Care Professionals from Overall demographic data were compared to Statistics Canada; the age and sex of patients with type 2 diabetes were compared to those in the Public Health Agency of Canada (PHAC) survey. Completeness was determined by visit for each variable. RESULTS Records from 845,243 patients were analyzed, of these 255,274 were active ( 1 visit) from Data are available for demographics, vitals, smoking status, labs, medications, history, diagnosis (ICD-9), short term absences and referrals. Completeness ranged from 26% for pulse to 10 for age, sex, lab results, referrals and sick notes. Smoking status was at 68%. The primary care population was slightly younger than the national average but there was no difference observed for sex. Similarly, demographics in patients with type 2 diabetes were consistent with PHAC; age was biased towards the younger population and sex was identical. CONCLUSIONS Validation of this primary care database indicated that it is highly comprehensive and representative of the Canadian population. It may serve as a valuable source for future observational studies. FIGURE 2: AGE DISTRIBUTION IN EMR COMPARED TO PUBLIC HEALTH AGENCY OF CANADA 10 % OF POPULATION 8 17% 18% 69% 7 17% 12% PHAC EMR AGE BAND REAL-WORLD EVIDENCE 4

5 Data Validation: How useful is it? How representative is the data? FIGURE 3: DIABETIC SEX DISTRIBUTION COMPARED TO PUBLIC HEALTH AGENCY OF CANADA % OF TYPE 2 DIABETES PATIENTS 10 8 Male Female 46% 47% 54% 53% EMR PHAC SOURCE FIGURE 4: COMPARISON OF MOST COMMONLY USED MEDICATIONS FOR TYPE 2 DIABETES (EMR VS. NATIONAL PRESCRIPTION UTILIZATION*) % 63% 13% 12% 12% 1 1% 1% 4% 3% 1% 1% 6% 9% Gliclazide Glyburide Liraglutide Metformin Pioglitazone Saxagliptin Sitagliptin DIABETES MEDICATION GLICLAZIDE GLYBURIDE LIRAGLUTIDE METFORMIN PIOGLITAZONE SAXAGLIPTIN SITAGLIPTIN PATIENT SHARE LRX 13.14% 11.91% 0.98% 58.69% 3.79% 0.89% 6.29% EMR PATIENT SHARE % 0.97% 62.91% 3.42% % *Source: IMS Brogan - LRx 2011 Patient counts/shares IMS Database Validity Study Abstract: Frise et al. Canadian Journal of Clinical Pharmacology 2013 REAL-WORLD EVIDENCE 5

6 Data Application: What can it be used for? Typical examples of types of reports that can be undertaken from the IMS Evidence 360 Cohort Builder FIGURE 5 RWE OFFERING KEY COMPONENTS APPLICATIONS Burden of Illness Prepare a Test and Control Cohort of patients Evaluate the direct, indirect, and societal costs to treat each cohort MD/ER visits, hospitalizations, diagnostic tests, patient monitoring, productivity Define the unmet need of a disease Quantify the impact of a disease on Canadians Raise awareness of the importance of improving disease management through publication Facilitate discussion with payer and policy makers Cost Effectiveness Lab results pre and post treatment Outcomes based on labs, diagnosis and Tx Drug cost Other health care costs Persistence & compliance Dose escalation Incidence & prevalence Lines of therapy Supplement evidence package for CDR and PCPA Understand the economic value of an individual treatment Often published in journals, at conferences to build credibility and raise awareness REAL-WORLD EVIDENCE 6

7 Data Application: What can it be used for? IMS EVIDENCE 360 COHORT BUILDER ENABLES FAST INSIGHTS TO DATA NOT READILY AVAILABLE PREVIOUSLY IN CANADA Example: Understanding HbA1c levels of patients taking diabetes medications and deeper insights into patients taking DPP4 medication - Patient Diagnostics from IMS Evidence 360 Cohort Builder, January 2013 December FIGURE 6: PERCENTAGE OF PATIENTS ON SELECTED DIABETES MEDICATIONS N= 6, % 15.8% 16.7% 10.5% 31.2% 12.1% Metformin Met+SU DPP4 Met+DPP4 HbA1c > 7% No HbA1c results less than 7% DIABETES MEDICATIONS HBA1C LEVELS In this study, 906 patients who have received a DPP4 prescription exhibit the following characteristics: FIGURE 7: DPP4 PATIENT CHARACTERISTICS N= % 56.2% 39.1% 40.7% 20.2% 30.1% 26.6% 17.2% 0.3% 3.6% 5.3% Male Female Not Recorded < Never Yes Quit SEX AGE SMOKING STATUS REAL-WORLD EVIDENCE 7

8 Data Application: What can it be used for? Patients taking DPP4s also typically receive the following medications: FIGURE 8: PERCENTAGE OF DPP4 PATIENTS RECEIVING OTHER MEDICATIONS N= % 56% 43% 35% 28% 25% 25% 21% Metformin Cholesterol & Triglyceride Regulators Sulphonylurea ACE Inhibitors, Plain Platelet Aggregation Inhibitors Oure Vaccines Antiulcerants Angiotensin - II Antagonists, Plain Calcium Antagonists, Plain MEDICATIONS Test results for DPP4 patients show a significant proportion are hypertensive and obese: FIGURE 9: PATIENT BLOOD PRESSURE TEST RESULTS n= Low Normal Prehypertension Hypertension Stage 1 Hypertension Stage 2 BLOOD PRESSURE STATUS* SYSTOLIC (MM HG) DIASTOLIC (MM HG) Low less than 90 OR less than 60 Normal less than 120 AND less than 80 Prehypertension OR Hypertension Stage OR Hypertension Stage or higher OR 100 or higher BLOOD PRESSURE STATUS *Mayo Clinic Guidance on BP ranges REAL-WORLD EVIDENCE 8

9 Data Application: What can it be used for? FIGURE 10: PATIENT BODY MASS INDEX^ n= OTHER AVAILABLE CHOLESTEROL TESTS 15.1 Obese (>30.00) Overweight ( ) Normal ( ) LDL HDL TC/HDL-C Thin (<18.50) TRIGLYCERIDES ^ Calculated Value, BMI definition from WHO With EMR data, we have the ability to observe test results to assess glycemic control and resulting costs: FIGURE 11: DPP4 PATIENT LAB TEST RESULTS % 39% 63% 37% Out of control > =.07 Within control <.07 Out of control > 6.9 Within control < = 6.9 HBA1C TEST RESULTS n= 460 FASTING GLUCOSE TEST RESULTS n=450 REAL-WORLD EVIDENCE 9

10 Data Application: What can it be used for? FIGURE 12: AVERAGE PHYSICIAN BILLING COSTS FOR DPP4 PATIENTS WITH GLYCEMIC LAB TESTS n=561 AVERAGE COST $900 $800 $700 $600 $500 $400 $300 $200 $100 $0 $614 Average Cost $832 71% Average Uncontrolled Patient Cost % Uncontrolled 8 PHYSICIAN BILLING COST LAB TEST RESULTS REAL-WORLD EVIDENCE 10

11 Data Access: How can the data be accessed? IMS Evidence 360 Cohort Builder enables the researcher to simply build cohorts using inclusion exclusion criteria on multiple parameters FIGURE 13 A recent paper from the Institute of Governance and the Institute of Health Economics Backgrounder on the use of real world evidence states the following: In economic evaluations of new therapeutics Canadian decision makers are increasingly utilizing health technology assessment processes that include formal methods of economic evaluation (i.e. analyses of value for money or return on investment) to inform investment and dis-investment decisions in the health care system. While such analyses can be conducted alongside clinical trials conducted to meet regulatory requirements, they are not ideal due to inherent limitations in the external validity of the results (i.e. efficacy vs. effectiveness). More commonly, economic evaluations, such as cost-effectiveness analysis are based on models (econometric or simulation). The primary limitation with modeling approaches are that the results are dependent on selection and availability of data sources as well as analytic and structural assumptions that are not always evident to those interpreting the findings of the model. Conducting economic evaluations using real-world data is a potential remedy being developed to address the limitations associated with current approaches. These data could be primary data collected for the purpose of evaluation, or secondary data routinely collected in administrative databases (or prospectively through other means such as surveys, patient registries, or electronic medical records). These approaches are become more common in the health sector, with international and Canadian examples being put in place. IMS Brogan has adressed this need with a comprehensive, representative Canadian primary care EMR database which meets or exceeds Canadian privacy requirements and is accessed by state-of-the-art software to build de-identified patient cohorts. REAL-WORLD EVIDENCE 11

12 IMS BROGAN, a Unit of IMS Health MONTREAL Trans-Canada Highway Kirkland (Québec) H9H 5M3 (514) OTTAWA 535 Legget Drive, Tower C, 7th Floor Kanata, Ontario K2K 3B8 (613) TORONTO 6700 Century Avenue, Suite 300 Mississauga, Ontario L5N 6A4 (905) ABOUT IMS HEALTH IMS Brogan is a unit of IMS Health, a leading worldwide provider of information, technology, and services dedicated to making healthcare perform better. With a global technology infrastructure and unique combination of real-world evidence, advanced analytics and proprietary software platforms, IMS Health connects knowledge across all aspects of healthcare to help clients improve patient outcomes and operate more efficiently. The company s expert resources draw on data from nearly 100,000 suppliers, and on insights from 39 billion healthcare transactions processed annually, to serve more than 5,000 healthcare clients globally. Customers include pharmaceutical, medical device and consumer health manufacturers and distributors, providers, payers, government agencies, policy makers, researchers and the financial community. TO FIND OUT MORE about how we can help you with your needs in RWE and HEOR, please contact your IMS Brogan representative. Scan the QR code to discover our range of information resources for Canada: IMS Health Incorporated and its affiliates. All rights reserved. Trademarks are registered in the United States and in various other countries.