Use of Claims Database Research in Medical Decision Making

1. Real-World Evidence and Claims
Databases
Craig I. Coleman, PharmD
Professor, University of Connecticut (UCONN)
Co-Director and Methods-Chief, UCONN Evidence-Based
Practice Center
Craig.coleman@hhchealth.org

2. “In essence, a clinical trial can tell us what a drug does, while
RWE can provide the context that tells us whether what it
does actually matters.”
1
• Pre-selected patient population
• Strict inclusion and exclusion criteria
• Strict study protocol
• Objectively adjudicated event rates
• Provide a good understanding of how the product performs in a
certain group of people, but not necessarily reflective of the real-
world
• Data collected outside of a RCT setting
• More diverse patient population treated in everyday practice
• Data collected prospectively (real-time) or retrospectively (using
past data)
• Treatment recommendations through guidelines and at the
discretion of the physician, not strict study protocol
• Over- and under-reporting of events is possible
RCT
RWE
1. PMLiVE. Get Real! The Rise of Observational Data In Healthcare. Available at:
http://www.pmlive.com/pharma_intelligence/get_real!
_the_rise_of_observational_data_in_healthcare_705710 Accessed May 2016.
RCT=Randomized clinical trial; RWE=Real-world evidence

3. Uses of Real-World Evidence*
• Fulfill regulatory body obligations
– Risk Evaluation and Mitigation Strategy (REMS) programs
• Surveillance, Epidemiology and End Results
Programs/Pharmacovigilance
– FDA Sentinel Program
– Post-Marketing Safety Surveillance (PMSS)
– FDA Safety Communications/Analyses (MedWatch)
• Comparative effectiveness research (CER)
• Evaluate prescribing patterns and medication utilization
• Quality improvement
*Individual studies may fall into multiple
categories

4. My Personal View: “Real-World Evidence is
Complementary to Rigorous But Tightly Controlled
Randomized Clinical Trials”
• Real-world evidence is a broad term for many different study
designs, including:
– Pragmatic (or naturalistic) randomized clinical trials (MERCURY-PE)
– Prospective registries
– Retrospective clinical studies
– Claims database analyses (e.g., MarketScan, Optum, Danish and
Swedish registries)
• Not all real-world evidence studies are created equal
– Internal (and external) validity can change markedly between and within real-world
study designs
– Differences (and their impact) are not always obvious to the reader (or peer reviewer,
or even editor-in-chief) 1. Am Heart J. 2012;163:13-19 e1; 2. Am Heart J 2011;162:606–612.e1.; 3. Eur
Heart J 2015; doi:10.1093/eurheartj/ehv466.

5. Claims and Claims Databases
• Claims data are collected by payers to track and assure reimbursement for
healthcare services provided
• Some claims databases are single payer:
– Medicare database1
or Department of Defense databases2
– National healthcare databases (e.g., Danish Nationwide Databases)3-5
• Some claims databases are drawn from multiple different payers and
assembled by health analytic companies6-8
:
– IBM MarketScan7
– Optum
• Regardless, rarely was this data originally collected for research
purposes!
1. Medicare. Available at https://www.cms.gov/Medicare/Medicare.html (accessed December 2016); 2. Tamayo S, et al. Clin Cardiol. 2015;38:63–68; 3. Lynge E, et al. Scand J Public Health. 2011;39(7 Suppl):30–3; 4. Kildemoes HW, et al. Scand J Public
Health. 2011;39(7 Suppl):38–41; 5. Pedersen CB, et al. Scand J Public Health. 2011;39(7 Suppl):22–5; 6. Real Health Data. Healthcare Database Information. Available at http://hinora.uncc.edu/sites/hinora.uncc.edu/files/media/Database
%20Narrative_19mar2013.pdf (accessed December 2016); 7. Hansen L. The MarketScan® Databases for Life Sciences Researchers. White Paper May 2016; 8. Optum Labs- Partners, Data and Design. Available at
https://www.optum.com/content/dam/optum/Landing%20Page/ls/OptumDay2015/1_OptumLabs_P.Wallace.pdf (accessed December 2016)

6. What is in a Claims Database?
• Typically only include data needed to facilitate reimbursement of healthcare services1-2
– Basic demographics (age, gender, race [sometimes], insurance type)
– Types and counts of healthcare encounters (hospitalisations, office visits, diagnostic and
laboratory tests)
– Diagnostic codes (ICD-10) associated with each healthcare encounter
– Prescription drug fill records
• Each healthcare encounter is (typically) associated with multiple diagnosis codes3
– The first or primary code listed should depict the main reason for the encounter
– Subsequent codes are used to demonstrate patient complexity/acuity (and possible justification
for higher reimbursement) by designating comorbidities
– Diagnostic codes are often chosen by a medical coder/biller based upon review of medical
records for an encounter
ICD=International Classification of Diseases
1. Real Health Data. Healthcare Database Information. Available at http://hinora.uncc.edu/sites/hinora.uncc.edu/files/media/Database%20Narrative_19mar2013.pdf (accessed December 2016); 2. Gandhi S, et al. J Manag Care Spec Pharm.
1999;5:215-222; 3. ProfNet. Questions and answers on ICD10 coding. Available at http://www.profnetmedical.co.za/media/1080/qa-on-icd-10-v2.pdf (accessed December 2016)

7. What Typically Isn’t in a Claims Database?1-
2
• Results of laboratory or diagnostic tests (serum creatinine, EKG, CT or
MRI results)
– Some claims databases have integrated EHRs, but this is often limited to a subset of
the entire database
• Vital sign or other clinical characteristics (heart rate)
• Out-of-hospital mortality data (government databases do, but access is
often restricted and do not reliably report cause of death)
• Qualitative data (quality-of-life, patient satisfaction, explanations for
treatment decisions)
• Simple description or listing of medical histories or comorbidities
EKG=Electrocardiogram ; CT=Computed tomography; MRI=Magnetic resonance imaging; EHR=Electronic health records
1. Real Health Data. Healthcare Database Information. Available at http://hinora.uncc.edu/sites/hinora.uncc.edu/files/media/Database%20Narrative_19mar2013.pdf (accessed December 2016); 2. Gandhi S, et al. J Manag
Care Spec Pharm. 1999;5:215-222

8. How are Claims Databases Used for
Research Purposes?
• Diagnostic coding (considering the presence of a code, its position and
type of encounter it is associated with) and prescription fill records are
used to determine if:
– Patients have the disease state of interest (ICD-10 of I48 suggests atrial fibrillation)
– Comorbidities of interest are present (e.g., CHA2DS2-VASc, HAS-BLED)
– The occurrence of an outcome of interest (ischaemic stroke, intracranial bleeding,
gastrointestinal bleeding) occurred
– What drug therapies (index oral anticoagulant, antiplatelet agents but not always
aspirin because its over-the-counter) and doses used (but are they appropriate
doses?)
– Persistence to index therapies (NOAC and VKA) and “on-treatment” status
• Procedures are coded as well and can be used in a similar fashion as
diagnostic codes (they are in no specific order)
NOAC=Non-vitamin K antagonist oral anticoagulant
Gandhi S, et al. J Manag Care Spec Pharm. 1999;5:215-222; Coleman CI, et al. Curr Med Res Opin. 2016;32:2047-2053

9. Common Limitations of Claims Database
Analyses1-2
• Misclassification bias (inaccurate or insufficient classification of patients):
– Error in diagnostic coding by the medical coder
– “Tactical” coding (upcoding)
– Coding is not always at the level of detail we would wish:
• There is a diagnosis code for heart failure, but cannot determine ejection fraction, NYHA
classification
• Multiple codes for cancer, but difficult to differentiate between history of vs. active, type
(e.g., prostate vs. pancreatic) or staging
• Important to use validated coding schemas for comorbidities and outcomes:
– US FDA Sentinel coding3
– Cunningham algorithm for major bleeding4
– CMS Data Warehouse
NYHA=York Heart Association ; US=United States; FDA=Food and Drug Administration
1. Gandhi S, et al. J Manag Care Spec Pharm. 1999;5:215-222; 2. Strengths and Limitations of CMS Administrative Data in Research. Available at https://www.resdac.org/resconnect/articles/156 (accessed December 2016); 3.
Go A. MINI-SENTINEL MEDICAL PRODUCT ASSESSMENT A PROTOCOL FOR ASSESSMENT OF DABIGATRAN. Available at https://www.sentinelsystem.org/sites/default/files/Drugs/Assessments/Mini-Sentinel_Protocol-for-
Assessment-of-Dabigatran_0.pdf (accessed December 2016); 4. Cunningham A, et al. Pharmacoepidemiol Drug Saf. 2011;20:560-6

10. Inconsistency in Major Bleeding
Definitions Using Claims
• Schemas to identify bleeding-
related hospitalizations in claims
data differ in both the specific
codes used and coding positions
allowed
• Within MarketScan claims data,
identified adults with NVAF and
newly started on OAC from 1/2012–
6/2015
• 151,738 new users of OACs with
NVAF (median CHA2DS2-VASc
score=3, HASBLED score=3
• The Cunningham, Yao and FDA
schemas identified bleeding-related
hospitalizations in 2,845 (1.9%),
7,065 (4.7%) and 4,027 (2.7%)
Kappa values: 0 – 0.20=no; 0.21 – 0.39=minimal; 0.40 –
0.59=weak; 0.60 – 0.79=moderate; 0.80 – 0.90=strong; > 0.90=near
perfect agreement

11. Common Limitations of Claims Database
Analyses
• Confounding (imbalance between “relevant” characteristics)
– Want to be able to say “All other things being equal…!”
– But it is highly likely patients receiving compared therapies vary in important characteristics
• Methods used to adjust or balance patients on “relevant” characteristics
– Regression and/or propensity scoring (adjustment, weighting, “greedy matching”)
• Characteristics adjusted/matched upon should take into account not just
the disease state, but also the endpoints of interest
– Matching on CHA2DS2-VASc and HAS-BLED criteria may be sufficient for stroke or major
bleeding in NVAF, but is it sufficient for all-cause mortality?
– Studies often match on ~30-50 “relevant” characteristics, but many more diagnoses and drugs
exist
• In the absence of randomisation, we will ALWAYS have residual
confounding
1. Gandhi S, et al. J Manag Care Spec Pharm. 1999;5:215-222; 2. Strengths and Limitations of CMS Administrative Data in Research. Available at https://www.resdac.org/resconnect/articles/156 (accessed December 2016);
3. Austin PC. Multivariate Behav Res. 2011;46:399–424; 4. Coleman CI, et al. Curr Med Res Opin. 2016;32:2047-2053

12. Study by Graham et al.
JAMA Intern Med. doi:10.1001/jamainternmed.2016.5954 Published online
October 3, 2016.

13. NSAIDs and Gastrointestinal Bleeding Risk:
Danish Registry Data
Br J Clin Pharmacol 2002;53:173-181

14. Among Patients with Atrial Fibrillation on
Dabigatran
Characteristic Adjusted Hazard Ratio (95%CI)
Age (yrs) (reference: < 55 yrs) ---
55–64 1.54 (0.89–2.68)
65–74 2.72 (1.59–4.65)
≥75 4.52 (2.68–7.64)
Heart Failure 1.25 (1.01–1.56)
Coronary artery disease 1.37 (1.10–1.69)
Renal impairment 1.67 (1.24–2.25)
Prior Bleeding 1.32 (1.01–1.72)
Alcohol abuse 2.57 (1.52–4.35)
Helicobacter pylori infection 4.75 (1.93–11.68)
Corticosteroids (and stress) 1.17 (0.95–1.45)
Dabigatran 150mg (reference: 1.14 (0.86–1.53) Pharmacotherapy. 2015; 35:560–568

15. How do GPs find real-world evidence to be most
useful to them?
validates Phase III trial
data
1
27
% shows how medicines are
being used by their peers
in a clinical setting1
25
%
Importance of Real-World Studies
(Including Claims Database Analyses)?
In a 2015 survey of more than 1,000 GPs, 97% found
real-world evidence (including claims analyses) to be
useful to them
1
GP=General practitioner; UK=United Kingdom
mhp health. ‘The tricky second album: From RCTs to RWE, does industry have the right mix for clinicians’ tastes?’. Available at http://www.mhpc.com/wp-
content/uploads/2016/01/MHP-Health-Tricky-Second-Album.pdf (accessed November 2016)

16. Case report or
expert opinion
Case series
Case-control studies
Cohort studies
RCTs
RCTs confirmed in daily care
Levels of Clinical Evidence As They Should Be?
Centre for Evidence-based Medicine. Oxford Centre for Evidence-based Medicine – Levels of
Evidence
(March 2009), http://www.cebm.net/oxford-centre-evidence-based-medicine-levels-evidence-march-
2009
(accessed March 30, 2017). The concept represented on this slide is a modification of the Centre for
Evidence-based Medicine’s hierarchy of clinical evidence.

17. Thank You for Your
Attention!