Bradley, C.J.

Foley, K.

Miller, J.

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 V A L U E  I N  H E A L T H  1 6  ( 2 0 1 3 )  A 1 - A 2 9 8  A25 
 
 
Spontaneous reports (SRs) of suspected adverse events associated with 
biopharmaceuticals and other medical products are key sources for identifying 
potential new drug hazards. They are required by most regulatory agencies as 
well as large drug distribution projects, e.g., HIV/TB programs. The WHO collects 
these pharmacovigilance data from >100 countries. Many databases conform to 
WHO’s Council for International Organizations of Medical Sciences (CIOMS) 
standard data fields. B.R.I.D.G.E. TO DATA (www.bridgetodata.org), an 
international resource of database profiles, can serve as a template and 
complement the CIOMS effort. OBJECTIVES: Analyze SR database profiles and 
classify use of data fields. METHODS: We identified databases profiled in 
B.R.I.D.G.E. collecting SR data using two search criteria: Database 
Type=Spontaneous reporting systems; and Database Source=Spontaneous reports. 
Twenty of 198 profiles matched ≥1 criteria; frequency of use of the 75 data fields 
were compared. Based on use frequency, fields were categorized into Group 1 (G1 
- consensus in use of field among the set), Group 2 (G2 - use by ≥50% databases), 
or Group 3 (G3 - use by <50% databases). RESULTS: Of the 75 data fields, 53 (71%) 
were frequently used among databases with SR data: 21 (28%) were classified 
into G1 (e.g., Diagnosis data, Drug data), and 32 (43%) into G2 (e.g., Death 
recorded, Physician specialty). Fields utilized less frequently (n=22; 29%) 
comprised G3 (e.g., Medical Record Access, Linkage Capabilities). Analysis of G1 
revealed that a majority of SR databases are funded by government agencies, 
capture OTC & prescription drug use in inpatient & outpatient settings; however, 
diagnosis data are heterogeneously coded. Of the 25 fields on the CIOMS 
reporting form, 17 corresponded to G1, and 8 to G2. CONCLUSIONS: In this 
analysis, B.R.I.D.G.E. served as a screening tool to categorize data fields used in 
SR databases and successfully identified additional fields to complement the 
CIOMS effort.  
 
PRM69  
WHAT IS THE OPTIMAL SEARCH ENGINE FOR RESULTS FROM EMBASE AND 
MEDLINE: OVID OR EMBASE.COM?  
Fortier KJ, Kiss N, Tongbram V 
Oxford Outcomes, Morristown, NJ, USA  
OBJECTIVES: Ovid and Embase.com are two search engines that are commonly 
used for searching the Medline and Embase databases. The standard of practice 
for conducting a search for a systematic literature review seems to support the 
use of Ovid. Our objective was to discern the advantages and disadvantages of 
both the Embase.com and Ovid search engines. We sought to compare the two 
databases in the following ways: results of a search, and ease of searching. 
METHODS: We conducted several searches to see if there were any differences in 
results. We analyzed the variation in results to determine why the disparities 
existed. We wrote a step-by-step search guide and highlighted the 
differences/difficulties encountered. Using a set of written directions, we also 
asked researchers to conduct searches using both interfaces and rate ease of use 
on a scale of 0 through 5. Research was also conducted through websites and 
help desks to ascertain the disparities in content between the two search 
engines. RESULTS: There were differences in the search results between Ovid 
and Embase.com using the same search criteria. The coding of articles 
sometimes differed, but there were very few, if any, relevant articles that were 
missed by either engine. In terms of ease of use, Embase.com had lower scores 
indicating greater ease of use in searching, exporting into a reference manager, 
saving searches, and recalling data compared to Ovid. In the research assessing 
the differences in content through the company websites, we found that 
Embase.com alleged to contain articles published further in the past, include 
more conference abstracts, and update Emtree terms more frequently than did 
Ovid. CONCLUSIONS: There is no notable difference between Ovid and 
Embase.com in terms of search results when searching Embase and Medline. 
However, Embase.com was rated as easier to use in several domains compared 
to Ovid.  
 
PRM70  
STATE-LEVEL, POPULATION-BASED CANCER REGISTRY AND ADMINISTRATIVE 
DATA LINKAGES IN THE UNITED STATES: A REVIEW OF THE LITERATURE  
Foley K1, Miller J1, Bradley CJ2 
1Truven Health Analytics, Cambridge, MA, USA, 2Virginia Commonwealth University, Richmond, 
VA, USA  
OBJECTIVES: Observational studies using administrative claims data are 
increasingly used for oncology outcomes research. The only population-based 
administrative data inclusive of disease stage and death from registries, 
however, are the SEER-Medicare data which exclude the under 65 population. 
Many state cancer registry and claims data linkages exist, but little is known 
about them. We conducted a literature review to identify and describe U.S. state-
level, population-based cancer data linkages. METHODS: PubMed was searched 
for all years using the keywords “registry,” “cancer OR oncology,” and “claims OR 
administrative.” Inclusion criteria were U.S. state cancer registries and 
administrative claims data linkages, exclusive of the SEER-Medicare database. 
RESULTS: A total of 687 abstracts were identified; 74 articles met the inclusion 
criteria. These papers represented 21 state cancer registries and linkages to 
Medicaid, Medicare, commercial insurance claims, state-level hospital discharge 
files, state-level screening program data, or state health care agency 
administrative files. Most linked Medicaid (N=12) and Medicare (N=9) data to 
assess quality of cancer care for specific types of cancer. Eighty percent of states 
specifically linked data on breast-cancer, 40% linked colorectal cancer, 25% 
linked on cervical, prostate or lung, and 40% were inclusive of all cancers. Only 
seven states (35%) linked commercial claims data or hospital discharge data 
(representing the under 65 population). CONCLUSIONS: Significant work is being 
conducted at the state level to generate cancer data, yet the under 65 population 
remains under-represented among linked cancer lives. Although fragmented, the 
existing linkages provide a foundation on which to build a more comprehensive 
oncology data system. Maintaining and expanding these data are critical for 
quality assessments, and disparity, outcomes and comparative effectiveness 
research. Barriers related to proprietary data and sustainable funding for 
linkages must be addressed before a data infrastructure that is broadly 
representative of the US national population can be generated.  
 
PRM71  
IDENTIFYING CHRONIC KIDNEY DISEASE STAGES USING PATIENT 
PRESCRIPTION INFORMATION  
Cai Y1, Han Y2, Jiao X3, Mu G1 
1IMS Health, Plymouth Meeting, PA, USA, 2IPSEN Biopharmaceuticals, Inc., Basking Ridge, NJ, 
USA, 3IMS Health, Alexandria, VA, USA  
OBJECTIVES: It’s important for health care policy makers, payers and drug 
manufacturers to identify Chronic Kidney Disease (CDK) patient stages in order 
to evaluate the prevalence, economic burden or market opportunities. The office 
based medical claims data, which contains ICD-9 diagnosis and treatment 
information of CDK stages, has very limited coverage. To identify more CDK 
patients, we built and compared varies of statistical models and machine 
learning algorithms to project CKD stages using prescription database. 
METHODS: The model data contained year 2011 patient level CKD stage 
indications, longitudinal drug therapies, days of supplies, titration rates, 
Demographic characteristics, payment type, and physician specialties, etc. The 
data were randomly divided into a training set (66.7%) and a validation set 
(33.3%). The classification models used were Logistic regression, linear/quadratic 
Discriminant, Classification and Regression Tree (CART), C4.5 decision tree, Logit 
Boost classification tree, Bayes learning networks, Support Vector Machine and 
Neural networks. Bagging and boosting techniques were also tested to improve 
the precision. RESULTS: Logistic regression showed the best classification 
accuracy out of all models. The overall correct classification rate was 66.8%, 
Kappa Statistics 0.35, F-measure 0.63 and receiver operating characteristic  
(ROC) area 0.75. Among all CDK stages, CDK stage 1-3 were predicted most 
accurately with True Positive Rate (TPR) 93% and 65% precision. ESRD  
was moderately identified with TPR 46% and precision 76%. CDK 4 was most 
difficult to identify, with TPR 9% and precision 40%. The bagging and boosting 
improved decision trees showed comparable results. CONCLUSIONS: The 
modern machine learning algorithms were proved to be more accurate in many 
cases but for the CKD dataset the classical logistic model worked out better. The 
logistic model could fairly classify severe CKD stages (4-5) patients from lower 
stages (1-3) using prescription information but it had hard time to separate CKD 
stage 4 from 5.  
 
PRM72  
THE IMPACT OF CIGARETTE SMOKING ON MORTALITY FOR DIABETIC 
PATIENTS: A PROPENSITY SCORE MATCHING APPROACH  
Cai B, McAdam Marx C, Nelson RE 
University of Utah, Salt Lake City, UT, USA  
OBJECTIVES: Confounding issues have not been accommodated adequately in 
the public health literature of smoking. This study examines the impact of 
smoking on mortality for patients with diabetes using a propensity score 
matching approach. METHODS: We employed the National Health Interview 
Survey (NHIS) data from 1997 to 2001. Patients who were older than 18 and have 
been diagnosed with diabetes are included in the study. The study outcome was 
all cause mortality, where death records obtained from National Death Index 
were available through Dec. 31, 2006. Propensity score matching (PSM) was 
performed based upon patients’ smoking status. Time to event model was 
performed on the PS matched samples. RESULTS: There were 165,057 patients 
older than 18 participated NHIS survey between 1997 and 2001, of which, 10,194 
(6.18%) and had prior diabetes. Our estimation results suggest stop smoking for 
less than 5 years was associated with a 67% greater risk of death than having 
never smoked; stop smoking for more than 5 years did not have a different risk 
of mortality than those who never smoked. In addition, smoking 16-34 cigarettes 
per day was associated with 52% greater risk of death, and smoking more than 
34 cigarettes per day more than doubles the risk relative to the never smokers. 
CONCLUSIONS: This study indicates smoking reduced survival significantly for 
patients with diabetes; smoking cessation for more than 5 years prolonged 
survival. It provides important evidence to decision makers who attempt to 
advocate smoking cessation programs to patients with diabetes.  
 
RESEARCH ON METHODS – Modeling Methods  
 
PRM73  
IDENTIFICATION OF PATIENTS AT HIGH RISK FOR SUBSEQUENT 
DEVELOPMENT OF ATRIAL FIBRILLATION OR STROKE USING A CLAIMS 
DATABASE  
Reynolds MR1, Hunter TD2, Mollenkopf SA3, Turakhia MP4 
1Harvard Clinical Research Institute, Boston, MA, USA, 2S2 Statistical Solutions, Inc., Cincinnati, 
OH, USA, 3Medtronic, Inc., Mounds View, MN, USA, 4Stanford University School of Medicine, 
Palo Alto, CA, USA  
OBJECTIVES: To evaluate the impact of epidemiologically established risk 
factors, both singular and multiple, on the rates of AF and stroke in a large 
claims database. METHODS: Using patients in the Truven Health MarketScan® 
Commercial and Medicare Supplemental Databases with enrollment during the 
entirety of 2007 (baseline), six conditions were considered as risk factors for 
stroke and AF: age (grouped as <65, 65-74, 75+), heart failure (HF), hypertension 
(HTN), diabetes, renal insufficiency, and coronary artery disease. A single 
categorical variable was created reflecting each patient’s exact combination of 
these conditions. Using this variable as the single predictor, Cox proportional 


PRM70 State-level, population-based cancer registry and administrative data linkages in the united states: A review of the literature 

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PRM70 State-level, population-based cancer registry and administrative data linkages in the united states: A review of the literature

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