Descriptive analyses of the integrity of a US Medicaid claims database.
OBJECTIVE
To examine the integrity of six Medicaid databases for use in pharmacoepidemiology research.


METHODS
We performed descriptive analyses to examine four categories of potential data errors: incomplete claims for certain time periods; absence of an accurate indicator of inpatient hospitalizations; missing hospitalizations for those aged 65 years and over; and diagnostic codes in demographic groups in which those conditions should be rare.


RESULTS
Prescription claims appeared to be missing intermittently in some states. No valid marker of inpatient hospitalizations could be found for three of six states. Hospitalizations appeared to be missing to varying degrees for those aged 65 years and over. Gross errors in diagnostic codes and demographic data did not appear to be widespread.


CONCLUSIONS
Whenever possible, investigators using administrative data should perform macro-level descriptive analyses on the parent data set. In particular, researchers should examine the number of medical and pharmacy claims over time, looking for gaps. Validity of markers of hospitalization should be assessed. The accuracy of diagnosis and demographic data should be examined. Such a descriptive macro-level approach should be used to supplement, and perhaps precede validation of study outcomes using clinical records.
Hennessy
Sean
S
Center for Clinical Epidemiology, Department of Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA. shenness@cceb.med.upenn.edu
Bilker
Warren B
WB
Weber
Anita
A
Strom
Brian L
BL
D000368:Aged; D016208:Databases, Factual / Q000706:statistics & numerical data*; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D017723:Drug Utilization Review; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data*; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D008875:Middle Aged; D011247:Pregnancy; D011248:Pregnancy Complications / Q000175:diagnosis; D012737:Sex Factors; D013997:Time Factors; D014481:United States