Early detection of adverse drug events within population-based health networks: application of sequential testing methods.
OBJECTIVE
Active surveillance of population-based health networks may improve the timeliness of detection of adverse drug events (ADEs). Active monitoring requires sequential analysis methods. Our objectives were to (1) evaluate the utility of automated healthcare claims data for near real-time drug adverse event surveillance and (2) identify key methodological issues related to the use of healthcare claims data for real-time drug safety surveillance.


METHODS
We assessed the ability to detect ADEs using historical data from nine health plans involved in the HMO Research Network's Center for Education and Research on Therapeutics (CERT). Analyses were performed using a maximized sequential probability ratio test (maxSPRT). Five drug-event pairs representing known associations with an ADE and two pairs representing 'negative controls' were analyzed.


RESULTS
Statistically significant (p < 0.05) signals of excess risk were found in four of the five drug-event pairs representing known associations; no signals were found for the negative controls. Signals were detected between 13 and 39 months after the start of surveillance. There was substantial variation in the number of exposed and expected events at signal detection.


CONCLUSIONS
Prospective, periodic evaluation of routinely collected data can provide population-based estimates of medication-related adverse event rates to support routine, timely post-marketing surveillance for selected ADEs.
Brown
Jeffrey S
JS
Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA, USA. jeff_brown@harvardpilgrim.org
Kulldorff
Martin
M
Chan
K Arnold
KA
Davis
Robert L
RL
Graham
David
D
Pettus
Parker T
PT
Andrade
Susan E
SE
Raebel
Marsha A
MA
Herrinton
Lisa
L
Roblin
Douglas
D
Boudreau
Denise
D
Smith
David
D
Gurwitz
Jerry H
JH
Gunter
Margaret J
MJ
Platt
Richard
R
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000465:Algorithms; D000068579:Celecoxib; D016536:Competitive Medical Plans / Q000458:organization & administration / Q000706:statistics & numerical data; D006279:Health Maintenance Organizations / Q000458:organization & administration / Q000706:statistics & numerical data; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D007783:Lactones / Q000009:adverse effects / Q000627:therapeutic use; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D009203:Myocardial Infarction / Q000139:chemically induced / Q000175:diagnosis; D009288:Naproxen / Q000009:adverse effects / Q000627:therapeutic use; D011159:Population Surveillance / Q000379:methods*; D011358:Product Surveillance, Postmarketing / Q000379:methods* / Q000706:statistics & numerical data; D011720:Pyrazoles / Q000009:adverse effects / Q000627:therapeutic use; D011725:Pyridines / Q000009:adverse effects / Q000627:therapeutic use; D012189:Retrospective Studies; D012206:Rhabdomyolysis / Q000139:chemically induced / Q000175:diagnosis; D013449:Sulfonamides / Q000009:adverse effects / Q000627:therapeutic use; D013450:Sulfones / Q000009:adverse effects / Q000627:therapeutic use; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States
D007783:Lactones; D011720:Pyrazoles; D011725:Pyridines; D013449:Sulfonamides; D013450:Sulfones; C116926:rofecoxib; D009288:Naproxen; C086276:cerivastatin; D000068579:Celecoxib