Impact of double counting and transfer bias on estimated rates and outcomes of acute myocardial infarction.
BACKGROUND
Ischemic heart disease is the leading cause of death in the United States. Recent studies report inconsistent findings on the changes in the incidence of hospitalizations for ischemic heart disease. These reports have relied primarily on hospital discharge data. Preliminary data suggest that a significant percentage of patients suffering acute myocardial infarction (MI) in rural communities are transferred to urban centers for care. Patients transferred to a second hospital may be counted twice for one episode of ischemic heart disease.


OBJECTIVE
To describe the impact of double counting and transfer bias on the estimation of incidence rates and outcomes of ischemic heart disease, specifically acute MI, in the United States.


METHODS
Analysis of state hospital discharge data from Kansas, Colorado (State Inpatient Database [SID]), Nebraska, Arizona, New Jersey, Michigan, Pennsylvania, and Illinois (SID) for the years 1995 to 1997. A matching algorithm was developed for hospital discharges to determine patients counted twice for one episode of ischemic heart disease. Validation of our matching algorithm.


METHODS
Patients reported to have suffered ischemic heart disease (ICD9 codes 410-414, 786.5).


METHODS
Number of patients counted twice for one episode of acute MI.


RESULTS
It is estimated that double count rates range from 10% to 15% for all states and increased over the 3 years. Moderate sized rural counties had the highest estimated double count rates at 15% to 20% with a few counties having estimated double count rates a high as 35% to 50%. Older patients and females were less likely to be double counted (P <0.05).


CONCLUSIONS
Double counting patients has resulted in a significant overestimation in the incidence rate for hospitalization for acute MI. Correction of this double counting reveals a significantly lower incidence rate and a higher in-hospital mortality rate for acute MI. Transferred patients differ significantly from nontransferred patients, introducing significant bias into MI outcome studies. Double counting and transfer bias should be considered when conducting and interpreting research on ischemic heart disease, particularly in rural regions.
Westfall
J M
JM
University of Colorado Health Sciences Center, and the High Plains Research Network, Denver, Colorado, USA. jack.westfall@uchsc.edu
McGloin
J
J
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001130:Arizona / Q000453:epidemiology; D015982:Bias; D003120:Colorado / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D017050:Episode of Care; D005260:Female; D006302:Health Services Research / Q000592:standards*; D017052:Hospital Mortality; D006801:Humans; D007087:Illinois / Q000453:epidemiology; D015994:Incidence; D007615:Kansas / Q000453:epidemiology; D008297:Male; D008824:Michigan / Q000453:epidemiology; D008875:Middle Aged; D009203:Myocardial Infarction / Q000453:epidemiology* / Q000628:therapy; D009329:Nebraska / Q000453:epidemiology; D009515:New Jersey / Q000453:epidemiology; D017063:Outcome Assessment, Health Care / Q000592:standards*; D010351:Patient Discharge / Q000706:statistics & numerical data*; D010360:Patient Transfer / Q000706:statistics & numerical data*; D010414:Pennsylvania / Q000453:epidemiology; D011159:Population Surveillance; D012680:Sensitivity and Specificity