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Evaluating false positives in two hospital discharge data sets of the Birth Defects Monitoring Program
F A Callif-Daley1, C A Huether, L D Edmonds
1Department of Biological Sciences, University of Cincinnati, OH 45221, USA.
Insights
This study quantified false positives in hospital discharge data for the Birth Defects Monitoring Program (BDMP). Data processing agencies showed significant differences in false positive rates, highlighting the need for improved data accuracy in birth defect surveillance.
Area of Science:
- Public Health
- Epidemiology
- Health Informatics
Background:
- Accurate data is crucial for public health surveillance programs like the Birth Defects Monitoring Program (BDMP).
- Hospital discharge data is a primary source for monitoring birth defects, but its accuracy can be affected by data processing and collection methods.
- Understanding sources of error, such as false positives, is essential for reliable epidemiological analysis.
Purpose of the Study:
- To quantify the rates of false positives in hospital discharge data used by the Centers for Disease Control and Prevention's (CDC) Birth Defects Monitoring Program (BDMP).
- To identify potential correlates of these false positives within the data.
- To assess the impact of data processing agencies on the accuracy of birth defect reporting.
Main Methods:
- Analysis of hospital discharge data from two major data processing agencies contributing to the BDMP.
- Statistical comparison of false positive rates between the two agencies.
- Evaluation of factors such as hospital size, diagnostic certainty, patient demographics (race, sex), and insurance source as potential correlates of false positives.
Main Results:
- The Commission on Professional and Hospital Activities (CPHA) reported a 13.2% false positive rate, while McDonnell Douglas Health Information Systems (MDHIS) reported 8.5%.
- These rates were statistically significantly different between the two agencies.
- Two-thirds of false positives resulted from miscoding correctly diagnosed anomalies; a quarter were contradicted by readily available discharge notes.
Conclusions:
- The identified false positive rates, while considered minimal, necessitate careful interpretation of BDMP data.
- Significant variations in false positive rates between data processing agencies underscore the importance of understanding data collection and processing methodologies.
- Further improvements in data coding and abstracting processes are recommended to enhance the accuracy and reliability of birth defect surveillance data.
Abstract:
The principal goal in this study was to quantify false positives in the hospital discharge data of the Birth Defects Monitoring Program conducted by the Centers of Disease Control and Prevention. The two hospital data processing agencies which contribute data to the Birth Defects Monitoring Program, the Commission on Professional and Hospital Activities and the McDonnell Douglas Health Information Systems, had respective levels of false positives of 13.2 percent and 8.5 percent, levels which were statistically different from each other. These false positive levels should be considered minimal because these data bases do not include information on sick babies who may be transferred into or out of member hospitals, and who may have their initial diagnoses significantly modified. Potential correlates of false positives were evaluated, including hospital size, diagnostic certainty, race, sex, and insurance source. Two-thirds of all false positives were due to the miscoding of correctly diagnosed anomalies, and another quarter were clearly contradicted in notes easily available before the patients were discharged. The authors hope that this study of false positives will enhance the interpretation of the Birth Defects Monitoring Program data and lead to improved understanding of data collection and processing.
