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Analysis of data-collection methods for an acute stroke care registry
Sung Sug Yoon1, Mary G George, Sharon Myers
1Division of Adult and Community Health NCCDPHP, Centers for Disease Control and Prevention, 4770 Buford Highway, NE, Mailstop-K47, Atlanta, GA 30341-3717, USA. syoon@cdc.gov
American Journal of Preventive Medicine
|December 21, 2006
Summary
Comparing data collection methods for the Paul Coverdell National Acute Stroke Registry, this study found no significant differences in completeness or reliability between prospective and retrospective approaches. A combined method may offer the best accuracy for stroke data.
Area of Science:
- Health Services Research
- Data Management
- Stroke Epidemiology
Background:
- The Paul Coverdell National Acute Stroke Registry collects critical data on stroke care.
- Assessing data collection methods is vital for registry accuracy and utility.
- Prospective and retrospective data collection have distinct advantages and disadvantages.
Purpose of the Study:
- To compare the completeness and reliability of prospective versus retrospective data collection methods for the Paul Coverdell National Acute Stroke Registry.
- To identify potential differences in data quality based on ascertainment approach.
Main Methods:
- An audit analysis was conducted on data from eight state prototypes of the registry.
- Prototypes utilized either retrospective or prospective (or combined) case ascertainment.
- Data completeness, exact match rates, and discrepancies were analyzed across 12 data element categories.
Main Results:
- Completeness was highest for "in-hospital complications" (99.6%) and lowest for "thrombolytic treatment" (53.5%).
- Exact match percentages with audit data ranged from 62.8% to 95.9%.
- No significant differences in completeness or audit match rates were observed between retrospective and prospective methods.
Conclusions:
- Both prospective and retrospective data collection methods demonstrated comparable completeness and reliability for the acute stroke registry.
- A combined approach to data collection, utilizing the strengths of both methods for different data types, may optimize overall accuracy and completeness.
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