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Agreement in race-ethnicity coding between a hospital discharge database and another database.
1Office of Policy, Planning and Evaluation, Connecticut Department of Public Health, Hartford 06134, USA.
Hospital discharge databases show good agreement for White and Black race coding but need improvement for Asian-Pacific Islanders and Hispanics. Birthplace and surname data can enhance accuracy.
Area of Science:
- Health Informatics
- Public Health
- Epidemiology
Background:
- Accurate race and ethnicity data are crucial for understanding health disparities.
- Hospital discharge databases are primary sources for health statistics but may have coding inconsistencies.
Purpose of the Study:
- To evaluate the agreement of race and Hispanic ethnicity coding between a hospital discharge database and a linked cancer registry.
- To identify potential improvements for race-ethnicity data ascertainment in hospital records.
Main Methods:
- Compared race-ethnicity codes for 72,276 cancer patients using linked hospital discharge data and a statewide cancer registry.
- Utilized patient birthplace and surname information for enhanced ethnicity ascertainment, including linkage to a Spanish surname list.
- Employed Kappa coefficients to quantify agreement between the two databases for various racial and ethnic categories.
Main Results:
- Substantial agreement was found for White (kappa=.74), Black (kappa=.93), Hispanic ethnicity (kappa=.73), and non-Hispanic White (kappa=.83) categories.
- Moderate agreement was observed for the Asian-Pacific Islander race category (kappa=.52).
- Significant discrepancies were noted, with only 42% of Asian-Pacific Islanders and 62% of Hispanics in the registry having matching codes in the discharge database.
Conclusions:
- Existing hospital discharge databases demonstrate variable accuracy in coding racial and ethnic groups.
- Incorporating birthplace and surname data into hospital discharge databases could significantly improve the ascertainment of specific racial-ethnic populations.
- Enhancing data collection methods is essential for reducing health disparities and improving public health surveillance.
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