Ascertaining variation in hospitalization risk among immigrants using small area analysis

Introduction. Data on hospitalization of immigrants to the United States are sparse, but small area analysis may improve the utility of existing data. Methods. Applying logistic regression analysis to 2000 census and hospitalization data for New York City, we examined the odds of hospital admission by major diagnostic category and global region of birth after controlling for covariates. We used individual-level covariates to control for age, race, and gender. By matching the patientʹs zip code of residence to census data, we then added median household income, the proportion of persons born in a particular global region, and the proportion of foreign-born persons living in the same zip code as independent variables. Results. The total proportion of foreign-born persons in a zip code predicts a lower hospitalization rate for most major diagnostic categories and most foreign-born groups. However, Africa-born persons have a higher odds of hospitalization for most major diagnostic categories – up to 1.79 (95% confidence interval 1.73, 1.86) for blood and blood forming disorders – relative to native-born persons. The odds of hospitalization among Africa-born persons for most conditions are over 3 times higher than other foreign-born groups. Hospitalization odds for Latin American-born persons were also higher than native-born persons across major diagnostic categories. Conclusion. Small area analysis generally predicts hospitalization rates that coincide with mortality studies and may serve as a useful tool for hypothesis testing in immigrant health.