Ascertaining variation in hospitalization risk among immigrants using small area analysis.

Abstract:

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 patient's 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.

journal_name

Prev Med

journal_title

Preventive medicine

authors

Muennig P,Jia H,Khan K,Pallin DJ

doi

10.1016/j.ypmed.2006.02.014

subject

Has Abstract

pub_date

2006-08-01 00:00:00

pages

145-9

issue

2

eissn

0091-7435

issn

1096-0260

pii

S0091-7435(06)00095-8

journal_volume

43

pub_type

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