Ascertaining variation in hospitalization risk among immigrants using small area analysis

Area of Science:

• Public Health
• Sociomedical Sciences
• Epidemiology

Background:

• Data on immigrant hospitalization in the United States is limited.
• Small area analysis can enhance the utility of existing data for studying immigrant health.
• Understanding hospitalization patterns is crucial for addressing health disparities.

Purpose of the Study:

• To examine the odds of hospital admission among immigrants in New York City based on their region of birth.
• To identify specific immigrant groups with higher hospitalization risks.
• To assess the utility of small area analysis in immigrant health research.

Main Methods:

• Logistic regression analysis was applied to 2000 census and hospitalization data for New York City.
• Hospital admission odds were analyzed by diagnostic category and global region of birth, controlling for age, race, and gender.
• Zip code-level data on income, regional foreign-born population proportion, and overall foreign-born population proportion were incorporated.

Main Results:

• Higher proportions of foreign-born residents in a zip code correlated with lower hospitalization rates for most groups and diagnostic categories.
• Africa-born individuals exhibited significantly higher odds of hospitalization across most diagnostic categories compared to native-born persons (e.g., 1.79 for blood disorders).
• Hospitalization odds for Africa-born persons were over three times higher than for other foreign-born groups, and Latin American-born persons also showed increased odds.

Conclusions:

• Small area analysis effectively predicts hospitalization rates, aligning with mortality study findings.
• This methodology serves as a valuable tool for hypothesis generation in immigrant health research.
• Findings underscore the need for targeted health interventions for specific immigrant populations, particularly those from Africa and Latin America.