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Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...

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Updated: Jul 5, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Using zip code-level mortality data as a local health status indicator in Mobile, Alabama.

Martha Arrieta¹, Harvey L White, Errol D Crook

¹Center for Healthy Communities, University of South Alabama College of Medicine, Mastin, 5th Floor, Suite 524, 2451 Fillingim Street, Mobile, AL 36617, USA. marrieta@usouthal.edu

The American Journal of the Medical Sciences

|May 9, 2008

Summary

Local health disparities are significant, with zip code-level data revealing higher mortality risks in impoverished areas. Interventions should target specific diseases and socioeconomic factors to reduce these health inequities.

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Last Updated: Jul 5, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Public Health
Health Disparities Research
Geographic Health Analysis

Background:

Local health data is crucial for addressing health disparities.
Zip code-level data remains an underutilized resource for identifying local health inequities.
Understanding geographic variations in health outcomes is essential for targeted interventions.

Purpose of the Study:

To utilize zip code-level mortality data to identify and quantify health disparities within a local area.
To compare disease-specific mortality rates between areas with different poverty levels.
To establish a foundation for designing local interventions to combat health disparities.

Main Methods:

Population aggregation into two groups based on 1999 poverty levels: study area population (SAP, >=20% poverty) and comparison area population (<20% poverty).
Calculation and comparison of disease-specific mortality rates per 100,000 population.
Determination of relative risk (RR) of death by comparing mortality rates between SAP and comparison areas.

Main Results:

The SAP exhibited higher percentages of African Americans and women, with significantly lower income, employment, and education levels.
Mortality rates were substantially higher in the SAP, with relative risks of death exceeding 1.4 for major cardiovascular disease and cancer.
Major disparities (RR >2.5) were identified for human immunodeficiency virus, homicide, hypertensive heart and renal disease, and kidney disease.

Conclusions:

Zip code-level data analysis provides a robust framework for understanding and addressing local health disparities.
The findings highlight specific diseases and geographic areas requiring targeted public health interventions.
Data-driven insights at the zip code level are vital for developing effective strategies to reduce health inequities.