Inequalities in Life Expectancy Across North Carolina
A Spatial Analysis of the Social Determinants of Health and the Index of Concentration at Extremes
Bibliographic Data
| ID | 13853780 |
|---|---|
| Authors | Jessica H Mitchell (Appalachian State University), Jennifer D Runkle, Lauren M Andersen (0000-0003-3654-570X), Elizabeth Shay (0000-0002-3529-151X), Margaret M Sugg (0000-0002-0315-4860, corresponding author) |
| Year | 2022 |
| Volume | 45 |
| Issue | 2 |
| Pages | 77-90 |
| Publication date | 2022-02-04 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Family & Community Health (JOURNAL) |
| Journal identifiers | ISSN: 0160-6379 • E-ISSN: 1550-5057 |
| Publisher | Lippincott Williams & Wilkins (PUBLISHER • US) |
| DOI | 10.1097/fch.0000000000000318 |
| PMID | 35125487 |
| OpenAlex | W4210716042 |
| Language | EN |
| Citations received | 4 |
| References cited | 41 |
Health inequalities are characterized by spatial patterns of social, economic, and political factors. Life expectancy (LE) is a commonly used indicator of overall population health and health inequalities that allows for comparison across different spatial and temporal regions. The objective of this study was to examine geographic inequalities in LE across North Carolina census tracts by comparing the performance of 2 popular geospatial health indices: Social Determinants of Health (SDoH) and the Index of Concentration at Extremes (ICE). A principal components analysis (PCA) was used to address multicollinearity among variables and aggregate data into components to examine SDoH, while the ICE was constructed using the simple subtraction of geospatial variables. Spatial regression models were employed to compare both indices in relation to LE to evaluate their predictability for population health. For individual SDoH and ICE components, poverty and income had the strongest positive correlation with LE. However, the common spatial techniques of adding PCA components together for a final SDoH aggregate measure resulted in a poor relationship with LE. Results indicated that both metrics can be used to determine spatial patterns of inequities in LE and that the ICE metric has similar success to the more computationally complex SDoH metric. Public health practitioners may find the ICE metric's high predictability matched with lower data requirements to be more feasible to implement in population health monitoring
Geospatial analysis · Health equity · Health indicator · Inequality · Life expectancy · Population · Population health · Public health · Social determinants of health · Data-Driven Disease Surveillance · Health disparities and outcomes · Spatial and Panel Data Analysis
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| Citations per year | 2 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |