Spatial Health Factors with Selection Among Multiple Causes: Lung Cancer in U.S. Counties

Abstract

Composite morbidity indices summarize geographic inequalities in disease, and are used to distribute resources. A spatial latent variable approach is developed for such an index, focusing on lung cancer in 3,141 U.S. counties. The model incorporates multiple indicators (cancer deaths and incidence), but also allows for population risk variables (area socio-economic, environmental, and smoking indicators) that affect lung cancer, and for missingness among indicators or risk variables. Selection of significant causes is illustrated, including nonadaptive and adaptive selection. To reflect geographic clustering in lung cancer, the latent morbidity index is spatially correlated, although the level of correlation is data determined.

Keywords:

• Common factor
• Multiple indicators and multiple causes
• Regression selection
• Spatial correlation

Mathematics Subject Classification:

• Primary 62F15
• Secondary 62P10

Acknowledgment

The support of the National Minority Quality Forum is acknowledged.