Assessing the advantages and disadvantages of dimensionality reduction methods in summarizing housing determinants of health in the United States.

Objectives: To evaluate and compare different dimensionality reduction techniques for quantifying housing conditions as a social determinant of health (SDOH) across various geographic levels in the United States. Materials and Methods: A total of 15 housing characteristics from the American Community Survey data were analyzed at county, ZIP code, and Census tract levels. The robustness of 3 dimensionality reduction techniques was assessed in reducing the 15 housing characteristics into 1 housing score. These summarization methods included principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP). We visualized geographic distributions of the housing scores, assessed methodological discrepancies between the techniques, and analyzed agreement between housing characteristic variability and housing score variability. Results: The selected dimensionality reduction methods generated housing scores that demonstrated acceptable face validity when visualized through choropleth maps. The PCA method provided the most stable and consistent results across geographic levels. The PCA method also resulted in the highest correlation between the variability of the underlying housing characteristics and the summarized housing score. Discussion: Data-driven summarization techniques provide an alternative approach to traditional expert-based indices in capturing housing conditions as a single SDOH factor. In this study, among the different summarized housing scores, the PCA-generated score offered superior robustness, persistent data structure, and higher stability across years. Conclusion: Principal component analysis was identified as the most reliable and interpretable approach for summarizing housing conditions across geographic levels. These findings contribute to the methodological foundation required to develop robust SDOH measures that can inform public health policies and address health disparities.