Staying on course with our exploration of social determinants of health (SDOH), next up are the area deprivation indices (ADIs). The basic idea behind the ADI, first formulated two decades ago and refined a couple of times since, is to utilize a well-defined set of socioeconomic indicators for all geographies and then generate a relative index of deprivation that allows one to compare and even rank geographies in terms of greater/lesser socioeconomic status.
Again, lower socioeconomic status is believed to shape healthcare experiences and health outcomes. In this post I want to understand how a particular R package calculates ADIs, and then also see a few alternative approaches before seeing how things look for Ohio in particular and then perhaps for Appalachia, more generally.
Of course, I could have always just downloaded the ADI data or maps from The Neighborhood Atlas but where would be the fun in that !! Before I go any further, I must thank Orman Hall for bringing Nik Krieger’s {sociome} package to my attention, and Nik for creating it.
Footnotes
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Citation
For attribution, please cite this work as
Ruhil (2022, Feb. 27). From an Attican Hollow: Area Deprivation Indices. Retrieved from https://aniruhil.org/posts/2022-02-27-area-deprivation-indices/
BibTeX citation
@misc{ruhil2022area,
author = {Ruhil, Ani},
title = {From an Attican Hollow: Area Deprivation Indices},
url = {https://aniruhil.org/posts/2022-02-27-area-deprivation-indices/},
year = {2022}
}
