Area Deprivation Indices

Census

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 Appalachia, more generally.

Ani Ruhil true
2022-02-27

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 would also like to thank Orman Hall for bringing Nik Krieger’s {sociome} package to my attention, and Nik for creating it.

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY-SA 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

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}
}