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Data processing, methodology, and Shiny app for the ARHQ data visualization challenge

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Introduction

This repository contains code for Mathematica's entry to Phase 2 of the AHRQ Visualization Resources of Community-Level Social Determinants of Health (SDoH) Challenge.

For more information, please contact Kelsey Skvoretz at KSkvoretz@mathematica-mpr.com and Margaret Luo at MLuo@mathematica-mpr.com.

Contents

  • data/
    • for storing intermediate and final datasets and dictionaries
  • pipeline/
    • for scraping and cleaning demographic, social determinant of health (SDoH), and outcome data
    • all of the cleaned data is output into the data/cleaned folder, to be picked up, merged, and utilized by pipeline.py
  • data-insights/
    • for examining preliminary insights
  • methodology/
    • for calculating county-level SDoH scores and similarities
  • DockerShinyApp/

Authors

  • Kelsey Skvoretz - back-end lead
  • Margaret Luo - front-end lead
  • Evelyn Cody - methodology & front-end support
  • Addison Larson - front-end support
  • Emma Pendl-Robinson - pipeline & front-end support

Advisors

  • Keri Calkins - research lead, methodology support, & back-end support
  • Elena Saavedra Jimenez - UX lead and graphics designer
  • Aaron White - technical consultant
  • George Gallo - AWS support
  • Additional thanks to Stephanie Tuerk, Alex Bohl, and Ravi Goyal

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Data processing, methodology, and Shiny app for the ARHQ data visualization challenge

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