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The Development of the Reproductive Healthcare Equity Algorithm (RHEA)

Shriya Karam, Lauren Shanos, Jessica Ford, Lorenzo Castaneda, Megan S. Ryerson, Rakesh Vohra

TL;DR

RHEA addresses the post-Dobbs travel burden for abortion care by marrying a pure-integer linear programming framework with a user-friendly web interface to optimize one-day logistics for nonprofits. The approach computes maximum feasible flow and, in a cost-aware variant, minimum transportation expense to route individuals from origin counties to clinics via ground and air transport while respecting budget, capacity, and time constraints. Key contributions include a detailed ILP formulation with sets, parameters, and decision variables, an Anvil-based front end, and a data pipeline that combines airport, flight, and clinic data with demand proxies. The work has practical impact by enabling nonprofits to plan scalable, equitable transport networks that better meet the needs of low-income, underserved populations seeking abortion care.

Abstract

After the repeal of Roe vs. Wade in June 2022, women face long-distance travel across state lines to access abortion care. For women who also face socioeconomic hardship, travel for abortion care is a significant burden. To ease this burden, abortion access nonprofits are funding and/or supplying transportation to abortion clinics. However, due to the uneven distribution of demand and supply for abortions, these nonprofits do not have efficient logistical operations. As a result, low-income, underserved women may not have access to adequate reproductive healthcare, thus widening healthcare inequity gaps. Nonprofits may also risk not serving the needs of vulnerable women without access to adequate reproductive healthcare, and in doing so, waste resources, money, and volunteer hours. To address these challenges, we create an interactive, web-based planning tool, the Reproductive Healthcare Equity Algorithm (RHEA), to guide nonprofits in strategically allocating resources and serving demand. RHEA leverages an optimization model to determine the maximum flow and minimum transportation cost to route women across a network of counties and abortion clinics, subject to transportation supply, budget, and time constraints for one day of operations for a nonprofit. In doing so, we collaborate with abortion access nonprofits to cater our model design and interface development to their needs and considerations. Ultimately, we seek to optimize resource allocation for nonprofits providing abortion care logistics and improve abortion access for low-income, underserved women.

The Development of the Reproductive Healthcare Equity Algorithm (RHEA)

TL;DR

RHEA addresses the post-Dobbs travel burden for abortion care by marrying a pure-integer linear programming framework with a user-friendly web interface to optimize one-day logistics for nonprofits. The approach computes maximum feasible flow and, in a cost-aware variant, minimum transportation expense to route individuals from origin counties to clinics via ground and air transport while respecting budget, capacity, and time constraints. Key contributions include a detailed ILP formulation with sets, parameters, and decision variables, an Anvil-based front end, and a data pipeline that combines airport, flight, and clinic data with demand proxies. The work has practical impact by enabling nonprofits to plan scalable, equitable transport networks that better meet the needs of low-income, underserved populations seeking abortion care.

Abstract

After the repeal of Roe vs. Wade in June 2022, women face long-distance travel across state lines to access abortion care. For women who also face socioeconomic hardship, travel for abortion care is a significant burden. To ease this burden, abortion access nonprofits are funding and/or supplying transportation to abortion clinics. However, due to the uneven distribution of demand and supply for abortions, these nonprofits do not have efficient logistical operations. As a result, low-income, underserved women may not have access to adequate reproductive healthcare, thus widening healthcare inequity gaps. Nonprofits may also risk not serving the needs of vulnerable women without access to adequate reproductive healthcare, and in doing so, waste resources, money, and volunteer hours. To address these challenges, we create an interactive, web-based planning tool, the Reproductive Healthcare Equity Algorithm (RHEA), to guide nonprofits in strategically allocating resources and serving demand. RHEA leverages an optimization model to determine the maximum flow and minimum transportation cost to route women across a network of counties and abortion clinics, subject to transportation supply, budget, and time constraints for one day of operations for a nonprofit. In doing so, we collaborate with abortion access nonprofits to cater our model design and interface development to their needs and considerations. Ultimately, we seek to optimize resource allocation for nonprofits providing abortion care logistics and improve abortion access for low-income, underserved women.
Paper Structure (22 sections, 23 equations, 5 figures, 1 table)

This paper contains 22 sections, 23 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Demand-side Abortion Restrictions
  4. Abortion Travel Burden and Accessibility
  5. Post-Dobbs Abortion Access
  6. Abortion Pills and Telemedicine
  7. Methodology
  8. Model Setup and Notation
  9. Sets
  10. Parameters
  11. Decision variables
  12. Maximum Flow Model
  13. Minimum Cost Model
  14. Front End Interface
  15. Implementation
  16. ...and 7 more sections

Figures (5)

  • Figure 1: Schematic of model inputs and outputs.
  • Figure 2: Map of origin and destination states and abortion clinics.
  • Figure 3: RHEA front end interface with inputted parameters and optimization results.
  • Figure 4: Map of total women transported by county in Missouri and selected open clinics in Illinois, as well as the minimum transportation cost.
  • Figure 5: Excess nonprofit resources for budget, general aircraft, and origin and destination volunteer drivers.