Using and Training AccessMod 5 in Travel Catchment Area Visualization for Maternal Mortality
Project Name: Using and Training AccessMod 5 in Travel Catchment Area Visualization for Maternal Mortality
Project Status: Proposed
Point of Contact: Florina Serbanescu
Center: National Center for Chronic Disease Prevention and Health Promotion
Keywords: travel catchment areas, maternal mortality, mapping, R Shiny, AccessMod, health services coverage
Maternal mortality, one of the legacy Millennium Development Goals and a current Sustainability Development Goal, is a highly preventable cause of death in developing countries. Delays in reaching facility-based care either during delivery, or during subsequent obstetric complications, frequently contributes to maternal deaths. Therefore, understanding the geographical distribution of patient’s travel times to emergency obstetric care (EmOC) facilities and identifying underserved areas is critical in improving obstetric care access efforts.
To improve surveillance efforts in capturing maternal mortality cases in rural areas, the Division of Reproductive Health, in conjunction with USAID and CDC-Uganda, launched data collection and surveillance efforts for four pilot districts in western Uganda under the Saving Mothers, Giving Life (SMGL) project. These data collection efforts have extended out both to other districts in Uganda, particularly in the northern region, and in other countries in sub-Saharan Africa, where there is an interest into access to EmOC facilities.
In order to gain comprehensive knowledge on the impact of travel time to EmOC facilities upon maternal deaths, we propose using the upcoming released version (Version 5) of AccessMod, a software package developed by the Department of Health Systems Financing (WHO/HSS/HSF) at the World Health Organization, in collaboration with the WHO Vulnerability and Risk Analysis & Mapping programme (VRAM). While use of previous versions of AccessMod have been contingent on the use of ArcGIS version 9.3, AccessMod version 5 is a cost-effective, standalone application designed using the open-source free R package, Shiny (http://shiny.rstudio.com/External). Shiny creates an easy-to-use dashboard while simultaneously performing powerful code in the background. With all software components included in the latest AccessMod version, AccessMod only requires an internet connection and geographic data related to landcover, population distribution, road networks, road speed limits, health facilities, and elevation to be uploaded into the system.
We wish to contribute to the creation of training materials (i.e., write documentation) for this software package, and help test and refine the software, in conjunction with the current creator of the software, Steeve Ebener, and his team. We also plan on piloting the use of this software with our in-country partners in Uganda and Tanzania, training local partners in procedures of travel time health catchment area estimation. Our project will include creation of a toolkit of training procedures, and a report detailing our project protocols, data collection and analysis, and information visualization.
Previous analyses using AccessMod from other country pilots (other parts of Uganda, Tanzania) have had to be performed by trained analysts at CDC headquarters in Atlanta. Conducting these analyses at headquarters has allowed for little local ownership over data, and also limits data use and analysis capacity, due to a significant lag time in producing results. The AccessMod software package has traditionally had a steep learning curve, both in understanding and manipulating software inputs, as well as in significant analytical/cartographic knowledge. By using AccessMod version 5, we hope to address some of these issues by identifying efficient ways to train individuals in the manipulation of spatial data, data processing, and improvement of current data display options.
We anticipate these efforts will lead to increased GIS capacity among local partners, and allow them to take ownership over maintenance and utilization of their data. If properly trained in GIS data manipulation, local partners would additionally be able to understand how building health facilities would affect their travel catchment areas and health systems coverage.