Mapping of Malnutrition from EMR Data in Southern Haiti

BACKGROUND: In Haiti, the problem of malnutrition is especially severe, exacerbated by the 2010 earthquake and a brutal rainy season. Local mapping of malnutrition with geographical information systems (GIS) makes it possible to analyze underlying geospatial risk factors of a region. In Port Salut, Klinik Timoun Nou Yo (KTNY) runs a nutrition program for affected children, and needs a tool for tracking their patient population. OBJECTIVE: A GIS tool is needed that is flexible and easy for clinical staff to operate. The tool will allow visualization of the geographical distribution of patients treated for malnutrition at KTNY. This information will aid in the analysis of malnutrition hotspots and seasonal admission trends, allowing the clinic to anticipate patient load and prepare resources effectively. METHODS: The GIS tool, called "KTNY Tracker" is written in python as a plug-in for the free software QGIS. As a proof of concept, patient data from 2013 and 2014 was uploaded and processed by the software, in order to qualitatively compare the yearly change in patient burden of the relatively new clinic, as well as assess the absolute patient distribution. RESULTS: The maps output by the software show a noticeable increase in patient load as well as catchment area from 2013 to 2014. This is most likely due to increased awareness about the clinic and its growing reputation, as well as extended efforts of patient pickup from remote areas. The map of overall patient load from 2013 to 2014 showed an expected high density of patients along the coastline, as well as low-density zones that may correlate with mountainous terrain, a lack of population, or other factors. These preliminary finding could be analyzed to elicit its true cause. DISCUSSIONS: KTNY Tracker in combination with QGIS's native functionality will prove to be a useful tool in visualizing clinical data and requires minimal training and experience to operate. Maps generated by this tool can serve as a visual advocate for more funding and resources for the clinic's malnutrition program. In the future, functionality will be added to assess follow-up success and failure rates, among other statistical tools for quantifying the visual data. The software is still in development, but it has promising potential as a clinical aid and as a launch pad for further studies.