mHAT app for automated malaria rapid test result analysis and aggregation: a pilot study

Abstract

Background There are a variety of approaches being used for malaria surveillance. While active and reactive case detection have been successful in localized areas of low transmission, concerns over scalability and sustainability keep the approaches from being widely accepted. Mobile health interventions are poised to address these shortcomings by automating and standardizing portions of the surveillance process. In this study, common challenges associated with current data aggregation methods have been quantified, and a web-based mobile phone application is presented to reduce the burden of reporting rapid diagnostic test (RDT) results in low-resource settings. Methods De-identified completed RDTs were collected at 14 rural health clinics as part of a malaria epidemiology study at Macha Research Trust, Macha, Zambia. Tests were imaged using the mHAT web application. Signal intensity was measured and a binary result was provided. App performance was validated by: (1) comparative limits of detection, investigated against currently used laboratory lateral flow assay readers; and, (2) receiver operating characteristic analysis comparing the application against visual inspection of RDTs by an expert. Secondary investigations included analysis of time-to-aggregation and data consistency within the existing surveillance structures established by Macha Research Trust. Results When compared to visual analysis, the mHAT app performed with 91.9% sensitivity (CI 78.7, 97.2) and specificity was 91.4% (CI 77.6, 97.0) regardless of device operating system. Additionally, an analysis of surveillance data from January 2017 through mid-February 2019 showed that while the majority of the data packets from satellite clinics contained correct data, 36% of data points required correction by verification teams. Between November 2018 and mid-February 2019, it was also found that 44.8% of data was received after the expected submission date, although most (65.1%) reports were received within 2 days. Conclusions Overall, the mHAT mobile app was observed to be sensitive and specific when compared to both currently available benchtop lateral flow readers and visual inspection. The additional benefit of automating and standardizing LFA data collection and aggregation poses a vital improvement for low-resource health facilities and could increase the accuracy and speed of data reporting in surveillance campaigns.

The SisaMob Information System: Implementation of Digital Data Collection as a Tool for Surveillance and Vector Control in the State of São Paulo

“…This APP is the first application for routine surveillance and vector control integrated with the vector-borne disease database aimed at state municipal managers in Brazil. Other applications successfully used were for managing outbreaks or restricted to research, such as SORMAS [23], for data collection, situation assessment, and coordination of response measures in outbreak surveillance processes, as well as the mHAT, concerning a study for the surveillance of malaria cases in remote communities [24]. Duncombe et al (2012) [25], in their review about the use of geographic information systems on dengue surveillance, discuss the need to identify variables that can explain the transmission of dengue fever and emphasize that the use of a geographic information system, especially in initial stages, could be a useful tool to visualize the relation amongst the variables and to establish cluster zones where the public managers should take priority actions.…”

Lessons for improved COVID-19 surveillance from the scale-up of malaria testing strategies

“…Concurrent digitalized data can be further improved by implementation of RDT reader software applications. These apps have the potential to support both malaria and COVID-19 antigen testing by improving the quality of results interpretation [ 133 ] whilst linking test results into national surveillance systems [ 134 ].…”

Efektivitas Adaptasi Teknologi pada Kinerja Sistem Surveilans Malaria di Era COVID-19 untuk Negara Berkembang: Sebuah Kajian Sistematis

“…Lima dari sembilan studi (Moore et al, Tobgay et al, Oo et al, Davies et al, Githinji et al) yang termasuk dalam kajian sistematik ini melaporkan kelengkapan dan keakuratan pelaporan sebagai salah satu indikator performa pada penggunaan mHealth atau intervensi teknologi lainnya dalam suatu sistem surveilans malaria [22][23][24][25][26]. Menurut Tobgay et al, inakurasi yang ditemukan pada data pelaporan yang diambil dari sistem informasi yang ia gunakan hanya sebesar 3.06% [31].…”