Abstract
Mobile health devices are emerging applications that could help deliver point-of-care (POC) diagnosis, particularly in settings with limited laboratory infrastructure, such as Sub-Saharan Africa (SSA). The advent of Severe acute respiratory syndrome coronavirus 2 has resulted in an increased deployment and use of mHealth-linked POC diagnostics in SSA. We performed a systematic review and meta-analysis to evaluate the accuracy of mobile-linked point-of-care diagnostics in SSA. Our systematic review and meta-analysis were guided by the Preferred Reporting Items requirements for Systematic Reviews and Meta-Analysis. We exhaustively searched PubMed, Science Direct, Google Scholar, MEDLINE, and CINAHL with full text via EBSCOhost databases, from mHealth inception to March 2021. The statistical analyses were conducted using OpenMeta-Analyst software. All 11 included studies were considered for the meta-analysis. The included studies focused on malaria infections, Schistosoma haematobium, Schistosoma mansoni, soil-transmitted helminths, and Trichuris trichiura. The pooled summary of sensitivity and specificity estimates were moderate compared to those of the reference representing the gold standard. The overall pooled estimates of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of mobile-linked POC diagnostic devices were as follows: 0.499 (95% CI: 0.458–0.541), 0.535 (95% CI: 0.401–0.663), 0.952 (95% CI: 0.60–1.324), 1.381 (95% CI: 0.391–4.879), and 0.944 (95% CI: 0.579–1.538), respectively. Evidence shows that the diagnostic accuracy of mobile-linked POC diagnostics in detecting infections in SSA is presently moderate. Future research is recommended to evaluate mHealth devices’ diagnostic potential using devices with excellent sensitivities and specificities for diagnosing diseases in this setting.
Highlights
Sub-Saharan Africa (SSA) bears the highest disease burden worldwide [1]
The receiver operating characteristic curve (ROC) curve confirmed the average diagnostic performance of these mobile-linked POC diagnostic devices. This means that mobile-linked POC diagnostics have less sensitivity and specificity abilities than the cut-off value of the gold standard described by the World Health Organization (WHO) [47]
The results demonstrated that two studies that used mobile-linked POC diagnostic devices to detect Schistosoma mansoni found an average sensitivity estimate of 0.500 and a low specificity estimate of 0.010 compared to the gold standard conventional light microscope [47]
Summary
Sub-Saharan Africa (SSA) bears the highest disease burden worldwide [1]. The high rate of infectious diseases, high recurrence of epidemics, increasing growth of chronic diseases, weak healthcare systems, insufficient funds to support healthcare, limited skilled health professionals, and poor healthcare infrastructure pose a significant challenge in improving healthcare provision in SSA [2,3,4]. Most patients have limited or no access to healthcare clinics and even essential healthcare services [2] With these challenges, digital health such as mobile health (mHealth) applications have demonstrated their potentials in screening communicable and non-communicable diseases at point-of-care diagnostics globally, including SSA [5,6,7,8]. A recent study in the USA showed that mHealth applications were used to screen healthcare workers for SARS-CoV-2 symptoms to control the spread of the infection [9]. Other studies conducted in the USA, Canada, and Taiwan have demonstrated the use mHealth for preliminary screening and early detection of possible SARS-CoV-2-infected persons and accelerating linkage to care [10,12,13]
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