Community-Based Hearing and Vision Screening in Schools in Low-Income Communities Using Mobile Health Technologies

Introduction Globally, more than 50 million children have hearing or vision loss. Most of these sensory losses are identified late due to a lack of systematic screening, making treatment and rehabilitation less effective. Mobile health (mHealth), which is the use of smartphones or wireless devices in health care, can improve access to screening services. mHealth technologies allow lay health workers (LHWs) to provide hearing and vision screening in communities. Purpose The aim of the study was to evaluate a hearing and vision school screening program facilitated by LHWs using smartphone applications in a low-income community in South Africa. Method Three LHWs were trained to provide dual sensory screening using smartphone-based applications. The hearScreen app with calibrated headphones was used to conduct screening audiometry, and the Peek Acuity app was used for visual acuity screening. Schools were selected from low-income communities (Gauteng, South Africa), and children aged between 4 and 9 years received hearing and vision screening. Screening outcomes, associated variables, and program costs were evaluated. Results A total of 4,888 and 4,933 participants received hearing and vision screening, respectively. Overall, 1.6% of participants failed the hearing screening, and 3.6% failed visual acuity screening. Logistic regression showed that female participants were more likely to pass hearing screening ( OR = 1.61, 95% CI [1.11, 2.54]), while older children were less likely to pass visual acuity screening ( OR = 0.87, 95% CI [0.79, 0.96]). A third (32.5%) of referred cases followed up for air-conduction threshold audiometry, and one in four (25.1%) followed up for diagnostic vision testing. A high proportion of these cases were confirmed to have hearing (73.1%, 19/26) or vision loss (57.8%, 26/45). Conclusions mHealth technologies can enable LHWs to identify school-age children with hearing and/or vision loss in low-income communities. This approach allows for low-cost, scalable models for early detection of sensory losses that can affect academic performance.