Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Sep 20, 2019
Date Accepted: Dec 15, 2019
The development and validation of smartphone guided algorithms for use by Community Volunteers to screen and refer people with eye problems in Trans Nzoia County, Kenya
ABSTRACT
Background:
Eyecare provision is currently insufficient to meet the requirement for eye care services. Many people remain unnecessarily visually impaired or at risk of becoming so due to treatable or preventable eye conditions. A lack of access and awareness of services are key barriers, in large part due to their being too few eye care providers in the health system for the unmet need.
Objective:
We hypothesized that by utilising novel smartphone-based clinical algorithms it is possible to task-shift eye screening to community volunteers (CVs) to accurately identify and refer patients to primary eye care services.
Methods:
We compared CVs referral decisions using smartphone-based clinical algorithms (Peek Community Screening App) to those by an experienced Ophthalmic Clinical officer (OCO), the reference standard. The same participants were assessed by a trained CV using the App and by an OCO using standard outreach equipment. The outcome was the proportion of all decisions that were correct when compared to the OCO’s results. All decisions about referral were used to calculate sensitivity, specificity, and predictive values (positive and negative). An iterative design approach was used to reach the required sensitivity and specificity. The final iteration sample size was 516 participants.
Results:
The required sensitivity and specificity was of the Peek Community Screening was reached after seven iterations. In the seventh iteration, the OCO identified referable eye problems in 378/574 (65.9 %) participants. CVs correctly identified 344/378 (sensitivity 91.0%, 95% CI 87.7% - 93.7%) of these and also correctly identified 153/196 (specificity 78.1%, 95% CI 71.6% - 83.6%) as not having a referable eye problem. The positive predictive value was 88.9%, (95% CI 85.3%-91.8%) and the negative predictive value was 81.8%, (95% CI 75.5%-87.1%).
Conclusions:
CVs can accurately use the Peek Community Screening App to identify and refer people with eye problems. An iterative design process is necessary to ensure validity in the local context.
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