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for healthcare providers to determine which adolescents
need urgent care and which require monitoring or lighter From Measurement to Meaningful Care
interventions.
AfriCAT is more than a testing tool; it is a bridge to effective
AfriCAT, short for African Computerised Adaptive Test, was mental health care. The platform supports measurement-
developed specifically for African adolescents to address based care, an approach where interventions are guided
these gaps. A Computerised Adaptive Test (CAT) is a type by systematic symptom monitoring rather than guesswork.
of assessment that adapts in real time: it selects questions Repeated assessments allow for early detection of
based on a person’s previous answers, allowing for faster worsening symptoms and ensure adolescents receive the
and more precise measurement of symptoms. Unlike appropriate level of support.
traditional questionnaires that ask the same questions to
everyone, CAT focuses on what is most relevant for each The development process involved extensive participatory
adolescent, reducing the time needed and minimising design. Adolescents, caregivers, educators, healthcare
fatigue or frustration. workers, and policymakers in South Africa and Kenya
contributed through workshops, interviews, and focus
Led by Dr Bianca Moffett at the SAMRC/Wits-Agincourt group discussions. Adolescents with lived experience of
Unit, and supported by the inaugural Mental Health Data depression or anxiety were employed as co-researchers,
Prize Africa, AfriCAT applies advanced psychometric providing insight into question phrasing, interface usability,
modelling and data science to identify, triage, and monitor and the overall assessment experience. This ensures the
adolescent depression and anxiety. The project uses large- tool resonates with the young people it aims to serve.
scale population data from the Kenya National Adolescent
Mental Health Survey, which provides one of the most Data from AfriCAT can also support health system planning.
comprehensive datasets on adolescent mental health in Aggregated, anonymised results may help clinics identify
Africa. Machine learning helps select the most informative service gaps, inform stepped-care models, and prioritise
questions, while stopping rules ensure accurate results with resources where they are most needed. The tool follows
minimal assessment length. an open science model, making algorithms, item banks,
and code freely available to other African research teams,
AfriCAT is designed for use by clinicians, lay counsellors, promoting adaptation and expansion in different contexts.
and other frontline workers. The tool is mobile-compatible By combining science, technological innovation, and youth
and web-based, featuring a youth-friendly interface design participation, AfriCAT represents an African-led solution
and culturally contextualised language. It can rapidly to a critical mental health challenge. It brings precision,
estimate the severity of depression, anxiety, social anxiety, efficiency, and relevance to adolescent mental health
and suicidal ideation, and track symptoms over time, assessment, making it easier for health systems to reach
enabling healthcare providers to make informed, evidence- those most in need and ultimately improving care outcomes
based decisions. across the continent.

14 THE SOUTH AFRICAN MEDICAL RESEARCH COUNCIL