Data Resource Profile: Harmonisation of a multimodal dataset to evaluate adolescent mental health in rural South Africa

Mental health disorders among adolescents and young adults in Africa are a growing concern, with most cases remaining undiagnosed or untreated due to limited resources. As the youth population increases, mental health issues are expected to rise, emphasising the need for targeted interventions. However, the lack of longitudinal data hinders researchers from understanding how social, behavioural, and clinical factors interact, which is essential for developing effective interventions. This study aimed to create a mental health data resource accessible to mental health researchers across Africa. We identified five HIV prevention studies conducted within the Africa Health Research Institute's Health and Demographic surveillance area that collected mental health data from adolescents and young adults. These were combined with data from annual household surveys and routine clinic data. We extracted mental health data and relevant exposure variables (socio-demographics, behaviour, general health, and clinic attendance) and harmonised them into a common format. The harmonisation process was conducted in accordance with the six steps framework outlined by the Maelstrom Research guidelines. We performed the harmonisation using Julia, with data stored in Microsoft SQL Server. Two mental health screening tools were used across the studies: the 14-item Shona Symptoms Questionnaire and the Patient Health Questionnaire (PHQ-9). From these tools, we generated key outcomes, including probable common mental disorders, depressive symptoms, and suicidal ideation. The harmonised dataset includes 6,253 participants aged 13-24 years, with up to six data points collected between 2012 and 2022. Approximately 43.8% are males, and 63% are aged 13-19 years. About 12.9% reported having experienced suicidal ideation, 23.9% and 4.6% were screened positive using SSQ-14 and PHQ-9, respectively. While this dataset provides rich longitudinal data, it has some limitations. These include differences in recall periods of the mental health screening tools, potential self-reporting bias due to stigma, variation in measurement depth across studies, and limited cultural adaptation of the tools. As such, users should interpret and apply the data with appropriate caution. Despite these limitations, it enables the investigation of mental health trajectories and associated risk factors over time, providing critical insights into how social and biological influences shape mental well-being as young people transition into adulthood.