Exploring the national prevalence of mental health risk, multimorbidity and the associations thereof: a repeated cross-sectional panel study
Bibliographic record
Abstract
Objective and methods: South Africans were affected by the COVID-19 pandemic and resultant economic hardships. As a result, mental health within this region may have worsened. Therefore, using large scale nationally representative data, we repeated the cross-sectional panel study to investigate mental health risk post COVID-19 to explore mental health and multimorbidity and to examine the relationship between adverse childhood experiences (ACEs) and comorbid health conditions in South African adults (aged 18 years and older). Results: Post-pandemic, 26.2, 17.0, and 14.8% of the South African respondents reported being probably depressed, anxious and had suffered high exposure to early life adversity, respectively. Nationally, the prevalence of mental health across the country remained alarmingly high when compared to Panel 1. The prevalence of multimorbidity (2 or more chronic morbidities) among the South African population was reported at 13.9%, and those with 2 or more morbidities were found to have increased odds of early adversity, irrespective of differing socio-demographics. Furthermore, early adversity was also associated with multimorbidity partly via mental health. Conclusion: This repeated cross-sectional national study reiterated that the prevalence of mental health across South African adults aged 18 years and older is widespread. Mental health remains worryingly high post-pandemic where more than a quarter of respondents are probably depressed, nearly one in every five respondents are anxious, and 14.8% reported high exposure ACEs. Public health interventions need to be upscaled with efforts to reduce the incidence of early adversity that may have the ability to lower adverse health outcomes and mental ill-health in adulthood.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".