Exploring the syndemic impact of COVID-19 and mental health on health services utilisation among adult Ontario population
Bibliographic record
Abstract
OBJECTIVES: There is a need to consider COVID-19 a syndemic; which calls for a comprehensive approach to tackle the associated interconnected challenges. The objective of this study is to investigate the potential syndemic nature of COVID-19, with a specific focus on understanding how viral infection, mental health (such as anxiety and depression), and pre-existing comorbidities interact and influence each other. STUDY DESIGN: Retrospective population-based cohort study. METHODS: We conducted a population-based retrospective cohort study using linked health administrative data from the Institute for Clinical Evaluative Sciences, Ontario. The study included 2,863,423 Ontario residents from January 2020 to March 2021. We analysed healthcare services utilisation (physician visits, emergency visits, and hospitalisations) for chronic conditions among individuals with both COVID-19 and either anxiety or depression, to understand the syndemic impact of COVID-19 and mental health issues among Ontario population. RESULTS: Multiple regression models were used to explore the study's objective. In the final adjusted regression model for the sample, it was found that the individuals who were COVID-19 positive and had either anxiety or depression were more likely to utilise health services for chronic conditions of interest during the pandemic than those who were COVID-19-negative with mental health issues (odds ratio [OR]:, 1.33; 95% confidence interval [CI]: 1.12-1.58). A higher risk of morbidity was observed among males (OR: 1.28; CI: 1.16-1.41), as well as in individuals with diverse ethnic backgrounds and low socioeconomic status. CONCLUSIONS: The impact of COVID-19 on mental health, particularly among vulnerable populations with chronic diseases, can be seen as a syndemic. This complex interaction emphasises the need for integrated public health strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".