An international comparison of longitudinal health data collected on long COVID in nine high income countries: a qualitative data analysis
Bibliographic record
Abstract
BACKGROUND: Long coronavirus disease (COVID) presents a significant health challenge. Long-term monitoring is critical to support understanding of the condition, service planning and evaluation. We sought to identify and examine longitudinal health data collected on long COVID to inform potential decisions in England regarding the rationale for data collection, the data collected, the sources from which data were collected and the methods used for collection. METHODS: We included datasets in high-income countries that experienced similar coronavirus disease 2019 (COVID-19) waves to England pre-vaccine rollout. Relevant datasets were identified through literature searches, the authors' networks and participants' recommendations. We undertook semi-structured interviews with individuals involved in the development and running of the datasets. We held a focus group discussion with representatives of three long COVID patient organisations to capture the perspective of those with long COVID. Emergent findings were tested in a workshop with country interviewees. RESULTS: We analysed 17 datasets from nine countries (Belgium, Canada, Germany, Italy, the Netherlands, New Zealand, Sweden, Switzerland and the United Kingdom). Datasets sampled different populations, used different data collection tools and measured different outcomes, reflecting different priorities. Most data collection was research (rather than health care system)-funded and time-limited. For datasets linked to specialist services, there was uncertainty surrounding how long these would continue. Definitions of long COVID varied. Patient representatives' favoured self-identification, given challenges in accessing care and receiving a diagnosis; New Zealand's long COVID registry was the only example identified using this approach. Post-exertion malaise, identified by patients as a critical outcome, was absent from all datasets. The lack of patient-reported outcome measures (PROMs) was highlighted as a limitation of datasets reliant on routine health data, although some had developed mechanisms to extend data collection using patient surveys. CONCLUSIONS: Addressing research questions related to the management of long COVID requires diverse data sources that capture different populations with long COVID over the long-term. No country examined has developed a comprehensive long-term data system for long COVID, and, in many settings, data collection is ending leaving a gap. There is no obvious model for England or other countries to follow, assuming there remains sufficient policy interest in establishing a long-term long COVID patient registry.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.088 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".