Identifying Potential Vulnerability to Long COVID Through Global‐to‐Local Inequalities in Years Lived With Disability Attributed to COVID‐19, 2020–2021, Across 920 Locations
Bibliographic record
Abstract
ABSTRACT The COVID‐19 pandemic has reshaped global health; however, the long‐term burden of long COVID remains poorly understood, especially in low‐ and middle‐income countries (LMICs), where limited surveillance and data gaps may obscure a substantial and sustained impact. Using the Global Burden of Disease (GBD) 2021 framework, we previously assessed the direct COVID‐19 burden—including incidence, prevalence, mortality, and disability‐adjusted life‐years (DALYs)—across 920 locations during 2020–2021. In this study, we focus on years lived with disability (YLDs), particularly in 2021, as a potential early indicator to identify locations and populations that may be at higher risk of long COVID burden in subsequent years (e.g., 2022–2023). We also examine patterns of inequality to highlight vulnerable groups. Our findings are consistent with multiple large‐scale studies on long COVID and suggest that YLDs may serve as a useful early proxy for ongoing burden. Importantly, we identify notably higher age‐standardized YLD rates in LMICs—especially in Sub‐Saharan Africa and in parts of South Asia and Eastern Europe. These areas, previously underexplored in long COVID research, might be particularly susceptible to its effects. Among the top 10 countries with the highest age‐standardized YLD rates in 2021, 80% fell within the low, low‐middle, and middle Socio‐demographic Index (SDI) categories. These high age‐standardized YLD rates may point to systemic vulnerabilities and entrenched structural health disparities, indicating a potential for considerable and enduring long COVID burden that could persist to the present day in the absence of targeted interventions. Furthermore, our inequality analysis underscores that while both advantaged and disadvantaged groups in LMICs require attention, the most disadvantaged groups warrant special focus due to their more severe resource constraints and restricted capacity for resilience‐building. Overall, this study supports calls for stronger surveillance, expanded access to rehabilitation, and better integration of long COVID care into universal health coverage. Continued GBD updates will be essential for monitoring trends and guiding responsive 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".