Socioeconomic drivers of encephalitis burden in the post-COVID era: a 204-country analysis from global burden of disease study 2021
Bibliographic record
Abstract
Background: Encephalitis, an inflammatory central nervous system disease causing significant morbidity and mortality, disproportionately affects low- and middle-income countries (LMICs) due to healthcare disparities. Encephalitis has diverse etiologies-viral, autoimmune, bacterial, parasitic-each with distinct clinical and epidemiological features. Despite declining global age-standardized rates since 1990, inequities in diagnostics, vaccine coverage, and critical care persist, worsened by COVID-19 pandemic, which delayed diagnoses and disrupted vaccinations. Methods: Using Global Burden of Disease (GBD) 2021 data, we analyzed age-standardized prevalence, incidence, mortality, and disability-adjusted life-years (DALYs) across 204 countries (1990-2021). We used the Bayesian Age-Period-Cohort model with integrated nested Laplace approximation to predict encephalitis' future trends, through 2040, enhancing the study's predictive value. Sociodemographic Index (SDI) stratification and Bayesian meta-regression models assessed trends, with significance determined via 95% uncertainty intervals and estimated annual percentage change (EAPC). Results: In 2021, 4.64 million individuals worldwide were affected by encephalitis (1.49 million new cases; 92,000 deaths), encompassing cases spanning acute, subacute, and chronic stages of the disease. Low-middle SDI regions bore 3-5 times higher burdens than high-SDI regions. South Asia had the highest burden (age-standardized prevalence rate [ASPR]: 140.9/100,000; incidence [ASIR]: 51.3/100,000), while Australasia reported the lowest (ASPR: 1.94/100,000). High-SDI countries showed distinct patterns, such as rising incidence in Australia. COVID-19 was associated with an 18% increase in DALYs in high-burden regions. National disparities were stark: Pakistan, India, and Nepal had the highest burdens; Canada, the lowest. The encephalitis burden was greater in children than in other age groups. Conclusion: This analysis advances prior GBD research by integrating post-COVID-19 insights and future burden forecasts, filling pre-pandemic study gaps. GBD dataset does not differentiate etiological subtypes, limiting our analysis granularity given encephalitis' clinical and epidemiological heterogeneity. Socioeconomic inequities drive encephalitis burden, necessitating targeted interventions: scaling Japanese encephalitis vaccination in South Asia, strengthening African diagnostic hubs, and integrating climate-resilient surveillance. Post-pandemic recovery must prioritize healthcare infrastructure, telehealth, and policies addressing poverty and education. Global collaboration is critical to mitigate disparities and optimize region-specific 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".