Sacred journeys and pilgrimages: health risks associated with travels for religious purposes
Bibliographic record
Abstract
BACKGROUND: Pilgrimages and travel to religious mass gatherings (MGs) are part of all major religions. This narrative review aims to describe some characteristics, including health risks, of the more well-known and frequently undertaken ones. METHODS: A literature search was conducted using keywords related to the characteristics (frequency of occurrence, duration, calendar period, reasons behind their undertaking and the common health risks) of Christian, Muslim, Hindu, Buddhist and Jewish religious MGs. RESULTS: About 600 million trips are undertaken to religious sites annually. The characteristics vary between religions and between pilgrimages. However, religious MGs share common health risks, but these are reported in a heterogenous manner. European Christian pilgrimages reported both communicable diseases, such as norovirus outbreaks linked to the Marian Shrine of Lourdes in France, and non-communicable diseases (NCDs). NCDs predominated at the Catholic pilgrimage to the Basilica of Our Lady of Guadalupe in Mexico, which documented 11 million attendees in 1 week. The Zion Christian Church Easter gathering in South Africa, attended by ~10 million pilgrims, reported mostly motor vehicle accidents. Muslim pilgrimages such as the Arbaeen (20 million pilgrims) and Hajj documented a high incidence of respiratory tract infections, up to 80% during Hajj. Heat injuries and stampedes have been associated with Hajj. The Hindu Kumbh Mela pilgrimage, which attracted 100 million pilgrims in 2013, documented respiratory conditions in 70% of consultations. A deadly stampede occurred at the 2021 Jewish Lag BaOmer MG. CONCLUSION: Communicable and NCD differ among the different religious MGs. Gaps exists in the surveillance, reporting and data accessibility of health risks associated with religious MGs. A need exists for the uniform implementation of a system of real-time monitoring of diseases and morbidity patterns, utilizing standardized modern information-sharing platforms. The health needs of pilgrims can then be prioritized by developing specific and appropriate guidelines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".