Sun exposure during childhood and the etiology of multiple sclerosis: measurement and analysis
Bibliographic record
Abstract
Introduction: The ultraviolet radiation (UVR) emitted by the sun has both beneficial and detrimental effects on human health. Low levels of sun exposure have been suggested to play a role in susceptibility to multiple sclerosis (MS). MS is a chronic, immune-mediated, degenerative disease of the brain and spinal cord. Sunlight is an interesting hypothesis given the many interactions between UVR and the immune system. To date, most epidemiological research has been focused on adults with MS, as pediatric-onset MS (onset≤18 years of age) has only recently been recognized and studied. The overall goal of this research is to advance our understanding of the relationship between sun exposure and the risk of MS. The research presented is divided into two methodological themes: (1) measurement and (2) analysis.Theme 1: The research on measurement of sun exposure focused on the development of the Pediatric MS Tool-Kit (Tool-Kit). The Tool-Kit is a measurement framework that will facilitate questionnaire design and data harmonization of pediatric MS etiological studies. I first designed and carried out a systematic review of measurement property studies that evaluated self-report questionnaires to assess children’s sun related behaviours. I then performed an international Delphi study that I used to define a minimal set of core variables to assess sun exposure in pediatric MS case-control studies. Studies included in the systematic review assessed sun protection (71%), sun exposure (34%), and host characteristics (31%; e.g. sun sensitivity), and focused on current (45%) or usual (45%) behaviours. I did not identify a validated questionnaire that was designed for a case-control study. Six core variables that measure sun exposure behaviours in children are included in Tool-Kit, and can be accessed at www.maelstrom-research.org/mica/network/tool-kit. Theme 2: The research on analysis of sun exposure focused on using novel analytical strategies to further elucidate the etiological model for MS. I used data collected in the Environmental Risk Factors in MS (EnvIMS) Study, a frequency matched case-control study that included adult MS cases and population-based controls from Canada, Italy and Norway (2251 cases and 4028 controls). Sun exposure behaviours, for 5-year age intervals, from birth to age 15 years were examined. I compared two life course epidemiology conceptual models (i.e. the critical period and accumulation models), to select the most etiologically relevant model. I also characterized latent sun exposure behaviour groups and compared risk across groups. The accumulation model was selected as the best model, and demonstrated a 47% increased risk of MS, comparing low summer sun exposure from birth to age 15, to high levels during the same period. Relative to sun-seekers (i.e. high exposure in summer and in winter, and rare use of sun protection), sun-avoiders (i.e. low exposure in summer and winter, and frequent use of sun protection) had a 76% greater risk. Interestingly, sun-avoiders had a 40% higher risk, when compared to a sun exposure behaviour group that had similar sun exposure levels, but that rarely used sun protection. Conclusions: Sun exposure is a modifiable risk factor that we can intervene on that may reduce burden of adult MS at the population level; and future studies, using the Tool-Kit variables, will be able to determine if sun exposure is also associated with risk of pediatric-onset MS. Targeted public health messages, which emphasize the benefits of sun exposure and how to maximize these benefits, while maintaining current recommendations aimed at reducing skin cancer, need to be tested.
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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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".