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Record W7020086874

Is high socioeconomic status a risk factor for multiple sclerosis?

2015· dissertation· en· W7020086874 on OpenAlexfundaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchUniversitetet i BergenMcGill UniversityFaculty of Medicine, McGill UniversityMultiple Sclerosis Society of CanadaMultiple Sclerosis SocietyHelse VestFondazione Italiana Sclerosi Multipla
KeywordsSocioeconomic statusDiseaseLogistic regressionRisk factorCohort studyAssociation (psychology)PopulationCohort
DOInot available

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is a chronic disease of the central nervous system characterised by inflammation and neurodegeneration. An increased risk of the disease among those of high socioeconomic status (SES) was first observed over 50 years ago. This is in contrast to a more common pattern whereby adverse health outcomes are generally associated with low SES. Most MS risk factors, such as smoking, obesity, and a late age of EBV infection, vary in their prevalence by SES, and thus all provide pathways through which SES could affect disease risk. Alternatively, stress-related immune changes linked to SES could influence disease susceptibility. To establish the strength and nature of the association between SES and MS, two studies were performed as part of this manuscript-based thesis.The first manuscript is a systematic review of published cohort and case-control studies that examined the association between SES and MS risk. 21 articles were included. 5 studies, all from countries with higher levels of income inequality, reported an association between high SES and increased MS risk. 13 studies reported insufficient evidence of an association, and 2 studies reported an association with low SES; these largely came from more egalitarian nations. Few studies adequately controlled for all important mediators and confounders, precluding clear conclusions about the nature of the SES-MS association.The second manuscript is an original analysis of the association between SES and MS, using data from the multinational Environmental Risk Factors in MS (EnvIMS) case-control study. The study population comprised 2,144 cases and 3,859 controls, from Norway, Canada, and Italy. Multiple logistic regression was used to evaluate the association between SES and MS, with SES measured by parental education level. Analyses were adjusted for age, sex, sunlight exposure, history of infectious mononucleosis, smoking, obesity, and family size. In Canada, the OR (95% CI) for MS among individuals with university-educated parents relative to those whose parents had primary school education or below was 1.47 (1.03-2.09), with a statistically significant dose-response relationship across education levels (p for trend = 0.029). In Norway, this association was only present for those who grew up during a period of rising inequality (p for trend = 0.031). No evidence for an association was found in Italy.These two studies provide only partial support for an association between high SES and increased MS risk. Differing ages of EBV infection by social class, or stress-related immune changes linked to SES, are both possible explanations for the findings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.077
GPT teacher head0.319
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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