The evolution of symptoms of nervous system dysfunction in a First Nation community with a history of mercury exposure: a longitudinal study
Bibliographic record
Abstract
BACKGROUND: Since the 1960's, mercury (Hg) contamination of the aquatic environment of Asubpeeschoseewagong Anishinabek (Grassy Narrows First Nation) territories has impacted the community members' traditions, culture, livelihood, diet and health. Despite decreasing Hg exposure over time, a recent study suggested that long-term exposure contributed to later-life symptom clusters of nervous system dysfunction. Here, the objective was to evaluate, 5 years later, the prevalence and progression of these symptoms and examine the contribution of long-term, past Hg exposure. METHODS: The symptom questionnaire, applied in the 2016/17 Grassy Narrows Community Health Assessment (GN-CHA) (Time 1), was re-administered in the 2021/22 Niibin study (Time 2). A total of 85 adults (median age: 47y; range: 29-75y) responded at both times. Paired statistics were used to test the differences (Time 2 - Time 1) in self-reported symptom frequencies. The symptom clustering algorithm, derived from the entire study group of the GN-CHA (n = 391), which had yielded 6 clusters, was applied at Time 1 and 2. Equivalent hair Hg measurements (HHg) between 1970 and 1997 were used in Longitudinal Mixed Effects Models (LMEM), with a sub-group with ≥ 10 repeated HHg mesurements (age > 40y), to examine its associations with symptom cluster scores and their progression. RESULTS: For most symptoms, paired analyses (Time 2 - Time 1) showed a significant increase in persons reporting " very often" or "all the time", and in the mean Likert scores for younger and older participants (< and ≥ 50y). The increase in cluster scores was not associated with age or sex, except for sensory impairment where a greater increase in symptom frequency was observed for younger persons. LMEM showed that, for the sub-group, long-term past Hg exposure was associated with most cluster scores at both times, and importantly, for all clusters, with their rate of increase over time (Time 2 - Time 1). CONCLUSIONS: The persistence of reported symptoms and their increase in frequency over the short 5-year period underline the need for adequate health care services. Results of the sub-group of persons > 40y, whose HHg reflects exposure over the 28-year sampling period, suggest that there may be a progressive impact of Hg on nervous system dysfunction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".