Effects of outdoor air pollution on vitamin D status and public health
Bibliographic record
Abstract
Air pollution has a direct impact on public health and is also accompanied by a decrease in serum vitamin D levels because airborne aerosols absorb and scatter UVB ultraviolet radiation, which is necessary for the production of vitamin D3. Due to the fact that most of the published literature sources reflect a qualitative picture of the processes, there was a need (goal) to quantify the impact of outdoor air pollution on the status of vitamin D and the state of health of the population. We carried out a correlation analysis of the dependence of the prevalence of vitamin D deficiency on the average annual concentration of pollutants (PM2.5, PM10 and NO2), as well as the dependencies of the DALY indicator and mortality associated with the environment on the prevalence of deficiency vitamin D in Europe, the USA and Canada. The main characteristics of the studied population (n=4,369,222), which included healthy, non-pregnant representatives of the Caucasian race (mean value ± standard deviation): age 43.4±26.4 years; serum 25 (OH) D concentration 25.5 ± 9.0 ng / mL; prevalence: vitamin D deficiency (25 (OH) D <20 ng / mL) 55.5 ± 11.7 %; insufficient vitamin D levels (25 (OH) D = 20‑29 ng / ml) 21.8 ± 1.6 %; sufficient vitamin D levels (25 (OH) D ≥30 ng / ml) 22.7 ± 11.2 %; body mass index 26.2 ± 4.6 kg / m2. Statistical studies suggest that air pollution in the range of real concentrations significantly increases the prevalence of vitamin D deficiency in Europe, the United States and Canada. With an increase in PM2.5 concentration by 10 μg / m3, the prevalence of vitamin D deficiency increases by 14.2 %, with an increase in PM10 concentration by 10 μg / m3, the prevalence of vitamin D deficiency increases by 11.2 %, with an increase in NO2 concentration by 10 μg / m3 the prevalence of vitamin D deficiency increases by 10.98 %. In turn, the increasing prevalence of vitamin D deficiency leads to an increase in the burden of DALY morbidity and environment-related mor tality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".