Ecological association between COVID-19 deaths and vitamin D deficiency among older adults: An international country-level systematic review and analysis
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has been consistently associated with disproportionately high mortality among older adults, regardless of nation of origin. Previous research on pneumotropic viruses demonstrated an association between host vitamin D levels and viral disease state. Vitamin D deficiency (VDD) also disproportionately impact older adults. The objective of this study was to evaluate VDD and COVID-19 mortality worldwide during the first 50 days of the pandemic as experienced by each country. Methods Country-specific VDD prevalence among older adults was evaluated according to a systematic literature search of PubMed, CINAHL, EMBASE, and SCOPUS during April 2020. Data were obtained from the public Our World in Data database which reports COVID-19 mortality from European Centre for Disease Prevention and Control and country population characteristics, size and proportion of the population greater than 65 years, from the United Nations Department of Economic and Social Affairs. Country-specific cumulative mortality was modeled via negative binomial random coefficient growth curve models. Results Among 29 countries included in this analysis, VDD prevalence among older adults ranged from 6.9% in Canada to 69% in France. Cumulative COVID-19 deaths up to day 50 within each country, modeled via exponential growth curves, demonstrated an association with country-level VDD (p = 0.0538). However, the association was not robust when controlling for proportion of older adults in each country (p = 0.2135). Conclusions This ecological analysis is further evidence for VDD correction as a component of COVID-19 treatment, especially among older individuals. Inclusion of data cross-culturally (i.e., collected from countries worldwide) supports the theory that VDD is a universal mechanism contributing to COVID-19 mortality. Though definitive randomized controlled trials are still needed, the current study provides support for VDD screening and correction as the influenza season rapidly approaches in the Northern hemisphere.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".