Estimation and Prediction of Avoidable Health Care Costs of Cardiovascular Diseases and Type 2 Diabetes Through Adequate Dairy Food Consumption: A Systematic Review and Micro Simulation Modeling Study.
Bibliographic record
Abstract
BACKGROUND: Recent evidence from prospective cohort studies show a relationship between consumption of dairy foods and cardiovascular diseases (CVDs) and type 2 diabetes mellitus (T2DM). This association highlights the importance of dairy foods consumption in prevention of these diseases and also reduction of associated healthcare costs. The aim of this study was to estimate avoidable healthcare costs of CVD and T2D through adequate dairy foods consumption in Iran. METHODS: This was a multistage modelling study. We conducted a systematic literature review in PubMed and EMBASE to identify any association between incidence of CVD and T2DM and dairy foods intake, and also associated relative risks. We obtained age- and sex-specific dairy foods consumption level and healthcare expenditures from national surveys and studies. Patient level simulation Markov models were constructed to predict the disease incidence, patient population size and associated healthcare costs for current and optimal dairy foods consumption at different time horizons (1, 5, 10 and 20 years). All parameters including costs and transition probabilities were defined as statistical distributions in the models, and all analyses were conducted by accounting for first and second order uncertainty. RESULTS: The systematic review results indicated that dairy foods consumption was inversely associated with incidence of T2DM, coronary heart disease (CHD) and stroke. We estimated that the introduction of a diet containing 3 servings of dairy foods per day may produce a $0.43 saving in annual per capita healthcare costs in Iran in the first year due to saving in cost of CVD and T2DM treatment. The estimated savings in per capita healthcare costs were $8.42, $39.97 and $190.25 in 5, 10 and 20-years' time, respectively. Corresponding total aggregated avoidable costs for the entire Iranian population within the study time horizons were $33.83, $661.31, $3,138.21 and $14,934.63 million, respectively. CONCLUSION: Our analysis demonstrated that increasing dairy foods consumption to recommended levels would be associated with reductions in healthcare costs. Further randomized trial studies are required to investigate the effect of dairy foods intake on cost of CVD and T2DM in the population.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.018 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".