Cost Savings of Reduced Constipation Rates Attributed to Increased Dietary Fibre Intakes in Europe: A Decision-Analytic Model
Bibliographic record
Abstract
Mounting evidence shows that functional constipation contributes to increased healthcare utilization, impairment in quality of life, and lost work productivity. Among those with functional constipation, relatively small dietary changes may alleviate symptoms and result in considerable constipation-related healthcare cost savings. The study objective was to estimate the economic impact of increased dietary fibre consumption on direct medical costs associated with constipation from a payer perspective. A decision-analytic spreadsheet model was created to perform the analysis. Literature searches identified sources for input parameters, including prevalence of functional constipation, dietary fibre intakes, proportion of the population meeting recommended intakes, and the percentage that would be expected to benefit from increased dietary fibre consumption. The model assumes that 25% of adults make no change in fibre intake, 25% increase intake by 3 g/day, 15% increase intake by 4 g/day, 25% increase intake by 5 g/day, and 10% increase intake by 11 g/day. A dose-response analysis of published data was conducted to estimate the percent reduction in constipation prevalence per 1 g/day increase in dietary fibre intake. Annual direct medical costs for constipation were derived from the literature and updated to 2014. Sensitivity analyses explored robustness of the model. Under base case assumptions, annual cost savings were estimated at ?127,037,383 in the United Kingdom, €8,791,992 / ?7,244,513 in Ireland, and €121,699,804 in Spain. Increasing dietary fibre consumption is associated with considerable cost savings, with these estimates being conservative given the exclusion of lost productivity costs in the model.
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 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".