The economic burden of ischaemic heart diseases on health systems: a systematic review
Bibliographic record
Abstract
INTRODUCTION: There is a dearth of evidence regarding the global economic burden of ischaemic heart diseases (IHDs). This systematic review aims to synthesise national-level studies worldwide quantifying the economic burden of IHDs from a provider's perspective. METHODS: We searched PubMed, Embase, Cochrane, DARE and EconLit databases from 1 January 2000 to 29 June 2022. We included observational, cost-of-illness and economic modelling studies reporting direct healthcare cost data for IHDs at the national level. At least two reviewers independently screened titles and abstracts and full texts, extracted data and assessed quality using a seven-question assessment tool. We synthesised findings by country, focusing on three key economic estimates: total annual costs of IHDs, costs of managing acute IHD episodes and chronic IHD care. We correlated these costs with country-specific macroeconomic measures and disease burden. RESULTS: We included 65 national-level studies conducted in 21 countries worldwide, with a majority in high-income countries. The median direct healthcare cost per episode of IHDs was 8062 Int$ 2019 (IQR: 5770-9580), and the median direct healthcare cost of IHDs per patient-year was 10 064 Int$ 2019 (IQR: 7619-14 818). These estimates positively correlated with country-specific macroeconomic and DALY measures. CONCLUSION: IHDs impose a substantial economic burden on health systems globally. Economic costs in countries exceed per capita public health expenditure, primarily driven by acute episodes. National-level data were available for only 21 countries, and none from low-middle-income and low-income countries. Economic costs of IHDs need to be quantified to inform resource allocation decisions at national and global levels.CRD42022337577.
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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 | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".