ECONOMIC EFFICIENCY OF PREVENTIVE PROGRAMS IN THE PUBLIC HEALTH SYSTEM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Preventive measures in healthcare are considered as one of the key instruments for ensuring sustainable development of the public health system. In the context of increasing burden on budget allocations, increasing prevalence of chronic diseases and aging population, the assessment of the economic feasibility of implementing preventive programs is of particular relevance. The objective of this work is to analyze scientific data and practical experience regarding the economic efficiency of such programs, as well as to identify factors that facilitate or hinder their implementation at the public policy level. The work considers various types of preventive measures — from primary prevention (e. g. educational campaigns, promotion of a healthy lifestyle) to secondary and tertiary (screening examinations, medical examinations, rehabilitation programs). Particular attention is paid to the methods of economic analysis in healthcare, including cost-effectiveness analysis (CEA), cost-benefit analysis (CBA), cost-utility analysis (CUA) and modeling of long-term consequences for the healthcare budget. Based on the analysis of international experience (EU countries, USA, Canada, Australia) and examples of the implementation of preventive programs in domestic practice, their potential economic benefit is demonstrated. It is shown that most programs aimed at preventing common diseases (cardiovascular diseases, diabetes, cervical cancer, etc.) provide a significant reduction in treatment costs, reduce the level of hospitalizations and disability, and increase the duration and quality of life of the population. The article is of interest to researchers, public health specialists, politicians and decision makers in the field of healthcare.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it