Cost-Effectiveness of Population Screening Programs for Cardiovascular Diseases and Diabetes in Low- and Middle-Income Countries: A Systematic Review
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
Almost all low- and middle-income countries (LMICs) have instated a program to control and manage non-communicable diseases (NCDs). Population screening is an integral component of this strategy and requires a substantial chunk of investment. Therefore, testing the screening program for economic along with clinical effectiveness is essential. There is significant proof of the benefits of incorporating economic evidence in health decision-making globally, although evidence from LMICs in NCD prevention is scanty. This systematic review aims to consolidate and synthesize economic evidence of screening programs for cardiovascular diseases (CVD) and diabetes from LMICs. The study protocol is registered on PROSPERO (CRD42021275806). The review includes articles from English and Chinese languages. An initial search retrieved a total of 2,644 potentially relevant publications. Finally, 15 articles (13 English and 2 Chinese reports) were included and scrutinized in detail. We found 6 economic evaluations of interventions targeting cardiovascular diseases, 5 evaluations of diabetes interventions, and 4 were combined interventions, i.e., screening of diabetes and cardiovascular diseases. The study showcases numerous innovative screening programs that have been piloted, such as using mobile technology for screening, integrating non-communicable disease screening with existing communicable disease screening programs, and using community health workers for screening. Our review reveals that context is of utmost importance while considering any intervention, i.e., depending on the available resources, cost-effectiveness may vary-screening programs can be made universal or targeted just for the high-risk population.
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 | 0.027 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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