Implementing essential interventions for cardiovascular disease risk management in primary healthcare: lessons from Eastern Europe and Central Asia
Bibliographic record
Abstract
Globally, non-communicable diseases (NCDs) are the leading cause of morbidity and mortality, including in the WHO European region. Within this region, the Member States with the greatest cardiovascular disease (CVD) burden are also some of the lowest resourced. As the need for technical support for the implementation of essential CVD/NCD interventions in primary healthcare (PHC) in these regions grew urgent, the WHO Regional Office for Europe has been directly supporting national governments in the development, assessment, scale-up and quality improvement of large scale PHC interventions for CVD. Herein, we synthesise the key learnings from providing technical support to national governments under the auspices of the WHO across the European region and share these learnings as a resource for public health professionals to consider when increasing coverage of quality essential health services. Based on our experience providing technical support to a diversity of Member States in the European Region (eg, Tajikistan, Republic of Moldova, Ukraine and Uzbekistan), we have identified six key lessons: prioritising NCDs for public health intervention, identifying and mapping existing resources, engaging key stakeholders, tailoring interventions to the local health system, generating local evidence and ensuring quality improvement while mainstreaming. Common challenges across all phases of implementation include multiple and inconsistent international toolkits and guidance, lack of national capacity for evidence-based healthcare, limited access to essential medicines and technologies, inconsistent national guidelines and limited experience in evaluation methodology, clinical epidemiology and guideline implementation. We map the lessons to the Consolidated Framework for Implementation Research and highlight key learnings and challenges specific to the region. Member States in the region are at various stages of implementation; however, several are currently conducting pragmatic clinical trials to generate local evidence for health policy. As this work expands, greater engagement with peer-to-peer sharing of contextual wisdom, sharing of resources, publishing methodology and results and development of region-specific resources is planned.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".