Evaluating Public Health Interventions: A Neglected Area in Health Technology Assessment
Bibliographic record
Abstract
Public health (PH) interventions are crucial for ensuring sustainable healthcare services. Nevertheless, they represent a neglected area in the field health technology assessment (HTA) due to various methodological issues and their complex design that goes beyond clinical setting. The present study provides an environmental scan of HTA initiatives related to the assessment of PH technologies on a global level. We conducted a cross-sectional survey among 85 HTA-related European and international societies, health bodies and networks from September 2018 to January 2019. The questionnaire contained four sections and 18 questions regarding activities related to evaluation of PH technologies, information on existing PH technologies and methodologies of assessment as well as barriers and facilitators to reaching a decision and implementing a PH technology. Among 52 survey responses, the majority of respondents came from European countries (35 %), followed by North American (27 %) and South America (19%) countries. Main type of organizations covered by our survey included HTA agencies, public administrations and research institutes. Seventy-one percent of institutions reported engagement in any aspect of HTA in the area of PH (N=37). Among those, 81% percent evaluated less than five PH technologies from 2013 to 2018. The most common barriers for reaching a decision on PH technologies were: lack of data, conflicting stakeholder priorities, and methodological issues. A total of 76 PH interventions were reported, and most cited initiatives were related to chronic disease screening, prevention of infectious diseases, and maternal, pre and neonatal screening. Our survey reported a rather limited involvement of HTA in the evaluation of PH technologies. In particular, evaluation of behavioral and lifestyle interventions remains extremely rare. Implementation of collaborative HTA approaches in the setting of PH practice and policy needs to be prioritized and further strengthened. Moreover, assuring reliable data structures and consolidation of HTA methods for the evaluation of PH technologies will be crucial for tackling the enormous burden of non-communicable diseases in societies.
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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 | 0.217 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".