ArticleClinicoEconomics and outcomes research : CEOR2016
Prioritizing health system and disease burden factors: an evaluation of the net benefit of transferring health technology interventions to different districts in Zimbabwe.
Article in ClinicoEconomics and outcomes research : CEOR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Tools for assessing the scalability of innovations in health: a systematic review.Health research policy and systems · 2022Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
introductionHealth-care technologies (HCTs) play an important role in any country's health-care system. Zimbabwe's health-care system uses a lot of HCTs developed in other countries. However, a number of local factors have affected the absorption and use of these technologies. We therefore set out to test the hypothesis that the net benefit regression framework (NBRF) could be a helpful benefit testing model that enables assessment of intra-national variables in HCT transfer.
methodWe used an NBRF model to assess the benefits of transferring cost-effective technologies to different jurisdictions. We used the country's 57 administrative districts to proxy different jurisdictions. For the dependent variable, we combined the cost and effectiveness ratios with the districts' per capita health expenditure. The cost and effectiveness ratios were obtained from HIV/AIDS and malaria randomized controlled trials, which did either a prospective or retrospective cost-effectiveness analysis. The independent variables were district demographic and socioeconomic determinants of health.
resultsThe study showed that intra-national variation resulted in different net benefits of the same health technology intervention if implemented in different districts in Zimbabwe. The study showed that population data, health data, infrastructure, demographic and health-seeking behavior had significant effects on the net margin benefit for the different districts. The net benefits also differed in terms of magnitude as a result of the local factors.
conclusionNet benefit testing using local data is a very useful tool for assessing the transferability and further adoption of HCTs developed elsewhere. However, adopting interventions with a positive net benefit should also not be an end in itself. Information on positive or negative net benefit could also be used to ascertain either the level of future savings that a technology can realize or the level of investment needed for the particular technology to become beneficial.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.