ReviewBMJ global health2026
The impact of investments in health systems: a rapid review of computational models.
Review in BMJ global health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Investments in health systems are essential for achieving effective sustainable and equitable health outcomes. However, national and global level stakeholders continue to grapple with how to quantify the impact of investments, its return of investment, and how to efficiently allocate health system inputs to improve health service delivery, health outputs and outcomes.This rapid review highlights the diverse computational models used to simulate investments in health systems strengthening. Our analysis revealed a range of methodologies, from agent-based simulations and dynamic computable general equilibrium models to structural equation models and the OneHealth Tool. Each model offers unique insights and has its strengths and limitations that need to be carefully considered for every scenario.A significant limitation across all models is the lack of external validation of the inputs and assumptions. The variability in data requirements and computational complexity also presents challenges in adapting these models to different contexts.To enhance the utility of these models, future research should focus on integrating ex-post impact evaluations and improving data collection to ensure accurate, context-specific inputs. Additionally, there is a need for better alignment between modelling results and policymaking processes to enhance the adoption and legitimacy among decision-makers of these tools.Overall, while current models provide valuable insights for planning and investment in health systems, addressing these limitations will be crucial for maximising their effectiveness and ensuring better-informed decision-making in health investments.
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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.