🟠 Moderate Evidence
A systematic review published in BMJ Global Health has examined how rigorously decision-analytic models (DAMs) have been applied to economic evaluations of community health worker (CHW) programmes worldwide. The review identified 37 studies using economic models to project the long-term cost-effectiveness of CHW interventions, finding that 84% concluded these programmes were cost-effective—yet revealing substantial methodological gaps that may compromise the reliability of policy decisions built on this evidence.
Key takeaways
- 84% of economic evaluations concluded CHW-led interventions were cost-effective, but only 67% met quality standards on average
- Decision trees (32% of studies) and Markov models (30%) dominated, with limited use of advanced methods in low-income settings where CHWs are deployed most
- Model validation was inadequate in most studies, and structural uncertainty was rarely explored—raising questions about comparability across countries
- Low-income countries conducted fewer evaluations despite having the highest burden of CHW deployment; high-income studies used sophisticated models but narrower scopes
Study at a Glance
| Source | BMJ Global Health |
| Study type | Systematic Review of Economic Evaluations |
| Sample size | 37 studies (1990–June 2025) |
| Population | Full economic evaluations of CHW-led or CHW-integrated health interventions |
| Country | Global (multi-country); majority in low- and middle-income countries |
Model Types and Quality Gaps in CHW Economic Evaluations
Distribution of decision-analytic models and methodological quality scores across 37 studies, BMJ Global Health 2025
Source: BMJ Global Health systematic review, 2025 | Georgian Medical Journal News
The Case for CHWs Remains Strong—But Based on Patchy Evidence
Community health workers are among the most cost-effective tools in global health, particularly in settings with limited physician capacity. The BMJ Global Health review confirms this intuition: across all 37 studies analysed, economic models consistently showed that CHW-led care delivery generated favourable incremental cost-effectiveness ratios—meaning more health gained per dollar spent compared to standard care. This consistency is reassuring for policymakers in low-resource settings that are expanding CHW programmes as part of primary healthcare strengthening.
However, the systematic review also exposes a critical vulnerability in the evidence base. According to the authors’ assessment using the Philips checklist for model quality, only 67% of studies met methodological standards. This means one in three economic evaluations used to justify CHW investments had significant flaws in their analytic structure, data sources, or validation procedures. The implications are substantial: if a policymaker scales a CHW programme based on a cost-effectiveness estimate from a poorly validated model, the actual health and financial outcomes may diverge sharply from projections.
The review identified recurring quality gaps across the field. Most critically, model validation—the process of testing whether a model’s predictions match real-world outcomes—was inadequate in the majority of studies. According to the BMJ Global Health report, few studies examined structural uncertainty, meaning analysts rarely tested whether different model architectures (decision trees vs. Markov models, for example) would yield different conclusions about cost-effectiveness. This is especially concerning because the choice of model type is often driven by data availability rather than biological plausibility—a risk factor for misleading estimates.
A Mismatch Between Where CHWs Are Deployed and Where They Are Studied
The geographic distribution of economic evaluations reveals a troubling gap. Most of the 37 studies were conducted in low- and middle-income countries, reflecting the reality that CHWs are deployed extensively in these settings. Yet few evaluations came from the poorest countries—low-income nations with the highest disease burden and greatest dependence on CHWs. This pattern is driven partly by capacity constraints: building economic models requires epidemiological data, health-system cost data, and analytic expertise that are scarcer in low-income settings.
The consequence is a vicious cycle. In low-income countries, data scarcity forces modellers to adopt simpler structures (such as decision trees) that may not capture long-term health impacts or disease dynamics adequately. Meanwhile, high-income country evaluations often employ more sophisticated methods—microsimulation, dynamic transmission models—but frequently focus on narrow intervention scopes or non-generalizable populations. This heterogeneity in model quality and structure across income groups undermines comparability: a cost-effectiveness estimate from a well-resourced high-income study may not translate to a low-income setting where CHW roles, health system contexts, and patient populations differ fundamentally.
Validation Gaps Threaten Policy Confidence
The systematic review highlights that most studies failed to validate their models against empirical data. Model validation involves comparing predicted outcomes (from the model) with observed outcomes (from randomized trials or real-world data collection). Without this step, there is no evidence that the model’s mathematical logic mirrors clinical reality. The review found that exploration of structural uncertainty was also limited—meaning analysts did not systematically test whether changing the model’s underlying structure (e.g., time horizon, transition probabilities) would reverse the cost-effectiveness conclusion.
This validation gap is not merely academic. A decision-analytic model that projects a CHW intervention as cost-effective without validation may mislead governments into allocating scarce health budgets to programmes that, in practice, deliver less value than promised. The risk is heightened when models are exported across settings: a validated model developed in one country may be inappropriately adapted for use in another without re-validation, especially if epidemiology or health system structure differs.
84% of CHW economic evaluations concluded cost-effectiveness, yet the mean methodological quality score was 67%, with substantial gaps in validation and exploration of structural uncertainty that could affect policy confidence.
— BMJ Global Health systematic review (2025)
Towards More Rigorous and Comparable Evidence
The systematic review implies that although the case for CHW cost-effectiveness is compelling, the strength of that case depends on methodological rigour that is not yet standard across the field. Moving forward, the authors recommend that CHW economic evaluations adopt uniform reporting standards (such as the CHEERS checklist for economic evaluations), invest in prospective data collection to validate model predictions, and explore structural uncertainty systematically to test robustness of conclusions.
For countries considering or scaling CHW programmes, this review signals caution without pessimism. The weight of evidence supports CHW cost-effectiveness, but policymakers should critically appraise the methodological quality of the economic evaluations informing their decisions. Commissioning new, locally-relevant economic evaluations—rather than importing cost-effectiveness estimates from other settings—may be worthwhile, especially in low-income countries where epidemiology and health system constraints are unique. Building research capacity in low-income countries to conduct rigorous economic evaluations of CHW programmes is therefore not a luxury but a prerequisite for evidence-informed scale-up.
Several health policy initiatives and global health programmes are beginning to prioritize standardized economic evaluation methods. The clinical updates in primary care will benefit from stronger, locally-validated evidence on CHW effectiveness and cost-efficiency.
What this means
Frequently asked questions
What is a decision-analytic model in health economics?
A decision-analytic model is a mathematical framework that projects long-term health and economic outcomes of an intervention by combining data on disease progression, treatment effectiveness, costs, and patient preferences. Models like decision trees (for short-term decisions) and Markov models (for chronic conditions over years) are used when randomized trials cannot measure all relevant outcomes or when a programme must be evaluated before deployment at scale.
Why does methodological quality matter if most studies reach the same conclusion (CHWs are cost-effective)?
If multiple low-quality studies reach the same conclusion, it may reflect shared methodological biases rather than true evidence of effectiveness. A poorly validated model may systematically overestimate cost-effectiveness by, for example, inflating health benefits or underestimating costs. High-quality validation would reveal whether the conclusion holds under alternative assumptions.
How can low-income countries improve economic evaluations of CHW programmes despite data constraints?
Low-income countries can prioritize prospective data collection alongside CHW programme implementation, partner with higher-income research institutions to build capacity, use simpler (but still rigorous) model structures suited to available data, and share data across countries to strengthen regional evidence bases. International funding mechanisms should support these efforts to ensure locally-relevant, validated cost-effectiveness evidence.
The findings from this BMJ Global Health systematic review indicate that while community health worker programmes remain economically justified, the field must now focus on strengthening the methodological foundations of that evidence. As countries scale CHW initiatives as part of their path to universal health coverage, investing in rigorous, validated economic evaluations—particularly in low-income settings—will ensure that policy decisions are informed by the most reliable evidence available.
Source: Decision-analytic models in the economic evaluation of community health worker programmes globally: a systematic review, BMJ Global Health, 2025
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Medically reviewed by Prof. Giorgi Pkhakadze, MD, MPH, PhD. Spotted an error? Contact the editorial team.
