The costs of digital health interventions to improve immunization and data in low- and middle- income countries: a multi-country study (Preprint)
Bibliographic record
Abstract
BACKGROUND Digital health interventions, such as electronic immunization registries (eIR) and electronic Logistic Management Information Systems (eLMIS), have the potential to significantly improve immunization data management and vaccine logistics in low- and middle-income countries (LMICs). Despite their growing adoption, there is limited evidence on the financial and economic costs associated with their implementation compared to traditional paper-based systems. OBJECTIVE We aimed to measure the costs of implementing and maintaining eIR and eLMIS systems in LMICs, and to estimate the affordability of their implementation as compared to the previous paper-based registries. METHODS The study was conducted across four countries: Guinea, Honduras, Rwanda, and Tanzania. A combination of primary and secondary data sources was used for the analysis. Expenditure information regarding the design, development and implementation of the tools was directly obtained from implementers and National Immunization Program offices in all countries. Primary survey data was collected to gauge the operational expenses of immunization information systems, both with and without electronic tools using an Activity Based Costing approach. The cost of immunization information system to the national level was then extrapolated and compared to national spending on immunization as a measure for affordability. RESULTS The total costs of designing, developing and deploying eIR and/or eLMIS were I$ 1.7, 5.4, 4.7 and 33 million in Guinea, Honduras, Rwanda and Tanzania respectively. Design costs were greatly affected by the degree of customization of the tool, whereas roll out costs were mostly driven by the costs of purchasing hardware and training of health workers. Overall, the implementation of the electronic systems was associated with higher costs in Honduras (I$ 535 per facility, 95% CI 441; 702) and Rwanda (I$ 278, 95%CI 75; 482), a cost reduction in Tanzania (I$ -1,770, 95%CI -2,990; -550) and no significant cost difference in Guinea. The percentage weight of the cost of managing data with the electronic systems over the total national immunization budgets was estimated at 8.6%, 1.1%, 3.7% and 1.8% for Honduras, Rwanda, Tanzania and Guinea, respectively CONCLUSIONS Digital health interventions such as eIR and eLMIS can potentially reduce costs and improve the efficiency of immunization data management and vaccine logistics in LMICs. However, the extent of cost savings is contingent upon the degree to which these digital systems replace traditional paper-based methods. Our study suggests that the economic impact of digital health solutions greatly depends on factors such as infrastructure, implementation, and the extent to which these technologies are integrated into existing healthcare systems. Careful planning and investment are essential to realizing the full economic potential of digital health in LMICs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".