The costs of digital health interventions to improve immunization and data in low- and middle- income countries: a multi-country study (Preprint)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».