Added Value of Electronic Immunization Registries in Low- and Middle-Income Countries: Observational Case Study in Tanzania
Notice bibliographique
Résumé
BACKGROUND: There is growing interest and investment in electronic immunization registries (EIRs) in low- and middle-income countries. EIRs provide ready access to patient- and aggregate-level service delivery data that can be used to improve patient care, identify spatiotemporal trends in vaccination coverage and dropout, inform resource allocation and program operations, and target quality improvement measures. The Government of Tanzania introduced the Tanzania Immunization Registry (TImR) in 2017, and the system has since been rolled out in 3736 facilities in 15 regions. OBJECTIVE: The aims of this study are to conceptualize the additional ways in which EIRs can add value to immunization programs (beyond measuring vaccine coverage) and assess the potential value-add using EIR data from Tanzania as a case study. METHODS: This study comprised 2 sequential phases. First, a comprehensive list of ways EIRs can potentially add value to immunization programs was developed through stakeholder interviews. Second, the added value was evaluated using descriptive and regression analyses of TImR data for a prioritized subset of program needs. RESULTS: The analysis areas prioritized through stakeholder interviews were population movement, missed opportunities for vaccination (MOVs), continuum of care, and continuous quality improvement. The included TImR data comprised 958,870 visits for 559,542 patients from 2359 health facilities. Our analyses revealed that few patients sought care outside their assigned facility (44,733/810,568, 5.52% of applicable visits); however, this varied by region; facility urbanicity, type, ownership, patient volume, and duration of TImR system use; density of facilities in the immediate area; and patient age. Analyses further showed that MOVs were highest among children aged <12 months (215,576/831,018, 25.94% of visits included an MOV and were applicable visits); however, there were few significant differences based on other individual or facility characteristics. Nearly half (133,337/294,464, 45.28%) of the children aged 12 to 35 months were fully vaccinated or had received all doses except measles-containing vaccine-1 of the 14-dose under-12-month schedule (ie, through measles-containing vaccine-1), and facility and patient characteristics associated with dropout varied by vaccine. The continuous quality improvement analysis showed that most quality issues (eg, MOVs) were concentrated in <10% of facilities, indicating the potential for EIRs to target quality improvement efforts. CONCLUSIONS: EIRs have the potential to add value to immunization stakeholders at all levels of the health system. Individual-level electronic data can enable new analyses to understand service delivery or care-seeking patterns, potential risk factors for underimmunization, and where challenges occur. However, to achieve this potential, country programs need to leverage and strengthen the capacity to collect, analyze, interpret, and act on the data. As EIRs are introduced and scaled in low- and middle-income countries, implementers and researchers should continue to share real-world examples and build an evidence base for how EIRs can add value to immunization programs, particularly for innovative uses.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».