Consumer reported digital immunization records : feasibility, applicability, and public health utility in the Canadian context
Notice bibliographique
Résumé
Background: Vaccination is one of the most effective public health interventions of all time. Public health programs collect data on Vaccination Coverage (VC) to determine levels of protection and guide resource allocation for further vaccination campaigns but VC data completeness and utility is a challenge in many settings. Self-report is a sensitive and relatively specific indicator of vaccination status but incorporation into VC analyses is limited. Mobile technologies can enhance Immunization Information Systems, not least by facilitating bi-directional communications with individuals. This permits collection of self-reported immunization records and a channel to deliver reminders and reliable information back to individuals. However, evaluations of consumer apps for immunization are still nascent, often small-scale, and conducted most in controlled research environments. \n \n \n \nAim: The overarching aim of this thesis is to shed light on the feasibility, public health utility and applicability of mHealth apps for recording, reporting, and encouraging immunization. \n \n \n \nMethods: Paper I was an ecological, quality-assurance study describing use of Pan- Canadian mobile immunization app for parental reporting of children’s primary immunization series in Ottawa, Ontario, Canada. Paper II was a single cohort interrupted time series analysis examining the impact of the COVID-19 pandemic in Canada on uptake of a Pan-Canadian mobile immunization app as well as parentally reported pneumococcal series completion rates at the child’s 13-months of age. Paper III was a cross-sectional study describing the characteristics of family/parental characteristics and their association of reporting vaccinating their children against influenza among Pan-Canadian mobile immunization app users in the 2018/2019 influenza season. Paper IV was a systematic review and meta-analysis examining effectiveness of digital push interventions compared to non-digital interventions at increasing vaccine uptake and series completion. \n \n \n \nResults: Feasibility and Acceptability: The first successful transmission of records occurred April 27, 2015 (Paper I). There were 63,833 pediatric records and 11,381 unique parent-child dyads that met inclusion criteria, respectively in Papers II and III. The onset of COVID-19 restrictions was associated with an abrupt and continued decline in enrollment of children in the app compared to expected values (Paper II). Public Health Utility: 530 (20%) of children were less than 12 months old when their record was first submitted via the app (Paper I). The onset of COVID-19 restrictions was associated with an initial increase in self-reported completion of pneumococcal series, followed by a modest decrease leading to a net effect of -20%, compared to expected values (Paper II). Influenza vaccination was reported for 32.3% (3,675/11,381) of children and 42.0% (4,788/11,381) of parents. Parents receiving the seasonal influenza vaccine was the most strongly associated characteristic with pediatric influenza vaccination (OR 17.05, 95% CI 15.08, 19.28) compared to parents who did not report being vaccinated against influenza that season (Paper III). \n \n \n \nApplicability: When comparing digital push with non-digital interventions, patients had 1.18 (95% CI 1.11, 1.25) the odds of receiving vaccination or series completion. Analyses had high statistical heterogeneity, but risk of bias was low (Paper IV). \n \n \n \nConclusions: Through four studies, this thesis provided supportive evidence on the potential to mobile apps to enhance IIS. Successful transmission of self-reported immunization records to public health via mobile app was demonstrated to be feasible and acceptable. The onset of COVID-19 was associated with decreased app use and reported pediatric pneumococcal series completion. Parents receiving the seasonal influenza vaccine was associated with reporting their children as immunized. Receiving digital push notifications increases the odds of vaccine uptake and series completion. These studies are subject to limitations but show potential for mHealth apps to facilitate self-report of vaccination data, assess trends and associations in vaccine behavior and encourage immunization through push interventions.
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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 | 0,003 | 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 | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 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 ».