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Record W2791761534 · doi:10.2196/publichealth.7026

Effect of Mobile Phone Text Message Reminders on Routine Immunization Uptake in Pakistan: Randomized Controlled Trial

2018· article· en· W2791761534 on OpenAlexaffvenue
Abdul Momin Kazi, Murtaza Ali, Khurram Zubair, Hussain Kalimuddin, Abdul Nafey Kazi, Saleem Perwaiz Iqbal, Jean‐Paul Collet, Asad Ali

Bibliographic record

VenueJMIR Public Health and Surveillance · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of British Columbia
FundersWorld Health Organization
KeywordsMobile phoneText messageText messagingRandomized controlled trialPhoneMedicineImmunizationComputer scienceInternet privacyMedical emergencyTelecommunicationsComputer networkInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Improved routine immunization (RI) coverage is recommended as the priority public health strategy to decrease vaccine-preventable diseases and eradicate polio in Pakistan and worldwide. OBJECTIVE: The objective of this study was to ascertain whether customized, automated, one-way text messaging (short message service, SMS) reminders delivered to caregivers via mobile phones when a child is due for an RI visit can improve vaccination uptake and timelines in Pakistan. METHODS: This was a randomized controlled trial, conducted in an urban squatter settlement area of Karachi, Pakistan. Infants less than 2 weeks of age with at least one family member who had a valid mobile phone connection and was comfortable receiving and reading SMS text messages were included. Participants were randomized to the intervention (standard care + one-way SMS reminder) or control (standard care) groups. The primary outcome was to compare the proportion of children immunized up to date at 18 weeks of age. Vaccine given at 6, 10, and 14 weeks schedule includes DPT-Hep-B-Hib vaccine (ie, diphtheria, pertussis, and tetanus; hepatitis B; and Haemophilus influenza type b) and oral poliovirus vaccine (OPV). Data were analyzed using chi-square tests of independence and tested for both per protocol (PP) and intention-to-treat (ITT) analyses. RESULTS: Out of those approached, 84.3% (300/356) of the participants were eligible for enrollment and 94.1% (318/338) of the participants had a working mobile phone. Only children in the PP analyses, who received an SMS reminder for vaccine uptake at 6 weeks visit, showed a statistically significant difference (96.0%, 86/90 vs 86.4%, 102/118; P=.03).The immunization coverage was consistently higher in the intervention group according to ITT analyses at the 6 weeks scheduled visit (76.0% vs 71.3%, P=.36). The 10 weeks scheduled visit (58.7% vs 52.7%, P=.30) and the 14 weeks scheduled visit (31.3% vs 26.0%, P=.31), however, were not statistically significant. CONCLUSIONS: Automated simple one-way SMS reminders in local languages might be feasible for improving routine vaccination coverage. Whether one-way SMS reminders alone can have a strong impact on parental attitudes and behavior for improvement of RI coverage and timeliness needs to be further evaluated by better-powered studies and by comparing different types and content of text messages in low-and middle-income countries (LMICs). TRIAL REGISTRATION: ClinicalTrials.gov NCT01859546; https://clinicaltrials.gov/ct2/show/NCT01859546 (Archived by WebCite at http://www.webcitation.org/6xFr57AOc).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.021
GPT teacher head0.411
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations98
Published2018
Admission routes2
Has abstractyes

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