Evidence map›Paper›PMID 34411136›Full record

ArticlePloS one2021

Improving routine immunization data quality using daily short message system reporting platform: An experience from Nasarawa state, Nigeria.

Adekunle Akerele, Belinda Uba, Matthew Aduloju, Sulaiman Etamesor, Jamila A Umar, Olorunsogo Bidemi Adeoye, Ameh Enyojo, Friday Josiah, Esther Ayandipo, Itse Olaoye and 8 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.2field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 12 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors at 3 institutions in 2 countries.

Adekunle AkereleAfrican Field Epidemiology Network, Abuja, Nigeria.ORCID 0000-0002-5951-5389
Belinda UbaAfrican Field Epidemiology Network, Abuja, Nigeria.
Matthew AdulojuAfrican Field Epidemiology Network, Abuja, Nigeria.
Sulaiman EtamesorNational Primary Health Care Development Agency, Abuja, Nigeria.
Jamila A UmarNational Primary Health Care Development Agency, Abuja, Nigeria.
Olorunsogo Bidemi AdeoyeAfrican Field Epidemiology Network, Abuja, Nigeria.ORCID 0000-0002-9239-4515
Ameh EnyojoAfrican Field Epidemiology Network, Abuja, Nigeria.
Friday JosiahAfrican Field Epidemiology Network, Abuja, Nigeria.ORCID 0000-0002-9066-4374
Esther AyandipoAfrican Field Epidemiology Network, Abuja, Nigeria.
Itse OlaoyeAfrican Field Epidemiology Network, Abuja, Nigeria.
Oluwasegun Joel AdegokeCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Sampson SidneySydani Initiative for International Development, Abuja, Nigeria.ORCID 0000-0001-5303-5475
Murtala BaganaNational Primary Health Care Development Agency, Abuja, Nigeria.
Okposen BasseyNational Primary Health Care Development Agency, Abuja, Nigeria.
Margherita E GhiselliCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID 0000-0001-5611-4777
Waziri NdadilnasiyaAfrican Field Epidemiology Network, Abuja, Nigeria.
Omotayo BoluCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Faisal ShuaibNational Primary Health Care Development Agency, Abuja, Nigeria.
African Field Epidemiology Network · NGNational Primary Health Care Development Agency · NGCenters for Disease Control and Prevention · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Routine immunization (RI) delivery was declared a public health concern in Nigeria in 2017 because of persistently low immunization coverage rates reported in independent surveys. However, administrative coverage rates remain high, suggesting serious data quality issues. We posit that a shorter timespan between service provision and data reporting can improve the monitoring of RI data, and developed a short message system (SMS) text reporting strategy to generate daily RI data points from health facilities (HFs). The goal was to assess whether daily data collection produces complete, reliable and internally consistent data points. The SMS reporting platform was piloted between December 2017 and April 2018 in two Local Government Areas (LGAs, equivalent to districts) of Nasarawa state, Nigeria. The 145 healthcare workers from 55 HFs received one mobile phone and pre-configured SIM card, and were trained to send data through predefined codes. Healthcare workers compiled the data after each vaccination session and transmitted them via SMS. We analyzed completeness, number of weekly sessions, and supportive supervision conducted. During the pilot phase, we received data from 85% (n = 47) of the 55 HFs. We expected 66 fixed-post sessions and 30 outreach sessions per week, but received data for 33 fixed-post and 8 outreach weekly session on average. More HFs reported on Tuesdays compared to other days of the week. When assessing internal consistency, we observed that the reported number of children vaccinated was sometimes higher than the number of doses available from opening a given number of vaccine vials. When found, this discrepancy was noted for all antigens during fixed-post and outreach vaccination sessions. Despite these initial discrepancies, transmitting RI data sessions via texting is feasible and can provide real-time updates to the performance of the RI services at the HF level.

Indexed as

Data AccuracyCell PhoneChildHealth FacilitiesHealth PersonnelHumansImmunizationImmunization ProgramsNigeriaSurveys and QuestionnairesText MessagingVaccinationVaccinesVaccines

Identifiers

PMID34411136
PMCPMC8376034
OpenAlexW3195266199

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.