Evidence map›Paper›PMID 32660509›Full record

ArticleBMC public health2020

Use of technology for public health surveillance reporting: opportunities, challenges and lessons learnt from Kenya.

Ian Njeru, David Kareko, Ngina Kisangau, Daniel Langat, Nzisa Liku, George Owiso, Samantha Dolan, Peter Rabinowitz, Daniel Macharia, Chinyere Ekechi and 1 more

Abstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Tropical medicine and infectious disease · 2026
    Article
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  5. Article
  6. Article
  7. Review
  8. Review
  9. Advance Warning and Response Systems in Kenya: A Scoping Review.medRxiv : the preprint server for health sciences · 2025
    Article
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  11. BMJ open · 2023
    Article
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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

11 authors.

Ian NjeruInternational Training and Education Centre for Health (I-TECH Kenya), Nairobi, Kenya. iannjeru2000@gmail.com.ORCID http://orcid.org/0000-0003-0610-3492
David KarekoMinistry of Health Kenya, Nairobi, Kenya.
Ngina KisangauMinistry of Health Kenya, Nairobi, Kenya.
Daniel LangatMinistry of Health Kenya, Nairobi, Kenya.
Nzisa LikuInternational Training and Education Centre for Health (I-TECH Kenya), Nairobi, Kenya.
George OwisoInternational Training and Education Centre for Health (I-TECH Kenya), Nairobi, Kenya.
Samantha DolanInternational Training and Education Centre for Health (I-TECH Kenya), Nairobi, Kenya.
Peter RabinowitzInternational Training and Education Centre for Health (I-TECH Kenya), Nairobi, Kenya.
Daniel MachariaDivision of Global Health Protection, Centers for Disease Control and Prevention, Nairobi, Kenya.
Chinyere EkechiDivision of Global Health Protection, Centers for Disease Control and Prevention, Nairobi, Kenya.
Marc-Alain WiddowsonDivision of Global Health Protection, Centers for Disease Control and Prevention, Nairobi, Kenya.

Funding

Centers for Disease Control and Prevention (US) Coag Number 1U2GGH00172-01
6 · The paper itself

Abstract

backgroundEffective public health surveillance systems are crucial for early detection and response to outbreaks. In 2016, Kenya transitioned its surveillance system from a standalone web-based surveillance system to the more sustainable and integrated District Health Information System 2 (DHIS2). As part of Global Health Security Agenda (GHSA) initiatives in Kenya, training on use of the new system was conducted among surveillance officers. We evaluated the surveillance indicators during the transition period in order to assess the impact of this training on surveillance metrics and identify challenges affecting reporting rates.

methodsFrom February to May 2017, we analysed surveillance data for 13 intervention and 13 comparison counties. An intervention county was defined as one that had received refresher training on DHIS2 while a comparison county was one that had not received training. We evaluated the impact of the training by analysing completeness and timeliness of reporting 15 weeks before and 12 weeks after the training. A chi-square test of independence was used to compare the reporting rates between the two groups. A structured questionnaire was administered to the training participants to assess the challenges affecting surveillance reporting.

resultsThe average completeness of reporting for the intervention counties increased from 45 to 62%, i.e. by 17 percentage points (95% CI 16.14-17.86) compared to an increase from 49 to 52% for the comparison group, i.e. by 3 percentage points (95% CI 2.23-3.77). The timeliness of reporting increased from 30 to 51%, i.e. by 21 percentage points (95% CI 20.16-21.84) for the intervention group, compared to an increase from 31 to 38% for the comparison group, i.e.by 7 percentage points (95% CI 6.27-7.73). Major challenges for the low reporting rates included lack of budget support from government, lack of airtime for reporting, health workers strike, health facilities not sending surveillance data, use of wrong denominator to calculate reporting rates and surveillance officers having other competing tasks.

conclusionsTraining plays an important role in improving public health surveillance reporting. However, to improve surveillance reporting rates to the desired national targets, other challenges affecting reporting must be identified and addressed accordingly.

Indexed as

Health FacilitiesHealth Information SystemsHealth PersonnelHumansKenyaPublic Health SurveillanceTime FactorsComparison groupIntervention groupPublic health surveillanceReporting rates

Identifiers

PMID32660509
PMCPMC7359619

What OpenQuestion holds

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LicenceCC BY
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.