Evidence map›Paper›PMID 34308238›Full record

ArticleThe East African health research journal2020

Assessment of Integrated Disease Surveillance Data Uptake in Community Health Systems within Nairobi County, Kenya.

Athanasio Japheth Omondi, Otieno George Ochieng, Khayo Eliud, Alison Yoos, Muli Rafael Kavilo

Abstract read
In one paragraph

Article in The East African health research journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Pooled it
  2. Article
  3. Advance Warning and Response Systems in Kenya: A Scoping Review.medRxiv : the preprint server for health sciences · 2025
    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

5 authors.

Athanasio Japheth OmondiDepartment of Health Management and Informatics, School of Public Health, Kenyatta University.
Otieno George OchiengDepartment of Health Management and Informatics, School of Public Health, Kenyatta University.
Khayo EliudSchool of Economics, University of Nairobi.
Alison YoosImproving Public Health Management for Action (IMPACT).
Muli Rafael KaviloNairobi City County, Department of Integrated Disease Surveillance Nairobi City.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundKenya has since independence struggled to restructure its health system to provide services to its entire population especially in outbreak responses. The last decade has seen the country witness disease outbreaks across the country i.e. Rift Valley fever in June 2018, and Chikungunya and Dengue fever in Mombasa in February 2018. This exposed the country's lack of preparedness in handling outbreaks at grass root level. Outbreak incidences tend to prevail at community level before a public health action is established, with the situation becoming dire in the lower tier health facilities.

objectiveThe purpose of the study was to assess the uptake of Integrated Disease Surveillance Response (IDSR) health data and utilisation at community level health systems in the six sub counties within Nairobi County of Kenya. METHODOLOGY: The study used cross-sectional descriptive research design on a target population of 1840 community health workers. The study used Yamane formula to calculate the sample size of 371 respondents, selected using stratified sampling and simple random sampling methods. The logistic regression model was used to assess the benefits of Integrated Data Surveillance and Response data in health facilities across Nairobi County. Data was collected using questionnaires, analysis done using Statistical Packages for Social Sciences, and findings presented in form of tables and bar graphs.

resultsThe study had 315 questionnaires were duly filled and returned, representing 85% response rate. The findings showed that 268(85%) Healthcare Workers lacked training on using disease surveillance data; 236(75%) cited lack of tools for disease surveillance in facilities, while 173(55%)cited lack of timely IDSR data as hindrance to IDSR data uptake. The regression findings showed that training of healthcare workers on IDSR, installation of disease surveillance system tools, and timely collection and dissemination of surveillance data increases the likelihood of IDSR data uptake in community health facilities.

conclusionThe study concluded that IDSR system tools should be installed in community health facilities across the six sub counties in Nairobi County. Training should be emphasised to ensure all health care workers have the required skills to use the IDSR data. There is need to ensure IDSR data is collected and disseminated on time to make it available for interpretation and use by health care workers in their respective facilities.

Identifiers

PMID34308238
PMCPMC8279160

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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.