Evidence map›Paper›PMID 34987048›Full record

ArticleBMJ open2022

Cross-sectional study to predict subnational levels of health workers' knowledge about severe malaria treatment in Kenya.

Beatrice Machini, Thomas No Achia, Jacqueline Chesang, Beatrice Amboko, Paul Mwaniki, Hillary Kipruto

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.3field-weighted citation impact, top 13% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

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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

6 authors at 4 institutions in 3 countries.

Beatrice MachiniDepartment of Public and Global Health, University of Nairobi, Nairobi, Kenya bkemunto2002@gmail.com.ORCID 0000-0001-6457-6541
Thomas No AchiaDepartment of Public and Global Health, University of Nairobi, Nairobi, Kenya.
Jacqueline ChesangDepartment of Public and Global Health, University of Nairobi, Nairobi, Kenya.
Beatrice AmbokoHealth Services Unit, KEMRI - Wellcome Trust Research Institute, Nairobi, Kenya.
Paul MwanikiHealth Services Unit, KEMRI - Wellcome Trust Research Institute, Nairobi, Kenya.
Hillary KiprutoWorld Health Organization, Regional Office for Africa, Eastern and Southern Africa, Harare, Zimbabwe.
Kenya Medical Research Institute · KEUniversity of Nairobi · KEUniversity of Zululand · ZAWorld Health Organization - Zimbabwe · ZW

Funding

World Health Organization 001
6 · The paper itself

Abstract

objectivesThis study applied a Bayesian hierarchical ecological spatial model beyond predictor analysis to test for the best fitting spatial effects model to predict subnational levels of health workers' knowledge of severe malaria treatment policy, artesunate dosing, and preparation.

settingCounty referral government and major faith-based hospitals across 47 counties in Kenya in 2019. DESIGN AND

participantsA secondary analysis of cross-sectional survey data from 345 health workers across 89 hospitals with inpatient departments who were randomly selected and interviewed. OUTCOME MEASURES: Three ordinal outcome variables for severe malaria treatment policy, artesunate dose and preparation were considered, while 12 individual and contextual predictors were included in the spatial models.

resultsA third of the health workers had high knowledge levels on artesunate treatment policy; almost three-quarters had high knowledge levels on artesunate dosing and preparation. The likelihood of having high knowledge on severe malaria treatment policy was lower among nurses relative to clinicians (adjusted OR (aOR)=0.48, 95% CI 0.25 to 0.87), health workers older than 30 years were 61% less likely to have high knowledge about dosing compared with younger health workers (aOR=0.39, 95% CI 0.22 to 0.67), while health workers exposed to artesunate posters had 2.4-fold higher odds of higher knowledge about dosing compared with non-exposed health workers (aOR=2.38, 95% CI 1.22 to 4.74). The best model fitted with spatially structured random effects and spatial variations of the knowledge level across the 47 counties exhibited neighbourhood influence.

conclusionsKnowledge of severe malaria treatment policies is not adequately and optimally available among health workers across Kenya. The factors associated with the health workers' level of knowledge were cadre, age and exposure to artesunate posters. The spatial maps provided subnational estimates of knowledge levels for focused interventions.

Indexed as

AntimalarialsMalariaArtesunateBayes TheoremCross-Sectional StudiesHumansKenyaAntimalarialsArtesunatepublic healthquality in health carestatistics & research methods

Identifiers

PMID34987048
PMCPMC8734019
OpenAlexW4205173792

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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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 Socratic graph.