Evidence map›Paper›PMID 37365602›Full record

ArticleMalaria journal2023

Predictors of mosquito bed net use among children under-fives in Ghana: a multilevel analysis of the 2019 malaria indicator survey.

Justice Moses K Aheto, Rahmatu Babah, Maxwell Kwame Dzokoto, Williams Kwarah, Yakubu Alhassan

Open access · goldAbstract read
In one paragraph

Article in Malaria journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
3.5field-weighted citation impact, top 7% 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. 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 at 1 institution in 1 country.

Justice Moses K AhetoDepartment of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, P. O. Box LG13, Legon-Accra, Ghana. justiceaheto@yahoo.com.
Rahmatu BabahDepartment of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, P. O. Box LG13, Legon-Accra, Ghana.
Maxwell Kwame DzokotoDepartment of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, P. O. Box LG13, Legon-Accra, Ghana.
Williams KwarahDepartment of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, P. O. Box LG13, Legon-Accra, Ghana.
Yakubu AlhassanDepartment of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, P. O. Box LG13, Legon-Accra, Ghana.
University of Ghana · GH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMorbidities and mortalities due to malaria can be prevented by the use of insecticide-treated mosquito bed nets (ITN), which has been proven for malaria control and elimination. The purpose of this study was to assess the critical factors that predict the use of ITN among children under-fives in Ghana.

methodsThe study utilized data from the 2019 Ghana Malaria Indicator Survey (GMIS). The outcome variable was mosquito bed net use among children under-fives. To determine critical factors that independently predict ITN use, multilevel multivariable logistic regression was employed using Stata version 16. Odds ratios and associated 95% confidence intervals and p-values were reported. A p < 0.05 was used to declare statistical significance.

resultsThe overall prevalence of ITN usage was 57.4%. Utilization of bed nets was 66.6% in the rural areas and 43.5% in the urban areas, was highest in the Upper West region (80.6%) even when stratified to rural (82.9%) and urban areas (70.3%) whilst Greater Accra region (30.5%, rural = 41.7%, urban = 28.9%) had the least. The community level multilevel analysis showed that bed net utilization was higher among children in rural areas [AOR = 1.99, 95% CI 1.32-3.01, p = 0.001] and in household with wooden wall materials [AOR = 3.29, 95% CI 1.15-9.40, p = 0.027]. Bed net utilization was however, less for households with 3 + children under-five [AOR = 0.29, 95% CI 0.19-0.46, p < 0.001), 4 years old (AOR = 0.66, 95% CI 00.48-00.92, p = 0.014], without universal access to bed net [AOR = 0.52, 95% CI 0.37-0.73, p < 0.001], those in the Greater Accra [AOR = 0.26, 95% CI 0.13-0.51, p < 0.001], Eastern [AOR = 0.47, 95% CI 0.23-0.95, p = 0.036], Northern [AOR = 0.42, 95% CI 0.20-0.88, p = 0.022], middle [AOR = 0.57, 95% CI 0.35-0.94, p = 0.026] and rich/richest [AOR = 0.51, 95% CI 0.29-0.92, p = 0.025] household wealth quintile. Substantial unobserved household and community level differences in bed net use were found.

conclusionThis study demonstrates the need to intensify promotion of ITN use to those in urban areas, Greater Accra, Eastern and Northern regions, houses without wooden wall materials, middle and rich/richest households. Interventions should be targeted at older children and households with more under-five children and to ensure full access and use of ITNs among all children under-fives in each household as part of the overall goal of achieving the health-related SDGs.

Indexed as

Insecticide-Treated BednetsMalariaAdolescentAnimalsChildChild, PreschoolCross-Sectional StudiesGhanaHumansMosquito ControlMultilevel AnalysisInsecticide treated netITNMalariaMosquitoPredictors, GhanaSub-Saharan AfricaUnder-fiveUnder-five malariaUtilization

Identifiers

PMID37365602
PMCPMC10291786
OpenAlexW4382138097

What OpenQuestion holds

Textmetadata
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.