Evidence map›Paper›PMID 31138216›Full record

ArticleMalaria journal2019

A subnational profiling analysis reveals regional differences as the main predictor of ITN ownership and use in Nigeria.

Andrew Andrada, Samantha Herrera, Uwem Inyang, Audu Bala Mohammed, Perpetua Uhomoibhi, Yazoumé Yé

Open access · goldAbstract read
In one paragraph

Article in Malaria journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
6.0field-weighted citation impact, top 4% 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

15 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Determinants of Malaria Insecticide-Treated Nets Use Among Women of Reproductive Age in Nigeria.Nigerian medical journal : journal of the Nigeria Medical Association
    Review
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 2 countries.

Andrew AndradaMEASURE Evaluation, ICF, 530 Gaither Road, Suite 500, Rockville, MD, 20850, USA. Andrew.Andrada@icf.com.ORCID http://orcid.org/0000-0001-8639-3914
Samantha HerreraMEASURE Evaluation, ICF, 530 Gaither Road, Suite 500, Rockville, MD, 20850, USA.
Uwem InyangPresident's Malaria Initiative/United States Agency for International Development, Plot 1075 Diplomatic Drive, Central District Area, Abuja, Nigeria.
Audu Bala MohammedNational Malaria Elimination Programme, Federal Ministry of Health, 1st Floor, Abia Plaza, 1 Avenue, Cadastral Zone A0, Central Business District, Abuja, Nigeria.
Perpetua UhomoibhiNational Malaria Elimination Programme, Federal Ministry of Health, 1st Floor, Abia Plaza, 1 Avenue, Cadastral Zone A0, Central Business District, Abuja, Nigeria.
Yazoumé YéMEASURE Evaluation, ICF, 530 Gaither Road, Suite 500, Rockville, MD, 20850, USA.
Federal Ministry of Health · NGICF International (United States) · USSave the Children · USUnited States Agency for International Development · US

Funding

United States Agency for International Development (US) AIDOAA-L-14-00004
6 · The paper itself

Abstract

backgroundTo reduce the malaria burden in Nigeria, the country is scaling up prevention and treatment interventions, especially household ownership and use of insecticide-treated nets (ITNs). Nevertheless, large gaps remain to achieve the goals of the National Malaria Strategic Plan 2014-2020 of universal access to ITNs and their increased use. To inform the targeting of intervention strategies and to maximize impact, the authors conducted a sub-national profiling of household ITN ownership and use in the general population to identify key predictors of ITN ownership and use, and the sub-groups that are at higher risk of low ITN coverage and use.

methodsThe authors conducted a secondary analysis of data from the 2015 Nigeria Malaria Indicator Survey. Using the Chi square automatic interaction detector (CHAID) and multiple logistic regression analysis, the authors examined the key predictors of ITN ownership and use in the general population throughout Nigeria.

resultsThe CHAID models identified region of the country as the best predictor of household ownership of at least one ITN and its use in the general population, with higher ownership and use observed in the northern regions. The odds of a household owning an ITN were five times greater in the North West region compared with the North Central region (odds ratio [OR] = 5.47, 95% confidence interval [CI] 4.46-6.72, p < 0.001). The odds of ITN use were two times greater for those living in the North West region compared with the North Central region (OR = 2.04, 95% CI 1.73-2.41, p < 0.001). Other significant predictors were household size, head of household education level, household wealth quintile, and place of residence. The CHAID gain index results identified households in the South West, North Central and South Central regions with low ITN ownership, and the general population in the South South, South East and North Central regions with low ITN use.

conclusionsThis study reveals regional differences in ITN ownership and use in Nigeria. Therefore, the findings from this analysis provide evidence that could inform the NMEP to better target future campaign and routine distribution of ITNs, to achieve universal access and increased use by 2020 in Nigeria.

Indexed as

Family CharacteristicsOwnershipCross-Sectional StudiesGeographyHumansInsecticide-Treated BednetsMalariaMosquito ControlNigeriaOdds RatioSurveys and QuestionnairesChi square automatic interaction detectionInsecticide-treated netsMalaria prevention and treatmentNet useNigeriaSub-Saharan Africa

Identifiers

PMID31138216
PMCPMC6540480
OpenAlexW2947435352

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.