Evidence map›Paper›PMID 29661245›Full record

ArticleMalaria journal2018

Socioeconomic health inequality in malaria indicators in rural western Kenya: evidence from a household malaria survey on burden and care-seeking behaviour.

Vincent Were, Ann M Buff, Meghna Desai, Simon Kariuki, Aaron Samuels, Feiko O Ter Kuile, Penelope A Phillips-Howard, S Patrick Kachur, Louis Niessen

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In one paragraph

Article in Malaria journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 2 pooled it
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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

32 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

9 authors.

Vincent WereCentre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya. vincentwere@gmail.com.ORCID http://orcid.org/0000-0002-3554-795X
Ann M BuffMalaria Branch, Division of Parasitic Diseases and Malaria,Center for Global Health, Centers for Disease Control and Prevention, Atlanta, USA.
Meghna DesaiMalaria Branch, Division of Parasitic Diseases and Malaria,Center for Global Health, Centers for Disease Control and Prevention, Atlanta, USA.
Simon KariukiCentre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.
Aaron SamuelsMalaria Branch, Division of Parasitic Diseases and Malaria,Center for Global Health, Centers for Disease Control and Prevention, Atlanta, USA.
Feiko O Ter KuileLiverpool School of Tropical Medicine, Liverpool, UK.
Penelope A Phillips-HowardLiverpool School of Tropical Medicine, Liverpool, UK.
S Patrick KachurMalaria Branch, Division of Parasitic Diseases and Malaria,Center for Global Health, Centers for Disease Control and Prevention, Atlanta, USA.
Louis NiessenLiverpool School of Tropical Medicine, Liverpool, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth inequality is a recognized barrier to achieving health-related development goals. Health-equality data are essential for evidence-based planning and assessing the effectiveness of initiatives to promote equity. Such data have been captured but have not always been analysed or used to manage programming. Health data were examined for microeconomic differences in malaria indices and associated malaria control initiatives in western Kenya.

methodsData was analysed from a malaria cross-sectional survey conducted in July 2012 among 2719 people in 1063 households in Siaya County, Kenya. Demographic factors, history of fever, malaria parasitaemia, malaria medication usage, insecticide-treated net (ITN) use and expenditure on malaria medications were collected. A composite socioeconomic status score was created using multiple correspondence analyses (MCA) of household assets; households were classified into wealth quintiles and dichotomized into poorest (lowest 3 quintiles; 60%) or less-poor (highest 2 quintiles; 40%). Prevalence rates were calculated using generalized linear modelling.

resultsOverall prevalence of malaria infection was 34.1%, with significantly higher prevalence in the poorest compared to less-poor households (37.5% versus 29.2%, adjusted prevalence ratio [aPR] 1.23; 95% CI = 1.08-1.41, p = 0.002). Care seeking (aPR = 0.95; 95% CI 0.87-1.04, p = 0.229), medication use (aPR = 0.94; 95% CI 0.87-1.00, p = 0.087) and ITN use (aPR = 0.96; 95% CI = 0.87-1.05, p = 0.397) were similar between households. Among all persons surveyed, 36.4% reported taking malaria medicines in the prior 2 weeks; 92% took artemether-lumefantrine, the recommended first-line malaria medication. In the poorest households, 4.9% used non-recommended medicines compared to 3.5% in less-poor (p = 0.332). Mean and standard deviation [SD] for expenditure on all malaria medications per person was US$0.38 [US$0.50]; the mean was US$0.35 [US$0.52] amongst the poorest households and US$0.40 [US$0.55] in less-poor households (p = 0.076). Expenditure on non-recommended malaria medicine was significantly higher in the poorest (mean US$1.36 [US$0.91]) compared to less-poor households (mean US$0.98 [US$0.80]; p = 0.039).

conclusionsInequalities in malaria infection and expenditures on potentially ineffective malaria medication between the poorest and less-poor households were evident in rural western Kenya. Findings highlight the benefits of using MCA to assess and monitor the health-equity impact of malaria prevention and control efforts at the microeconomic level.

Indexed as

Cost of IllnessHealth Status DisparitiesSocioeconomic FactorsAdolescentAdultAgedAged, 80 and overChildChild, PreschoolCross-Sectional StudiesFamily CharacteristicsFemaleHumansInfantInfant, NewbornKenyaInequalitiesKenyaMalariaMedicationSocioeconomic

Identifiers

PMID29661245
PMCPMC5902919

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.