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

Overview of attention for article published in Malaria Journal, April 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

policy
1 policy source
twitter
1 X user

Citations

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31 Dimensions

Readers on

mendeley
173 Mendeley
Title
Socioeconomic health inequality in malaria indicators in rural western Kenya: evidence from a household malaria survey on burden and care-seeking behaviour
Published in
Malaria Journal, April 2018
DOI 10.1186/s12936-018-2319-0
Pubmed ID
Authors

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

Abstract

Health 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. Data 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. Overall 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). Inequalities 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.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 173 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 173 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 33 19%
Researcher 23 13%
Student > Ph. D. Student 15 9%
Student > Doctoral Student 12 7%
Student > Bachelor 10 6%
Other 25 14%
Unknown 55 32%
Readers by discipline Count As %
Medicine and Dentistry 25 14%
Nursing and Health Professions 20 12%
Social Sciences 16 9%
Agricultural and Biological Sciences 13 8%
Biochemistry, Genetics and Molecular Biology 7 4%
Other 29 17%
Unknown 63 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 June 2019.
All research outputs
#7,041,787
of 23,043,346 outputs
Outputs from Malaria Journal
#2,156
of 5,605 outputs
Outputs of similar age
#111,684
of 296,868 outputs
Outputs of similar age from Malaria Journal
#48
of 116 outputs
Altmetric has tracked 23,043,346 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 5,605 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 296,868 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.