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01.12.2018 | Research | Ausgabe 1/2018 Open Access

Malaria Journal 1/2018

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

Zeitschrift:
Malaria Journal > Ausgabe 1/2018
Autoren:
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

Background

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.

Methods

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.

Results

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).

Conclusions

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.
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