American Journal of Public Health Research
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American Journal of Public Health Research. 2019, 7(1), 27-32
DOI: 10.12691/ajphr-7-1-5
Open AccessArticle

Application of Principal Component Analysis to Assess Health Systems Capacity Using Cross Sectional Data in Rural Western Kenya

Maximila N. Wanzala1, , J. A Oloo1, Gordon Nguka1 and Vincent Were2

1Department of Public Health, Masinde Muliro University of Science and Technology

2Kenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya

Pub. Date: February 15, 2019

Cite this paper:
Maximila N. Wanzala, J. A Oloo, Gordon Nguka and Vincent Were. Application of Principal Component Analysis to Assess Health Systems Capacity Using Cross Sectional Data in Rural Western Kenya. American Journal of Public Health Research. 2019; 7(1):27-32. doi: 10.12691/ajphr-7-1-5

Abstract

Introduction: Strong health systems are essential platforms for accessible, quality health services, and population health and attainment of the Sustainable Development Goal (SDGs). Descriptive methods have been used to assess the health systems strength and impact, however, there is inadequate knowledge on methods of analyzing huge number of indices to provide systematic evidence that service readiness is improving or deteriorating over time. Methods: We utilized data from a cross section survey of 71 health facilities in Kakamega County of western Kenya. A total of 151 indices of the health system building blocks were reduced using Principal Component Analysis (PCA) model which generated factor weights for the individual indicators. These included indices from human resources, service delivery, infrastructure, finance, health information systems, commodities and governance. Factors weights were then summed and ranked in order of their relative contribution to better performance. These were then summed and average to rank health facilities. Sum of indicators within each health system block was used as explanatory variables in a linear regression model with overall average of all indicators. Coefficients of the regression was used to assess marginal effects and p-value<0.05 were considered statistics significant. Results: The top ranked indicators were basic service deliver for testing and diagnosis and the lowest ranked were infrastructure such as availability of public taps, water, toilet or privacy. The department that were highly ranked whose indicators performed better in terms of weighting, were service delivery (p<0.0001), health financing (p<0.0001), health workforce (p=0.005) and medical supplies and commodities (p<0.0001) in relation to overall service provision denoted by overall weighting for all indicators. Health governance was not a significant factor influencing service provision. Conclusion: PCA is an essential methodology for assessing health system readiness and preparedness to provide accessible and quality service delivery in resource poor settings.

Keywords:
principal component analysis health systems devolution

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