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Principal component analysis of socioeconomic factors and their association with malaria and arbovirus risk in Tanzania: a sensitivity analysis
  1. Esha Homenauth1,
  2. Debora Kajeguka2,
  3. Manisha A Kulkarni1
  1. 1School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
  2. 2Department of Microbiology and Immunology, Kilimanjaro Christian Medical University College, Moshi, Tanzania
  1. Correspondence to Dr Manisha A Kulkarni, School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON K1G 5Z3, Canada; manisha.kulkarni{at}uottawa.ca

Abstract

Principal component analysis (PCA) is frequently adopted for creating socioeconomic proxies in order to investigate the independent effects of wealth on disease status. The guidelines and methods for the creation of these proxies are well described and validated. The Demographic and Health Survey, World Health Survey and the Living Standards Measurement Survey are examples of large data sets that use PCA to create wealth indices particularly in low and middle-income countries (LMIC), where quantifying wealth-disease associations is problematic due to the unavailability of reliable income and expenditure data. However, the application of this method to smaller survey data sets, especially in rural LMIC settings, is less rigorously studied.

In this paper, we aimed to highlight some of these issues by investigating the association of derived wealth indices using PCA on risk of vector-borne disease infection in Tanzania focusing on malaria and key arboviruses (ie, dengue and chikungunya). We demonstrated that indices consisting of subsets of socioeconomic indicators provided the least methodologically flawed representations of household wealth compared with an index that combined all socioeconomic variables. These results suggest that the choice of the socioeconomic indicators included in a wealth proxy can influence the relative position of households in the overall wealth hierarchy, and subsequently the strength of disease associations. This can, therefore, influence future resource planning activities and should be considered among investigators who use a PCA-derived wealth index based on community-level survey data to influence programme or policy decisions in rural LMIC settings.

  • malaria
  • socioeconomic
  • communicable diseases
  • international health

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Footnotes

  • Contributors EH and MAK conceived and designed the study. EH contributed to data analysis and interpretation, and wrote the manuscript. DK and MAK contributed to data collection and interpretation and critically revised the manuscript. All authors approved the final version to be published.

  • Competing interests None declared.

  • Ethics approval Ottawa Health Sciences Research Network Research Ethics Board.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement Additional data used in construction of the principal component analysis are available upon request by contacting the corresponding author.

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