Analysis of binary multivariate longitudinal data via 2-dimensional orbits: An application to the Agincourt Health and Socio-Demographic Surveillance System in South Africa.

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dc.contributor.authorVisaya, M.V.
dc.contributor.authorSherwell, D.
dc.contributor.authorSartorius, B.
dc.contributor.authorCromieres, F.
dc.date.accessioned2016-07-26T07:20:19Z
dc.date.available2016-07-26T07:20:19Z
dc.date.issued2015-04
dc.description© 2015 Visaya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.en_ZA
dc.description.abstractWe analyse demographic longitudinal survey data of South African (SA) and Mozambican (MOZ) rural households from the Agincourt Health and Socio-Demographic Surveillance System in South Africa. In particular, we determine whether absolute poverty status (APS) is associated with selected household variables pertaining to socio-economic determination, namely household head age, household size, cumulative death, adults to minor ratio, and influx. For comparative purposes, households are classified according to household head nationality (SA or MOZ) and APS (rich or poor). The longitudinal data of each of the four subpopulations (SA rich, SA poor, MOZ rich, and MOZ poor) is a five-dimensional space defined by binary variables (questions), subjects, and time. We use the orbit method to represent binary multivariate longitudinal data (BMLD) of each household as a two-dimensional orbit and to visualise dynamics and behaviour of the population. At each time step, a point (x, y) from the orbit of a household corresponds to the observation of the household, where x is a binary sequence of responses and y is an ordering of variables. The ordering of variables is dynamically rearranged such that clusters and holes associated to least and frequently changing variables in the state space respectively, are exposed. Analysis of orbits reveals information of change at both individual- and population-level, change patterns in the data, capacity of states in the state space, and density of state transitions in the orbits. Analysis of household orbits of the four subpopulations show association between (i) households headed by older adults and rich households, (ii) large household size and poor households, and (iii) households with more minors than adults and poor households. Our results are compared to other methods of BMLD analysis.en_ZA
dc.identifier.citationVisaya, M.V. et al. 2015. Analysis of binary multivariate longitudinal data via 2-dimensional orbits: An application to the Agincourt Health and Socio-Demographic Surveillance System in South Africa. 2015. PLoS ONE 10(4): e0123812.en_ZA
dc.identifier.issn1932-6203
dc.identifier.other10.1371/journal.pone.0123812
dc.identifier.urihttp://hdl.handle.net/10539/20702
dc.language.isoenen_ZA
dc.publisherPublic Library of Scienceen_ZA
dc.subjectdata visualizationen_ZA
dc.subjectchartsen_ZA
dc.subjectMarkov modelsen_ZA
dc.subjectdemographyen_ZA
dc.subjectSouth Africaen_ZA
dc.subjectstatistical modelsen_ZA
dc.subjecthealthen_ZA
dc.subjectsocio-economicen_ZA
dc.subjectmultivariate data analysisen_ZA
dc.subjectbinary multivariate longitudinal dataen_ZA
dc.subjectcluster analysisen_ZA
dc.subjectdata analysisen_ZA
dc.subjectfamily sizeen_ZA
dc.subjecthealth care organizationen_ZA
dc.subjecthealth surveyen_ZA
dc.subjecthouseholden_ZA
dc.subjectintermethod comparisonen_ZA
dc.subjectlongitudinal studyen_ZA
dc.subjectmathematical modelen_ZA
dc.subjectpovertyen_ZA
dc.subjectrural areaen_ZA
dc.subjectsocial statusen_ZA
dc.titleAnalysis of binary multivariate longitudinal data via 2-dimensional orbits: An application to the Agincourt Health and Socio-Demographic Surveillance System in South Africa.en_ZA
dc.typeArticleen_ZA
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