Assessing joint spatial autocorrelations between mortality rates due to cardiovascular conditions in South Africa

  • Timotheus B. Darikwa | timotheus.darikwa@ul.ac.za Department of Statistics and Operations Research, University of Limpopo, Polokwane, South Africa.
  • Samuel Manda Biostatistics Research Unit, South African Medical Research Council, Pretoria; Department of Statistics, University of Pretoria, Hatfield, South Africa.
  • ‘Maseka Lesaoana Department of Statistics and Operations Research, University of Limpopo, Polokwane, South Africa.

Abstract

South Africa is experiencing an increasing burden of noncommunicable diseases (NCDs). There is evidence of co-morbidity of several NCDs at small geographical areas in the country. However, the extent to which this applies to joint spatial autocorrections of NCDs is not known. The objective of this study was to derive and quantify multivariate spatial autocorrections for NCDrelated mortality in South Africa. The study used mortality attributable to cerebrovascular, ischaemic heart failure and hypertension captured by the country’s Department of Home Affairs for the years 2001, 2007 and 2011. Both univariate and pairwise spatial clustering measures were derived using observed, empirical Bayes smoothed and age-adjusted standardised mortality rates. Cerebrovascular and ischaemic heart co-clustering was significant for the years 2001 and 2011. Cerebrovascular and hypertension co-clustering was significant for the years 2007 and 2011, while hypertension and ischaemic heart co-clustering was significant for the year 2011. Co-clusters of cerebrovascular-ischaemic heart disease are the most profound and located in the south-western part of the country. It was successfully demonstrated that bivariate spatial autocorrelations can be derived for spatially dependent mortality rates as exemplified by mortality rates attributed to three cardiovascular conditions. The identified co-clusters of spatially dependent health outcomes may be targeted for an integrated intervention and monitoring programme.

Downloads

Download data is not yet available.
Published
2019-11-06
Section
Original Articles
Keywords:
Bivariate spatial autocorrelation, Indirect standardised mortality rate, Cardiovascular mortality, Empirical Bayes smoothing, South Africa
Statistics
Abstract views: 95

PDF: 59
APPENDIX: 15
Share it

PlumX Metrics

PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.

How to Cite
Darikwa, T., Manda, S., & Lesaoana, ‘Maseka. (2019). Assessing joint spatial autocorrelations between mortality rates due to cardiovascular conditions in South Africa. Geospatial Health, 14(2). https://doi.org/10.4081/gh.2019.784