Does Economic Status Matter for the Regional Variation of Malnutrition-Related Diabetes in Romania? Temporal Clustering and Spatial Analyses

Druica, Elena and Goschin, Zizi (2016): Does Economic Status Matter for the Regional Variation of Malnutrition-Related Diabetes in Romania? Temporal Clustering and Spatial Analyses. Published in: Journal of Applied Quantitative Methods , Vol. 11, No. 4 (2016)

Abstract

Among the different types of diabetes, in Romania the malnutrition-related diabetes displays the highest territorial inequality. In this paper, we combined two types of statistical tools, temporal clustering and spatial analysis, to find some relevant patterns in its territorial distribution. Firstly we conducted a time series clustering for the 41 counties and Bucharest Municipality, over 2007-2014, based on CORT and ACF dissimilarity distances and choose four clusters in each case. Within each cluster the evolution of malnutrition diabetes is similar. The clusters were then included as dummy variables in a spatial model testing the determinants of malnutrition-related diabetes incidence at county level. Malnutrition-related diabetes is a disease that might be linked to the economic status, therefore GDP per capita and average wage have been tested and found significant as factors of influence in various model specifications. The dummies representing the temporal clusters are also significant determinants of the regional incidence of malnutrition-related diabetes in Romania. We found that when introducing the cluster dummies in the spatial model, it becomes less appropriate than classic OLS regression, which suggests that temporal clusters were able to capture the spatial dependence in our data. The contribution of our work is three folded. First, we applied time series clustering in R and in doing so we added a real – data application to this scarce stream of literature. Secondly, we combined two techniques relatively new in Romanian data: spatial analysis and time series clustering. Last, but not least, we discussed the malnutrition – related diabetes, mellitus, as a possible proxy of poverty, and tried to advocate our claim by relating this disease’s territorial distribution with some economic variables.

• Item Type: MPRA Paper
• Language: English
• Keywords: malnutrition-related diabetes, time series clustering, spatial clustering, spatial-lag model, county.
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; I - Health, Education, and Welfare > I1 - Health > I15 - Health and Economic Development

References

Anselin, L. (2005), Exploring Spatial Data with GeoDaTM: A Workbook, Spatial Analysis Laboratory Department of Geography, University of Illinois, Urbana, available at: http://sal.agecon.uiuc.edu.

Anselin, L., Rey, S. (1991), “Properties of Tests for Spatial Dependence in Linear Regression Models”, Geographical Analysis, 23, pp. 112–131.

Banerjee A., Dave, R.N (2004) Validate cluster tendency using Hopkins statistics, Conference Paper in IEEE International Conference on Fuzzy Systems 1:149 - 153 vol.1 / 54, August 2004.

Blakely T, Hales S, Woodward A. (2004) Poverty : assessing the distribution of health risks by socioeconomic position at national and local levels. Geneva, World Health Organization, 2004. (WHO Environmental Burden of Disease Series, No. 10).

Chiriac C, Scorțan A. (2015) Evidența diabetului zaharat în perioada 2007 – 2014, Raport al Ministerului Sănătății, Institutul Național de Sănătate Publică, Centrul Național de Statistică și Informatică în Sănătate Publică

Fotheringham, A. S. (1997), “Trends in quantitative methods I: stressing the Local”, Progress in Human Geography 21 (1), pp. 88-96.

Galeano P, Pena D (2000). “Multivariate Analysis in Vector Time Series.” Resenhas do Instituto de Matematica e Estatıstica da Universidade de Sao Paulo, 4(4), 383–403.

GeoDa (2014), The GeoDa Center for Geospatial Analysis and Computation, available at: http://geodacenter.asu.edu/about

George AM, Jacob AG, Fogelfeld L. (2015) Lean diabetes mellitus: An emerging entity in the era of obesity. World Journal of Diabetes. 2015; 6(4):613-620. doi:10.4239/wjd.v6.i4.613.

Hotez P., Herricks J. (2015) Helminth elimination in the pursuit of Sustainable Development Goals: a “worm index” for human development (PLOS Neglected Tropical Diseases, 30 April 2015)

Kaufman L., Rousseeuw P. J. (2005) Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken, NJ: Wiley

Kidd S., Wylde E., (2011) Targeting the poorest: An assessment of the proxy means test methodology, Published by the Australian Agency for International Development (AusAID), Canberra, September 2011, available at http://www.unicef.org/socialpolicy/files/targeting-poorest.pdf, retrieved on 8th November 2016

LeSage, J.P., Pace R.K. (2009), Introduction to Spatial Econometrics, Boca Raton, CRC Press

Lester F. T. (1993) A search for malnutrition-related diabetes mellitus among Ethiopian patients. Diabetes Care. 1993 Jan; 16(1): 187–192.

Liao T. W., (2005) Clustering of time series data – a survey, Pattern Recognition (38) (2005), 1857 – 1874

Montero P., Vilar J (2014) TSclust: An R Package for Time Series Clustering, Journal of Statistical Software, November 2014, Volume 62, Issue 1.

National Institute of Statistics: TEMPO database - time series, 2016, available at https://statistici.insse.ro/shop/

Nosek, V., Netrdová, P. (2014), Measuring Spatial Aspects of Variability. Comparing Spatial Autocorrelation with Regional Decomposition in International Unemployment Research, Historical Social Research, Vol. 39, 2, pp. 292-314.

Online reference: Food poverty and health, a contribution of the Faculty of Public Health of the Royal College of Physicians in the UK, available at http://www.fph.org.uk/uploads/bs_food_poverty.pdf retrieved on 9 November 2016

Paparrizos J., Gravano L., (2015) K-Shape: Efficient and Accurate Clustering of Time Series, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, p. 1855-1870

Press V. (2004) Nutrition and food poverty: a toolkit for those involved in developing or implementing a local nutrition and food poverty strategy. London: National Heart Forum

Samuels F., Pose R. R. (2013) Why neglected tropical diseases matter in reducing poverty, Working Paper for Development Progress, UK, available at http://unitingtocombatntds.org/sites/default/files/resource_file/Why%20neglected%20tropical%20diseases%20matter%20in%20reducing%20poverty.pdf, retrieved 8 November 2016

Schneider M.C. et al. (2002) Trends in infant mortality inequalities in the Americas: 1955 – 1995, Journal of Epidemiology Community Health 2002; 56:538-541 doi:10.1136 / jech.56.7.538

Taksande A., Taksande B., Kumar A., Vilhekar KY (2008) Malnutrition-related Diabets Mellitus, JMGIMS, Vol. 13, No(ii),19-24

Unwin, A., Unwin, D. (1998), “Exploratory Spatial Data Analysis with Local Statistics”, The Statistician, 47 (3), pp. 415-421.

World Health Organization. Diabetes mellitus. Tech Rep Ser 1985; 727: 20-4.

Worrall E., Basu S., Hanson K. (2003) The relationship between socio – economic status and malaria: a review of the literature, Background paper for “Ensuring that malaria control interventions reach the poor”, London, 5 – 6 September 2002, available at http://siteresources.worldbank.org/INTMALARIA/Resources/SESMalariaBackgroundPaper.pdf, retrieved on 8th November 2016