Clustering of OECD Countries Out of Pocket Health Expenditure Time Series Data

Abstract

Out of pocket health expenditures points out to the payments made by households at the point they receive health services. Frequently these include doctor consultation fees, purchase of medication and hospital bills. In this study hierarchical clustering method was used for classification of 34 countries which are members of OECD (Organization for Economic Cooperation and Development) in terms of out of pocket health expenditures for the years between 1995-2011. Longest common subsequences (LCS), correlation coefficient and Euclidean distance measure was used as a measure of similarity and distance in hierarchical clustering. At the end of the analysis it was found that LCS and Euclidean distance measures were the best for determining clusters. Furthermore, study results led to understand grouping of OECD countries according to health expenditures.

Causality Distance Measures for Multivariate Time Series with Applications

“…Policy-makers have expressed the view that continued increases in health care spending may be "unsustainable", particularly in light of current and projected government budget deficits [24]. Today, health care is one of the most complex expenditures area and health and budget officials face the challenge to develop policies (a) for risk-sharing between the state and citizens, (b) an effort to increase the efficiency and effectiveness of funding ( [5].…”

“…As the health expenditures are 'dynamic' and evolve, the classification should not be based on 'static' measures. Measures based on the correlation and causality of time series may be more useful [5].…”

“…Detection of these complex physiological patterns not only enables demarcation of important clinical events, but can also elucidate hidden dynamical structures that may be suggestive of disease processes. Distance measures in conjunction with visual analysis tools enable analysts to achieve their tasks reliably and accurately including clustering and classification purposes (see, e.g., in [5,6]). For further readings, the interested reader may refer to the works in [7][8][9][10].…”

“…is the restricted sum of squares of residuals associated with the restricted regression ( 6), SSE = ∑ Xi − X i 2 is the unrestricted sum of squares of residuals, n is the sample size, h is the number of lags, k the number of parameters of Y t in (4), and X * i and Xi are the predictions of X * i and X i , respectively. For the hypothesis that X t is causing Y t , the test statistic F Y,X is given by (7), where SSE * is the restricted SSE associated with (5). Under the null hypothesis, the test statistic F follows an F-distribution with h and (n − k) degrees of freedom.…”