A dataset of healthcare systems for cross-efficiency evaluation in the presence of flexible measure

Data Article A dataset of healthcare systems for cross-efficiency evaluation in the presence of flexible measure Sepideh Abolghasem a , * , Mehdi Toloo b , Santiago Am ezquita a a Department of Industrial Engineering, Universidad de los Andes, Bogot a, Colombia b Department of Systems Engineering, Faculty of Economics, V  SB-Technical University of Ostrava, Czech Republic article info Article history: Received 24 February 2019 Received in revised form 2 June 2019 Accepted 1 July 2019 Available online 6 July 2019 Keywords: Healthcare system Efficiency measure Data envelopment analysis abstract This article presents the dataset of the healthcare systems indicators of 120 countries during 2010e2017, which is related to the research article “Cross-efficiency evaluation in the presence of flexible measures with an application to healthcare systems”[1]. The data is collected from the World Bank and selected for the 120 countries. Depending on their role in the performance of the healthcare systems, the indicators are categorized into input (I), output (O) and flexible measure (FM) where the FM measure can play either role of input or output in the healthcare system. The dataset can be used to perform efficiency as well as crossefficiency analysis of the healthcare systems using methods such as data envelopment analysis (DEA) in the presence of flexible measure. ©2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/). 1. Data The data comprises various indicators of the healthcare systems in 120 countries which are selected according to their availability of the data in the World Bank [2] during 2010e2017. The distribution of *Corresponding author. E-mail addresses: ag.sepideh1[email protected],[email protected] (S. Abolghasem). Contents lists available at ScienceDirect Data in brief journal homepage: www.elsevier.com/locate/dib https://doi.org/10.1016/j.dib.2019.104239 2352-3409/©2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). Data in brief 25 (2019) 104239 the selected countries among the continents is shown in Fig. 1. The indicators and their type: input (I), output (O), or flexible measure (FM), as well as the summary of descriptive statistics of the indicators, are provided in Tables 1e6for each continent. 2. Experimental design, materials and methods The data for the healthcare systems was collected from the World Bank [2] containing information for different indicators. Then according to the availability of the information during 2010e2017, the dataset was selected and compiled for the 120 countries. The countries are arranged in the ascending order of their Decision-Making Unit identity (DMU ID) in the first column. The DMU ID, starting from 1 to 120, corresponds to the country name organized in alphabetical order. Subsequently, for the performance analysis of the healthcare systems using the data envelopment analysis (DEA) methodology, the indicators are categorized into input (I), output (O), and flexible measure (FM) according to Fig. 2. The healthcare system measures were divided into three categories based on their role in the performance of the healthcare system. The population, specialist surgical, birthrate, total fertility rate, hospital beds, nurses and midwives, physicians were categorized as the input of the study and mortality was treated as the output. The aforementioned categorization is in accordance with similar studies on the healthcare system performance in literature [3]. The categorization of the indicators was done according to their natural impact on the performance of the healthcare system. For instance, population, birthrate and total fertility rate were categorized as input since it is supposed that lower level of population, birthrate, and fertility rate results in better housing, nutrition, and access to healthcare. Besides, we categorized specialist surgical, hospital beds, nurses and midwives, and physicians as input since it is preferred that the healthcare system achieve the maximum performance requiring the minimum number of specialist surgical workforce, hospital beds, nurses and midwives, and physicians. Mortality was selected as the output of the healthcare system as by definition it is considered to be a direct measure on the performance of the healthcare system and finally, life expectancy was categorized as the only flexible measure of the study. Specifications table Subject area Operations research and management science More specific subject area Data envelopment analysis Type of data Table How data was acquired Using a macro developed in Excel Visual Basic to acquire data which is available on World bank open data Data format Raw, analyzed with descriptive and statistical data Experimental factors The most updated data of healthcare systems for 120 countries available from 2010 to 2017. Experimental features indicators of interest were selected and collated. Data source location Global data Data accessibility Data is within this article and also accessible from the database of the World Bank open data: https:// data.worldbank.org Related research article S. Abolghasem, M. Toloo, S. Am ezquita, “Cross-efficiency evaluation in the presence of flexible measures with an application to healthcare systems,”Health Care Manag. Sc., 2019, 1e22 [1]. Value of the data The raw data contains the indicators for healthcare systems of 120 countries selected during 2010e2017, which can be used for performance assessment of the countries in terms of their efficiency in their healthcare system in comparison to their peers. The provided data is useful for decision makers to perform efficiency analysis using methodologies such as data envelopment analysis on the healthcare systems of the 120 countries. The data is worthwhile to the researchers for efficiency as well as cross-efficiency evaluation of the healthcare systems for the 120 countries under consideration. The data is useful to evaluate a wide range of efficiency measures for the 120 countries under consideration besides comparative analysis of continental performance and beyond. S. Abolghasem et al. / Data in brief 25 (2019) 1042392 Fig. 1. Distribution of countries within the continents. Table 1 Descriptive statistics of inputs, outputs and flexible measures for Africa. Mean Std. Dev. Min Max Inputs Population 32237224.5 38877689.9 94677 185989640 Specialist surgical 5.41 11.96 0.17 50.08 Birthrate 33.35 8.93 10.40 48.14 Total Fertility Rate 4.43 1.38 1.40 7.24 Hospital beds 1.39 1.27 0.10 6.30 Nurses and midwives 1.19 1.34 0.08 5.23 Physicians 0.45 0.80 0.02 3.06 Output Mortality 0.52 0.18 0.32 0.93 Flexible Measure Life expectancy 62.84 6.54 52.17 75.82 Table 2 Descriptive statistics of inputs, outputs and flexible measures for Asia. Mean Std. Dev. Min Max Inputs Population 86283051.8 247794083.2 427756 1324171354 Specialist surgical 36.21 36.03 0.03 125.01 Birthrate 19.61 6.82 7.80 33.21 Total Fertility Rate 2.41 0.84 1.24 4.64 Hospital beds 4.08 3.61 0.50 13.70 Nurses and midwives 4.15 3.28 0.24 12.50 Physicians 1.84 1.25 0.08 4.78 Output Mortality 0.83 0.10 0.43 0.97 Flexible Measure Life expectancy 72.99 5.45 61.16 83.98 S. Abolghasem et al. / Data in brief 25 (2019) 104239 3 It is noteworthy that regarding the selected input, we performed a Pearson correlation analysis, which is a measure of the strength of the association among the measures. The results of the correlation analysis revealed no correlation among the inputs. It should be noted that with the aim to prevent scaling problems of the data, we transformed all the data, by dividing each value of the data set by the maximum value of the corresponding indicator. Lastly, we would like to confirm that the provided data excludes any type of statistical or scaling oriented modification of the data. The aforementioned modifications such as standardization of the data was performed for the analysis executed in the main manuscript and are not reflected in the data table provided here. Table 3 Descriptive statistics of inputs, outputs and flexible measures for Europe. Mean Std. Dev. Min Max Inputs Population 20949260.07 32028667.21 437418 144342396 Specialist surgical 88.86 31.50 0.81 166.81 Birthrate 10.59 1.28 7.80 32.22 Total Fertility Rate 1.57 0.20 1.24 4.10 Hospital beds 5.44 2.13 0.60 11.30 Nurses and midwives 8.69 3.99 0.70 18.23 Physicians 3.50 0.94 0.31 6.26 Output Mortality 0.96 0.02 0.75 0.99 Flexible Measure Life expectancy 78.82 3.84 64.74 82.90 Table 4 Descriptive statistics of inputs, outputs and flexible measures for North America. Mean Std. Dev. Min Max Inputs Population 26023144.56 79772622.31 100963 45004645 Specialist surgical 19.37 12.80 3.40 113.12 Birthrate 17.18 4.61 10.30 25.27 Total Fertility Rate 2.12 0.45 1.40 2.97 Hospital beds 2.28 1.56 0.60 9.00 Nurses and midwives 3.21 2.92 0.10 11.88 Physicians 1.05 0.80 0.10 4.19 Output Mortality 0.86 0.07 0.69 0.96 Flexible Measure Life expectancy 74.76 4.38 63.33 82.30 Table 5 Descriptive statistics of inputs, outputs and flexible measures for Oceania. Mean Std. Dev. Min Max Inputs Population 1442360.63 2715989.62 109643 323127513 Specialist surgical 3.70 2.00 2.30 54.71 Birthrate 26.69 4.51 12.40 28.71 Total Fertility Rate 3.76 0.84 1.75 3.85 Hospital beds 2.49 1.68 1.30 5.20 Nurses and midwives 2.35 1.37 0.53 9.88 Physicians 0.32 0.27 0.06 2.57 Output Mortality 0.77 0.12 0.66 0.95 Flexible Measure Life expectancy 70.24 3.24 65.54 78.69 S. Abolghasem et al. / Data in brief 25 (2019) 1042394 Acknowledgements This research was supported by the 2019 grant of the school of engineering at Universidad de los Andes Bogot a, Colombia and the Czech Science Foundation through project No. 17-23495S. Conflict of interest None. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104239. References [1] S. Abolghashem, M. Toloo, S. Am ezquita, Cross-efficiency evaluation in the presence of flexible measures with an application to healthcare systems, Health Care Manag. Sci. (2019) 1e22. [2] The World Bank, 2018. https://data.worldbank.org/. [3] L. Asandului, M. Roman, P. Fatulescu, The efficiency of healthcare systems in Europe: a data envelopment analysis approach, Procedia Econ. Financ. 10 (2014) 261e268, https://doi.org/10.1016/S2212-5671(14)00301-3. Table 6 Descriptive statistics of inputs, outputs and flexible measures for South America. Mean Std. Dev. Min Max Inputs Population 43378930.22 64011970.53 107122 207652865 Specialist surgical 27.97 17.59 0.58 61.12 Birthrate 17.87 2.79 14.16 35.05 Total Fertility Rate 2.23 0.30 1.73 5.50 Hospital beds 2.04 1.10 1.00 5.90 Nurses and midwives 2.68 2.47 1.08 7.44 Physicians 1.95 1.19 0.08 3.91 Output Mortality 0.86 0.05 0.56 0.90 Flexible Measure Life expectancy 74.38 3.19 68.88 76.58 Fig. 2. Input, output, and flexible measures with countries as DMUs. S. Abolghasem et al. / Data in brief 25 (2019) 104239 5