Herein we investigate the health development of East Asia and Pacific Region during the period of 2001 to 2015. The data for the investigation has been taken from world bank. The research team is particularly interested in the relation of crude birth and death rate to the health expenditure of the region. Moreover, in this research we intend to study how crude birth and deaths rate have varied across countries for last 15 years. In addition, the research intends to study the relation between growth in population of the region (the difference between crude death rate and crude birth rate) and health expenditure. Further, the relation between immunization rate and crude birth rate is studied.
The outcomes of the analysis have importance to government and planners. The analysis can be utilised for resource mobilisation to improve the health of the country.
The analysis is limited to the region of East Asia and Pacific only.
For analysing the health of the region the present study is segregated into four groups. Initially the researcher evaluates the attributes using a one variable study. Box-plots and histograms are used to undertake a one-attribute study. In the second part two of variables are studies. in order to undertake the two variable study box-plots were found to be best suited. In the next section the researcher has evaluated patterns in the relation between crude birth and death rate. The countries of the region having similar death and birth rates have been grouped together. Finally, in the last section, growth in population of the region is related to health expenditure and birth rate to immunization rate.
Quantitative data got from world bank is used for the present analysis. The data pertains to the time period of 2001 to 2015 and East Asia and Pacific Region.
In order to undertake the analysis, the data file should be loaded into the R program. Thus the first line of the code requests the user to load the required data file. During the process of running the first line of code a window pops up and the data file to be analysed is requested. The pre-processing of the data file also takes place when the data file is loaded. All missing values are tagged and the first row is taken as the header row. In the second stage the required library files are loaded. In order to undertake the analysis certain library files are required. The library files are loaded in the second stage.
The crude death rate of the region per 1000 people in 2014 is studied as a one-variable analysis. In 2014 the average death rate was 6.37 with a standard deviation of 1.44 for every 1000 people. The minimum and maximum death rate was 2.99 and 10 respectively in the region. The median death rate in 2014 was 6.16 for every 1000 people. From the analysis it can be seen that the minimum and maximum death rates of the region are outliers.
The crude birth rate of the region per 1000 people in 2014 is studied as a one-variable analysis. In 2014 the average birth rate was 20.92 with a standard deviation of 7.7 for every 1000 people. The minimum and maximum birth rate was 8 and 37.78 respectively in the region. The median birth rate in 2014 was 23.53 for every 1000 people. From the boxplot it can be viewed that the birth rate of the region is left skewed, average birth rate is higher than median birth rate.
The distribution of Crude Death Rate of the region in 2014 is studied with the help of a histogram. From the histogram it can be felt that the crude death rate is approximately normally distributed.
The spread of crude death rate of the region from 2001 to 2015 was studied in the two-variable analysis. A boxplot with the names of the countries in the x-axis and the values in the y-axis is plotted. The box-plots shows that the crude death rate of BRN (Brunei Darussalam) during the period of study is the least. Further, there are wide variations in crude death rate of different countries. Moreover, the crude death rate is skewed for most of the countries during the period.
The spread of crude birth rate of the region from 2001 to 2015 was studied in the two-variable analysis. A boxplot with the names of the countries in the x-axis and the values in the y-axis is plotted. The analysis shows that there is a wide variation of Birth Rates amongst the countries of the region during the 15-year period. The plot shows that the crude birth rate was the highest for the period for Timor. Moreover, variations in birth rate during the period have outliers.
The use of k-means clustering is to segregated the dataset into groups such that the mean of the group represents the clusters (Witten et al., 2016). In the process a centroid is first evaluated from the dataset. The observations then nearest to the centroids are allocated to the centroid. Clustering with k-means is in two steps. In the initial step a centroid is first selected. As more number of observations are added more centroids are added. The observations are assigned to the centroid wherein the mean is the least (Ghosh and Dubey 2013). The optimal number of clusters are first calculated. Then the k-means are calculated.
The countries of the region are grouped on the basis of their crude death and birth rates. The time period of analysis was from 2001 to 2014. From the scaling process it was found that the optimal number of solution can be found with grouping the countries into four clusters. Through the process of k-means cluster it is found that there are 9, 10,10 and 2 countries in the four clusters. Thus, it can be seen that while three of the clusters are of similar size one cluster contains only two countries. In both the countries (Solomon Islands and Timor-Leste) there are high birth rate and death rates. The within cluster, SSE of the clusters is 90.7%.
Linear regression is used to find the relation between two variables. One of the variables is independent variable, while the other is dependent variable. The independent variable is used to predict the dependent variable (Holmes and Rinaman 2014). The equation through which the two variables are related is:
Here, “Y” is the dependent variable and “X” is the independent variable. The slope of the equation is “m.” The slope signifies the change in the value of “Y” for each unit change in value of “X.”
The “growth in population” of the region in 2014 is related to the health expenditure. The “growth in population” of a country is derived by subtracting the crude death rate from the crude birth rate of the country.
The relation between ‘population growth” and ‘health expenditure” is given as:
From the above equation it is found that with an increase in population of the region (for every 1000) there is a decrease in health expenditure by 0.06202 (%GDP) (Casson and Farmer 2014).
In the second Linear Regression model the crude birth rate of the region is related to the immunization rate. Immunization enables a child to get immunity from diseases. The process of immunization is useful from the sense that it decreases the child mortality rates. Thus, the crude birth rate of the region was related to the immunization process. From the analysis it is found that:
From the above equation it is found that with increase in Crude Birth Rate (per 1000) there is a decrease in Immunization (BCG) by 0.7701. Thus the chances of child mortality of the region increases.
Conclusion
The investigation into the health statistics shows that the crude birth and death rates of the regions is skewed. Moreover, there are extremely higher and lower death rates in the region in 2014. The crude birth and deaths rates of the region for the last 14 years have varied a lot. While one of the country (Brunei Darussalam) has extremely lower crude death rates there are other countries. Conversely country like Timor-Leste has very high crude birth rate. Moreover, it is found that the countries of the region can be grouped into four groups. In addition, we created another variable – Population Growth. The population growth is the difference between crude birth and death rates. The population growth is poorly related to health expenditure of the region. In addition, the crude birth rate is also poorly related to the rate of immunization.
While making the analysis the initial difficulty faced was the selection of the correct attributes. Some of the attributes had enough data for analysis but relating them to other variables was difficult. However, after analysing the dataset we found that crude death rate and birth rates could be easily related. Moreover, the difference between birth rates and death rates provided us with “population growth.” So we thought of creating a new variable and making further analysis. Further, the years were in different rows. So to change the rows into columns we faced some problems.
Reference
Holmes, W.H. and Rinaman, W.C., 2014. Simple Linear Regression. In Statistical Literacy for Clinical Practitioners (pp. 341-366). Springer, Cham.
Casson, R.J. and Farmer, L.D., 2014. Understanding and checking the assumptions of linear regression: a primer for medical researchers. Clinical & experimental ophthalmology, 42(6), pp.590-596.
Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., 2016. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Ghosh, S. and Dubey, S.K., 2013. Comparative analysis of k-means and fuzzy c-means algorithms. International Journal of Advanced Computer Science and Applications, 4(4).
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