DATA COLLECTION
All the data collected are conducted as primary data from the World Bank database in the year of 2017; and they are considered quantitative data According to the World Bank, 1 current international $ would equal 1 USD The collecting data process began when we downloaded the excel files of the world’s Gross Net Income (GNI) per capita from the World Bank Database In a separate excel file, we randomly selected 30 countries for each income category (low, lower- middle, upper-middle, and high income) as classified in the GNI’s file, which also qualified with the requirement for each income category in the assignment After that, we downloaded the excel files for Domestic General Government Health Expenditure Per Capita, PPP (current international $), Immunization, Measles (% of children ages 12-23 months), Compulsory
Education Duration (Years); and Child Mortality rate under 5 (per 1,000 live births) respectively on World Bank Afterward, we transferred the data from those categories into the excel file of
120 countries using VLOOKUP function Finally, in order to have a complete data set, we eliminated countries that missed the data for any of those categories.
DESCRIPTIVE STATISTIC
MEASURE OF CENTRAL TENDENCY
Lower-Middle Income countries (LMI)
Upper-Middle Income countries (UMI)
Mode No mode No mode
Figure 2: Central Tendency of each countries category child mortality rate under the age of 5 in
2017 (unit: death per 1,000 live births)
Because there are extreme values in the dataset, they will skew the results and the Mean can no longer be the correct representation of the dataset The mode is not an ideal approach, too As it can be seen, the data are numerical, no value repeats in the groups of Low-Income and Lower- Middle income countries, so Mode cannot be found in these groups of the dataset At last, Median, as a central tendency method, seems effective to describe the matter From Figure 2, it should be noted that the Median of Low-Income Countries is the highest with 64.9 points, inferring that 50% of countries of this group has a total of child death per 1000 live births fewer than 64.9 cases Follow this descending pattern, lower-middle-income countries are ranked second with a Median of
31.3 points and upper-middle-income countries are ranked third with 13.7 points Finally, half of the High-Income Countries has fewer than 4 deaths per 1000 child live births This supposes a possible causal relationship between low income and high child mortality.
MEASURE OF VARIATION
Lower-Middle Income countries (LMI)
Upper-Middle Income countries (UMI)
Figure 3: Measure of variation of each countries category on the child mortality rate under the age of 5 in 2017 (unit: death per 1,000 live births).
Due to them endures an aggregate of five upper outliers in the case, Interquartile Range (IQR) can be applied as the best measure Since it partitions the arrangement into four equivalent parts and measures the distance average in the range of first and third quartile, causing it to get unaffected toward outliers.
Based on the figure 3, the IQR of low-income countries’ group is the highest with 42.55 The following one is a group of lower-middle income countries with 37.33 Meanwhile, 6 and 2.65 are the lower figures of upper-middle and high-income country categories, respectively with the lowest is high-income countries’ group It can be said that the child mortality rate under the age of 5 of low-income and lower-middle income countries differ greatly from the remaining country categories.
MEASURE OF SHAPE
Figure 4: Measure of shape of low-income countries group on the child mortality rate under the age of 5 (unit: death per 1,000 live births).
Figure 5: Measure of shape of lower-middle income countries group on the child mortality rate under the age of 5 (unit: death per 1,000 live births).
It is clearly seen that the range of low-income countries’ group is lower than the one of lower-middle income countries’ group with 103.2, meanwhile, the range of lower-middle income country category is 122.8 Furthermore, there is clear allocation between two groups through figure 4 and figure 5 The child mortality rate under the age of 5 in low-income countries is 97.5 deaths per 1,000 live births Nevertheless, in the lower-middle income country category, the rate is only 60.73.
Figure 6: Measure of shape of upper-middle income countries group on the child mortality rate under the age of 5 (unit: death per 1,000 live births).
Figure 7: Measure of shape of high-income countries group on the child mortality rate under the age of 5 in 2017 (unit: death per 1,000 live births).
Through the box and whisker plots above (figure 6 and 7), it can be easily recognized that the range of upper-middle income country category is twice as high as the group of high-income countries is with 31.2 and 15.9 in turn Similar to figure 4 and 5, both figures 6 and 7 also have distinct division In upper-middle income countries’ group, the rate of child mortality under the age of 5 is 14.8, while 5.9 deaths per 1,000 live births is the child mortality rate under the age of
5 of the high-income country categories.
From all the box and whisker plots above, it can be said that the rates of child mortality of low-income and lower-middle income country category are much higher than the other two groups It displays how vital income is to mortality in children under 5 years of age.
Factors Left Comparison Right Result Conclusio n
Figure 8: Country categories’ box, whisker, and median comparison in 2017 (unit: death per
It is obvious that four country categories have right-skewed through comparing the box, whisker, and median The median of low-income countries’ group is the highest rate with 64.9 deaths per 1,000 live births Then it is a group of lower-middle income countries with a median of 31.3 And upper-middle and high-income countries categories are lower with the median are 13.7 and 4, respectively.
MULTIPLE REGRESSION (FROM YOUR COLLECTED DATA)
REGRESSION MODEL FOR THE DATA SET LI: THE LOW-INCOME COUNTRIES
The approach to backward elimination is used to choose the best regression model that attempts to remove independent variables that are irrelevant at the level of significance of 0.05 and considered adding those variables that are related to the dependent variable indicated in Appendix 1 Although, the final regression output model is found, its p-value is still higher than the significant level of 0.05; Based on that and the fact that all the regression output models before it also does not contain any significant variables, therefore, the regression model for the Low-Income countries is irrelevant
Figure 9: Final regression output for the data set of The Low income in 2017
Figure 9 above is the final regression output model with the independent variable of the immunization, measles (% of children ages 12-23 months) a.2 Scatter plot:
Figure 10: Scatter plot of Low-Income countries’ child mortality rate under the age of 5 (per 1,000 live births) vs Immunization, measles (% of children ages 12-23 months) in 2017 b Regression equation:
In order to estimate the number of the child mortality rate under the age of 5 the final regression model will be used by the following equation:
^Y is denoted for the predicted number of the child mortality rate under the age of 5 and is X the percentage of children ages 12-23 months has taken the immunization for measles c Interpret the regression coefficient of the significant independent variable/s in the context of your research: β 1 =−0.4672
The slope value of 0.4672 indicates that the number of child mortality rate under the age of 5 will decrease by 0.4672 per 1,000 live births when the percentage of children ages 12-23 months has taken the immunization for measles increase d Interpret the coefficient of determination in the context of your research:
R 2 =0.0733 The coefficient of determination equals 0.0733 or 7.33% illustrates that 7.33% of variation in number of child mortality rate under the age of 5 will be explained by variation in the percentage of children ages 12-23 months has taken the immunization for measles Based on that, for Low- Income counties, the link between the child mortality rate and the immunization, measles is rather very weak
The remaining 24.11% of the variation in mortality rate of children under 5 years old could be impacted by other factors like infection, disorder, or other factors that we do not include in our study However, since the independent variable is insignificant due to its p-value lower than the significance level 0.05, thus, it is considered invalid, and it will be rejected.
REGRESSION MODEL FOR THE DATA SET LMI: THE LOWER-MIDDLE INCOME COUNTRIES
The final regression output model for the Lower-Middle Income countries is developed based on the application of the backward elimination process demonstrated in Appendix 2.
Figure 11: Final regression output for the data set of The Lower upper income a.2 Scatter plot:
Figure 12: Scatter plot of Lower-Middle Income countries’ child mortality rate under the age of
5 (per 1,000 live births) vs Immunization, measle
GNI per Capita, Atlas meth b Regression equation:
The final regression model will be used to estimat age of 5 by the following regression equation:
In this case, ^Y is denoted for the predicted num while X2 is the Immunization, measles (percent of ch per capita, Atlas method (US$) c Interpret the regression coefficient of the significant independent variable/s in the context of your research: β 1=−1.4195
The slope value is 1.4195 shows that the number of child mortality rate under the age of 5 will decrease by 1.4195 per 1,000 live births when the percentage of children ages 12-23 months has taken the immunization for measles increase β 1 =−0.0157
Likewise, the slope value is 0.0157 indicated that the number of child mortality rate under the age of 5 will decrease by 0.0157 per 1,000 live births when the Gross Net Income per capita (US$) increase d Interpret the coefficient of determination in the context of your research:
R 2 =0.7589 The coefficient of determination equals to 0.7589 or 75.89% means that 75.89% variation in the child mortality rate under the age of 5 will be explained by variations in the Gross Net Income per capita (US$) and the percentage of children ages 12-23 months has taken the immunization for measles.
On the other hand, the remaining 24.11% of the variation in mortality rate of children under 5 years old could be impacted by other factors like infection, disorder, or other factors that we do not include in our study.
REGRESSION MODEL FOR THE DATA SET UMI: THE UPPER-MIDDLE INCOME COUNTRIES
The final regression output model for the Upper-Middle Income countries is developed based on the application of the backward elimination process demonstrated in Appendix 3.
Figure 13: Final regression output for the data set of The High-Income countries a.2 Scatter plot:
Figure 14: Scatter plot of Upper-Middle Income countries’ child mortality rate under the age of
5 (per 1,000 live births) vs Domestic general government health expenditure per capita, PPP
(current international $) in 2017 b Regression equation:
The final regression model the Upper-Middle Income countries will be used to estimate the mortality rate of children under 5 years old per 1,000 live births of the High-Income countries by the following regression equation:
Whereas, ^Y is the mortality rate of children under 5 years old per 1,000 live births of the High- Income countries in 2017; and X is the domestic general government health expenditure per capita, Purchasing Power Parity (PPP), in current international $, in 2017 c Interpret the regression coefficient of the significant independent variable/s in the context of your research: β 1=−0.0142 The slope value of -0.0142 indicates that the mortality rate of children under 5 years old of the Upper-Middle Income countries would decrease by 0.0142 per 1,000 live births when the domestic general government health expenditure per capita, Purchasing Power Parity increase. d Interpret the coefficient of determination in the context of your research:
The coefficient of determination equals to 0.0.3185 or 31.85% means that 31.85% of the variance of the child mortality rate under 5 years old in the Upper-Middle Income countries category can be explain by the domestic general government health expenditure per capita, Purchasing Power Parity
On the other hand, the remaining 68.15% of the variation in mortality rate of children under 5 years old could be impact by other factors like infection, disorder, or other factors that we do not include in our study.
REGRESSION MODEL FOR THE DATA SET HI: THE HIGH-INCOME COUNTRIES
The backward elimination was applied to reach to the final regression output model for High- Income countries, that contained only the independent variable It also means that its p-value has to be lower than the significant level 0.05 which is indicated in Appendix 4
Figure 15: Final regression output for the data set of The High-Income countries
The above figure 15 is the final regression output model for High-Income countries where domestic general government health expenditure, PPP is the only significant variable a.2 Scatter plot:
Figure 16: Scatter plot of High-Income countries’ child mortality rate under the age of 5 (per 1,000 live births) vs Domestic general government health expenditure per capita, PPP (current international $) in 2017 b Regression equation:
The regression equation above will be used to estimate the mortality rate of children under 5 years old per 1,000 live births of the High-Income countries For this case, ^Y is the mortality rate of children under 5 years old per 1,000 live births of the High-Income countries in 2017; and X is the domestic general government health expenditure per capita, Purchasing Power Parity (PPP), in current international $, in 2017 c Interpret the regression coefficient of the significant independent variable/s in the context of your research: β 1 =−0.0012 The slope value of -0.0012 indicates that the mortality rate of children under 5 years old of the High-Income countries would decrease by 0.0012 per 1,000 live births for every dollar decrease in the domestic general government health expenditure per capita, Purchasing Power Parity d Interpret the coefficient of determination in the context of your research:
R 2 =0.2600 The coefficient of determination equals to 0.2600 or 26.00% means that 26.00% of the variance of the child mortality rate under 5 years old in the High-Income countries category can be explain by the domestic general government health expenditure per capita, Purchasing Power Parity
On the other hand, the remaining 74% of the variation in mortality rate of children under 5 years old could be impacted by other factors like infection, disorder, or other factors that we do not include in our study.
TEAM REGRESSION CONCLUSION
Hi g h- I n c o me Co unt r i e s ( HI )
S i g ni fic a n t i nde pe nde nt v a r i a bl e ( s )
- Do me s t i c g e ne r a l g o v e r n me n t h e a l t h e x pe n d i t u r e p e r c a pi t a , PPP ( c u r r e n t i n t e r n a t i on a l $)
- Do me s t i c g e ne r a l g o v e r n me n t h e a l t h e x pe n d i t u r e p e r c a p i t a , PPP ( c ur r e nt i nt e r n a t i o na l $ )
Figure 17: The compilation of final regression output for the data set of all country’s categories in
Based on figure 17, only the data sets of Upper-Middle Income, and High-Income countries contain the same significant independent variable with 95% of confidence, which is the domestic general government health expenditure per capita, PPP (current international $) after backward elimination process to remove three independent variables that do not help to explain the variation in the child mortality rate under the age of 5 (per 1,000 live births) On the other hand, in the Lower-Middle Income countries data sets, it contains different independent variables After eliminating 2 variables, there still remain two variables with the significant variable with 95% of confidence which are GNI per capita, Atlas method (per 1,000 live births), and immunization, measles (percent of children aged 12-23 months) Therefore, the mortality rate of children under 5 years old per 1,000 life births can be estimated using domestic general government health expenditure per capita for Upper-Middle Income, and High-Income countries; and using GNI per capita, and immunization, measles for Lower-Middle Income countries
According to Frost (2015), the higher the coefficient of determination (R square), the better the regression model fits the estimation Although, domestic general government health expenditure contributes to estimate the mortality rate of children under 5 years old per 1,000 life births for both countries income categories, Upper-Middle Income countries regression model seems better suited than High-Income countries because R of UMI (31.19%) is slightly higher 2 than of HI (26%) However, Lower-Middle Income countries contains the highest coefficient of determination at 75.89%, which means that 75.89% of variation in Lower-Middle Income countries’ child mortality rate under 5 years old per 1,000 life births can be explained by GNI per capita, and immunization, measles Thus, the estimation of child mortality rate under 5 years old per 1,000 life births in Lower-Middle Income countries is a better fit to the observed data in reality
As shown in figured 17, Lower-Middle Income countries category contains the highest R square value, which mean that there is a connection between the child mortality rate with GNI and immunization for measles Not only that, looking at the regression equation of all four countries’ income categories, they all have different slop values Therefore, in order to decide which countries income categories have more child mortality rate under 5 per 1,000 life births would be considered based on the intercept value In this case, Lower-Middle Income countries category have the highest intercept value (about 200.866) and the median is 31.3 per 1,000 life births which is second to the highest only below Low-Income countries However, since Low- Income countries category doesn’t have significant independent variable at 95% significant level, thus, the child mortality rate under 5 per 1,000 life births in Lower-Middle Income countries categories would be more affected if there is any increase or decrease in the GNI or immunization for measles.
TIME SERIES
LINEAR TREND
According to Appendix 5, all any four countries, namely Afghanistan, India, China and Japan have a linear trend based on hypothesis testing to determine the trend models.
A.1: Low-income country: Afghanistan a) Regression Output
Figure 18: Regression output of the Linear Trend for the low-income country dataset b) Regression Formula
In which ^Y presented the predicted value of child mortality under the age of 5 in Afghanistan, whereas T is the period of time used to forecast (trend)
A.2: Lower-Middle Income country: India a) Regression Output
Figure 19: Regression output of the Linear Trend for the lower-middle income country dataset b) Regression Formula
Where ^Y presented the predicted value of child mortality under the age of 5 in India, while
T is the period of time used to forecast (trend)
A.3: Upper-Middle Income country: China a) Regression Output
Figure 20: Regression output of the Linear Trend for the upper-middle income country dataset b) Regression Formula
In which ^Y presented the predicted value of child mortality under the age of 5 in China, whereas T is the period of time used to forecast (trend)
A.4: High-income country: Japan a) Regression Output
Figure 21: Regression output of the Linear Trend for the high-income country dataset b) Regression Formula
Where ^Y is referred to the predicted value of child mortality under the age of 5 in Japan, while T is the period of time used to forecast (trend).
QUADRATIC TREND
By testing the hypothesis in Appendix 6, it is concluded that Quadratic trend is recognized in all selected countries: Afghanistan, India, China and Japan.
B.1: Low-income country: Afghanistan a) Regression Output
Figure 22: Regression output of the Quadratic Trend for the low-income country dataset b) Regression Formula
In which ^Y presented the predicted value of child mortality under the age of 5 in Afghanistan, whereas T is the period of time used to forecast (trend).
B.2: Lower-Middle Income country: India a) Regression Output
Figure 23: Regression output of the Quadratic Trend for the lower-middle income country dataset b) Regression Formula
Where ^Y presented the predicted value of child mortality under the age of 5 in India, while
T is the period of time used to forecast (trend)
B.3: Upper-Middle Income country: China a) Regression Output
Figure 24: Regression output of the Quadratic Trend for the upper-middle income country dataset b) Regression Formula
In which ^Y presented the value of prediction for child mortality under the age of 5 in China, whereas T is the period of time used to forecast (trend)
B.4: High-Income country: Japan a) Regression Output
Figure 25: Regression output of the Quadratic Trend for the high-income country dataset b) Regression Formula
Where ^Y is referred to the predicted value of child mortality under the age of 5 in Japan, whereas T is the period of time used to forecast (trend)
EXPONENTIAL TREND
Due to the outcomes of hypothesis testing in Appendix 7, the same as other trends, the existence of Exponential trend is confirmed in all four countries: Afghanistan, India, China and Japan.
C.1: Low-income country: Afghanistan a) Regression Output
Figure 26: Regression output of the Exponential Trend for the low-income country dataset b) Regression Formula
In which ^Y presented the value of prediction for child mortality under the age of 5 in
Afghanistan, whereas T is the period of time used to forecast (trend)
C.2: Lower-Middle Income country: India a) Regression Output
Figure 27: Regression output of the Exponential Trend for the lower-middle income country dataset b) Regression Formula
In Non-Linear Form: ^Y1.253×0.959 T Where ^Y is referred to the predicted value of child mortality under the age of 5 in India, while T is the period of time used to forecast (trend).
C.3: Upper-Middle Income country: China a) Regression Output
Figure 28: Regression output of the Exponential Trend for the upper-income country dataset b) Regression Formula:
In Non-linear Form: ^Yp.469×0.933 T
In which ^Y presented the predicted value of child mortality under the age of 5 in China, whereas T is the period of time used to forecast (trend)
C.4: High-Income country: Japan a) Regression Output
Figure 29: Regression output of the Exponential Trend for the high-income country dataset b) Regression Formulas
In Non-linear Form: ^Y=6.761×0.966 T Where ^Y presented the predicted value of child mortality under the age of 5 in Japan, while
T is the period of time used to forecast (trend)
From above information of trend models in part 5.1, the child mortality rate under the age of 5 can be predicted by using both linear, quadratic, or even exponential trend However, according to Li & Wang (2013), the trend model, which has the smallest error, will be considered as the best one to apply for the prediction in order to gain the most accurate result.
Figure 30: Comparison between SSE and MAD between three trends model in Afghanistan data set
Looking at the above table, it obviously illustrates that SSE and MAD of exponential trend has the smallest values compared with others, which means exponential trend will make fewer errors than other trend models Thus, it should be used to carry out the forecast of the child mortality rate under 5 in Afghanistan. b India
The same as Afghanistan, the child mortality in India also can be forecasted by all types of three trend models In spite of several trend models, we suggest the most optimal trend model with the smallest calculation errors should be applied to find out the prediction result.
Figure 31: Comparison between SSE and MAD between three trends model in India data set
By providing the outputs of SSE and MAD from the above table, the application of quadratic trend, rather than the other two models, is regarded as the most suitable method to predict the child mortality rate under the age of 5 in India. c China
Despite the conclusion of the existence of linear, quadratic and also exponential trend in part 5.1, we still recommend to select the best one among them to measure the rate of child mortality under 5 in China.
Figure 32: Comparison between SSE and MAD between three trends model in China data set
Due to the lowest SSE and MAD, exponential trend should be made the best use of predicting the child mortality rate under 5 in China. d Japan
By analyzing the trend models in part 5.1, we certainly jump into the conclusion that all three types of trend models, respectively Linear, Quadratic and Exponential Trend can be utilized in prediction the child mortality rate under 5 in Japan.
Figure 33: Comparison between SSE and MAD between three trends model in Japan data set
In case of strengthening the accuracy of prediction result, exponential trend should be applied to limit measuring errors, which results from the lowest values of both SSE and MAD.
The rate of child mortality under the age of 5 in Afghanistan in 2018, 2019 and 2020 would be predicted by applying the regression of formula from Exponential trend, which is
^Y8.799×0.964 T , then results will be shown in the following table:
Time Period (T) Formula Application Result of
Figure 34: Predict calculations of Child mortality under the age of 5 in Afghanistan in 2018,
Based on Quadratic trend, the utilization of its regression formula, which is
^Y0.519−3.657 T + 0.011 T 2 , will be used for calculation of the child mortality rate under the age of 5 in India in 2018, 2019 and 2020, presented in the following table:
Figure 35: Predict calculations of Child mortality under the age of 5 in India in 2018, 2019 and
The rate of child mortality under the age of 5 in China in 2018, 2019 and 2020 would be measured in the below table by extrapolating from the regression formula of Exponential trend, which is ^Yp.469×0.933 T
Time Period (T) Formula Application Result of
Figure 36: Predict calculations of Child mortality under the age of 5 in China in 2018, 2019 and 2020 d Japan
So as to improve the accuracy of the prediction rate of child mortality under the age of 5 in Japan, we will use the regression formula of Exponential trend, which is
^Y=6.761×0.966 T to calculate in the below table:
Time Period (T) Formula Application Result of
Figure 37: Predict calculations of Child mortality under the age of 5 in Japan in 2018, 2019 and
TIME SERIES CONCLUSION
LINE CHART
A line chart for child mortality rate under the age of 5 (per 1,000 live births) in four Asia countries: Afghanistan, India, China and Japan from 1990 to 2015.
Figure 38: Line chart of child mortality rate under the age of 5 for years 1990 - 2015 of
Afghanistan, India, China and Japan
INTERPRETATION
According to the above line chart, it is obvious that every country has had a downward trend in child mortality rate under the age of 5 since 1990, especially Afghanistan, which dropped more than a half during this period time Additionally, its reduction followed the ratio of
5 units per a year Besides, a lower-income country as India, which also decreased dramatically from nearly 130 to 43.5 In China, the rate only dropped slightly in the early time, however since
2000, it continuously followed with a steep fall and reached only 10.7 in 2015 Meanwhile,
Japan, which is evaluated as a high-income country, tends to decline steadily with a small drop each year but in the end, its child mortality rate under the age of 5 is still the lowest by jumping down to 2.8 in 2015.
Child Mortality rate under 5 (per 1,000 live births)
Country Preferred trend model SSE MAD
Figure 39: Comparison of SSE, MAD and preferred trend model between countries
From the above table, only India is appropriate with the utilization of Quadratic trend for prediction On the other hand, Exponential trend seems to be the most optimal with the rest of countries, hence it is regarded as the best trend model, which should be used to predict the rate of child mortality under the age of 5 More tellingly, by carrying out the comparison of both SSE and MAD for countries preferring Exponential trend, we can recognize that Japan has the smallest values in both SSE (0.011426887 < 1.675800671 < 13.78522176) and MAD
(0.062744159 < 0.915230088 < 2.624347435) so that means it will make fewer errors compared with other countries, which will ameliorate the accuracy considerably Consequently, it is summarized that the Exponential trend of Japan is the best method to apply for predicting the child mortality rate under the age of 5 in the world.
OVERALL CONCLUSION
The health status of a country is measured best by data on child mortality (Wang, 2002) The world has made significant progresses in child survival for the past decades, and the risk of children dying in their first years has decreased In order to get further achievement, it is vital to analyze the main determinants of child survival From the above calculations, it is suggested that income, along with other factors, has a significant impact on child mortality and there is a possible causal relationship between these two matters Taking the Median into consideration, we can see that one of low-income countries’ group is the highest rate with 64.9 deaths per 1,000 live births The next ones are of lower-middle income countries, upper-middle and high-income countries categories with 31.3, 13.7 and 4, respectively In addition, with the coefficient of determination 75.89%, it means that 75.89% variation in the child mortality rate under the age of 5 of lower-middle income countries will be explained by variations in the Gross Net Income per capita (US$) and the percentage of children ages 12-23 months has taken the immunization for measles Moreover, the slope value is 1.4195 shows that the number of child mortality rate under the age of 5 will decrease by 1.4195 per 1,000 live births when the percentage of children ages 12-23 months has taken the immunization for measles increase From these findings, we can perceive the importance of income, along with other factors heavily affected by it, in the relationship with child mortality It is suggested that wealthier people are healthier people (Pritchett & Summers, 1996), better health outcomes correlates closely with higher income of a population (Preston, 1975), and income has a remarkable impact on ways to prevent several causes of death (such as immunization), which are sensitive to the level of technology and nutrition (Birchenall, 2007) However, some researchers argue that when income increases, it is not compulsory to spend it on projects helping to develop the health care system Although the true nature of this relationship has not been discovered and analyzed thoroughly yet, it is still noted that income is one of the main determinants of health status of a country This report confirms the importance of income in developing child health, especially in developing countries, and gives policymakers vital ideas about this matter.
2 What will be the predicted child mortality rate under the age of 5 in year 2030?
As mentioned above, Exponential trend of Japan is recognized as the most optimal predictor in the world for measuring the rate of child mortality under the age of 5 from Part 6 Therefore, applying T= 41, which is the time period of year 2030 in order to calculate the child mortality rate under 5 in 2030 based the aforementioned formula:
In 2030, with each 1000 births under the age of 5, the world mortality rate will be 1.637 deaths, which will continuously follow the same downward trend as previous years but it can suffer an intensive slump In conclusion, it is likely that the world rate mortality of child under 5 will keep going down gradually in 2030 That sayings, it must be one of the most glorious success of world healthcare as well as pharmacy industry in the process of achieving SDG 3. However, some limitations by using available data as a method to predict the future numbers need to be concerned For instance, extrapolation, which occurs due to exceeding the existing observation range (Uddin & Ullah, 2013) More tellingly, it can likely lead to the outcomes of unintelligible approach as well as inaccurate results in case of a far distance between the current dataset and the forecasted time period In this circumstance, because of the further spread of the predicted time period to the right from the available data, it can result in the imprecise calculation in the future child mortality rate under the age of 5
Besides that, Japan seems to be unable to represent and reflect the world’s change in child mortality because of its inappropriate features compared with the world’s pattern For example, as a high-income country with the enormous development in technology and facilities in the past as well as at the present, Japan has had a smaller rate in child mortality than other countries in the world so its modification in child mortality rate is marginally not much The method, which is based on Japan to foresee the world’s future trend, is called generalization According to Watt
& Berg (1995), it is said that if the sample (Japan) and the world have inconsistence traits, the utilization of generalization will lead to erroneous assumptions
3 Based on your research what kind of recommendations can you provide.
Under-five mortality rates have large disparities across regions and countries, especially income (UNICEF, 2020) It can be clearly seen from the measure of shape in part 2 To be more specific, the child mortality rates under the age of 5 of low-income and lower-middle income country categories are much higher than the other two gatherings with the numbers are 97.5 and 60.73 deaths per 1,000 live births, respectively Meantime, upper-middle and high-income country categories’ rates are 14.8 and 5.9 deaths per 1,000 live births, in turn Nevertheless, according to World Health Organization (WHO), generous worldwide advancement has been made in lessening child deaths since 1990 The absolute number of under-five passing’s worldwide has declined from 12.6 million out of 1990 to 5.2 million out of 2019.
Contagious illnesses, including pneumonia, loose bowels and pre-term birth remain the main sources of death for youngsters under five Moreover, Plecher’s research (2020) reveals that gender is also the reason which influences the rate of child mortality, notably in India There have many solutions that may help to cut down the child mortality rate under the age of 5 Having a solid handle of knowledge about nutrition just as perils to the health of youngsters is essential in each family (Huber, 2016) When families have a firm grasp of that knowledge, taking care of the child will not only be less complex but also easy to recognize when the child is sick so that they can be treated promptly Furthermore, enhancement in the soundness of pregnant ladies and new mothers will assume a significant job in producing further decreases in child mortality Poor sustenance in women can prompt preterm births and infants with low-birth weight (Veneman, n.d) Last but not least, education plays a necessary role in life because it may subsidize to the healthiness and well-being of women, children and their nations as well Mainly, changing the mindset of favoring men and women in some countries to avoid abortion or be killed right after birth
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APPENDIX 1 Backward elimination process in the regression model for the Low-Income : Countries
Since we use multiple regression with significance level at 5%, we will determine if the coefficients in each independent variable have an effect on the dependent variable (Y) - the total number of the child mortality rate under the age of 5 (per 1,000 live births) by testing the null hypothesis below:
The null hypothesis � �; �� = � (no independent variable explains the change in Y) The alternative hypothesis � �; �� ≠ � (at least one of the independent variables X1, X2, X3, X4 contributes to the variation in Y) where j = 1,2,3,4
To achieve that, we test whether these independent variables are (1) significant or (2) insignificant compare to the p-value with the significance level = � � ��:
(1) − � ����� < � = 0.05 => we reject the null hypothesis ( 0) => the independent variable is � significant
(2) − � ����� > = 0.05 => we do not reject the null hypothesis ( 0) => the independent � � variable is insignificant.
Figure I: Part of the regression outputs for the full data set of Low-Income countries
According to that figure I, there are no independent variables that is significant All of their P-value are greater than the significance level of 0.05 To search for the significant, independent variable, we will have to remove the one variable with the highest p-value Hence, the GNI per capita, Atlas method (US$) variable is eliminated.
Figure II: Part of the regression outputs when GNI per capita, Atlas method (US$) is eliminated
In figure II, there are still no sign of independent variables with p-value less than 0.05, thus we continue with the backward elimination method and eliminate the one with the highest p-value, Compulsory Education Duration (years)
Figure III: Part of new regression outputs when Compulsory Education Duration is eliminated
Since there are still no significant independent variables found in figure III, domestic general government health expenditure, PPP (current international $) with the highest level of p- value (0.5848) will be eliminate
Figure IV: Final regression output for the data set of The Low-Income countries
Coming to an end with figure IV, the significant independent variables are still unseen, thus, the regression model for the Low-Income Countries will be rejected; and it is considered irrelevant
APPENDIX 2 Backward elimination process in the regression model for The Lower-:
Figure V: Part of the regression outputs for the full data set of the Lower-Middle Income countries
According to that figure V, there is one independent variable, which is important at a significance level of 0.05, and three other variables are insignificant since the p-values (0.0575, 0.00472, 0.80847) are higher than the significance level (0.05) The highest p-value, but is eliminated first (0.92193) from the Compulsory Education Duration variable The figure 2 will remove the Compulsory Education Duration first.
Figure VI: Part of the regression when Compulsory Education Duration is eliminated
At the 0.05 significance level, there are three variables here, but the variable in Domestic general government level health expenditure will first be excluded because of the highest p-value, which is 0.8131 Therefore, the new regression output after domestic general government level health expenditure is eliminated is shown on the figure 3 below.
Figure VII: Part of the regression outputs when Domestic General Government Health Expenditure,
Based on figure VII, the Immunization, measles (% of children ages 12-23 months) and GNI per Capita, Atlas method (US$) are significant independent variables, since its p-value is smaller than the significance level (0.05) In turn make figure VII the final regression output model for the Lower- Middle Income countries.
APPENDIX 3 Backward elimination process in the regression model for The Upper-Middle : Income Countries
Figure VIII: Part of the regression outputs for the full data set of the Upper-Middle Income countries
Based on figure VIII, there is only one independent variable that is significant; the other three independent variables are insignificant since its p-value is higher than the significant level α=0.05 However, the variable for compulsory education duration (0.93) highlights the highest p-value, thus, compulsory education duration is eliminated
Figure IX: Part of the new regression outputs when Compulsory Education Duration is eliminated
In figure IX, there are still two independent variables that are insignificant Hence, we have to continue eliminate the category with the highest p-value In this case, immunization, measles contains the highest p-value at 0.83
Figure X: Part of the new regression outputs when Immunization, Measles is eliminated
Next in figure X, GNI’s p-value is 0.28 which is higher than the significant level of 0.05, therefore, GNI is an independent variable that is insignificant Thus, it is eliminated
Figure XI: Final regression output for the Upper-Middle Income countries