Statistical analysis of mortality transition in Saudi Arabia

The following variables will be investigated for their impacts on birth rates; gross enrollment ratio at tertiary level, life expectancy, urban population , total population , maternal mortality and mortality rate under age of 5. INTERDICTION 5 1. Background 5 1. Objectives of study 5 1. Statistical Methods 6 1. Death rates 12 3. Birth rates 12 3. Maternal Mortality 13 4. RESULTS AND ANALYSIS 14 4. Trends of Mortality 14 4. REFERENCES 39 LIST OF FIGURES Figure 1. Trend of Crude Death Rate 14 Figure 2. Trend of Child Mortality under Age 5 15 Figure 3. Trend of Infant Mortality Rate 16 Figure 4. Trend of Maternal Mortality 17 Figure 5. Plot Matrix of Pairwise Scatter Diagram of Selected Variables 28 LIST OF TABLES Table 1: Pearson correlations coefficient …………………………………. …………………32 Table 2: descriptive statistics …………………………………………………………………34 Table 3: Multiple Regression Analysis of Crude Death Rate…………………………………34 Table 4a: Multiple Regression Analysis of Child Mortality under Age 5……………………. Table 4b: multiple regression of child mortality under age of 5 on selected variables, in Backwards Elimination Approach…………………………………………………………….

Table 5a: multiple regression of infant mortality on selected variables ………………………. Table 5b: multiple regression of infants mortality on selected variables, in backwards elimination approach ………………………………………………………………………………………. To come up with techniques of addressing mortality in Saudi Arabia Research questions 1. How is mortality transition in Saudi Arabia? 2. What are the causes of mortality transition in Saudi Arabia? 3. How can mortality rate in Saudi Arabia be addressed? 1. Statistical Methods The section of the paper presents the various statistical methods used in analyzing the data collected for the study. There are three major things that are known to be the sole cause of population change, birth rates, death rates and migration normally referred components of population change. Fertility is the number of live-born children an average normal woman has as calculated considering women of child bearing age (15-49 years).

Maternal mortality – refers to the death of woman while pregnant or in 42 days of termination of pregnancy regardless of the duration and other factors except accidental or incidental causes. Data transformations In most cases biological data does not meet the assumptions of parametric statistical tests and therefore it is likely to lack homogeneity and is not normally distributed for analysis. Data has to be normally distributed in order to use statistical tests such as linear analysis regression so as to give accurate results son analysis. Formula Where: X and Y represent the pair of variable under consideration and n the number of the pair of variables 1. Simple linear regression model Simple linear regression is used in statistical analysis tool used to study the relationship between two quantitative values.

This model indicates the relationship between variable and how they are associated to one another but does not give the kind of relations that they have. The simple linear relationship explores the relations between variables for example, x to represent a independent variable and y a dependent variable. The two variables when plotted on a graph their relationship is given by: Y= a +bx +c Where; a is the intercept value b is the slope of the graph c is residual or the error term. Mortality transition describes the passage from high mortality rate due to infectious and parasitic diseases to times of lower mortality rate usually associated with communicable diseases. The causes of death changes are as a result of natural causes, the life expectancy, age structure of a population, the measure of fertility of a population and quality of health care of a population of a country.

The standard of living also forms an important part in the well being of a population, mentally, physically and socially. Mortality transition in Saudi Arabia Health care is an increasing concern in many countries today due to increasing non communicable diseases which are a contributing factor to increasing deaths. Saeedi, in his assessment report on cardiovascular diseases makes it clear that it is a major leading cause of increased mortality rate in Saudi Arabia every year accounting at least 20% of the total deaths. There is however a decline in fertility rate in Saudi Arabia which is believed to be attributed to delay in marriage, spatial distribution of development and household characteristics (Gietel-Basten, 2017). Birth rates Bamufleh notes that use of contraceptive enhances birth spacing although it has no significant effect on birth control.

New strategies and policies have been advanced that are patient centered and focus on social determinants of health, promotion and protection. More health centers have been accredited by the national agency so as to improve safety and quality of services. Despite this improvement, the demand for human resources at the health centers is increasing rapidly which is a challenge. He therefore comes to a conclusion that fertility of women in Saudi Arabia has fallen which could be as a result of late marriages low economic status and studies. Also, cases of abortions have risen among women over the age of 40 years with increased rates of infertility attributed to unhealthy foods, obesity, hypertension, smoking and unsuitable living environment. However, the population of Saudi Arabia has increased over the years as compared to mortality rates (Braham, 2017.

RESULTS AND ANALYSIS 4. Trends of Mortality 4. From the graph, under-five mortality rate(per 1000 live births) was recorded as from the year 1972. According to the graph, there was a gradual decrease in under-five mortality( per 1000) between 1996 and 2014. A steady decline was also recorded between the years 1972 and 1992. The year 1972 had the highest record of over 150 deaths( per 1000) compared to a lower value of less than 25 (per 1000) by 2014. Figure 3. The graph shows a great decrease in maternal mortality ratio between 1990 and 2014. MMR reduced to 12 (per 100000 live births) in the year 2014. According to the presented data, there were no records of maternal mortality ratio from 1960 to 1990. There was a sharp decrease in MMR between the year 1990 and 2001. The years 2009 and 2014 recorded a gradual change in maternal mortality ratio with the years 2009,2011 and 2014 recording almost zero changes.

The highest life expectancy recorded (in years) was 2014 at 74 years and the lowest recorded in the year 1960 at 46 years. From the graph, there was a gradual change in life expectancy at birth between 1992 and 2014. The largest increase in life expectancy occurred between 1967 and 1976. Figure 7. Trend of Life Expectancy by Gender The above figure is a graph representing the average number of years a newborn is expected to live till death if neonatal mortality rate and under-five mortality rate remain constant for both male and female. Female adults experienced a more gradual decrease in mortality rate than male adults between the year 1996 and 2014. Figure 9. Trends of Child Mortality Rate under Age 5 by Gender The above under-five mortality rate graph represents the probability of live births dying between birth and exactly five years per 1000 live births in KSA between 1060 and 2014.

According to the graph, under-five mortality rate records begun to be kept from the year 1990 up to 2014 and from the presented data, male children had a higher probability of dying below five years compared to female live births( per 1000 live births). The highest records for male were 46 (per 1000 live births) while for the female were 42 (per 1000 live births) There was an even but sharp decrease in under-five mortality rate between 1990 and 2000 compared to an even but steady decline from the year 2000 to 2014. From the graph, the degree of certainty of the data provided was 0. The graph depicts a steady but even decline in the crude death rate for the urban population (per 1000 people). The highest recorded crude death rate was 21( per 1000 people) of 31% urban population of the percentage total in KSA.

The lowest was 3 (per 1000 people) of 80% of the total urban population. Death crude rate remained constant at 2. per 1000 people) and remained constant when health expenditure per capita was increased to between 600 and 1200 USD. Figure 13. Scatter Diagram between Crude Death Rate and log (Gross Domestic Product), and Fitted Line From the above graph of death crude rate (per 1000 people) versus logGDPmar in KSA, according to the egression line, the degree of certainty of tis data was 0. Athough the trend is not even, it’s evident that KSA experienced a dramatic reduction death crude rate ( per 1000 people) as the logGDPmar increased. However, according to the data provided, although the logGDPmar reduced from 26 to 25. These are associated with pre-mature birth and labor complication at birth.

Other factors that contribute to infants’ death include lack of quality health care and suffering malnutrition in the case of poor communities. From this analysis it clear that the rate of death increases at infants and under -5. This trend has a direct impact on life expectancy in the sense that due to many factors contributing to increase inn deaths, the life expectancy is significantly reduced with time. Life expectancy shows a negative association with the other variables since it largely depends on the rate mortality; when the rate of mortality decrease, the life expectancy increases. This is due to ease of access of health facilities and clinics in urban centers as compared to remote rural areas which are challenged by transportation facilities in order to access quality care.

The primary completion rate, both sexes shows little correlation with logGDPmar, while the ogGDPmar shows a positive association with the urban population, that id the GDP is expected to increase with increase in urban population. The primary completion rate, both sexes increases with increase in health expenditure per capita (US$). As a result, greater investment in health care significantly contributes to completion rate. Generally, the death crude rate, is affected by a number of issues such as income, affordability of quality health care and accessibility to health care facilities. Death rate shows a negative correlation with other variables such as life expectancy (-0. urban population (-0. gross enrollment (-0. Most of the other variables have an effect on each other either directly or indirectly therefore, they have a correlation.

Table 1 shows the correlations between the various variable under study. In this case, table 2: analysis will be based on 20 cases in which the various independent variables have been statistically analyzed and organized in such a way that the maximum and minimum expected values of each variable are determined. The average and standard deviation have a significant importance in decision making regarding the mortality rate in Saudi Arabia. The maximum and minimum gives the range over which variable that can be analyzed while the mean average is important for general decision. The table below shows the statistical properties of the various variables. Multiple Regression Analysis of Crude Death Rate Table 3 compares the relative importance of each of the standardized variables. and gross enrollment (0.

shows some relation with the variable under study. The economic status does not have great effect on the child mortality. All other factors show a negative relationship and effect therefore do not provide sufficient basis of comparison since at tender age , computation of the variable ignores the number of children and also it is not possible to continually update the values since births occur continually every minute. Therefore, log GDP market does not take it consideration of new birth and so does to death of the infants. People e through interaction, improvement with technology people are able to access change quality health abroad while conducting researches on how to solve most of the problems that have been contributing negatively to child survival.

REFERENCES Bamufleh, R. A. Al-Zahrani, A. E. Swedan, N. B. Al Dawish, M. A. Reproductive disturbances among Saudi adolescent girls and young women with type 1 diabetes mellitus. Rubin, D. B.  Statistical analysis with missing data (Vol. John Wiley & Sons. Saeedi, M. A. Cardiovascular risk assessment in general population at primary health care centers in Saudi Arabia: Using the World Health Organization/International Society of Hypertension risk prediction charts.