An Assessment of Fertility Rate and Differentials in Women Within the Reproductive Age in Kaduna State
Chapter One
Aim and Objectives of the Study
The aim of this dissertation is to estimate the fertility rate and fit a model that can best determine the fertility pattern in women within the reproductive age in General Hospital Kafanchan and Dantsoho Memorial Hospital Tudun Wada, Kaduna State of Nigeria using binary logistic and multiple regression. The specific objectives are:
- To measure the fertility rate in women within the reproductive age in the two Hospitals.
- To determine the fertility pattern in women within the reproductive age in these two hospitals in Kaduna state of Nigeria.
- To determine major diseases that causes fertility differentials in women within the reproductive age in these two hospitals in Kaduna state of Nigeria.
- To compare the result of the two methods used.
CHAPTER TWO
LITERATURE REVIEW
Introduction
This chapter provides a review of related research work done by other researchers on the use of multivariate techniques to assess fertility and factors that causes fertility differential.
Fertility in Nigeria
Nigeria is the most populous country in Africa with a rapid population growth that outweighs the growth of resources and having a current population of over 150 million and a growth rate of approximately 2.4 percent per annum (Olootu, et al. 2012).
Fertility rates in Nigeria have been put at approximately between 5 and 6 children per woman not withstanding a “high rate of pregnancy wastage.” (Oluyemi, and Earnest, 2011). The World Health Organization Report shows a value of 6.5 children per woman for 1994 and 5.7 for 2004. These data seem at odds with growing concerns about infertility as such. As Hollos put it, “The problem of infertility in sub-Saharan Africa (including Nigeria) received comparatively little attention until recently.” Hollos further emphasized that the problem of infertility “was obscured by the region’s high fertility rates, which gave rise to a global climate of concern over population growth and high fertility that is not conducive to the perception of infertility as a real problem.” (Oluyemi, and Earnest, 2011).
Low prevalence of contraceptive use has been attributed to be a prominent cause of high fertility rate in Nigeria. High total fertility rate (TFR) in Nigeria and sub-Saharan African countries resulted from low level of contraceptive use in terms of acceptability, affordability and accessibility (Stephen, et al. 20011). The relatively high birth rate in Nigeria has contributed to high rates of population growth. This low prevalence showed clearly that the goal set by Federal Government of Nigeria in 2004 to reduce fertility through increased adoption of contraception has not been actualized (Stephen, et al. 2011). Contraceptive use reduces the likelihood of having higher completed fertility (Stephen, et al. 2011)
Mohammed, (2012) said fertility rate of 5.7 in Nigeria is higher than many other countries of about similar level of development with huge regional differences, North-West and North-East having high total fertility rates. He, however, called for a review of the nation’s population policy in order to foster Nigeria’s development. He attributed the high fertility to early marriage, girl-child education and the role of women in the society. The minister spoke on the nation’s increasing population, arguing that by 2050, Nigeria would be among the most populous countries in the world. It was estimated that by 2050, we will be among the top five most populous nations in the world (Mohammed, 2012).
According to National Population Commission (NPC) Publication (2013), Fertility varies considerably by region of residence, with lower rates in the south and higher rates in north. Fertility also has a strong negative correlation with a woman’s educational attainment. Most Nigerians, irrespective of their number of living children, want large families. The ideal number of children is 6.7 for all women and 7.3 for currently married women. Nigerian men want even more children than women. The ideal number of children for all men is 8.6 and for currently married men is 10.6
(NPC, 2013). Clearly, one reason for the slow decline in Nigerian fertility is the desire for large families. A 36-month interval between deliveries is best for mother and child; longer birth intervals also contribute to reduction in overall levels of fertility. The median birth interval in Nigeria is 31 months, which is close to the optimal interval. The median interval is lowest among mothers aged 15-19 (26 months) and highest among mothers aged 40-49 (39 months). While there is no difference in birth intervals between urban and rural women, birth intervals do vary considerably by region of residence. Women in the South West have the longest median birth interval (37 months) and women in the South East have the shortest median birth interval (27 months), a difference of almost one year (NPC, 2013).
According to Nigerian Newsworld (March, 2014): Years of high fertility produce a young population age structure, which generates momentum for future growth as these youth begin having their families. Today it is 3.2 children per woman or one child more than “replacement-level fertility,” in which couples have about two children each and replace themselves in the population.
Replacement fertility is the total fertility rate at which women give birth to enough babies to sustain population levels if there were no mortality in the female population until the end of the childbearing years. The replacement fertility rate is roughly 2.1 births per woman for most industrialized countries (2.075 in the UK, for example), but ranges from 2.5 to 3.3 in developing countries because of higher mortality rates (Wikipedia, 2014).
CHAPTER THREE
MATERIALS AND METHOD
Introduction
In this chapter, section 3.1 discusses about the data used to carry out the analysis. Section 3.2 talked about Statistical Techniques and Modelling while section 3.3 discusses about the Test Statistics.
Materials
The data used for this dissertation were secondary data collected from Health record department of General Hospital Kafanchan and Yusuf Dantsoho Memorial Hospital Tudun Wada Kaduna from 1994 to 2013 and all the data available were collected. The variables used are: Total Life Birth (TLB), Sexually Transmitted Disease (STD), Fibroid (FB), Ovarian Cyst (OVC) and Ectopic Pregnancy (ECT). The dependent variable (Y) used is TLB and the independent variables are STD (X1), FB (X2), OVC (X3) and ECT (X4). The data are presented in tables as Appendices I to XIII
CHAPTER FOUR
RESULTS AND DISCUSSION
Introduction
In this chapter, results and discussions about the analysis of the data used is carried out.
Section 4.2 discusses about the normality test for the data while section 4.3 talked about the result of the binary logistic regression using the collected samples. In section 4.4, the result of the multiple regression of both samples was discussed. Section 4.5 discusses about the findings of the analysis of both logistic and multiple regression of the two hospitals.
CHAPTER FIVE
SUMMARY AND CONCLUSION
Summar
In this dissertation, descriptive statistics was used to estimate the fertility rate while binary logistic and multiple regression were used to determine fertility pattern and factors that causes fertility differentials in women within the reproductive age in the two hospitals in Kaduna State. The dependent variable that was used was TLB and the independent variables were STD, FB, OVC and ECT. The independent variables that were used were diseases that cause fertility problems by delaying pregnancy or causes miscarriages in women if left uncured.
The fertility rate was estimated using descriptive statistics which gives an approximate value of five (5) children per woman in sample I, seven (7) children in sample II and six (6) in sample III. The histogram and the line graph of Total Fertility Rate (TFR) shows a gradual declining in the fertility rate. Also logistic regression result of sample I revealed STD as a significant factor that affects TLB negatively, Result of sample II showed STD and ECT were statistically significant and have a negative value indicating decrease in TLB. Also the logistic regression result of sample III showed a negative value in STD and OVC which also means decrease in TLB.
The multiple regression of sample I showed that only OVC is negative but in not significant in the model that is it has not contributed to the model. STD was significant out of the four independent variables used. The result indicated a positive coefficien which means that STD will increase TLB. The multiple regression of sample II showed that STD, FB and ECT were significant. STD was negative which means a unit increase in STD will reduce TLB while FB and ECT were positive that is a unit increase in each of these variables will increase TLB. The multiple regression result of sample III indicated that STD and OVC were significant. The positive value of STD and OVC shows increase in TLB.
Conclusion
The fertility rate is five (5) children per woman in sample I, seven (7) children in sample II and six (6) children in sample III. The fertility rate is still high but is gradually decreasing looking at the histogram and the line graph of Total Fertility Rate (TFR). The result showed STD to be significant in both logistic and multiple regression in sample I and in sample II with a negative coefficient in logistic regression. The negative value of STD shows a gradual reduction in fertility and it means among the variables STD, FB OVC and ECT, STD was the major factor that affects TLB in sample I while STD and ECT were the major factors that affect TLB in sample II. In sample III, STD and OVC affect TLB by reducing fertility. In multiple regression, some of the variables that were significant such as STD in sample I, FB and ECT in sample II showed a positive result which means increase in TLB. While the negative value of STD in sample II indicates decrease in TLB. In conclusion, the negative value in STD, OVC and ECT in logistic regression, indicated decrease in fertility which means STD, OVC and ECT are the major factors that affect TLB
Suggestion for Further Research
Among the variables that were used for this research work, STD seems to be the variable that affects fertility negatively in both samples. Further research can be carried out on various STDs such as pelvic inflamentary disease, gonorrhea, chlamydia; syphilis etc. A different statistical tool can be use to measure the effect of such variables on fertility.
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