prevalence of kidney renal failure reported at Sir Yahaya Memorial Hospital BirninKebbi
ABSTRACT
This research based on analysis of the prevalence of kidney (renal) failure in Kebbi State, taking into account that the variables of interest are gender and age group. The nature of the data used is secondary data, which was obtained from Sir Yahaya Memorial Hospital, (Birnin Kebbi) medical record for consecutive ten (10) years (20072016), while monthly reported cases was collected and analyzed. My present study has been carried out in order to determine whether the effect of renal failure depends on age and gender, and to look at the prevalence of kidney (renal) failure, over the period of study. Appropriate statistical techniques have been used to test the difference of means (ttest) and contingency table (X2 test), based on the analysis of results. The analysis has been done for significant at 5% level of significance. The empirical results are obtained from the tests of two different means which reveal that there is a significant difference in the prevalence of renal failure between male and female. Resultantly, the impact of kidney renal failure has been focused both on two parameters of age and gender. Finally, some significant suggestions based on my empirical results from data analysis and observations have also been proposed for preventing kidney renal failure and future scope of present study.
Keywords: Chronic kidney disease (CKD), kidney (renal) failure, chisquare test, Ttest, level of significance,
TABLE OF CONTENTS
Title Page i
Certification ii
Dedication iii
Acknowledgement iv
Abstract v
Table of content x
CHAPTER ONE
INTRODUCTION
1.1 Introduction 1
1.2 Statement of the Problem 2
1.3 Aim and Objectives 2
1.4 Research Question 2
1.5 Research Hypothesis 3
1.6 Significance of the study 3
1.7 Scope and Limitation 3
1.8 Definition of Term 4
CHAPTER TWO:
LITERATURE REVIEW
2.1 Introduction 4
2.2 Stressors of Patients Undergoing Haemodialysis 4
2.3 Individual Psycological Factors of Patients Undergoing Haemodialysis 5
2.4 Personality 5
2.5 Perception of Disease 5
2.6 Period of Adjustment 6
2.7 Self Esteem 7
2.8 Physical Factors of patients undergoing Haemodialysis 7
2.9 Pain 7
2.10 Behavioral Factors of Patients Undergoing Haemodailysis 7
2.11 Diet Eating Disorders 8
2.12 Exercise 9
2.13 Sleep 9
2.14 Sexual Dysfunction 9
CHAPTER THREE:
METHODOLOGY
3.1 Introduction 11
3.2 Source of Data Collection 11
3.3 Problems Encountered During Collection 12
3.4 Method of Data Collection 12
3.5 Analysis of Result 21
CHAPTER FOUR:
CONCLUSION AND RECOMMENDATION
4.1 Conclusion 26
4.2 Recommendation 26 References 28
Appendix 30
1.0 CHAPTER ONE
1.1 Introduction
A kidney renal failure is a serious disease, which has major impact on life and can be accidentally fatal; several studies have demonstrated the high incidence of renal failure, which are of two types i.e. acute and chronic renal failures. Kidney disease is an important public health issue. It is common and the prevalence increases with age, which means that the disease burden will increase with our aging population. Chronic kidney disease is an independent risk factor for other diseases, particularly cardiovascular disease. It often coexists with other cardiovascular conditions meaning that it needs to be managed alongside other diseases and risk factors such as diabetes and hypertension as well as the social needs that come with frailty and multiple conditions. (William, et al, 2003).
In a minority of cases, chronic kidney disease progresses to end stage renal disease, which may require renal replacement therapy. This progression and the risks of other vascular events, such as stroke and heart failure can be reduced if chronic kidney disease is identified and managed, early diagnosis is therefore essential. The acute renal failure (ARF) is characterized usually reversible deterioration of renal function, which develops over a period of days or week. It occurs suddenly, by causing bacterial infection, injuries, shock, congestive heart failure, drug poisoning and severed bleeding which results in uremia. A marked reduction in urine volume is usual and the clinical features, while the rapid problems of diagnosis and management arises. Many of the disorder giving rise to acute renal failure carry high rate of mortality in human beings, but if the patients survives, then the renal function usually returns to normal or near normal. (Dibal, 2006).
Chronic kidney disease (CKD) describes abnormal kidney function and/or structure. It is common, frequently unrecognized and often exists together with other conditions (for example, cardiovascular disease and diabetes). CKD can progress to end stage renal disease in a small but significant percentage of people. CKD is usually asymptomatic until the late stages, but it is detectable usually by measurement of serum creatinine or urine testing for protein. In the UK clinical practice has been standardized using the 4 factor Modification of Diet in Renal Disease (MDRD) equation and albumin creating ratio, consistent with the National Institute for Health and Clinical Excellence (NICE) guidance. Other measurement methods exist for specific indications such as the CKDEPI equation and the CockroftGault in children. The CKDEPI equation is more accurate than the MDRD especially in categorizing CKD stages 35 and may be used in future CKD guidelines. (Maurya et al 2013).
There is evidence that treatment can prevent or delay the progression of CKD, reduce or prevent the development of complications and reduce the risk of cardiovascular disease. Statistical simulations techniques and sampling tests are widely used to explore significant empirical results and implications in different allied fields of biology, natural science, and life sciences. In this direction, we refer recent work of Maurya, 2014 and references therein. Literature shows that several previous researchers and authors paid their attention to contribute in this connection.
1.2 Statement of the Problem
The cases of kidney renal failure over the years especially in Nigeria, has been observed to be fluctuating, despite the fact that the disease can be accidentally fatal, therefore, renal failure may be caused by any condition, which destroys the normal structures and functions of the kidney. By the aims and objectives mentioned below, one will be able to know the discrepancies arising the effects of renal failure and how it can be cured. As a result of its great economic importance, (Timothy . 2005).
There is a need to address people so as to know the implications and protections in human societies. It is this development that prompted the desire to look at the situation morally and clearly, so as to draw a valid conclusion.
1.3. The Aim and Objectives
The aim of this project work is to study the prevalence of kidney (renal) failure using logistic regression with a view to achieve the following objectives:
 To verify whether the effect of renal failure depends on age and gender.
 To verify whether there is any difference in the prevalence of renal failure between genders.
 To analyze, verify, recommend and conclude based on the result of the analysis made to the research on the effect of renal failure.
1.4. Research Questions
 Does the number of renal failure increases or decreases over a period of time at different age group?
 Does the number of renal failure depend on age and gender?
 At what age is the renal failure more rampant and prevalent?
1.5. Research Hypothesis 1.
Null hypothesis Ho: kidney (renal) failure does not depends on age and gender, i.e Ho: Âµ1 = Âµ2.
Alternative hypothesis H1: kidney (renal) failure depends on age and gender, i.e
H1: Âµ1 ≠ Âµ2
Hypothesis 2
Null hypothesis Ho: there is no significant difference in the prevalence of kidney (renal) failure between gender, i.e Ho: Âµ1 = Âµ2.
Alternative hypothesis: there is a significant difference in the prevalence of kidney (renal) failure between gender, i.e H1:Âµ1 ≠ Âµ2.
1.6. Significance of the Study:
Apart from this major sector to the pursuance of my education it can also serve as baseline information. Researchers that may be willing to carryout relevant study in the future and also the resulting analysis will be great significance to the SIR YAYA MEMORIAL SPECILIST HOSPITAL, BIRNIN KEBBI, KEBBI STATE and Kebbi State Ministry of Health in general.
1.7. Scope and Limitation of the Study:
This study is limited to the number of reported cases on the rate of kidney (renal) failure at Sir. Yahaya Memorial Specialist Hospital, Kebbi for the year (20072016). It entails some limitation especially in the field of data collection. The study is restricted to only Sir. Yahaya Memorial Specialist Hospital, Kebbi state. Considering the wideness of this topic, the analysis is based on the ten (10) years monthly reported cases of the renal failure in the hospital.
1.8 Causes CKD
The two main causes of chronic kidney disease are diabetes and high blood pressure, which are responsible for up to twothirds of the cases. Diabetes happens when your blood sugar is too high, causing damage to many organs in your body, including the kidneys and heart, as well as blood vessels, nerves and eyes. High blood pressure, or hypertension, occurs when the pressure of your blood against the walls of your blood vessels increases. If uncontrolled, or poorly controlled, high blood pressure can be a leading cause of heart attacks, strokes and chronic kidney disease. Also, chronic kidney disease can cause high blood pressure.
Other conditions that affect the kidneys are:
Glomerulonephritis, a group of diseases that cause inflammation and damage to the kidney's filtering units. These disorders are the third most common type of kidney disease.
Inherited diseases, such as polycystic kidney disease, which causes large cysts to form in the kidneys and damage the surrounding tissue.
Malformations that occur as a baby develops in its mother's womb. For example, a narrowing may occur that prevents normal outflow of urine and causes urine to flow back up to the kidney. This causes infections and may damage the kidneys.
Lupus and other diseases that affect the body's immune system.
Obstructions caused by problems like kidney stones, tumors or an enlarged prostate gland in men.
1.8 Definitions of Term:
1. Chronic Kidney Disease (CKD): is a condition characterized by a gradual loss of kidney function over time. CKD includes conditions that damage your kidneys and decrease their ability to keep you healthy by doing the jobs listed. If kidney disease gets worse, wastes can build to high levels in your blood and make you feel sick. You may develop complications like high blood pressure, anemia (low blood count), weak bones, poor nutritional health and nerve damage.
2. A ChiSquare Test{\displaystyle \chi ^{2}}: Is any statistical hypothesis test wherein the sampling distribution of the test statistic is a chisquared distribution when the null hypothesis is true. Without other qualification, 'chisquared test' often is used as short for Pearson's chisquared test.
3. The ttest: Is any statistical hypothesis test in which the test statistic follows a Student's tdistribution under the null hypothesis.
A ttest is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistics (under certain conditions)
2.0 CHAPTER TWO
2.1 Literature Review
Chronic Renal Failure (CRF) is an irreversible and progressive kidney failure where body fails to maintain metabolic and electrolytic balance, resulting in uremia, metabolic acidosis, anemia, electrolyte imbalances and endocrine disorders. Its main causes are diabetes, hypertension, glomerulonephritis, and polycystic kidney disease.
Haemodialysis is the most frequent treatment method for CRF. However, it has been argued that a number of restrictions and modifications accompany this treatment, which have a detrimental impact on the quality of patient’s life and affect individuals’ physical and psychological wellbeing.
Chronic Renal failure is a continuous psychological process for patients and their families in order to accept their new image and to be adjusted to the new condition of haemodialysis. The quality of life of patients requiring dialysis is affected significantly, since it is associated with changes in their daily habits and in their lifestyle for both themselves and their families. At the same time, their physical health, their functional status, their personal relationships and their social and economic status are greatly affected. (Dingwell, 1997).
2.2 Stressors of Patients Undergoing Haemodialysis
Many authors have dealt with the stressors of individuals with chronic kidney renal failure undergoing regular dialysis, since they increase both the psychological and socioeconomic problems of these patients. The most common stressors are financial difficulties, changes in social and marital relationships, regular hospital admission, inability of holiday vacation, restriction of leisure time, relationships with nursing and medical staff, fear of disability or death, increased dependence on artificial kidney machine, uncertainness about the future and physical fatigue.
Furthermore, according to Gerogianni, (2014). Liquids and foods is the most frequent stressor for the patients. That is because the daily consumption of fluids should not exceed of 500 ml per day due to the risk of causing pulmonary edema. An equally distressing factor is the requiring effort to follow the dietary guidelines, as the excessive intake of potassium and phosphorus is responsible for causing heart failure and possible itching or renal osteodystrophy respectively.
A recent research study conducted in Greece by Kaitelidou et al, (2016), showed that 60.2% of patients receiving dialysis were not able to keep their profession and 36.7% had to retire after the beginning of dialysis. Loss of employment is responsible for the appearance of intense anxiety and problems of gender function while employment positively affects the psychological status and libido of spouses.
Also, Ormandy (2010), argues that stressors affecting appetite of patients are body image issues (weight loss, muscle wasting, change of skin color, visible signs of venous puncture) after starting dialysis. Another one significant stressor is fatigue, which can negatively affect a person’s ability to work and to participate in various daily activities. Physical or mental fatigue can be caused by sleep disorders or fatigue after dialysis.
2.3 Individual Psychological Factors of Patients Undergoing Haemodialysis
2.3.1 Psychopathology
Psychopathology of patients undergoing haemodialysis plays an important role in the final outcome of renal disease and in occurrence of psychiatric symptoms. Social or family support of the patient and medical staff support are very important factors, as they affect patient’s compliance to the disease. In a patient with such a chronic health problem, a powerful 'Ego' tries to mobilize adaptable strategies of the disease, while a weak ‘Ego’ is leading him to the development of psychiatric disorders, noncompliance with the treatment and usually disruption of interpersonal and family relations. (Reyner and Best, 1989).
2.4 Personality
Psychosocial patient’s compliance with a regular haemodialysis program depends on the personality of the patient in conjunction with the support received from health professionals, family and social environment.
According to the research study of Maurya et al., (2014), about the effect of chronic dialysis on the personality of patients with CRF, patients who are forced to join in a program of periodic dialysis, exhibit personality disorders, which are different, regarding gender and age. Specifically, these patients, before the enrollment in a program of CRF, are facing a health problem as most people with a chronic problem. After the beginning of dialysis, they exhibit considerable psychological personality disorders, such as alexithymia, neuroticism, introversion and psychotism.
2.5 Perception of Disease
2.5.1 Depression
Depression is the most common psychological complication which has serious impact on the quality of life of haemodialysis patients and their caregivers, affecting negatively their social, economic and psychological wellbeing.
Depression is associated with important aspects of the clinical course, including mortality, increased number of hospital admissions, reduced compliance with drugs and reduced quality of life. However, Beaver et al. (2003), in a research study reported that depression is responsible for the highest annual mortality of haemodialysis patients in Pakistan, compared with patients in Western countries.
The incidence of depression is 73% and most of the patients are included in the category of moderate to severe depression. Personality traits and cognitive evaluation of each patient have a strong correlation with the occurrence of depression in patients with end stage renal failure. The association between psychosocial factors and depression depends on gender, age and type of dialysis.
Depression occurs more frequently in patients with CRF mainly between the third to ninth years of treatment and affects females with greater frequency. Also, depressions manifests mainly with sadness, anxiety, depressed mood, poor selfesteem, pessimism about the future, decreased libido, sleep disorders and limited appetite. Moreover, during the period of starting a dialysis program, one in 500 patients attempt a suicide or violate the dietary rules.
2.6 Periods of adjustment
During the period of adjustment to the dialysis procedure, patient is going through three periods:
a) The period of honeymoon, beginning 13 weeks from the first dialysis.
b) The period of frustration that takes about 312 months and
c) The long period of adjustment.
In honeymoon period, patients with acute renal impairment accept with relatively greater ease the process of dialysis and their dependence from the artificial kidney machine and the health professionals. On the other hand, patients with progressive decline of CRF come upon intense fear and anxiety for any disability or death and concern about professional and social decline or financial problems. During this period, coexisting sleep disorders, depressive symptoms and intense concern about the loss of autonomy, employment, family role and gender function.
During the period of discouragement and frustration, patients feel strongly symptoms of grief, sadness and exhaustion. Typically, a domestic, private or professional stressful event is the first opportunity for the patient to pass at this stage. This stage manifests itself in outbursts of anger and aggression of the patient towards the family or the unit staff.
Finally, the longterm period of adjustment is characterized by partial acceptance of dialysis limitations by the patients were they experience periods of satisfaction and depression, (Maniadakis et al, 2016).
2.7 SelfEsteem
Additionally, patients with chronic kidney disease have difficulties in participating in sports and social activities. This has a negative effect on feelings of autonomy and selfesteem. Regarding the psychosocial picture of patients undergoing chronic dialysis program, selfesteem seems to be moderate to high, in patients who have interests, to those of them who are in good economic condition and being employed.
According to the theory of selfdetermination, autonomy is one of the basic human psychological needs contributing to daily wellbeing and psychological wellbeing. When the fulfillment of autonomy need is hampered by various factors, patients experience poor selfesteem and bad psychological condition.
2.8 Physical Factors of Patients Undergoing Haemodialysis
2.8.1 Anemia
Anemia is the most common complication of CKD. According to existing data from the U.S. (National Health and Nutrition Examination Survey), the incidence of anemia in patients of 3rd Stage of CKD is 5.2%, in patients of 4th stage is 44.1%, and in end stage renal disease patients is universal. Furthermore, in certain patient groups, such as African Americans and patients with diabetes, the incidence of anemia is greater in all stages of kidney disease. Anemia appears clinically as fatigue and / or depression, due to reduced secretion of erythropoietin by the kidneys and adversely affects the quality of life of these patients. Key components of quality of life of patients, such as motor activity, sleep, morbidity, social activity, emotional relationships, anxiety, depression and mental satisfaction are being influenced favorably by correcting anemia.
The administration of erythropoietin, folic acid, vitamin B12, vitamin complexes and iron is important in the treatment of anemia and iron deficiency while transfusions are recommended for severe anemia. (Frank and Althoen, 1995).
2.9 Pain
The appearance of chronic pain in dialysis patients is usually in rate 37% to 50%, while 82% of them show a moderate to severe intensity pain. The etiology of pain is multifactorial and may be either due to the process of dialysis (puncture, muscle cramps, headaches) or due to the existence of accompanying systemic diseases and painful syndromes. Pain is the most common symptom  discomfort of patients which causes significantly impaired quality of life. This is because the incidence of chronic pain is associated with the onset of affective disorders (anxiety, depression), social disorders (isolation, negligence) and economic impact (e.g. inability to keep the job).
2.10 Behavioral Factors of Patients Undergoing Haemodialysis
2.10.1 Compliance
In the field of periodic dialysis, wellbeing and patient’s health depend on the active participation of patient in the treatment program and the compliance with medical instructions, requirements regimen and recommendations.
Patient’s compliance in the treatment process is very important as it affects the prognosis of a chronic illness such as End Stage Renal Disease (ESRD) and thus the quality of life of those patients. Compliance, involves adherence to a treatment regimen (dialysis schedule, duration of treatment, special diet, fluid limitation, right medication), changes in behavior, habits and lifestyle and sometimes adjustment of the personality aspects to the dialysis treatment.
A percentage of haemodialysis patients do not comply with medical instructions, medication and dosage, monitoring of the planned treatment, completing the required treatment time, restriction of fluids and dietary restriction. Reduced compliance is often a result of a depressive phenomenon and associated with increased mortality and poor medical results.
Also, a research study of Tijerina on psychosocial factors affecting the compliance of Mexican  American women during the dialysis procedure, classifies poverty, longterm periodic haemodialysis, immigrant status, loss of identity and family dysfunction among the factors affecting patients’ compliance in the dialysis treatment. Regarding poverty, patients who are of low income are compatible in the use of limited resources, buying lowcost drugs, using lowcost health services and not following specific dietary restrictions.
Finally, frequent hospitalization of haemodialysis patients and long life medication are routine situations that overwhelm patients, remind them constantly that they are suffering from this disease and urge them to drift. However, patient is the first one who will ultimately decide to follow or not the directives, to take or not drugs, to the right dosage and timing. (Hedayati and Bosworth, 2010).
2.11. Diet  Eating disorders
Poor nutrition is a major factor of quality of life because it increases the rate of morbidity and mortality and reduces physical activity. Malnutrition, weight loss and subsequent increased loss of energy of these patients are being presented with fatigue, discomfort and exhaustion. In the mean time, there is an increased susceptibility to infections as stockpiles of body protein and fat are in low levels. A problem of malnutrition such as anorexia, due to uremia, hypoalbuminemia and reduced intake of protein, appears to be exacerbated by the presence of depressive symptoms. Furthermore, there is evidence for a molecular mechanism causing cachexia in patients with CRF.
Also, in patients with ESRD who are adapted to dialysis program, the levels of TNF a, a cytokine associated with cachexia, and cortisol, a stress hormone associated with depression and deregulation of carbohydrate metabolism, are higher.
Home dialysis offers patients adequate nutrition and lack of dietary restrictions, contributes significantly to the improvement of malnutrition factors and has a direct impact on the quality of life of haemodialysis patients.
2.12 Exercise
Any type of systematic exercise, implemented in a proper manner and methods, can be a safe supplement, and non pharmaceutical therapeutic agent for patients with CKD on dialysis. Physical exercise is positively associated with quality of life. That is because it makes patients more active ‘physically’, in terms of their general fitness and reduces the severity of complications that occur during dialysis. The beginning of exercise in those patients need to be progressive and individualized according to the limitations of the patient (type of exercise, frequency of exercise, exercise intensity) and the pathophysiological condition. For this reason, patients on haemodialysis should be encouraged to exercise individually and collectively with the limitations imposed by their health condition.
2.13 Sleep
Sleep disorders are a very common problem for dialysis patients and have occupied many researchers in the past. In a research study of JuYeh Yang et al., (2014), 85% of haemodialysis patients didn’t have good quality of sleep. The main psychosocial factors affecting the quality of sleep are major depression, educational level, occupational status and marital status.
It is also important to note that dependence of haemodialysis patients from the artificial kidney machine, the medical and nursing unit staff and their family and loss of control of physical function leads to insomnia and permanent stress.
2.14 Gender dysfunction
Gender dysfunction is a frequent and common problem in patients with CRF as 50% of men with ESRD and 55% of women undergoing dialysis report difficulty achieving orgasm. A case  control study of Yun Seob Song et al., (2014), on gender function and quality of life of women in Korea with CRF on dialysis, reports that 70% of patients display gender dysfunction. According to that study, gender dysfunction is higher in women on haemodialysis or peritoneal clearance due to the lack of gender interest.
Gender dysfunction is a group of disorders characterized by physical and psychological changes and result in weakness for satisfaction in gender performance and decreased quality of life. In terms of gender, women undergoing chronic dialysis have a lower quality of life and significantly greater gender dysfunction compared with healthy women. Also, gender disorders greatly affect the quality of life of many men and their partners and have negative impact on their self esteem and on their interpersonal relationships.
This condition has been shown to be significantly more common in men and women with renal disease than in the general population. Men more often have problems with the features of gender dysfunction, such as difficulty in gender arousal, erectile dysfunction, premature or delayed ejaculation and difficulty in achieving orgasm. At the same time, women have very frequent abnormalities of the menstrual cycle or decreased libido. In the meanwhile, on haemodialysis Korean women hormonal disorders and premature menopause are often problems. In CKD, problems in the gender area are closely related to reducing of the frequency of gender intercourse, impotence and reduced libido.
Another important problem of chronic patients is the fear of loss of gender partners, as expressed by a patient: “Maintaining my relationship is the first thing on your mind, just overcoming health problems. This is the most important test you have to experience, how he will react while watching a woman with bandages, unable to help herself. Even if there is love, there are things that remind him of your situation”.
These possible problems may be due to various physical and psychological factors. Such factors are hormonal disorders (hyperthyroidism, hyperprolactinemia, hypogonadism in men and changes in hypothalamic pituitary in women), anemia, bone disorders, psychosocial factors (depression, denial of illness, anxiety, low selfesteem, social isolation, negative perception of body image, fear of disability and death, job loss, financial difficulties), autonomic neuropathy, medication and coexisting diseases (diabetes, cardiovascular disease, malnutrition).
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 INTRODUCTION
In this chapter, the researcher set out to explain the methodology used in gathering data and also this chapter describes how the study is designed and progressed with study of statistically analyzing the rate of kidney (renal) failure in Kebbi State using Sir Yahaya Memorial Hospital, Birnin Kebbi as a case study. A chisquared test also referred to as chisquare test or test is any statistical hypothesis in which the sampling distribution of the test statistic is a chisquared distribution when the null hypothesis is true. Also considered a chisquared test is a test in which this is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chisquared distribution as closely as desired by making the sample size large enough. He computed the sampling distribution of the sample variance of a normal population. Thus in German this was traditionally known as the Helmertsche ("Helmertian") or "Helmert distribution". The name "chisquared" ultimately derives from Pearson's shorthand for the exponent in a multivariate normal distribution with the Greek letter Chi, writing Ï‡² for what would appear in modern notation. The idea of a family of "chisquared distributions" is however not due to Pearson but arose as a further development due to Fisher in the 1920s. The main objectives of this study are mentioned in chapter one above.
3.2 SOURCES OF DATA COLLECTION
Basically we have two sources of data collection namely
1. Primary source
2. Secondary Source
Primary Source
A data collected for any research or project work is said to be from a primary source if the researcher is the one that generated the data and he/she is the first to make use of such generated data. A primary source of data can come or be generated through personal interview, questionnaire, experiment and direct observation etc.
Secondary Source
A data collected for any research or project work is said to be secondary source if such data were extracted from an already existing record or source example are data gotten from the internet, journals record files etc.
For the purpose of this project work, the data used was from a secondary source. i.e it was gotten from the statistical health record, nephrology unit Sir Yahaya Memorial Hospital Birnin Kebbi, Kebbi State and it covers the period of ten(10) years from 20072016.
3.3 PROBLEMS ENCOUNTERED IN DATA COLLECTION.
They are indeed problems encountered during the process of data collection among many which are
Financial Problem
Unwillingness for the company to release such vital information
Extraction of the data from different record books or file.
Time constraint and also clashed lectures on the days of collection of data from the company
3.4 METHOD OF DATA ANALYSIS
Analysis is a process by which conclusions are drawn after detailed examination or manipulation has been done.
However, there are different methods of analyzing the data such as X2 analysis, correlation and regression and logistic regression were used to analyze the data in order to determine whether the effect of kidney (renal) failure depends on age and gender and to look at the prevalence of kidney (renal) failure, over the period of study.
3.2.1 METHOD OF ANALTYSIS
The method used, is logistic regression, and X2 to determine whether the effect of kidney (renal) failure depends on age and gender and to look at the prevalence of kidney (renal) failure. The statistical Software used is SPSS20.
3.2.2 METHODOLOGY OF LOGISTIC REGRESSION
In statistics, logistic regression (sometime called the logit model) is used to predict the probability of occurrence of an event by fitting data to a logit function logistic curve. It is a generalized linear model used for binomial regression by many forms of regression analysis, makes use of several d = predictor variables that may be either numerical or categorical. For example the probability that a person has a kidney failure within a specified time period might be predicted from knowledge of person’s age and gender.
In other words, logistic regression or logit regression is a type of probabilistic statistical classification model. It is also used to predict a binary response from a binary predictor, used for predicting the outcome of a categorical dependent variable (i.e., a class label) base on one or more predictor variables (features). That is, it is used in estimating empirical values of the parameter in a qualitative response model. The probabilities describing the possible outcomes of a single trial are modeled, as a function of the explanatory (predictor) variables using a logistic function. Frequently (and subsequently in this article) “logistic regression” is used to refer specifically to the problem in which the dependent variable is binary that is, the number of available categories is twoand problems with more than two categories are referred to as multinomial logistic regression or, if the multiple categories are ordered, as ordered logistic regression.
Logistic regression is used extensively in the medical and social science field, as well as marketing applications such as prediction of a customer’s propensity to purchase a product of cease a subscription. Logistic regression can be binomial or multinomial. Binomial or binary logistic regression deals with situation in which the observed outcome for a dependent variable can have only two possible types (for example, “prone to the disease “vs. “not prone to the disease”). Multinomial logistic deals with situation where the outcome can have three or more chance of been affected by the disease. In binary logistic regression, the outcome is usually coded as “0” or “1”, as this leads to most straightforward interpretation. If a particular observed for the dependent variable is the noteworthy possible outcome (referred to as a “positive” or a “prone”) it is usually coded as “1” and the contrary outcome (referred to as “negative” or a “not prone”) as “0”. Logistic regression is used to predict the odds of being a case based on the values of the independent variables (predictors). The odds are defined as the probability that a particular outcome is a case divided by the probability that it is a non case.
An example of logistic regression begins with an explanation of the logistic function
The input is z and the output is. The logistic function is useful because it can take as an input value from negative infinity to positive infinity, whereas the output is confined to value from 0 and 1. The variable ‘z’ represent the exposure to some set of independent variable. While f(z) represent the probability of a particular outcome, given that set of explanatory variable. The variable ‘z’ is a measure of the total contribution of all the independent variables used in the model and is known as the logit.
Logistic regression generates the coefficients (and it is standard errors and significance levels) of a formula to predict a logit transformation of the probability of presence of the characteristic of interest:
Logit (p) =
Where p is the probability of presence of the characteristic of interest. The logit transformation is defined as the logged odds:
And
Logit (p) = in =
Rather than choosing parameters that minimize the sum of squared errors (like in ordinary regression), estimation in logistic regression chooses parameters that maximize the likelihood of observing the samples values.
3.2.3 Regression coefficients
The regression coefficients are the coefficient, regression equation.
Logit (p) =
Where is the intercept and are the regression coefficient of . The intercept is the value of all independent variables is zero(e.g. the value of z in someone with no risk factors). Each of the regression coefficient describes the size of the contribution to the risk factors.
A positive regression coefficient means that the explanatory variables increase the probability of the outcome, while the negative regression coefficient means that the explanatory variable decreases the probability of that outcome.
Logistic regression is a useful way of describing the relationship between one or more variable (age, gender, e.t.c.) and a binary response variable expressed as a probability that has only two possible values such as (prone or not prone.)
An independent with a regression coefficient not significantly difference from 0(p>0.05) can be removed from the regression model. If P<0.05 then the variable contribute significantly to the prediction of the outcome variable. The logistic regression show the change (increase when, decrease when ) in the predicted logged odds of having the characteristics of interest for a oneunit change in the independent variables. When the independent variable dichotomous variables (e.g. smoking,. gender) then the influence of these variables on the dependent variable can simply be compared by comparing their regression coefficients
3.3.6 Hosmer and Lemeshow Test
The hosmerlemeshow test is statistical for goodness of a fit for the logistic regression model. The data are divided into approximately ten groups defined by increasing order of estimated risk. The observed and expected number of cases in each group is calculated and a chi squared statistic is calculated as follows:
X2 =
Where, and the observed events, expected event and the number of observation for the risk deciles group, and n the of groups. The test statistic follows a chi squared distribution with n2 degrees of freedom.
A large value of chi squared (with small pvalue <0.05) indicate poor fit and small chi squared values (with larger pvalue closer to 1) indicate a good logistic regression model fit.
3.2.9 OMNIBUS TEST
Omnibus test are kind of statistical test that test whether the explained variance in a set of data is significantly greater than the unexplained variance, overall. One example is the Ftest in the analysis of variance. There can be legitimate significant effects within a model even if the omnibus test is not significant. For instance, in a model with two independent variables, if only one variable exert a significant effect on the dependent variable and the other does not, then the monist test may be nonsignificant. This fact does not affect the conclusions that may be drawn from one significant variable. In other to test the effect within an omnibus test, researcher often use contrast. In addition, Omnibus test is a general name refer to an overall or a global test and In most cases, omnibus test is called in other expressions such as: Ftest or Chisquared test. Omnibus test is statistical test it is implemented on an overall hypothesis that tend to find general significance between parameters variance, while examining the parameters of the same type, such as: Hypotheses regarding equality vs. inequality between k expectancies vs. at least one pair ≠;where j =1,…K and j ; in Analysis Of Variance (ANOVA); or regarding equality between standard deviations vs. K at least one pair in testing equality of variance in ANOVA; or regarding coefficients Î²1=Î²2=…Î² k vs. at least one pair Î²j≠Î²j in multiple linear regression or in logistic regression.
3.3.10 Omnibus Test in Logistic Regression
In statistics, logistic regression is a type of regression analysis use for predicting the outcome of a categorical dependent variable (with a limit number of categories) or dichotomic dependent variable based on one o more predictor variables. The probabilities describing the possible outcome of a single trial are modeled, as a function of explanatory (independent) variables, using a logistic function or multinomial distribution. Logistic regression measures the distribution between a categorical or dichotomic dependent variable and usually a continuous independent variable (or several), by converting the dependent variable to probability score. The probabilities can be retrieved using the logistic function or the multinomial distribution, while those probabilities, like I probability theory, takes on values between zero and one, whereas is the category of the dependent variable for the observation and is the j independent variable (j=1,2,…k) for that observation, Î²j is the coefficient of and indicates its influence on the expected from the fitted model. Note: Independent variable from the logistic regression can also be continuous. The omnibus test relates to the hypotheses.
Î²k
3.3.11 Classification, sensitivity and specification
Classification table shows the rule that allow us to correctly classify of the subjects where thee predicted event (negative or positive) was observed. This is known as the sensitivity of prediction, the p(positive or negative), that is the percentage of occurrences correctly predicted. It also show us the rule that enable us to correctly classify. This is known as the specificity of prediction.
3.3.12 Model fitting: Maximum likelihood method
The omnibus test, among the other part of logistic regression procedure, is a likelihoodtest based on the maximum likelihood method. Unlike the linear regression procedure in which estimation of the regression coefficients can be derive from least5 square procedure or by minimizing the sum of squared residuals as in maximum likelihood method, in logistic regression there is no such an analytical solution or a set of equations from which one can derive a solution to estimate the regression coefficients. So logistic regression uses maximum likelihood procedure to estimate the coefficients that maximize the likelihood of the regression coefficients given the predictors and criterion. The maximum likelihood solution is an interactive process that begins with a tentative solution, revises it slightly to see if it can be improved, and repeat this process until improvement is minute, at which point the model is said to have converged. Applying the procedure in conditioned on convergence (see also in the following “remarks and other considerations”). In general, regarding simple hypotheses on parameters Î¸ (for example): . . The likelihood test ration statistic can be referred as: , where L (y i/Î¸) is the likelihood function, which refer to the specific Î¸. The numerators correspond to the maximum likelihood of an observed outcome under the null hypothesis. The denominators correspond to the maximum likelihood of an observed outcome varying parameters over the whole parameter space. The numerator of this ratio is less than the denominator. The likelihood ratio hence is between 0 and 1. Lower values of the likelihood ratio means that the observed result was much less likely to occur under the null hypothesis as compared to alternative. Higher values of the statistic mean that the observed outcome was more than or equally likely or nearly as likely to occur under the null hypothesis as compared to alternative, and the null hypothesis cannot be rejected. The likelihood ratio test provide the following decision rule: If do not reject , otherwise if reject and also reject with probability q if, where as the critical value c, q are usually choosen to obtain a specified significance level of the test Î±, through the relation: . Thus, the likelihoodratio test reject the null hypothesis if the value of this statistic is too small. How small is too small depend on the significance level of the test, on what the probability of Type I error is considered tolerable The NeymanPearson lemma state that this likelihood ratio test is the most powerful among all level Î± test for this problem. Test’s statistic and Distribution: Wilks’ theorem First we define the test statistic as the deviate which indicates testing ratio: While the saturated model is a model with a theoretical fit. Given that deviance is a measure of the difference between a given model and the saturated model, smaller value indicates better fit as the fitted model deviates less from the saturated model. When assessed upon a Chisquare distribution, non significant chisquare value indicates very little unexplained variance and thus, good model fit. Conversely, a significant chisquare value indicates that significant amount of the variance unexplained. Two measure of deviance D are particularly important on the logistic regression: null deviance and model deviance. The null deviances represent the difference between a model with at least one predictor and the saturated. In this respects, the null model provide a baseline upon which to compare predictor model. Therefore, to assess the contribution of a predictors, one can subtract the model deviance from the null deviance and assess the difference in a chi square distribution with one degree of freedom. If the model deviance is significantly smaller than the null deviance then one can conclude that the predictor or set of predictors significantly improved model fit. This is analogous to the ftest used in linear regression analysis to assess the significance of prediction. In most cases, the exact distribution of the likelihood ratio corresponding to specific hypothesis is very difficult to determine. A convenient result, attributed to Samuel S. Wilks, says as the sample size n approaches the test statistic has asymptotically distribution with degree of freedom equal to the difference in dimensionality of and parameters the Î² coefficients as mentioned before on the omnibus test. E.g., if n is large enough and if the fitted model assuming the null hypothesis consist of 3predictors and the saturated (full) model consist of 5 predictors, the Wilks statistic is approximately distributed (with 2 degrees of freedom). This means that we can retrieve the critical value C from the chisquared with 2 degrees of freedom under a specific significance level.
3.5 ANALYSIS OF RESULT
SPSS was used for the analysis and result shown below.
Table 3.4.1: Statistics
Statistics
GENDER
AGE
N
Valid
166
166
Missing
0
0
Table 3.4.1 above shows that there are 166 gender and age group present in analysis and there is no missing value.
Table 3.4.2: Gender
GENDER
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
MALE
89
53.6
53.6
53.6
FEMALE
77
46.4
46.4
100.0
Total
166
100.0
100.0
Table 3.4.2 above shows that male has the high rate of kidney failure (53.6% ) compare with female (46.4).
Table: 3.4.3 AGE
AGE
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
115
16
9.6
9.6
9.6
1630
34
20.5
20.5
30.1
3145
51
30.7
30.7
60.8
4660
31
18.7
18.7
79.5
6175
17
10.2
10.2
89.8
7690
16
9.6
9.6
99.4
44.00
1
.6
.6
100.0
Total
166
100.0
100.0
Table 3.4.3 shows that the age range of 3145 (30.7%) has the high rate of kidney failure within year 2007 2016.
Table 3.4.4 ANOVA
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
96.693
1
96.693
8.441
.004b
Residual
1878.656
164
11.455
Total
1975.349
165
a. Dependent Variable: AGE
b. Predictors: (Constant), GENDER
Table 3.4.4 above shows that the effect of kidney (renal) failure depends on age and gender since pvalue <0.05, and concluded that gender and age are significantly different, from the overall analysis it is observed that the factors are significant because the overall pvalue < 0.05.
Table 3.4.5 Correlation
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
T
Sig.
B
Std. Error
Beta
1
(Constant)
1.290
.815
1.583
.115
GENDER
1.530
.527
.221
2.905
.004
a. Dependent Variable: AGE
Table 3.4.6 Logistic regression
Case Processing Summary
Unweighted Casesa
N
Percent
Selected Cases
Included in Analysis
166
100.0
Missing Cases
0
.0
Total
166
100.0
Unselected Cases
0
.0
Total
166
100.0
a. If weight is in effect, see classification table for the total number of cases.
Table 3.4.7dependent variable encoding
Dependent Variable Encoding
Original Value
Internal Value
MALE
0
FEMALE
1
Block 0: Beginning Block
Table 3.4.8 classification table
Classification Tablea,b
Observed
Predicted
GENDER
Percentage Correct
MALE
FEMALE
Step 0
GENDER
MALE
89
0
100.0
FEMALE
77
0
.0
Overall Percentage
53.6
a. Constant is included in the model.
b. The cut value is .500
Out of 166 people that are included in the analysis at which 89 are male and 77 are female and the result also indicate that about 53.6% are well treated .
Table 3.4.9 variable in the equation
Variables in the Equation
B
S.E.
Wald
df
Sig.
Exp(B)
Step 0
Constant
.145
.156
.866
1
.352
.865
Table 3.4.10 variable not in the equation
Variables not in the Equation
Score
df
Sig.
Step 0
Variables
AGE
8.126
1
.004
Overall Statistics
8.126
1
.004
From the table above, the variable in the question at step 0 on constant is significant because p –value < 0.05 .therefore we say that the effect of kidney (renal) failure depend on age and gender while age and gender are insignificant. From the overall statistics it is observed that the two factors are significant because the overall pvalue < 0.05.
Block 1: Method = Enter
Table 3.4.11 Omnibus Tests of Model Coefficients
Omnibus Tests of Model Coefficients
Chisquare
Df
Sig.
Step 1
Step
23.592
1
.000
Block
23.592
1
.000
Model
23.592
1
.000
The table above shows that the omnibus test of the model coefficient based on chisquare test, that implies the overall model of the test is predictive for awareness. Since p> 0.05 the result means that the model predict the degree of awareness is suitable to the data.
Table 3.4.12 Hosmer and Lemeshow Test
Hosmer and Lemeshow Test
Step
Chisquare
Df
Sig.
1
28.987
4
.000
Since p value which is 0.000 is less than 0.05 that is to say that the data is not good for the model.
Table 3.4.13 classification table
Classification Tablea
Observed
Predicted
GENDER
Percentage Correct
MALE
FEMALE
Step 1
GENDER
MALE
80
9
89.9
FEMALE
21
56
72.7
Overall Percentage
81.9
a. The cut value is .500
From the table above 81.9% of the data are correctly classified because the model has increase from step 0.
Table 3.4.14 variable in the equation
Variables in the Equation
B
S.E.
Wald
df
Sig.
Exp(B)
Step 1a
AGE
.563
.130
18.759
1
.000
1.756
Constant
2.018
.461
19.194
1
.000
.133
a. Variable(s) entered on step 1: AGE.
Table 3.4.14 above shows that 0.563 support the fact that the effect of kidney (renal) failure depend on age while 1.756 insist that the effect of kidney (renal) failure doesn’t depend on age.
Table 3.4.15 Correlation matrix .
Correlation Matrix
Constant
AGE
Step 1
Constant
1.000
.932
AGE
.932
1.000
Fig. 3.1.1: shows the age limit and their percentage at which the kidney (renal) failure has more effect of which the age limit3145 has the highest number of kidney failure.
CHAPTER FOUR
4.0 Conclusion and Recommendations
4.1 Conclusion
The data analyzed on monthly and yearly basis of reported cases on the prevalence of kidney renal failure, at the Sir Yahaya Memorial hospital Birnin Kebbi, reveal that there is a little reduction in the number of patients. From the result of chisquare test (contingency table) it was observed that the average rate at which real failure affect people, the result shows that it is significant and indicates that the age of person depends on gender, at the same time the coefficient of contingency shows a weak relationship between the age and gender. Despite the fact that, the rate at which kidney (renal) failure is failed, it is observed and believed that it will have influence on its great economic importance and this shows from the test that there are a little bit decreases as a result of the improvement in treatments. Also from the test of difference of two means it was observed that the renal failure is significant difference between the male and female gender. From the analysis, we observed an increase and decrease at both time.
4.2 Recommendation
Having analyzed the prevalence of kidney renal failure reported at Sir Yahaya Memorial Hospital BirninKebbi, the research recommends that:
There is need for improvement especially in the area of supply of genuine drugs to the hospital and the affected patients. Since one of the analysis shows that there is a difference among the gender at both time.
then the inspectorate division of national agencies for health, drugs administrative and control should continue inspecting, advising the illiterate and educated people so as to know the implication of the kidney diseases.
Generally, government should provide more equipments and provision for more health workers and better coordination of services to professional doctors and nurses so as to take care of the affected persons in the society.
REFERENCES
Cochran W.G.(1954),Some methods of strengthening the common x2 tests.Biometrics,10,417
15
Dibal, N.P (2006), Elementary Statistics, Loud Books Publishers, KonjiBodija, Ibadan, 2nd
Edition.
Dibal, N.P. (2006), Research Methods, books publishers, KonjiBodija, Ibadan, 1st Edition.
Dingwall RR.(1997). Living with renal failure: the psychological issues. European Dialysis
and Transplant Nurses Association/ European Renal Care Association Journal.
Frank H. and Althoen, S.C. (1995), Statistics Concepts and Applications.
Gerogianni KG. Stressors of patients undergoing chronic Hemodialysis. Nursing, 2014.
Kaitelidou D, Liaropoulos L, Siskou O, Mamas T, Zirogiannis P, Maniadakis N, Papakonstantinou V, Prezerakos P. (2016).The social and economic consequences of
Dialysis inpatients' lives with chronic renal insufficiency. Nursing 2007.
Maurya V.N., Maurya A.K. and Kaur D. (2013), A survey report on nonparametric
hypothesis testing including KruskalWallis ANOVA and Kolmogorov–Smirnov
goodnessfittest, International Journal of Information Technology & Operations Management, Academic and Scientific Publishing, New York, USA, Vol. 1, No. 2, pp. 2940, ISSN: 23288531
Maurya V.N. (2013), Numerical simulation for nutrients propagation and microbial growth using finite difference approximation technique, International Journal of
Mathematical Modeling and Applied Computing, Academic & Scientific Publishing, New York, USA,Vol. 1, No. 7, pp. 6476, November, ISSN: 23323744.
Maurya V.N., Maurya A.K. & Arora D.K. (2014), Elements of Advanced Probability Theory
and Statistical Techniques, Scholar’s Press Publishing Co.,Saarbrucken, Germany, ISBN 978 3639518498.
Maxwell E. (1971), Analyzing Qualitative Data. 4th Edition. Chapman and Hall Ltd. Library of Congress Catalog Card Number 7510907.
Murray R.S, John S.R and Srinivasan, R.A (2004), Probability and Statistics, 2nd Edition.
Ormandy P.(2010) Dialysis (part 2): Haemodialysis. Nursing Standard
Rayner J.C.W. and Best D.J. (1989), Smooth Tests of Goodness of Fit. Oxford University
Press, Inc., ISBN 0195056108.
Urdan Timothy C. (2005), Statistics in Plain English. 2nd Edition. Lawrence Erlbaum
Associates, Inc., London, UK.
William Mendenhall, Beaver Robert J., and Beaver Barbara M. (2003), Introduction to Probability and Statistics. Brooks/Cole, Division of Thomson Learning, Inc., 2003.
Hedayati and Bosworth. (2010): A practical approach to the treatment of depression in
Patients with chronic kidney disease and end – stage renal disease
APPENDIX
YEARLY STATISTICAL RECORD OF PATIENT THAT AFFECTED BY KIDNEY RENAL FAILURE IN KEBBI STATE
FROM 20072016
(SIR YAHAYA MEMORIAL HOSPITAL BIRNIN KEBBI, KEBBI STATE)
AGE GROUP
Age group.
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
115
2
5
3
0
4
2
0
0


1630
3
2
2
1
2
4
9
6
5
3145
5
6
8
5
6
5
3
4
8
1
4660
2
4
1
2
4
2
6
3
4
4
6175
2
1

4

1
2

1
4
7690

1
1
1
3


6
6
1
GENDER
YEAR
MALE
FEMALE
TOTAL
2007
8
6
14
2008
11
8
19
2009
5
10
15
2010
7
6
13
2011
10
9
19
2012
9
5
14
2013
7
13
20
2014
8
4
12
2015
15
10
25
2016
10
5
15
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