THE RELATIONSHIP BETWEEN HEMODIALYSIS PATIENTS’ FLUID COMPLIANCE AND SELECTED SOCIO-DEMOGRAPHIC FACTORS IN NORTHERN ALBERTA, CANADA by Shyvi Jacob B.S.N., Universal College of Learning, 2007 THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NURSING UNIVERSITY OF NORTHERN BRITISH COLUMBIA June 2016 © Shyvi Jacob, 2016 UNIVERSITY OF NORTHERN BRITISH COLUMBIA PARTIAL COPYRIGHT LICENCE I hereby grant the University of Northern British Columbia Library the right to lend my project/thesis/dissertation to users of the library or to other libraries. Furthermore, I grant the University of Northern British Columbia Library the right to make single copies only of my project/thesis/dissertation for users of the library or in response to a request from other libraries, on their behalf or for one of their users. Permission for extensive copying of this project/thesis/dissertation for scholarly purposes may be granted by me or by a member of the university designated by me. It is understood that copying or publication of this thesis/dissertation for financial gain shall not be allowed without my written permission. Title of Project/Thesis/Dissertation: THE RELATIONSHIP BETWEEN HEMODIALYSIS PATIENTS’ FLUID COMPLIANCE AND SELECTED SOCIODEMOGRAPHIC FACTORS IN NORTHERN ALBERTA, CANANDA Author: Shyvi Jacob Printed Name Signature Date ii ABSTRACT Compliance with fluid restrictions is an important factor in the health and well-being of hemodialysis patients. Non-compliance is a common and increasing problem for patients, leading to morbidity and death. This study aimed to investigate the relationship between socio-demographic factors and fluid compliance among hemodialysis patients. This retrospective, cross-sectional, descriptive study included 153 patients, a majority of whom were residing in an urban location, and were receiving dialysis treatment in a metropolitan area in Northern Alberta, Canada. About 42% were fluid non-compliant. Younger and male patients found more likely to be fluid non-compliant. Patients who resided and received care in metropolitan areas, had median income above $35,000, and attended all dialysis sessions were less likely to be fluid non-compliant. Psychological support, continuous educational interventions, and follow-up to remind patients about the importance of complying with their fluid restrictions are recommended to minimize the risk of fluid non-compliance. iii TABLE OF CONTENTS ABSTRACT .............................................................................................................................. ii TABLE OF CONTENTS ......................................................................................................... iii LIST OF TABLES ................................................................................................................... vi LIST OF FIGURES ................................................................................................................ vii GLOSSARY .......................................................................................................................... viii ACKNOWLEDGEMENTS ..................................................................................................... ix CHAPTER 1 Introduction..........................................................................................................1 Background ........................................................................................................5 Normal kidneys and their functions .......................................................5 Chronic kidney disease and its etiology.................................................6 Chronic kidney disease and its prevalence and incidence .....................7 End-stage renal disease and treatment ...................................................8 End-stage renal disease prevalence in Canada.......................................9 Principles of hemodialysis ...................................................................10 Fluid overload ......................................................................................11 Fluid non-compliance measurements ..................................................11 Common complications of fluid non-compliance in hemodialysis patients .................................................................................................12 Treatment compliance and patient mortality .......................................13 Guiding concepts related to health and fluid non-compliance.............14 Significance of the Study .................................................................................16 Research Question ...........................................................................................17 Northern Alberta Renal Program .....................................................................18 Summary ..........................................................................................................22 CHAPTER 2 Literature Review...............................................................................................23 Factors Affecting Fluid Non-compliance ........................................................24 Socio-Demographic Factors and Their Role in Health ....................................25 Age .......................................................................................................26 Gender ..................................................................................................28 Income..................................................................................................30 Length of time on dialysis....................................................................31 Rural definition ....................................................................................32 Geographical location of patient’s residence .......................................33 Location where patients received care .................................................33 Missed dialysis appointments ..............................................................34 Chapter Summary ............................................................................................36 iv CHAPTER 3 Methods .............................................................................................................37 Study Design ....................................................................................................37 Sources of data .....................................................................................38 Sample..................................................................................................39 Study Variables ................................................................................................40 Age .......................................................................................................40 Gender ..................................................................................................41 Income..................................................................................................41 Length of time on dialysis....................................................................41 Geographical location of patient’s residence .......................................42 Location where dialysis treatment was received .................................42 Number of missed dialysis ...................................................................43 Interdyalitic weight gain ......................................................................43 Data Collection ................................................................................................45 Data Analysis ...................................................................................................46 Ethical Considerations .....................................................................................47 CHAPTER 4 Results................................................................................................................49 Socio-Demographic Characteristics .................................................................49 CHAPTER 5 Discussion ..........................................................................................................57 Socio-Demographic Factors.............................................................................57 Age .......................................................................................................57 Gender ..................................................................................................58 Income..................................................................................................58 Length of time in dialysis ....................................................................59 Geographical location of patients’ residence .......................................60 Location where care was received .......................................................61 Number of missed dialysis appointments ............................................62 Fluid compliance status........................................................................62 Limitations of the Study...................................................................................64 Implications and Recommendations ................................................................66 Implications for research......................................................................66 Implications for practice ......................................................................67 Implications for education ...................................................................68 Conclusion .......................................................................................................69 References ................................................................................................................................71 Appendix A: Introductory Letter of Information from NARP.................................................81 Appendix B: Criteria for Satellite Waitlist...............................................................................82 Appendix C: Researcher’s Confidentiality Agreement............................................................83 Appendix D: University of Northern British Columbia Research Ethics Committee Approval ..................................................................................................................................84 v Appendix E: Health Research Ethics Board of Alberta Ethics Approval ................................85 Appendix F: Permission for Data collection from Alberta Health Service .............................86 vi LIST OF TABLES Table 1 Stages of Chronic Kidney Disease..........................................................................7 Table 2 Socio-Demographic Characteristics of Patients who Received Hemodialysis in Four Sites in Northern Alberta, Canada, from Oct-Nov 2015(N=153)..........50 Table 3 Comparison of Socio-Demographic Characteristics by Urban and Rural Patients who Received Hemodialysis in Four Sites in Northern Alberta, Canada, Oct-Nov, 2015(N=153) ........................................................................52 Table 4 Compliance of Patients who Received Hemodialysis in Four Sites in Northern Alberta, Canada, from Oct-Nov, 2015(N=153) ..................................55 vii LIST OF FIGURES Figure 1. Northern Alberta Renal Program patient flow diagram. ....................................20 Figure 2. Map of Northern Alberta Renal Program’s (NARP) satellite units. ..................21 Figure 3. Selection process for eligible patients in this study in four sites in Northern Alberta, Canada, from October to November 2015. ..........................................40 Figure 4. Formula used in this study to calculate length of time on dialysis. ....................42 Figure 5. Formula used in this study to calculate daily IDWG. ........................................44 viii GLOSSARY AHS Alberta Health Services CKD Chronic Kidney Disease ESRD End-Stage Renal Disease IDWG Interdyalitic Weight Gain NARP Northern Alberta Renal Program RIC Renal Insufficiency Clinic ix ACKNOWLEDGEMENTS It is my pleasure to thank those who supported me through this thesis process. I would like to express my sincere gratitude to my supervisor, Dr. Martha MacLeod, for the continuous support of my thesis study, for her patience, motivation, encouragement, and immense knowledge. I am grateful to Erin Wilson, for her guidance, encouragement, and practical advice. I am thankful to her for commenting on my views and helping me understand and enrich my ideas, which helped me focus my concepts. My sincere thanks also goes to Dr. Shannon Freeman, who has always been there to listen and give advice. Her motivating discussions and guidance helped me throughout all the time of research and writing of this thesis. I was continually challenged to improve my thesis, and she had confidence in my abilities to succeed. I am grateful to many people, and too many to mention on this page, especially to my friends, colleagues, and entire NARP team for their support through this journey to thesis completion. Of these people, I would like to thank Tracy Delorme for her continuous help and support since the beginning of this thesis study. Most importantly, none of this would have been possible without the love and patience of my family. My husband Aji Jacob encouraged and supported me from the beginning of this thesis through to the end of this journey. I could not have finished this without your support! I would like to thank my children, Alan and Nathan, for their love and patience. 1 CHAPTER 1 Introduction Chronic kidney disease (CKD) and its irreversible final stage, end-stage renal disease (ESRD), affect populations around the world (Matteson & Russell, 2010). One of the most safe and viable treatments for ESRD is hemodialysis, which has widespread acceptance from patients and health care professionals (Boyer, Ronald, Gregory, & George, 1990; Kugler, Vlaminck, Haverich, & Maes, 2005; Pang, Ip, & Chang, 2001; Victoria, Evangelos, & Sofia, 2015). However, this medical intervention requires many lifestyle adjustments from patients. Once the body loses the ability to excrete fluid, as in ESRD, accumulation of fluid occurs; hence, patients need to limit their fluid intake (Kammerer, Garry, Hartigan, Carter, & Erlich, 2007). Poor fluid compliance among hemodialysis patients can lead to short-term consequences, such as shortness of breath, peripheral and lung edema, and heart failure (Pang et al., 2001). It can also be fatal (Kauric-Klein, 2013; Pang et al., 2001; Rambod, Peyravi, Shokrpour, & Sareban, 2010; Sharp, Wild, & Gumley, 2005). In addition, multiple healthrelated complications due to fluid overload affect patients’ quality of life (Ahrari, Moshki, & Bahrami, 2014; Kim, Evangelista, Phillips, Pavlish, & Kopple, 2012). Therefore, patients need to be highly compliant for both better treatment outcomes and to achieve a better quality of life (Ahrari et al., 2014; Ibrahim, Hossam, & Belal, 2015; Victoria et al., 2015). However, fluid non-compliance is common among many hemodialysis patients, as fluid compliance to the hemodialysis treatment regime is very challenging to maintain (Chan, Zalilah, & Hii, 2012; Curtin, Svarstad, Andress, Keller, & Sacksteder, 1997; Kim & Evangelista, 2010; Kugler et al., 2005). 2 A thorough understanding of the relationship between factors that influence fluid non-compliance among dialysis patients can be helpful for health care professionals in order to create effective strategies to improve fluid compliance among patients. This research will add knowledge that is specific to a selection of factors affecting hemodialysis patients’ fluid compliance status. The results may improve the knowledge of hemodialysis nurses concerning fluid non-compliance among hemodialysis patients. The study findings can be used to inform evidence-based policies and individualised treatment plans to help patient achieve desired outcomes (Baraz, Parvardeh, Mohammadi, & Broumand, 2010; Lindberg, 2010; Kutner, 2001; Mollaoğlu & Kayataş 2015). It is well known that social, demographic, cultural, and environmental factors influence the health status of individuals (Mikkonen & Raphael, 2010). As such, it is helpful to know about the association between a patient’s fluid compliance and the sociodemographic factors that affect their lives. Socio-demographic factors are the major components of social determinants of health. Social determinants of health are the living conditions a person experiences that can shape the health of the individual (Mikkonen & Raphael, 2010). As identified by the Public Health Agency of Canada (2011), these factors may be: “Income and Social Status, Social Support Networks, Education and Literacy, Employment/Working Conditions, Social Environments, Physical Environments, Personal Health Practices and Coping Skills, Healthy Child Development, Biology and Genetic Endowment, Health Services, Gender, and Culture” (Key Determinants section). In other words, social determinants of health include social, cultural, economic, environmental, and demographic factors (Mikkonen & Raphael, 2010), and sociodemographic factors are the social and demographic factors within the social determinants of 3 health that affect individuals’ health status. Mikkonen and Raphael (2010) explained that age, gender, income, and health services are some of the factors affecting health status of an individual. Evidence has shown that these factors have a strong effect on the health of individuals (Raphael, 2009). According to the World Health Organization (as cited in Sabate, 2003), social, economic, and demographic factors are some of the important risk factors that can be associated with patients’ non-compliance. It has been observed that fluid non-compliance is one of the major issues for a vast majority of dialysis patients (Ahrari et al., 2014; KauricKlein, 2013; Victoria et al., 2015). Fluid non-compliance causes a significant increase in interdyalitic weight gain (IDGW), resulting in substantial fluid removal during dialysis treatment, leading to decreased blood pressure, cramps, headache, and dizziness (Lee et al., 2014; Onofriescu, Hogas, Voroneanu, & Covic, 2011). Researchers from different parts of the world have completed many studies focused on efforts to find and address related factors related to non-compliance (Ahrari et al., 2014; Kauric-Klein, 2013; Kugler, Maeding, & Russell, 2011; Victoria et al., 2015). While social and demographic factors influence the health of an individual, it was imperative to examine the relationship between socio-demographic factors and fluid intake status of hemodialysis patients within the Northern Alberta Renal Program (NARP) jurisdiction. Although previous studies have examined the relationship between different socio-demographic factors and fluid compliance, no such studies have been conducted within Northern Alberta or similar regions in Canada. Canada has a different social, economic, and environmental context than other countries (Mikkonen & Raphael, 2010), including different provincial and federal 4 government policies, family benefits, income inequality, differences in access to health care and social assistance, and availability of affordable housing (Mikkonen & Raphael, 2010). The majority of the studies were conducted outside Canada (Ahrari et al., 2014; Chan et al., 2014; Ibrahim et al., 2015; Kauric-Klein, 2013; Pang et al., 2001). No studies were found that were completed in Northern Alberta to show whether different socio-demographic backgrounds affect fluid compliance status among Northern Alberta hemodialysis patients. Therefore, it was interesting and helpful to learn about the factors that affect Northern Alberta hemodialysis patients’ ability to comply with their fluid intake. This thesis attempts to fill this gap in the knowledge. This study investigated the relationship between sociodemographic factors, such as age, gender, length of time on dialysis, number of missed dialysis appointments, care site location, geographic location, and income distribution by geographic area, and fluid compliance of patients undergoing hemodialysis in Northern Alberta outpatient settings. This study used socio-demographic factors, which were collected by Northern Alberta Renal Program (NARP) for clinical purposes, to ascertain their relationship to fluid compliance status. Research using routinely collected retrospective data is both time and cost effective. In addition, routinely collected data avoid nonresponse and reporting and recall biases (Jorm, 2015). While it would have been useful to identify the relationship with hemodialysis patients’ fluid compliance status and other factors, such as education, employment status, ethnicity or race, family support, social support, and housing status. These are not available in the NARP records. 5 Background To have an in-depth understanding of fluid non-compliance and the subsequent complications among hemodialysis patients, it is necessary to discuss normal kidneys and their functions. In addition, CKD, its etiology, and various stages of the disease progression are explained in the following discussion along with the etiology of ESRD, its treatments, and prevalence in Canada. Normal kidneys and their functions. The kidneys are a pair of bean-shaped organs located on each side of the spine in the lower middle of the back. The kidneys are situated on the posterior abdominal wall between the twelfth thoracic and third lumbar vertebra and are protected by abdominal muscles, fat, fascia, intestines, and ribs (Schira, 2008). Each kidney weighs about 120 g, measures 5 to 7 cm in width and 11 to 13 cm in length, and has around one million filtering units called nephrons, which are composed of a glomerulus and a tubule (Kallenbach, 2015; Schira, 2008). The glomerulus is a tiny filtering device, and the tubule is a small tube-like structure connected to the glomerulus. The three basic functions of the nephrons are filtration, secretion, and re-absorption. Urine is produced in the body through these three functions; therefore, the amount of urine produced in the human body is equal to filtration plus secretion minus re-absorption (Kallenbach, 2015; Schira, 2008). The kidneys connect to the urinary bladder through ureters, from where urine is expelled through the urethra. The most important purpose of the kidney is to maintain a healthy balance of fluids and electrolytes in the body and to flush away toxins, surplus water, and waste products from the blood through urine (Ferguson & Waikar, 2012). Each day, the kidneys process about 200 litres of blood, 6 regulating levels of calcium, sodium, and potassium in the blood (Kallenbach, 2015; Schira, 2008) and producing about two litres of urine in the process. Another major function of the kidneys is to produce erythropoietin, which is a hormone produced by interstitial fibroblasts within the kidney. Erythropoietin stimulates the bone marrow to create red blood cells. Renin, an enzyme produced, stored, and secreted by the juxtaglomerular cells of the kidney, plays a role in regulation of blood volume blood pressure (Kallenbach, 2015; Schira, 2008). The kidneys are also responsible for blood pressure control, vitamin D synthesis, and red blood cell production. In some cases, the kidneys fail to perform their normal functions. Kidney failure is a serious medical condition, in which the kidneys go through cellular death and become incapable of filtering waste, maintaining fluid balance, and producing urine (Kallenbach, 2015; Schira, 2008). Kidney failure will cause a build-up of toxins in the body that can negatively affect the blood, heart, and brain. All these complications will alter the normal health status of a person and lead to health complications (Schira, 2008). Chronic kidney disease and its etiology. Kidney damage can be detected in many forms, including abnormalities in serology, urinalysis, or imaging studies. The stage of kidney disease is evaluated by the glomerular filtration rate (GFR) as the GFR is the best test to measure renal function to determine the stage of kidney disease (Kallenbach, 2015) . The glomerular filtration rate is the quantity of blood that passes through the glomeruli per minute, and glomeruli are microscopic capillaries in the kidneys that filter blood (Kallenbach, 2015; Stevens, Coresh, Greene, & Levey, 2006). GFR is a measure of the filtering capacity of kidneys or how well the kidneys are removing excess water and waste 7 from the body. The GFR number, which is calculated from a blood test, tells how much kidney function a person has. The GFR number goes down as kidney disease get worse (Kallenbach, 2015). CKD occurs gradually; kidneys lose their function over time, and in CKD, the loss of kidney function is permanent. It causes an accumulation of toxic substances, water, and waste in the body that would normally be excreted through the kidney (Kallenbach, 2015; Schira, 2008). Kidney failure can also create problems like metabolic acidosis, anemia, hypertension, as well as cholesterol disorders. CKD is classified into five stages based on the glomerular filtration rate level (see Table 1). Table 1 Stages of Chronic Kidney Disease Glomerular Filtration Rate (Millilitre/minute 1.73 m2) Stage Description 1(G1) Normal or high kidney function Greater than 90 2(G2) Mild decrease in kidney function 60-89 3a(G3a) Mildly to moderately decreased kidney function 45-59 3b(G3b) Moderately to severely decreased kidney function 30-44 4(G4) Severely decreased kidney function 15-29 5(G5) Kidney failure Less than 15 Note. Adapted from KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease, Kidney International Supplement, 3(1), p. 5, by A. Levin & P. E. Stevens (Eds.), 2013. Adapted with permission. Chronic kidney disease and its prevalence and incidence. CKD is defined as a condition in which the glomerular filtration rate goes below 60 ml/min for more than three months (Kallenbach, 2015). CKD is the result of progressive loss of kidney function over the duration of months or years (Levin & Stevens, 2013). 8 The prevalence and incidence of CKD followed by ESRD is rising globally (Hussein, Winkelman, El-Wahab, Ali, & Abdeen, 2012; Kazemi, Nasrabadi, Hasanpour, Hassankhani, & Mills, 2011; Kring & Crane, 2009). Curtin, Mapes, Schatell, and Burrows-Hudson (2005) predicted that by the year 2025, more than 53 million people over the age of 65 will have CKD in the United States. This trend is evident in Canada as well. A fact sheet published in 2012 by the Kidney Foundation of Canada showed that in 2010, an estimated 2.6 million Canadians had kidney disease or were at the risk of developing kidney disease. In Canada, every year, an average of 5,840 people are diagnosed with kidney failure. Among new renal failure patients, 53% are 65 years of age or older (Kidney Foundation of Canada, 2012). Considering the growing incidence of kidney failure among Canadians, it is imperative to investigate how to improve fluid compliance among kidney patients who are undergoing hemodialysis. End-stage renal disease and treatment. Progressive or irreversible loss of kidney function over many months or years’ results in kidney failure, and this kidney damage can be defined as functional or structural abnormalities or a GFR of less than 60 ml/min/1.73 m2 for three or more months (Kallenbach, 2015). ESRD is the irreversible final stage of CKD and is defined as GFR below 15 ml/min/1.73 m2 (Kallenbach, 2015). The two leading causes of kidney failure in newly diagnosed patients are diabetes (35%) and renal vascular disease (18%) (Kidney Foundation of Canada, 2012). According to the Canadian Institute for Health Information (CIHI) (2011), the third highest cause of ESRD in Canada is glomerulonephritis. Glomerulonephritis is defined as damage of the filtration system due to kidney inflammation (Border, Okuda, Languino, Sporn, & Ruoslahti, 1990; Kallenbach, 2015). 9 Once the kidneys have failed, major treatment approaches include peritoneal dialysis, hemodialysis, and kidney transplants. Hemodialysis is the most common method available for removing excess fluid and waste products such as creatinine and urea from the blood (Hailey & Moss, 2000; Matteson & Russell, 2010). Since hemodialysis can remove large quantities of fluid in a short period of time, the majority of kidney failure patients choose this treatment method (Graham, 2006; Tsay, 2003). End-stage renal disease prevalence in Canada. Consistent with the worldwide upward trend in people undergoing dialysis (Matteson & Russell, 2010), the number of hemodialysis patients in Canada is also growing (Fincham, Kagee, & Moosa, 2008). In 2012, there were 5,431 newly diagnosed ESRD patients reported in Canada (Canadian Institute for Health Information, 2014, p. 12). At the end of 2012, 41,252 Canadians were living with ESRD (p. 21). Since 2003, this number has grown by 40%, and in 2012, 58% of those with ESRD were receiving some form of dialysis (peritoneal dialysis or hemodialysis), and 42% had a functioning kidney transplant in 2012 (p. 21). In 2012, 1,358 kidney transplants were performed, which was a 14% increase from 2003. By the end of 2012, 3,428 patients were waiting for a kidney transplant, and a total of 84 patients died while waiting for a kidney transplant (p. 38). Since 1990, in Canada, the rate of patients undergoing dialysis has increased by 212%, reflecting an increased rate from 211.6 per million population (RPMP) to 661.2 RPMP (CIHI, 2011). During the same period, the rate of patients with kidney transplants increased significantly from 187.1 RPMP to 457.4 RPMP (CIHI, 2011). In conclusion, as per statistics over the past 20-year period, prevalence rates of ESRD have been increasing across all age groups in Canada. 10 Principles of hemodialysis. The fundamental principle of the hemodialysis process is the diffusion of solutes across a semi-permeable membrane (Daugirdas, Blake, & Ing, 2001; Kallenbach, 2015; King, 2008). Dialysis systems include dialysis machine, dialyzer, and dialysate. The main goal of dialysis is to artificially replace the lost kidney functions, such as removing waste and excess water from the blood (Kallenbach, 2015; King, 2008). To achieve this goal, the patient’s blood is circulated outside the body through an artificial kidney, which is called the dialyzer. A dialyzer has two chambers separated by a membrane (Daugirdas et al., 2001; Kallenbach, 2015; King, 2008). One chamber is perfused by the patient’s blood, while the other chamber is perfused by a special dialysate. During hemodialysis, blood is drawn from an artery through a system of tubes, to pass through the dialyzer unit of the artificial kidney, which separates the blood from the bath fluid using a membrane (Daugirdas et al., 2001; Kallenbach, 2015; King, 2008). While the blood flows across the membrane, the waste products and electrolytes are exchanged across the membrane, and the waste is removed. Individuals with ESRD normally require three dialysis treatments per week to prevent complications related to kidney failure (Chan, Thadhani, & Maddux, 2014). The rationale for thrice-weekly hemodialysis was derived from a landmark trial that identified several physiological experiments, costs, and other logistics that can impact treatment adherence and outcomes (Frequent Hemodialysis Network Trial Group, 2010). While hemodialysis is the main treatment, successful dialysis also depends on other factors that require patients to modify their lifestyle and habits substantially (Victoria et al., 2015). Patients need to strictly attend dialysis sessions, restrict fluid intake, limit potassium- and phosphorous-containing foods, and comply with medication 11 regimes (Victoria et al., 2015). Failure to restrict fluid intake often leads to fluid overload. This is significant because one of the major reasons for cardiac-related mortality among hemodialysis patients is fluid overload due to poor fluid compliance (Lee et al., 2014). Fluid overload. Limiting fluid intake is vital for a successful dialysis outcome. Fluid overload in hemodialysis patients is from the accumulation of fluid in patients’ body due to excessive fluid intake (Lindberg, 2010). To avoid fluid overload complications related to non-compliance, patients undergoing hemodialysis are required to limit their fluid intake to a fluid allowance derived by adding 600 ml to the urine output and extrarenal water losses (Pace, 2007). The 600 ml represents the net daily water loss from insensible processes, such as transepidermal diffusion in which water passes through the skin and is lost by evaporation, and evaporative water loss from the respiratory tract (Kopple & Massry, 2004). Extrarenal water losses include diarrhea and vomitus. Fluid non-compliance measurements. Fluid non-compliance is measured by IDWG, representing the increase of body weight between two consecutive hemodialysis sessions (Lee et al., 2014). Due to decreased or impaired renal function, fluid and food intake during the interdialytic period will increase extracellular water volume (Lindberg, 2010). Interdialytic weight gain is a biological measure of fluid intake that refers to the quantity of fluid consumption between two successive dialysis sessions, which is a reliable way of calculating fluid non-compliance among hemodialysis patients because IDWG is a function of oral fluid intake (Kalantar-Zadeh et al., 2009). Dry weight is the weight of the patient at the end of hemodialysis treatment without any complications related to fluid removal (Onofriescu et al., 2011), and this “dry weight” is 12 a prescribed set weight by a nephrologist. Accurate estimation of dry weight assessment is important for a hemodialysis patient in order to provide effective and safe hemodialysis treatment to patients with minimum adverse reactions (Onofriescu et al., 2011). If the dry weight is too low, the patient may be at risk of having hypotension, dizziness, and cramps. Alternately, if the dry weight set too high, the patient may have the signs and symptoms of high hypertension, fluid overload-related problems, and cardiovascular problems (Onofriescu et al., 2011). This implies that effective dialysis depends on correct dry weight assessment. Therefore, dry weight assessment is essential to achieving an ideal and comfortable level of fluid removal for dialysis patients (Onofriescu et al., 2011). Common complications of fluid non-compliance in hemodialysis patients. Several studies have analyzed patients’ fluid non-compliance status, and researchers found that few people are compliant with fluid restrictions; almost half of the patients experienced minor to serious complications due to fluid overload (Curtin et al., 1997; Graham, 2006; Kauric-Klein, 2013; Tsay, 2003; Victoria et al., 2015). Victoria et al. (2015) stated that hemodialysis patients had multiple complications due to fluid non-compliance, such as fluid overload, hypertension, heart disease, shortness of breath, and related complications, which affected their quality of life. Similarly, Kauric-Klein (2013) stated that 50% of the dialysis patients suffered from cardiovascular diseases, hypertension and related problems due to fluid noncompliance. Fluid overload in patients with ESRD causes hypertension, which was a common problem among dialysis patients (Kugler et al., 2005; Tapolyai et al., 2011). Excess consumption of salt and fluid was found to be the main reason for uncontrolled hypertension among dialysis patients (Kauric-Klein, 2013). Fluid overload increased blood pressure, 13 which has led to pulmonary edema, cardiovascular damage, and death (Khalil, & Darawad, 2014). Non-compliance with fluid intake has contributed to many other serious health problems, such as peripheral edema, left ventricular hypertrophy, congestive heart failure, pulmonary edema, and pulmonary vascular congestion (Lee et al., 2014; Rambod et al., 2010; Sharp et al., 2005). These complications often lead to impaired physical abilities, depression, and premature death (Ahrari et al., 2014; Banerjee, Ma, Collins, & Herzog, 2007; Charra, 2007; Ibrahim et al., 2015; Pace, 2007; Welch, 2001). For patients receiving hemodialysis, fluid non-compliance aggravates the probability of negative treatment outcomes (Hailey & Moss, 2000; Ibrahim et al., 2015; Kutner, 2001), including increased morbidity, and thus related health care costs, and hospitalization (Clark, Farrington, & Chilcot, 2014; Kammerer et al., 2007). In summary, fluid compliance is an important factor for hemodialysis patients’ health and well-being. Treatment compliance and patient mortality. The survival rate and treatment outcome of patients undergoing hemodialysis is poor, and a significant number will die each year (Ozkahya et al., 2006). According to Matteson and Russell (2010), the one-year survival rate for ESRD patients on hemodialysis is 78.3%, but this decreases to 32.1% at five years. The most common reasons for death are cardiac-related issues caused by factors such as volume overload, hypertension, arterio-venous, and uremia-related myocardial cell injury (Kauric-Klein, 2013; Lee et al., 2014; Locatelli, Del Vecchio, & Manzoni, 1998). In Hailey and Moss’s (2000) literature review, wherein they reviewed studies conducted between 1990 and 2000, IDWG due to fluid non-compliance caused a 35% higher risk of death rate among hemodialysis patients. 14 Guiding concepts related to health and fluid non-compliance. The World Health Organization (1948) defined health as “a state of complete physical, mental and social wellbeing and not merely the absence of disease or infirmity” (p. 100). This definition is still in use today, and unchanged. Health is the cumulative result of biomedical, biological, rational, social, personal, and political factors (Davies, 2007). According to Kim and Saada (2013), socio-demographic factors, in which people are born, grow, live, work, and age, can influence the individual’s health status. In order to treat health-related issues successfully, health care providers need to have clear understanding of socio-demographic factors that lead to non-compliance, and this study intended to create that clear understanding. Many factors, like medical, psychosocial, socio-demographic, and culture, can affect a hemodialysis patient’s ability to be compliant with fluid restrictions, and cope with the disease (Goldman & Smith, 2002; Moattari, Ebrahimi, Sharifi, & Rouzbeh, 2012; Wheeler & Becker, 2013). Instead of using the term compliance, Quinan (2007) suggested the term adherence, as adherence relates to patient choice. However, Berg, Evangelista, Carruthers, and DunbarJacob (2006), stated that both terms transmit the same meaning. Therefore, this study used the term compliance. Some researchers (Bissonnette, 2008; Quinan, 2007) have defined treatment compliance within the framework of an authoritarian relationship of the health care provider with the patient, where the patient is required to strictly comply to the directions from the provider, which, in other words, can be defined as the patient being submissive to others’ control (Calvin, 2004). Other studies have defined treatment compliance as the cooperative activity that a patient exhibits concerning to the treatment requirements, linked to the environment in which the patient functions as an individual (Lindberg, 2010). 15 The term compliance can simply define how patients follow their recommended treatment regimen. Therefore, non-compliance may be defined as refusing the prescribed orders or showing denial (Bissonnette, 2008). However, patients may not be blamed solely for their non-compliance, as non-compliance can be due to underlying psychological and environmental conditions that impact the patient (Lindberg, 2010). A respectful partnership between the patients and health care providers, such as dialysis nurses, is essential to address the underlying issues of non-compliance among dialysis patients (Lindberg, 2010). Compliance can therefore be considered to be a combined product of active participation from both patients and health care providers, which can be achieved through proper communication, encouragement, follow-up, and continuous education (Baraz et al., 2010; Lindberg, 2010; Kutner, 2001). Mollaoğlu and Kayataş (2015) argued that medical professionals should have adequate knowledge about the related factors of non-compliance to help patients develop strategies to prevent non-compliance. Health care professionals such as dialysis nurses may not be able to modify factors like gender and age. However, awareness of the role of these factors that affect noncompliance might help nurses to develop effective strategies to help patients achieve desired goals. Educational interventions, especially one-on-one educational programs, have been shown to improve compliance among dialysis patients (Lingerfelt & Thornton, 2011; Tsay, 2003). A research study conducted by Barnett, Li Yoong, Pinikahana, and Si-Yen (2008) provided evidence that dialysis patient’s IDWG decreased from 2.64 kg to 2.21 kg following an educational intervention, and fluid compliance improved from 47% to 71%. This implies that even though health care professionals cannot change the non-modifiable factors, the effect of these non-modifiable factors can be reduced through the teamwork of health care 16 professionals and patients through effective strategies and educational interventions. In addition, better understanding of non-modifiable factors can assist nurses better target resources to patients at higher risk for non-compliance. Significance of the Study There is a need to promote hemodialysis patients’ fluid compliance since patients’ non-compliance to the treatment regimen influences poor health outcomes and increased health care costs (Simpson et al., 2006). Based on their study, Kim and Evangelista (2010) stated that 95% of patients were aware or knowledgeable about their fluid restrictions while they are on dialysis, yet 63% of patients still had difficulty in following their fluid restrictions due to an inability to control their urge to drink, or they simply did not understand how to control their fluid consumption. This implies the importance of finding out the factors that could affect the fluid non-compliance among dialysis patients. Evidence-based knowledge can help nurses to collaborate with patients in creating individualised treatment plans so patients can more successfully be compliant with their treatment regimen. Health care professionals, including nephrology nurses, should have evidence-based knowledge, as they are the people who do continuous teaching and follow-up with dialysis patients in their day-to-day life. Being a health care profession, nursing has a responsibility to develop practices based on evidence to benefit the well-being of patients under their care (Forbes & While, 2009). Improvement in compliance rates can be achieved through education, counselling, coordination of the service of social support professionals, tailoring the treatment to the patient’s lifestyle, use of reminders, encouragement of family support, and informing patients about side-effects (Cegala, Marinelli, & Post, 2000; Vermeire, Hearnshaw, Van Royen, & 17 Denekens, 2001). According to Van Camp, Huybrechts, Van Rompaey, and Elseviers (2012), nurse‐led education and counselling enhance treatment compliance among patients undergoing hemodialysis. Various studies emphasized the importance of early identification of fluid noncompliance, as early identification can help health care professionals to intervene immediately to avoid further non-compliance-related health issues (Martin, Williams, Haskard, & DiMatteo, 2005; Ulrich, 2006; Victoria et al., 2015). Once health care professionals recognise factors potentially associated with fluid non-compliance, they can intervene at the earliest time to help patients to modify their non-compliance status. This noncompliance status modification can be achieved through individualised care and patient education as well as by implementing changes in the patient’s care plan and by collaborating with other health care professionals (Ulrich, 2006). Furthermore, identifying factors potentially associated with non-compliance may provide economic benefits in terms of less health care spending (Martin et al., 2005; Victoria et al., 2015). Findings from this study may raise awareness to create effective strategies to address underlying issues and to improve fluid compliance among dialysis patients. Victoria et al. (2015) suggested that early identification of non-compliance risk factors and underlying issues may be helpful in developing effective patient-focused strategies, such as collaborating with patients to create care plans, counselling and educational interventions, and simplifying or modifying the therapeutic regimens. Research Question This study intended to fill a gap in the knowledge that exists in northern Canadian hemodialysis settings concerning hemodialysis patients’ fluid compliance status. The goal of this study was to investigate the relationship between available socio-demographic factors 18 and fluid compliance of patients undergoing hemodialysis in Northern Alberta outpatient settings. The research question was: “Is there any relationship between hemodialysis patients’ fluid compliance and age, gender, length of time on dialysis, number of missed dialysis appointments, geographic location, income distribution by geographic area, and care site location in Northern Alberta out-patient hemodialysis settings?” Northern Alberta Renal Program The mission of NARP is to deliver assessment, treatment, and follow-up to patients with kidney failure from different parts of Northern Alberta, Northwestern Saskatchewan, Northeastern British Columbia, and the Northwest Territories (Alberta Health Services, 2016). The aim of the NARP is to prevent or delay the onset of renal failure (Alberta Health Services, 2016). NARP provides various services including general nephrology clinics, Renal Insufficiency Clinics, diabetic nephropathy prevention clinics, renal transplant surveillance, living donor services, hemodialysis (in-centre, satellites, dialysis bus, and home), peritoneal dialysis, vascular access, and in-patient renal services (Alberta Health Services, 2016). This study focused on hemodialysis. To access NARP, a patient requires a referral from the family physician or nephrologist. When a patient is diagnosed with renal failure, the family doctor refers the patient to a nephrologist or to a Renal Insufficiency Clinic (RIC). The RIC Clinic is a program within NARP (Alberta Health Services, 2016). Patients with less than 30% kidney function are eligible to access care at the RIC through a nephrologist referral (Alberta Health Services, 2016). The RIC aims to delay the progression of CKD; manage metabolic complications related to renal failure; provide relevant education, psychosocial support, and 19 nutrition recommendations; and prepare the patient for dialysis if needed (Alberta Health Services, 2016). At the RIC, patient care is provided by a team of health care professionals, which includes the nephrologist, registered and licensed practical nurses, dietitian, pharmacist, and the social worker. Patients with CKD can access this program through the General Nephrology Clinic and Rural General Nephrology Clinic. The General Nephrology Clinic provides assessment and follow-up treatments for patients with CKD. The Rural General Nephrology Clinic delivers the same services to rural communities so patients do not have to travel. The General Nephrology Clinics under NARP are located at Fort McMurray, High Level, Grande Prairie, Peace River, Whitecourt, Westlock, St. Paul, Vermillion, and Bonneville (Alberta Health Service, 2016). The NARP patient flow is depicted in Figure 1. In the city of Edmonton, NARP has seven units, which include Aberhart, Edmonton General, Grey Nuns, Royal Alexandra Hospital (two units), and the University of Alberta Hospital (two units). NARP also delivers a mobile dialysis service, where hemodialysis service is provided within a bus. NARP has several satellite units, which include Lloydminster, Drayton Valley, Grand Prairie, Vegreville, High Level, Edmonton General Hospital, Peace River, Red Deer, Rocky Mountain House, St. Paul, Slave Lake, Fort McMurray, Stettler, Westlock, and Wetaskiwin. A map of the NARP dialysis centres within Alberta Health Services is presented in Figure 2 (Alberta Health Services, 2016). 20 Figure 1. Northern Alberta Renal Program patient flow diagram. Note: Adapted from Northern Alberta Renal Program by Alberta Health Services, 2016. Retrieved from http://www.albertahealthservices.ca/services.asp?pid=service&rid=5854 Reproduced with permission from NARP. 21 Figure 2. Map of Northern Alberta Renal Program’s (NARP) satellite units. Note: Adapted from: Northern Alberta Renal Program by Alberta Health Services, 2016. Retrieved from http://www.albertahealthservices.ca/services.asp?pid=service&rid=5854 Reproduced with permission from NARP Each patient with ESRD under NARP jurisdiction starts his/her dialysis in Edmonton, and when patients are clinically stable during their dialysis treatment, they are transferred to outpatient satellite units (see Appendix A). Once patients become mobile, reasonably stable, and have a reliable transportation to get to dialysis unit, rural patients 22 are transferred to satellite units that are closer to their home depending on space availability (see Appendix B; see also Figure 2). The availability of dialysis closer to patients’ homes depends on individual dialysis units’ infrastructure and number of patients waiting to be admitted into that specific dialysis unit. Summary Hemodialysis is the common and preferred treatment method for patients with kidney failure. There is a lack of knowledge about the factors that affect hemodialysis patients’ ability to comply with their treatment regimen that includes restricting fluid intake within NARP. This study aimed to expand the knowledge about the underlying factors affecting hemodialysis patient’s fluid non-compliance. In the next chapter, findings from a current review of literature are presented, which will include a discussion about gaps in the existing knowledge. 23 CHAPTER 2 Literature Review According to Welch (2001), kidney failure patients’ daily therapeutic regimens are restrictive, stressful, and difficult to follow. In more recent and detailed studies, self-reported prevalence of non-compliance with fluid restrictions on hemodialysis patients ranged from 30% to 74% (Denhaerynck et al., 2007; Ibrahim et al., 2015; Kauric-Klein, 2013; Kugler et al., 2005; Lee &, Molassiotis, 2002; Lin & Liang, 1997; Vlaminck, Maes, Jacobs, Reyntjens, & Evers, 2001). Non-compliance measured using calculated IDWG had a similarly wide range, from 10% to 60% (Bame, Petersen, & Wry, 1993; Hecking et al., 2004). These wider ranges are due to the sample’s heterogeneity, as patients from different countries had varying customs and healthcare systems, and the wider range can also be due to the bias in processes (Denhaerynck et al., 2007). Often, self-report measures were biased, as patients consistently overestimated their compliance (De Geest, Abraham, & Dunbar-Jacob, 1996; Liu et al., 2001). Another bias was associated with the length of dialysis sessions and the frequency between consecutive dialysis sessions. Based on their study, Stragier and Jadoul (2003) observed variation in daily IDWG between the longer weekend intervals and the shorter midweek intervals. From the literature, it was evident that fluid non-compliance was an ongoing issue among dialysis patients. Fluid non-compliance puts hemodialysis patients at risk of fluid overload (Ahrari et al., 2014), which can lead to life-threatening complications and unpredictable progression of ESRD (Boyer et al., 1990, Hussein et al., 2012; Ibrahim et al., 2015; Kazemi et al., 2011; Kring & Crane, 2009). Various factors influence fluid compliance of hemodialysis patients (Lindberg, 2010). There was ample evidence in the literature that 24 socio-economic and demographic factors can influence hemodialysis patients’ compliance (Bame et al., 1993; Bissonnette, 2008; Ibrahim et al., 2015). Research has also confirmed that a person’s geographical location is a powerful determinant of physical health, psychological health, and mortality (Link, Bruce, & Phelan, 1995), and this is true at all stages of life (Cairney & Krause, 2005). Similarly, people of lower socio-economic status are at higher risk for poor health outcomes (Marmot, 2005; Wilkinson & Marmot, 2003). Relevant literature is explored in this chapter to examine the relationship between fluid compliance and socio-demographic factors of patients undergoing hemodialysis. Factors Affecting Fluid Non-compliance A person’s fluid intake can have two dimensions. The first dimension is the person’s need to meet the physical necessities, and the second dimension is the person’s urge to satisfy psychological needs (Lindberg, 2010). Fluid non-compliance among hemodialysis patients can be triggered from physical needs, habits, customs, and social rituals or from the disease itself (Lindberg, 2010). For example, the onset of drinking among dialysis patients can be from the thirst sensation due to high sodium in their body. Patients also may drink when they see others drinking (Lindberg, 2010). The physical necessities can arise from the need to reduce mouth dryness or from the body’s regulatory response to thirst (Abuelo, 1998; Lindley, 2009; Porcu, Fanton, & Zampieron, 2007). Abuelo (1998) and Mistiaen (2001) explained that thirst is one of the major physical causes of chronic fluid overload and excessive IDWG among hemodialysis patients. Psychological needs can be more complex than the physiological needs when it comes to fluid intake (Fisher, 2004). People drink often to take pleasure in the taste of beverages such as juices or soft drinks (Lindberg, 2010). In addition, people consume fluids 25 to experience the psychotropic effect of liquid such as alcoholic drinks (McKinley et al., 2004). Various studies have documented that cognitive, emotional, and motivational disturbances are experienced when an individual is living in a stressful environment over which he or she has no control (Wilson, Kliewer, Plybon, & Sica, 2000). When it comes to hemodialysis patients context, this stress and tension can be associated with a patient’s constant struggle to restrict fluid intake and the desire to drink (Sinclair & Parker, 2009). In addition to physiological and psychological factors, social and demographic factors influence fluid compliance of hemodialysis patients (Baines & Jindal, 2000; Leggat et al., 1998). In a study conducted by Leggat et al. (1998), socio demographic factors such as age, race, and length of time on dialysis were found to be predictors of fluid noncompliance. Positioned on this understanding that socio-demographic factors influence an individual’s health status, the effect of socio-demographic factors on the health of a person merits closer attention (Kugler et al., 2011). Socio-Demographic Factors and Their Role in Health The environment to which an individual is exposed comprises important factors affecting the health of Canadians (Mikkonen & Raphael, 2010). Socio-demographic factors, such as age, gender, race, income, education, occupation, and marital status, have been found to affect the health of an individual, and most of these factors intermingle with each other (Leggat et al., 1998; Mikkonen & Raphael, 2010; Victoria et al., 2015). Evidence-based knowledge derived from exploring the relationship between socio-demographic factors and non-compliance may help nurses understand how such factors may be taken into account towards effective early interventions to increase compliance for these patients (Ahrari et al., 2014; Smith & Egger, 1996). 26 Contemporary literature sources were examined to identify the gap in knowledge related to fluid non-compliance status among hemodialysis patients across the world. Environmental, social, and cultural dissimilarities between hemodialysis patients were observed throughout the literature review, and these differences were taken into consideration in this study. Age. In a 2010 study completed in Iran, Ahrari et al. (2014) found that age had a significant relationship with fluid compliance among hemodialysis patients. This correlational study had 237 hemodialysis patients from two large dialysis centres in Iran. The inclusion criteria were that participants had to be a minimum of 18 years old with a history of receiving hemodialysis for the last three consecutive months. Ahrari et al. concluded that increase in age significantly decreases the level of non- compliance. This variance in compliance between younger and older people was attributed to the conservative beliefs of older patients compared to younger patients. Kutner, Zhang, McClellan, and Cole (2002) found similar relationship between age and compliance, as they found elder patients were more fluid compliant than younger patients. Kutner et al. attributed this to the lifestyle of older people, who adapt to the treatment regimen, unlike younger patients who consider themselves to be non-vulnerable to complications resulting from noncompliance (Chan et al., 2012; Kutner et al., 2002; Victoria et al., 2015). Similarly, based on their 90-day randomized controlled study of 118 patients in the US, Kauric-Klein (2013) found that young age significantly related to excessive IDGW among dialysis patients. The study included a variety of socio-demographic factors, such as age, gender, race, education, employment status, IDWG, and marital status. Kauric-Klein held that the reason behind the noncompliance of younger-age hemodialysis patients is due to 27 their reluctance to accept that they are chronically ill, and they are required to fulfill their responsibility towards work and family. Because of everyday responsibilities, younger-age patients were stressed and more prone to skip hemodialysis sessions or comply with fluid restrictions. Based on their recent literature review study that explored 16 research studies done in different parts of the world, which included North America, Europe, Australia, New Zealand, and Asian countries, Victoria et al. (2015) posited that young age was the major demographic factor associated with treatment non-compliance. Victoria et al. argued that older people may have adapted to the fluid-restricted lifestyle, as they have been undergoing hemodialysis for a longer time and might have developed a self-care strategy over a longer period. Bame et al. (1993) found that non-compliance with fluid restrictions increased among younger dialysis patients compared to older patients. While Leggat et al. (1998) found higher noncompliance among 20-39 years, others studies suggested that non-compliance and related problems were more common among patients between 20 and 30 years of age (Baines & Jindal, 2000; Ward, 2008). Several other studies also found age as a demographic factor that influences fluid compliance of hemodialysis patients (Ifudu et al., 1996; Kara, Caglar, & Kilic, 2007; Kim & Evangelista, 2010; Kugler et al., 2005; Mellon, Regan, & Curtis, 2013; Morduchowicz et al., 1993). In contrast, based on a study conducted in 2007 in Iran, which included 63 hemodialysis patients, Baraz et al. (2010) found that younger patients exhibited better fluid compliance than older patients. This result may not be comparable with previously discussed studies, as the mean age of participants in this study was 34.85 years, and the population age 28 range was narrow, ranging from 18 to 50 years. Further, the sample was smaller, with only 66 participants. These limitations might have influenced the study results. These literature sources showed there were few similarities and many differences among different studies, such as differences in sample size, study method, geographical differences, different health care systems, and cultural differences. These study results are not generalizable to the Canadian context and emphasize the requirement of further investigation. In addition, there was no comparable study found in Canada. Therefore, this study intended to explore the relationship of age and hemodialysis patients’ fluid compliance status in a Canadian context. Gender. Gender has a direct relationship with the health of an individual (Lane & Cibula, 2000; Mikkonen & Raphael, 2010). In Israel, a multivariate regression analysis conducted in a study of 50 hemodialysis patients found compliance to be multi-dimensional and emphasized the fact that gender influences treatment compliance of hemodialysis patients (Morduchowicz et al., 1993). The eligibility criteria included patients who received dialysis for four hours three times a week. A questionnaire collected patients’ sociodemographic factors. However, the major limitation observed in the study was the limited sample size (n = 50), which affected the generalizability of the study results. Boyer et al. (1990) examined the relationship between treatment compliance and demographic factors among 60 in-centre patients and found significant association between gender and compliance, with women being more compliant to the treatment regime. KauricKlein (2013) stated that male patients were two-thirds less fluid compliant than female patients. Similarly, Bame et al. (1993), showed from their study that male patients were approximately two-thirds less likely to be fluid compliant than female patients. Several other 29 researchers also concluded that female patients exhibited better fluid compliance (Chan et al., 2012; Ifudu et al., 1996; Kara et al., 2007; Kauric-Klein, 2013; Kugler et al., 2005). Barnett et al. (2008) found significant positive relationship between fluid compliance and gender among hemodialysis patients, noting that female patients were exhibiting higher fluid compliance. Kugler et al. (2005) attributed higher compliance among women to their higher self-awareness with respect to their health status. Other studies disputed the argument that women are more compliant. Based on their recent research study in Turkey, Mollaoğlu and Kayataş (2015) showed the non-compliance rate was higher among female patients. A total of 186 hemodialysis patients participated in their descriptive study. Data were collected from a personal information form and questionnaire. Similarly, Kugler et al. (2011) and Mollaoğlu and Kayataş (2015) found that male patients exhibited increased rates of compliance when compared to female patients. However, Baraz et al. (2010) and Rambod et al. (2010) found no association between hemodialysis patients’ compliance and gender. Similarly, Ibrahim et al. (2015) stated that there was no relationship between gender and compliance. Ibrahim et al. conducted a recent study in Egypt, with a total of 100 hemodialysis patients. They concluded that no statistical significance existed on gender and hemodialysis patients’ fluid compliance. Victoria et al. (2015) stated, “Fluid compliance results are fuzzy as far as gender is concerned” (p. 63). These findings indicated a need for further investigation about the relationship between gender and fluid compliance in a Canadian context. From the literature, conflicting findings related to gender and fluid compliance among hemodialysis patients were evident. This could be attributed to heterogeneity in research studies, especially the difference in study protocols and cultural differences. As such, these 30 study results are not generalizable in a Canadian context, and further investigation is required to understand how gender is associated with fluid compliance among hemodialysis patients. Therefore, this study intended to explore the relationship of gender and hemodialysis patients’ fluid compliance status in a Canadian context. Income. Income is another socio-demographic factor that can affect the fluid compliance (Kim & Evangelista, 2010). A total of 151 patients participated in Kim and Evangelista’s (2010) study in Los Angeles County, California. The researchers adopted a self-reporting design using a ESRD compliance questionnaire to measure patients’ compliance. Kim and Evangelista provided evidence that patients with low income showed higher non-compliance compared to patients with higher income. However, the inadequate sample size limited the generalization of the study findings. Further, a variation in reporting is a limitation in self-reporting compliance measures, as often patients overestimate their compliance (Liu et al., 2001; De Geest, et al., 1996) Similarly, Bame et al. (1993) showed that lower income can have a negative impact on a patient’s health. This was a large study of hemodialysis patients (N = 1,230) in a variety of facility types (N = 29) conducted in United States. Bame et al. investigated the incidence and related demographic factors of noncompliance with fluid restrictions. Even though their study was conducted in 1993, the study findings may still be relevant due to the large sample size (N = 1,230) with a 96% response rate, inclusion of a variety of important sociodemographic factors, and inclusion of multiple locations (i.e., Houston, Dallas, Fort Worth, and Austin). Bame et al. (1993) concluded that among hemodialysis patients, people with high income exhibited 1.6 times higher compliance (p = 0.032). Overall, income was 31 identified as having a great impact on quality and overall health status of a patient (Lemos, Rodrigues, & Veiga, 2015). In contrast, Pang et al. (2001) indicated that low income was a predictor of higher compliance. A sample of 92 Chinese hemodialysis patients from two dialysis centres participated in this cross-sectional, descriptive, correlational study. Pang et al. concluded that patients with low family income had a higher compliance rate. However, this study had limitations, such as smaller sample size, convenient sample, geographical diversities, and lack of ethnic minorities. Therefore, the generalizability of the findings to other hemodialysis populations is cautioned. The above literature indicated inconsistent conclusions in terms of the relationship between income and fluid compliance. In addition to the above inconsistent findings, along with a lack of Canadian studies, it was useful to investigate the relationship between income and fluid non-compliance in a Canadian context. Length of time on dialysis. Research studies conducted among hemodialysis patients found a substantial relationship (p < 0.01) between patients’ fluid compliance and length of time on dialysis (Chan et al., 2012). A total of 188 patients from 14 dialysis centres in Malaysia between 2008 and 2011 participated in a purposive sampling, self-reported questionnaire study Chan et al. (2012). In their cross-sectional study, Chan et al. found the non-compliance rate was higher among patients who had been on dialysis for a longer duration. The researchers attributed their finding to the boredom and frustration with the restrictive fluid and dietary requirements. According to Chan et.al, younger male patients who were on dialysis for longer duration were at higher risk for fluid non-compliance. Further, newly diagnosed patients may get more motivation from social support and family 32 support. However, the cross-sectional nature of the data, small sample size, and potential for selection bias limited the generalizability of the study findings. Similarly, Kugler et al. (2005) conducted a large study with a sample of 916 patients from 18 dialysis centres in Germany and Belgium and found a positive significant correlation (p = 0.003) between length of time on dialysis and fluid non-compliance. However, the study was limited, as the researchers were analysing compliance for a shorter period of time (14 days). Cultural preferences regarding participants’ food habits might have affected the results, as all the participants were from Europe (Kugler et al., 2005). Another limitation of the study was that it was a self-reported study, in which the patients might have tried to present themselves in a positive way, which could have confounded the study results. In contrast, Pang et al. (2001) did not find any significant relationship between length of time on dialysis and fluid compliance (p > 0.05) among Chinese patients receiving hemodialysis at two Hong Kong hospitals. The literature indicated conflicting results concerning the relationship between fluid compliance and length of length on dialysis. These contrasting results in the literature emphasized the need to further investigate the relationship between length of time on dialysis and fluid compliance among hemodialysis patients in a Canadian context. Rural definition. Two geographically related categories were established for this research: (a) those who received care within or outside of a metropolitan area, and patients who resided in an urban or a rural area. The first category reflected patients who received care outside the commuting zones of a Census Metropolitan Area or a Census Agglomerations and their neighbouring Census Subdivisions, which Du Plessis, Beshiri, Bollman, & Clemenson (2002) define as rural and small town areas. According to Statistics 33 Canada (2015), a metropolitan area is one that has a total population of at least 100,000, of which 50,000 or more live in the core. The second category in this study concerned location of residence (patients who resided in urban or rural areas). The basis of the categorisation was Statistics Canada’s (2011b) definition of rural as obtained from a patient’s postal code. On Statistic Canada’s website, each postal code reflects its rural and urban status. Rural areas include people living outside the main commuting zone of communities of 10,000 and over (Du Plessis et al., 2002; Statistics Canada, 2011b). In addition to this, agricultural, undeveloped, and nondevelopable land, as well as remote and wilderness areas are considered rural (Statistics Canada, 2011b). Geographical location of patient’s residence. Where people live has a direct impact on their health, and there is a certain relationship between where people live and the quality of their health (Pong, DesMeules, & Lagacé, 2009; Sundquist, 1995). According to Kulig and Williams (2011), rural Canadians experience higher health risks compared to urban Canadians. Rural Canadians have comparatively lower socio-economic status than urban Canadians due to a higher unemployment rate (Kirby & LeBreton, 2002; Kulig & Williams, 2011; Kurpas, Mroczek, & Bielska, 2014; Pong et al., 2009). Patients from rural areas can struggle with paying higher costs for transportation, accommodation, and meals when travelling to receive care, and this may affect their overall health status (Luo et al., 2004). Location where patients received care. Location of care has a direct impact on peoples’ health as rural health care facilities have fewer services, such as doctors, dieticians, social worker, and nurses (Romanow, 2002). According to Vanasse, Courteau, Cohen, Orzanco, & Drouin (2010), access to better care and specialist doctors are often limited in 34 rural areas. Medical resources utilization rates and the specialist consultation rates may be statistically lower in small towns and rural areas when compared with metropolitan areas, which might result in lower health status among rural and nonmetropolitan Canadians (Pong et al., 2009). Other studies support these arguments. According to Pong (2007), many rural areas lack adequate infrastructure, which negatively affect the health of rural Canadians. Therefore, patients who resided in a rural location and patients who received dialysis treatment outside metropolitan area may have been more susceptible to poorer health status due to varying factors. This study intended to examine if there was any relationship between the area where patients resided and received dialysis treatment and their fluid compliance status. Missed dialysis appointments. Hemodialysis patients normally need three treatments per week to avoid serious complications from ESRD (Chan et al., 2014). Chan et al. (2014) conducted a large study investigating the factors related to missed dialysis appointments and the effects on hemodialysis population. The study observed 44 million hemodialysis treatments for 182,536 patients in the United States. The observational cohort analysis study abstracted data from the Fresenius Medical Care North America ESRD database from the period of January 1, 2005, to December 31, 2009. Over 1,500 hemodialysis clinics in 48 states, the District of Columbia, and the Territory of Puerto Rico were represented in the database, which reflected 30% of the US dialysis population. Chan et al. (2014) concluded that after a missed dialysis appointment, the probability of hospitalization due to non-compliance was 5%, whereas it was only 1.2% if the patient received dialysis. The chances of emergency room visits were 5% when treatment was missed and only 1.6% in patients who received dialysis. Similarly, the risk for ICU-CCU 35 admission was 2% after a missed dialysis, and only 0.5% in patients who attended dialysis. Chan et al. stated that the number of hospitalizations and/or emergency room/ coronary care room visits had significantly increased after missing dialysis appointments. The observational study had it limitations, including that it did not report accurate mortality outcomes and failed to include health literacy information, as the data were not available. It was evident from their study that missed dialysis treatment was a key factor, as it could affect the fluid compliance status and related complications. Chan et al. conclude that missing treatment can affect dialysis patients’ overall health status. Kugler et al. (2005) conducted a study in two European countries to assess the factors related to non-compliance among hemodialysis patients and concluded that sociodemographic factors have strong positive correlation between frequency and degree of fluid non-compliance among dialysis patients. In contrast to the above-mentioned research studies, Pang et al. (2001) found that demographic factors were not a major predictor for compliance with fluid restrictions among hemodialysis patients. Pang et al. stated that none of the demographic factors had significant relationship with IDWG. Based on their research study conducted in Malaysia, Barnett et al. (2008) agreed with the findings of Pang et al., for all the demographic factors except for gender. In conclusion, a number of studies investigated the correlation between fluid compliance and demographic factors, such as age, gender, and length of time on dialysis (Ahrari et al., 2014; Baines & Jindal, 2000; Bame et al., 1993; Kugler et al., 2011; Kutner, 2001; Mollaoğlu & Kayataş, 2015; Morduchowicz et al., 1993; Victoria et al., 2015). It was evident from the international literature that prevalence and frequencies of fluid non- 36 compliance varied considerably throughout the global studies when reviewing different socio-demographic factors (Bame et al., 1993; Chan et al., 2012; Ifudu et al., 1996; Kara et al., 2007; Kauric-Klein, 2013; Kugler et al., 2005; Mollaoğlu & Kayataş, 2015). Incidence of fluid non-compliance varied globally, depending on variations in sociodemographic factors. The identified literature gaps included a lack of rural geographical studies and a lack of Canadian studies. Therefore, this study is intended to address some of the above-mentioned gaps specific to variation in non-compliance between urban and rural patients as well as the relationship between socio-demographic factors and non-compliance on Northern Alberta, Canada patients. Chapter Summary From the literature review, it was apparent that there have been no studies linking hemodialysis patients’ fluid non-compliance to socio-demographic factors in the Northern Alberta or another similar Canadian context. Since society influences the health status of individuals (Mikkonen & Raphael, 2010), it is vital to better understand the association between patients’ fluid compliance and socio-demographic factors. Even though some of the socio-demographic factors are non-modifiable, by understanding the underlying factors of fluid non-compliance, a hemodialysis nurse can more effectively collaborate with a patient to achieve a better treatment outcome by providing frequent follow-ups, educational sessions, and psychological support, and hemodialysis nurses have broader responsibility to help the patients manage their illness. The findings from this study may assist nurses to provide better evidence-based quality care to their patients. 37 CHAPTER 3 Methods The study design and the rationale behind selecting this specific method, along with the independent and dependent variables, are explained in this chapter. In the sampling section, the selection process for sampling, sampling method, sample size, population location, and sample approach are described. The exclusion and inclusion criteria, considerations and protection of human subjects, and location selection criteria are outlined in the sampling section. An overview of the data collection section provides an understanding of the type of data collected and the data collection methodology. An explanation of the techniques used for data analysis is also provided. In the last section, an outline of ethical considerations and the strategies to address confidentiality are discussed. Study Design A retrospective, multicentre, cross-sectional descriptive study design was undertaken to examine the relationship between selected socio-demographic factors and hemodialysis patients’ fluid compliance in Northern Alberta, Canada. Descriptive statistics, such as frequencies and percentages, and Pearson Chi-square test, were used to analyze the data to examine the variables and their statistical association. This study did not use regression analysis due to limited sample size. The research study compared hemodialysis patients’ fluid compliance outcomes between patients residing in rural versus urban areas, and patients who received care in metropolitan versus nonmetropolitan areas. This study was conducted in four dialysis units under NARP, which included Edmonton General Hospital, Lloydminster Hospital, St. Therese Healthcare Centre, St. Paul, St. Joseph’s General Hospital, and Vegreville. 38 Sources of data. This study utilized data collected for clinical purposes by NARP. NARP collects patient-related socio-demographic information at the time of admission, and this information is updated and verified every month by dialysis nurses. The dialysis nurses and unit clerks enter this patient-related information as part of their clinical practice across NARP. It is NARP policy that dialysis nurses should verify patient information with each treatment as well as record IDGW for clinical purposes. This information is kept in each dialysis unit for at least three months before being forwarded to NARP medical records. The researcher collected data from hemodialysis patients’ charts and run sheets in the dialysis units. Patients’ dialysis treatment related information or dialysis record is also known as hemodialysis run sheet. The hemodialysis run sheet and chart provide patient-specific information such as age, gender, length of time on dialysis, postal code, and IDWG. The hemodialysis run sheet is the only place where a hemodialysis patient’s IDWG is recorded, and the chart provides other patient-related information. Therefore, the researcher had to use both hemodialysis run sheets and charts to collect required information for this study. Age, gender, length of time on dialysis, number of missed dialysis appointments, and IDWG (which was already calculated and recorded on the hemodialysis run sheet) of patients undergoing hemodialysis were collected from patients’ hemodialysis run sheets for a 10weeks period at the four dialysis centres that were the focus of this research. The patients’ residential postal codes were collected from their charts to determine the geographic location and income distribution by geographical area. By using the postal code, income distribution corresponding to the geographic area was accessed from the National Household Survey (Statistics Canada, 2011a). 39 All the dialysis units mentioned in this report are outpatient dialysis units with patients of varying backgrounds, which were suitable for this study. Various geographical locations and socio-demographic variations may affect the generalizability of the study findings. No direct contact between the researcher and the patient occurred during this study. The key dependent variable, which is the indicator for compliance, was the IDWG, and the independent variables were age, gender, length of time on dialysis, number of missed dialysis appointments, geographic location, income distribution by geographic area, and care site location. Sample. Data were collected from 153 patients from four dialysis centres: Edmonton General Hospital, Edmonton (n = 102), Lloydminster Hospital, Lloydminster (n = 17), St. Therese Healthcare Centre, St. Paul (n = 15), and St. Joseph’s General Hospital, Vegreville (n = 19). This multiple sites sampling approach (i.e., patients from four multiple sites) was used to find out if there was any difference in fluid compliance status among patients residing in and receiving care from different geographical locations. All patients who received care from the metropolitan city of Edmonton at Edmonton General Hospital were categorized as “patients who received care in metropolitan area,” and all patients who received care from Lloydminster Hospital, St. Therese Healthcare Centre, St. Paul, St. Joseph’s General Hospital, and Vegreville were categorized as “patients who received care in nonmetropolitan area.” Similarly, patients were categorized into rural and urban residents (i.e., patient resided in urban or rural location) by using the postal code associated with the geographical location of their respective residence. The inclusion criteria reflected the following: (a) outpatients receiving hemodialysis in one of the study dialysis centres of NARP over a 10-week period from the first week of October 2015 to last week of 40 November 2015, (b) patients who received dialysis treatment on October 1, 2015, and those above 18 years of age were eligible to be included in this study. The selection process to decide eligibility for this study is shown in Figure 3. Figure 3. Selection process for eligible patients in this study in four sites in Northern Alberta, Canada, from October to November 2015. Note. This figure reflects the 208 dialysis patients who received care in Edmonton General Hospital, Lloydminster, Vegreville, and St. Paul dialysis units who were eligible to participate in the research. Exact numbers for each category, such as number of patients transplanted or moved to peritoneal dialysis, were not available, and were not provided by NARP, as gathering such data could breach the anonymity of patients. Study Variables The study variables in this study were age in years, gender, postal code, site where patient received care, income, length of time on dialysis, total number of missed dialysis, and IDWG. Each variable is detailed in this section. Age. Age was one of the independent variables in this study to examine the relationship with fluid non-compliance. The patients’ ages were calculated in years. 41 Gender. Patients were divided into male and female by as stated on the NARP form, and this information was collected directly from hemodialysis patients’ run sheets. Income. Income distribution of the geographic location of patients’ residence was an independent variable in this study. Since income is not recorded in NARP documents, each patients’ postal code was used to determine the median income distribution in the geographical location where the patient resided. Each patient’s postal code was entered into Statistics Canada’s (2011b) website, and the median income corresponding to that postal code was used as income for this study. Median income is the single most commonly used measure of income, and it is the best value to use in a set of data (Beddoe, 2016). Collected income data for this study were not evenly distributed. Based on the nature of the data distribution of income, $35,000 was decided as a cut off for this study. All the participating patients were divided into two groups based on their income group: (a) one group had a median income of $35,000 and below, and (b) the other group had a median income above $35,000. Length of time on dialysis. Length of time on dialysis in months was included in the study as an independent variable to find out if that had any connection to hemodialysis patients’ fluid compliance status. The patients were divided into four groups. Length of time on dialysis was calculated in months by subtracting the first week of data collection (October 1, 2015) and the start date of dialysis. The formula used in this study to calculate length of time on dialysis is shown in Figure 4. 42 Data collection start date (October 1, 2015) Subtract patient’s start date on dialysis Divide by 30 days = Duration of dialysis in months Figure 4. Formula used in this study to calculate length of time on dialysis. Note: This study used 30 days as one month, as 30 days are commonly considered as one month. In addition, 365 days (one year) divided by 12 months is 30.4 days, rounded to 30 for consistency for this formula. Geographical location of patient’s residence. Patients were divided into urban and rural by their geographical location of residence. Patients’ residential postal codes were collected to determine the population size of the geographic location of their residence, and based on the postal code, they were categorized as urban residents or rural residents. The urban and rural areas of each patient’s residence were determined by Statistics Canada’s (2011b) definition of rural and urban. Therefore, this study used the National Household Survey (Statistic Canada, 2011a) to categorize patients’ rural and urban areas based on the patients’ postal code. Location where dialysis treatment was received. Patients were divided into metropolitan and nonmetropolitan by the location where they received dialysis treatments. All patients who received care in Edmonton General Hospital were considered as “patients who received care in a metropolitan area,” and all patients from the three smaller units (i.e., Lloydminster, St. Pauls, and Vegreville) were categorized as “patients who received care in a nonmetropolitan area.” 43 Number of missed dialysis. Missed dialysis treatment was an important key independent variable in this study. Number of missed dialysis treatments was collected from hemodialysis run sheets in each week of the data collection. If a patient did not attend a scheduled dialysis treatment, hemodialysis nurses were to chart as “cancelled treatment” or “no show” based on the reason for missing dialysis. All “cancelled treatments” and “no shows” were considered as missed dialysis treatment for this study. If a patient ever shortened his/her dialysis treatment, patient were supposed to a sign a specific form stating that he/she is shortening the dialysis treatment against physicians advice, and this information also gets recorded electronically. This study did not collect such information, as such data were not available to the researcher. Interdyalitic weight gain. The dependent variable IDWG, which was already calculated with each dialysis treatment, was gathered from the hemodialysis run sheets. IDWG is a biological measure and was the key indicator of hemodialysis patients’ fluid compliance. IDWG was measured in kilograms and refers to the quantity of fluid consumption between two successive dialysis sessions. Weight gain between each hemodialysis treatment, calculated by specially trained hemodialysis nurses, was collected for this study to analyze the fluid compliance status. The patients’ average weight gain in each week was calculated, and then the fluid compliance status was decided by their mean weight gain during the study period (see formula presented in Figure 5). This formula was created based on the information used in previous research studies to measure IDWG on hemodialysis patients (Chan et al., 2012; Kauric-Klein, 2013; Pang et al., 2001). 44 Assessed weight before dialysis Subtract post-dialysis weight from previous dialysis treatment Divide by 7 days (whole week) = Mean daily IDWG. Figure 5. Formula used in this study to calculate daily IDWG. The cut-off defining fluid non-compliance varied throughout the literature (Denhaerynck et al., 2007). There was no standardized guideline evident in the literature. However, this study used two North American studies as guidelines for the cut-off to decide fluid non-compliance (Bame et al., 1993; Matteson & Russell, 2010). Matteson and Russell (2010) used a well-established Kidney Foundation Dialysis Outcome and Quality Initiative (KDOQI) guideline to measure non-compliance among dialysis patients. Matteson and Russell defined patients as fluid non-compliant if their IDWG was > 1.0 kg per day. Similarly, Bame et al.’s (1993) larger study done in the US on 1,230 patients in 29 facilities defined patients as fluid non-compliant if their IDWG was > 1.0 kg per day. This evidence implies that this cut-off of 1.0 kg daily weight gain to decide fluid non-compliance was still relevant even after 17 years (1993-2010). Therefore, the purpose of this study, patients who gained one or more kilograms per day were considered fluid non-compliant, and those who gained less than one kilogram were considered fluid compliant. Data from 10 weeks of dialysis run sheets were used to calculate the IDWG data to compare to independent variables. The IDWG values were gathered over a 10-week period, and daily weight gain between dialysis sessions for each week was calculated. The mean of the daily weight gain was again calculated over the 10-week data collection period. If the mean of 45 daily weight gain value was below one kilogram per day, then that patient was considered to be fluid compliant. If the mean was one or more kg per day, then that patient was considered non- compliant. Data Collection This study used retrospective data, which were originally collected and maintained for NARP clinical purposes. Chart records were used to collect each patient’s postal code and length of time on dialysis. Information about each patient’s age, gender, number of missed dialysis treatments, location where dialysis treatment was received, and IDWG was collected from their respective hemodialysis run sheets. It should be noted that the IDWG was recorded with each dialysis treatment. A meaningless but unique number (MBUN), which is a number that does not have any purpose, value, significance, or meaning, was assigned to each patient to protect anonymity of the patient. Eligible patients’ data from the beginning of the first week (Sunday to Saturday) of October 2015 to last week of November 2015 were collected. All retrospective/secondary data were originally collected as part of routine dialysis treatment sessions. The designated NARP staff assigned by the corresponding Unit Managers provided data to the researcher. Data were blinded before the data collection; therefore, there was no chance of identifying the patients. The researcher had no direct contact with the patients. The researcher did not have any personal interaction outside of the clinical setting with patients potentially eligible for inclusion in this study. Furthermore, the researcher will not disclose any data to third persons, as the researcher has signed a confidentiality agreement (see Appendix C). Hence, the privacy of the patients was protected. 46 The researcher directly accessed original data in paper form stored on the hemodialysis units in each location. The researcher manually copied the data from the paper forms and inputted it directly into an excel file. The researcher then checked for errors in data entry following each record entry. All necessary data were uploaded to a secure University of Northern British Columbia (UNBC) G drive, which is a dedicated secured server maintained at UNBC. This dedicated password-protected server was accessible only to the researcher and her supervisors. All data were directly entered into an Excel table stored on the UNBC G drive, which was accessed remotely from the UNBC website. Photocopies of all de-identified patient-related information were destroyed on the same day of data collection. Destruction of database information generated for this study will take place no later than five years after data collection. Data Analysis The Statistical Package for the Social Sciences (IBM SPSS 23) software (available through UNBC) was used for the statistical analysis. Descriptive, frequency, and crosstab statistics examined the association between socio-demographic variables. Descriptive statistics are mainly useful to find the basic features of the data in a study, and they describe powerful summaries of the sample (Trochim, 2006). Crosstabs compared these percentages in categorical variables. As this study intended to examine the relationship between the variables, Chi-square was the most appropriate test to analyze the data, as a Chi-square is generally used to assess the association between categorical variables (Ahrari et al., 2014). Pearson Chi square test was used for categorical variables to examine the relationship/ association between fluid compliance of hemodialysis patients and socio-demographic factors. 47 All independent variables were statistically analyzed with the dependent variable IDWG. The mean IDWG of 10 weeks was used for all patients, except for four patients who had eight weeks of data and three patients who had nine weeks of data. These exceptions reflect those who missed dialysis occasionally or more than one time per week. After reviewing the literature, mean imputation was determined to be the best method for treating occasional missing data for this study (Lindberg, 2010); therefore, occasional missing data were imputed by using a mean imputation method to attain a complete data set. The missing IDWG was imputed by the mean IDWG of that specific week. Of the total data, 1.9% of missing data were imputed by a mean imputation method to obtain a complete data set. Further, a data cleaning file was prepared for data hygiene in order to improve the quality of data and ensure there were no missing data. There was no missing data in independent variables. Variables were categorized into groups without violating assumptions and also by considering the nature and distribution of the data. The rationale behind this categorization of variables was to align with the previous studies (Khattak, Sandhu, Desilva, & Goldfarb‐Rumyantzev, 2012). Ethical Considerations This study met the ethical requirements as detailed in the Tri-Council Ethics Policy Statement II, Article 5.5 (Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, & Social Sciences, & Humanities Research Council of Canada, 2014). Ethical approval from both the University of Northern British Columbia Research Ethics Committee (see Appendix D: #E2015.0617.053.00, October 9, 2015), and the Health Research Ethics Board of Alberta (see Appendix E: HREBA. CHC-150039, October 5, 2015) as well as permission from Alberta Health Services (see Appendix F) 48 were obtained prior to initiating data collection. Approval to collect data was granted on December 11, 2015, by Alberta Health Services research administration. 49 CHAPTER 4 Results An overview of the study findings is presented in this chapter. This study used deidentified data from a total of 153 hemodialysis patients, as collected from NARP. After the exclusion criteria, data were collected from 102 patients receiving care at a metropolitan dialysis unit and 51 patients at nonmetropolitan dialysis units. SPSS was used to analyze the data. SPSS is a commonly used software designed to assist researchers and students learn how to analyze and interpret research data (Leech, Barrett, & Morgan, 2005). A description of frequencies and distribution of the socio-demographic factors, comparison of urban and rural status with socio-demographic factors, and the differences in fluid compliance with each independent variable are provided in this chapter. A description of frequencies and distribution of the socio-demographic factors is presented in Table 2. Socio-Demographic Characteristics Among the 153 hemodialysis patients, 20.9% of the patients were between the ages of 18 to 49, and 48.4% of patients from this study group were 65 years and older. Over half of the patients were male (58.8%). Substantial differences existed between the percentage of urban and rural population. About 26.1% of patients resided in rural areas while 73.9% resided in urban locations. Two in five hemodialysis patients (41.8%) within the study sample were non-compliant in regards to their fluid compliance status. 50 Table 2 Socio-Demographic Characteristics of Patients who Received Hemodialysis in Four Sites in Northern Alberta, Canada, from Oct-Nov 2015(N=153) Socio-Demographic Characteristics Total Population % (N = 153) Age Group (in years) 18-49 50-64 65-79 80 and greater 20.9 (32) 30.7 (47) 32.7 (50) 15.7 (24) Gender Female Male 41.2 (63) 58.8 (90) Geographic Location of Participant’s Residence Resides in rural area Resides in urban area 26.1 (40) 73.9 (113) Compliance Status Non-Compliant Compliant 41.8 (64) 58.2 (89) Location Where Care was Received Metropolitan (Edmonton General Hospital) Nonmetropolitan Lloydminster Hospital, Lloydminster St. Therese Healthcare Centre, St. Paul St. Joseph’s General Hospital, Vegreville 66.7 (102) 33.3 (51) 11.1 (17) 9.8 (15) 12.4 (19) Income Distribution by Geographic Area Median income  $35,000 Median income > $35,000 26.1 (40) 73.9 (113) Length of Time on Dialysis Less than one year 12-23 months 24-35 months 36 and greater 21.6 (33) 20.9 (32) 15.7 (24) 41.8 (64) Number of Missed Dialysis Appointments None missed Missed one or more 76.5 (117) 23.5 (36) 51 Two thirds of patients received care in Edmonton General Hospital (metropolitan area). In comparison, 33.3% of patients received care in nonmetropolitan areas. The median income in areas where patients resided ranged from $27,670 to $40,553, with a mean of $36,089 and a standard deviation (SD) of $2,539. The majority of the participants resided in an areas with a median income of $37,243 (n = 100). Variation existed in the income distribution of the patients among the study population. The length of time on dialysis ranged from one month to 284 months (mean 41.6 months, SD 41.3 months). A greater percentage (76.5%) attended all dialysis appointments, while 23.5% missed one or more dialysis appointments. However, 11.8% of patients missed one dialysis treatment during the 10-week period, and approximately the same percentage of people (11.7%) missed two to nine dialysis treatments during the same study period. In summary, the majority of the patients resided in an area with a median income above $35,000, resided in an urban location, and received care in a metropolitan area. A detailed comparison of the socio-demographic characteristics of patients who received care in in four sites in Northern Alberta, Canada, from October to November 2015 is presented in Table 3. Of patients resided in a rural area, 52.5% were aged 18-64, and 47.5% were aged 65 and older. Similarly, of patients who resided in urban area, 51.4% were aged 18-64, and 48.7% were aged 65 and older. 52 Table 3 Comparison of Socio-Demographic Characteristics by Urban and Rural Patients who Received Hemodialysis in Four Sites in Northern Alberta, Canada, Oct-Nov, 2015(N=153) Socio-Demographic Characteristics Resided in Resided in Total Population Rural Area Urban Area % (N = 153) 26% (n = 40) 73.9% (n = 113) Age Group (in years) 18-49 50-64 65-79 80 and greater 20.9 (32) 30.7 (47) 32.7 (50) 15.7 (24) 25.0 (10) 27.5 (11) 27.5 (11) 20.0 (8) 19.5 (22) 31.9 (36) 34.5 (39) 14.2 (16) Gender Female Male 41.2 (63) 58.8 (90) 35.0 (14) 65.0 (26) 43.4 (49) 56.6 (64) Compliance Status Non-compliant Compliant 41.8 (64) 58.2 (89) 50.0 (20) 50.0 (20) 38.9 (44) 61.1 (69) Location Where Care was Received Metropolitan 66.7 (102) Nonmetropolitan 33.3 (51) 5.0 (2) 95.0 (38) 88.5 (100) 11.5 (13) Income Distribution by Geographic Area 26.1 (40) Median income  $35,000 Median income > $35,000 73.9 (113) 97.5 (39) 2.5 (1) 0.9 (1) 99.1 (112) Length of Time on Dialysis (in Months) Less than one year 21.6 (33) 12-23 months 20.9 (32) 24-35 months 15.7 (24) 36 and greater 41.8 (64) 25.0 (10) 27.5 (11) 12.5 (5) 35.0 (14) 20.4 (23) 18.6 (21) 16.8 (19) 44.2 (50) Total Number of Missed Dialysis None missed 76.5 (117) Missed one or more 23.5 (36) 85.0 (34) 15.0 (6) 73.5 (83) 26.5 (30) Pearson’s Chi-Square (df) p value 1.70 (3) 0.6 0.85 (1) 0.4 1.48 (1) 0.2 _ _ _ _ 2.37 (3) 0.5 2.19 (1) 0.1 Note 1. Could not calculate statistical significance of income and care site location (patients’ received hemodialysis treatment) due to limited sample size. Note 2. df=degrees of freedom 53 Of patients who resided in the urban area, 61.1% were fluid compliant, while 38.9% were fluid non-compliant. Of patients who resided in rural areas, fluid compliant status was equally distributed; 50% were fluid compliant and 50% were fluid non-compliant. Within the urban and rural population, 11.5% of the patients residing in the urban area received care in a nonmetropolitan area (i.e., Lloydminster, St. Paul, and Vegreville), while 95% of patients residing in rural areas received care at nonmetropolitan area. Within the urban and rural population, 88.5% of the patients residing in the urban area received care in a metropolitan area (Edmonton General Hospital), while 5.0% of patients residing in rural areas received care at nonmetropolitan area. There were no significant differences found by patients’ geographical location of residence (urban or rural), nor by the location where they received hemodialysis treatment (metropolitan vs. nonmetropolitan). Data analysis indicated a difference in income distribution for patients from urban versus rural areas of residence. The majority of patients who resided in the urban areas (99.1%) resided in a geographic location where the median income was above $35,000 compared to 97.5% of patients who resided in the rural area who resided in a geographic location where the median income was less than or equal to $35,000. Only 2.5% of patients who resided in a rural area had a median income above 35,000, whereas 99.1% of patients who resided in an urban area had a median income of above $35,000. Within the urban and rural population, 44.2% of patients who resided in an urban area and 35% of patients who resided in the rural area had been receiving dialysis for more than three years. Rural residents showed a higher incidence of compliance in terms of attending all dialysis treatments. Approximately 85.0% of patients who resided in the rural areas never missed any of their dialysis treatments whereas 73.5% of patients who resided in an urban 54 area attended all dialysis appointments. No statistical significance was found between sociodemographic factors and hemodialysis patients’ urban and rural status (i.e., geographical location where they resided). A statistically significant difference was noted between age groups and hemodialysis patients’ fluid compliance (p=0.0001). A higher incidence of fluid non-compliance was indicated among younger-aged patients (see Table 4). Of patients who were fluid noncompliant, 79.7% were aged 18-65, and 20.3% were aged above 65. Similarly, of patients who were fluid compliant, 39.3% were aged 18-65, and 60.7% were aged above 65. Of the patients who were compliant, 44.9% were female compared to 55.1% male patients. Of patients who were non-compliant, 31.1% resided in a rural area, while among persons who were compliant, only 22.5% resided in a rural area. Although there were no statistically significant differences observed between fluid compliance status and sociodemographic characteristics, there was a higher prevalence of fluid compliance was observed among older adults aged greater than 65 years. Of the patients who were fluid compliant, 69.7% received care in a metropolitan area (i.e., Edmonton General Hospital). Whereas, of the patients who were fluid compliant, only 30.4% received care in a nonmetropolitan area. Lloydminster and St. Paul dialysis unit patients were similar in fluid compliance status. Of patients who were fluid noncompliant, 31.3% resided in an areas where the median income was of less than or equal to $35,000, and 68.8% of the patients who were fluid non-compliant resided in an area where the median income was greater than $35,000. Of the patients who were fluid compliant, 77.5% resided in an area where the median income was greater than $35,000. 55 Table 4 Compliance of Patients who Received Hemodialysis in Four Sites in Northern Alberta, Canada, from Oct-Nov, 2015(N=153) Total Population Socio-Demographic Characteristics % (N = 153) Pearson’s Non-Compliant Compliant Chi-Square 41.8% (n = 64) 58.2% (n = 89) (df) p value 24.85 (2) 0.0001 1.24 (1) 0.3 1.48 (1) 0.2 3.94 (3) 0.3 1.48 (1) 0.2 2.39 (3) 0.5 2.31 (1) 0.1 Age Group (in years) 18-49 50-65 >65 20.9 (32) 35.3 (54) 43.8 (67) 31.3 (20) 48.4 (31) 20.3 (13) 13.5 (12) 25.8 (23) 60.7 (54) Gender Female Male 41.2 (63) 58.8 (90) 35.9 (23) 64.1 (41) 44.9 (40) 55.1 (49) 31.1 (20) 68.8 (44) 22.5 (20) 77.5 (69) Geographic Location of Participant’s Residence Resides in rural area 26.1 (40) Resides in urban area 73.9 (113) Location Where Care was Received Metropolitan 66.7 (102) 66.7 (102) 33.3 (51) 11.1 (17) 9.8 (15) 62.5 (40) 62.5 (40) 37.5 (24) 15.6 (10) 12.5 (8) 69.7 (62) 69.7 (62) 30.4 (27) 7.9 (7) 7.9 (7) 12.4 (19) 9.4 (6) 14.6 (13) Income Distribution by Geographic Area 26.1 (40) Median income  $35,000 Median income > $35,000 73.9 (113) 31.3 (20) 68.8 (44) 22.5 (20) 77.5 (69) Length of Time on Dialysis Less than one year 12-23 months 24-35 months 36 and greater 21.6 (33) 20.9 (32) 15.7 (24) 41.8 (64) 15.6 (10) 23.4 (15) 17.2 (11) 43.8 (28) 25.8 (23) 19.1 (17) 14.6 (13) 40.4 (36) Number of Missed Dialysis Appointments None Missed 76.5 (117) Missed one or more 23.5 (36) 70.3 (45) 29.7 (19) 80.9 (72) 19.1(17) Edmonton General Hospital Nonmetropolitan Lloydminster Hospital St. Therese Healthcare Centre, St. Paul St. Joseph’s General Hospital, Vegreville Note 1. df =degrees of freedom There was no significant difference found in the fluid compliance status and the length of time on dialysis. However, of patients who were fluid non-compliant, 56 15.6% had been receiving dialysis for less than one year, while 43.8% of patients who were fluid non- compliant had been receiving dialysis for 36 and greater months. Of the patients who were fluid compliant, 80.9% attended all dialysis appointments. Of Patients who were fluid compliant, 19.1% missed one or more dialysis appointments. In summary, there were no statistically significant differences observed between these socio-demographic factors and hemodialysis patients’ fluid compliance with the exception of age where there was a statistically significant difference. The following discussion chapter will interpret these results along with implications for research, education, and practice. 57 CHAPTER 5 Discussion In this research study, de-identified data of 153 hemodialysis patients from four dialysis sites under NARP were examined to explore the relationship between hemodialysis patients’ fluid compliance and selected socio-demographic factors in Northern Alberta, Canada. Socio-Demographic Factors Socio-demographic factors, such as age, gender, urban versus rural status (based on geographic location of patients’ residence), metropolitan versus nonmetropolitan (based on the location where hemodialysis treatment was received), income, length of time on dialysis, and number of missed dialysis appointments were compared with the key dependent variable IDWG to examine the fluid compliance status of patients. The result of this study did not find any significant relationship between socio-demographic factors and hemodialysis patients’ fluid compliance with the exception of age where there was a statistically significant difference. The study results showed 41.8% of the patients as non-compliant with their fluid restrictions based on a 10 weeks period IDWG calculation. This is in alignment with the existing literature. The existing literature showed a range of 10 to 60% fluid non-compliance among hemodialysis patients based on IDWG calculation (Bame et al., 1993; Hecking et al., 2004). This study result is closer to one of the largest studies (N = 1,230) conducted in a North American context, in which Bame et al. (1993) observed 49.5% fluid noncompliance among hemodialysis patients. Age. Older patients were more compliant with their fluid intake restrictions. Existing literature supported this finding that compliance is higher among older patients (Ahrari et al., 58 2014; Bame et al., 1993; Chan et al., 2012; Ifudu et al., 1996; Kara et al., 2007; Kauric-Klein, 2013; Kim & Evangelista, 2010; Kugler et al., 2005; Mellon et al., 2013; Morduchowicz et al., 1993; Victoria et al., 2015). A probable explanation for this observation may be the more controlled lifestyle of older patients that complies with the demands of the lifestyle and treatment regimen of a hemodialysis patient (Kutner et al., 2002; Victoria et al., 2015). As literature suggested, older patients might have developed a conservative life style as well as self-care strategies that might have helped them to positively respond to fluid compliance demands. Gender. In this study’s results, female patients exhibited more fluid compliance in comparison to male patients. This result was consistent with the study of Kauric-Klein (2013), in which male patients were two-thirds less fluid compliant than female patients. Similarly, Bame et al. (1993) found that male patients were approximately 66% less likely to be fluid non-compliant than females. Several studies showed similar results: namely, that female patients had increased incidence of compliance compared to males (Chan et al., 2012; Kauric-Klein, 2013; Kugler et al., 2011). This higher compliance incidence among women could be attributed to higher self-awareness and their ability to avoid risk-taking behaviour (Chan et al., 2012; Kugler et al., 2005). Income. The analysis of the results indicated that patients who resided in the geographical area that had a median income $35,000 exhibited lower compliance than patients who lived in a geographical area with a median income > $35,000. Other studies showed that low income was a predictor of non-compliance (Bame et al., 1993), though Chan et al. (2012) found no significant relationship between income and compliance status. This 59 study was consistent with Chan et al., as no significant relationship was found between income and fluid compliance among hemodialysis patients. A few previous studies about hemodialysis patients used personal or family income either directly from the patient’s medical records or by questionnaire (Chan et al., 2012; Kim & Evangelista, 2010; Pang et al., 2001). This study used the median income of the geographical location of the patients based on their postal code. Further investigation into the reliability of using median income based on the patient’s postal code is suggested, as this approach took only the median income in that region where the patient lives, instead of personal or individualized income. Moreover, there was no comparable study done in Canada to identify the differences in income distribution among the hemodialysis populations and fluid-compliance status. Future research is recommended to determine the relationship between income and fluid-compliance status. Length of time in dialysis. No significant statistical relationship was found between length of time on dialysis and hemodialysis patients’ fluid compliance status. However, further analysis of the data indicated that non-compliance was greater with a longer period on dialysis. Patients who had been dialyzing less than one year had lower incidences of noncompliance, while patients who had been dialyzing 12 to 23 months, 24 to 35 months, and 36 and greater months had shown incremental incidences of non-compliance. The findings from this study were consistent with some other studies (Chan et al., 2012; Lee & Molassiotis, 2002). A possible explanation of this observation is that being on dialysis for longer durations may make the patient feel bored and frustrated with the strict treatment regimen, especially with fluid restrictions, while new patients to dialysis usually get follow-up and social support. This suggests the importance of continuous education and follow-up to 60 remind and motivate patients to stay compliant with their fluid restrictions. Often, nephrology nurses lead these teaching efforts as part of their role, which has evolved into an educator role as seen in the literature (Lindberg, 2010). Current practices in nephrology nursing are focused on teaching, prevention related to complications associated with kidney failure, and helping patients to achieve better treatment outcome (Murphy, Jenkins, McCann, & Sedgewick, 2008). Another observation deduced from this study was that dialysis patients’ length of time on dialysis ranged from one month to 284 months, indicating that some Northern Alberta patients had been on dialysis for a longer duration of time compared to dialysis patients from other parts of the world as mentioned in the literature. A study done among Chinese patients indicated their length of time on dialysis ranged from three to 252 months (Pang et al., 2001). Similarly, another study done among Malaysian patients indicated that their length of time on dialysis ranged from five to 162 months (Chan et al., 2012). The most probable reason Canadian patients had higher lengths of time on dialysis could be due to Canada’s universal health care system, whereby every patient can access dialysis treatment regardless of their financial situation. This is not true for many patients in other countries where economically disadvantaged patients may succumb to death sooner as they could not afford costly dialysis treatment for a longer duration of time. Geographical location of patients’ residence. This descriptive study did not find statistically significant influence of geographical locations of residence over patients’ fluid compliance status. This could be mainly due to small sample size. However, among this study population, it was observed that patients who resided in rural areas had a higher prevalence of non-compliance than urban patients. Not enough literature existed to compare 61 this finding with hemodialysis patients from different populations. Possible explanations for this finding may be due to the difficulty of access to health care or distance to travel to get health care (Romanow, 2002). Location where care was received. Analysis of the study results indicated that patients dialyzed in nonmetropolitan dialysis units had more incidences of non-compliance as compared to their metropolitan counterparts. The reasons why patients receiving care in metropolitan areas had a higher rate of compliance compared to patients receiving care in nonmetropolitan areas are not clear, even though the entire NARP followed the same treatment plan, policies, and procedures. The possible explanation for this could be that nonmetropolitan health care facilities may offer fewer services compared to metropolitan centres. In NARP, no nephrologists, dieticians, or social workers are physically present in nonmetropolitan areas to conduct routine educational intervention or follow-up with patients. Patients receive dialysis in nonmetropolitan dialysis units get to see their nephrologist typically only once or a maximum of twice a year through telehealth conversation and once a year in person to discuss their health concerns. In metropolitan units, nephrologists and other health care team members, such as dieticians and social workers, routinely assess patients in metropolitan units. Evidence has shown that continuous educational intervention and inclusion of patients in their treatment plan could improve compliance status of dialysis patients (Victoria et al., 2015). The geographical location where patients received care precluded significant statistical findings due to the limited sample size. Therefore, statistical significance between location where care was received and fluid compliance status is unknown. 62 Number of missed dialysis appointments. It was observed from this study group that patients who missed dialysis treatment had greater incidence of non-compliance, and patients who never missed dialysis had lower incidence of non-compliance. This may be due to the fact that patients with kidney failure have three dialysis treatments per week, usually a maximum of four hours each, and missing at least one dialysis session will lead to accumulation of fluid and related complications (Chan et al., 2014). The reason for missing dialysis is not recorded in NARP charts; therefore, it was not possible to find the root cause of missing dialysis treatments among Northern Alberta patients. Harsher weather could be one reason for patients missing dialysis sessions among Northern Alberta dialysis patients compared to patients from other parts of the world. In a recent study that included 48 states in the Unites States, Chan et al. (2014) explained transportation problems and extreme weather as some of the major factors associated with missed dialysis treatment. Future study might take place during summer and winter to compare if weather is associated with missed dialysis treatment among Northern Alberta dialysis patients. It was also evident in Chan et al.’s (2014) study that patients who came to dialysis through a transportation van had more missed dialysis appointments. Similarly, a number of patients coming from rural areas of Alberta used a transportation van or medical taxis to get to dialysis units in Northern Alberta. Since information about the mode of transportation was not recorded on patients’ chart, it was not possible to determine if patients travelling in transportation van or medical taxis were more likely to miss dialysis treatment or not. Fluid compliance status. From the study, it was evident that 41.8% of the hemodialysis population in Northern Alberta, under NARP, were non-compliant with their 63 fluid restrictions. The fluid non-compliant rate found in this study was consistent with some other studies. For example, Ahrari et al. (2014) found a 45.2% noncompliance rate among study population. Bame et al. (1993) found a 49.5% non-compliance rate, and Denhaerynck et al. (2007) found a 42.0% % non-compliance rate. The literature consistently revealed that the degree of fluid non-compliance varied significantly from 20% to 79% in studies done across the world (Chan et al., 2012; Ibrahim et al., 2015; Kugler et al., 2011; Mollaoğlu& Kayataş, 2015; Pang et al., 2001; Rambod et al., 2010). It became evident from these literature sources and the findings from this study that hemodialysis patients from Northern Alberta were somewhere in the midpoint (41.8%) of that range, in regards to their fluid noncompliance rate compared to hemodialysis populations from across the world. The reason for this mid-point non-compliance could be the support and health care benefits that Canada offers. Diverse health care systems, traditional and cultural differences, social supports, and follow-up treatments can influence fluid compliance status of hemodialysis patients (Ahrari et al., 2014). Dialysis patients in Canada may get many supportive services, such as medical taxies for transportation, employment insurance, sick benefits, and disability benefits (Kidney Foundation of Canada, 2015). Moreover, there are many Canadian federal and provincial programs to help dialysis patients with their physical and emotional well-being (Kidney Foundation of Canada, 2015). Dialysis patients within NARP get transportation for free if they have to travel more than 40 km one way for dialysis, and travel, accommodation and meal costs are covered if they have to travel more than 80 km one way for dialysis (Kidney Foundation of Canada, 2015). This kind of support system may increase with compliance to the treatment regimen. 64 Limitations of the Study Socio-demographic factors available from patients’ charts and hemodialysis run sheets were used for this study; however, missing data from the records may have influenced the study results. One limitation of this study was the availability of data focused more on socio demographic factors. Another limitation of the study was that the researcher used multiple mean imputations to address missing IDWG values, which were 1.9% of total IDWG data. Mean imputation is the replacement of missing values with the mean of the nonmissing values (Saunders et al., 2006). One of the major limitations of the mean imputation approach is that it can introduce bias to a study (Saunders et al). However, by taking multiple mean imputation approaches, this study tried to reduce the probability of bias. Future research should increase sample size to reduce the probability of bias. The researcher was unable to add more hemodialysis units to the study at this point due to limited time and resources. This small sample size might have made the study to be susceptible to Type II error. Type II error is the failure to reject a false negative conclusion (Taylor, 2014). A larger sample size could have improved power in this study and reduced the risk of Type II error. Retrospective data from the four dialysis units with limited number of patients, especially in nonmetropolitan locations, posed a difficulty to increase power in this study. Any study with a small sample size will have high sampling process error compared to a study with larger sample size. As the sample size increases, it approaches entire characteristics of the study population, and thereby, sampling process error decreases. Future research with a larger sample size is recommended to decrease the probability of Type II error. 65 The generalizability of the study is limited, since the sample in this study was taken from four dialysis centres under NARP within a relatively small region. This study did not control many other non-demographic factors that can influence dialysis patients’ fluid compliance, such as knowledge of renal diet, language, frequency of food consumption, and family/social support (Rambod et al., 2010). A larger study including the above-mentioned factors along with all major socio-demographic factors, such as age, gender, race, income, education, marital and housing status, and occupation, would provide valuable information for health care professionals to understand the fluid non-compliance of hemodialysis patients. Due to the limitations of data availability, this study offers an initial exploration into the correlation of fluid non-compliance among hemodialysis patients and the selected sociodemographic factors. Some confounding variables may have affected the study results, which include the education, attitudes, behaviours, emotional and physical feelings, and beliefs of patients, such as perceived family support and social support. Unfortunately, these data were not available from the data sources. However, this study was the first research done in Northern Alberta concerning fluid compliance among hemodialysis patients and it could provide some initial descriptive information and knowledge. This study may provide an opportunity for future researchers to explore this research topic. A larger study is recommended, which could include a bigger sample size from different areas, provinces, or from across the country to find the risk and protecting factors associated with noncompliance. To address the issue effectively, evidence-based knowledge would help health care professionals make modifications on the policies and procedures. In conclusion, this study is an initial exploration, and this information can be used to plan for future study in a Canadian context in different provinces and/or across Canada. 66 Implications and Recommendations Implications for research. Modern nursing is founded on research and evidencebased practice (Ulrich, 2006). Nurses use problem-solving, critical thinking, and clinical judgement abilities to provide safe and effective care to their patients (Ulrich, 2006). If health care professionals are able to identify linkages between fluid compliance and sociodemographic factors, interventions can be made as early as possible to minimize risks associated with non-compliance. Early intervention with all ESRD patients could help to promote better treatment compliance (Kauric-Klein, 2013; Victoria et al., 2015). Evidence from this study indicated the necessity for further research. Statistical evidence is still lacking to determine whether Northern Alberta patients are less or more likely to comply with fluid restrictions than other dialysis patients from other parts of Canada or different countries. Therefore, the relationship between fluid compliance status and sociodemographic factors needs to be statistically determined by further research with a larger sample size within NARP, different provinces, or across Canada. A gap in knowledge still exists about the relationship between the fluid compliance and socio-demographic factors in a Canadian context. Future research should fill the gap in the literature, such as a lack of Canadian studies, the absence of rural studies, a lack of diverse ethnical studies, and a lack of studies with a larger sample size. This study revealed that 41.8% of the study population is non-compliant to their fluid regime, which is at about the midpoint of the non-compliance range that other studies have identified. Further studies are required to explore the strategies to reduce this noncompliance rate. A mix of future quantitative studies with larger sample sizes and qualitative studies that include patients’ and families’ perceptions of the contributing factors to fluid non-compliance 67 may provide some solid evidence to make changes in the practice to help patients comply with their treatment plan. Implications for practice. Nephrology nurses have vital roles in improving fluid compliance status of hemodialysis patients. Nephrology nursing did not emerge as a specialty until 1960s, and the nurse’s role was limited to symptomatic relief, with nursing care focused on dietary control (Hoffart, 2009). Currently, nephrology nursing is an established speciality, and change in nursing practice has always been a part of nephrology nursing (Hoffart, 2009). Nephrology nurses’ roles are constantly changing based on evidence-based protocols, and nephrology nurses have started to take more responsibilities with implementing a maximum scope of practice and autonomy in dialysis patients’ treatment plans (Chamney, Pugh‐Clarke, & Kafkia, 2009). Contemporary nephrology nursing involves many situations that require complex decision-making practices, holistic nursing care, and skilful practice to enhance dialysis patients’ treatment compliance (Chamney et al., 2009). Current practices in nephrology nursing are focused on teaching, preventing complications associated with kidney failure, and helping patients to achieve better treatment outcome (Murphy et al., 2008). Understanding factors related to non-compliance will help nurses to provide teaching, training, encouragement, continuity and coordination of care, and support to dialysis patients (Chamney et al., 2009). In this context, key findings from this study provide nephrology nurses and other health care professionals some basic evidence about the relationship between sociodemographic factors and hemodialysis patients’ fluid compliance. The existing prevalence of non-compliance among Northern Albertan dialysis patients has emphasized the necessity for renal care professionals to spend time and resources to recognize and address the root cause 68 of non-compliance among dialysis patients. Creating early intervention strategies focused on factors affecting patient compliance and integrating them into practice could be beneficial in achieving improvements in compliance status. In addition to written instructions, good rapport and communication might encourage patients to comply with their fluid intake and treatment regimen. Apart from the usual dialysis routine of receiving care and going back home, strategies focused on patients by nurses spending more available time for active listening and identifying the area where patients struggle to achieve compliance and addressing those issues might bring more compliance among patients. Socio-demographic factors that affect patients’ ability to comply may vary from patient to patient. Therefore, it is recommended that health care providers, such as dialysis nurses, follow strategies, such as encouraging patients to ask questions when they are in doubt, appreciating/motivating patients when they follow the treatment regimen, and developing short-term and long-term plans for patients who struggle to achieve recommended treatment regimen. As the study results showed that non-compliance existed among Northern Alberta patients, it is imperative to provide continuous education and follow-up to remind all patients about the importance of complying with their treatment plan. This can be achieved by specialized nutritional and educational interventions along with continuous psychological support (Baraz et al., 2010; Chamney et al., 2009; Kutner, 2001; Lindberg, 2010). Reinforcement of instructions in a simple, practical way might encourage patients to comply with their fluid restrictions. Implications for education. Continuous ongoing education is important to improve knowledge and performance of health care provides. Evidence showed that after an 69 educational session, knowledge and performance of hemodialysis nurses improved as well as patient outcome (Hassona, Winkelman, El-Wahab, Ali, & Abdeen, 2012). This evidencebased knowledge can be used to create an effective nurse-directed and patient-centred model of care to provide efficient care to patients and to develop strategies to address the barriers that affect fluid compliance status. It was evident from the literature that a nursing model of care, focused on patient education, communication, treatment plans, and follow-up, in collaboration with nephrologist improved efficiency and quality of care (Neyhart et al., 2010). Education related to fluid compliance is an integral part of improving fluid compliance among hemodialysis population. It has been documented in the literature that one-on-one educational programs significantly improved ESRD patients’ overall knowledge, leading to better self-management and treatment outcomes (Lingerfelt & Thornton, 2011; Tsay, 2003.). Integrating concepts based on factors affecting fluid compliance in an educational session may benefit both hemodialysis patient and staff and improve the overall quality of life for hemodialysis patients. Conclusion The aim of this research study was to investigate the relationship between sociodemographic factors and fluid compliance among hemodialysis patients. This study did not find any significant relationship between socio-demographic factors and fluid compliance with the exception of age group. Therefore, interventions including better education and support should focus on the entire dialysis population equally to decrease the incidents of fluid non-compliance. This study revealed that fluid non-compliance is an issue among Northern Alberta Canada patients, and the study results and analysis provided potentially 70 useful information to develop some individualized approaches and strategies to improve fluid compliance among hemodialysis patients. 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Hypertension, 35(1), 496500. 81 Appendix A: Introductory Letter of Information from NARP 82 Appendix B: Criteria for Satellite Waitlist 83 Appendix C: Researcher’s Confidentiality Agreement 84 Appendix D: University of Northern British Columbia Research Ethics Committee Approval 85 Appendix E: Health Research Ethics Board of Alberta Ethics Approval 86 Appendix F: Permission for Data collection from Alberta Health Service 87