Abstract:
Background: Caring for the patients with chronic diseases are not an easy task. End stage renal disease is one of the chronic diseases and the patients suffering from these disease needs lifelong hemodialysis treatment. Caregivers of hemodialysis patients encounter lots of pressures in taking care of their loved ones with chronic conditions. It could negatively affect all aspects of their health including their quality of life. Diminish quality of life could increase their pressures or burdens and interfere with the proper patient care. Thus, the present study was designed to examine the characteristics of caregivers and patients undergoing hemodialysis, the caregivers’ burden and determine the factors which predict the quality of life of these caregivers.
Method: A cross-sectional study conducted in three units of hemodialysis centers in Yangon, Myanmar during May 2019 involving 199 caregivers of End-stage Renal Disease Patients using the self-administered questionnaire for demographic assessment (age, gender, education, occupation, income, marital status, relationship with the patient, extra household works, having children or not), caregiving activities (duration of caregiving, incentive from the patient, caring hours per day) and patients’ characters (age, sex, occupation, comorbid conditions). Purposive sampling was used for data collection and Zarit burden interview and WHO QoL BREF in Myanmar version were used to evaluate caregiver’s burden and their QOL. Hierarchical linear regression was used to find out the predictors of caregiver’s quality of life.
Results: The variables which are significant in hierarchical linear regression were caregiver’s level of burden (p value < 0.001), caregiver’s age (p value = 0.002) and caregiver’s monthly family income (p value < 0.001). Caregiver’s burden and caregiver’s age were negatively affected the quality of life whereas monthly family income is positively affected quality of life. So, the best model to predict caregiver’s quality of life was [Quality of life = β0 + β1 (level of burden) + β2 (caregiver’s age) + β3 (caregiver’s monthly family income)] where β0, β1, β2 and β3 were 97.333, (-0.395), (-0.149) and 0.010 respectively.
Conclusion: Health professionals and governments should consider the predictors revealed in the findings in dealing with the caregivers and do more research on other different types of caregivers to develop strategies and programs for improving the caregiver’s quality of life.
