A 2018/2019 Summer Studentship research project
The potential impact of this work will be to show whether there is any disparity in referral practices for kidney transplantation among patients who start treatment for kidney failure. Identifying the factors that are associated with referral for kidney transplantation assessment can lead to targeted ways to improve health systems for patients who would benefit from a kidney transplant. Ultimately, this could support health services to provide timely referral for kidney transplantation for Māori and Pacific patients and increase the chances of a kidney transplant.
Student: Josie Nicholas-McAnergney
Supervisor: Professor Suetonia Palmer (Associate Dean (Māori), UOC); Dr Nick Cross (Department of Nephrology, CDHB)
Kidney transplantation rates are increasing rapidly due to new government investment and improved care processes. However, despite increases in access to kidney transplantation, transplantation rates for Māori and Pacific patients remains less than one-third of rates for New Zealand European patients. The reasons for this inequity remain incompletely explored. Analysis of patient-level data can identify root causes of inequity and inform national quality improvement to increase access to kidney transplantation, which is the gold-standard therapy for kidney failure.
- To evaluate the rates of referral for kidney transplantation assessment according to ethnicity in Canterbury.
- To evaluate the possible factors associated with referral for transplantation assessment that might be modifiable in quality improvement processes in the transplant services at Christchurch Hospital.
The study will be a retrospective cohort analysis of all patients <65 years old who commence dialysis at the Canterbury DHB between 2007 and 2016 (estimated to be 800–1200 patients) and who have not yet received a kidney transplant. The initial decision made by clinicians about whether the patient can commence medical assessment for kidney transplantation will be extracted from a comprehensive clinical database. The factors associated with the medical decision will be analysed using logistic regression with adjustment for clinical comorbidity and demographic factors. Analysis will be conducted by ethnicity identified within the National Health Index and using best practice approaches to categorise ethnicity. We will identify ethnicity categories as principles in the analysis (New Zealand Māori, Pacific, and New Zealand European). Rates of referral will be calculated using age and ethnicity standardized populations. The rate of referral will be adjusted for age, sex, comorbidity (coronary artery disease, diabetes, chronic lung disease), body mass index, distance from hospital, smoking history, and late referral to specialist services.
Student researcher’s component of the study
The student will receive the full dataset and will learn how to link data from the National Health Service for ethnicity with the assistance of DHB data support systems. The student will learn how to clean the data and conduct analysis to generate baseline characteristics of the cohort and the initial transplant referral decision. The student will learn how to conduct logistic regression within statistical software and be given guidance about the interpretation of the findings. We will aim for the student to generate a report on the findings and a presentation to the clinical transplant service at Christchurch Hospital (supported by the supervisors).