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Influence of Socioeconomic Determinants on Access to Kidney Transplantation and Outcomes: A Systematic Review of Cohort Studies
Table of Content
Social determinants of health are factors which influence the populations and an individual’s state of health. These factors are a determined by the complex integration and overlapping of the conditions in which people are born, grow, work, live and age and the distribution of wealth, power and resources at a global, national and local level. (WHO, 2008) These factors have been classified into five groups: biological, socioeconomic, psychosocial, behavioural and social in nature. (CDC; WHO) It is these factors which contribute to the social patterning of illness, disease and health and which are responsible for the health inequities which are present within local communities, nationally and globally. (WHO, 2008)
1.1.1 Health Inequity
Health inequity can be defined as the differences, variations and disparities that are seen between individuals, populations and countries health status and access to health care resources, its benefits and outcomes. (CDC; WHO) Health inequities exist between countries and within countries. Generally, the poorest have the worst health and highest levels of mortality, however low health status is not confined to those who are in the poorest countries. Health inequities are observed in all countries and in all levels of income. Health inequities result from the differences observed between the underlying levels of social advantages or disadvantages i.e. social hierarchy. (Braveman and Gruskin, 2003) The differences observed between an individual’s social position is closely linked to education, ethnicity, gender, income and occupation and creates this social gradient in health and the global phenomenon of health inequity. (WHO) In 2008 the World Health Organisation (WHO) developed a global commissioning report on how to address the social determinants that create disparities in healthcare and to achieve health equity, which is defined as when everyone has the opportunity to attain their full health potential regardless of social or economic status. (Breenan and Metzler, 2008)
1.1.2 Socioeconomic Status
Socioeconomic refers to society related economic factors which are defined as education, income and occupation, and an individual’s position within society is referred to as socioeconomic status (SES). (PDPHE) An individual’s education, income and occupation are all closely linked to access to healthcare and it is well recognised that differences in SES have a significant impact on health and mortality across all populations and it continues to be one of the main challenges for public health globally. (PDPHE; Mackenback, 2017; Singh, 2017) Measuring SES is extremely complex, particularly when discussing health disparities, and there is no direct measure in existence. Over the year’s scientists have attempted to create scales or methods which can be used to classify populations and individuals by SES. There are two proposed methods to measure SES; composite scales and univariate measures. (Singh, et.al, 2017; Oakes, e-source). Composite scales are generally utilised by governments to produce national statistics and by researchers as they provide more sophisticated and in-depth reporting of SES amongst populations. Examples of composite scales are the Duncan SEI and Nam-Powers SS which is the scales used predominantly by the Unites States (US) for research and the census. (Oakes, e-source) In the United Kingdom (UK), the National Statistics Socioeconomic classification (NS-SEC) is the primary scale used by the government for census reporting and surveys. (ONS) Results from composite scales are usually expressed in 5-6 quintiles as social deprivation or social class.
Univariate scales provide a simpler and easier means of collating gradients of education, income and occupation and SES is classed as low, middle or high.
Income refers to an individual’s salary and many studies is measured as household income. Individuals will be asked to either report their absolute income or place themselves in pre-defined categories. Income however is variable and unlike the other indicators will change over time whether this be in the short term or in the long term due to the cumulative effect it has over the life course. Income provides an insight into an individual’s material resources and behaviours, which in turn can influence health status i.e. better access and resources to purchase food and shelter, access to exercise facilities, healthy lifestyle activities and education. (Galobardes. et.al, 2006)
Education is measured as a continuous variable and is unlikely to change in the short or long term. Education is categorised into educational milestones such as primary education, secondary education, higher education and degrees and is comparable globally. It is assumed that the longer an individual spends in education then the higher their SES. Education is a strong determinant of future employment and income and individuals of higher education are considered to have a greater level of health education and are more receptive to accessing healthcare services. (Galobardes. et.al, 2006)
Occupation is measured as continuous variables and individuals are asked to select pre-defined categories for example unemployed, unskilled manual, non-manual and professional. These are comparable between countries and are unlikely to change in the short term. An individual’s occupation is closely linked to education which in turn influences income. An individual with an unskilled manual occupation is considered to earn less than a professional, which will in turn influence their available resources as discussed above. An individual with non-professional occupation is also considered to be less educated and therefore will have a lower level of health education. Occupations also present health hazards and an individual who has a manual occupation will likely be exposed to more harmful environments or have more physical demands than an individual who has a professional occupation, which will potentially impact short- and long-term health outcomes. (Galobardes. et.al, 2006)
All three indicators are closely related and when measuring SES all three need to be taken into consideration. There is no single indicators that can be used to asses SES, each indicator has their own measurement which affects an individual’s health status and outcomes in different ways and at different stages of their life. (Galobardes. et.al, 2006)
1.1.3 Chronic Kidney Disease
Chronic Kidney Disease (CKD) is a condition that affects approximately 10-15% of the adult population worldwide with more than 50% in high-risk subpopulations. (Levin. et.al, 2017; Eckardt. et.al, 2013; National Kidney Foundation (NKF)) It is one of the most common causes of premature death and is considered a global public health issue. (Kidney care UK; NKF) with the fastest growth in prevalence and the largest associated burden to the global health economy occurring in the low-income and middle-income countries. (Levin. et.al, 2017; Jha. et.al, 2013) In 2015 the Global Burden of Disease (GBD) study estimated that 5-10 million people die annually directly from CKD (Luyckx. et.al, 2017) Chronic kidney disease is associated with an impaired quality of life and reduced life expectancy, there is an increased risk of cardiovascular disease and other complications resulting from diabetes, impaired immune systems and hypertension (Levin. et.al, 2017; Hill. et.al, 2016; Eckardt. et.al, 2013) which further increase the burden on the global health economy. The cost of treating CKD is unaffordable for many countries which gives rise to the disparities observed between low income and high-income countries in the prevalence and management of the disease and its related complications. The leading causes of CKD in the developed world are diabetes and hypertension, however in low-income, developing countries causes are more likely to be related to infection as a result of poor sanitation, inadequate and unsafe supply of water and unregulated use of medication, including herbal medicines and additives. Environmental factors such as pollution, pesticides and illicit drug use also contribute. (Jha. et.al, 2013) Early identification of CKD can significantly slow disease progression and reduce morbidity and mortality, however awareness of the disorder remains low in many developing countries and there is a global shortage of nephrologists who are able to manage the increasing global burden of this non-communicable disease. (Levin. et.al, 2017; Jha. et.al, 2013)
1.1.4 End Stage Renal Disease
End Stage Renal Disease (ESRD) is the most severe form of CKD and the number of patients treated for ESRD has doubled during the last decade in Europe and the US due to the ageing populations. (Satayathum. et.al, 2005). When an individual reaches ESRD their kidneys are only function at 10-15% capacity and it is at this point that a form of Renal Replacement Therapy (RRT) will need to initiated in order for the individual to survive. There are three different types of RRT available to patients; haemodialysis (HD), peritoneal dialysis (PD) and transplantation. Access to RRT varies between countries and within countries, with the highest income countries typically spending around 2-3% of their annual healthcare budget on the management of patients with ESRD (Luyckx. et.al, 2017), whilst those in the lowest income countries have insufficient or no access to RRT. Worldwide it is estimated that only half of those who require life-saving RRT receive it and between 2.5 million to 5 million people go untreated. (Levin. et.al, 2017) Haemodialysis is the costliest and high-risk form of RRT. (Garg, 2018)
Kidney transplantation is considered the optimal form of RRT for patient with ESRD. (KDIGO, 2009; Oniscu. et.al, 2016) Patients who undergo kidney transplantation have a two- to threefold increase in life expectancy and a significantly improved quality of life (QoL) compared to those on HD. (Satayathum. et.al, 2015; Oniscu. et.al, 2015; Wang. et.al, 2016) There are two types of kidney transplantation; deceased donor transplants and living donor transplants. Living donor transplant is associated with superior outcomes compared to deceased donor transplant and is the preferred form of transplantation as it also allows for patients to be transplanted pre-emptively i.e. before they reach ESRD, which further incurs greater outcomes than patients who are transplant post dialysis start. (Gill. Et.al, 2013; Bailey. et.al, 2014) To receive a transplant a patient must be referred and reviewed by a specialist’s nephrologists and transplant surgeon. Following a review, they are then added to the national kidney transplant waiting list. This list will provide the patient access to a potential deceased kidney donor. Wait times for a kidney transplant can range from 6 months to 10 years. Patients are also encouraged to seek living kidney donors and many schemes such as paired pooling exists which means patients with incompatible live donors are placed into a national pool and blindly matched with suitable live donors. (Udayaraj. et.al, 2010)
Kidney transplantation is also highly cost effective compared to HD with an average cost saving of $40,000 per annum observed globally. (NKF) However, despite these considerable benefits less than 15% of patients with ESRD join the wait list for a kidney transplant. (NFK; Kucirka. et.al, 2015)
1.1.6 Allograft outcomes
Allograft is defined as the transplant of an organ or tissue from one individual to another of the same species with a different genotype and is the term used by transplant surgeons to describe the transplanted organ and the associated outcomes. (KDIGO, 2009) Kidney allograft outcomes are measured as unadjusted allograft survival at 1 year, 5 years and 10 years. (Tong. Et.al, 2016; KDIGO, 2009; Wang. et.al, 2016)
Kidney transplantation is the optimal or ‘gold standard’ RRT modality, it offers a substantially superior quality of life and longer life expectancy compared to dialysis and is considerably more cost effective. (Kucirka, 2015; Abecassis, 2008) All eligible patients with ESRD should be offered this option, however, several ecological and longitudinal studies from Europe, Australia and the US have shown that there are disparities in access to kidney transplantation amongst renal centres and that socioeconomically disadvantaged individuals are less likely to be wait-listed or receive a kidney transplant compared to those with higher SES. (Bailey. et.al, 2014; Kucirka. et.al, 2015; Abecassis. et.al, 2008; Udayaraj.et.al, 2010) A large number of studies from the US have shown that access to renal transplantation is reduced in those with inadequate health insurance, ethnic minorities and black African Americans and that considerable variations in access exits due to racial disparities. (Laging. et.al, 2014) One study reported that only 13% of individuals eligible for transplant reached the wait list due to socioeconomic inequalities and that there was a lack of access to follow-up care of those who did receive a transplant and consequently an impact on allograft survival outcomes. (Kucirka, 2015; Goldfarb. et.al, 2006)An association between access to living donor transplants, which are associated with superior outcomes, and SES has also been reported, with those individuals of lower SES less likely to receive a living donor transplant. (Gill, et.al, 2013) In the UK and Netherlands, where healthcare access is government funded and free at the point of delivery, studies demonstrated that those who have a lower SES are more likely to develop CKD and require RRT, however they were the least likely to receive a kidney transplant. (Bailey. et.al, 2014)
The impact of socioeconomic determinants on access to transplantation is complex and affects each stage of the pathway, from access to wait list and follow up several years post transplantation. (Zhang, 2017) Data demonstrates that there is a correlation between low SES and access to healthcare including transplantation in patient with CKD. (Morton, 2016) The influence of socioeconomic inequalities on health is well recognised, those of lower SES have a higher incidence of illness and greater morbidity and mortality. (Mackenbach. et.al, 2008) Possible explanations for the disparities observed is that individuals of lower SES are more likely to have co-morbidities which preclude them to transplantation. Patients of lower SES are likely to have a lower level of health education and are less likely to access healthcare when required. It is well recognised that patient of higher SES are more likely to access healthcare pre-emptively and at an earlier stage of their disease state, which in turn will impact health status and outcomes.
However there has been little research on the influence of SES on patients with ESRD and access to kidney transplantation.
Why is it important to do this review?
It is important that access to the transplant waiting list and transplantation is fair and that equity exists on a local, national and global level. Longitudinal studies done on the influence of SES on outcomes in patients with CKD including ESRD have demonstrated that there is a relationship between an individual’s SES and access to treatment and long-term allograft survival outcomes. However large studies or reviews are yet to be done and there is little evidence and guidance for healthcare professionals. This report will aim to identify inequalities in access to kidney transplantation and allograft survival outcomes and provide healthcare professionals with the necessary evidence and support to be able to address these issues. (Morton, 2016; Naik, 2017)
The Cochrane library for systematic reviews was searched prior to developing this protocol. There are no published protocols or systematic reviews on the influence of socioeconomic factors on access to renal transplantation and allograft survival outcomes.
In adults with Chronic Kidney Disease (CKD), what influence do socioeconomic factors have on access, or time to kidney transplantation and transplant outcomes, measured as allograft survival at 5 years?
To conduct a systematic review to determine if socioeconomic determinants defined as education level, income and occupation influence access to the kidney transplantation, i.e. access to listing and wait time to transplantation, and allograft survival at 5 years.
The systematic review will answer the following questions:
- Do patients of lower SES have decreased access to the transplant wait list compared to patients with a higher SES?
- Do patients with lower SES have to wait longer until transplantation compared to patients with a higher SES?
- What influence does SES have on allograft survival?
The protocol will be reported according to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 statement. (Shamseer et al, 2016) The protocol has not been registered with PROSPERO International Prospective Register of Systematic Reviews.
Eligibility criteria were developed based on international guidelines and the initial search for relevant literature.
The criteria for considering studies in the review if based on a variation of the PICO structure which has been adapted from the PRISMA preferred reporting of systematic review guidelines. (Shamseer et al, 2016)
2.2.1 Types of participants
22.214.171.124 Inclusion criteria
Studies looking any patients >18 years with CKD or ESRD who are eligible for a kidney transplant.
Studies looking at pre-emptive and post dialysis start transplantation will be included.
Studies looking at all types of kidney transplantation – living related or unrelated donor, deceased brain death (DBD) and deceased cadaveric death (DCD) or extended criteria donor (ECD).
Kidney transplants can be first or subsequent
126.96.36.199 Exclusion criteria
Studies including patient with other solid organ transplants will only be included if the data on kidney transplant recipients can be analysed separately
Studies on children and adolescents will not be included as transplant guidelines and outcomes are not comparable with adults.
Studies that only include kidney donors
2.2.2 Types of Exposure
Socioeconomic status (SES) is defined as a measure of an individual’s education, employment and income. (NS-SEC and Singh, 2017)The review will consider studies that evaluate differences in socioeconomic status i.e. low-income vs moderate to high income, level of education e.g. secondary education vs higher education and occupation in the adult population with ESRD and access to kidney transplantation and/or outcomes in kidney transplant recipients. We will review studies that address SES factors on an individual level and a population level. Studies addressing issues affecting access to kidney transplantation and transplant outcomes where SES is not a primary focus but discussed within the study will be considered for inclusion if the data can be analysed separately. Studies addressing the SES factors that affect access to other solid organ transplants and transplant outcomes will also be considered if strong parallels can be drawn in the conclusion. Studies addressing ethnicity, age and gender will be excluded.
2.2.3 Types of outcome measures
Access to renal transplant waiting list calculated as time from first dialysis to waiting list
Access, to renal transplantation calculated as time from being placed on waiting list to transplant
Allograft survival at 1 and 5 years
Studies reviewing allograft survival outcomes will be selected based on length of follow-up. All studies included for review on allograft survival should have a follow-up time of at least 1-year post transplantation. This falls in line with international guidelines which measures allograft survival based on 1, 5- and 10-years post transplantation (KDIGO 2009)
No time restrictions are placed on studies reporting access to and time to transplantation.
2.2.5 Types of Studies
The following types of quantitative studies will be eligible for inclusion:
- Prospective longitudinal cohort studies
- Retrospective longitudinal cohort studies
- Case-control studies
- Cross-sectional studies
Due to the nature of the topic it is unlikely that any randomised controlled trails (RCTs) will be available however if found then they will be considered for inclusion.
The review will only include published studies as the scope of the review will not enable me to verify the credibility of unpublished studies (grey literature).
I will include studies from Western Europe, the US and Australia because they are comparable in terms of socioeconomic factors (education, income and occupation), organ allocation systems and outcome measures for kidney transplants. (KDIGO 2009) Data on inequalities in health is often also available for all of these regions.
Only articles reported in the English language will be included. It has been limited to English as the primary language for reporting in international medical journals in Europe and the US is English.
2.2.8 Publication dates
Access to kidney transplantation and outcomes have improved significantly over the last 20 years (reference??) due to advances in medicine and access to healthcare therefore to limit the scope of this search to the most recent published data the search will only include articles published since 2000. This will also provide allow for papers reviewing allograft survival up to 10 years.
The following electronic databases were searched:
- Embase via OVID (1996 to current date)
- MEDLINE via OVID (1946 to current date)
The search was limited to abstracts and full text, publication year 2000 to 2018 and English language and humans. A manual search of Google scholar and scanning of the reference lists of all included publications and reports were screened for additional relevant publications.
The OVID platform (Embase 1996 to 2018 Week 30 and MEDLINE 1946 to July 23 2018) were searched using the following keywords which were mapped to subject headings: “renal insufficiency”; “kidney failure”; “end stage renal disease”; “chronic kidney disease”; “socioeconomic factors”; “education”; “income”; “occupation”; “poverty”; “salary”; “kidney transplant”; “renal replacement therapy”; “kidney transplantation”; “list”; “listing”; “pre-emptive”. The subheadings for each search term were included.
The Boolean operator ‘OR’ and ‘AND’ were used to combine synonyms and searches. The searches were confined to abstracts and full text, English language and publication year 2000 to date and humans.
The full search strategy can be viewed in Appendix I
Best practice for systematic reviews, as guided by PRISMA, is for two independent authors to screen all identified articles for inclusion/exclusion and for a third independent party to resolve any disagreements. However due to the nature of the assignment the study selection was only undertaken by a single individual. The titles and abstracts identified were screened for relevance based on the eligibility criteria set out above. Any studies considered irrelevant were discarded, whilst the full text was obtained for those which were considered relevant. The electronic searches were also screened for duplicates and any duplicates were removed. The full text was obtained for studies which met the inclusion criteria in the initial screening process and then further assessed for eligibility using a study eligibility form which is a modified version of the Cochrane template (Appendix II). (Higgins and Green, 2011) Articles that did not meet the inclusion criteria were discarded and those that did meet the inclusion criteria were included in the review.
The eligibility criteria and the rationale for inclusion into the review is based on the inclusion and exclusion criteria defined above i.e. studies on adult patients with ESRD who are eligible for transplantation that reviewed the influcenes of SES on, time to wait-list, time to transplantation and/or allograft survival; all types of transplants; studies from Australia, Europe and the US; English language.
Data extraction will be carried out independently using the standard data extraction tool in Appendix III. Data collected will include publication type and year, study design, methodology and background information on the study. The form will also extract demographic and socioeconomic data on the participants and primary and secondary outcome data.
The data extraction from was piloted on two articles prior to undertaking a full review of all the eligible studies.
The data items extracted from the individual studies are as follows:
- Population characteristics i.e. age, gender, ethnicity
- Study characteristics i.e. population size, type of study, country of study, time period study covers, publication date
- Exposure(s) reviewed i.e. Socioeconomic determinants (education, income and occupation
- Types of kidney donors included in the study – pre-emptive, live, DBD, DCD
- Outcome measures – primary and secondary (if applicable)
A risk of bias assessment was undertaken using the Newcastle-Ottawa Scale (NOS) Quality Assessment Form for Cohort Studies(Appendix IV). (Higgins, 2011 and Wells et al, 2004) The tool evaluates the selection of the study groups, the comparability of the groups and the quality of the assessment of the primary outcome and adequacy of follow up. The NOS for cohort studies allocates a maximum of nine stars to studies of the highest quality based on three parameters: selection of study groups, comparability of groups and ascertainment of the outcome of interest. Studies will be considered as high quality (7–9 stars), moderate quality (4–6 stars) or low quality (0–3 stars).
Each study was reviewed for bias using this tool and the results are displayed in Table ?
It is expected that a high degree of heterogeneity will be found between the studies identified for this review therefore for the purpose of this assignment and time constraints a statistical analysis and a meta-analysis of the data will not be undertaken.
I will undertake a narrative descriptive synthesis of the studies identified. A systematic method will be used to develop textual summaries for each of the individual studies. If possible, studies will be combined into groups based on key themes and findings and reported in the form of tables for ease of comparison and key themes will be elaborated on in a textual form. Where it is not possible to combine studies into groups then the data presented in each study will be reported in the same order in a tabular fashion.
Key information on the study design, quality of the data and the effects of the exposures on primary outcomes will the tabulated. Studies of high bias will only be reported if the information provided in the study is deemed significant to the review and outcomes. The following characteristics will be presented in a table form to allow visual comparison across studies:
- Number of participants
- Target population
- Country of study
- Study period
- Study design
- Exposure (s) measured
- Primary outcome measure of study
A textual narrative will be used to elaborate key findings within the studies and the relationships identified within studies and between studies.
2.9.1 Analysis of subgroups or subsets
This is not planned
2.9.2 Proposed data synthesis and analysis
If a meta-analysis was to be undertaken and the studies were sufficiently homogenous and data was appropriate for synthesis then the following methods would be applied to conduct a random effects meta-analysis.
Measure of Outcomes
For dichotomous outcomes i.e., mortality we will express results as risk ratio (RR) with 95% confidence interval (CI). (Higgins, 2011 and Wells et al, 2004) Continuous outcomes i.e. time to waiting list, transplantation and graft survival and mortality will be assessed using weighted mean differences (with 95% confidence interval) or standardised mean difference differences (95% CI) if different measurement scales are used.
Unit of analysis
With respect to the exposures i.e. education, income and occupation then we will expect some difference in the definitions between countries. We do not anticipate difference in the definition of ESRD and allograft survival.
Dealing with missing data
We will consider and record the number of patients that drop out of studies if such information is provided in prospective cohort studies. If data is found to be missing we will consider this in the risk of bias analysis and perform a sensitivity analysis addressing the impact of this ‘missing data’ in the discussion.
Assessment of heterogeneity
If studies are considered to be significantly homogenous then heterogeneity between studies will be assessed using the Chi2 test (significance level: 0.1) and I2 statistic(Higgins, 2011 and Wells et al, 2004) which are interpreted as follows:
- 0% to 40% – might not be important
- 30% to 60% – may represent moderate heterogeneity
- 50% to 90% – may represent substantial heterogeneity
- 75% to 100% – considerable heterogeneity
Visual inspection of the forest plots will also be undertaken.
Assessment of reporting biases
If possible, funnel plots will be used to assess the existence of bias or heterogeneity. (Scanlon, 2007) The Eggers regression test(Egger et al, 1997) was used to evaluate publication bias.
RevMan 5software will be used for data management and analysis. If statistical heterogeneity is observed (I2 >=50% or P<0.1) the random effects model of DerSimonian-Laird (PROSPERO) will be used to quantify the relationship between SES and the primary outcomes. A narrative and qualitative synthesis will be provided where heterogeneity is substantial. The narrative synthesis will explore the relationship and finding both within and between the studies as per guidance from Centre of Reviews and Dissemination. (CRD, 2009)
Subgroup analysis and investigation of heterogeneity
Sub group analysis using the random effects meta regression analysis will be performed to explore possible sources of heterogeneity and identify potential study-level factors contributing to heterogeneity between studies. Characteristics investigated will be gender, age, income, education, occupation, geographical area, study design, follow-up period, publication year, type of kidney transplant. We will also perform univariate meta-regression analysis for each SES factor using a backward stepwise approach to identify the significant variable to be included. Proposed sub-group analyses:
- Comparison between countries
- Comparison between SES factors i.e. income, education and occupation
Sensitivity analysis will be performed once we have assessed heterogeneity. We will assess risk of bias by omitting one study at a time and repeating the analysis. We will assess the following factors:
- Risk of bias by the co-authors
- Influence of large studies on the results
- Unpublished studies
The Embase (1996 to 2018 Week 30) search yielded 471 results and the MEDLINE (146 to 2018 week 30) search yielded 198 results. In total the OVID platform search produced 669 results which once duplicates were removed gave a total of 517 articles. A manual search yielded 17 articles which were found via a combination of google scholar and scanning of the reference lists of publications found during the initial background research. In total, 534 titles and abstracts were identified for initial screening. The titles and abstracts of all articles were screened and of these 502 were exclude as they did not fit the eligibility. The full text of the remaining 32 articles were sought for further review. Using the eligibility form (Appendix II) a further 22 articles were excluded due to full text not being available (7); incorrect study design (3); incorrect population including one containing children (6); comparators focused on ethnicity or did not include SES (6). The remaining 10 articles were eligible for inclusion in the full narrative review and were classed into ? groups for review.
The results of the study selection process are displayed in Figure 1 following the PRISMA guidelines (Shamseer et al, 2016).
A full list of articles that were deemed unsuitable for inclusion in the review can be found in Appendix V
Records identified through database searching
(n = 669)
Additional records identified through other handsearching
(n = 17)
Records after duplicates removed
(n = 152)
(n = 502)
(n = 534)
Full-text articles excluded, with reasons
(n = 22)
Conference abstracts n=7
Study design n=3
Socioeconomic variables not fully studied n=6
Population does not meet criteria n=6
Full-text articles assessed for eligibility
(n = 32)
Studies included in narrative synthesis
Figure 1. PRISMA flow diagram showing study selection process. The flow diagram depicts the different phases (identification-screening-eligibility-included) of the systematic review. It maps out the number of records in each phase and shows how many studies were included or excluded respectively.
Table -see example paper
3.3.1 Outcome Measures
3.3.2 Risk of bias within studies
4.1.1 Summary of evidence for main outcomes
4.1.2 Summary of findings from included studies
- Kidney Care UK. An estimated 1 in 10 people have worldwide have chronic kidney disease. Available from: https://www.kidneycareuk.org/news-and-campaigns/news/estimated-1-10-people-worldwide-have-chronic-kidney-disease/ (Accessed 25 April 2018)
- National Kidney Foundation. Global facts: About kidney disease. Available from: https://www.kidney.org/kidneydisease/global-facts-about-kidney-disease (Accessed 25 April 2018)
- Kucirka, L. Purnell, T. Segev, D. (2015) Improving access to kidney transplantation: referral is not enough. JAMA. 314(6): 565-567
- Abecassis, M. Bartlett, S. Collins, A. Davis, C. Delmonico. Friedwald, J. et al. (2008) Kidney transplantation as primary therapy in End Stage Renal Disease: A National Kidney Foundation/Kidney Disease Outcomes Quality Initiative (NKF/KDOQI) Conference. Clinc J Am Soc Nephrol. 3(2): 471-480
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- Zhang, Y. Jarl, J. Gerdtham, U. (2017) Are there inequities in treatment of End Stage Renal Disease in Sweden? A longitudinal register-based study on socioeconomic status related access to kidney transplantation. Int. J. Envrion. Res. Public Health. 2017; 14:119
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Appendix I – Search Strategy
The OVID platform (Embase 1996 to 2018 Week 30 and MEDLINE 1946 to July 23 2018)
Appendix II– Study Eligibility From
Appendix III – Data Extraction Form
Appendix IV – Newcastle–Ottawa Scale (NOS) Quality Assessment Form for Cohort Studies
Appendix V – List of studies not included for review