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HRB Open Res
HRB Open Res
HRB Open Research
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10.12688/hrbopenres.13941.1
Study Protocol
Articles
The impact of social deprivation on development and progression of diabetic kidney disease
[version 1; peer review: 2 approved]

Casey Caoimhe Conceptualization Data Curation Formal Analysis Investigation Methodology Project Administration Resources Writing – Original Draft Preparation https://orcid.org/0000-0002-5968-3649
a12
Buckley Claire M Formal Analysis Methodology Resources Supervision Writing – Review & Editing https://orcid.org/0000-0002-3174-7022
1
Kearney Patricia M Formal Analysis Methodology Supervision Writing – Review & Editing https://orcid.org/0000-0001-9599-3540
1
Griffin Matthew D Conceptualization Formal Analysis Methodology Supervision Writing – Review & Editing https://orcid.org/0000-0002-8701-8056
34
Dinneen Sean F Conceptualization Formal Analysis Methodology Resources Supervision Writing – Review & Editing 25
Griffin Tomas P Conceptualization Data Curation Formal Analysis Investigation Methodology Project Administration Resources Supervision Writing – Review & Editing 25
1 School of Public Health, University College Cork, Cork, County Cork, Ireland
2 Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, Galway, County Galway, Ireland
3 Regenerative Medicine Institute (REMEDI) at CURAM SFI Research Centre for Medical Devices, School of Medicine, University of Galway, Galway, County Galway, Ireland
4 Department of Nephrology, Galway University Hospital, Galway, County Galway, Ireland
5 School of Medicine, University of Galway, Galway, County Galway, Ireland
a caoimhe.casey@umail.ucc.ie
Competing interests: CC received funding to attend national and international diabetes conferences from Novo Nordisk. TPG received honoraria for speaker fees and/or advisory boards from Novo Nordisk, Sanofi Aventis, Mundipharma Pharmaceuticals, Dexcom, Abbott Diabetes Care, AbbVie, Astra Zeneca and Eli Lilly.

8 8 2024
2024
7 5329 7 2024
Copyright: © 2024 Casey C et al.
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction

Diabetes is one of the leading causes of chronic kidney disease. Social deprivation is recognised as a risk factor for complications of diabetes, including diabetic kidney disease. The effect of deprivation on rate of decline in renal function has not been explored in the Irish Health System to date. The objective of this study is to explore the association between social deprivation and the development/progression of diabetic kidney disease in a cohort of adults living with diabetes in Ireland.

Methods

This is a retrospective cohort study using an existing dataset of people living with diabetes who attended the diabetes centre at University Hospital Galway from 2012 to 2016. The variables included in this dataset include demographic variables, type and duration of diabetes, clinical variables such as medication use, blood pressure and BMI and laboratory data including creatinine, urine albumin to creatinine to ratio, haemoglobin A1c and lipids. This dataset will be updated with laboratory data until January 2023. Individual’s addresses will be used to calculate deprivation indices using the Pobal Haase Pratschke (HP) deprivation index. Rate of renal function decline will be calculated using linear mixed-effect models. The relationship between deprivation and renal function will be assessed using linear regression (absolute and relative rate of renal function decline based on eGFR) and logistic regression models (rapid vs. non-rapid decline).

Deprivation
diabetes
diabetic kidney disease
Health Research Board(DIFA-2023-018 SFD is the lead applicant on the D1 now study which has been funded by the Health Research Board (DIFA-2023-018).
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pmcIntroduction

In 2021, 537 million adults aged 20–79 were estimated to live with diabetes worldwide, with projections suggesting an increase to 643 million by 2030 1 . Diabetes accounted for approximately 6.7 million deaths globally in 2021. Diabetes is one of the leading causes of chronic kidney disease (CKD) 2 . The number of new cases of CKD due to type 2 diabetes increased worldwide from approximately 1.4 million in 1990 to 2.4 million in 2017 2 . There is an inverse association between CKD incidence and a country’s sociodemographic index 2 . Mortality is increased in people with diabetic kidney disease (DKD) 3 . Those with kidney disease and type 2 diabetes have a standardised mortality rate of 31.3%, compared to 11.5% in those with diabetes without kidney disease 3 . CKD due to diabetes is defined as persistent albuminuria (an albumin-to-creatinine ratio >3mg/mmol), persistently reduced renal function (an estimated Glomerular Filtration Rate <60ml/min per 1.73m2) or both for greater than 3 months 4 . Hypertension, dyslipidaemia, hyperglycaemia and smoking are some modifiable risk factors for DKD 5 . Socioeconomic deprivation is associated with DKD through its influence on these risk factors, but also through other mechanisms such as access to healthcare and health literacy 6 .

Socioeconomic deprivation, is characterized by restricted access to societal resources due to poverty, discrimination and other disadvantages 7 , The prevalence of type 2 diabetes is linked to social deprivation, with a higher prevalence in deprived groups 8, 9 , likely due to barriers to healthy living conditions, education, and behavioural factors imposed by economic constraints. The relationship between deprivation and type 1 diabetes is less clearly understood with studies reporting varied relationships ranging from no association 10 , to an inverse association 11 to a positive association 12 . Socioeconomic deprivation is also a risk factor for complications in both type 1 and type 2 diabetes including diabetic retinopathy 13, 14 , cardiovascular disease 15, 16 , diabetic foot ulceration and amputation 17 and mortality in type 1 and 2 diabetes 18– 20 . There is also significant evidence of an influence of social deprivation on development of DKD. The EURODIAB IDDM complications study showed a significant association between lower educational attainment and macroalbuminuria 21 . Gonzales et al. demonstrated in the UK that among people living with type 1 diabetes, those living in the most deprived circumstances had a hazard ratio of 2.92 for incident DKD compared to the least deprived. Similarly in type 2 diabetes, the most deprived had a hazard ratio of 1.39 for incident DKD compared to the least deprived 22 .

Studies to date in Ireland on deprivation and diabetes have focussed on diabetes prevalence and predominantly use individual level markers of socioeconomic deprivation such as education and occupation 23– 25 . Educational attainment, a component used in determining deprivation status, has been shown to be inversely associated with the prevalence of multimorbidity in a cohort study in Ireland 23 . Participants completed health and lifestyle questionnaires and attended for a physical exam. Multimorbidity was defined as the presence of two or more chronic diseases including diabetes. Educational attainment was ascertained from the questionnaire and divided into primary level or secondary level and above. O Connor et al. looked at the determinants of undiagnosed and diagnosed diabetes and used social class (as defined by the European Socioeconomic Classification System), education and medical insurance as covariates 24 . Insurance was classified into “private insurance” (paid for by the individual), means tested state assisted –“state insurance” and no insurance and results showed that those with private insurance were less likely to have a diagnosis of type 2 diabetes and those with who finished education at primary level were more likely to have a diagnosis of type 2 24 . Leahy et al. used the Irish Longitudinal Study of Ageing (TILDA), a study on adults over 50 years in Ireland and looked at social class based on fathers’ occupation. Compared with professional/managerial occupations, belonging to the "manual” social class in childhood was associated with an increased risk of type 2 diabetes 25 . Socioeconomic deprivation is associated with non-attendance at the Irish national diabetic retinopathy screening service 26 . The most deprived quintile had approximately 12% higher non-attendance rates compared to the middle quintile. Deprivation is associated with an increased rate of admission to hospital for diabetes complications – diabetic ketoacidosis, renal complications, retinopathy, neuropathy, peripheral vascular disease and “other” complications 27 . This was shown after adjusting for population density and medical-card (state insurance) coverage 27 . Interestingly, a study of over 1000 people with type 1 and type 2 diabetes attending primary care showed no difference in haemoglobin A1c (HbA1c) values between deprivation categories, even after adjusting for whether the diabetes care was shared with secondary care or managed in primary care alone 28 . However, this does not take into account that attendance rates may be affected by deprivation and those with higher HbA1c values may be less likely to attend for monitoring. Another study using the TILDA dataset, looked at risk factors for macro and micro vascular complications in type 2 diabetes 29 and showed that higher educational attainment was associated with a lower likelihood of microvascular complications 29 .

Identifying social determinants affecting people living with diabetes is key to comprehending morbidity contributors and customizing management strategies for diverse populations, recognizing that disadvantaged backgrounds may lead to increased healthcare utilization and emergency care reliance 30, 31 . Issues that affect their health may not be fixed by physical healthcare alone, and a framework of care encompassing a biopsychosocial model may be needed. The Frome model of primary care is a project in Somerset in the UK that leverages existing social networks to improve health outcomes. It works on the basis that health is heavily influenced by social factors and uses community assets such as peer support groups to tackle social determinants of health and provide support to people in the community. This reduced hospital admissions by 14% over a 4 year period 32 . We are also at a time period where diabetes technology is ever advancing, and it is imperative that access to technology is equitable across all social classes. Literature from the UK suggests disparities in access to insulin pump and continuous glucose monitoring devices 33 . Another study looking at flash glucose monitors showed that “time in range” did not differ between deprivation categories 34 . Therefore, it is important to have equitable access to these technologies to allow everyone to benefit.

The objective of this study is to explore the association between area level social deprivation and diabetic kidney disease in a cohort of adults living with diabetes in Ireland. Area based deprivation indices are well established and widely used and facilitate gradients to be demonstrated at a population level 35– 39 . To our knowledge, this will be the first study in Ireland to look at the association between deprivation and rate of decline in renal function, using a composite, area level measure of deprivation.

Protocol

Methods

This is a retrospective cohort study of people diagnosed with diabetes attending University Hospital Galway, a tertiary referral centre serving a large catchment area in the west of Ireland. We will use an existing dataset from a previous cohort study of people with diabetes, who attended the diabetes centre between 2012 and 2016.

This dataset contains clinical and laboratory data which was obtained from DIAMOND. DIAMOND is the electronic record that is used in University Hospital Galway for people living with diabetes. Demographic data are input by administration staff on registering with the diabetes service. At each clinic visit DIAMOND is then used to record clinical details- anthropometric measures such as blood pressure and weight, medications used, complications and laboratory results.

The remaining laboratory data in the dataset was obtained from the laboratory IT system at University Hospital Galway. This system records longitudinal laboratory measurements on all samples analysed at University Hospital Galway. Longitudinal values for serum creatinine, urine albumin to creatinine ratio (uACR), serum HbA1c, cholesterol, HDL and triglycerides were obtained for each person in the dataset. Isotope dilution mass spectrometry was to measure serum creatinine, conventional Roche Diagnostics assays were used to measure lipids and urine creatinine and high-performance liquid chromatography was used to measure Hba1c.

The dataset contains the datapoints as shown in Table 1. It was previously used in a study looking at the prevalence of diabetic kidney disease and rapid renal function decline in adults with diabetes 40 .

Table 1. Data in existing dataset.

Clinical and Demographic Data	Laboratory Data	
Age	Creatinine (μmol/l)	
Gender	eGFR (mL/min/1.73 m 2)	
Ethnicity	Urine ACR (mg/mmol)	
Type of Diabetes	HbA1c (mmol/mol)	
Duration of Diabetes	Cholesterol (mmol/l)	
Smoking Status	HDL cholesterol (mmol/l)	
Diabetes Medications Used	LDL cholesterol (mmol/l)	
Antihypertensive Medications Used	Triglycerides (mmol/l)	
Body Mass Index (BMI) (kg/m2)	Urine ACR (mg/mmol)	
Systolic Blood Pressure (mmHg)		
Diastolic Blood Pressure (mmHg)		
eGFR= estimated glomerular filtration rate

ACR= albumin creatinine ratio

Inclusion criteria:

-People living with diabetes, attending the diabetes service at Galway University Hospitals with a diagnosis of diabetes (type 1, type 2 and other)

-Over 18 years of age

Exclusion criteria:

-Insufficient follow up laboratory data (at least 2 values of creatinine >3months apart required for inclusion)

-Insufficient address details to determine address-based deprivation index

-Primary diagnosis of gestational diabetes, impaired glucose tolerance or impaired fasting glucose

The Pobal Haase Pratschke (HP) deprivation index provides a sophisticated indication of deprivation across Ireland 41 . It uses small area geography which was developed in 2011 by the Ordnance Survey of Ireland and the Central Statistics office. Small area geography is useful when mapping social and economic data as the areas are homogeneous in their social composition and population size, with a mean of just under 100 households per small area. The HP index maps the overall levels of affluence and deprivation at the level of 18,488 small areas. We will use the index based off the 2016 census as this most accurately reflects deprivation status at the baseline visit for participants in the study. The HP index is constructed based on three dimensions of affluence/disadvantage- demographic profile, social class composition and labour market situation. Demographic profile includes indicators such as level of educational attainment and age of the population, social class composition includes indicators such as type of profession and labour market situation looks indicators such as unemployment rate.

The Pobal HP deprivation index has previously been used in medical research looking at polypharmacy and dependency in older adults and on survival post renal transplant and on chronic dialysis 42– 44 .

Individual’s addresses will be used to determine their deprivation index using the Pobal HP 2016 deprivation index. This will be done with the assistance of colleagues in the Health Intelligence Unit using Health Atlas Ireland 45 . The addresses in the dataset will first be matched to small area ID. This will be done through 2 processes. Firstly, an automated process will be carried out using health atlas Ireland which will match all addresses with a small area ID where possible. Secondly, a manual address matching process will be carried out where the unmatched addresses will be reviewed individually. The addresses will be refined and matched to suggested addresses on health atlas or alternatively searched on the online interactive HP Pobal deprivation map to determine the corresponding small area. The small area will then enable us to determine a deprivation index for each individual in the dataset. These indices are reported as a numerical value from roughly -40(most disadvantaged) to +40(most affluent) or category of relative index score which are defined by HP as per Table 2 below. Categories of “extremely” and “very” disadvantaged will be merged into the “disadvantaged” category and similarly for the “affluent” category due to anticipation of low numbers in these categories. For each small area it is also possible to obtain data from which the deprivation scores are constructed such as population change, age dependency ratio, lone parents ratio, education level, unemployment rate, proportion of professional and manual workers, percentage of owner occupied households and rented households and average persons per room.

Table 2. Pobal HP deprivation Indices 2016.

Relative Index Score	Standard Deviation	Label	
Over 30	>3	Extremely affluent	
20-30	2 to 3	Very affluent	
10-20	1 to 2	Affluent	
0 to 10	0 to 1	Marginally above average	
0 to -10	0 to -1	Marginally below average	
-10 to -20	-1 to -2	Disadvantaged	
-20 to -30	-2 to -3	Very disadvantaged	
Below -30	<3	Extremely Disadvantaged	

For a proportion of the cohort, the address may not be detailed enough to identify a corresponding small area but a larger area may be identified. In these cases, the average deprivation index of each of the small areas within the larger area will be used.

The existing dataset will be updated with serial laboratory measurements of creatinine, urine ACR, Hba1c and lipids until January 2023. eGFR will be calculated using the CKD-EPI 2021 equation 46 for all creatinine values from the last clinical episode date in the existing dataset until January 2023. These eGFR values will then be censored to exclude those on dialysis or who have received renal transplant as these variations in eGFR are not reflective of renal function decline. Rate of renal function decline will be calculated as per the methods in the previous study utilising this dataset 40 . Linear mixed-effects models (incorporating random within-subject trajectories of eGFR over time) will be used to generate individual-specific eGFR slopes. These models will be applied to untransformed eGFR measurements to estimate absolute change in eGFR (mL/min/1.73 m 2/year), and to log-transformed eGFR measurements to estimate percentage change (% change per year). These slopes represent the change in renal function over time for each participant incorporating all eGFR measurements. Only individuals with at least 2 eGFR readings 3 months apart will be included to calculate rate of decline. Progressive or rapid decline in renal function among participants with DM will be defined as either an absolute reduction in eGFR of >3.5ml/min/1.73m 2/year 47 or proportionate eGFR loss per year of >3.3% 48 .

The primary outcome will be rate of renal function decline (absolute and percentage values as per formula above). Secondary outcomes will include time to reaching end stage kidney disease (eGFR<15ml/min/1.73 m 2), dialysis or renal transplant and variability in eGFR and ACR measurements which may reflect fluctuation in health status which could have been influenced by external social factors.

Analysis

Stata V.17 will be used for statistical analysis. Data will be assessed for normality. Descriptive analysis will be performed, comparing the distribution of variables between deprivation categories. Age, duration of diabetes, number of antihypertensive medications used, BMI, blood pressure, baseline creatinine, eGFR, uACR, HbA1c and lipids will be described with median/mean with standard deviation values depending on distribution and minimum/maximum values. Frequencies and proportions will be used to describe categorical variables- gender, type of diabetes, ethnicity, smoking status and medications used. ꭓ 2 squared test will be used to compare categorical variables between deprivation groups. Analysis of variance (ANOVA) will be used to compare means of continuous variables between groups.

Linear regression models will be used to explore the relationship between explanatory variables (including deprivation) and the primary outcome of rate of decline in renal function (absolute and relative based on eGFR). Logistic regression models will be used to explore categories of rapid and non-rapid decline. A “time to event” analysis will be carried out using the endpoints of ESKD/dialysis or renal transplant as described above. Confounders such as diabetes duration and HbA1c will be adjusted for in the models with careful consideration to differentiate confounders from mediating factors on the causal pathway.

Missing data will be dealt with using a complete case analysis approach. It is anticipated that there will be data missing from the exposure variable (deprivation) as a result of inadequate/incomplete address data and outcome variable (rate of renal function decline) as a result of lack of follow up laboratory data which may be due to change in location of diabetes management or death. Missing data will therefore be excluded from the analysis. A sensitivity analysis will be carried out using only the addresses matched to exact small area (i.e., excluding those with an averaged deprivation index).

A data protection impact assessment (DPIA) has been carried out to ensure the research methodology is in line with general data protection regulation (GDPR). Consent has not been obtained from participants as the data will be anonymised when accessed and analysed and therefore ethical approval was granted without the requirement to obtain consent. All data will be stored on a password protected Health Service Executive laptop with approved encryption software and anti-virus software. The file containing the dataset will be password protected.

Results

The baseline characteristics of each individual will be described as per table 3 [Refer extended data] using the information available from the existing dataset.

Discussion

There is a large body of evidence internationally demonstrating that social deprivation has a detrimental outcome on people’s health 49– 60 . The association between deprivation and diabetes complications has been explored internationally. This study will be the first in Ireland to explore the relationship between area level social deprivation and rate of decline in renal function among people with diabetes. It will be a starting point in a specific geographical area which can then be replicated across the country. If we identify an association between social deprivation and diabetic kidney disease in Ireland, it will highlight a need for targeted interventions for vulnerable subgroups with diabetes. This may be achieved in part through the enhanced community care programme, a national programme in Ireland designed to move the management of chronic diseases (including diabetes) to the community and away from tertiary or hospital care. Delivering care in the community and perhaps targeting more deprived areas could help to address the effect of deprivation. We may need to consider the impact of deprivation at each clinical assessment of a person with diabetes and how we can help modify the effects of same. Our models of care for diabetes management would need to be amended to address the impact of deprivation and how we can address this, for example through equitable access to technology, tailored diabetes education and virtual clinics where appropriate. We would need to ensure that our health system provides equal access to care and ensure there are governmental policies to subsidise healthy foods and provide equitable access to recreational facilities The results of this study may have significant impact on informing new health care and governmental policies.

Strengths and limitations of this study

First study in Ireland exploring an association between deprivation and rate of decline in renal function using a composite deprivation index

Long duration of follow up of longitudinal laboratory data (7+years)

Follow up results limited to a single tertiary public centre

Quality of data dependent on accuracy of data entry into electronic patient record system by healthcare professionals

Ethics and dissemination

Ethical approval was granted from the Clinical Research Ethics Committee at Galway University Hospitals in March 2023- Ref C.A. 2956. A data protection impact assessment (DPIA) was also completed.

The results will be written up for publication in a peer reviewed journal and presentation at national/and or international diabetes and public health conferences. The findings will be relevant to clinicians managing diabetes, public health specialists and healthcare service managers/policy makers.

Consent

Consent has not been obtained from participants as the data will be anonymised when accessed and analysed and therefore ethical approval was granted without the requirement to obtain consent.

Acknowledgements

This study protocol has been accepted as a preprint on medRxiv. This is accessible using the following link: https://medrxiv.org/cgi/content/short/2024.04.24.24306283v1

Underlying data

No data are associated with this article

Extended data availability

Open Science Framework: Study Protocol- The impact of social deprivation on development and progression of diabetic kidney disease. http://doi.org/10.17605/OSF.IO/9XTHR 61

This project contains the following extended data:

File- “Table 3”- (Description of the planned data collection of baseline characteristics of individuals categorised by deprivation status)

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Reporting guidelines

This study will adhere to the STROBE reporting guidelines.

10.21956/hrbopenres.15297.r42243
Reviewer response for version 1
Hansen Christian Stevns 1Referee https://orcid.org/0000-0002-5782-3476

1 Steno Diabetes Center Copenhagen, Herlev, Denmark
18 9 2024 Copyright: © 2024 Hansen CS
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Version 1recommendationapprove
Review of  HRB Open Research: Study protocol: The impact of social deprivation on development and progression of diabetic kidney disease [version 1; peer review:1 approved]

Overall considerations

The protocol describes an important topic: social deprivation indices and diabetic kidney disease. If these links are established in the Irish population new focused interventions are warranted in the deprived areas. The protocol is clear and concise.  

Abstract

Clear and concise

Introduction

Good background overview and clear and valid objectives

Study design

Is appropriate. It seems that the study database is appropriate in size, follow-up time and detail to allow for the analyses to be used.

Statistical analyses

Are appropriate.

Results

Is appropriately planned.

Conclusion

Is appropriately planned.

Is the study design appropriate for the research question?

Yes

Is the rationale for, and objectives of, the study clearly described?

Yes

Are sufficient details of the methods provided to allow replication by others?

Yes

Are the datasets clearly presented in a useable and accessible format?

Yes

Reviewer Expertise:

Diabetes epidemiology. Diabetic neuropathy. randomized trails. Risk factor analyses

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

10.21956/hrbopenres.15297.r41919
Reviewer response for version 1
Deo Salil 1Referee
1 Case School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
10 9 2024 Copyright: © 2024 Deo S
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Version 1recommendationapprove
This is a retrospective study that will use the DIAMOND data to evaluate the association between social determinants and kidney function decline in Galway Ireland. The study introduction is well written. CKD is an important adverse event among people with T2D. Understanding the longitudinal decline in kidney function would be important to understand in this cohort & the importance of SDoH in this regard is also important. 

I congratulate the authors on this well written study protocol & look forward to the results.

Is the study design appropriate for the research question?

Yes

Is the rationale for, and objectives of, the study clearly described?

Yes

Are sufficient details of the methods provided to allow replication by others?

Yes

Are the datasets clearly presented in a useable and accessible format?

Yes

Reviewer Expertise:

Cardiovascular disease, Social determinants of health, Cardiac Surgery, Risk prediction models

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Competing interests: No competing interests were disclosed.

Competing interests: No competing interests were disclosed.
==== Refs
1 IDF diabetes atlas. International Diabetes Federation,2021; [01/02/2023]. Reference Source
2 IDF atlas reports- diabetes and kidney disease. International Diabetes Federation,2023; [20/03/2024]. Reference Source
3 Afkarian M Sachs MC Kestenbaum B : Kidney disease and increased mortality risk in Type 2 Diabetes. J Am Soc Nephrol. 2013;24 (2 ):302–8. 10.1681/ASN.2012070718 23362314
4 Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work Group: KDIGO 2022 clinical practice guideline for diabetes management in Chronic Kidney Disease. Kidney Int. 2022;102 (5S ):S1–S127. 10.1016/j.kint.2022.06.008 36272764
5 Pacilli A Viazzi F Fioretto P : Epidemiology of Diabetic Kidney Disease in adult patients with Type 1 Diabetes in Italy: the AMD-annals initiative. Diabetes Metab Res Rev. 2017;33 (4 ): e2873. 10.1002/dmrr.2873 27935651
6 Casey C Buckley CM Kearney PM : Social deprivation and Diabetic Kidney Disease: a European view. J Diabetes Investig. 2024;15 (5 ):541–556. 10.1111/jdi.14156 38279774
7 American Psychological Association: APA dictionary of psychology. Washington,2023; [27/03/2023]. Reference Source
8 Stringhini S Tabak AG Akbaraly TN : Contribution of modifiable risk factors to social inequalities in Type 2 Diabetes: prospective Whitehall II cohort study. BMJ. 2012;345 : e5452. 10.1136/bmj.e5452 22915665
9 Agardh E Allebeck P Hallqvist J : Type 2 Diabetes incidence and Socio-Economic Position: a systematic review and meta-analysis. Int J Epidemiol. 2011;40 (3 ):804–18. 10.1093/ije/dyr029 21335614
10 Connolly VM Kesson CM : Socioeconomic Status and clustering of cardiovascular disease risk factors in diabetic patients. Diabetes Care. 1996;19 (5 ):419–22. 10.2337/diacare.19.5.419 8732702
11 du Prel JB Icks A Grabert M : Socioeconomic conditions and Type 1 Diabetes in childhood in North Rhine–Westphalia, Germany. Diabetologia. 2007;50 (4 ):720–8. 10.1007/s00125-007-0592-5 17294165
12 Liese AD Puett RC Lamichhane AP : Neighborhood level risk factors for Type 1 Diabetes in youth: the SEARCH case-control study. Int J Health Geogr. 2012;11 (1 ): 1. 10.1186/1476-072X-11-1 22230476
13 Funakoshi M Azami Y Matsumoto H : Socioeconomic Status and Type 2 Diabetes complications among young adult patients in Japan. PLoS One. 2017;12 (4 ): e0176087. 10.1371/journal.pone.0176087 28437472
14 Alvarez-Ramos P Jimenez-Carmona S Alemany-Marquez P : Socioeconomic deprivation and development of Diabetic Retinopathy in patients with Type 1 Diabetes Mellitus. BMJ Open Diabetes Res Care. 2020;8 (2 ): e001387. 10.1136/bmjdrc-2020-001387 33177040
15 Tatulashvili S Fagherazzi G Dow C : Socioeconomic inequalities and Type 2 Diabetes complications: a systematic review. Diabetes Metab. 2020;46 (2 ):89–99. 10.1016/j.diabet.2019.11.001 31759171
16 Talbo MK Katz A Dostie M : Associations between Socioeconomic Status and patient experience with Type 1 Diabetes management and complications: cross-sectional analysis of a cohort from Québec, Canada. Can J Diabetes. 2022;46 (6 ):569–77. 10.1016/j.jcjd.2022.02.008 35864032
17 Hurst JE Barn R Gibson L : Geospatial mapping and data linkage uncovers variability in outcomes of foot disease according to multiple deprivation: a population cohort study of people with diabetes. Diabetologia. 2020;63 (3 ):659–67. 10.1007/s00125-019-05056-9 31848633
18 Saydah S Lochner K : Socioeconomic Status and risk of diabetes-related mortality in the U.S. Public Health Rep. 2010;125 (3 ):377–88. 10.1177/003335491012500306 20433032
19 Roper NA Bilous RW Kelly WF : Excess mortality in a population with diabetes and the impact of material deprivation: longitudinal, population based study. BMJ. 2001;322 (7299 ):1389–93. 10.1136/bmj.322.7299.1389 11397742
20 Chaturvedi N Jarrett J Shipley MJ : Socioeconomic gradient in morbidity and mortality in people with diabetes: cohort study findings from the Whitehall study and the WHO multinational study of vascular disease in diabetes. BMJ. 1998;316 (7125 ):100–5. 10.1136/bmj.316.7125.100 9462313
21 Chaturvedi N Stephenson JM Fuller JH : The relationship between socioeconomic status and diabetes control and complications in the EURODIAB IDDM complications study. Diabetes Care. 1996;19 (5 ):423–30. 10.2337/diacare.19.5.423 8732703
22 González-Pérez A Saéz ME Vizcaya D : Impact of Chronic Kidney Disease definition on assessment of its incidence and risk factors in patients with newly diagnosed Type 1 and Type 2 Diabetes in the UK: a cohort study using primary care data from the United Kingdom. Prim Care Diabetes. 2020;14 (4 ):381–7. 10.1016/j.pcd.2019.11.002 31791904
23 Sinnott C Mc Hugh S Fitzgerald AP : Psychosocial complexity in multimorbidity: the legacy of adverse childhood experiences. Fam Pract. 2015;32 (3 ):269–75. 10.1093/fampra/cmv016 25900675
24 Connor JMO Millar SR Buckley CM : The prevalence and determinants of undiagnosed and diagnosed Type 2 Diabetes in middle-aged Irish adults. PLoS One. 2013;8 (11 ): e80504. 10.1371/journal.pone.0080504 24282548
25 Leahy S Canney M Scarlett S : Life-course social class is associated with later-life diabetes prevalence in women: evidence from the Irish longitudinal study on ageing. Longitudinal and Life Course Studies. 2020;11 (3 ):353–81. 10.1332/175795920X15786655004305
26 Kelly SR Loiselle AR Pandey R : Factors associated with non-attendance in the Irish National Diabetic Retinopathy screening programme (INDEAR study report no. 2). Acta Diabetol. 2021;58 (5 ):643–50. 10.1007/s00592-021-01671-4 33483856
27 Sexton E Bedford D : GP supply, deprivation and emergency admission to hospital for COPD and diabetes complications in counties across Ireland: an exploratory analysis. Ir J Med Sci. 2016;185 (2 ):453–61. 10.1007/s11845-015-1359-5 26399613
28 O'Connor R Houghton F Saunders J : Diabetes Mellitus in Irish general practice: level of care as reflected by HbA1c values. Eur J Gen Pract. 2006;12 (2 ):58–65. 10.1080/13814780600780858 16945878
29 Tracey ML McHugh SM Fitzgerald AP : Risk factors for macro- and microvascular complications among older adults with diagnosed Type 2 Diabetes: findings from the Irish longitudinal study on ageing. J Diabetes Res. 2016;2016 : 5975903. 10.1155/2016/5975903 27294152
30 Loef B Meulman I Herber GCM : Socioeconomic differences in healthcare expenditure and utilization in the Netherlands. BMC Health Serv Res. 2021;21 (1 ): 643. 10.1186/s12913-021-06694-9 34217287
31 Unwin M Crisp E Stankovich J : Socioeconomic disadvantage as a driver of non-urgent emergency department presentations: a retrospective data analysis. PLoS One. 2020;15 (4 ): e0231429. 10.1371/journal.pone.0231429 32282818
32 Relatioships project: Case study: frome model of enhanced primary care. Reference Source
33 Fallon C Jones E Oliver N : The impact of socio-economic deprivation on access to diabetes technology in adults with type 1 diabetes. Diabet Med. 2022;39 (10 ): e14906. 10.1111/dme.14906 35751860
34 Kingsnorth AP Wilson C Choudhary P : Comparing glucose outcomes following face-to-face and remote initiation of flash glucose monitoring in people living with diabetes. J Diabetes Sci Technol. 2023;17 (4 ):887–894. 10.1177/19322968231176531 37226777
35 Bihan H Laurent S Sass C : Association among individual deprivation, glycemic control, and diabetes complications: the EPICES score. Diabetes Care. 2005;28 (11 ):2680–5. 10.2337/diacare.28.11.2680 16249539
36 Townsend deprivation index: national centre for research methods.2022. Reference Source
37 Nitsch D Burden R Steenkamp R : Patients with diabetic nephropathy on renal replacement therapy in England and Wales. QJM. 2007;100 (9 ):551–60. 10.1093/qjmed/hcm062 17681992
38 Deprivation guidance for analysts public health Scotland: GPD team.2020. Reference Source
39 Höhn A McGurnaghan SJ Caparrotta TM : Large socioeconomic gap in period life expectancy and life years spent with complications of diabetes in the Scottish population with type 1 diabetes, 2013–2018. PLoS One. 2022;17 (8 ): e0271110. 10.1371/journal.pone.0271110 35951518
40 Griffin TP O'Shea PM Smyth A : Burden of chronic kidney disease and rapid decline in renal function among adults attending a hospital-based diabetes center in Northern Europe. BMJ Open Diabetes Res Care. 2021;9 (1 ): e002125. 10.1136/bmjdrc-2021-002125 37077135
41 Haase T PJ : The pobal HP deprivation index for small areas in the Republic of Ireland. Dublin: Pobal,2018.
42 Swan L Horgan NF Fan CW : Residential area socioeconomic deprivation is associated with physical dependency and polypharmacy in community-dwelling older adults: an analysis of health administrative data in Ireland. J Multidiscip Healthc. 2022;15 :1955–1963. 10.2147/JMDH.S380456 36081581
43 Ward FL O'Kelly P Donohue F : Influence of socioeconomic status on allograft and patient survival following kidney transplantation. Nephrology (Carlton). 2015;20 (6 ):426–33. 10.1111/nep.12410 25641402
44 Ward FL O'Kelly P Donohue F : The influence of socioeconomic status on patient survival on chronic dialysis. Hemodial Int. 2015;19 (4 ):601–8. 10.1111/hdi.12295 25854991
45 Health atlas Ireland national health intelligence unit: health information and quality authority. Reference Source
46 CKD-EPI creatinine equation: National Kidney Foundation.2021. Reference Source
47 Krolewski AS : Progressive renal decline: the new paradigm of diabetic nephropathy in type 1 diabetes. Diabetes Care. 2015;38 (6 ):954–62. 10.2337/dc15-0184 25998286
48 Krolewski AS Niewczas MA Skupien J : Early progressive renal decline precedes the onset of microalbuminuria and its progression to macroalbuminuria. Diabetes Care. 2014;37 (1 ):226–34. 10.2337/dc13-0985 23939543
49 Singh GK : Area deprivation and widening inequalities in US mortality, 1969–1998. Am J Public Health. 2003;93 (7 ):1137–43. 10.2105/ajph.93.7.1137 12835199
50 ELHadi A Ashford-Wilson S Brown S : Effect of social deprivation on the stage and mode of presentation of colorectal cancer. Ann Coloproctol. 2016;32 (4 ):128–32. 10.3393/ac.2016.32.4.128 27626022
51 Kargoli F Shulman E Aagaard P : Socioeconomic status and predictors of survival in patients admitted with atrial fibrillation. J Am Coll Cardiol. 2015;65 (10 ):A471. 10.1016/S0735-1097(15)60471-5
52 Lipman TH Hawkes CP : Racial and socioeconomic disparities in pediatric type 1 diabetes: time for a paradigm shift in approach. Diabetes Care. 2021;44 (1 ):14–16. 10.2337/dci20-0048 33444165
53 Phillimore P Beattie A Townsend P : Widening inequality of health in Northern England, 1981–91. BMJ. 1994;308 (6937 ):1125–8. 10.1136/bmj.308.6937.1125 8173452
54 Mackenbach JP Stirbu I Roskam AJ : Socioeconomic inequalities in health in 22 European countries. N Engl J Med. 2008;358 (23 ):2468–81. 10.1056/NEJMsa0707519 18525043
55 Eachus J Williams M Chan P : Deprivation and cause specific morbidity: evidence from the Somerset and Avon survey of health. BMJ. 1996;312 (7026 ):287–92. 10.1136/bmj.312.7026.287 8611787
56 Nadrowski P Drygas W Bielecki W : The higher socioeconomic status, the lower risk of cardiovascular death ? Eur J Prev Cardiol. 2018;25 (2 ):S87–S8.
57 Guan TR Zhang XH Dong LG : Low socioeconomic status is a significant predictor of incidental stroke risk in Chinese population from a prospective cohort study in Beijing. Eur Heart J. 2011;32 :59–60.
58 Zhang D Earp BE Blazar P : Association of economic well-being with comorbid conditions in patients undergoing carpal tunnel release. J Hand Surg Am. 2022;47 (12 ):1228.e1–1228.e7. 10.1016/j.jhsa.2021.09.012 34716055
59 Kargoli F Shulman E Aagaard P : Socioeconomic status as a predictor of mortality in patients admitted with atrial fibrillation. Am J Cardiol. 2017;119 (9 ):1378–1381. 10.1016/j.amjcard.2017.01.041 28400027
60 Jones DA Howard JP Rathod KS : The impact of socio-economic status on all-cause mortality after percutaneous coronary intervention: an observational cohort study of 13,770 patients. EuroIntervention. 2015;10 (10 ):e1–8. 25701263
61 Casey C : Study protocol- the impact of social deprivation on development and progression of diabetic kidney disease.2024. 10.17605/OSF.IO/9XTHR
