An informatics-based data-led prioritization strategy to facilitate objective and equitable care for an ethnically diverse urban cohort of people with type 1 diabetes: A proof-of-concept study

Abstract

Background

Ethnicity and socioeconomic factors contribute to higher morbidity and mortality in people with type 1 diabetes (pwT1D), partly due to reduced access to specialised care and technology. Objectively prioritising high-risk individuals in resource-limited settings is challenging. Data-led prioritisation (DLP) uses health informatics to stratify pwT1D based on new-onset risk factors since their last review. This may help overcome implicit bias, structural racism, and care barriers. However, data on its use in pwT1D are limited.

Methods

In this proof-of-concept study, DLP was implemented from July to September 2023 in a university hospital serving an ethnically diverse population. Clinical and demographic data were collected from 697 adults with T1D (50.5% female, 23.5% non-White, 37% from poor socioeconomic backgrounds). DLP identified 76 individuals (10.9%) as highest risk.

Results

Non-White patients were more likely to be in the highest-risk group (30/164, 18.3%) than White patients (35/372, 9.4%), p=0.004. Those from the most deprived socioeconomic backgrounds were also more likely to be high-risk (40/256, 15.7%) vs others (36/433, 8.3%), p=0.008.

Conclusion

DLP may enable objective risk stratification in pwT1D and could help reduce bias linked to ethnicity and deprivation. Further large-scale research is warranted to demonstrate the use of such systems in diabetes care.

Keywords

social determinants of health type 1 diabetes data-led prioritisation risk stratification

Introduction

Type 1 diabetes (T1D) is a chronic condition that requires proactive management to achieve glycaemic targets, thereby reducing the risk of end-organ dysfunction. Effective management also involves minimising iatrogenic hypoglycaemia and glycaemic variability through careful titration of insulin regimes. To achieve optimal glycaemic control and mitigate complications, regular follow-up with a specialist diabetes care team is a standard aspect of care for people with T1D (pwT1D). However, there is no universally agreed-upon schedule for follow-up intervals, which are often determined on a case-by-case basis, influenced by clinical judgment, clinic capacity, local policies and local standards of care.

Social determinants of health, play a crucial role in the management of pwT1D and patient outcomes. Psychosocial and socioeconomic disparities (psychological, social, and structural barriers to care) have been shown to adversely affect mortality and the incidence of diabetes-related complications in people with T1D.^1,2 Additionally, ethnicity is a significant factor in susceptibility to diabetes complications, with African-Caribbean ethnicity identified as an independent risk factor for developing advanced chronic kidney disease and sight-threatening diabetic retinopathy in individuals with T1D.^3–5

Young adults from ethnic minority and lower socioeconomic status have been shown to have worse glycaemic control and lower use of diabetes technology compared to their peers from a White background.⁶ Interestingly, socioeconomic status was found to have a significant correlation with HbA1c levels in Hispanic young adults, whereas Black young adults had higher HbA1c even when socioeconomic status is accounted for.⁷ In parallel, data from the T1D Exchange Quality Improvement Collaborative demonstrate higher HbA1c, higher rates of DKA, severe hypoglycaemia, acute diabetes related complications and lower rates of continuous glucose monitor and insulin pump use among non-Hispanic Black people with T1D.⁸ Black heritage has also been associated with higher likelihood of diabetic retinopathy and lower likelihood of insulin pump use.⁹ Though access to specialist healthcare and utilisation of advanced medical technologies are essential components of T1D care, these are often limited for individuals from lower socioeconomic backgrounds and ethnic minority groups. Potential contributing factors to these disparities may include implicit bias, systemic discrimination, cultural differences in health-related attitudes, and language barriers.^10–12

Data-led prioritization (DLP) is an approach that utilises regular, automated application of rulesets based on routinely collected clinical data to prioritise people based on new onset of modifiable risk event since last clinical review.¹³ Risk stratification is achieved through a clinical risk score (P-score), which determines the level of priority (P1 highest, P4 lowest) and can guide appropriate follow-up intervals based on clinical needs. This methodology offers an objective assessment of clinical risk, free from socioeconomic or racial biases, positioning DLP as a potentially valuable tool in advancing equity in healthcare.¹⁴ Given its potential use in the field of diabetes, we sought to use DLP to risk stratify the caseload of pwT1D attending specialist diabetes clinics in a tertiary centre and examine the demographic characteristics of different risk categories.

Materials and methods

The cohort consisted of adult pwT1D attending routine outpatient clinics in a tertiary university hospital based in an urban, ethnically diverse area. We implemented a DLP risk prioritisation system from July 2023 to September 2023.

The DLP tool has previously been validated across the entire outpatient diabetes service, including T1D, identifying low-risk patients with a specificity of 81%, and high-risk patients with a sensitivity of 83%.¹⁴ Electronic health record (EHR) data was reviewed over the course of 12 weeks from July to September 2023 in 697 adult pwT1D. Data was obtained from EHR and socioeconomic status determined by Index of Multiple Deprivation (IMD) from Office for National Statistics United Kingdom data. In brief, IMD deciles are small area measures of relative deprivation across each of the constituent nations of the United Kingdom. Areas are ranked from the most deprived area to the least deprived area. The IMD encompasses broad themes including income, employment, education, health, crime, barriers to housing and services, and the living environment. IMD scores are ranked according to population deciles, with 1 indicating highest level of deprivation and 10 being the most affluent.¹⁵

Criteria for “high risk” and “ambiguous risk” events relating to diabetes control since last outpatient review were established through consultation with a panel of diabetes clinicians and reviewing national guidelines.^14,16 These included emergency department attendance, HbA1c concentration, rate of change of HbA1c, estimated glomerular filtration (eGFR) decline and diabetic eye disease treatment since last consultant led consultation. These criteria were discussed within a group of 10 diabetes specialists who focussed on a pragmatic selection and consensus approach, with emphasis on modifiability of risk, accessibility of data/information currently available in the EHR database and inclusion and exclusion criteria for the cohort. Following this review and discussion, a list of risk criteria/flags with clinical parameters and criteria were agreed upon by the group and validated as described previously¹⁴(Table 1).

Table 1.

Risk factor ruleset for DLP model.

Criteria	Concerning	Ambiguous	Not concerning
Emergency Department attendances	One or more diabetes-related A&E attendances since patient was last seen in a relevant clinic	-	-
High HbA1c	Highest HbA1c since patient was last seen in a relevant clinic > 86 mmol/mol	Highest HbA1c since patient was last seen in a relevant clinic between 64 and 86 (inclusive) mmol/mol	Highest HbA1c since patient was last seen in a relevant clinic < 64 mmol/mol
Increasing HbA1c	Largest difference between relevant HbA1c preceding and following date patient was last seen in a relevant clinic > 20 mmol/mol AND following HbA1c ≥ 64 mmol/mol	Not ‘Concerning’ AND Largest difference between relevant HbA1c preceding and following date patient was last seen in a relevant clinic ≥ 5 mmol/mol	Largest difference between relevant HbA1c preceding and following date patient was last seen in a relevant clinic < 5 mmol/mol
Decreasing eGFR*	Largest difference between relevant eGFR* preceding and following date patient was last seen in a relevant clinic < -15 ml/min OR annualised rate of change < -15 ml/min AND following eGFR < 90 ml/min	Not ‘Concerning’ AND Largest difference between relevant eGFR* preceding and following date patient was last seen in a relevant clinic ≤ -5 ml/min OR annualised rate of change⁴ ≤ -5 ml/min	Not ‘Concerning’ AND Not ‘Ambiguous’ AND Largest difference¹ between relevant eGFR* preceding² and following³ date patient was last seen in a relevant clinic > -5 ml/min OR annualised rate of change⁴ > -5 ml/min
Diabetic Eye Disease Treatments** (DEDT)	One or more diabetes eye treatment procedures since patient was last seen in a relevant clinic where the treatment series appears to have started after the patient was last seen	One or more DEDT procedures since patient was last seen in a relevant clinic where the treatment series appears to have started before the patient was last seen	-
Low HbA1c	Lowest HbA1c since patient was last seen in a relevant clinic < 48 mmol/mol (6.5%)	-	Lowest HbA1c since patient was last seen in a relevant clinic ≥ 48 mmol/mol (6.5%)
Decreasing HbA1c	Largest difference between relevant HbA1c preceding and following date patient was last seen in a relevant clinic < -20 mmol/mol (1.85%) AND following HbA1c < 64 mmol/mol (8%)	Not ‘Concerning’ AND Largest difference between relevant HbA1c preceding and following date patient was last seen in a relevant clinic <= -5 mmol/mol (0.45%)	Largest difference between relevant HbA1c preceding and following date patient was last seen in a relevant clinic > -5 mmol/mol (0.45%)

*eGFR – estimated glomerular filtration rate.

**Anti-VEGF injection or retinal photocoagulation.

A third-party analytics partner, organised people into DLP categories based on available data for risk events. New high-risk events that were entered into the DLP scoring matrix were high risk encounters/interactions or changes in measures since the persons’ last clinic visit and included emergency department attendance related to diabetes, HbA1 >86 mmol/mol, increase in HbA1c >20 mmol/mol, decreasing eGFR> 15ml/min with latest eGFR <90 ml/min, initiation of treatment for diabetic eye disease, HbA1c < 48 mmol/mol and decrease in HbA1c >20 mmol/mol with latest HbA1c <64 mmol/mol. People who scored one or more in these criteria were defined as the high-risk category (“Concerning” P1). If these parameters did not change beyond the threshold for “Concerning”, they were categorised as “Ambiguous” or “Not concerning”. PwT1D were characterised as lower-risk (no concerning criteria, and one or more encouraging criteria); moderate-risk (no concerning or encouraging criteria, and one or more ambiguous criteria); and unknown risk (no new data since the last consultant-led appointment). Encouraging criteria consisted of highest recorded HbA1c < 64 mmol/mol since last clinic review, HbA1c change < 5 mmol/mol, absence of any concerning or ambiguous flags and eGFR decline of < 5 ml/min. The risk score was updated 24 hours following a change in risk category. Risk categories are outlined in Figure 1 and the risk criteria in Table 1.

Figure 1.

Informatics-led risk stratification segments and default outpatient review. $⋆$ PIFU – Patient initiated follow up.

The resulting DLP classification of each patient was presented to the clinic administrative team between appointments and the clinician at the time of the next scheduled follow-up, alongside its prespecified outcome (Figure 1). By utilising this information, the administrators could expedite and book (P1) appointments in reserved slots for people who experienced new onset high-risk events since their last review. At clinic appointments, clinicians were presented with an up-to-date risk score, conducted the consultation and decided on an outcome regarding follow-up; deviation from the prespecified DLP segment outcome required a documented reason for this choice. Risk scores were also used to determine intervals for no-show/missed appointments.

Furthermore, we examined the demographic characteristics and analysed their distribution across risk categories with respect to ethnicity and socioeconomic deprivation. This was a retrospective data analysis of a proof-of-concept study conducted in line with local protocols using existing anonymized routine clinical data accessed directly by the teams and approved by hospital information and data governance committees and related data protection agreements. Data are presented as mean and standard deviation (SD) or median (interquartile range). Independent continuous variables were compared using Student’s t-test or the Mann–Whitney test. Categorical variables were compared using the chi-squared test. All statistical tests were 2-tailed, with P < 0.05 considered significant. Statistical analysis was performed using SPSS 21.0 software (SPSS, Chicago, IL, USA).

Results

The baseline characteristics of the cohort included were as follows: 50.5% were female, and 23.5% were from non-White ethnicity. Median IMD category was 4 (IQR 3–7); and 36.7% were in the lowest three deciles of the IMD.

When the DLP model was applied, 76 patients (10.9%) had new onset risk events since their last clinical review which placed them in the highest-risk group (P1). Across all DLP categories, non-White individuals were disproportionately represented in higher-risk strata. Among non-White patients, 30 of 164 (18.3%) were classified as highest risk (P1), compared with 35 of 372 (9.4%) White patients (χ² test, p<0.001) (Table 2). The majority of non-White and White individuals were categorised within the intermediate-risk group (P3: 91/164, 55.5% non-White; 238/372, 64% White), reflecting the overall cohort distribution. No significant association was observed between DLP risk category and gender.

Table 2.

DLP stratification by ethnicity.

DLP classification	Ethnicity			Grand total
DLP classification	Non-White	White	Not Known/not statedNot Stated	Grand total
P1	30	35	11	76
P2	2	12	3	17
P3	91	238	121	450
P4	10	39	4	53
P5	31	48	22	101
Grand Total	164	372	161	697

Risk categories: P1= Concerning; P2= Ambiguous; P3= None or insufficient new data; P4= Not concerning; P5= Suitable for PIFU.

Abbreviations: DLP = Data led prioritisation.

A gradient effect was observed across socioeconomic strata. Patients from more deprived IMD deciles were increasingly represented in higher-risk DLP categories, with the highest concentration of P1 classifications occurring within IMD deciles 2 and 3 (17/97, 17.5% and 22/151, 14.6%, respectively). In contrast, individuals from less deprived IMD deciles were more frequently classified into lower-risk categories (P4–P5), with higher IMD deciles (7–10) collectively accounting for 33 of 101 (32.7%) of all P5 classifications. Those in the lowest 3 IMD categories were significantly more likely to be in the highest clinical risk group (40/256, 15.7%) than those outside of this group (36/433, 8.3%), p<0.001 (Table 3). Of those patients attending clinic during the study period, 1.9% were considered suitable for discharge to their GP, and 12.2% were suitable for patient initiated follow up (PIFU) to clinical review, roughly corresponding to the incidence of patients assigned to the low risk (P5) category (14.5%).

Table 3.

DLP stratification for each IMD category.

Data led pripritisation (DLP) risk classification	Indices of Multiple Deprivation (IMD deciles)											Grand total
Data led pripritisation (DLP) risk classification	1	2	3	4	5	6	7	8	9	10	NULL	Grand total
P1	1	17	22	6	6	13	1	2	4	4		76
P2		4	3	1	2	1			2	2	2	17
P3	5	55	85	73	39	45	41	33	42	27	5	450
P4	1	6	11	6	6	6	2	5	5	5		53
P5	1	15	30	13	9	14	6	3	4	4	2	101
Grand Total	8	97	151	99	62	79	50	43	57	42	9	697

Risk categories: P1= Concerning; P2= Ambiguous; P3= None or insufficient new data; P4= Not concerning; P5= Suitable for PIFU.

IMD interpretation: Score of 1 indicates the highest level of deprivation and 10 the most affluent background.

Abbreviations: DLP = Data led prioritisation, IMD = Index of multiple deprivation.

This informatics-led model has been incorporated into the hospital EHR and is currently being used to manage the outpatient clinic waiting list for the diabetes department (Supplementary figures (S)F1, SF2).

Discussion

Our findings show that people with T1D from ethnic minority backgrounds and lower socioeconomic status (SES) as defined by IMD, are more likely to experience new-onset high risk events between clinic appointments. We hypothesise that our informatics-led approach may help uncover hidden risk within the diabetes clinic waiting lists and allocate resources to those at greatest need, in a way that may help facilitate clinicians to address socioeconomic disparities in diabetes care. Our novel risk stratification system combines risk criteria derived from national guidelines and expert consensus in a unique risk scoring matrix utilised to manage the caseload of a specialist diabetes outpatient clinic; the first one to adopt this. Increasing numbers of people to a PIFU to clinical review pathway as demonstrated in our results, can help reduce capacity constraints and allows higher risk people (P1) to be offered earlier review within existing clinic and staffing resources.

SES is an amalgamation of several factors, with emphasis on educational, economic, and occupational status. It is strongly associated with progression and outcomes of many different conditions, including diabetes; it has been shown that low SES is linked to increased risk of experiencing diabetes related complications and early mortality in people with diabetes.¹⁷ Individuals from low SES are more likely to live in environments that are conducive to the development of chronic conditions such as diabetes and their associated complications, due to poor availability of high quality food, exposure to pollutants and toxic environmental factors, tobacco use, unstable housing conditions, areas with less safety and opportunity for an active lifestyle and finally poor access to specialist healthcare.^18,19 SES has been identified as a key element influencing outcomes in T1D, as it is significantly linked to healthcare engagement, use of diabetes technologies and health literacy.

Young adults and children from lower SES are less likely to successfully use or have access to diabetes technologies. The importance of T1D technologies in improving T1D outcomes is undeniable with an ever-growing body of evidence and best practice guidelines supporting this.^20,21 Those with lower levels of literacy or children with carers of low literacy are less likely to be successfully established on insulin pump therapies or to be even offered that option to begin with.²² Furthermore, focus groups conducted with adults with T1D who have poor glycaemic control or experienced acute complications, have shown that people of lower SES are less likely to be initiated on diabetes technologies and have higher rates of discontinuation.^23,24 Provider-level factors and interpersonal communications during clinical encounters were often cited as the level where barriers to technology were met.

Ethnicity has a widely recognised role in health disparities in people with T1D. It is well documented that people from ethnic minority groups have on average, worse glycaemic control, higher prevalence of complications, lower attainment of diabetes technologies and lower levels of healthcare engagement.⁷ These disparities may be partly explained by the significant prevalence of lower SES in ethnic minority groups and cultural differences in communication, decision making approach in matters of health. However, emerging evidence suggest that ethnicity may also be an independent factor in glycaemic control and susceptibility to diabetes related complications.^3,7,9 Agarwal et al.⁷ performed a cross-sectional study examining racial-ethnic disparities in young adults with T1D. It was found that individuals from ethnic minority groups had higher HbA1c levels and lower use of diabetes technologies. When their analysis accounted for SES, the difference in HbA1c and technology use disappeared between White and Hispanic people with T1D. Even with SES being accounted for, people with Black heritage still had higher HbA1c levels. Similar results were observed by Willi et al.,²⁵ with higher HbA1c levels, increased prevalence of DKA and severe hypoglycaemia and lower insulin pump use among Black individuals with T1D compared to those of White and Hispanic background. Recent evidence proposes SES, racial and systemic bias as potential causes of lower technology uptake.^11,12 Considering these disparities in technology use in people of ethnic minority background and lower SES, the implementation of an informatics-based system with objective criteria could help eliminate the implicit bias that may act as a barrier to technology access and user supportive healthcare contact in these groups. However further research is required to confirm this hypothesis.

Mangelis et al. studied a diverse cohort of 1,876 people with T1D attending for eye screening surveillance, with the objective of examining the effect of ethnicity on developing sight threatening diabetic retinopathy.⁵ Among others, African Caribbean ethnicity emerged as a significant independent risk factor for developing sight threatening diabetic retinopathy. The same group, with similar methodology studied a population of 5,261 people with T1D, showing significant and independent association between African Caribbean ethnicity and significant progression of diabetic kidney disease.³ Use of informatics-based system could identify such high-risk individuals early and objectively, independent of systemic and social barriers, thereby enabling risk-modifying interventions that could significantly alter the progression of such complications.

Initial use of DLP took place during the COVID-19 pandemic in surgical fields, as a standardised approach and framework that was developed to enable clinicians to target more accurately those patients with the greatest need and those who would gain the greatest benefit.¹³ After the pandemic surgical specialties used scoring-system based prioritisation approaches that provided a means to identifying people who needed prompt care and also as a means to reduce waiting list times that grew due to the COVID-19 pandemic.²⁶

We have successfully implemented this approach into the hospital electronic health record system (Epic) and it is now being used in clinical practice and to manage clinic waiting lists. To our knowledge, our systems was the first designed for use in medical specialist services and outpatient clinical care setting. The risk stratification tool and related methods used in our work are novel and have not been previously implemented in diabetes care. Our use of a health informatics approach highlighted and better identifies hidden disparities and clinical risk that were most pronounced in low SES and ethnic minority groups of people with type 1 diabetes. This, as far as we are aware, is a novel clinical application of health informatics in the care of people with diabetes.

In specialist chronic disease outpatient services such as diabetes, clinicians frequently do not have ‘sight’ of emergent or new risk between clinic appointments unless there is an unscheduled or emergency care episode. DLP based on risk stratification may help fill that gap and reduce patient harm during those ‘blind’ time periods with prompt follow up, while also improving healthcare equity by eliminating racial and socioeconomic bias from the decision-making process of providing access to a clinic. Furthermore, by identifying high-risk individuals who may often have low healthcare engagement due to challenging personal and socioeconomic factors as well as structural barriers, this data led approach could act as a safety net, providing additional support and enhanced opportunity for these individuals to better engage with care and clinical services.^17,19

Innovative data led approaches are urgently needed to effectively deal with backlog of care in ambulatory medicine and especially diabetes, as it is associated with a high burden of related complications often resulting in hospitalisation, premature morbidity and mortality.²⁷ Many of these complications can be delayed or prevented with prompt intervention and high level of care.²⁸

In this pilot involving outpatients with T1D attending a university hospital diabetes service, DLP has demonstrated that non-White people with T1D and those with the highest level of socioeconomic deprivation, were over-represented in the high clinical risk category (P1). These groups are historically the ones with poorer access to healthcare and lower engagement with services, which highlights their need for closer support and timely follow up.¹⁹ By improving equity and minimising systemic bias in healthcare access, DLP could help improve outcomes in these high-risk groups. Indeed, in a previous operational pilot pathway over 3 months using this approach, it was demonstrated that people in the high risk P1 category were more likely to be of lower SES and ethnic minority background as compared to higher SES and White individuals. Interventions and care optimisation prevented deterioration in health in 40% of this group within this 3-month pilot.¹⁴ The ability of this model to identify both high and low risk pwT1D is crucial in order to create clinical capacity to facilitate prioritisation based on clinical risk, as people at low risk could be offered less frequent or patient-initiated consultations, while those at highest risk (P1) offered prompt review. Such an approach creates a ‘dynamic capacity’ for those at highest need without increasing clinic numbers or staffing; a pragmatic approach in a setting where resources and time are often limited.

Strengths of this approach include the validated nature of the DLP tool and its utilisation of routinely collected objectively recorded clinical parameters. Limitations include the short period it was conducted over which was in the context of a proof-of-concept study to assess the feasibility and application of this new approach to care in a busy real world clinical environment. Glycaemic data from diabetes technology were not incorporated and we acknowledge their addition could enhance DLP performance. Another limitation is that people from non-White ethnic backgrounds were analysed as a single group; this was primarily due to sample size constraints within individual ethnic categories. Several individual ethnic groups were represented by small numbers, precluding meaningful stratified comparisons without a high risk of spurious findings. We recognise that racial and ethnic groups are heterogeneous and that combining them may obscure important differences in risk profiles and care experiences, therefore further research incorporating more detailed analysis of ethnic subgroups is needed.

Our findings in this proof-of-concept study, support that non-White people and those with high index of socioeconomic deprivation with T1D are on average at higher clinical risk. We hypothesise that these high-risk groups would benefit from the objectivity introduced by DLP in outpatient clinic caseload management. This study was not designed to answer these questions, and to do so longer-term follow-up and further research is needed to confirm this hypothesis.

Conclusion

A pragmatic, data-driven methodology can risk-stratify an outpatient population with type 1 diabetes, utilising objective measures of clinical risk, thereby minimising implicit and systemic bias in the provision of healthcare access and determination of follow up intervals. In addition, facilitating the implementation of such strategies to improve capacity may lead to greater availability of timely care and resources for high-risk pwT1DM, as the process of DLP identifies not only high-risk people, but also those at lower risk that require less frequent consultations. Our data suggest considerable overlap between socioeconomic deprivation, ethnic minority status and high clinical risk; these findings corroborate those of other publications on the adverse effects of socioeconomic, racial and ethnic disparities on glycaemic control and diabetes related clinical risk. The objective process through which DLP operates could prove valuable in improving healthcare equity for pwT1D, by helping reduce the impact of SES and racial bias and facilitating the provision of fairer care for those at highest need.

Supplemental material

Supplemental material - An informatics-based data-led prioritization strategy to facilitate objective and equitable care for an ethnically diverse urban cohort of people with type 1 diabetes: A proof-of-concept study

Supplemental material for An informatics-based data-led prioritization strategy to facilitate objective and equitable care for an ethnically diverse urban cohort of people with type 1 diabetes: A proof-of-concept study by Panagiotis Pavlou, Khuram Chaudhry, Ollie French, Sarah Keane, Thomas Johnston, Anna Brackenridge, Stephen Thomas, Yuk-Fun Liu, Daghni Rajasingam, Dulmini Kariyawasam, Janaka Karalliedde in Health Informatics Journal

Footnotes

ORCID iD

Panagiotis Pavlou

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a research grant from Guy’s and St Thomas’ Charity funding and by the King’s British Heart Foundation (BHF) Centre of Excellence Award RE/18/2/34213.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

References

Will

Connell

. The preventability of ‘premature mortality’: an investigation of early diabetes deaths. Am J Public Health 1988; 78(7): 831–833. https://doi.org/10.2105/ajph.78.7.831

Laing

Jones

Swerdlow

, et al. Psychosocial and socioeconomic risk factors for premature death in young people with type 1 diabetes. Diabetes Care 2005; 28(7): 1618–1623. https://doi.org/10.2337/diacare.28.7.1618

Mangelis

Fountoulakis

Corcillo

, et al. African Caribbean Ethnicity Is an Independent Predictor of Significant Decline in Kidney Function in People With Type 1 Diabetes. Diabetes Care 2022; 45(9): 2095–2102. https://doi.org/10.2337/dc22-0815

Brancati

Whittle

Whelton

, et al. The excess incidence of diabetic end-stage renal disease among blacks. A population-based study of potential explanatory factors. JAMA 1992; 268(21): 3079–3084.

Mangelis

Wijewickrama

Nirmalakumaran

, et al. People With Type 1 Diabetes of African Caribbean Ethnicity Are at Increased Risk of Developing Sight-Threatening Diabetic Retinopathy. Diabetes Care 2023; 46(5): 1091–1097. https://doi.org/10.2337/dc22-2118

Evans

. Widening health inequalities related to type 1 diabetes care in children and young people in the UK: A time to act now. Diabet Med 2021; 38(11): e14620. https://doi.org/10.1111/dme.14620

Agarwal

Kanapka

Raymond

, et al. Racial-Ethnic Inequity in Young Adults With Type 1 Diabetes. J Clin Endocrinol Metab 2020; 105(8): e2960–e2969. https://doi.org/10.1210/clinem/dgaa236

Majidi

Ebekozien

Noor

, et al. Inequities in Health Outcomes in Children and Adults With Type 1 Diabetes: Data From the T1D Exchange Quality Improvement Collaborative. Clin Diabetes 2021; 39(3): 278–283. https://doi.org/10.2337/cd21-0028

Ferm

DeSalvo

Prichett

, et al. Clinical and Demographic Factors Associated With Diabetic Retinopathy Among Young Patients With Diabetes. JAMA Netw Open 2021; 4(9): e2126126. https://doi.org/10.1001/jamanetworkopen.2021.26126

10.

Ajayi Sotubo

. A perspective on health inequalities in BAME communities and how to improve access to primary care. Future Healthc J 2021; 8(1): 36–39. https://doi.org/10.7861/fhj.2020-0217

11.

Singh

Garcia

Grishman

, et al. Disparities in diabetes technology utilization in youth with diabetes. BMJ Open Diabetes Res Care 2025; 13(6): e005067. https://doi.org/10.1136/bmjdrc-2025-005067

12.

Ebekozien

Fantasia

Farrokhi

, et al.

Technology and health inequities in diabetes care: How do we widen access to underserved populations and utilize technology to improve outcomes for all?

Diabetes Obes Metab 2024; 26(Suppl 1): 3–13. https://doi.org/10.1111/dom.15470

13.

Wathes

Malhotra

Flott

, et al. Towards a standardised method of patient prioritisation that accounts for clinical harm. Future Healthc J 2021; 8(1): e42–e46. https://doi.org/10.7861/fhj.2020-0109

14.

Karalliedde

French

Burnhill

, et al. A pragmatic digital health informatics based approach for aiding clinical prioritisation and reducing backlog of care: A study in cohort of 4022 people with diabetes. Diabetes Res Clin Pract 2023; 203: 110834. https://doi.org/10.1016/j.diabres.2023.110834

15.

Noble

McLennan

Wilkinson

, et al. The English Indices of Deprivation 2007. 2008.

16.

Choudhary

Wilmot

Owen

, et al. A roadmap to recovery: ABCD recommendations on risk stratification of adult patients with diabetes in the post-COVID-19 era. Diabet Med 2021; 38(3): e14462. https://doi.org/10.1111/dme.14462

17.

Brown

Ettner

Piette

, et al. Socioeconomic position and health among persons with diabetes mellitus: a conceptual framework and review of the literature. Epidemiol Rev 2004; 26: 63–77. https://doi.org/10.1093/epirev/mxh002

18.

Diez Roux

Mair

. Neighborhoods and health. Ann N Y Acad Sci 2010; 1186: 125–145. https://doi.org/10.1111/j.1749-6632.2009.05333.x

19.

Hill-Briggs

Adler

Berkowitz

, et al. Social Determinants of Health and Diabetes: A Scientific Review. Diabetes Care 2020; 44(1): 258–279. https://doi.org/10.2337/dci20-0053

20.

Karakus

Akturk

Alonso

, et al. Association Between Diabetes Technology Use and Glycemic Outcomes in Adults With Type 1 Diabetes Over a Decade. Diabetes Care 2023; 46(9): 1646–1651. https://doi.org/10.2337/dc23-0495

21.

American Diabetes Association Professional Practice, C . 9. Pharmacologic Approaches to Glycemic Treatment: Standards of Care in Diabetes-2025. Diabetes Care 2025; 48(Supplement_1): S181–S206. https://doi.org/10.2337/dc25-S009

22.

Dlugatch

Rankin

Evans

, et al. Understanding inequities in access to diabetes technologies in children and young people with type 1 diabetes: Qualitative study of healthcare professionals’ perspectives and views. Diabet Med 2024; 42: e15486. https://doi.org/10.1111/dme.15486

23.

Walker

Hood

Gurka

, et al. Barriers to Technology Use and Endocrinology Care for Underserved Communities With Type 1 Diabetes. Diabetes Care 2021; 44(7): 1480–1490. https://doi.org/10.2337/dc20-2753

24.

Sabben

Telfort

Morales

, et al. Technology and Continuous Glucose Monitoring Access, Literacy, and Use Among Patients at the Diabetes Center of an Inner-City Safety-Net Hospital: Mixed Methods Study. JMIR Diabetes 2024; 9: e54223. https://doi.org/10.2196/54223

25.

Willi

Miller

DiMeglio

, et al. Racial-ethnic disparities in management and outcomes among children with type 1 diabetes. Pediatrics 2015; 135(3): 424–434. https://doi.org/10.1542/peds.2014-1774

26.

Rathnayake

Clarke

Jayasinghe

. Patient prioritisation methods to shorten waiting times for elective surgery: A systematic review of how to improve access to surgery. PLoS One 2021; 16(8): e0256578. https://doi.org/10.1371/journal.pone.0256578

27.

Crasto

Patel

Davies

, et al. Prevention of Microvascular Complications of Diabetes. Endocrinol Metab Clin North Am 2021; 50(3): 431–455. https://doi.org/10.1016/j.ecl.2021.05.005

28.

Umpierrez

Korytkowski

. Diabetic emergencies - ketoacidosis, hyperglycaemic hyperosmolar state and hypoglycaemia. Nat Rev Endocrinol 2016; 12(4): 222–232. https://doi.org/10.1038/nrendo.2016.15

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.46 MB