Skip to main content
NCBI home page
Journal of Diabetes Research logoLink to Journal of Diabetes Research
. 2025 Jul 19;2025:5588397. doi: 10.1155/jdr/5588397

Public Health System Funding for isCGM Reduces Socioeconomic Disparities in Type 2 Diabetes Control: A Cohort Study

Fernando Sebastian-Valles 1, Jessica Jimenez-Diaz 2, Carolina Sager-La Ganga 1,✉, Jon Garai-Hierro 3, Alicia Justel Enriquez 1, Alejandra Santamaría Barrena 3, Jon Portu Gamazo 3, Luis Eduardo Lander-Lobariñas 2, María Sara Tapia-Sanchiz 1, María Ausín Carrera 3, Julia Martínez-Alfonso 4, Jose Alfonso Arranz-Martín 1, Miguel Antonio Sampedro-Núñez 1, Victor Navas-Moreno 1, Purificación Martinez-Icaya 2, Mónica Marazuela 1, Iñigo Hernando-Alday 3
PMCID: PMC12463513  PMID: 41019918

Abstract

Objective: The objective of this study is to assess whether the provision of free intermittently scanned continuous glucose monitoring (isCGM) systems can reduce socioeconomic disparities in glycemic control among individuals with Type 2 diabetes mellitus (T2D) treated with multiple daily insulin injections.

Methods: This is a cohort study involving 402 T2D patients from three hospitals, all of whom initiated isCGM use as part of routine clinical practice. The isCGM systems were provided free of charge through public healthcare funding, with no out-of-pocket cost to the patients. Glycated hemoglobin (HbA1c) levels were recorded before and after at least 3 months of sensor use. Socioeconomic status (SES) was determined based on the average annual net income per person within the census tract for each patient.

Results: Prior to the sensor placement, the mean HbA1c was 8.9% for patients in the lowest SES quartile and 8.2% for those in the highest quartile (p = 0.009). Following isCGM implementation, significant HbA1c reductions were observed across all SES groups, with decreases of 1.0% in the lowest quartile and 0.6% in the highest (p < 0.001). Postintervention differences in HbA1c between SES quartiles were not statistically significant (p = 0.509).

Conclusion: Public funding of isCGM systems is associated with a significant improvement in glycemic control and contributes to the reduction of pre-existing socioeconomic disparities in healthcare among T2D patients treated with insulin.

1. Introduction

Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycemia and disturbances in carbohydrate metabolism [1]. Due to its high prevalence and the burden associated with its chronic complications, DM currently represents one of the leading global public health challenges [2, 3]. Estimates of the number of individuals affected by DM exceed 800 million [4]. The vast majority (85%–95%) of diabetes cases in adults are classified as Type 2 diabetes mellitus (T2D) [5]. Notably, the highest prevalence rates of DM in 2022 were recorded in countries within Polynesia and Micronesia, the Caribbean, the Middle East, and North Africa, as well as in Pakistan and Malaysia [4].

Type 1 diabetes (T1D) is an autoimmune condition characterized by absolute insulin deficiency, typically diagnosed in younger individuals, and managed from the outset with intensive insulin therapy [1]. In contrast, T2D is a heterogeneous disease of progressive insulin resistance and relative insulin deficiency, often associated with obesity and aging, and managed initially with lifestyle interventions and oral antidiabetic agents [6]. Insulin therapy in T2D is usually reserved for more advanced stages or cases with poor glycemic control.

Glycemic control, typically assessed using glycated hemoglobin (HbA1c) determination, has been consistently linked to a reduced risk of microvascular and macrovascular complications [7, 8]. In this context, the advent of continuous glucose monitoring (CGM) systems has revolutionized diabetes management. Numerous randomized clinical trials [9, 10] and real-world studies [11, 12] have demonstrated that these devices improve glycemic control in individuals with T1D.

Recently, evidence has also emerged indicating improved glycemic control in large cohorts of patients with T2D [13, 14], including cohorts with patients without insulin therapy [15]. However, access to these technologies may be influenced by economic factors, potentially perpetuating or even exacerbating disparities in glycemic control across different socioeconomic status (SES) levels. Social determinants of health have been extensively studied in T1D, revealing their impact on metabolic control [16, 17], development of complications [18, 19], and even mortality [20–22]. Previous studies from our group showed that free access to CGM systems in individuals with T1D can reduce SES-related disparities [23] in accordance with prior reports in the literature [24].

Nevertheless, there is limited evidence regarding the impact of universal access to these technologies on individuals with T2D. Therefore, the objective of the present study was to evaluate whether the provision of free intermittently scanned continuous glucose monitoring (isCGM) sensors can reduce social inequalities in glycemic control among individuals with T2D.

2. Materials and Methods

This is a retrospective pre–post observational study involving a cohort of 402 individuals with T2D on multiple daily insulin injections. Participants were recruited from three Spanish hospitals across different geographic regions: Hospital Universitario de La Princesa (Madrid), Hospital Universitario Severo Ochoa (Leganés), and Hospital Universitario Basurto (Bilbao). All subjects were active users of isCGM systems, specifically the FreeStyle Libre (Abbott).

Inclusion criteria were confirmed diagnosis of T2D and consistent use of an isCGM device, defined as sensor usage exceeding 70% in accordance with current clinical guidelines. Exclusion criteria included diagnoses of T1D, maturity-onset diabetes of the young (MODY), or other specific diabetes forms. HbA1c levels at two different time points were collected, one within 1 month prior to sensor initiation and another one at least 3 months postinitiation, at the time of glucose metrics extraction from the LibreView platform.

The study was approved by the Research Ethics Committee of Hospital Universitario de La Princesa (5917 -04/25) and was conducted in accordance with the principles of the Declaration of Helsinki and the strengthening of the reporting of observational studies in epidemiology (STROBE) guidelines [25].

2.1. Procedures

The Spanish National Health System provides individuals with T2D requiring multiple daily insulin injections with isCGM systems at no personal cost upon prescription by a healthcare professional (see Supporting Information section). Participation in a structured education session is mandatory to access the device, and all patients received individualized training on isCGM use prior to sensor initiation, in line with international recommendations [26, 27]. The isCGM system utilizes an electrochemical glucose oxidase sensor implanted subcutaneously, replaced every 14 days. Interstitial glucose readings are wirelessly transmitted to a reader and stored in the cloud via the LibreView platform. Participants were provided with written instructions on interpreting and utilizing sensor data, including real-time insulin dose adjustments, hypoglycemia management, and retrospective data review through LibreView for therapeutic decision-making.

2.2. Data Collection

Sociodemographic and clinical variables were retrieved from electronic health records, including sex, age, T2D duration, body mass index (BMI), smoking status, baseline and follow-up HbA1c levels, isCGM usage duration, insulin dosage, number of antidiabetic agents, presence of diabetic nephropathy and retinopathy, and history of ischemic heart disease or ischemic stroke. HbA1c level was measured using liquid chromatography (ADAMS A1c HA8180 V, ARKRAY). Microvascular complications were classified according to international criteria [28–30].

2.3. SES

SES was assessed using the mean annual net income per capita of the census tract for each patient, as reported by the Spanish National Statistics Institute [31]. This measure correlates closely [23] with the 2011 Spanish deprivation index [32], but offers a more up-to-date data. Thus, it was utilized as a proxy for SES, analogous to the deprivation index (source: Instituto Nacional de Estadística de España. Atlas de Distribución de Renta de los Hogares 2022. Available in: https://www.ine.es/componentes_inebase/ADRH_total_nacional.htm (visited on 2 April 2025)).

2.4. Statistical Analysis

Outliers were examined, and the normality of data distributions was assessed using the Kolmogorov–Smirnov test and normal probability plots. Validated extreme values were truncated at the 1st and 99th percentiles. Continuous variables are presented as mean ± standard deviation (SD), and categorical variables as absolute frequencies and percentages.

Differences in HbA1c across SES quartiles were analyzed using ANOVA and multiple linear regression, adjusting for age, sex, diabetes duration, insulin dosage, BMI, and smoking status.

To evaluate changes in HbA1c over time and the potential modifying effect of SES, we fitted a multivariable mixed-effects linear regression model with HbA1c as the dependent variable. The model included the following fixed effects: time (post vs. pre-isCGM initiation), SES quartile (reference: Quartile 1), interaction terms between time and SES quartiles, age, sex, smoking status (current vs. not), BMI); number of noninsulin antidiabetic agents; and total basal insulin dose per kilogram. The inclusion of time × SES quartile interactions aimed to assess whether the magnitude of HbA1c change following isCGM initiation differed across socioeconomic strata. A random intercept at the individual level was specified to account for within-subject correlation due to repeated measures.

All analyses were performed using STATA v17.0 BE-Basic Edition (StataCorp, College Station, Texas, United States), with statistical significance set at p < 0.05.

3. Results

A total of 511 individuals with T2D on insulin therapy who were provided with an isCGM sensor were enrolled. Of these, 402 were selected after exclusion of individuals with missing data for certain variables (Figure 1) with a mean isCGM usage duration of 1.1 ± 0.7 years as of March 2025. The mean age of this cohort was 67.3 ± 10.4 years, and 37.1% of the patients were women (n = 149). The mean duration of diabetes was 17.8 ± 4.8 years, and the age at diagnosis was 47.8 ± 10.1 years. The mean BMI was 28.7 ± 4.7 kg/m2; 15.3% (n = 61) of the patients were current smokers. Diabetic retinopathy was present in 36.3% (n = 146) of the patients, chronic kidney disease or microalbuminuria in 54.7% (n = 220), a history of ischemic heart disease in 24.6% (n = 99), and ischemic stroke in 9.0% (n = 36). All complications were assessed prior to isCGM initiation. A total of 384 patients underwent an HbA1c measurement at least 3 months after sensor placement. In this group (final cohort for analysis, Figure 1), HbA1c was reduced after the intervention compared to the basal level (8.5% ± 1.5% [95% CI: 8.4–8.7] vs 7.7% ± 1.1% [95% CI: 7.6–7.8]; p < 0.001).

Figure 1.

Figure 1

Open in a new tab

Flowchart. HbA1c, glycated hemoglobin; isCGM, intermittently scanned continuous glucose monitoring; T2D, Type 2 diabetes.

3.1. Baseline Differences According to SES

The clinical variables were analyzed across quartiles of net income, which was used as a proxy for SES. Notably, baseline HbA1c levels were significantly higher in the lowest income quartile compared to the highest quartile (8.9% ± 1.8% vs. 8.2% ± 1.3%; p = 0.009). Additionally, there was a trend toward higher BMI in individuals with lower income (29.8 ± 4.9 kg/m2; p = 0.029). No significant differences were observed in other clinical variables across SES quartiles. Detailed results are presented in Table 1.

Table 1.

Baseline characteristics of the study sample in the different categories of socioeconomic status.

Variable Obs, n = 402 Quartile 1 income, n = 101 Quartile 2 income, n = 102 Quartile 3 income, n = 99 Quartile 4 income, n = 100 p value
Age 67.3 ± 10.4 67.8 ± 11.7 66.5 ± 10.8 67.5 ± 8.4 67.4 ± 10.5 0.848
Sex, women 149 (37.1%) 42 (41.6) 40 (39.2) 37 (37.4) 30 (30.0) 0.357
Duration of diabetes (years) 17.8 ± 4.8 18.0 ± 3.8 17.3 ± 5.6 18.2 ± 6.0 17.7 ± 3.5 0.608
Net income/person/year (€) 15,379 ± 5260 10,484 ± 1186 12,635 ± 521 15,488 ± 1205 23,015 ± 4223 < 0.001
BMI (kg/m2) 28.7 ± 4.7 29.8 ± 4.9 27.9 ± 4.0 28.5 ± 4.4 28.5 ± 5.1 0.029
Smokers 61 (15.3%) 18 (18.2) 19 (18.6) 13 (13.3) 11 (11.0) 0.355
HbA1c (%) 8.5 ± 1.5 8.9 ± 1.8 8.4 ± 1.5 8.5 ± 1.3 8.2 ± 1.3 0.009
Insulin dose (IU/kg) 0.62 ± 0.31 0.58 ± 0.29 0.66 ± 0.30 0.62 ± 0.31 0.60 ± 0.36 0.359
Other antidiabetic agents 2 (1–2) 1 (0–2) 2 (1–2) 2 (1–2) 2 (1–2) 0.315
Retinopathy 146 (36.3%) 46 (45.5) 36 (35.3) 30 (30.3) 34 (34.0) 0.136
Nephropathy 220 (54.7) 54 (53.5) 51 (50.0) 53 (53.5) 62 (62.0) 0.365
Ischemic heart disease 99 (24.6) 21 (20.8) 19 (18.6) 33 (33.3) 26 (26.0) 0.074
Stroke 36 (9.0) 8 (7.9) 10 (9.8) 7 (7.1) 11 (11.0) 0.761
Open in a new tab

Note: Continuous variables are expressed as mean ± standard deviation and categorical variables as n (%). Differences between quartiles were analyzed with ANOVA.

Abbreviations: BMI, body mass index; HbA1c, glycated hemoglobin.

A multivariable linear regression model was constructed with baseline HbA1c as the dependent variable. The analysis revealed an inverse association between age and HbA1c levels (β = –0.023; 95% CI: −0.038 to –0.007; p = 0.004). Conversely, female sex (β = 0.316; 95% CI: 0.010 to 0.621; p = 0.043) and active smoking (β = 0.625; 95% CI: 0.205 to 1.046; p = 0.004) were positively associated with higher HbA1c values. Neither BMI (β = 0.001; p = 0.970) nor diabetes duration (β = 0.012; p = 0.472) showed a significant association. Compared to the lowest income quartile, individuals in Quartile 2 (β = –0.509; p = 0.016) and Quartile 4 (β = –0.505; p = 0.020) exhibited significantly lower HbA1c levels; the difference in the Quartile 3 was not statistically significant (β = –0.361; p = 0.091).

3.2. Changes Following isCGM Implementation

HbA1c levels decreased significantly across all SES quartiles after isCGM initiation. In the lowest SES quartile, HbA1c declined from 8.9% ± 1.7% to 7.9% ± 1.0% (Δ = –1.0%; 95% CI: 0.65 to 1.32; p < 0.001). In Quartile 2, the reduction was from 8.4% ± 1.6% to 7.7% ± 0.9% (Δ = –0.7%; 95% CI: 0.42 to 1.05; p < 0.001); in Quartile 3, from 8.5% ± 1.3% to 7.7% ± 1.0% (Δ = –0.8%; 95% CI: 0.55 to 1.08; p < 0.001); and in the highest quartile, from 8.3% ± 1.3% to 7.8% ± 1.2% (Δ = –0.6%; 95% CI: 0.30 to 0.83; p = 0.001) (Figure 2). Although all groups experienced a significant improvement in glycemic control, the magnitude of HbA1c reduction was greatest in the lowest SES quartile. In addition, linear regression models showed that lower HbA1c levels were significantly associated with higher SES prior to isCGM initiation, whereas this association was weaker after isCGM implementation (Figure 3). Accordingly, no statistically significant differences in post sensor placement HbA1c levels were observed among SES quartiles (p = 0.509), suggesting that isCGM implementation mitigated previously observed disparities in glycemic control associated with SES.

Figure 2.

Figure 2

Open in a new tab

Difference in HbA1c (%) across SES quartiles before and after isCGM initiation. The figure illustrates HbA1c levels before and after the implementation of isCGM in patients with Type 2 diabetes, stratified by SES quartile. HbA1c, glycated hemoglobin; isCGM, intermittently scanned continuous glucose monitoring; SES, socioeconomic status Quartile 1 SES, lowest income; Quartile 4 SES, highest income.

Figure 3.

Figure 3

Open in a new tab

Socioeconomic status-associated HbA1c gradient before and after isCGM implementation. Forest plot displaying the beta coefficients (with 95% confidence intervals) from linear regression models assessing baseline (yellow markers) and postintervention (orange markers) HbA1c levels across SES quartiles, using Quartile 1 (lowest SES) as the reference. HbA1c, glycated hemoglobin; isCGM, intermittently scanned continuous glucose monitoring; SES, socioeconomic status.

3.3. Longitudinal Analysis

A mixed-effects linear regression model with random intercepts at the individual level was applied to evaluate changes in HbA1c over time. Fixed effects included SES quartile, time point (pre/post sensor placement), their interaction, age, sex, smoking status, BMI, number of oral antidiabetic agents, and basal insulin dose per kilogram.

The model revealed a significant overall reduction in HbA1c following isCGM implementation (β = –1.04; 95% CI: −1.34 to −0.74; p < 0.001). Compared to the lowest income quartile (reference group), individuals in Quartiles 2 and 4 exhibited significantly lower HbA1c levels (β = –0.82 and −1.02; p = 0.020 and p = 0.005, respectively). A significant interaction was observed in the highest SES quartile, indicating a smaller HbA1c reduction over time compared to the reference group (β = 0.47; 95% CI: 0.03 to 0.91; p = 0.037).

Additionally, active smoking (β = 0.42; p = 0.007) and higher basal insulin dose (β = 0.39; p = 0.033) were significantly associated with higher HbA1c levels, whereas older age was associated with lower HbA1c (β = –0.01; p = 0.034). The mixed-effects model demonstrated significantly better fit than a standard linear model (likelihood ratio test, p < 0.001; see Table 2).

Table 2.

Mixed-effects linear regression model assessing HbA1c variation over time according to SES quartiles.

Variable Coefficient ( β ) 95% confidence interval p value
Time (post vs. pre) −1.04 –1.34 to –0.74 < 0.001
SES Quartile 2 −0.82 –1.52 to –0.13 0.020
SES Quartile 3 −0.59 –1.29 to 0.12 0.102
SES Quartile 4 −1.02 –1.73 to –0.30 0.005
Interaction: time × Quartile 2 0.30 –0.13 to 0.72 0.169
Interaction: time × Quartile 3 0.20 –0.23 to 0.62 0.361
Interaction: time × Quartile 4 0.47 0.03 to 0.91 0.037
Age −0.012 –0.023 to –0.001 0.034
Sex (female) 0.19 –0.04 to 0.42 0.104
Smoking 0.42 0.12 to 0.73 0.007
BMI 0.007 –0.019 to 0.032 0.607
Other antidiabetics agents 0.061 –0.049 to 0.170 0.276
Basal insulin (kg) 0.39 0.03 to 0.75 0.033
Open in a new tab

Note: Results of the multivariable mixed-effects linear regression model evaluating changes in HbA1c before and after isCGM initiation, incorporating SES quartiles, time point (pre/post), and their interaction as fixed effects. The model is also adjusted for age, sex, smoking status, BMI, number of oral antidiabetic agents, and baseline insulin dose per kilogram. A random intercept at the patient level was included to account for repeated measures.

Abbreviations: BMI, body mass index; HbA1c; glycated hemoglobin; SES, socioeconomic status.

4. Discussion

The primary aim of this study was to evaluate the impact of publicly funded isCGM implementation on socioeconomic disparities in glycemic control among individuals with T2D treated with multiple daily insulin injections. Our findings indicate that the introduction of this technology was associated with a significant improvement in glycemic control across all socioeconomic strata and a substantial reduction in differences between them.

Numerous studies have demonstrated that lower SES is linked to a higher incidence of T2D [33, 34], poorer glycemic control, suboptimal cardiovascular risk factor management, increased psychological burden [35], accelerated progression of microvascular [36, 37] and macrovascular complications [38], and elevated mortality [39]. In line with this evidence, our study determined a clinically relevant baseline HbA1c difference of 0.7 percentage points between the lowest and highest SES quartiles. A significant trend toward higher BMI in the lower SES groups was also observed. Importantly, the association between SES and baseline HbA1c remained significant even after adjusting for BMI and other covariates in the multivariable analysis. The effect of SES on glycemic control is mediated by several factors. Individuals in socioeconomically deprived areas are less likely to access high-quality healthcare [40]. In our context, limited access to sports and exercise facilities in lower SES neighborhoods has been described, which may partially contribute to differences in obesity and T2D prevalence [41], although our findings suggest that other SES-related factors also play a role. Health literacy tends to be lower in these populations—even in countries with universal healthcare [42]—while unhealthy behaviors such as smoking [43] and high consumption of fast food [44] are more common, further impacting glycemic outcomes.

The benefits of continuous and intermittently scanned glucose monitoring systems have extended beyond their initial indications in T1D [26], with growing evidence supporting their efficacy in T2D [13]. However, their impact on health equity has not been clearly elucidated. Robust evidence suggests that the introduction of new technologies without equitable access strategies can exacerbate existing health disparities [45, 46]. In our study, publicly funded isCGM was associated with a significant narrowing of HbA1c differences across SES groups. These findings are consistent with prior observations in T1D populations [23]. They suggest that universal access to such technologies may help mitigate structural inequities, supporting public health policies aimed at reducing the influence of SES on clinical outcomes. In this context, several countries have begun implementing policies similar to those in Spain to provide glucose monitoring sensors for individuals with T2D. For instance, the 2023 NICE guidelines recommend offering such devices to patients who face difficulties in performing capillary blood glucose testing [47, 48]. Although smoking was not specifically associated with SES in our sample, as reported in other studies [43, 49], it remained consistently linked to a higher baseline HbA1c and less improvement after sensor initiation. Similar findings were also reported in T1D populations [50]. It is worth noting that our study did not assess physical activity. Since this factor is likely associated with both SES and glycemic control, it should be addressed in future research.

There are currently concerns regarding the sustainability of increasing pharmaceutical expenditure in publicly funded systems, particularly those related to the risk of diverting resources from cost-effective interventions toward high-cost technologies with uncertain benefit [51]. However, our findings suggest that isCGM may represent a justifiable investment due to its potential to improve metabolic outcomes and reduce socioeconomic disparities.

This study has several limitations. Its retrospective observational design limits causal inference. Data on physical activity and dietary habits—key factors in glycemic control—were not collected. Although the study was conducted across three tertiary hospitals in different geographic regions, the generalizability of the results to the broader insulin-treated T2D population cannot be guaranteed. Moreover, the SES proxy used was based on census tract income, which may not fully reflect individual-level socioeconomic conditions and could introduce ecological bias. Nevertheless, the use of area-based socioeconomic indicators is well supported in the literature [52–54]. Although in our setting isCGM is universally available to individuals with T2D on multiple daily insulin injections, and the lack of device use is most commonly due to personal preference, we did not include a matched control group of individuals without isCGM. Therefore, we cannot fully exclude the possibility that the observed reduction in SES-related disparities was influenced by unmeasured confounders rather than sensor use itself. Finally, we did not evaluate the impact of SES on other glycemic metrics. This effect should be explored in future studies.

In conclusion, the publicly funded implementation of isCGM in individuals with T2D on insulin therapy was associated with improved glycemic control across all socioeconomic levels and a marked reduction in previously observed disparities. These findings support the role of publicly financed diabetes technologies as a strategic tool to reduce health inequities associated with SES.

Acknowledgments

We thank Manuel Gómez for his valuable contribution to the study.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

Author Contributions

Fernando Sebastian-Valles: conceptualization, methodology, writing – original draft, writing – review and editing, supervision, project administration. Jessica Jimenez-Diaz: investigation, writing – review and editing. Carolina Sager-La Ganga: investigation, resources. Jon Garai-Hierro: investigation, resources. Alicia Justel Enriquez: investigation, data curation. Alejandra Santamaría Barrena: resources, writing – review and editing. Jon Portu Gamazo: visualization, data curation. Luis Eduardo Lander-Lobariñas: investigation, resources, data curation. María Sara Tapia-Sanchiz: investigation, writing – review and editing. María Ausín Carrera: investigation, writing – review and editing. Julia Martínez-Alfonso: investigation, writing – review and editing. Jose Alfonso Arranz-Martín: conceptualization, supervision, writing – review and editing. Miguel Antonio Sampedro-Núñez: supervision, writing – review and editing. Victor Navas-Moreno: resources, writing – review and editing. Purificación Martinez-Icaya: data curation, writing – review and editing. Mónica Marazuela: conceptualization, methodology, supervision, project administration, writing – review and editing. Iñigo Hernando-Alday: formal analysis, data curation, writing – review and editing.

Funding

This study was supported by the Instituto de Salud Carlos III, 10.13039/501100004587 (PI19/00584, PI22/01404, and PMP22/00021).

Supporting Information

Supporting Information

Additional supporting information can be found online in the Supporting Information section. Official bulletin issued by the Community of Madrid (June 15, 2023) announcing the public funding of intermittently scanned continuous glucose monitoring (isCGM) systems for individuals with Type 2 diabetes treated with multiple daily insulin injections.

5588397.f1.pdf (163.9KB, pdf)

References

  • 1.American Diabetes Association Professional Practice Committee. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2022. Diabetes Care . 2022;45(supplement 1):S17–S38. doi: 10.2337/dc22-S002. [DOI] [PubMed] [Google Scholar]
  • 2.Conget I. Diagnosis, Classification and Pathogenesis of Diabetes Mellitus. Revista Española de Cardiología. . 2002;55(5):459–561. doi: 10.1016/s0300-8932(02)76646-3. [DOI] [PubMed] [Google Scholar]
  • 3.Soriguer F., Goday A., Bosch-Comas A., et al. Prevalence of Diabetes Mellitus and Impaired Glucose Regulation in Spain: The Di@bet.es Study. Diabetologia . 2012;55(1):88–93. doi: 10.1007/s00125-011-2336-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.NCD Risk Factor Collaboration (NCD-RisC) Worldwide Trends in Diabetes Prevalence and Treatment From 1990 to 2022: A Pooled Analysis of 1108 Population-Representative Studies With 141 Million Participants. Lancet . 2024;404(10467):2077–2093. doi: 10.1016/S0140-6736(24)02317-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Tuomilehto J. The Emerging Global Epidemic of Type 1 Diabetes. Current Diabetes Reports . 2013;13(6):795–804. doi: 10.1007/s11892-013-0433-5. [DOI] [PubMed] [Google Scholar]
  • 6.American Diabetes Association Professional Practice Committee. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2025. Diabetes Care . 2025;48(supplement_1):S27–S49. doi: 10.2337/dc25-S002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Barrot J., Real J., Vlacho B., et al. Diabetic Retinopathy as a Predictor of Cardiovascular Morbidity and Mortality in Subjects With Type 2 Diabetes. Frontiers in Medicine . 2022;9 doi: 10.3389/fmed.2022.945245.945245 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.UK Prospective Diabetes Study (UKPDS) Group. Intensive Blood-Glucose Control With Sulphonylureas or Insulin Compared With Conventional Treatment and Risk of Complications in Patients With Type 2 Diabetes (UKPDS 33) Lancet . 1998;352(9131):837–853. doi: 10.1016/S0140-6736(98)07019-6. [DOI] [PubMed] [Google Scholar]
  • 9.Beck R. W., Riddlesworth T., Ruedy K., et al. Effect of Continuous Glucose Monitoring on Glycemic Control in Adults With Type 1 Diabetes Using Insulin Injections: The DIAMOND Randomized Clinical Trial. Jama . 2017;317(4):371–378. doi: 10.1001/jama.2016.19975. [DOI] [PubMed] [Google Scholar]
  • 10.Leelarathna L., Evans M. L., Neupane S., et al. Intermittently Scanned Continuous Glucose Monitoring for Type 1 Diabetes. New England Journal of Medicine . 2022;387(16):1477–1487. doi: 10.1056/NEJMoa2205650. [DOI] [PubMed] [Google Scholar]
  • 11.Moreno-Fernandez J., Sastre J., Pinés P., Calderon-Vicente D., Gargallo J., Muñoz-Rodriguez J. R. To Evaluate the Use and Clinical Effect of Intermittently Scanned Continuous Glucose Monitoring in Adults With Type 1 Diabetes: Results of a Multicentre Study. Endocrinología, Diabetes y Nutrición . 2023;70(4):270–276. doi: 10.1016/j.endien.2023.03.013. [DOI] [PubMed] [Google Scholar]
  • 12.Sebastian-Valles F., Martínez-Alfonso J., Arranz Martin J. A., et al. Scans per day as predictors of optimal glycemic control in people with type 1 diabetes mellitus using flash glucose monitoring: what number of scans per day should raise a red flag? Acta Diabetologica . 2024;61(3):343–350. doi: 10.1007/s00592-023-02204-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Seidu S., Kunutsor S. K., Ajjan R. A., Choudhary P. Efficacy and Safety of Continuous Glucose Monitoring and Intermittently Scanned Continuous Glucose Monitoring in Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis of Interventional Evidence. Diabetes Care . 2024;47(1):169–179. doi: 10.2337/dc23-1520. [DOI] [PubMed] [Google Scholar]
  • 14.Jancev M., Vissers T. A. C. M., Visseren F. L. J., et al. Continuous Glucose Monitoring in Adults With Type 2 Diabetes: A Systematic Review and Meta-Analysis. Diabetologia . 2024;67(5):798–810. doi: 10.1007/s00125-024-06107-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Ferreira R. O. M., Trevisan T., Pasqualotto E., et al. Continuous Glucose Monitoring Systems in Noninsulin-Treated People With Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Diabetes Technology & Therapeutics . 2024;26(4):252–262. doi: 10.1089/dia.2023.0390. [DOI] [PubMed] [Google Scholar]
  • 16.Huo L., Deng W., Shaw J. E., et al. Factors Associated With Glycemic Control in Type 1 Diabetes Patients in China: A Cross-Sectional Study. Journal of Diabetes Investigation . 2020;11(6):1575–1582. doi: 10.1111/jdi.13282. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Mönkemöller K., Müller-Godeffroy E., Lilienthal E., et al. The Association Between Socio-Economic Status and Diabetes Care and Outcome in Children With Diabetes Type 1 in Germany: The DIAS Study (Diabetes and Social Disparities) Pediatric Diabetes . 2019;20(5):637–644. doi: 10.1111/pedi.12847. [DOI] [PubMed] [Google Scholar]
  • 18.Anderson S. G., Shoo H., Saluja S., et al. Social Deprivation Modifies the Association Between Incident Foot Ulceration and Mortality in Type 1 and Type 2 Diabetes: A Longitudinal Study of a Primary-Care Cohort. Diabetologia . 2018;61(4):959–967. doi: 10.1007/s00125-017-4522-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Alvarez-Ramos P., Jimenez-Carmona S., Alemany-Marquez P., Cordoba-Doña J. A., Aguilar-Diosdado M. Socioeconomic Deprivation and Development of Diabetic Retinopathy in Patients With Type 1 Diabetes Mellitus. BMJ Open Diabetes Research & Care . 2020;8(2) doi: 10.1136/bmjdrc-2020-001387.e001387 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Campbell R. A. S., Colhoun H. M., Kennon B., et al. Socio-economic status and mortality in people with type 1 diabetes in Scotland 2006-2015: a retrospective cohort study. Diabetic Medicine . 2020;37(12):2081–2088. doi: 10.1111/dme.14239. [DOI] [PubMed] [Google Scholar]
  • 21.Rawshani A., Svensson A. M., Rosengren A., Eliasson B., Gudbjörnsdottir S. Impact of Socioeconomic Status on Cardiovascular Disease and Mortality in 24,947 Individuals With Type 1 Diabetes. Diabetes Care . 2015;38(8):1518–1527. doi: 10.2337/dc15-0145. [DOI] [PubMed] [Google Scholar]
  • 22.Scott A., Chambers D., Goyder E., O’Cathain A. Socioeconomic Inequalities in Mortality, Morbidity and Diabetes Management for Adults With Type 1 Diabetes: A Systematic Review. PLoS One . 2017;12(5) doi: 10.1371/journal.pone.0177210.e0177210 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Sebastian-Valles F., Martínez-Alfonso J., Arranz Martin J. A., et al. Impact of Socioeconomic Status on Chronic Control and Complications of Type 1 Diabetes Mellitus in Users of Glucose Flash Systems: A Follow-Up Study. BMC Medicine . 2024;22(1):p. 37. doi: 10.1186/s12916-024-03254-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Everett E. M., Wisk L. E. Relationships Between Socioeconomic Status, Insurance Coverage for Diabetes Technology and Adverse Health in Patients With Type 1 Diabetes. Journal of Diabetes Science and Technology . 2022;16(4):825–833. doi: 10.1177/19322968211050649. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.von Elm E., Altman D. G., Egger M., et al. The Strengthening The Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies. Journal of Clinical Epidemiology . 2008;61(4):344–349. doi: 10.1016/j.jclinepi.2007.11.008. [DOI] [PubMed] [Google Scholar]
  • 26.Battelino T., Danne T., Bergenstal R. M., et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range. Diabetes Care . 2019;42(8):1593–1603. doi: 10.2337/dci19-0028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Battelino T., Alexander C. M., Amiel S. A., et al. Continuous Glucose Monitoring and Metrics for Clinical Trials: An International Consensus Statement. Lancet Diabetes & Endocrinology . 2023;11(1):42–57. doi: 10.1016/S2213-8587(22)00319-9. [DOI] [PubMed] [Google Scholar]
  • 28.Rao V., Rao L. V., Tan S. H., Candasamy M., Bhattamisra S. K. Diabetic Nephropathy: An Update on Pathogenesis and Drug Development. Diabetes & Metabolic Syndrome: Clinical Research & Reviews . 2019;13(1):754–762. doi: 10.1016/j.dsx.2018.11.054. [DOI] [PubMed] [Google Scholar]
  • 29.Vassalotti J. A., Centor R., Turner B. J., et al. Practical Approach to Detection and Management of Chronic Kidney Disease for the Primary Care Clinician. American Journal of Medicine . 2016;129(2):153–162.e7. doi: 10.1016/j.amjmed.2015.08.025. [DOI] [PubMed] [Google Scholar]
  • 30.López Gálvez M. I. International Severity Scale of Diabetic Retinopathy and Diabetic Macular Edema. Archivos de la Sociedad Espanola de Oftalmologia . 2004;79(4):149–150. doi: 10.4321/s0365-66912004000400001. [DOI] [PubMed] [Google Scholar]
  • 31.Instituto Nacional de Estadística (INE) Atlas de Distribución de Renta de los Hogares. 2022. Available at: https://www.ine.es/componentes_inebase/ADRH_total_nacional.htm (accessed 2 April 2025)
  • 32.Duque I., Domínguez-Berjón M. F., Cebrecos A., et al. Deprivation Index by Enumeration District in Spain, 2011. Gaceta Sanitaria . 2021;35(2):113–122. doi: 10.1016/j.gaceta.2019.10.008. [DOI] [PubMed] [Google Scholar]
  • 33.Wu H., Meng X., Wild S. H., Gasevic D., Jackson C. A. Socioeconomic Status and Prevalence of Type 2 Diabetes in Mainland China, Hong Kong and Taiwan: A Systematic Review. Journal of Global Health . 2017;7(1) doi: 10.7189/jogh.07.011103.011103 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Hawkins Carranza F., Corbatón-Anchuelo A., Bermejo Pareja F., et al. Incidence of Type 2 Diabetes in the Elderly in Central Spain: Association With Socioeconomic Status, Educational Level, and Other Risk Factors. Primary Care Diabetes . 2022;16(2):279–286. doi: 10.1016/j.pcd.2021.12.016. [DOI] [PubMed] [Google Scholar]
  • 35.Crielaard L., Motazedi E., Galenkamp H., et al. Socioeconomic Inequalities in Type 2 Diabetes: Mediation Through Status Anxiety? International Journal of Public Health . 2023;68:p. 68. doi: 10.3389/ijph.2023.1606069. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Alobaid T., Karalliedde J., O’Connell M. D. L., et al. The Prevalence and Progression of Microvascular Complications and the Interaction With Ethnicity and Socioeconomic Status in People With Type 2 Diabetes: A Systematic Review and Meta-Analysis. Journal of Diabetes Research . 2025;2025:10. doi: 10.1155/jdr/3307594.3307594 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Elgart J. F., Caporale J. E., Asteazarán S., et al. Association Between Socioeconomic Status, Type 2 Diabetes and its Chronic Complications in Argentina. Diabetes Research and Clinical Practice . 2014;104(2):241–247. doi: 10.1016/j.diabres.2014.02.010. [DOI] [PubMed] [Google Scholar]
  • 38.Tatulashvili S., Fagherazzi G., Dow C., Cohen R., Fosse S., Bihan H. Socioeconomic Inequalities and Type 2 Diabetes Complications: A Systematic Review. Diabetes & Metabolism . 2020;46(2):89–99. doi: 10.1016/j.diabet.2019.11.001. [DOI] [PubMed] [Google Scholar]
  • 39.Rawshani A., Svensson A. M., Zethelius B., Eliasson B., Rosengren A., Gudbjörnsdottir S. Association Between Socioeconomic Status and Mortality, Cardiovascular Disease, and Cancer in Patients With Type 2 Diabetes. JAMA Internal Medicine . 2016;176(8):1146–1154. doi: 10.1001/jamainternmed.2016.2940. [DOI] [PubMed] [Google Scholar]
  • 40.Kurani S. S., Lampman M. A., Funni S. A., et al. Association Between Area-Level Socioeconomic Deprivation and Diabetes Care Quality in US Primary Care Practices. JAMA Network Open . 2021;4(12) doi: 10.1001/jamanetworkopen.2021.38438.e2138438 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Cereijo L., Gullón P., Del Cura I., et al. Exercise Facilities and the Prevalence of Obesity and Type 2 Diabetes in the City of Madrid. Diabetologia . 2022;65(1):150–158. doi: 10.1007/s00125-021-05582-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Innes C. W. D., Henshall D. E., Wilson B., Poon M. T. C., Morley S. D., Ritchie S. A. Socioeconomic Deprivation Is Associated With Reduced Efficacy of an Insulin Adjustment Education Program for People With Type 1 Diabetes. Diabetic Medicine . 2022;39(10) doi: 10.1111/dme.14902.e14902 [DOI] [PubMed] [Google Scholar]
  • 43.Sebastian-Valles F., Martinez-Alfonso J., Navas-Moreno V., et al. Influence of Smoking on Glycaemic Control in Individuals With Type 1 Diabetes Using Flash Glucose Monitoring and its Mediating Role in the Relationship Between Socioeconomic Status and Glycaemic Control. Journal of Diabetes & Metabolic Disorders . 2025;24(1):p. 11. doi: 10.1007/s40200-024-01535-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Jaffiol C., Fontbonne A., Vannereau D., Olive J. P., Passeron S. Diabetes and Social Deprivation. Bulletin de l'Académie Nationale de Médecine . 2012;196(4-5):953–976. doi: 10.1016/S0001-4079(19)31782-0. [DOI] [PubMed] [Google Scholar]
  • 45.Burnside M. J., Williman J. A., Davies H. M., et al. Inequity in Access to Continuous Glucose Monitoring and Health Outcomes in Paediatric Diabetes, a Case for National Continuous Glucose Monitoring Funding: A Cross-Sectional Population Study of Children With Type 1 Diabetes in New Zealand. Lancet Regional Health–Western Pacific . 2023;31 doi: 10.1016/j.lanwpc.2022.100644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Agarwal S., Simmonds I., Myers A. K. The Use of Diabetes Technology to Address Inequity in Health Outcomes: Limitations and Opportunities. Current Diabetes Reports . 2022;22(7):275–281. doi: 10.1007/s11892-022-01470-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Diabetes UK; 2025. Getting a Free CGM, Flash Glucose Monitor (Freestyle Libre) or Insulin Pump on the NHS – Who Should Qualify? Available from: https://www.diabetes.org.uk/about-diabetes/looking-after-diabetes/technology/cgm-flash-pump-who-qualifies-on-nhs. [Google Scholar]
  • 48.NICE: 2023. Quality Statements|Type 2 Diabetes in Adults|Quality Standards|NICE. Available from: https://www.nice.org.uk/guidance/qs209/chapter/Quality-statements?utm_source=chatgpt.com. [Google Scholar]
  • 49.Dover A. R., Strachan M. W. J., McKnight J. A., et al. Socioeconomic Deprivation, Technology Use, C-Peptide, Smoking and Other Predictors of Glycaemic Control in Adults With Type 1 Diabetes. Diabetic Medicine . 2021;38(3) doi: 10.1111/dme.14445.e14445 [DOI] [PubMed] [Google Scholar]
  • 50.Sebastian-Valles F., Hernando Alday I., Tapia-Sanchiz M. S., et al. Association of Smoking With the Efficacy of Continuous Glucose Monitoring in type1 Diabetes: A Propensity Score-Matched Cohort Study. Medicina Clínica . 2025;164(8):396–402. doi: 10.1016/j.medcli.2024.10.023. [DOI] [PubMed] [Google Scholar]
  • 51.Naci H., Murphy P., Woods B., Lomas J., Wei J., Papanicolas I. Population-Health Impact of New Drugs Recommended by the National Institute for Health and Care Excellence in England During 2000–20: A Retrospective Analysis. Lancet . 2025;405(10472):50–60. doi: 10.1016/S0140-6736(24)02352-3. [DOI] [PubMed] [Google Scholar]
  • 52.Bilal U., Auchincloss A. H., Diez-Roux A. V. Neighborhood Environments and Diabetes Risk and Control. Current Diabetes Reports . 2018;18(9):p. 62. doi: 10.1007/s11892-018-1032-2. [DOI] [PubMed] [Google Scholar]
  • 53.Rehkopf D. H., Haughton L. T., Chen J. T., Waterman P. D., Subramanian S. V., Krieger N. Monitoring Socioeconomic Disparities in Death: Comparing Individual-Level Education and Area-Based Socioeconomic Measures. American Journal of Public Health . 2006;96(12):2135–2138. doi: 10.2105/AJPH.2005.075408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Fallon C., Jones E., Oliver N., Reddy M., Avari P. The Impact of Socio-Economic Deprivation on Access to Diabetes Technology in Adults With Type 1 Diabetes. Diabetic Medicine . 2022;39(10) doi: 10.1111/dme.14906.e14906 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information

Additional supporting information can be found online in the Supporting Information section. Official bulletin issued by the Community of Madrid (June 15, 2023) announcing the public funding of intermittently scanned continuous glucose monitoring (isCGM) systems for individuals with Type 2 diabetes treated with multiple daily insulin injections.

5588397.f1.pdf (163.9KB, pdf)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Journal of Diabetes Research are provided here courtesy of Wiley

RESOURCES