Return to Article Details Investigating Racial Disparities in Insulin Pump Use Among People with Type 1 Diabetes Across the United States: A Retrospective Multicenter Study

Investigating Racial Disparities in Insulin Pump Use Among People with Type 1 Diabetes Across the United States: A Retrospective Multicenter Study

Mahmoud NassarⒾ1*, Ahmed Y. AzzamⒾ2, Mahmoud M. MorsyⒾ3, Iqra PatoliⒾ1, Angad GillⒾ1, Erlin J. MarteⒾ4

  • 1Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, New York, USA
  • 2Montefiore-Einstein Cerebrovascular Research Lab, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
  • 3Faculty of Medicine, October 6 University, 6th of October City, Giza, Egypt
  • 4Endocrine and Diabetes Department, VA Western New York Health Care, Veterans Affairs, Buffalo, NY, USA
Vol. 1(1): 18-23 · 2024 · DOI: 10.71079/ASIDE.IM.0000012262412

Abstract

Introduction: Despite technological advances in managing Type 1 diabetes mellitus (T1D), racial disparities in insulin pump utilization persist. We investigated patterns of insulin pump adoption across different racial groups using a large-scale, multi-institutional database to quantify these disparities and identify potential intervention points.

Methods: We conducted a retrospective cohort study using the TriNetX research network, analyzing data from 978,665 T1D patients across 66 healthcare organizations. Propensity score matching was employed to balance cohorts, with a focused sub-analysis of Buffalo, NY (n=6,080) to examine regional variations compared to the United States nationwide present data.

Results: Nationwide data revealed significant racial disparities in insulin pump utilization, with White patients showing the highest adoption rate (11.74%) compared to Black or African American (AA) patients (4.056%). Buffalo cohort demonstrated higher overall adoption rates but maintained similar disparity patterns (White: 30.18%, Black or AA: 13.75%). Post-matching analysis confirmed these disparities persisted independent of demographic factors.

Conclusions: Our findings reveal significant racial disparities in insulin pump adoption, with regional variations suggesting the influence of institutional factors. These results highlight the need for targeted interventions to promote equitable access to diabetes technology and prevent the widening of health disparities in T1D care.

Keywords: Racial Disparity, Insulin Pump, Diabetes, technology, Continuous Glucose Monitoring

Article information

Article typeOriginal Article
JournalASIDE Internal Medicine
ISSN3065-9671 (print); 3065-968X (online)
PublisherAmerican Society for Inclusion, Diversity, and Equity in Healthcare
Received02 December 2024
Revised24 December 2024
Accepted26 December 2024
Published26 December 2024
Corresponding authorMahmoud Nassar — Dr.Nassar@aucegypt.edu
Copyright© 2024 Mahmoud Nassar, Ahmed Y. Azzam, Mahmoud M. Morsy, Iqra Patoli, Angad Gill, Erlin J. Marte
LicenceCreative Commons Attribution 4.0 International (CC BY 4.0) — This is an open-access article.

Introduction

Advanced technologies, particularly insulin pumps, have revolutionized the management of type 1 diabetes mellitus (T1D). These pumps have significantly improved glycemic control, quality of life and reduced diabetes-related complications [1]. However, despite these well-documented benefits, we continue to observe substantial disparities in access to and utilization of these vital technologies across different racial and ethnic groups in the United States [2]. Previous and current literature has highlighted concerning patterns of inequitable access to diabetes technology [3], with studies suggesting that racial and ethnic minorities face disproportionate barriers to insulin pump adoption. These disparities persist even when controlling socioeconomic factors and insurance coverage, indicating deeper systemic issues in healthcare delivery and access [4]. While existing literature has documented these disparities, comprehensive analyses of large-scale [5] and multi-institutional data examining racial patterns in insulin pump utilization remain limited [6]. Understanding and addressing these disparities has become increasingly crucial as diabetes technology advances. Recent studies have shown that early adoption of insulin pump therapy is associated with better long-term outcomes, including reduced rates of diabetic ketoacidosis, severe hypoglycemia, and diabetes-related hospitalizations [7]. However, if certain racial and ethnic groups systematically experience delayed access to or reduced utilization of these technologies [8], we risk perpetuating and potentially widening existing health disparities in diabetes care [9]. Our study aims to comprehensively analyze racial disparities in insulin pump utilization among adults with T1D across the United States, leveraging data from a large network of healthcare organizations. By highlighting and addressing both nationwide patterns and focused regional data from Buffalo, New York, we aim to understand how these disparities manifest at different geographic and institutional levels using the TriNetX database. The TriNetX database and research network represents a federated health research platform that integrates de-identified electronic health records from several healthcare organizations across the United States, providing real-world data from over 197 million unique patient records. This network enables large-scale observational studies through standardized data collection and analysis tools while maintaining compliance with privacy regulations and institutional policies [9]. This dual-perspective approach allows us to identify broad systemic patterns and local variations in technology access and adoption. Our study’s significance concerns its potential to inform targeted interventions and policy changes. We can better understand where interventions are most needed by quantifying the extent of racial disparities in insulin pump utilization and identifying specific patterns across different healthcare settings.

Methods

Study Design and Data Source:

We conducted a retrospective cohort study utilizing the TriNetX research network platform (TriNetX Inc., Cambridge, MA, USA). This federated health research network aggregates de-identified electronic health records from 66 healthcare organizations across the United States (https://trinetx.com/solutions/live-platform/). The study period concluded with data extraction on September 25, 2024, employing a standardized query approach through the TriNetX platform to identify eligible participants and extract relevant clinical and demographic data.

Study Population:

The study population comprised adults (≥18 years) with a confirmed diagnosis of T1D, identified using the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) code E10. Participants were stratified into two distinct cohorts based on their insulin delivery method: individuals using insulin pump therapy (pump cohort, n=84,903) and those not using insulin pump therapy (no pump cohort, n=893,762), forming an initial nationwide sample of 978,665 patients. Insulin pump usage was identified through Current Procedural Terminology (CPT) codes and medical device records within the electronic health record system. Additionally, data on Continuous Glucose Monitoring (CGM) utilization was collected.

Data Collection and Variables:

Demographic and clinical data collection encompassed age (calculated at the time of data extraction), sex (male/female), and self-reported race/ethnicity. Race and ethnicity categories followed U.S. Census Bureau classifications, including White, Black, or African American (AA), Hispanic or Latino, Asian, Native Hawaiian or Other Pacific Islander, and American Indian or Alaska Native. Clinical variables included insulin pump usage status and comprehensive healthcare utilization metrics.

Statistical Analysis:

Our statistical approach employed propensity score matching to minimize selection bias and ensure robust analysis. We implemented 1:1 matching considering age, sex, and race/ethnicity as covariates, resulting in balanced cohorts of 84,723 patients each. Post-matching balance was confirmed with standardized mean differences less than 0.1 for all variables. Descriptive statistics were calculated with continuous variables presented as means ± standard deviations and categorical variables as frequencies and percentages. Between-group comparisons utilized Chi-square tests for categorical variables and Student’s t-tests for continuous variables, with statistical significance set at p<0.05. To evaluate factors associated with pump utilization, we performed multivariate logistic regression analyses, adjusting for potential confounders, including age, sex, and race/ethnicity, with results presented as adjusted odds ratios and 95% confidence intervals.

Geographic Sub-analysis:

A focused sub-analysis was conducted on a cohort from Buffalo, New York (n=6,080) to examine regional variations in insulin pump utilization patterns. This analysis employed identical statistical methodologies, with propensity score matching yielding 1,360 patients per group, matched for age, sex, and race/ethnicity, followed by comparative analyses between matched cohorts.

Ethical Approvals:

The study protocol was exempted from the University at Buffalo Institutional Review Board (IRB) committee (STUDY00007618). Data handling and analysis adhered to Health Insurance Portability and Accountability Act (HIPAA) guidelines, using de-identified data through the TriNetX platform, ensuring the protection of patient privacy, compliance with federal regulations, and maintenance of data integrity.

Results

Baseline Characteristics:

In our nationwide cohort, we initially identified 978,665 eligible participants, comprising 84,903 patients in the pump cohort and 893,762 in the no-pump cohort Table 1. Before propensity score matching, we observed significant demographic differences between the cohorts (all p<0.0001). The pump cohort was notably younger (mean age 40.3 ± 20.9 years vs 58.5 ± 21.9 years) and had a higher proportion of female patients (54.41% vs 48.56%). We found substantial racial/ethnic disparities in pump utilization, with White patients representing a markedly higher proportion of the pump cohort than the no-pump cohort (85.12% vs 67.37%). Conversely, Black or AA (7.80% vs. 17.98%), Hispanic or Latino (5.65% vs. 10.83%), and Asian patients (1.38% vs. 2.27%) were underrepresented in the pump cohort. After propensity score matching, we achieved well-balanced cohorts of 84,723 patients each, with no significant differences in demographic characteristics (all p>0.05). In the matched cohorts, both groups maintained identical distributions of sex (54.41% female), age (40.3 ± 20.9 years), and racial/ethnic composition (White: 85.12%, Black or AA: 7.80%, Hispanic or Latino: 5.63-5.65%, Asian: 1.38%). Our Buffalo sub-analysis included 6,080 patients (pump: n=1,580; no-pump: n=4,500) before matching Table 2. Similar to our nationwide findings, we observed significant pre-matching disparities. The pump cohort was younger (27.9 ± 16.7 years vs 50.4 ± 24.7 years, p<0.0001) and showed comparable gender distribution (48.10% female vs 48.44%, p=0.8143). Racial disparities were evident, with White patients comprising a larger proportion of the pump cohort (82.91% vs 67.56%, p<0.0001) and Black or AA patients being underrepresented (6.96% vs 15.56%, p<0.0001). Following propensity score matching in the Buffalo cohort, we achieved balanced groups of 1,360 patients each, with no significant demographic differences (all p>0.05). The matched cohorts showed comparable age (pump: 29.5 ± 17.3 years; no-pump: 29.6 ± 17.6 years), gender distribution (pump: 49.27% female; no-pump: 47.06%), and racial/ethnic composition (White: 80.88%, Black or AA: 8.09%, Hispanic or Latino: 3.68-4.41%).

Nationwide vs. Buffalo Comparison:

In our analysis of insulin pump and CGM usage across different racial groups, we observed significant disparities in adoption rates both nationally and in Buffalo. Our findings revealed substantial variations in technology utilization across racial and ethnic groups, with particularly notable differences in insulin pump usage Table 3.

At the national level, we found that White individuals had the highest insulin pump adoption rate at 11.74%, markedly higher than all other racial groups. In contrast, Black or AA individuals showed the lowest insulin pump utilization rate at 4.056%, representing a nearly threefold difference. Other racial groups demonstrated intermediate adoption rates: Asian (5.79%), American Indian or Alaska Native (5.52%), Native Hawaiian or Other Pacific Islander (5.09%), and individuals of Unknown Race (5.01%). While looking at Buffalo specifically, we observed generally higher adoption rates across all racial groups compared to national averages, though racial disparities persisted. In Buffalo, White individuals maintained the highest insulin pump usage rate at 30.18%, while Black or AA individuals showed a usage rate of 13.75%. Considerably, Asian individuals in Buffalo demonstrated a relatively high adoption rate of 37.5%. Similar patterns of disparity were evident in CGM usage. Nationally, White individuals showed the highest CGM adoption rate at 11.55%, while Black or AA individuals had substantially lower usage at 6.2%. Asian individuals demonstrated relatively higher CGM adoption at 8.92%, followed by American Indian or Alaska Native (7.34%), Unknown Race (6.59%), and Native Hawaiian or Other Pacific Islander showing the lowest rate at 1.873%. In the Buffalo system, CGM adoption patterns showed some variation from national trends. White individuals maintained relatively high usage at 11.98%, while Asian individuals showed adoption rates of 12.5%. Black or AA individuals in the Buffalo system had CGM usage rates of 6.25%, similar to national figures. American Indian or Alaska Native individuals showed higher adoption at 20%, though this finding should be interpreted cautiously given potential sample size limitations. It is demonstrated that there are persistent racial disparities in diabetes technology adoption within the United States, with particularly pronounced differences in insulin pump usage between White and Black or AA individuals, both nationally and regionally.

Table 1
Demographic Characteristics Before and After Propensity Score Matching in the U.S.-based cohort.
Before Matching After Matching
Characteristic No Pump (n=893,762) Pump (n=84,903) P-value No Pump (n=84,723) Pump (n=84,723) P-value
Sex, n (%)
Female 356,119 (48.56) 43,810 (54.41) <0.0001 43,820 (54.41) 43,810 (54.41) 0.9612
Male 377,182 (51.44) 36,723 (45.59) <0.0001 36,730 (45.59) 36,723 (45.59) 0.9726
Age (years)
Current Age, mean ± SD 58.5 ± 21.9 40.3 ± 20.9 <0.0001 40.3 ± 20.9 40.3 ± 20.9 0.9546
Race/Ethnicity, n (%)
White 464,764 (67.37) 65,453 (85.12) <0.0001 65,477 (85.12) 65,453 (85.12) 0.8894
Black / African American 124,058 (17.98) 5,999 (7.80) <0.0001 5,997 (7.80) 5,999 (7.80) 0.9849
Hispanic or Latino 74,688 (10.83) 4,344 (5.65) <0.0001 4,336 (5.63) 4,344 (5.65) 0.9298
Asian 15,678 (2.27) 1,065 (1.38) <0.0001 1,058 (1.38) 1,065 (1.38) 0.8785
Native Hawaiian or Other Pacific Islander 8,474 (1.23) 455 (0.59) <0.0001 450 (0.58) 455 (0.59) 0.8676
American Indian or Alaska Native 3,395 (0.32) 217 (0.28) <0.0001 200 (0.26) 217 (0.28) 0.4046

SD, Standard Deviation; n, Number (sample size)

Table 2
Demographic Characteristics Before and After Propensity Score Matching in Buffalo cohort.
Before Matching After Matching
Characteristics No Pump (n=4,500) Pump (n=1,580) P-value No Pump (n=1,360) Pump (n=1,360) P-value
Sex, n (%)
Female 2,180 (48.44) 760 (48.10) 0.8143 640 (47.06) 670 (49.27) 0.2496
Male 2,070 (46.00) 760 (48.10) 0.1497 680 (50.00) 650 (47.79) 0.2498
Age (years)
Current Age, mean ± SD 50.4 ± 24.7 27.9 ± 16.7 <0.0001 29.6 ± 17.6 29.5 ± 17.3 0.9187
Race/Ethnicity, n (%)
White 3,040 (67.56) 1,310 (82.91) <0.0001 1,100 (80.88) 1,100 (80.88) 1.0000
Black / African American 700 (15.56) 110 (6.96) <0.0001 110 (8.09) 110 (8.09) 1.0000
Hispanic or Latino 230 (5.11) 70 (4.43) 0.2825 50 (3.68) 60 (4.41) 0.3304
Asian 60 (1.33) 30 (1.90) 0.1094 30 (2.21) 20 (1.47) 0.1535
American Indian or Alaska Native 40 (0.89) 10 (0.63) 0.3324 10 (0.74) 10 (0.74) 1.0000

SD, Standard Deviation; n, Number (sample size)

Table 3
Prevalence of Insulin Pump and CGM Usage by Race in the USA and Buffalo, New York (2010-2024) among patients with T1D.
Race/Ethnicity Insulin Pump (USA) Insulin Pump (Buffalo) CGM (USA) CGM (Buffalo)
White 11.74% 30.18% 11.55% 11.98%
Asian 5.79% 37.50% 8.92% 12.50%
Native Hawaiian or Other Pacific Islander 5.09% 100% 1.87% 0%
American Indian or Alaska Native 5.52% 20% 7.34% 20%
Unknown Race 5.01% 17.28% 6.59% 7.41%
Black or African American 4.06% 13.75% 6.20% 6.25%

T1D, Type 1 Diabetes; CGM, Continuous Glucose Monitoring; USA, United States of America

Discussion

Our study reveals significant racial disparities in insulin pump utilization among individuals with T1D across the United States, with particularly pronounced differences between White and Black or AA populations. These findings carry significant clinical implications, given that insulin pumps provide more precise insulin delivery and reduce risks of both hypoglycemia and hyperglycemia compared to MDI [10]. Integrating insulin pumps with CGM systems, enabling automated insulin delivery adjustments, further amplifies the importance of addressing these disparities [11]. Our disparity patterns align with previous research demonstrating that advanced diabetes technologies significantly enhance glycemic control [12] and reduce adverse events [13]. Our findings of lower insulin pump adoption rates among racial minorities are particularly concerning given that CGM use has been associated with improved self-management and enhanced quality of life [14], with continuous application leading to reduced HbA1c levels and decreased glucose variability [15]. The contrast in insulin pump utilization between White (11.74%) and Black or AA individuals (4.056%) in our nationwide cohort reflects broader systemic inequities in healthcare access. These differences persist despite evidence that insulin pump therapy provides more stable glycemic control [16] and significantly reduces HbA1c levels compared to MDI [17]. The higher adoption rates observed in the Buffalo cohort (White: 30.18%, Black or AA: 13.75%) suggest that regional variations and institutional factors may influence technology access, though racial disparities remain evident. Our findings of persistent disparities, even in settings with higher over adoption rates highlight multiple barriers to insulin pump access. These include high initial and ongoing costs [18], technical complexity requiring comprehensive education [19], and challenges related to healthcare provider biases [20]. The impact of these barriers is particularly pronounced among Black or AA populations, who often face additional socioeconomic challenges [21] and healthcare access limitations [22]. Geographic variations in our data, particularly between national and Buffalo-specific cohorts, suggest that local healthcare delivery systems significantly influence technology access [23]. While encouraging, the higher overall adoption rates in the Buffalo cohort also demonstrate that addressing systemic barriers [24] and insurance coverage issues [25] may help reduce but not eliminate racial disparities. The lower insulin pump utilization rates among racial minorities likely contribute to poorer health outcomes [26], as previous studies have shown that limited access to advanced diabetes technologies is associated with higher rates of complications [27]. Our findings of persistent disparities, even after controlling for demographic factors, are consistent and parallel with some of the literature studies [28] showing that socioeconomic status alone does not fully explain these gaps [29]. Our results suggest the need for multi-level interventions to address these disparities. These should include improving insurance coverage, enhancing provider education about cultural competency, and developing targeted outreach programs for underserved communities [30]. The higher adoption rates in our Buffalo cohort, while still showing racial disparities, suggest that institutional policies and focused efforts to improve access can have positive impacts. Our study has important considerations, as well as future clinical practice and health policy directions. First, healthcare systems should systematically evaluate and address barriers to insulin pump adoption among racial minorities. Second, provider education should emphasize both the technical aspects of insulin pump therapy and cultural competency in technology prescription. Third, insurance policies should be reviewed and modified to ensure equitable access to diabetes technologies. The limitations of our study include its retrospective nature, potential selection bias in the TriNetX database, and inability to capture detailed socioeconomic factors or insurance status. Additionally, while our regional analysis provides valuable insights, the smaller sample sizes for certain racial groups may limit generalizability. Future studies should focus on prospective studies examining the impact of targeted interventions to reduce racial disparities in insulin pump adoption. Additionally, investigating successful institutional policies and practices that have reduced disparities could provide valuable guidance for broader implementation. These findings underscore the urgent need for systematic changes to address racial disparities in diabetes technology access. While technological advances continue to improve diabetes management capabilities, ensuring equitable access to these technologies remains a critical challenge, requiring coordinated efforts from healthcare providers, institutions, and policymakers.

Conclusions

Our comprehensive analysis of racial disparities in insulin pump utilization among T1D patients reveals systemic inequities that require urgent attention. The present contrast in adoption rates between racial groups, particularly the threefold difference between White and Black or AA populations, suggests that technological advances in diabetes care may inadvertently widen existing health disparities if access barriers remain unaddressed. The regional variations observed between our nationwide and Buffalo cohorts provide valuable insights into the potential impact of institutional policies and local healthcare delivery systems. While higher overall adoption rates in the Buffalo cohort demonstrate that targeted interventions can improve access, the persistence of racial disparities, even in this setting, underscores the need for more comprehensive solutions. We propose a three-tiered approach to address these disparities: implementing systematic screening for technology eligibility across all racial groups, developing culturally competent diabetes education programs, and establishing institutional policies prioritizing equitable access to diabetes technologies. Future research should focus on evaluating the effectiveness of these interventions and identifying additional strategies to promote equitable adoption of insulin pump therapy.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding Source

None

Acknowledgments

None

Institutional Review Board (IRB)

The IRB Board at the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, NY, USA, reviewed the study and determined it was exempt from IRB review as it does not constitute human subjects research, given the study design and type.

Large Language Model

We have employed an advanced large language model to enhance and refine English-language writing. This process focused solely on improving the text’s clarity and style without generating or adding new information to the content.

Authors Contribution

MN conceptualized the study and developed the methodology, with AYA leading the investigation alongside MMM; MN and AYA performed data analysis, while IP, AG, and EM contributed to data curation; MN prepared the original draft; MN and AYA created the visualizations; MN supervised the project and provided administrative oversight; all authors participated in manuscript review and editing, validated the findings, and approved the final version of the manuscript.

Data Availability

Available on TriNetX Database Based on Institutional Collaborations.

References

  1. Syed F. Z.. Type 1 Diabetes Mellitus. Ann Intern Med. 2022;175(3):ITC33-ITC48. doi:10.7326/AITC202203150 PMID: 35254878
  2. Dayan C. M., Besser R. E. J., Oram R. A., Hagopian W., Vatish M., Bendor-Samuel O., Snape M. D., Todd J. A.. Preventing type 1 diabetes in childhood. Science. 2021;373(6554):506-510. doi:10.1126/science.abi4742 PMID: 34326231
  3. Zajec A., Trebusak Podkrajsek K., Tesovnik T., Sket R., Cugalj Kern B., Jenko Bizjan B., Smigoc Schweiger D., Battelino T., Kovac J.. Pathogenesis of Type 1 Diabetes: Established Facts and New Insights. Genes (Basel). 2022;13(4). doi:10.3390/genes13040706 PMID: 35456512 PMCID: PMC9032728
  4. Bluestone J. A., Buckner J. H., Herold K. C.. Immunotherapy: Building a bridge to a cure for type 1 diabetes. Science. 2021;373(6554):510-516. doi:10.1126/science.abh1654 PMID: 34326232
  5. Tonnies T., Brinks R., Isom S., Dabelea D., Divers J., Mayer-Davis E. J., Lawrence J. M., Pihoker C., Dolan L., Liese A. D., Saydah S. H., D'Agostino R. B., Hoyer A., Imperatore G.. Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged <20 Years Through 2060: The SEARCH for Diabetes in Youth Study. Diabetes Care. 2023;46(2):313-320. doi:10.2337/dc22-0945 PMID: 36580405 PMCID: PMC9887625
  6. Lawrence J. M., Divers J., Isom S., Saydah S., Imperatore G., Pihoker C., Marcovina S. M., Mayer-Davis E. J., Hamman R. F., Dolan L., Dabelea D., Pettitt D. J., Liese A. D., SEARCH for Diabetes in Youth Study Group. Trends in Prevalence of Type 1 and Type 2 Diabetes in Children and Adolescents in the US, 2001-2017. JAMA. 2021;326(8):717-727. doi:10.1001/jama.2021.11165 PMID: 34427600 PMCID: PMC8385600
  7. Rafferty J., Stephens J. W., Atkinson M. D., Luzio S. D., Akbari A., Gregory J. W., Bain S., Owens D. R., Thomas R. L.. A retrospective epidemiological study of type 1 diabetes mellitus in wales, UK between 2008 and 2018. Int J Popul Data Sci. 2021;6(1):1387. doi:10.23889/ijpds.v6i1.1387 PMID: 34007896 PMCID: PMC8103995
  8. Wass John A. H., Arlt Wiebke, Semple Robert K., Hammond Peter, Campbell Fiona. Strategies for the Management of Type 1 Diabetes. Oxford Textbook of Endocrinology and Diabetes 3e. 2022:2024-2031. doi:10.1093/med/9780198870197.003.0255
  9. Janez A., Guja C., Mitrakou A., Lalic N., Tankova T., Czupryniak L., Tabak A. G., Prazny M., Martinka E., Smircic-Duvnjak L.. Insulin Therapy in Adults with Type 1 Diabetes Mellitus: a Narrative Review. Diabetes Ther. 2020;11(2):387-409. doi:10.1007/s13300-019-00743-7 PMID: 31902063 PMCID: PMC6995794
  10. Visser M. M., Charleer S., Fieuws S., De Block C., Hilbrands R., Van Huffel L., Maes T., Vanhaverbeke G., Dirinck E., Myngheer N., Vercammen C., Nobels F., Keymeulen B., Mathieu C., Gillard P.. Comparing real-time and intermittently scanned continuous glucose monitoring in adults with type 1 diabetes (ALERTT1): a 6-month, prospective, multicentre, randomised controlled trial. Lancet. 2021;397(10291):2275-2283. doi:10.1016/S0140-6736(21)00789-3 PMID: 34089660
  11. Varkevisser R. D. M., Birnie E., Mul D., van Dijk P. R., Aanstoot H. J., Wolffenbuttel B. H. R., van der Klauw M. M.. Type 1 diabetes management: Room for improvement. J Diabetes. 2023;15(3):255-263. doi:10.1111/1753-0407.13368 PMID: 36808864 PMCID: PMC10036258
  12. Zaharieva D. P., Messer L. H., Paldus B., O'Neal D. N., Maahs D. M., Riddell M. C.. Glucose Control During Physical Activity and Exercise Using Closed Loop Technology in Adults and Adolescents with Type 1 Diabetes. Can J Diabetes. 2020;44(8):740-749. doi:10.1016/j.jcjd.2020.06.003 PMID: 33011134
  13. Pratley R. E., Kanapka L. G., Rickels M. R., Ahmann A., Aleppo G., Beck R., Bhargava A., Bode B. W., Carlson A., Chaytor N. S., Fox D. S., Goland R., Hirsch I. B., Kruger D., Kudva Y. C., Levy C., McGill J. B., Peters A., Philipson L., Philis-Tsimikas A., Pop-Busui R., Shah V. N., Thompson M., Vendrame F., Verdejo A., Weinstock R. S., Young L., Miller K. M., Wireless Innovation for Seniors With Diabetes Mellitus Study Group. Effect of Continuous Glucose Monitoring on Hypoglycemia in Older Adults With Type 1 Diabetes: A Randomized Clinical Trial. JAMA. 2020;323(23):2397-2406. doi:10.1001/jama.2020.6928 PMID: 32543682 PMCID: PMC7298607
  14. Galindo R. J., Aleppo G.. Continuous glucose monitoring: The achievement of 100 years of innovation in diabetes technology. Diabetes Res Clin Pract. 2020;170:108502. doi:10.1016/j.diabres.2020.108502 PMID: 33065179 PMCID: PMC7736459
  15. Laffel L. M., Kanapka L. G., Beck R. W., Bergamo K., Clements M. A., Criego A., DeSalvo D. J., Goland R., Hood K., Liljenquist D., Messer L. H., Monzavi R., Mouse T. J., Prahalad P., Sherr J., Simmons J. H., Wadwa R. P., Weinstock R. S., Willi S. M., Miller K. M., Teens C. G. M. Intervention in, Young Adults with T. D. Study Group, Cde. Effect of Continuous Glucose Monitoring on Glycemic Control in Adolescents and Young Adults With Type 1 Diabetes: A Randomized Clinical Trial. JAMA. 2020;323(23):2388-2396. doi:10.1001/jama.2020.6940 PMID: 32543683 PMCID: PMC7298603
  16. Keyu G., Jiaqi L., Liyin Z., Jianan Y., Li F., Zhiyi D., Qin Z., Xia L., Lin Y., Zhiguang Z.. Comparing the effectiveness of continuous subcutaneous insulin infusion with multiple daily insulin injection for patients with type 1 diabetes mellitus evaluated by retrospective continuous glucose monitoring: A real-world data analysis. Front Public Health. 2022;10:990281. doi:10.3389/fpubh.2022.990281 PMID: 36091534 PMCID: PMC9454013
  17. McAuley S. A., Vogrin S., Lee M. H., Paldus B., Trawley S., de Bock M. I., Abraham M. B., Bach L. A., Burt M. G., Cohen N. D., Colman P. G., Davis E. A., Hendrieckx C., Holmes-Walker D. J., Jenkins A. J., Kaye J., Keech A. C., Kumareswaran K., MacIsaac R. J., McCallum R. W., Sims C. M., Speight J., Stranks S. N., Sundararajan V., Ward G. M., Jones T. W., O'Neal D. N., Australian Jdrf Closed-Loop Research Group. Less Nocturnal Hypoglycemia but Equivalent Time in Range Among Adults with Type 1 Diabetes Using Insulin Pumps Versus Multiple Daily Injections. Diabetes Technol Ther. 2021;23(6):460-466. doi:10.1089/dia.2020.0589 PMID: 33351699
  18. Huo L., Deng W., Lan L., Li W., Shaw J. E., Magliano D. J., Ji L.. Real-World Application of Insulin Pump Therapy Among Patients With Type 1 Diabetes in China: A Cross-Sectional Study. Front Endocrinol (Lausanne). 2022;13:891718. doi:10.3389/fendo.2022.891718 PMID: 35757419 PMCID: PMC9226667
  19. Harper Andy, Aflatoony Leila, Wilson Wendell, Wang Wei. Exploring the Challenges and Potential Alternatives to Insulin Pump Technologies. Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare:371-374. doi:10.1145/3421937.3421950
  20. Gu Minjeong. Effective Use of Insulin Pump in Patients with Type 1 Diabetes. The Journal of Korean Diabetes. 2020;21(1):36-40. doi:10.4093/jkd.2020.21.1.36
  21. DemİR Gunay, ÇUbukcu Emine, Akcay Nurdan. Problems in Insulin Pump Management and Suggestions for Solutions in Children and Adolescents with Type 1 Diabetes. Turkish Journal of Diabetes and Obesity. 2022;6(2):187-194. doi:10.25048/tudod.1105407
  22. O'Donnell H. K., Vigers T., Johnson S. B., Pyle L., Wright N., Deeb L. C., Driscoll K. A.. Pump It Up! A randomized clinical trial to optimize insulin pump self-management behaviors in adolescents with type 1 diabetes. Contemp Clin Trials. 2021;102:106279. doi:10.1016/j.cct.2021.106279 PMID: 33440262 PMCID: PMC8341128
  23. Boscari F., Avogaro A.. Current treatment options and challenges in patients with Type 1 diabetes: Pharmacological, technical advances and future perspectives. Rev Endocr Metab Disord. 2021;22(2):217-240. doi:10.1007/s11154-021-09635-3 PMID: 33755854 PMCID: PMC7985920
  24. Almogbel E.. Impact of insulin pump therapy on glycemic control among adult Saudi type-1 diabetic patients. An interview-based case-control study. J Family Med Prim Care. 2020;9(2):1013-1019. doi:10.4103/jfmpc.jfmpc86919 PMID: 32318460 PMCID: PMC7114031
  25. Biester T., Schwandt A., Heidtmann B., Rami-Merhar B., Haak T., Festa A., Kostow S., Muller A., Monkemoller K., Danne T., DPV Initiative. Declining Frequency of Acute Complications Associated with Tubeless Insulin Pump Use: Data from 2,911 Patients in the German/Austrian Diabetes Patienten Verlaufsdokumentation Registry. Diabetes Technol Ther. 2021;23(8):527-536. doi:10.1089/dia.2020.0675 PMID: 33684335 PMCID: PMC8377506
  26. Agarwal S., Schechter C., Gonzalez J., Long J. A.. Racial-Ethnic Disparities in Diabetes Technology use Among Young Adults with Type 1 Diabetes. Diabetes Technol Ther. 2021;23(4):306-313. doi:10.1089/dia.2020.0338 PMID: 33155826 PMCID: PMC7994432
  27. Everett E. M., Wright D., Williams A., Divers J., Pihoker C., Liese A. D., Bellatorre A., Kahkoska A. R., Bell R., Mendoza J., Mayer-Davis E., Wisk L. E.. A Longitudinal View of Disparities in Insulin Pump Use Among Youth with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study. Diabetes Technol Ther. 2023;25(2):131-139. doi:10.1089/dia.2022.0340 PMID: 36475821 PMCID: PMC9894603
  28. Lipman T. H., Willi S. M., Lai C. W., Smith J. A., Patil O., Hawkes C. P.. Insulin Pump Use in Children with Type 1 Diabetes: Over a Decade of Disparities. J Pediatr Nurs. 2020;55:110-115. doi:10.1016/j.pedn.2020.08.007 PMID: 32889433
  29. Kanbour S., Jones M., Abusamaan M. S., Nass C., Everett E., Wolf R. M., Sidhaye A., Mathioudakis N.. Racial Disparities in Access and Use of Diabetes Technology Among Adult Patients With Type 1 Diabetes in a U.S. Academic Medical Center. Diabetes Care. 2023;46(1):56-64. doi:10.2337/dc22-1055 PMID: 36378855 PMCID: PMC9797654
  30. Carrillo-Iregui A., Lopez-Mitnik G., Cossio S., Garibay-Nieto N., Arheart K. L., Messiah S. E.. Relationship between aminotransferases levels and components of the metabolic syndrome among multiethnic adolescents. J Pediatr Endocrinol Metab. 2010;23(12):1253-61. doi:10.1515/jpem.2010.199 PMID: 21714459