Publications
1.
Shao, Hui; Thorpe, Lorna E.; Islam, Shahidul; Bian, Jiang; Guo, Yi; Li, Piaopiao; Bost, Sarah; Dabelea, Dana; Conway, Rebecca; Crume, Tessa; Schwartz, Brian S.; Hirsch, Annemarie G.; Allen, Katie S.; Dixon, Brian E.; Grannis, Shaun J.; Lustigova, Eva; Reynolds, Kristi; Rosenman, Marc; Zhong, Victor W.; Wong, Anthony; Rivera, Pedro; Le, Thuy; Akerman, Meredith; Conderino, Sarah; Rajan, Anand; Liese, Angela D.; Rudisill, Caroline; Obeid, Jihad S.; Ewing, Joseph A.; Bailey, Charles; Mendonca, Eneida A.; Zaganjor, Ibrahim; Rolka, Deborah; Imperatore, Giuseppina; Pavkov, Meda E.; Divers, Jasmin
In: Diabetes Care, vol. 48, iss. 6, pp. 914-921, 2025.
Abstract | Links | BibTeX | Tags: diabetes mellitus, electronic health records
@article{nokey,
title = {Developing a Computable Phenotype for Identifying Children, Adolescents, and Young Adults With Diabetes Using Electronic Health Records in the DiCAYA Network},
author = {Hui Shao and Lorna E. Thorpe and Shahidul Islam and Jiang Bian and Yi Guo and Piaopiao Li and Sarah Bost and Dana Dabelea and Rebecca Conway and Tessa Crume and Brian S. Schwartz and Annemarie G. Hirsch and Katie S. Allen and Brian E. Dixon and Shaun J. Grannis and Eva Lustigova and Kristi Reynolds and Marc Rosenman and Victor W. Zhong and Anthony Wong and Pedro Rivera and Thuy Le and Meredith Akerman and Sarah Conderino and Anand Rajan and Angela D. Liese and Caroline Rudisill and Jihad S. Obeid and Joseph A. Ewing and Charles Bailey and Eneida A. Mendonca and Ibrahim Zaganjor and Deborah Rolka and Giuseppina Imperatore and Meda E. Pavkov and Jasmin Divers},
doi = {10.2337/dc24-1972},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
journal = {Diabetes Care},
volume = {48},
issue = {6},
pages = {914-921},
abstract = {Objective: The Diabetes in Children, Adolescents, and Young Adults (DiCAYA) network seeks to create a nationwide electronic health record (EHR)-based diabetes surveillance system. This study aimed to develop a DiCAYA-wide EHR-based computable phenotype (CP) to identify prevalent cases of diabetes.
Research design and methods: We conducted network-wide chart reviews of 2,134 youth (aged <18 years) and 2,466 young adults (aged 18 to <45 years) among people with possible diabetes. Within this population, we compared the performance of three alternative CPs, using diabetes diagnoses determined by chart review as the gold standard. CPs were evaluated based on their accuracy in identifying diabetes and its subtype.
Results: The final DiCAYA CP requires at least one diabetes diagnosis code from clinical encounters. Subsequently, diabetes type classification was based on the ratio of type 1 diabetes (T1D) or type 2 diabetes (T2D) diagnosis codes in the EHR. For both youth and young adults, the sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively) in finding diabetes cases were >90%, except for the specificity and NPV in young adults, which were slightly lower at 83.8% and 80.6%, respectively. The final DiCAYA CP achieved >90% sensitivity, specificity, PPV, and NPV in classifying T1D, and demonstrated lower but robust performance in identifying T2D, consistently maintaining >80% across metrics.
Conclusions: The DiCAYA CP effectively identifies overall diabetes and T1D in youth and young adults, though T2D misclassification in youth highlights areas for refinement. The simplicity of the DiCAYA CP enables broad deployment across diverse EHR systems for diabetes surveillance.},
keywords = {diabetes mellitus, electronic health records},
pubstate = {published},
tppubtype = {article}
}
Objective: The Diabetes in Children, Adolescents, and Young Adults (DiCAYA) network seeks to create a nationwide electronic health record (EHR)-based diabetes surveillance system. This study aimed to develop a DiCAYA-wide EHR-based computable phenotype (CP) to identify prevalent cases of diabetes.
Research design and methods: We conducted network-wide chart reviews of 2,134 youth (aged <18 years) and 2,466 young adults (aged 18 to <45 years) among people with possible diabetes. Within this population, we compared the performance of three alternative CPs, using diabetes diagnoses determined by chart review as the gold standard. CPs were evaluated based on their accuracy in identifying diabetes and its subtype.
Results: The final DiCAYA CP requires at least one diabetes diagnosis code from clinical encounters. Subsequently, diabetes type classification was based on the ratio of type 1 diabetes (T1D) or type 2 diabetes (T2D) diagnosis codes in the EHR. For both youth and young adults, the sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively) in finding diabetes cases were >90%, except for the specificity and NPV in young adults, which were slightly lower at 83.8% and 80.6%, respectively. The final DiCAYA CP achieved >90% sensitivity, specificity, PPV, and NPV in classifying T1D, and demonstrated lower but robust performance in identifying T2D, consistently maintaining >80% across metrics.
Conclusions: The DiCAYA CP effectively identifies overall diabetes and T1D in youth and young adults, though T2D misclassification in youth highlights areas for refinement. The simplicity of the DiCAYA CP enables broad deployment across diverse EHR systems for diabetes surveillance.
Research design and methods: We conducted network-wide chart reviews of 2,134 youth (aged <18 years) and 2,466 young adults (aged 18 to <45 years) among people with possible diabetes. Within this population, we compared the performance of three alternative CPs, using diabetes diagnoses determined by chart review as the gold standard. CPs were evaluated based on their accuracy in identifying diabetes and its subtype.
Results: The final DiCAYA CP requires at least one diabetes diagnosis code from clinical encounters. Subsequently, diabetes type classification was based on the ratio of type 1 diabetes (T1D) or type 2 diabetes (T2D) diagnosis codes in the EHR. For both youth and young adults, the sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively) in finding diabetes cases were >90%, except for the specificity and NPV in young adults, which were slightly lower at 83.8% and 80.6%, respectively. The final DiCAYA CP achieved >90% sensitivity, specificity, PPV, and NPV in classifying T1D, and demonstrated lower but robust performance in identifying T2D, consistently maintaining >80% across metrics.
Conclusions: The DiCAYA CP effectively identifies overall diabetes and T1D in youth and young adults, though T2D misclassification in youth highlights areas for refinement. The simplicity of the DiCAYA CP enables broad deployment across diverse EHR systems for diabetes surveillance.
2.
Hai, Ameen Abdel; Weiner, Mark G.; Livshits, Alice; Brown, Jeremiah R.; Paranjape, Anuradha; Hwang, Wenke; Kirchner, Lester H.; Mathioudakis, Nestoras; French, Esra Karslioglu; Obradovic, Zoran; Rubin, Daniel J.
Domain generalization for enhanced predictions of hospital readmission on unseen domains among patients with diabetes Journal Article
In: Artificial Intelligence in Medicine, vol. 158, pp. 103010, 2024.
Abstract | Links | BibTeX | Tags: deep learning, diabetes mellitus, domain generalization
@article{nokey,
title = {Domain generalization for enhanced predictions of hospital readmission on unseen domains among patients with diabetes},
author = {Ameen Abdel Hai and Mark G. Weiner and Alice Livshits and Jeremiah R. Brown and Anuradha Paranjape and Wenke Hwang and Lester H. Kirchner and Nestoras Mathioudakis and Esra Karslioglu French and Zoran Obradovic and Daniel J. Rubin},
doi = {10.1016/j.artmed.2024.103010},
year = {2024},
date = {2024-11-10},
urldate = {2024-11-10},
journal = {Artificial Intelligence in Medicine},
volume = {158},
pages = {103010},
abstract = {A prediction model to assess the risk of hospital readmission can be valuable to identify patients who may benefit from extra care. Developing hospital-specific readmission risk prediction models using local data is not feasible for many institutions. Models developed on data from one hospital may not generalize well to another hospital. There is a lack of an end-to-end adaptable readmission model that can generalize to unseen test domains. We propose an early readmission risk domain generalization network, ERR-DGN, for cross-domain knowledge transfer. ERR-DGN internalizes the shared patterns and characteristics that are consistent across source domains, enabling it to adapt to a new domain. It transforms source datasets to a common embedding space while capturing relevant temporal long-term dependencies of sequential data. Domain generalization is then applied on domain-specific fully connected linear layers. The model is optimized by a loss function that integrates distribution discrepancy loss to match the mean embeddings of multiple source distributions with the task-specific loss. A model was developed using electronic health record (EHR) data of 201,688 patients with diabetes across urban, suburban, rural, and mixed hospital systems to enhance 30-day readmission predictions among patients with diabetes on 67,066 unseen patients at a rural hospital. We also explored how model performance varied by the number of sites and over time. The proposed method outperformed the baseline models, yielding a 6 % increase in F1-score (0.79 ± 0.006 vs. 0.73 ± 0.007). Model performance peaked with the inclusion of three sites. Performance of the model was relatively stable for 3 years then declined at 4 years. ERR-DGN may be a proficient tool for learning data from multiple sites and subsequently applying a hospitalization readmission prediction model to a new site. Including a relatively small number of varied sites may be sufficient to ac},
keywords = {deep learning, diabetes mellitus, domain generalization},
pubstate = {published},
tppubtype = {article}
}
A prediction model to assess the risk of hospital readmission can be valuable to identify patients who may benefit from extra care. Developing hospital-specific readmission risk prediction models using local data is not feasible for many institutions. Models developed on data from one hospital may not generalize well to another hospital. There is a lack of an end-to-end adaptable readmission model that can generalize to unseen test domains. We propose an early readmission risk domain generalization network, ERR-DGN, for cross-domain knowledge transfer. ERR-DGN internalizes the shared patterns and characteristics that are consistent across source domains, enabling it to adapt to a new domain. It transforms source datasets to a common embedding space while capturing relevant temporal long-term dependencies of sequential data. Domain generalization is then applied on domain-specific fully connected linear layers. The model is optimized by a loss function that integrates distribution discrepancy loss to match the mean embeddings of multiple source distributions with the task-specific loss. A model was developed using electronic health record (EHR) data of 201,688 patients with diabetes across urban, suburban, rural, and mixed hospital systems to enhance 30-day readmission predictions among patients with diabetes on 67,066 unseen patients at a rural hospital. We also explored how model performance varied by the number of sites and over time. The proposed method outperformed the baseline models, yielding a 6 % increase in F1-score (0.79 ± 0.006 vs. 0.73 ± 0.007). Model performance peaked with the inclusion of three sites. Performance of the model was relatively stable for 3 years then declined at 4 years. ERR-DGN may be a proficient tool for learning data from multiple sites and subsequently applying a hospitalization readmission prediction model to a new site. Including a relatively small number of varied sites may be sufficient to ac
3.
Johnson, Steven G.; Abedian, Sajjad; Sturmer, Til; Huling, Jared D.; Lewis, Colby; Buse, John B.; Brosnahan, Shari B.; Mudumbi, Praveen C.; Erlandson, Kristine M.; McComsey, Grace A.; Arnold, Jonathan; Wiggen, Talia D.; Wong, Rachel; Murphy, Shawn N.; Rosen, Clifford; Kaushal, Rainu; Weiner, Mark G.; Bramante, Carolyn T.
Prevalent Metformin Use in Adults With Diabetes and the Incidence of Long COVID: An EHR-Based Cohort Study From the RECOVER Program Journal Article
In: Diabetes Care, vol. 47, iss. 11, pp. 1930-1940, 2024.
Abstract | Links | BibTeX | Tags: COVID-19, diabetes mellitus, long COVID
@article{nokey,
title = {Prevalent Metformin Use in Adults With Diabetes and the Incidence of Long COVID: An EHR-Based Cohort Study From the RECOVER Program},
author = {Steven G. Johnson and Sajjad Abedian and Til Sturmer and Jared D. Huling and Colby Lewis and John B. Buse and Shari B. Brosnahan and Praveen C. Mudumbi and Kristine M. Erlandson and Grace A. McComsey and Jonathan Arnold and Talia D. Wiggen and Rachel Wong and Shawn N. Murphy and Clifford Rosen and Rainu Kaushal and Mark G. Weiner and Carolyn T. Bramante },
doi = {10.2337/DCa24-0032},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
journal = {Diabetes Care},
volume = {47},
issue = {11},
pages = {1930-1940},
abstract = {Objective: Studies show metformin use before and during SARS-CoV-2 infection reduces severe COVID-19 and postacute sequelae of SARS-CoV-2 (PASC) in adults. Our objective was to describe the incidence of PASC and possible associations with prevalent metformin use in adults with type 2 diabetes mellitus (T2DM).
Research design and methods: This is a retrospective cohort analysis using the National COVID Cohort Collaborative (N3C) and Patient-Centered Clinical Research Network (PCORnet) electronic health record (EHR) databases with an active comparator design that examined metformin-exposed individuals versus nonmetformin-exposed individuals who were taking other diabetes medications. T2DM was defined by HbA1c ≥6.5 or T2DM EHR diagnosis code. The outcome was death or PASC within 6 months, defined by EHR code or computable phenotype.
Results: In the N3C, the hazard ratio (HR) for death or PASC with a U09.9 diagnosis code (PASC-U09.0) was 0.79 (95% CI 0.71-0.88; P < 0.001), and for death or N3C computable phenotype PASC (PASC-N3C) was 0.85 (95% CI 0.78-0.92; P < 0.001). In PCORnet, the HR for death or PASC-U09.9 was 0.87 (95% CI 0.66-1.14; P = 0.08), and for death or PCORnet computable phenotype PASC (PASC-PCORnet) was 1.04 (95% CI 0.97-1.11; P = 0.58). Incident PASC by diagnosis code was 1.6% metformin vs. 2.0% comparator in the N3C, and 2.1% metformin vs. 2.5% comparator in PCORnet. By computable phenotype, incidence was 4.8% metformin and 5.2% comparator in the N3C and 24.7% metformin vs. 26.1% comparator in PCORnet.
Conclusions: Prevalent metformin use is associated with a slightly lower incidence of death or PASC after SARS-CoV-2 infection. PASC incidence by computable phenotype is higher than by EHR code, especially in PCORnet. These data are consistent with other observational analyses showing prevalent metformin is associated with favorable outcomes after SARS-CoV-2 infection in adults with T2DM.},
keywords = {COVID-19, diabetes mellitus, long COVID},
pubstate = {published},
tppubtype = {article}
}
Objective: Studies show metformin use before and during SARS-CoV-2 infection reduces severe COVID-19 and postacute sequelae of SARS-CoV-2 (PASC) in adults. Our objective was to describe the incidence of PASC and possible associations with prevalent metformin use in adults with type 2 diabetes mellitus (T2DM).
Research design and methods: This is a retrospective cohort analysis using the National COVID Cohort Collaborative (N3C) and Patient-Centered Clinical Research Network (PCORnet) electronic health record (EHR) databases with an active comparator design that examined metformin-exposed individuals versus nonmetformin-exposed individuals who were taking other diabetes medications. T2DM was defined by HbA1c ≥6.5 or T2DM EHR diagnosis code. The outcome was death or PASC within 6 months, defined by EHR code or computable phenotype.
Results: In the N3C, the hazard ratio (HR) for death or PASC with a U09.9 diagnosis code (PASC-U09.0) was 0.79 (95% CI 0.71-0.88; P < 0.001), and for death or N3C computable phenotype PASC (PASC-N3C) was 0.85 (95% CI 0.78-0.92; P < 0.001). In PCORnet, the HR for death or PASC-U09.9 was 0.87 (95% CI 0.66-1.14; P = 0.08), and for death or PCORnet computable phenotype PASC (PASC-PCORnet) was 1.04 (95% CI 0.97-1.11; P = 0.58). Incident PASC by diagnosis code was 1.6% metformin vs. 2.0% comparator in the N3C, and 2.1% metformin vs. 2.5% comparator in PCORnet. By computable phenotype, incidence was 4.8% metformin and 5.2% comparator in the N3C and 24.7% metformin vs. 26.1% comparator in PCORnet.
Conclusions: Prevalent metformin use is associated with a slightly lower incidence of death or PASC after SARS-CoV-2 infection. PASC incidence by computable phenotype is higher than by EHR code, especially in PCORnet. These data are consistent with other observational analyses showing prevalent metformin is associated with favorable outcomes after SARS-CoV-2 infection in adults with T2DM.
Research design and methods: This is a retrospective cohort analysis using the National COVID Cohort Collaborative (N3C) and Patient-Centered Clinical Research Network (PCORnet) electronic health record (EHR) databases with an active comparator design that examined metformin-exposed individuals versus nonmetformin-exposed individuals who were taking other diabetes medications. T2DM was defined by HbA1c ≥6.5 or T2DM EHR diagnosis code. The outcome was death or PASC within 6 months, defined by EHR code or computable phenotype.
Results: In the N3C, the hazard ratio (HR) for death or PASC with a U09.9 diagnosis code (PASC-U09.0) was 0.79 (95% CI 0.71-0.88; P < 0.001), and for death or N3C computable phenotype PASC (PASC-N3C) was 0.85 (95% CI 0.78-0.92; P < 0.001). In PCORnet, the HR for death or PASC-U09.9 was 0.87 (95% CI 0.66-1.14; P = 0.08), and for death or PCORnet computable phenotype PASC (PASC-PCORnet) was 1.04 (95% CI 0.97-1.11; P = 0.58). Incident PASC by diagnosis code was 1.6% metformin vs. 2.0% comparator in the N3C, and 2.1% metformin vs. 2.5% comparator in PCORnet. By computable phenotype, incidence was 4.8% metformin and 5.2% comparator in the N3C and 24.7% metformin vs. 26.1% comparator in PCORnet.
Conclusions: Prevalent metformin use is associated with a slightly lower incidence of death or PASC after SARS-CoV-2 infection. PASC incidence by computable phenotype is higher than by EHR code, especially in PCORnet. These data are consistent with other observational analyses showing prevalent metformin is associated with favorable outcomes after SARS-CoV-2 infection in adults with T2DM.
4.
Min, Jea Young; Williams, Nicholas; Simmons, Will; Banerjee, Samprit; Wang, Fei; Zhang, Yongkang; Reese, April B.; Mushlin, Alvin I.; Flory, James H.
Baseline hemoglobin A1c and the risk of COVID-19 hospitalization among patients with diabetes in the INSIGHT Clinical Research Network Journal Article
In: Diabetic Medicine: A Journal of the British Diabetic Association, vol. 39, iss. 5, no. e14815, 2022.
Abstract | Links | BibTeX | Tags: COVID-19, diabetes mellitus
@article{nokey,
title = {Baseline hemoglobin A1c and the risk of COVID-19 hospitalization among patients with diabetes in the INSIGHT Clinical Research Network},
author = {Jea Young Min and Nicholas Williams and Will Simmons and Samprit Banerjee and Fei Wang and Yongkang Zhang and April B. Reese and Alvin I. Mushlin and James H. Flory
},
doi = {10.1111/dme.14815},
year = {2022},
date = {2022-02-28},
urldate = {2022-02-28},
journal = {Diabetic Medicine: A Journal of the British Diabetic Association},
volume = {39},
number = {e14815},
issue = {5},
abstract = {Aims: To examine the association between baseline glucose control and risk of COVID-19 hospitalization and in-hospital death among patients with diabetes.
Methods: We performed a retrospective cohort study of adult patients in the INSIGHT Clinical Research Network with a diabetes diagnosis and haemoglobin A1c (HbA1c) measurement in the year prior to an index date of March 15, 2020. Patients were divided into four exposure groups based on their most recent HbA1c measurement (in mmol/mol): 39-46 (5.7%-6.4%), 48-57 (6.5%-7.4%), 58-85 (7.5%-9.9%), and ≥86 (10%). Time to COVID-19 hospitalization was compared in the four groups in a propensity score-weighted Cox proportional hazards model adjusting for potential confounders. Patients were followed until June 15, 2020. In-hospital death was examined as a secondary outcome.
Results: Of 168,803 patients who met inclusion criteria; 50,016 patients had baseline HbA1c 39-46 (5.7%-6.4%); 54,729 had HbA1c 48-57 (6.5-7.4%); 47,640 had HbA1c 58-85 (7.5^%-9.9%) and 16,418 had HbA1c ≥86 (10%). Compared with patients with HbA1c 48-57 (6.5%-7.4%), the risk of hospitalization was incrementally greater for those with HbA1c 58-85 (7.5%-9.9%) (adjusted hazard ratio [aHR] 1.19, 95% confidence interval [CI] 1.06-1.34) and HbA1c ≥86 (10%) (aHR 1.40, 95% CI 1.19-1.64). The risk of COVID-19 in-hospital death was increased only in patients with HbA1c 58-85 (7.5%-9.9%) (aHR 1.29, 95% CI 1.06, 1.61).
Conclusions: Diabetes patients with high baseline HbA1c had a greater risk of COVID-19 hospitalization, although association between HbA1c and in-hospital death was less consistent. Preventive efforts for COVID-19 should be focused on diabetes patients with poor glucose control.},
keywords = {COVID-19, diabetes mellitus},
pubstate = {published},
tppubtype = {article}
}
Aims: To examine the association between baseline glucose control and risk of COVID-19 hospitalization and in-hospital death among patients with diabetes.
Methods: We performed a retrospective cohort study of adult patients in the INSIGHT Clinical Research Network with a diabetes diagnosis and haemoglobin A1c (HbA1c) measurement in the year prior to an index date of March 15, 2020. Patients were divided into four exposure groups based on their most recent HbA1c measurement (in mmol/mol): 39-46 (5.7%-6.4%), 48-57 (6.5%-7.4%), 58-85 (7.5%-9.9%), and ≥86 (10%). Time to COVID-19 hospitalization was compared in the four groups in a propensity score-weighted Cox proportional hazards model adjusting for potential confounders. Patients were followed until June 15, 2020. In-hospital death was examined as a secondary outcome.
Results: Of 168,803 patients who met inclusion criteria; 50,016 patients had baseline HbA1c 39-46 (5.7%-6.4%); 54,729 had HbA1c 48-57 (6.5-7.4%); 47,640 had HbA1c 58-85 (7.5^%-9.9%) and 16,418 had HbA1c ≥86 (10%). Compared with patients with HbA1c 48-57 (6.5%-7.4%), the risk of hospitalization was incrementally greater for those with HbA1c 58-85 (7.5%-9.9%) (adjusted hazard ratio [aHR] 1.19, 95% confidence interval [CI] 1.06-1.34) and HbA1c ≥86 (10%) (aHR 1.40, 95% CI 1.19-1.64). The risk of COVID-19 in-hospital death was increased only in patients with HbA1c 58-85 (7.5%-9.9%) (aHR 1.29, 95% CI 1.06, 1.61).
Conclusions: Diabetes patients with high baseline HbA1c had a greater risk of COVID-19 hospitalization, although association between HbA1c and in-hospital death was less consistent. Preventive efforts for COVID-19 should be focused on diabetes patients with poor glucose control.
Methods: We performed a retrospective cohort study of adult patients in the INSIGHT Clinical Research Network with a diabetes diagnosis and haemoglobin A1c (HbA1c) measurement in the year prior to an index date of March 15, 2020. Patients were divided into four exposure groups based on their most recent HbA1c measurement (in mmol/mol): 39-46 (5.7%-6.4%), 48-57 (6.5%-7.4%), 58-85 (7.5%-9.9%), and ≥86 (10%). Time to COVID-19 hospitalization was compared in the four groups in a propensity score-weighted Cox proportional hazards model adjusting for potential confounders. Patients were followed until June 15, 2020. In-hospital death was examined as a secondary outcome.
Results: Of 168,803 patients who met inclusion criteria; 50,016 patients had baseline HbA1c 39-46 (5.7%-6.4%); 54,729 had HbA1c 48-57 (6.5-7.4%); 47,640 had HbA1c 58-85 (7.5^%-9.9%) and 16,418 had HbA1c ≥86 (10%). Compared with patients with HbA1c 48-57 (6.5%-7.4%), the risk of hospitalization was incrementally greater for those with HbA1c 58-85 (7.5%-9.9%) (adjusted hazard ratio [aHR] 1.19, 95% confidence interval [CI] 1.06-1.34) and HbA1c ≥86 (10%) (aHR 1.40, 95% CI 1.19-1.64). The risk of COVID-19 in-hospital death was increased only in patients with HbA1c 58-85 (7.5%-9.9%) (aHR 1.29, 95% CI 1.06, 1.61).
Conclusions: Diabetes patients with high baseline HbA1c had a greater risk of COVID-19 hospitalization, although association between HbA1c and in-hospital death was less consistent. Preventive efforts for COVID-19 should be focused on diabetes patients with poor glucose control.
5.
Mayer, Victoria; Mijanovich, Tod; Egorova, Natalia; Flory, James H.; Mushlin, Alvin I.; Calvo, Michele; Deshpande, Richa; Siscovick, David
Impact of New York State’s Health Home program on access to care among patients with diabetes Journal Article
In: BMJ Open Diabetes Research & Care, vol. 9, iss. Supplement 1, pp. e002204, 2021.
Abstract | Links | BibTeX | Tags: diabetes mellitus, medicaid
@article{nokey,
title = {Impact of New York State’s Health Home program on access to care among patients with diabetes},
author = {Victoria Mayer and Tod Mijanovich and Natalia Egorova and James H. Flory and Alvin I. Mushlin and Michele Calvo and Richa Deshpande and David Siscovick},
doi = {10.1136/bmjdrc-2021-002204},
year = {2021},
date = {2021-12-01},
journal = {BMJ Open Diabetes Research & Care},
volume = {9},
issue = {Supplement 1},
pages = {e002204},
abstract = {Introduction: Access to care is essential for patients with diabetes to maintain health and prevent complications, and is important for health equity. New York State's Health Homes (HHs) provide care management services to Medicaid-insured patients with chronic conditions, including diabetes, and aim to improve quality of care and outcomes. There is inconsistent evidence on the impact of HHs, and care management programs more broadly, on access to care.
Research design and methods: Using a cohort of patients with diabetes derived from electronic health records from the INSIGHT Clinical Research Network, we analyzed Medicaid data for HH enrollees and a matched comparison group of HH non-enrollees. We estimated HH impacts on several access measures using natural experiment methods.
Results: We identified and matched 11 646 HH enrollees; patients were largely non-Hispanic Black (29.9%) and Hispanic (48.7%), and had high rates of dual eligibility (33.0%), Supplemental Security Income disability enrollment (49.1%), and multiple comorbidities. In the 12 months following HH enrollment, HH enrollees had one more month of Medicaid coverage (p<0.001) and 4.6 more outpatient visits than expected (p<0.001, evenly distributed between primary and specialty care). There were also positive impacts on the proportions of patients with follow-up visits within 7 days (4 percentage points (pp), p<0.001) and 30 days (6pp, p<0.001) after inpatient care, and on the proportion of patients with follow-up visits within 30 days after emergency department (ED) care (4pp, p<0.001). We did not find meaningful differences in continuity of care. We found small positive impacts on the proportion of patients with an inpatient visit and the proportion with an ED visit.
Conclusions: New York State's HH program improved access to care for Medicaid recipients with diabetes. These findings have implications for New York State Medicaid as well as other providers and care management programs.},
keywords = {diabetes mellitus, medicaid},
pubstate = {published},
tppubtype = {article}
}
Introduction: Access to care is essential for patients with diabetes to maintain health and prevent complications, and is important for health equity. New York State's Health Homes (HHs) provide care management services to Medicaid-insured patients with chronic conditions, including diabetes, and aim to improve quality of care and outcomes. There is inconsistent evidence on the impact of HHs, and care management programs more broadly, on access to care.
Research design and methods: Using a cohort of patients with diabetes derived from electronic health records from the INSIGHT Clinical Research Network, we analyzed Medicaid data for HH enrollees and a matched comparison group of HH non-enrollees. We estimated HH impacts on several access measures using natural experiment methods.
Results: We identified and matched 11 646 HH enrollees; patients were largely non-Hispanic Black (29.9%) and Hispanic (48.7%), and had high rates of dual eligibility (33.0%), Supplemental Security Income disability enrollment (49.1%), and multiple comorbidities. In the 12 months following HH enrollment, HH enrollees had one more month of Medicaid coverage (p<0.001) and 4.6 more outpatient visits than expected (p<0.001, evenly distributed between primary and specialty care). There were also positive impacts on the proportions of patients with follow-up visits within 7 days (4 percentage points (pp), p<0.001) and 30 days (6pp, p<0.001) after inpatient care, and on the proportion of patients with follow-up visits within 30 days after emergency department (ED) care (4pp, p<0.001). We did not find meaningful differences in continuity of care. We found small positive impacts on the proportion of patients with an inpatient visit and the proportion with an ED visit.
Conclusions: New York State's HH program improved access to care for Medicaid recipients with diabetes. These findings have implications for New York State Medicaid as well as other providers and care management programs.
Research design and methods: Using a cohort of patients with diabetes derived from electronic health records from the INSIGHT Clinical Research Network, we analyzed Medicaid data for HH enrollees and a matched comparison group of HH non-enrollees. We estimated HH impacts on several access measures using natural experiment methods.
Results: We identified and matched 11 646 HH enrollees; patients were largely non-Hispanic Black (29.9%) and Hispanic (48.7%), and had high rates of dual eligibility (33.0%), Supplemental Security Income disability enrollment (49.1%), and multiple comorbidities. In the 12 months following HH enrollment, HH enrollees had one more month of Medicaid coverage (p<0.001) and 4.6 more outpatient visits than expected (p<0.001, evenly distributed between primary and specialty care). There were also positive impacts on the proportions of patients with follow-up visits within 7 days (4 percentage points (pp), p<0.001) and 30 days (6pp, p<0.001) after inpatient care, and on the proportion of patients with follow-up visits within 30 days after emergency department (ED) care (4pp, p<0.001). We did not find meaningful differences in continuity of care. We found small positive impacts on the proportion of patients with an inpatient visit and the proportion with an ED visit.
Conclusions: New York State's HH program improved access to care for Medicaid recipients with diabetes. These findings have implications for New York State Medicaid as well as other providers and care management programs.
6.
Forthal, Sarah; Choi, Sugy; Yernei, Rajeev; Zhang, Zhongjie; Siscovick, David; Egorova, Natalia; Mijanovich, Todor; Mayer, Victoria; Neighbors, Charles
Substance Use Disorders and Diabetes Care: Lessons From New York Health Homes Journal Article
In: Medical Care, vol. 59, iss. 10, pp. 881–887, 2021.
Abstract | Links | BibTeX | Tags: diabetes mellitus, substance use disorder
@article{nokey,
title = {Substance Use Disorders and Diabetes Care: Lessons From New York Health Homes},
author = {Sarah Forthal and Sugy Choi and Rajeev Yernei and Zhongjie Zhang and David Siscovick and Natalia Egorova and Todor Mijanovich and Victoria Mayer and Charles Neighbors},
doi = {10.1097/MLR.0000000000001602},
year = {2021},
date = {2021-10-01},
journal = {Medical Care},
volume = {59},
issue = {10},
pages = {881--887},
abstract = {Background: Individuals that have both diabetes and substance use disorder (SUD) are more likely to have adverse health outcomes and are less likely to receive high quality diabetes care, compared with patients without coexisting SUD. Care management programs for patients with chronic diseases, such as diabetes and SUD, have been associated with improvements in the process and outcomes of care.
Objective: The aim was to assess the impact of having coexisting SUD on diabetes process of care metrics.
Research design: Preintervention/postintervention triple difference analysis.
Subjects: Participants in the New York State Medicaid Health Home (NYS-HH) care management program who have diabetes and a propensity-matched comparison group of nonparticipants (N=37,260).
Measures: Process of care metrics for patients with diabetes: an eye (retinal) exam, HbA1c test, medical attention (screening laboratory measurements) for nephropathy, and receiving all 3 in the past year.
Results: Before enrollment in NYS-HH, individuals with comorbid SUD had fewer claims for eye exams and HbA1c tests compared with those without comorbid SUD. Diabetes process of care improvements associated with NYS-HH enrollment were larger among those with comorbid SUD [eye exam: adjusted odds ratio (AOR)=1.08; 95% confidence interval (CI): 1.01-1.15]; HbA1c test: AOR=1.20 (95% CI: 1.11-1.29); medical attention for nephropathy: AOR=1.21 (95% CI: 1.12-1.31); all 3: AOR=1.09 (95% CI: 1.02-1.16).
Conclusions: Individuals with both diabetes and SUD may benefit moderately more from care management than those without comorbid SUD. Individuals with both SUD and diabetes who are not enrolled in care management may be missing out on crucial diabetes care.},
keywords = {diabetes mellitus, substance use disorder},
pubstate = {published},
tppubtype = {article}
}
Background: Individuals that have both diabetes and substance use disorder (SUD) are more likely to have adverse health outcomes and are less likely to receive high quality diabetes care, compared with patients without coexisting SUD. Care management programs for patients with chronic diseases, such as diabetes and SUD, have been associated with improvements in the process and outcomes of care.
Objective: The aim was to assess the impact of having coexisting SUD on diabetes process of care metrics.
Research design: Preintervention/postintervention triple difference analysis.
Subjects: Participants in the New York State Medicaid Health Home (NYS-HH) care management program who have diabetes and a propensity-matched comparison group of nonparticipants (N=37,260).
Measures: Process of care metrics for patients with diabetes: an eye (retinal) exam, HbA1c test, medical attention (screening laboratory measurements) for nephropathy, and receiving all 3 in the past year.
Results: Before enrollment in NYS-HH, individuals with comorbid SUD had fewer claims for eye exams and HbA1c tests compared with those without comorbid SUD. Diabetes process of care improvements associated with NYS-HH enrollment were larger among those with comorbid SUD [eye exam: adjusted odds ratio (AOR)=1.08; 95% confidence interval (CI): 1.01-1.15]; HbA1c test: AOR=1.20 (95% CI: 1.11-1.29); medical attention for nephropathy: AOR=1.21 (95% CI: 1.12-1.31); all 3: AOR=1.09 (95% CI: 1.02-1.16).
Conclusions: Individuals with both diabetes and SUD may benefit moderately more from care management than those without comorbid SUD. Individuals with both SUD and diabetes who are not enrolled in care management may be missing out on crucial diabetes care.
Objective: The aim was to assess the impact of having coexisting SUD on diabetes process of care metrics.
Research design: Preintervention/postintervention triple difference analysis.
Subjects: Participants in the New York State Medicaid Health Home (NYS-HH) care management program who have diabetes and a propensity-matched comparison group of nonparticipants (N=37,260).
Measures: Process of care metrics for patients with diabetes: an eye (retinal) exam, HbA1c test, medical attention (screening laboratory measurements) for nephropathy, and receiving all 3 in the past year.
Results: Before enrollment in NYS-HH, individuals with comorbid SUD had fewer claims for eye exams and HbA1c tests compared with those without comorbid SUD. Diabetes process of care improvements associated with NYS-HH enrollment were larger among those with comorbid SUD [eye exam: adjusted odds ratio (AOR)=1.08; 95% confidence interval (CI): 1.01-1.15]; HbA1c test: AOR=1.20 (95% CI: 1.11-1.29); medical attention for nephropathy: AOR=1.21 (95% CI: 1.12-1.31); all 3: AOR=1.09 (95% CI: 1.02-1.16).
Conclusions: Individuals with both diabetes and SUD may benefit moderately more from care management than those without comorbid SUD. Individuals with both SUD and diabetes who are not enrolled in care management may be missing out on crucial diabetes care.
7.
Leonard, Charles E.; Flory, James H.; Likic, Robert; Ogunleye, Olayinka O.; Wei, Li; Wong, Ian
Spotlight commentary: A role for real-world evidence to inform the clinical care of patients with diabetes mellitus Journal Article
In: British Journal of Clinical Pharmacology, vol. 87, iss. 12, pp. 4549-4551, 2021.
Links | BibTeX | Tags: diabetes mellitus
@article{nokey,
title = {Spotlight commentary: A role for real-world evidence to inform the clinical care of patients with diabetes mellitus},
author = {Charles E. Leonard and James H. Flory and Robert Likic and Olayinka O. Ogunleye and Li Wei and Ian Wong},
doi = {10.1111/bcp.14882},
year = {2021},
date = {2021-05-04},
journal = {British Journal of Clinical Pharmacology},
volume = {87},
issue = {12},
pages = {4549-4551},
keywords = {diabetes mellitus},
pubstate = {published},
tppubtype = {article}
}
8.
Flory, James H.; Li, Jing; Dawwas, Ghadeer K.; Leonard, Charles E.
Compared to commercially insured patients, Medicare advantage patients adopt newer diabetes drugs more slowly and adhere to them less Journal Article
In: Endocrinology, Diabetes & Metabolism, vol. 4, iss. 3, pp. e00245, 2021.
Abstract | Links | BibTeX | Tags: diabetes mellitus, Medicare
@article{nokey,
title = {Compared to commercially insured patients, Medicare advantage patients adopt newer diabetes drugs more slowly and adhere to them less},
author = {James H. Flory and Jing Li and Ghadeer K. Dawwas and Charles E. Leonard },
doi = {10.1002/edm2.245},
year = {2021},
date = {2021-04-02},
journal = {Endocrinology, Diabetes & Metabolism},
volume = {4},
issue = {3},
pages = {e00245},
abstract = {Aims: To compare rates of use and adherence for newer versus older second-line diabetes drug classes in commercially insured, Medicare Advantage and dual-eligible (covered by both Medicare and Medicaid) patients.
Materials and methods: Longitudinal cohort study using insurance claims data from 1/1/2012 to 12/31/2016 to identify patients with a first prescription, after metformin, of a second-line diabetes drug (eg sulphonylurea, DPP-4 inhibitor, thiazolidinedione, SGLT-2 inhibitor or GLP-1 receptor agonist) and to estimate their adherence to that drug class. Univariate analysis and multivariable logistic regression were used to examine the association between insurance type and use of each drug class, and between insurance type and adherence to each drug class.
Results: The study population included 96,663 patients. Trends in drug use differed by insurance type. For example, sulphonylurea use declined among the commercially insured (from 46% to 39%, p < .001) but not among Medicare Advantage or dual-eligible patients. Patterns of adherence also differed between insurance groups. For example, compared to commercial insurance, Medicare Advantage was associated with higher adherence to sulphonylurea (odds ratio [OR] 1.32, 95% CI 1.21-1.43)) but lower adherence to SGLT-2 inhibitors (OR 0.43 (95% CI 0.33-0.56)).
Conclusions: This study finds differences in utilization and adherence for diabetes drugs across insurance types. Older medications such as sulphonylureas appear to be more used and better adhered to among Medicare Advantage recipients, while the opposite is true for newer medication classes. These findings suggest a need to personalize selection of diabetes drugs according to insurance status, particularly when adherence needs optimization.},
keywords = {diabetes mellitus, Medicare},
pubstate = {published},
tppubtype = {article}
}
Aims: To compare rates of use and adherence for newer versus older second-line diabetes drug classes in commercially insured, Medicare Advantage and dual-eligible (covered by both Medicare and Medicaid) patients.
Materials and methods: Longitudinal cohort study using insurance claims data from 1/1/2012 to 12/31/2016 to identify patients with a first prescription, after metformin, of a second-line diabetes drug (eg sulphonylurea, DPP-4 inhibitor, thiazolidinedione, SGLT-2 inhibitor or GLP-1 receptor agonist) and to estimate their adherence to that drug class. Univariate analysis and multivariable logistic regression were used to examine the association between insurance type and use of each drug class, and between insurance type and adherence to each drug class.
Results: The study population included 96,663 patients. Trends in drug use differed by insurance type. For example, sulphonylurea use declined among the commercially insured (from 46% to 39%, p < .001) but not among Medicare Advantage or dual-eligible patients. Patterns of adherence also differed between insurance groups. For example, compared to commercial insurance, Medicare Advantage was associated with higher adherence to sulphonylurea (odds ratio [OR] 1.32, 95% CI 1.21-1.43)) but lower adherence to SGLT-2 inhibitors (OR 0.43 (95% CI 0.33-0.56)).
Conclusions: This study finds differences in utilization and adherence for diabetes drugs across insurance types. Older medications such as sulphonylureas appear to be more used and better adhered to among Medicare Advantage recipients, while the opposite is true for newer medication classes. These findings suggest a need to personalize selection of diabetes drugs according to insurance status, particularly when adherence needs optimization.
Materials and methods: Longitudinal cohort study using insurance claims data from 1/1/2012 to 12/31/2016 to identify patients with a first prescription, after metformin, of a second-line diabetes drug (eg sulphonylurea, DPP-4 inhibitor, thiazolidinedione, SGLT-2 inhibitor or GLP-1 receptor agonist) and to estimate their adherence to that drug class. Univariate analysis and multivariable logistic regression were used to examine the association between insurance type and use of each drug class, and between insurance type and adherence to each drug class.
Results: The study population included 96,663 patients. Trends in drug use differed by insurance type. For example, sulphonylurea use declined among the commercially insured (from 46% to 39%, p < .001) but not among Medicare Advantage or dual-eligible patients. Patterns of adherence also differed between insurance groups. For example, compared to commercial insurance, Medicare Advantage was associated with higher adherence to sulphonylurea (odds ratio [OR] 1.32, 95% CI 1.21-1.43)) but lower adherence to SGLT-2 inhibitors (OR 0.43 (95% CI 0.33-0.56)).
Conclusions: This study finds differences in utilization and adherence for diabetes drugs across insurance types. Older medications such as sulphonylureas appear to be more used and better adhered to among Medicare Advantage recipients, while the opposite is true for newer medication classes. These findings suggest a need to personalize selection of diabetes drugs according to insurance status, particularly when adherence needs optimization.