scholarly journals Early Identification of Type 2 Diabetes: Policy should be aligned with health systems strengthening

Diabetes Care ◽  
2010 ◽  
Vol 34 (1) ◽  
pp. 244-246 ◽  
Author(s):  
K. M. V. Narayan ◽  
J. Chan ◽  
V. Mohan
2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. e002153
Author(s):  
Scott J Pilla ◽  
Jennifer L Kraschnewski ◽  
Erik B Lehman ◽  
Lan Kong ◽  
Erica Francis ◽  
...  

IntroductionHypoglycemia is the most common serious adverse effect of diabetes treatment and a major cause of medication-related hospitalization. This study aimed to identify trends and predictors of hospital utilization for hypoglycemia among patients with type 2 diabetes using electronic health record data pooled from six academic health systems.Research design and methodsThis retrospective open cohort study included 549 041 adults with type 2 diabetes receiving regular care from the included health systems between 2009 and 2019. The primary outcome was the yearly event rate for hypoglycemia hospital utilization: emergency department visits, observation visits, or inpatient admissions for hypoglycemia identified using a validated International Classification of Diseases Ninth Revision (ICD-9) algorithm from 2009 to 2014. After the transition to ICD-10 in 2015, we used two ICD-10 code sets (limited and expanded) for hypoglycemia hospital utilization from prior studies. We identified independent predictors of hypoglycemia hospital utilization using multivariable logistic regression analysis with data from 2014.ResultsYearly rates of hypoglycemia hospital utilization decreased from 2.7 to 1.6 events per 1000 patients from 2009 to 2014 (p-trend=0.023). From 2016 to 2019, yearly event rates were stable ranging from 5.6 to 6.6, or 6.3 to 7.3, using the limited and expanded ICD-10 code sets, respectively. In 2014, the strongest independent risk factors for hypoglycemia hospital utilization were chronic kidney disease (OR 2.86, 95% CI 2.33 to 3.57), ages 18–39 years (OR 2.43 vs age 40–64 years, 95% CI 1.78 to 3.31), and insulin use (OR 2.13 vs no diabetes medications, 95% CI 1.67 to 2.73).ConclusionsRates of hypoglycemia hospital utilization decreased from 2009 to 2014 and varied considerably by clinical risk factors such that younger adults, insulin users, and those with chronic kidney disease were at especially high risk. There is a need to validate hypoglycemia ascertainment using ICD-10 codes, which detect a substantially higher number of events compared with ICD-9.


2020 ◽  
Vol 54 (4s) ◽  
pp. 117-120
Author(s):  
Roberta Lamptey ◽  
Stephen T. Engmann ST ◽  
Boateng Asante ◽  
Ernest Yorke ◽  
Yaw B. Mensah ◽  
...  

This is a case report of a 55-year-old man with Type 2 Diabetes Mellitus who presented with progressive breathlessness, chest pain and hyperglycaemia. An initial impression of a chest infection was made. Management was initiated with antibiotics, but this was unsuccessful, and he continued to desaturate. A screen for Coronavirus Disease of 2019 (COVID-19) returned positive. There was no prodrome of fever or flu-like illness or known contact with a patient known to have COVID-19. This case is instructive as he didn’t fit the typical case definition for suspected COVID-19. There is significant community spread in Ghana, therefore COVID-19 should be a differential diagnosis in patients who present with hyperglycaemia and respiratory symptoms in the absence of a febrile illness. Primary care doctors must have a high index of suspicion in cases of significant hyperglycaemia and inability to maintain oxygen saturation.Patients known to have diabetes and those not known to have diabetes may develop hyperglycaemia subsequent to COVID-19. A high index of suspicion is crucial for early identification, notification for testing, isolation, treatment, contact tracing and possible referral or coordination of care with other specialists. Early identification will protect healthcare workers and patients alike from cross-infection.


2021 ◽  
Author(s):  
Thomas A. Peterson ◽  
Valy Fontil ◽  
Suneil K. Koliwad ◽  
Ayan Patel ◽  
Atul J. Butte

<b>Objective:</b> Using the newly created University of California Health Data Warehouse (UCHDW), we present the first study to analyze antihyperglycemic treatment utilization across the five large University of California (UC) academic health systems (Davis, Irvine, Los Angeles, San Diego, and San Francisco). <p><b>Research Design:</b> Retrospective analysis using deidentified Electronic Health Records (EHRs; 2014-2019) including 97,231 type 2 diabetes patients from 1,003 UC-affiliated clinical settings. Significant differences between health systems and individual providers were identified using binomial probabilities with cohort matching.</p> <p><b>Results</b>: Our analysis reveals statistically different treatment utilization patterns not only between health systems but also among individual providers within health systems. We identified 21 differences among health systems, and 29 differences among individual providers within these health systems, with respect to treatment intensifications within existing guidelines on top of either metformin monotherapy or dual therapy with metformin and a sulfonylurea. Next, we identified variation for medications within the same class (e.g., glipizide vs. glyburide among sulfonylureas), with 33 differences among health systems and 86 among individual providers. Finally, we identified two health systems and 55 individual providers that more frequently utilized medications with known cardioprotective benefits for patients with high cardiovascular disease risk, but also one health system and 8 providers who prescribed such medications less frequently for these patients.</p> <p><b>Conclusions:</b> Our study utilized cohort matching techniques to highlight real-world variation in care between health systems and individual providers. This demonstrates the power of EHRs to quantify differences in treatment utilization, a necessary step towards standardizing precision care for large populations.</p>


Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 349-P
Author(s):  
SCOTT J. PILLA ◽  
JENNIFER L. KRASCHNEWSKI ◽  
ERIK LEHMAN ◽  
LAN KONG ◽  
ERICA FRANCIS ◽  
...  

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0195086 ◽  
Author(s):  
Suan Ee Ong ◽  
Joel Jun Kai Koh ◽  
Sue-Anne Ee Shiow Toh ◽  
Kee Seng Chia ◽  
Dina Balabanova ◽  
...  

2018 ◽  
Vol 6 (12) ◽  
pp. 992-1002 ◽  
Author(s):  
Andrew P Hills ◽  
Anoop Misra ◽  
Jason M R Gill ◽  
Nuala M Byrne ◽  
Mario J Soares ◽  
...  

BMC Neurology ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Angelica Carbajal-Ramírez ◽  
Julián A. Hernández-Domínguez ◽  
Mario A. Molina-Ayala ◽  
María Magdalena Rojas-Uribe ◽  
Adolfo Chávez-Negrete

2013 ◽  
Vol 19 (2) ◽  
pp. 69-76 ◽  
Author(s):  
Naomh Gallagher ◽  
Kathleen Bennett ◽  
Susan M Smith ◽  
Dermot O’Reilly

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