Evidence map›Paper›PMID 39347973›Full record

ArticleJournal of managed care & specialty pharmacy2024

Impact of continuous glucose monitoring on emergency department visits and all-cause hospitalization rates among Medicaid beneficiaries with type 2 diabetes treated with multiple daily insulin or basal insulin therapy.

Irl B Hirsch, Bhavya Sree Burugapalli, Laura Brandner, Yeesha Poon, Marie Frazzitta, Lakshmi Godavarthi, Naunihal Virdi

Abstract read
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Article in Journal of managed care & specialty pharmacy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Irl B HirschUniversity of Washington School of Medicine, Seattle.
Bhavya Sree BurugapalliAbbott Diabetes Care, Alameda, CA.
Laura BrandnerAbbott Diabetes Care, Alameda, CA.
Yeesha PoonAbbott Diabetes Care, Alameda, CA.
Marie FrazzittaAbbott Diabetes Care, Alameda, CA.
Lakshmi GodavarthiAbbott Diabetes Care, Alameda, CA.
Naunihal VirdiAbbott Diabetes Care, Alameda, CA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe increasing prevalence of diabetes in the United States continues to drive a steady rise in health care resource utilization, especially emergency department visits and all-cause hospitalizations, and the associated costs.

objectiveTo investigate the impact of continuous glucose monitoring (CGM) on emergency department visits and all-cause hospitalizations among Medicaid beneficiaries with type 2 diabetes (T2D) treated with multiple daily insulin injections (MDIs) or basal insulin therapy (BIT) in a real-world setting.

methodsIn this retrospective, 12-month analysis, we used the Inovalon Insights claims dataset to evaluate the effects of CGM acquisition on emergency department visits and all-cause hospitalizations in the Managed Medicaid population. The analysis included 44,941 beneficiaries with T2D who were treated with MDIs (n = 35,367) or BIT (n = 9,574). Primary outcomes were changes in the number of emergency department visits and all-cause hospitalizations following 6 months after acquisition of CGM (post-index period) compared with 6 month prior to CGM acquisition (pre-index period). The first claim for CGM was the index date. Inclusion criteria were as follows: aged younger than 65 years, diagnosis of T2D, claims for short- or rapid-acting insulin (MDI group) or basal insulin (not rapid-acting) (BIT group), acquisition of a CGM device between January 1, 2017, and September 30, 2022, and continuous enrollment in their health plan throughout the pre-index and post-index periods.

resultsIn the MDI group, all-cause inpatient hospitalization rates decreased from 3.25 to 2.29 events/patient-year (hazard ratio = 0.12; 95% CI = 0.11-0.13;

conclusionsAcquisition of CGM is associated with significant reductions in emergency department visits and all-cause hospitalizations among people with T2D treated with MDIs or BIT.

Indexed as

Blood Glucose Self-MonitoringDiabetes Mellitus, Type 2Emergency Service, HospitalHospitalizationHypoglycemic AgentsInsulinMedicaidAdultAgedBlood GlucoseContinuous Glucose MonitoringEmergency Room VisitsFemaleHumansMaleMiddle AgedBlood GlucoseHypoglycemic AgentsInsulin

Identifiers

PMID39347973
PMCPMC11443977

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.