The impact of initial antidiabetic agent and use of monitoring agents on prescription costs in newly treated type 2 diabetes: A retrospective cohort analysis

Grimes, Ronan T.; Bennett, Kathleen; Canavan, Ronan J.; Tilson, Lesley; Henman, Martin

doi:10.1016/j.diabres.2015.12.020

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Only one study included investigates less-frequent SMBG versus usual care; more significant decreases in HbA1c are reported (-0.6%, -6 mmol/mol) (Franciosi et al, 2011). SMBG carries significant financial burden in diabetes (participants who perform SMBG incur 80% more costs) (Grimes et al, 2016), and by reducing the number of readings with this seven-point SMBG REVIEW: SMBG AND GLYCEMIC CONTROL (HBA1C) IN T2DM 17 regimen e.g., by 65% and 83% compared to daily and twice daily SMBG respectively, leads to significantly improved cost-effectiveness. Therefore, this less-frequent SMBG regimen is presented as a favorable structured SMBG regimen, both in terms of clinical-and costeffectiveness; though evaluation of this specific regimen may be merited.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review of Self-Monitoring of Blood Glucose in Patients With Type 2 Diabetes

2021

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The benefit of self-monitoring of blood glucose (SMBG) in the reduction of HbA1c in non-insulintreated participants remains unclear. HbA1c may be improved in this population with SMBG. We aimed to investigate this. Materials and methodsMeta-analyses of randomized controlled trials (RCTs) were performed comparing SMBG versus usual care and structured versus unstructured SMBG; the effect of clinician therapy adjustment based on SMBG readings was examined. Medline, Embase and Cochrane Central were electronically searched to identify articles published from 1 January 2000 to 30 June 2020. Trials investigating changes in HbA1c were selected. Screening was performed independently by two investigators. Two investigators extracted HbA1c at baseline and follow-up for each trial. ResultsNineteen RCTs, involving 4,965 participants were included. Overall, SMBG reduced HbA1c. Preplanned subgroup analysis showed that using SMBG readings to adjust therapy contributed significantly to the reduction. No significant improvement in HbA1c was shown in SMBG without therapy adjustment). The same difference was observed in structured SMBG compared to unstructured SMBG. ConclusionsHbA1c is improved with therapy adjustment based on structured SMBG readings. Implications are for clinicians to prescribe structured SMBG with an aim for therapy adjustment based on the readings, and not prescribing unstructured SMBG. Participants with suboptimal glycemic control may benefit most. A SMBG regimen that improves clinical-and cost-effectiveness is presented. Future studies can investigate this regimen specifically.

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Section: Discussionmentioning

confidence: 99%

Systematic Review of Self-Monitoring of Blood Glucose in Patients With Type 2 Diabetes

2021

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“…338 Real-world pharmacy reimbursement data assists in determining whether evidence from highly selected populations in randomized controlled trials can be translated to populations in everyday clinical practice that have not been selected. 339 The existing pharmacy reimbursement data can be used to calculate the number of eligible recipients for a new drug, which allows for estimating the potential budgetary impact of introducing or reimbursing these new drugs. 340 Analysis of diverse populations in medical databases enables the provision of comprehensive education, monitoring, and premium services, while also contemplating the implementation of streamlined drug administration strategies, including initiatives aimed at augmenting patient medication adherence.…”

Section: The Prospects and Challenges Of Medication Adherence Mining ...mentioning

confidence: 99%

“…Real‐world pharmacy reimbursement data assists in determining whether evidence from highly selected populations in randomized controlled trials can be translated to populations in everyday clinical practice that have not been selected 339 . The existing pharmacy reimbursement data can be used to calculate the number of eligible recipients for a new drug, which allows for estimating the potential budgetary impact of introducing or reimbursing these new drugs 340 .…”

Section: The Prospects and Challenges Of Medication Adherence Mining ...mentioning

confidence: 99%

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Xu,

Zheng,

et al. 2023

J Evidence Based Medicine

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BackgroundIncreasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data‐rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.MethodsWe conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.ResultsOur study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision‐making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.ConclusionAdvanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

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The Health Service Executive—Primary Care Reimbursement Services Database (HSE-PCRS) in Ireland

Sinnott

Cahir

Bennett

2021

Databases for Pharmacoepidemiological Research

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The impact of initial antidiabetic agent and use of monitoring agents on prescription costs in newly treated type 2 diabetes: A retrospective cohort analysis

Cited by 7 publications

References 28 publications

Systematic Review of Self-Monitoring of Blood Glucose in Patients With Type 2 Diabetes

Systematic Review of Self-Monitoring of Blood Glucose in Patients With Type 2 Diabetes

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

The Health Service Executive—Primary Care Reimbursement Services Database (HSE-PCRS) in Ireland

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