Evidence map›Paper›PMID 36844798›Full record

ArticlePatient preference and adherence2023

SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes.

Joshua Wells, Chao Wang, Kevin Dolgin, Reem Kayyali

Abstract read
In one paragraph

Article in Patient preference and adherence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Joshua WellsDepartment of Pharmacy, Kingston University, Kingston upon Thames, KT1 2EE, UK.ORCID 0000-0003-2021-6055
Chao WangFaculty of Health, Science, Social Care and Education, Kingston University, Kingston upon Thames, KT2 7LB, UK.ORCID 0000-0002-7873-0594
Kevin DolginBehavioural Science Department, Observia, Paris, 75015, France.ORCID 0000-0002-4818-8415
Reem KayyaliDepartment of Pharmacy, Kingston University, Kingston upon Thames, KT1 2EE, UK.ORCID 0000-0002-7300-8738

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Poor medication adherence (MA) is linked to an increased likelihood of hospital admission. Early interventions to address MA may reduce this risk and associated health-care costs. This study aimed to evaluate a holistic Patient Reported Outcome Measure (PROM) of MA, known as SPUR, as a predictor of general admission and early readmission in patients living with Type 2 Diabetes. Patients and Methods: An observational study design was used to assess data collected over a 12-month period including 6-month retrospective and 6-month prospective monitoring of the number of admissions and early readmissions (admissions occurring within 30 days of discharge) across the cohort. Patients (n = 200) were recruited from a large South London NHS Trust. Covariates of interest included: age, ethnicity, gender, level of education, income, the number of medicines and medical conditions, and a Covid-19 diagnosis. A Poisson or negative binomial model was employed for count outcomes, with the exponentiated coefficient indicating incident ratios (IR) [95% CI]. For binary outcomes (Coefficient, [95% CI]), a logistic regression model was developed. Results: Higher SPUR scores (increased adherence) were significantly associated with a lower number of admissions (IR = 0.98, [0.96, 1.00]). The number of medical conditions (IR = 1.07, [1.01, 1.13]), age ≥80 years (IR = 5.18, [1.01, 26.55]), a positive Covid-19 diagnosis during follow-up (IR = 1.83, [1.11, 3.02]) and GCSE education (IR = 2.11, [1.15,3.87]) were factors associated with a greater risk of admission. When modelled as a binary variable, only the SPUR score (-0.051, [-0.094, -0.007]) was significantly predictive of an early readmission, with patients reporting higher SPUR scores being less likely to experience an early readmission. Conclusion: Higher levels of MA, as determined by SPUR, were significantly associated with a lower risk of general admissions and early readmissions among patients living with Type 2 Diabetes.

Indexed as

logistic regressionpatient reported outcome measurepredictive modeltype 2 diabetes

Identifiers

PMID36844798
PMCPMC9948632

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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.