Cargando…
SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes
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 pred...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9948632/ https://www.ncbi.nlm.nih.gov/pubmed/36844798 http://dx.doi.org/10.2147/PPA.S397424 |
_version_ | 1784892824097914880 |
---|---|
author | Wells, Joshua Wang, Chao Dolgin, Kevin Kayyali, Reem |
author_facet | Wells, Joshua Wang, Chao Dolgin, Kevin Kayyali, Reem |
author_sort | Wells, Joshua |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9948632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-99486322023-02-24 SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes Wells, Joshua Wang, Chao Dolgin, Kevin Kayyali, Reem Patient Prefer Adherence Original Research 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. Dove 2023-02-19 /pmc/articles/PMC9948632/ /pubmed/36844798 http://dx.doi.org/10.2147/PPA.S397424 Text en © 2023 Wells et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Wells, Joshua Wang, Chao Dolgin, Kevin Kayyali, Reem SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes |
title | SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes |
title_full | SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes |
title_fullStr | SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes |
title_full_unstemmed | SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes |
title_short | SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes |
title_sort | spur: a patient-reported medication adherence model as a predictor of admission and early readmission in patients living with type 2 diabetes |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9948632/ https://www.ncbi.nlm.nih.gov/pubmed/36844798 http://dx.doi.org/10.2147/PPA.S397424 |
work_keys_str_mv | AT wellsjoshua spurapatientreportedmedicationadherencemodelasapredictorofadmissionandearlyreadmissioninpatientslivingwithtype2diabetes AT wangchao spurapatientreportedmedicationadherencemodelasapredictorofadmissionandearlyreadmissioninpatientslivingwithtype2diabetes AT dolginkevin spurapatientreportedmedicationadherencemodelasapredictorofadmissionandearlyreadmissioninpatientslivingwithtype2diabetes AT kayyalireem spurapatientreportedmedicationadherencemodelasapredictorofadmissionandearlyreadmissioninpatientslivingwithtype2diabetes |