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Does potentially inappropriate prescribing predict an increased risk of admission to hospital and mortality? A longitudinal study of the ‘oldest old’

BACKGROUND: Potentially inappropriate prescribing (PIP) is associated with negative health outcomes, including hospitalisation and mortality. Life and Living in Advanced Age: a Cohort Study in New Zealand (LiLACS NZ) is a longitudinal study of Māori (the indigenous population of New Zealand) and non...

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Detalles Bibliográficos
Autores principales: Cardwell, Karen, Kerse, Ngaire, Hughes, Carmel M., Teh, Ruth, Moyes, Simon A., Menzies, Oliver, Rolleston, Anna, Broad, Joanna B., Ryan, Cristín
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6986145/
https://www.ncbi.nlm.nih.gov/pubmed/31992215
http://dx.doi.org/10.1186/s12877-020-1432-4
Descripción
Sumario:BACKGROUND: Potentially inappropriate prescribing (PIP) is associated with negative health outcomes, including hospitalisation and mortality. Life and Living in Advanced Age: a Cohort Study in New Zealand (LiLACS NZ) is a longitudinal study of Māori (the indigenous population of New Zealand) and non-Māori octogenarians. Health disparities between indigenous and non-indigenous populations are prevalent internationally and engagement of indigenous populations in health research is necessary to understand and address these disparities. Using LiLACS NZ data, this study reports the association of PIP with hospitalisations and mortality prospectively over 36-months follow-up. METHODS: PIP, from pharmacist applied criteria, was reported as potentially inappropriate medicines (PIMs) and potential prescribing omissions (PPOs). The association between PIP and hospitalisations (all-cause, cardiovascular disease-specific and ambulatory-sensitive) and mortality was determined throughout a series of 12-month follow-ups using binary logistic (hospitalisations) and Cox (mortality) regression analysis, reported as odds ratios (ORs) and hazard ratios (HRs), respectively, and the corresponding confidence intervals (CIs). RESULTS: Full demographic data were obtained for 267 Māori and 404 non-Māori at baseline, 178 Māori and 332 non-Māori at 12-months, and 122 Māori and 281 non-Māori at 24-months. The prevalence of any PIP (i.e. ≥1 PIM and/or PPO) was 66, 75 and 72% for Māori at baseline, 12-months and 24-months, respectively. In non-Māori, the prevalence of any PIP was 62, 71 and 73% at baseline, 12-months and 24-months, respectively. At each time-point, there were more PPOs than PIMs; at baseline Māori were exposed to a significantly greater proportion of PPOs compared to non-Māori (p = 0.02). In Māori: PPOs were associated with a 1.5-fold increase in hospitalisations and mortality. In non-Māori, PIMs were associated with a double risk of mortality. CONCLUSIONS: PIP was associated with an increased risk of hospitalisation and mortality in this cohort. Omissions appear more important for Māori in predicting hospitalisations, and PIMs were more important in non-Māori in predicting mortality. These results suggest understanding prescribing outcomes across and between population groups is needed and emphasises prescribing quality assessment is useful.