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Much more medicine for the oldest old: trends in UK electronic clinical records.

ABSTRACT: BACKGROUND:the oldest old (85+) pose complex medical challenges. Both underdiagnosis and overdiagnosis are claimed in this group. OBJECTIVE:to estimate diagnosis, prescribing and hospital admission prevalence from 2003/4 to 2011/12, to monitor trends in medicalisation. DESIGN AND SETTING:observational study of Clinical Practice Research Datalink (CPRD) electronic medical records from general practice populations (eligible; n = 27,109) with oversampling of the oldest old. METHODS:we identified 18 common diseases and five geriatric syndromes (dizziness, incontinence, skin ulcers, falls and fractures) from Read codes. We counted medications prescribed ?1 time in all quarters of studied years. RESULTS:there were major increases in recorded prevalence of most conditions in the 85+ group, especially chronic kidney disease (stages 3-5: prevalence <1% rising to 36.4%). The proportions of the 85+ group with ?3 conditions rose from 32.2 to 55.1% (27.1 to 35.1% in the 65-84 year group). Geriatric syndrome trends were less marked. In the 85+ age group the proportion receiving no chronically prescribed medications fell from 29.6 to 13.6%, while the proportion on ?3 rose from 44.6 to 66.2%. The proportion of 85+ year olds with ?1 hospital admissions per year rose from 27.6 to 35.4%. CONCLUSIONS:there has been a dramatic increase in the medicalisation of the oldest old, evident in increased diagnosis (likely partly due to better record keeping) but also increased prescribing and hospitalisation. Diagnostic trends especially for chronic kidney disease may raise concerns about overdiagnosis. These findings provide new urgency to questions about the appropriateness of multiple diagnostic labelling.

SUBMITTER: Melzer D

PROVIDER: S-EPMC4255615 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Much more medicine for the oldest old: trends in UK electronic clinical records.

Melzer David D Tavakoly Behrooz B Winder Rachel E RE Masoli Jane A H JA Henley William E WE Ble Alessandro A Richards Suzanne H SH

Age and ageing 20140807 1

<h4>Background</h4>the oldest old (85+) pose complex medical challenges. Both underdiagnosis and overdiagnosis are claimed in this group.<h4>Objective</h4>to estimate diagnosis, prescribing and hospital admission prevalence from 2003/4 to 2011/12, to monitor trends in medicalisation.<h4>Design and setting</h4>observational study of Clinical Practice Research Datalink (CPRD) electronic medical records from general practice populations (eligible; n = 27,109) with oversampling of the oldest old.<h4 ...[more]

PMID: 25103030

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Project description:ObjectivesMedical behaviors are playing significant roles in the delivery of high quality and cost-effective health services. Timely discovery of changing frequencies of medical behaviors is beneficial for the improvement of health services. The main objective of this work is to discover the changing trends of medical behaviors over time.MethodsThis study proposes a two-steps approach to detect essential changing patterns of medical behaviors from Electronic Medical Records (EMRs). In detail, a probabilistic topic model, i.e., Latent Dirichlet allocation (LDA), is firstly applied to disclose yearly treatment patterns in regard to the risk stratification of patients from a large volume of EMRs. After that, the changing trends by comparing essential/critical medical behaviors in a specific time period are detected and analyzed, including changes of significant patient features with their values, and changes of critical treatment interventions with their occurring time stamps.ResultsWe verify the effectiveness of the proposed approach on a clinical dataset containing 12,152 patient cases with a time range of 10 years. Totally, 135 patients features and 234 treatment interventions in three treatment patterns were selected to detect their changing trends. In particular, evolving trends of yearly occurring probabilities of the selected medical behaviors were categorized into six content changing patterns (i.e, 112 growing, 123 declining, 43 up-down, 16 down-up, 35 steady, and 40 jumping), using the proposed approach. Besides, changing trends of execution time of treatment interventions were classified into three occurring time changing patterns (i.e., 175 early-implemented, 50 steady-implemented and 9 delay-implemented).ConclusionsExperimental results show that our approach has an ability to utilize EMRs to discover essential evolving trends of medical behaviors, and thus provide significant potential to be further explored for health services redesign and improvement.

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Much more medicine for the oldest old: trends in UK electronic clinical records.

Publications

Much more medicine for the oldest old: trends in UK electronic clinical records.

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