The effectiveness and cost effectiveness of a hospital avoidance program in a residential aged care facility: a prospective cohort study and modelled decision analysis
Hannah CarterXing J. LeeTrudy DwyerBarbara O’NeillDee JeffreyChristopher M. DoranLynne ParkinsonSonya OsborneKerry Reid‐SearlNicholas Graves
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Abstract Background Residential aged care facility residents experience high rates of hospital admissions which are stressful, costly and often preventable. The EDDIE program is a hospital avoidance initiative designed to enable nursing and care staff to detect, refer and quickly respond to early signals of a deteriorating resident. The program was implemented in a 96-bed residential aged care facility in regional Australia. Methods A prospective pre-post cohort study design was used to collect data on costs of program delivery, hospital admission rates and length of stay for the 12 months prior to, and following, the intervention. A Markov decision model was developed to synthesize study data with published literature in order to estimate the cost-effectiveness of the program. Quality adjusted life years (QALYs) were adopted as the measure of effectiveness. Results The EDDIE program was associated with a 19% reduction in annual hospital admissions and a 31% reduction in the average length of stay. The cost-effectiveness analysis found the program to be both more effective and less costly than usual care, with 0.06 QALYs gained and $249,000 health system costs saved in a modelled cohort of 96 residents. A probabilistic sensitivity analysis estimated that there was an 86% probability that the program was cost-effective after taking the uncertainty of the model inputs into account. Conclusions This study provides promising evidence for the effectiveness and cost-effectiveness of a nurse led, early intervention program in preventing unnecessary hospital admissions within a residential aged care facility. Further research in multi-site randomised studies is needed to confirm the generalisability of these results.<p>Eligible patients of different cohorts. Eligible patients in cohort I, cohort II, cohort III and TCGA cohort.</p>
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<p>(A-B) Overall survival curve of three prognostic groups in the training cohort (Cohort Mono N=149) and the validation cohort (Cohort Multi, N=205, 5 patients in the Cohort Multi without enough clinicopathological data were excluded). (A) Training cohort. (B) Validation cohort.</p>
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<p>(A-B) Overall survival curve of three prognostic groups in the training cohort (Cohort Mono N=149) and the validation cohort (Cohort Multi, N=205, 5 patients in the Cohort Multi without enough clinicopathological data were excluded). (A) Training cohort. (B) Validation cohort.</p>
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Characterization of disease progression course is important for prevention and treatment of the disease. However, for the disease with slow progression such as Alzheimer disease (AD), the development of a progression course is not easy because of the difficulty to follow up for a long time. A solution to overcome this limitation is to use multiple single cohorts of successive stages of the disease, for example preclinical AD cohort and MCI cohort for AD. We present a method to integrate the two single cohorts to model a disease progression course over time. We suggested the four steps 1) estimating the model according to follow up time for each cohort (figure 1) 2) generating the predicted outcome and its 95% confidence interval for each subject 3) checking the overlapped region of the predicted values between the two successive cohorts and searching the time to start to overlap between the two cohorts postulating the cohort of the late stage (cohort 2) comes after the cohort of the early stage (cohort 1) at this time (figure 2) 4) finally estimating the linear mixed model of one whole course of the disease using cohort 1 and cohort 2 (figure 3). We examined the validity of our approach using the simulated data. The data of 100 subjects for each cohort was generated as the following setting assuming cohort 2 starts to overlap to cohort 1 at t=4. For cohort 1, ln(Y)=b10+b11*t+ε (t=0∼5), where b10∼N(1.0, 0.12), b11=0.025; ε∼N(0, σ112), σ11=0.1, 0.2. For cohort 2, ln(Y)=b20+b21*t+ε (t=0∼5), where b20∼N(2.2, 0.22), b21=0.03; ε∼N(0, σ212), σ12=0.2, 0.3. For each data of all combinations according to the random variability of intercept and residual in cohort 1 and cohort 2, the time to start to overlap between the two cohorts was estimated to very close to true value of t=4 (range 4.00∼4.03). The disease progression model over t=0∼10 was estimated to ln(Y)=0.920+0.028*t.
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Cohort effect
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Abstract The aim of this study was to compare the prevalence of the different types of eardrum pathology in a cohort of adults not previously treated by grommet insertion with corresponding findings obtained in a cohort previously treated with grommet insertion. A cohort born in 1955 were invited to a screening examination including otomicroscopy. In the untreated cohort, retraction of Shrapnell's membrane was found in four per cent of the ears compared to 20 per cent in the cohort treated with grommets. Tensa pathology, including atrophy and myringosclerosis, was found in six per cent of the ears in the untreated cohort and in 17 per cent in the treated cohort. Normal eardrums were found in 91 per cent of the ears. Despite the increased awareness of secretory otitis, as well as the increased rate of surgical treatment, the prevalence of eardrum pathology seems to be increasing. The reasons for this are discussed.
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In autumn 1977 91% of the women who had graduated from United Kingdom medical schools in 1949-51 (early cohort) and 1965 (late cohort) were practising medicine. Over the first 12 years after qualification the late cohort was marginally more active in medicine and had more members in career and training posts than the early cohort. On the survey date 1 October 1977 (26-28 years after qualification) the participation index of the early cohort was 0.73 and of the late cohort (12 years) 0.65. Both cohorts show the bimodal career pattern characteristic of British women's occupational experience.
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Background: In the past 5 years, type 2 diabetes practice guidelines have shifted to prioritize agents with cardiovascular (CV) and renal benefit. This study assessed whether real-world medication use has changed in line with these evolving guidelines. Methods: We retrospectively examined the treatment patterns (12 months pre and post) of people with T2D (PwT2D) who initiated basal insulin (BI) in either 2015 (cohort 1) , 2017 (cohort 2) , or 2019 (cohort 3) using IBM® MarketScan® Databases. Between-group differences were analyzed via t-test or chi-square as appropriate. Results: In the study period, 6,396,441 PwT2D were identified. The number of PwT2D initiating BI was 17,801/948,805 (1.88%) in 2015 vs. 10,870/622,455 (1.75%) in 2019 and a progressively larger share of BI initiators had prior GLP-1RA use (14.8% in cohort 1 vs. 25.2% in cohort 3; p < 0.001) or SGLT-2i use (11.4% in cohort 1 vs. 20.5% in cohort 3; p < 0.001) . Furthermore, in patients without prior GLP-1RA, SGLT-2i, or bolus use who initiated GLP-1RA or SGLT-2i in the first year of BI, time to first GLP-1RA (132.4 days in cohort 1 vs. 120.5 days in cohort 3; p = 0.02) or SGLT-2i (131.5 days in cohort 1 vs. 113.3 days in cohort 3; p = 0.002) decreased. Conclusions: Real-world data suggests growing and earlier use of GLP-1RA and SGLT-2i among BI users, and a growing proportion of PwT2D on BI-GLP-1RA combination treatment, in line with changing practice guidelines. Disclosure D. Schapiro: Employee; Eli Lilly and Company. A. Meeks: Employee; Eli Lilly and Company. D. Liu: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. F. Gelsey: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. R. Juneja: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. M. Perez-nieves: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. A. Huang: None. Funding Eli Lilly and Company
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summary We introduced an email teleneurology service for patients referred to a neurologist by general practitioners. Over 14 months, 76 referrals (of 75 patients) were received. To determine the sustainability of the service, we studied a second cohort of 76 consecutive patients referred after our first study. We also followed up the first cohort of patients to get information on longer-term safety. The second cohort was obtained in one month less than the first, and had similar characteristics in terms of age, sex and the time taken by the neurologist to reply to the general practitioner. It contained fewer patients requiring clinic appointments (34% versus 43%). Fewer patients from the second cohort were referred for second opinions (4 versus 10) and there were no resulting changes in diagnosis. Follow-up of the first cohort from a mean of 6 months to a mean of 23 months led to seven more re-referrals and no additional changes in diagnosis. We conclude that teleneurology by email is sustainable for this group of patients, and confirm that it is safe, effective and efficient.
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