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      Hip fracture incidence and post-fracture mortality in Victoria, Australia: a state-wide cohort study

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          Abstract

          Summary

          Hip fractures are a major public health concern. Number of hip fractures cases increased by 20% from 2012 to 2018. Factors associated with post-fracture mortality included men, those who are frail, living in a non-metropolitan region, or residing in a residential aged care facility. Our results are useful for planning healthcare interventions.

          Purpose

          Hip fractures are a major public health concern in Australia. Data on hip fracture incidence and mortality are needed to plan and evaluate healthcare interventions. The aims of the study were to investigate (1) the time-trend in absolute number and incidence of first hip fractures, and (2) factors associated with mortality following first hip fractures in Victoria, Australia.

          Methods

          A state-wide cohort study of all patients aged \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document} 50 years admitted to a Victorian hospital for first hip fracture between July 2012 and June 2018. Annual age-standardized incidence rates were calculated using population data from Australian Bureau of Statistics. Multivariate negative binomial regression was used to investigate factors associated with post-fracture mortality.

          Results

          Overall, 31,578 patients had a first hip fracture, of whom two-thirds were women and 47% were \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document} 85 years old. Absolute annual numbers of first hip fractures increased by 20%. There was no significant change in age- and sex-adjusted incidence. In total, 8% died within 30 days and 25% within 1 year. Factors associated with 30-day mortality included age (≥ 85 years old versus 50–64 years old, mortality rate ratio [MRR] 8.05, 95% confidence interval [CI] 5.86–11.33), men (MRR 2.11, 95% CI 1.88–2.37), higher Hospital Frailty Risk Scores (high frailty versus no frailty, MRR 3.46, 95% CI 2.66–4.50), admission from a residential aged care facility (RACF) (MRR 2.28, 95% CI 1.85–2.82), and residing in a non-metropolitan region (MRR 1.22, 95% CI 1.09–1.38). The same factors were associated with 1-year mortality.

          Conclusion

          The absolute increase in hip fractures highlights the need for interventions to reduce fracture risk, especially for those at higher risk of post-fracture mortality, including men and those who are frail, living in a non-metropolitan region, or residing in a RACF.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s11657-023-01254-6.

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          Most cited references37

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          Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

          Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
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            Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study

            Summary Background Older people are increasing users of health care globally. We aimed to establish whether older people with characteristics of frailty and who are at risk of adverse health-care outcomes could be identified using routinely collected data. Methods A three-step approach was used to develop and validate a Hospital Frailty Risk Score from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes. First, we carried out a cluster analysis to identify a group of older people (≥75 years) admitted to hospital who had high resource use and diagnoses associated with frailty. Second, we created a Hospital Frailty Risk Score based on ICD-10 codes that characterised this group. Third, in separate cohorts, we tested how well the score predicted adverse outcomes and whether it identified similar groups as other frailty tools. Findings In the development cohort (n=22 139), older people with frailty diagnoses formed a distinct group and had higher non-elective hospital use (33·6 bed-days over 2 years compared with 23·0 bed-days for the group with the next highest number of bed-days). In the national validation cohort (n=1 013 590), compared with the 429 762 (42·4%) patients with the lowest risk scores, the 202 718 (20·0%) patients with the highest Hospital Frailty Risk Scores had increased odds of 30-day mortality (odds ratio 1·71, 95% CI 1·68–1·75), long hospital stay (6·03, 5·92–6·10), and 30-day readmission (1·48, 1·46–1·50). The c statistics (ie, model discrimination) between individuals for these three outcomes were 0·60, 0·68, and 0·56, respectively. The Hospital Frailty Risk Score showed fair overlap with dichotomised Fried and Rockwood scales (kappa scores 0·22, 95% CI 0·15–0·30 and 0·30, 0·22–0·38, respectively) and moderate agreement with the Rockwood Frailty Index (Pearson's correlation coefficient 0·41, 95% CI 0·38–0·47). Interpretation The Hospital Frailty Risk Score provides hospitals and health systems with a low-cost, systematic way to screen for frailty and identify a group of patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful. Funding National Institute for Health Research.
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              The frailty syndrome: definition and natural history.

              This article reviews the current state of knowledge regarding the epidemiology of frailty by focusing on 6 specific areas: (1) clinical definitions of frailty, (2) evidence of frailty as a medical syndrome, (3) prevalence and incidence of frailty by age, gender, race, and ethnicity, (4) transitions between discrete frailty states, (5) natural history of manifestations of frailty criteria, and (6) behavior modifications as precursors to the development of clinical frailty. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                miriam.leung77@monash.edu
                Journal
                Arch Osteoporos
                Arch Osteoporos
                Archives of Osteoporosis
                Springer London (London )
                1862-3522
                1862-3514
                29 April 2023
                29 April 2023
                2023
                : 18
                : 1
                : 56
                Affiliations
                [1 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, , Monash University (Parkville Campus), ; 381 Royal Parade, Victoria 3052 Parkville, Melbourne, Australia
                [2 ]GRID grid.14848.31, ISNI 0000 0001 2292 3357, Faculty of Pharmacy, , University of Montreal, ; Québec, Canada
                [3 ]GRID grid.294071.9, ISNI 0000 0000 9199 9374, Centre de Recherche, , Institut Universitaire de Gériatrie de Montréal, ; Québec, Canada
                [4 ]GRID grid.23856.3a, ISNI 0000 0004 1936 8390, Faculty of Pharmacy, , Laval University, ; Québec, Canada
                [5 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Department of Epidemiology and Preventive Medicine, , Monash University, ; Melbourne, Australia
                [6 ]GRID grid.410678.c, ISNI 0000 0000 9374 3516, Pharmacy Department, , Austin Health, ; Melbourne, Australia
                [7 ]GRID grid.9668.1, ISNI 0000 0001 0726 2490, Faculty of Health Sciences, , University of Eastern Finland, ; Kuopio, Finland
                Article
                1254
                10.1007/s11657-023-01254-6
                10148778
                37119328
                98ae02f1-f4ea-4951-a8d4-c2bb58035e58
                © The Author(s) 2023

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 February 2023
                : 19 April 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008710, Dementia Australia Research Foundation;
                Award ID: Yulgilbar Innovation Grant
                Funded by: National Health and Medical Research Council
                Award ID: Dementia Leadership Fellowship
                Award Recipient :
                Funded by: Australian Government
                Award ID: Research Training Scholarship
                Award Recipient :
                Funded by: Monash University
                Categories
                Original Article
                Custom metadata
                © International Osteoporosis Foundation and Bone Health and Osteoporosis Foundation 2023

                Orthopedics
                hip fracture,risk factors,incidence,mortality,frailty
                Orthopedics
                hip fracture, risk factors, incidence, mortality, frailty

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