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  • 1
    In: Annals of Oncology, Elsevier BV, Vol. 33 ( 2022-09), p. S924-S925
    Type of Medium: Online Resource
    ISSN: 0923-7534
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 2003498-2
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  • 2
    In: The journal of nutrition, health & aging, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2019-1), p. 105-110
    Type of Medium: Online Resource
    ISSN: 1279-7707 , 1760-4788
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2082520-1
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  • 3
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 80, No. Suppl 1 ( 2021-06), p. 852.2-853
    Abstract: Evidence suggests that gout is associated with high health care costs and that many inpatient admissions are preventable (1). Understanding the driver of health care costs will allow more targeted intervention. Objectives: To examine factors associated with high health service utilisation and cost in patients admitted to hospital with gout, using whole-population linked hospital, WA cancer registration, Emergency Department (ED) and death data (2). Methods: The study included patients (18 to 84 years) who had been admitted to hospital with a primary or co-diagnosis of gout for the first time between 1 Jan 02 and 31 Dec 09 Hospital costs were calculated per patient using DRG codes and ED costs were calculated from URG codes. Costs are presented in Australian dollars. Follow-up was completed at five years post their initial gout hospitalisation, at death, or at the 31th of December 2014. Both univariable and multivariable analysis was conducted for each patient characteristic. Independent variables were assessed for collinearity. Collinearity was assumed present where the correlation co-efficient was greater than 0.7. Results: 4,379 individuals were included. In the following five years, there was 22,222 ED attendances (median cost, $1826 per patient (IQR: $433 - $4,414)), and 58,920 hospital admissions, (median cost, $25,009 per patient (IQR: $6,844 - $60,535)). 4,059 (18.3%) ED attendances and 3,834 (6.5%) hospital admissions were potentially preventable. Gout was not a major driver of events, with 341 (1.5%) ED attendances and 620 (1.1%) hospital admissions coded with a primary diagnosis of gout. In the univariable analysis (Table 1), Aboriginality and smoking were associated with an increased number of both ED attendances and hospital admissions. Increased socio-economic status was associated with a reduction in ED attendances, however, this was not reflected in hospital admissions. Conclusion: Patients admitted to hospital with gout are highly likely to be re-admitted or attend ED in the following 5 years. Many of these contacts are preventable, but are usually driven by comorbidities rather than gout. References: [1]Loh K, . Intern Med J. 2020 Mar;50(3):386. [2] https://www.datalinkage-wa.org.au . Acknowledgements: The authors wish to thank the staff at the Western Australian Data Linkage Branch and the Hospital Morbidity Data Collection, and the Death Registrations and the Emergency Department Data Collection. Disclosure of Interests: None declared.
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 1481557-6
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  • 4
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 80, No. Suppl 1 ( 2021-06), p. 52.1-53
    Abstract: The worldwide incidence of PyA is reportedly rising due to a combination of increased longevity, multi- comorbidity, iatrogenic complications and increasing use of immunomodulating therapies, while there is limited data on longterm outcomes of PyA. Objectives: To describe the recent incidence, risk factors and long-term outcomes in adults hospitalised with non-gonococcal pyogenic arthritis (PyA) of native joints in Western Australia (WA). Methods: We extracted population-based longitudinally linked administrative health data for patients 16 years or older with a first diagnostic code of 711.xx (ICD9-CM) and M00.xx (ICD10-AM) in WA in the period 1990-2010. Annual incidence rates (IR), risk factors during 14.5 years lookback and outcomes including standardized mortality rates (SMR) during 10.1 years follow-up are reported. Results: A total of 2,777 patients (67% male, mean age 49.8 ± 20.5) received a first diagnostic code for PyA. The AIR increased from 4.5 to 11.8 /100,000 over time as did age at onset (45.1 to 55.4 years) and proportion of female patients (23 to 36%). There was no seasonal variation in PyA incidence but a higher rate of predisposing comorbidities in female patients. Knees (33.6%) and hands (22%) were most frequently affected with 28.4 % of positive cultures not due to Gr+ cocci. Mean hospital stay was 8 days, 30-day readmittance and mortality rate was 12 .8% and 3.1% respectively. During ten years follow-up serious infections (43%), new diagnosis of osteoarthrosis (20%), joint replacement (10.8%), osteomyelitis (6%), and crystal arthropathy (6.3%) were the most common morbidities. SMR were increased across all age and gender categories (Table) but highest in females aged 16-40 (SMR 25.9). Table 1. Mortality rates (MR) per 1000 person years in patients with pyogenic arthritis compared with age (at death) and gender matched categories from the general population by standardised mortality rate (SMR) Gender Age Deaths Person years MR PyA MR Gen pop * SMR Male 16-40 27 4015 6.72 0.892 7.53 40-59 80 7106 11.25 2.972 3.78 〉 60 331 7366 44.93 21.55 2.08 All 438 18487 23.69 5.820 4.07 Female 16-40 11 1026 10.72 0.41 25.95 40-59 40 2769 14.44 1.75 8.21 〉 60 208 4088 50.88 24.20 2.10 All 259 7883 32.85 5.50 5.96 * Based on WA death data from Australian Bureau of statistics in 2011 Conclusion: The incidence of PyA has increased significantly between 1990 and 2010 in WA. PyA associates with a 3% in-hospital mortality rate and significant late bone and joint morbidity including osteomyelitis. PyA associated with excess mortality across age and gender categories, most markedly in younger female patients. References: [1]Ross JJ. Septic arthritis of native joints. Infect Dis Clin North Am 2017;31:203-18 Acknowledgements: The authors would like to acknowledge the support of the Western Australian Data Linkage Branch, the Western Australian Department of Health, and the data custodians of, the Hospital and Morbidity Data Collection, the Emergency Department Data Collection the WA Cancer Register and the WA Death Register for their assistance with the study Disclosure of Interests: None declared
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2021
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  • 5
    Online Resource
    Online Resource
    BMJ ; 2022
    In:  Annals of the Rheumatic Diseases Vol. 81, No. Suppl 1 ( 2022-06), p. 1065.1-1065
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 81, No. Suppl 1 ( 2022-06), p. 1065.1-1065
    Abstract: Rheumatoid arthritis (RA) is a heterogeneous chronic autoimmune disease that affects the synovial joint lining and may result in permanent joint destruction, premature death, and socio-economic burden. 1 Although RA is one of Australia’s national health priority areas and gathering information about the RA burden of disease was one of the national action plans 2 , no published epidemiological study adequately describes RA prevalence and risk factors for frequent hospitalisations in Western Australia (WA) to date. An accurate prevalence estimate of this disease offers a framework for predicting present and growing healthcare service requirements in the future. 3 Objectives We estimated RA period prevalence and identified risk factors of frequent RA hospitalisations, using linked administrative health and state-specific Australian Pharmaceutical Benefits Scheme (PBS) datasets in WA from 1995–2014. Methods RA prevalence was calculated per 1000 hospital separations and biological therapy users. RA patients were identified in the WA linked health dataset using ICD codes 714.0–714.9 and M05.00–M06.99. Dispensing data on biological therapy for RA were obtained from PBS records and converted to defined daily doses/1000 population/day. Multivariate logistic regression was used to analyse risk factors for frequent RA hospitalisations ( 〉 2/year), controlling for sex, age, and geographic locations. Results A total of 17,125 RA patients were admitted to WA hospitals between 1995–2014. The total number of RA hospital separations was 50,353, averaging three hospitalisations per patient over 20 years. The RA period prevalence was 3.4 per 1,000 separations (0.34%), while the RA period prevalence based on biological therapy use was 0.36%. The corrected RA prevalence based on biological therapy usage was 0.36% and 0.72% for the 2005–2009 and 2010–2014 periods, respectively (Table 1). Female gender, age 60–69 years, and living in rural areas were all risk factors for frequent RA hospitalisations. Table 1. Total number of Rheumatoid Arthritis patients in Western Australia taking a standard dose daily (DDD) of RA biological therapy from 1995 to 2014. Year Total RA bDMARDs utilisation (DDD/1000 population/day) WA general population Prevalence of RA bDMARDs use in WA population (%) Number of RA patients use standard dose daily of bDMARDs at WA 2003 0.01 1,952,741 0.00 14 2004 0.08 1,979,542 0.01 158 2005 0.16 2,011,207 0.02 329 2006 0.23 2,050,581 0.02 476 2007 0.31 2,106,139 0.03 643 2008 0.50 2,171,700 0.05 1,094 2009 0.60 2,240,250 0.06 1,338 2010 0.59 2,290,845 0.06 1,361 2011 0.63 2,353,409 0.06 1,475 2012 0.77 2,425,507 0.08 1,859 2013 0.66 2,486,944 0.07 1,649 2014 1.00 2,517,608 0.10 2,510 Abbreviations: bDMARDs, biologic disease-modifying anti-rheumatic drugs included Abatacept, Adalimumab, Certolizumab, Etanercept, Golimumab, Infliximab, Rituximab, Tocilizumab; DDD, defined daily doses; RA, Rheumatoid arthritis; WA, Western Australia. Conclusion Based on hospital and biological therapy data, the minimal prevalence of RA in Western Australia is 0.34–0.36%, which falls within the literature range. Older female RA patients in rural areas were more likely to be hospitalised, suggesting unmet needs in primary care access. References [1] Guo Q, Wang Y, Xu D, Nossent J, Pavlos NJ, Xu J. (2018) Rheumatoid arthritis: pathological mechanisms and modern pharmacologic therapies. Bone Res. 6, 15. [2] Australian Institute of Health and Welfare. (2006) National indicators for monitoring osteoarthritis, rheumatoid arthritis, and osteoporosis. pp. 55. AIHW, Canberra. [3]Hanly JG, Thompson K, Skedgel C. (2015) The use of administrative health care databases to identify patients with rheumatoid arthritis. Open access rheumatology: research and reviews. 7(6), 69-75. Acknowledgements The authors thank the data custodians of Hospital Morbidity Data Collection, Emergency Department Data Collection, the Death Registrations and staff at the Western Australian Data Linkage Branch to assist in the provision of data. Special thanks to the University of Western Australia to support KA with an Australian Government Research Training Program PhD Scholarship and the Australian Rheumatology Association WA for Research Fellowship Award. Disclosure of Interests Khalid Almutairi: None declared, Charles Inderjeeth Speakers bureau: Eli Lilly, David Preen: None declared, Helen Keen Speakers bureau: Pfizer Australia, Abbvie Australia, Johannes Nossent Speakers bureau: Janssen
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2022
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  • 6
    Online Resource
    Online Resource
    BMJ ; 2022
    In:  Annals of the Rheumatic Diseases Vol. 81, No. Suppl 1 ( 2022-06), p. 671.2-671
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 81, No. Suppl 1 ( 2022-06), p. 671.2-671
    Abstract: The association between systemic lupus erythematosus (SLE) and cancer risk is unclear. Objectives Describe the association between systemic lupus erythematosus (SLE) and the risk of cancer development and subsequent 5-year mortality in Western Australia (WA). Methods Population-level cohort study of SLE patients (n=2,111) and general population comparators (n=21,110) hospitalised between 1980–2014. SLE patients (identified by ICD-9-CM: 695.4, 710.0, and ICD-10-AM: L93.0, M32.0) were nearest matched (10:1) for age, sex, Aboriginality, and temporality. Follow-up was from timezero (index SLE hospitalisation) to cancer development, death or 31/12/2014. Using longitudinal linked health data, we determined the risk of cancer development and subsequent 5-year mortality between SLE patients and comparators with Cox proportional hazards regression models. Results SLE patients had similar multivariate-adjusted risk (aHR 1.03, 95%CI 0.93, 1.15; P=0.583) of cancer development. Cancer development risk was higher in SLE patients 〈 40 years old (aHR 1.51), and from 1980-1999 (aHR 1.28). SLE patients had higher risk of developing cancer of the oropharynx (aHR 2.13); vulvo-vagina (aHR 3.22); skin (aHR 1.20), and, lymphatic and haematopoietic tissues (aHR 1.78), all P 〈 0.05. SLE patients had reduced risk of breast cancer (aHR 0.64). After cancer development, SLE patients had increased risk of 5-year mortality (aHR 1.16, 95%CI 1.01, 1.33); highest in 40-49 years old (aHR 1.89), and in those with skin (aHR 1.65) or prostate cancer (aHR 4.74). Conclusion Hospitalised SLE patients had increased risk of multiple cancers, but a reduced risk of breast cancer. Following cancer development, SLE patients had increased risk of 5-year mortality. Together there is scope to improve cancer prevention and surveillance in SLE patients. Acknowledgements The authors wish to thank the staff at the Western Australian Data Linkage Branch and Emergency Department Data Collection, Hospital Morbidity Data Collection, Western Australian Cancer Registry, and Death Registrations. The authors wish to thank the Australian Co-ordinating Registry, the Registries of Births, Deaths and Marriages, the Coroners, the National Coronial Information System and the Victorian Department of Justice and Community Safety for enabling COD URF data to be used for this publication. Disclosure of Interests None declared
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2022
    detail.hit.zdb_id: 1481557-6
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  • 7
    Online Resource
    Online Resource
    BMJ ; 2022
    In:  Annals of the Rheumatic Diseases Vol. 81, No. Suppl 1 ( 2022-06), p. 661.1-661
    In: Annals of the Rheumatic Diseases, BMJ, Vol. 81, No. Suppl 1 ( 2022-06), p. 661.1-661
    Abstract: Patients with autoimmune connective tissue disease (CTD) and systemic vasculitis (SV) often require aggressive immune-modulating therapy to prevent organ damage. This however increases the risk for common and uncommon infections. Objectives To compare the temporal rates and associated mortality of hospitalisation with opportunistic infections (OI) for CTD and SV patients in Western Australia between 1985 and 2015. Methods All patients hospitalized in Western Australia in the period 1985-2015 with ≥ 2 ICD based diagnostic codes for SLE (n=1432), other CTD (o-CTD; incl DM/PM, systemic sclerosis, Sjogren’s syndrome; n=2161) and Systemic vasculitis (SV; n=1599) and a microbiologically confirmed OI (Mycobacterial, Fungal and viral infections) were included. Descriptive data are given as median (IQR) and frequency (%). Incidence rates per 1000 person years (IR) were calculated during 100.410 person years. Results OI occurred in 12.4 % of lupus, 11.5% of SV and 10.4% of o-CTD patients (p=0.72), but overall IR rates for OI were higher for lupus patients (9.87, CI 5 .49-15.76) than for SV (5.94, CI-2.81-10.24) and o-CTD patients (3.40, CI 1.62-7.23). However, whereas the IR for OI in lupus decreased over time, the IR increased for SV and o-CTD patients (Figure 1). Viral infections were the most frequent specific OI followed by tuberculosis and mycotic infections. Cryptococcal infections were observed in lupus patients only and the limited cases of pneumocystis occurred predominantly in SV patients with no cases observed after 2000 (Figure 2). In hospital mortality during OI admission was 11.5% for SV, 5.6 % for lupus and 3.5% for o-CTD patients (p=0.004). Figure 1. Figure 2. Conclusion Hospitalization rates for OI have decreased for lupus patients especially since 2005, whereas viral and mycotic OI rates have increased for both SV and o-CTD patients. Hospitalization for OI associated with significant case fatality in especially SV patients, indicating a need for increased prevention of OI. References [1]Esposito S, Bosis S, Semino M, Rigante D. Infections and systemic lupus erythematosus. Eur J Clin Microbiol Infect Dis. 2014 Sep;33(9):1467-75 Acknowledgements Supported by an unrestricted grant from The Arthritis Foundation of Western Australia Disclosure of Interests None declared
    Type of Medium: Online Resource
    ISSN: 0003-4967 , 1468-2060
    RVK:
    Language: English
    Publisher: BMJ
    Publication Date: 2022
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  • 8
    In: Osteoporosis International, Springer Science and Business Media LLC, Vol. 29, No. 8 ( 2018-8), p. 1759-1770
    Type of Medium: Online Resource
    ISSN: 0937-941X , 1433-2965
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 1480645-9
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  • 9
    In: Medical Journal of Australia, Wiley, Vol. 193, No. 6 ( 2010-09), p. 376-376
    Type of Medium: Online Resource
    ISSN: 0025-729X , 1326-5377
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2010
    detail.hit.zdb_id: 2035730-8
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  • 10
    In: Medical Journal of Australia, Wiley, Vol. 193, No. 3 ( 2010-08), p. 149-153
    Type of Medium: Online Resource
    ISSN: 0025-729X , 1326-5377
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2010
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