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D Bond-Smith,1 R Seth,1 N de Klerk,1,2 L Nedkoff,1 M Anderson,3 J Hung,1 J Cannon,1,2 K Griffiths,4,5 JM Katzenellenbogen1,2 1School of Population and Global Health, The University of Western Australia, Perth, Australia; 2Telethon Kids Institute, Perth, Australia; 3Queensland Health, Brisbane, Australia; 4Centre for Big Data Research, The University of New South Wales, Sydney, Australia; 5Menzies School of Health Research, Charles Darwin University, Darwin, AustraliaCorrespondence: D Bond-Smith Email daniela.bond-smith@uwa.edu.auBackground: Previous research has raised substantial concerns regarding the validity of the International Statistical Classification of Diseases and Related Health Problems (ICD) codes (ICD-10 I05–I09) for rheumatic heart disease (RHD) due to likely misclassification of non-rheumatic valvular disease (non-rheumatic VHD) as RHD. There is currently no validated, quantitative approach for reliable case ascertainment of RHD in administrative hospital data.Methods: A comprehensive dataset of validated Australian RHD cases was compiled and linked to inpatient hospital records with an RHD ICD code (2000– 2018, n=7555). A prediction model was developed based on a generalized linear mixed model structure considering an extensive range of demographic and clinical variables. It was validated internally using randomly selected cross-validation samples and externally. Conditional optimal probability cutpoints were calculated, maximising discrimination separately for high-risk versus low-risk populations.Results: The proposed model reduced the false-positive rate (FPR) from acute rheumatic fever (ARF) cases misclassified as RHD from 0.59 to 0.27; similarly for non-rheumatic VHD from 0.77 to 0.22. Overall, the model achieved strong discriminant capacity (AUC: 0.93) and maintained a similar robust performance during external validation (AUC: 0.88). It can also be used when only basic demographic and diagnosis data are available.Conclusion: This paper is the first to show that not only misclassification of non-rheumatic VHD but also of ARF as RHD yields substantial FPRs. Both sources of bias can be successfully addressed with the proposed model which provides an effective solution for reliable RHD case ascertainment from hospital data for epidemiological disease monitoring and policy evaluation.Keywords: rheumatic heart disease, international classification of diseases, prediction, case ascertainment, acute rheumatic fever, non-rheumatic valvular heart disease, administrative data, validation, discrimination, receiver operating curve, Australia

Judith M Katzenellenbogen,1,2,* Daniela Bond-Smith,1,* Rebecca J Seth,1 Karen Dempsey,3 Jeffrey Cannon,2 Lee Nedkoff,1 Frank M Sanfilippo,1 Nicholas de Klerk,1,2 Joe Hung,1 Elizabeth Geelhoed,4 Daniel Williamson,5 Rosemary Wyber,2,6,7 Anna P Ralph,3 Dawn Bessarab1 On behalf of the ERASE Collaboration Study Group1School of Population and Global Health, The University of Western Australia, Perth, WA, Australia; 2Group A Streptococcus Research Group, Telethon Kids Institute, Perth, WA, Australia; 3Global and Tropical Health, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia; 4School of Allied Health, The University of Western Australia, Perth, WA, Australia; 5Aboriginal and Torres Strait Islander Health Branch, Queensland Health, Brisbane, QLD, Australia; 6Office of the Chief Scientist, The George Institute for Global Health, Sydney, NSW, Australia; 7The University of Sydney, Sydney, NSW, Australia*These authors contributed equally to this workCorrespondence: Judith M KatzenellenbogenSchool of Population and Global Health, The University of Western Australia, M431 35 Stirling Highway, Crawley, Perth, WA 6009, AustraliaTel +61 8 6488 1001Fax +61 8 6488 1188Email Judith.katzenellenbogen@uwa.edu.auPurpose: Acute rheumatic fever (ARF) and rheumatic heart disease (RHD) persist as public health issues in developing countries and among disadvantaged communities in high-income countries, with rates in Aboriginal and Torres Strait Islander peoples in Australia among the highest recorded globally. A robust evidence base is critical to support policy recommendations for eliminating RHD, but available data are fragmented and incomplete. The End RHD in Australia: Study of Epidemiology (ERASE) Project aims to provide a comprehensive database of ARF and RHD cases in Australia as a basis for improved monitoring and to assess prevention and treatment strategies. The objective of this paper is to describe the process for case ascertainment and profile of the study cohort.Patients and methods: The ERASE database has been built using linked administrative data from RHD registers, inpatient hospitalizations, and death registry data from 2001 to 2017 (mid-year). Additional linked datasets are available. The longitudinal nature of the data is harnessed to estimate onset and assess the progression of the disease. To accommodate systematic limitations in diagnostic coding for RHD, hospital-only identified RHD has been determined using a purposefully developed prediction model.Results: Of 132,053 patients for whom data were received, 42,064 are considered true cases of ARF or RHD in the study period. The patient population under 60 years in the compiled dataset is more than double the number of patients identified in ARF/RHD registers (12,907 versus 5049). Non-registered patients were more likely to be older, non-Indigenous, and at a later disease stage.Conclusion: The ERASE Project has created an unprecedented linked administrative database on ARF and RHD in Australia. These data provide a critical baseline for efforts to end ARF/RHD in Australia. The methodological work conducted to compile this database resulted in significant improvements in the robustness of epidemiological estimates and entails valuable lessons for ARF/RHD research globally.Keywords: rheumatic fever, rheumatic heart disease, indigenous, epidemiology, linked data

Melanie C Matheson,1,2,* Gayan Bowatte,1,3,* Jennifer L Perret,1,4 Adrian J Lowe,1,2 Chamara V Senaratna,1,5 Graham L Hall,6–8 Nick de Klerk,6,8 Louise A Keogh,9 Christine F McDonald,4 Nilakshi T Waidyatillake,1 Peter D Sly,10 Deborah Jarvis,11,12 Michael J Abramson,13 Caroline J Lodge,1,2,* Shyamali C Dharmage1,2,* 1Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia; 2Murdoch Children’s Research Institute, Melbourne, VIC, Australia; 3National Institute of Fundamental Studies, Kandy, Sri Lanka; 4Department of Respiratory and Sleep Medicine, Institute for Breathing and Sleep, Austin Health, University of Melbourne, Melbourne, VIC, Australia; 5Department of Community Medicine, University of Sri Jayewardenepura, Nugegoda, Sri Lanka; 6Telethon Kids Institute, Perth, WA, Australia; 7School of Physiotherapy and Exercise Science, Curtin University, Perth, WA, Australia; 8Centre of Child Health Research, University of Western Australia, Perth, WA, Australia; 9Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia; 10Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; 11MRC-PHE Centre for Environment and Health, Imperial College London, London, UK; 12Population Health and Occupational Diseases, National Heart and Lung Institute, Imperial College London, London, UK; 13School of Public Health & Preventive Medicine, Monash University, Melbourne, VIC, Australia *These authors contributed equally to this work Abstract: Early identification of people at risk of developing COPD is crucial for implementing preventive strategies. We aimed to systematically review and assess the performance of all published models that predicted development of COPD. A search was conducted to identify studies that developed a prediction model for COPD development. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was followed when extracting data and appraising the selected studies. Of the 4,481 records identified, 30 articles were selected for full-text review, and only four of these were eligible to be included in the review. The only consistent predictor across all four models was a measure of smoking. Sex and age were used in most models; however, other factors varied widely. Two of the models had good ability to discriminate between people who were correctly or incorrectly classified as at risk of developing COPD. Overall none of the models were particularly useful in accurately predicting future risk of COPD, nor were they good at ruling out future risk of COPD. Further studies are needed to develop new prediction models and robustly validate them in external cohorts. Keywords: COPD, early detection, predictors and risk prediction models

Seasonal influenza can cause significant morbidity in pregnant women. Much of the existing epidemiological evidence on influenza during pregnancy has focused on the 2009 A/H1N1 pandemic. To measure the epidemiological characteristics of seasonal influenza infection among pregnant women and the impact on infant health, a cohort of 86 779 pregnancies during the influenza season (2012-2014) was established using probabilistic linkage of notifiable infectious disease, hospital admission, and birth information. A total of 192 laboratory-confirmed influenza infections were identified (2·2 per 1000 pregnancies), 14·6% of which were admitted to hospital. There was no difference in the proportion of infections admitted to hospital by trimester or subtype of infection. Influenza B infections were more likely to occur in second trimester compared with influenza A/H3N2 and influenza A/H1N1 infections (41·3%, 23·6%, and 33·3%, respectively), and on average, infants born to women with influenza B during pregnancy had 4·0% (95% CI 0·3-7·6%) lower birth weight relative to optimal compared with infants born to uninfected women (P = 0·03). Results from this linked population-based study suggest that there are differences in maternal infection by virus type and subtype and support the provision of seasonal influenza vaccine to pregnant women.