Background In 2018, the World Health Organization prioritized control of acute rheumatic fever (ARF) and rheumatic heart disease (RHD), including disease surveillance. We developed strategies for estimating contemporary ARF/RHD incidence and prevalence in Australia (2015–2017) by age group, sex, and region for Indigenous and non‐Indigenous Australians based on innovative, direct methods. Methods and Results This population‐based study used linked administrative data from 5 Australian jurisdictions. A cohort of ARF (age <45 years) and RHD cases (<55 years) were sourced from jurisdictional ARF/RHD registers, surgical registries, and inpatient data. We developed robust methods for epidemiologic case ascertainment for ARF/RHD. We calculated age‐specific and age‐standardized incidence and prevalence. Age‐standardized rate and prevalence ratios compared disease burden between demographic subgroups. Of 1425 ARF episodes, 72.1% were first‐ever, 88.8% in Indigenous people and 78.6% were aged <25 years. The age‐standardized ARF first‐ever rates were 71.9 and 0.60/100 000 for Indigenous and non‐Indigenous populations, respectively (age‐standardized rate ratio=124.1; 95% CI, 105.2–146.3). The 2017 Global Burden of Disease RHD prevalent counts for Australia (<55 years) underestimate the burden (1518 versus 6156 Australia‐wide extrapolated from our study). The Indigenous age‐standardized RHD prevalence (666.3/100 000) was 61.4 times higher (95% CI, 59.3–63.5) than non‐Indigenous (10.9/100 000). Female RHD prevalence was double that in males. Regions in northern Australia had the highest rates. Conclusions This study provides the most accurate estimates to date of Australian ARF and RHD rates. The high Indigenous burden necessitates urgent government action. Findings suggest RHD may be underestimated in many high‐resource settings. The linked data methods outlined here have potential for global applicability.

on behalf of the END RHD CRE Investigators Collaborators Acknowledgements: • Children, families and communities living with RHD-We thank the Aboriginal and Torres Strait Islander people for sharing their stories in the Endgame Strategy, and acknowledge that the research and data in this publication reflect the experiences of Aboriginal and Torres Strait Islander people and communities affected by the ongoing trauma of ARF and RHD. • END RHD Review Working Group-We thank the following members of the END RHD Alliance, who formed an expert working group to review content of the Endgame Strategy for feasibility and acceptability, including review from a cultural perspective:

PurposeAcute 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 methodsThe 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.ResultsOf 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.ConclusionThe 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.

BACKGROUND: Recent vaccine mandates in Australia, as in other high income settings, have sought to change the behavior of parents, including those who would otherwise access nonmedical exemptions. Since 2014, Australian state governments have introduced and progressively tightened policies restricting the access of unvaccinated children to early education and child care. In 2016, the Federal Government removed financial entitlements and subsidies from nonvaccinating families. We sought to ascertain the impact of these policies on vaccine coverage rates by state, and also to consider their impact on communities with high numbers of registered refusers. METHODS: Interrupted time series models were fitted by using the Autoregressive Integrated Moving Average framework to test for changes in trend in vaccination rates following implementation of government policies. RESULTS: Australian vaccine coverage rates were rising before the vaccine mandates and continued to do so subsequently, with no statistically significant changes to coverage rates associated with the interventions. The exception was New South Wales, where vaccine coverage rates were static before the policy intervention, but were increasing at an annual rate of 1.25% after (P &lt; .001). The impact of the policies was indistinguishable between communities with high, medium and low numbers of registered vaccine refusers. CONCLUSIONS: In our study, we show that childhood vaccine coverage continued on its positive trajectory without any conclusive evidence of impact of mandatory policies. Overseas policymakers looking to increase coverage rates would be well-advised to examine the contribution of pre-existing and parallel nonmandatory interventions employed by Australian governments to the country’s enhanced coverage.

Background 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.