Pioneering Methods Developed to Investigate the Burden of Acute Rheumatic Fever and Rheumatic Heart Disease Using Multiple Linked Data Sources
| Autor: | Daniela Bond-Smith, Judith Katzenellenbogan, Rebecca Seth |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Burden of disease
medicine.medical_specialty Information Systems and Management Heart disease business.industry Incidence (epidemiology) Public health Annual average Health Informatics Acute rheumatic fever Linked data medicine.disease lcsh:HB848-3697 Emergency medicine lcsh:Demography. Population. Vital events Medicine Rheumatic fever business Information Systems Demography |
| Zdroj: | International Journal of Population Data Science, Vol 5, Iss 5 (2020) |
| ISSN: | 2399-4908 |
| DOI: | 10.23889/ijpds.v5i5.1632 |
| Popis: | IntroductionAcute Rheumatic Fever (ARF) and Rheumatic Heart Disease (RHD) remain a major public health concern in Australia. Government action requires reliable burden estimates, however data from single or unlinked sources are only partial and likely to be skewed, exacerbated by systemic problems with ICD-10 codes for RHD. Linked data provide an opportunity to address these shortcomings. Objectives and ApproachObjectives: to develop a methodology using harmonised linked data across five Australian jurisdictions to determine the burden of ARF and RHD 90 days from the previous one across both register and hospital data. For first-ever episodes we applied a lookback to mid-2001 for both ARF and RHD. For Western Australia, we evaluated the effect of look-back period on prevalence pooling. ResultsFor total ARF incidence over 3 years (2015-2017), there was 1425 episodes compared to 1027 episodes for first-ever ARF. There was an annual average of 5241 cases of RHD identified using our new methods (0-54yrs) – substantially higher than 2634 and 4255 RHD cases reported by Global Burden of Disease Study and Australian Institute of Welfare estimates respectively for 2017. Increased lookback had no effect on first-ever ARF but increased RHD prevalence >25 years. Conclusion / ImplicationsBy using multiple sources and cross-jurisdictional data we were able to provide contemporary and robust estimates for the burden of ARF and RHD in Australia. The prediction algorithm we developed can also be used in other countries, where only hospital data is available, to quantify RHD burden. |
| Databáze: | OpenAIRE |
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