Estimating the true number of people with acute rheumatic fever and rheumatic heart disease from two data sources using capture–recapture methodology
Joanne Thandrayen A * , Ingrid Stacey B C , Jane Oliver D , Carl Francia E , Judith M. Katzenellenbogen B D and Rosemary Wyber A DA
B
C
D
E
Abstract
In Australia, accurate case ascertainment of acute rheumatic fever (ARF) and rheumatic heart disease (RHD) diagnoses for disease surveillance and control purposes requires the use of multiple data sources, including RHD registers and hospitalisation records. Despite drawing on multiple data sources, the true burden of ARF/RHD is likely to be underestimated.
This study used capture–recapture methods to quantify the missing number of ARF/RHD cases in data from hospitals and jurisdictional RHD registers. Linked datasets comprised reported cases of ARF/RHD in register records and administrative hospital data.
Capture–recapture analyses indicated the total number of new ARF/RHD cases in three Australian jurisdictions (Queensland, South Australia and Western Australia), among people aged 3–54 years, was 3480 (95% CI = 3366–3600) during 2011–2016. This included 894 (25.7%) individuals who were not listed in either the hospital or register datasets. Non-Indigenous, urban and older people with ARF/RHD were least likely to be identified in either the hospital or register data sources.
The 894 likely ARF/RHD cases our analyses detected that are not included in the routine surveillance datasets are concerning and quantify the magnitude and characteristics of under-notification to RHD registers in Australia, especially for groups that are not typically at high risk of ARF.
Keywords: acute rheumatic fever, capture–recapture method, disease surveillance, hospitalisations, linked databases, population size, registers, rheumatic heart disease.
References
1 Carapetis JR, Beaton A, Cunningham MW, et al. Acute rheumatic fever and rheumatic heart disease. Nat Rev Dis Primers 2016; 2: 15084.
| Crossref | Google Scholar | PubMed |
2 Stacey I, Hung J, Cannon J, et al. Long-term outcomes following rheumatic heart disease diagnosis in Australia. Eur Heart J Open 2021; 1(3): oeab035.
| Crossref | Google Scholar | PubMed |
3 Wyber R, Noonan K, Halkon C, et al. Ending rheumatic heart disease in Australia: the evidence for a new approach. Med J Aust 2020; 213(10): S1-31.
| Crossref | Google Scholar | PubMed |
4 Casey D, Turner P. Australia’s rheumatic fever strategy three years on. Med J Aust 2024; 220(4): 170-1.
| Crossref | Google Scholar | PubMed |
5 Department of Health, Victoria. Notification of Rheumatic Heart Disease and Acute Rhematic Fever. 2023. Available at https://www.health.vic.gov.au/health-advisories/notification-of-rheumatic-heart-disease-and-acute-rhematic-fever [accessed 13 September 2024].
7 Katzenellenbogen JM, Bond-Smith D, Seth RJ, et al. The End Rheumatic Heart Disease in Australia Study of Epidemiology (ERASE) Project: Data sources, case ascertainment and cohort profile. Clin Epidemiol 2019; 11: 997-1010.
| Crossref | Google Scholar | PubMed |
8 Agenson T, Katzenellenbogen JM, Seth RJ, et al. Case Ascertainment on Australian Registers for Acute Rheumatic Fever and Rheumatic Heart Disease. Int J Environ Res Public Health 2020; 17(15): 5505.
| Crossref | Google Scholar | PubMed |
9 Stacey I, Knight Y, Ong CMX, et al. Notification of acute rheumatic fever and rheumatic heart disease in hospitalised people in the Midwest region of Western Australia, 2012-2022: a retrospective cohort study. MJA 2024. 10.5694/mja2.52477
10 Rossi PG, Mantovani J, Ferroni E, et al. Incidence of bacterial meningitis (2001–2005) in Lazio, Italy: the results of a integrated surveillance system. BMC Infect Dis 2009; 9(1): 13.
| Google Scholar |
11 Bitar D, Morizot G, Van Cauteren D, et al. Estimating the burden of mucormycosis infections in France (2005–2007) through a capture–recapture method on laboratory and administrative data. Rev Epidemiol Sante Publique 2012; 60(5): 383-7.
| Crossref | Google Scholar | PubMed |
12 Oliver J, Pierse N, Williamson DA, Baker MG. Estimating the likely true changes in rheumatic fever incidence using two data sources. Epidemiol Infect 2018; 146(2): 265-75.
| Crossref | Google Scholar | PubMed |
13 Amiri H, Mohammadi MJ, Alavi SM, et al. Capture–recapture based study on the completeness of smear positive pulmonary tuberculosis reporting in southwest Iran during 2016. BMC Public Health 2021; 21: 2318.
| Crossref | Google Scholar | PubMed |
14 Balasubramani GK, Nowalk MP, Clarke LG, et al. Using capture–recapture methods to estimate influenza hospitalization incidence rates. Influenza Other Respir Viruses 2022; 16(2): 308-15.
| Crossref | Google Scholar | PubMed |
15 Australian Bureau of Statistics, Canberra. Estimates of Aboriginal and Torres Strait Islander Australians. 2021. Available at https://www.abs.gov.au/statistics/people/aboriginal-and-torres-strait-islander-peoples/estimates-aboriginal-and-torres-strait-islander-australians/latest-release [accessed 13 September 2024].
16 Independent Hospital Pricing Authority, Australia. Australian Consortium for Classification Development. International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification. 2017. Available at https://www.ihacpa.gov.au/resources/icd-10-amachiacs-tenth-edition [accessed 13 September 2024].
17 Bond-Smith D, Seth R, de Klerk N, et al. Development and evaluation of a prediction model for ascertaining rheumatic heart disease status in administrative data. Clin Epidemiol 2020; 12: 717-30.
| Crossref | Google Scholar | PubMed |
18 Katzenellenbogen JM, Nedkoff L, Cannon J, et al. Low positive predictive value of International Classification of Diseases, 10th Revision codes in relation to rheumatic heart disease: A challenge for global surveillance. Intern Med J 2019; 49: 400-3.
| Crossref | Google Scholar | PubMed |
19 Katzenellenbogen JM, Bond-Smith D, Seth RJ, et al. Contemporary incidence and prevalence of rheumatic fever and rheumatic heart disease in Australia using linked data: The case for policy change. J Am Heart Assoc 2020; 9(19): e016851.
| Crossref | Google Scholar | PubMed |
20 Australian Bureau of Statistics, Canberra. Australian Statistical Geography Standard (ASGS) Edition 3. 2023. Available at https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/remoteness-structure/remoteness-areas [accessed 13 September 2024].
21 Huggins RM. On the statistical analysis of capture experiments. Biometrika 1989; 76: 133-40.
| Crossref | Google Scholar |
22 Alho JM. Logistic regression in capture–recapture models. Biometrics 1990; 46: 623-35.
| Google Scholar | PubMed |
23 Wang Y, Thandrayen J. Multiple-record systems estimation using latent class models. Aust N Z J Stat 2009; 51: 101-11.
| Crossref | Google Scholar |
24 Stajic J, Carson A, Ward J. Rationale and plan for a focus on First Nations urban health research in Australia. Med J Aust 2024; 220(2): 64-6.
| Crossref | Google Scholar | PubMed |
25 Rheumatic Heart Disease, Australia. The 2020 Australian guideline for prevention, diagnosis and management of acute rheumatic fever and rheumatic heart disease. 2020. Available at https://www.rhdaustralia.org.au/system/files/fileuploads/arf_rhd_guidelines_3.2_edition_march_2022.pdf [accessed 13 September 2024].
26 Oliver J, Fualautoalasi-Lam L, Ferdinand A, et al. Living with rheumatic fever and rheumatic heart disease in Victoria, Australia: a qualitative study. 2024. Available at https://www.medrxiv.org/content/10.1101/2024.03.04.24303586v1.full.pdf [accessed 13 September 2024].
27 Tilling K, Sterne JAC. Capture–recapture models including covariate effects. Am J Epidemiol 1999; 149: 392-400.
| Crossref | Google Scholar | PubMed |