ArticleEpidemiology and infection2018
Estimating the likely true changes in rheumatic fever incidence using two data sources.
Article in Epidemiology and infection, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Acute rheumatic fever and rheumatic heart disease in children and adolescents in Victoria, Australia.Journal of paediatrics and child health · 2023Article
- C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features.International journal of molecular sciences · 2022Article
- Acute rheumatic fever and rheumatic heart disease in Victoria, 2006-18.The Medical journal of Australia · 2022Article
- Preceding group A streptococcus skin and throat infections are individually associated with acute rheumatic fever: evidence from New Zealand.BMJ global health · 2021Article
- Ethnically Disparate Disease Progression and Outcomes among Acute Rheumatic Fever Patients in New Zealand, 1989-2015.Emerging infectious diseases · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute rheumatic fever (ARF) continues to produce a significant burden of disease in New Zealand (NZ) Māori and Pacific peoples. Serious limitations in national surveillance data mean that accurate case totals cannot be generated. Given the changing epidemiology of ARF in NZ and the major national rheumatic fever prevention programme (2012-2017), we updated our previous likely true case number estimates using capture-recapture analyses. Aims were to estimate the likely true incidence of ARF in NZ and comment on the changing ability to detect cases. Data were obtained from national hospitalisation and notification databases. The Chapman Estimate partially adjusted for bias. An estimated 2342 likely true new cases arose from 1997 to 2015, giving a mean annual incidence of 2·9 per 100 000 (95% CI 2·5-3·5). Compared with the pre-intervention (2009-2011) baseline incidence (3·4 per 100 000, 95% CI 2·9-4·0), the likely true 2015 incidence declined 44%. Large gaps in data completeness are slowly closing. During the period 2012-2015, 723 cases were identified; 83·8% of notifications were matched to hospitalisation data, and 87·2% of hospitalisations matched to notifications. Despite this improvement, clinicians need to remain aware of the need to notify atypical patients. A possible unintended consequence of the national ARF prevention programme is increased misdiagnosis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.