A Nationwide, Population-Based Prevalence Study of Genetic Muscle Disorders.


Journal

Neuroepidemiology
ISSN: 1423-0208
Titre abrégé: Neuroepidemiology
Pays: Switzerland
ID NLM: 8218700

Informations de publication

Date de publication:
2019
Historique:
received: 20 08 2018
accepted: 26 09 2018
pubmed: 21 1 2019
medline: 26 12 2019
entrez: 21 1 2019
Statut: ppublish

Résumé

Previous epidemiological studies of genetic muscle disorders have relied on medical records to identify cases and may be at risk of selection biases or have focused on selective population groups. This study aimed to determine age-standardised prevalence of genetic muscle disorders through a nationwide, epidemiological study across the lifespan using the capture-recapture method. Adults and children with a confirmed clinical or molecular diagnosis of a genetic muscle disorder, resident in New Zealand on April 1, 2015 were identified using multiple overlapping sources. Genetic muscle disorders included the muscular dystrophies, congenital myopathies, ion channel myopathies, GNE myopathy, and Pompe disease. Prevalence per 100,000 persons by age, sex, disorder, ethnicity and geographical region with 95% CIs was calculated using Poisson distribution. Direct standardisation was applied to age-standardise prevalence to the world population. Completeness of case ascertainment was determined using capture-recapture modelling. Age standardised minimal point prevalence of all genetic muscle disorders was 22.3 per 100,000 (95% CI 19.5-25.6). Prevalence in Europeans of 24.4 per 100,000, (95% CI 21.1-28.3) was twice that observed in NZ's other 3 main ethnic groups; Māori (12.6 per 100,000, 95% CI 7.8-20.5), Pasifika (11.0 per 100,000, 95% CI 5.4-23.3), and Asian (9.13 per 100,000, 95% CI 5.0-17.8). Crude prevalence of myotonic dystrophy was 3 times higher in Europeans (10.5 per 100,000, 9.4-11.8) than Māori and Pasifika (2.5 per 100,000, 95% CI 1.5-4.2 and 0.7 per 100,000, 95% CI 0.1-2.7 respectively). There were considerable regional variations in prevalence, although there was no significant association with social deprivation. The final capture-recapture model, with the least deviance, estimated the study ascertained 99.2% of diagnosed cases. Ethnic and regional differences in the prevalence of genetic muscle disorders need to be considered in service delivery planning, evaluation, and decision making.

Sections du résumé

BACKGROUND
Previous epidemiological studies of genetic muscle disorders have relied on medical records to identify cases and may be at risk of selection biases or have focused on selective population groups.
OBJECTIVES
This study aimed to determine age-standardised prevalence of genetic muscle disorders through a nationwide, epidemiological study across the lifespan using the capture-recapture method.
METHODS
Adults and children with a confirmed clinical or molecular diagnosis of a genetic muscle disorder, resident in New Zealand on April 1, 2015 were identified using multiple overlapping sources. Genetic muscle disorders included the muscular dystrophies, congenital myopathies, ion channel myopathies, GNE myopathy, and Pompe disease. Prevalence per 100,000 persons by age, sex, disorder, ethnicity and geographical region with 95% CIs was calculated using Poisson distribution. Direct standardisation was applied to age-standardise prevalence to the world population. Completeness of case ascertainment was determined using capture-recapture modelling.
RESULTS
Age standardised minimal point prevalence of all genetic muscle disorders was 22.3 per 100,000 (95% CI 19.5-25.6). Prevalence in Europeans of 24.4 per 100,000, (95% CI 21.1-28.3) was twice that observed in NZ's other 3 main ethnic groups; Māori (12.6 per 100,000, 95% CI 7.8-20.5), Pasifika (11.0 per 100,000, 95% CI 5.4-23.3), and Asian (9.13 per 100,000, 95% CI 5.0-17.8). Crude prevalence of myotonic dystrophy was 3 times higher in Europeans (10.5 per 100,000, 9.4-11.8) than Māori and Pasifika (2.5 per 100,000, 95% CI 1.5-4.2 and 0.7 per 100,000, 95% CI 0.1-2.7 respectively). There were considerable regional variations in prevalence, although there was no significant association with social deprivation. The final capture-recapture model, with the least deviance, estimated the study ascertained 99.2% of diagnosed cases.
CONCLUSIONS
Ethnic and regional differences in the prevalence of genetic muscle disorders need to be considered in service delivery planning, evaluation, and decision making.

Identifiants

pubmed: 30661069
pii: 000494115
doi: 10.1159/000494115
pmc: PMC6518995
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

128-135

Informations de copyright

© 2019 The Author(s) Published by S. Karger AG, Basel.

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Auteurs

Alice Theadom (A)

National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand, alice.theadom@aut.ac.nz.

Miriam Rodrigues (M)

Muscular Dystrophy Association of New Zealand, Auckland, New Zealand.
Department of Neurology, Auckland City Hospital, Auckland, New Zealand.

Gemma Poke (G)

Genetic Health Service NZ, Capital and Coast District Health Board, Wellington, New Zealand.

Gina O'Grady (G)

Paediatric Neuroservices, Starship Children's Health, Auckland District Health Board, Auckland, New Zealand.

Donald Love (D)

Diagnostic Genetics, LabPLUS, Auckland City Hospital, Auckland, New Zealand.

Graeme Hammond-Tooke (G)

Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.

Priya Parmar (P)

Department of Biostatistics and Epidemiology, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand.

Ronelle Baker (R)

Muscular Dystrophy Association of New Zealand, Auckland, New Zealand.

Valery Feigin (V)

National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand.

Kelly Jones (K)

National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand.

Braden Te Ao (B)

Population Health, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand.

Anna Ranta (A)

Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.

Richard Roxburgh (R)

Department of Neurology, Auckland City Hospital, Auckland, New Zealand.

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Classifications MeSH