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Using Bayesian methodology to explore the profile of mental health and well-being in 646 mothers of children with 13 rare genetic syndromes in relation to mothers of children with autism

Using Bayesian methodology to explore the profile of mental health and well-being in 646 mothers of children with 13 rare genetic syndromes in relation to mothers of children with autism
Using Bayesian methodology to explore the profile of mental health and well-being in 646 mothers of children with 13 rare genetic syndromes in relation to mothers of children with autism

BACKGROUND: It is well documented that mothers of children with intellectual disabilities or autism experience elevated stress, with mental health compromised. However, comparatively little is known about mothers of children with rare genetic syndromes. This study describes mental health and well-being in mothers of children with 13 rare genetic syndromes and contrasts the results with mothers of children with autism.

METHODS: Mothers of children with 13 genetic syndromes (n = 646; Angelman, Cornelia de Lange, Down, Fragile-X, Phelan McDermid, Prader-Willi, Rett, Rubenstein Taybi, Smith Magenis, Soto, Tuberous Sclerosis Complex, 1p36 deletion and 8p23 deletion syndromes) and mothers of children with autism (n = 66) completed measures of positive mental health, stress and depression. Using Bayesian methodology, the influence of syndrome, child ability, and mother and child age were explored in relation to each outcome. Bayesian Model Averaging was used to explore maternal depression, positive gain and positive affect, and maternal stress was tested using an ordinal probit regression model.

RESULTS: Different child and mother factors influenced different aspects of mental well-being, and critically, the importance of these factors differed between syndromes. Maternal depression was influenced by child ability in only four syndromes, with the other syndromes reporting elevated or lower levels of maternal depression regardless of child factors. Maternal stress showed a more complex pattern of interaction with child ability, and for some groups, child age. Within positive mental health, mother and child age were more influential than child ability. Some syndromes reported comparable levels of depression (SMS, 1p36, CdLS) and stress (SMS, AS) to mothers of children with autism.

CONCLUSIONS: Bayesian methodology was used in a novel manner to explore factors that explain variability in mental health amongst mothers of children with rare genetic disorders. Significant proportions of mothers of children with specific genetic syndromes experienced levels of depression and stress similar to those reported by mothers of children with autism. Identifying such high-risk mothers allows for potential early intervention and the implementation of support structures.

Journal Article
1750-1172
Adams, Dawn
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Hastings, Richard P.
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Alston-Knox, Clair
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Cianfaglione, Rina
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Eden, Kate
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Felce, David
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Griffith, Gemma
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Moss, Jo
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Stinton, Chris
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Oliver, Chris
071a4420-edd3-4054-99cc-e8374a644de2
Adams, Dawn
d3da9682-1fbd-4539-bb7e-1ba5e1418976
Hastings, Richard P.
4fd1ea2a-233f-461b-94c0-769e7d9e2c3c
Alston-Knox, Clair
5b1cace7-caa0-4a83-b50b-c8607768f8bb
Cianfaglione, Rina
bf9b4507-4a79-4f72-b7e2-7244b9dea9ef
Eden, Kate
f1b2b93c-096d-4b17-80b2-42f53de3f550
Felce, David
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Griffith, Gemma
48bed165-1941-4e39-8990-6b9d365ec928
Moss, Jo
df665849-2315-4983-8098-f3af42745a41
Stinton, Chris
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Oliver, Chris
071a4420-edd3-4054-99cc-e8374a644de2

Adams, Dawn, Hastings, Richard P., Alston-Knox, Clair, Cianfaglione, Rina, Eden, Kate, Felce, David, Griffith, Gemma, Moss, Jo, Stinton, Chris and Oliver, Chris (2018) Using Bayesian methodology to explore the profile of mental health and well-being in 646 mothers of children with 13 rare genetic syndromes in relation to mothers of children with autism. Orphanet Journal of Rare Diseases, 13 (1), [185]. (doi:10.1186/s13023-018-0924-1).

Record type: Article

Abstract

BACKGROUND: It is well documented that mothers of children with intellectual disabilities or autism experience elevated stress, with mental health compromised. However, comparatively little is known about mothers of children with rare genetic syndromes. This study describes mental health and well-being in mothers of children with 13 rare genetic syndromes and contrasts the results with mothers of children with autism.

METHODS: Mothers of children with 13 genetic syndromes (n = 646; Angelman, Cornelia de Lange, Down, Fragile-X, Phelan McDermid, Prader-Willi, Rett, Rubenstein Taybi, Smith Magenis, Soto, Tuberous Sclerosis Complex, 1p36 deletion and 8p23 deletion syndromes) and mothers of children with autism (n = 66) completed measures of positive mental health, stress and depression. Using Bayesian methodology, the influence of syndrome, child ability, and mother and child age were explored in relation to each outcome. Bayesian Model Averaging was used to explore maternal depression, positive gain and positive affect, and maternal stress was tested using an ordinal probit regression model.

RESULTS: Different child and mother factors influenced different aspects of mental well-being, and critically, the importance of these factors differed between syndromes. Maternal depression was influenced by child ability in only four syndromes, with the other syndromes reporting elevated or lower levels of maternal depression regardless of child factors. Maternal stress showed a more complex pattern of interaction with child ability, and for some groups, child age. Within positive mental health, mother and child age were more influential than child ability. Some syndromes reported comparable levels of depression (SMS, 1p36, CdLS) and stress (SMS, AS) to mothers of children with autism.

CONCLUSIONS: Bayesian methodology was used in a novel manner to explore factors that explain variability in mental health amongst mothers of children with rare genetic disorders. Significant proportions of mothers of children with specific genetic syndromes experienced levels of depression and stress similar to those reported by mothers of children with autism. Identifying such high-risk mothers allows for potential early intervention and the implementation of support structures.

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Accepted/In Press date: 2 October 2018
e-pub ahead of print date: 25 October 2018
Published date: 25 October 2018
Keywords: Journal Article

Identifiers

Local EPrints ID: 426567
URI: http://eprints.soton.ac.uk/id/eprint/426567
ISSN: 1750-1172
PURE UUID: 9208bfe1-d81a-4086-bb52-95c51c84a343
ORCID for Rina Cianfaglione: ORCID iD orcid.org/0000-0001-8739-0598

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Date deposited: 30 Nov 2018 17:30
Last modified: 23 Jan 2020 17:31

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Contributors

Author: Dawn Adams
Author: Richard P. Hastings
Author: Clair Alston-Knox
Author: Rina Cianfaglione ORCID iD
Author: Kate Eden
Author: David Felce
Author: Gemma Griffith
Author: Jo Moss
Author: Chris Stinton
Author: Chris Oliver

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