Abstract
Background
It is understood that many veterans in the UK suffer mental health difficulties and those difficulties are comorbid, however, little is known about Northern Ireland (NI) veteran mental health specifically. This is of concern as mental health seems to be particularly poor across NI generally and support services for all are limited. A deeper understanding of NI veteran mental health is needed to provide adequate support.
Methods
Latent class analysis (LCA) was conducted on NI Veteran Health and Wellbeing Survey data (n=647). Six dichotomous indicator variables were used: PTSD, complex PTSD, depersonalisation/derealisation, anxiety, depression and alcohol.
Results
Four latent classes were identified: (1) high probability of PTSD, alcohol and anxiety comorbidity (3.71%); (2) high probability of complex PTSD with moderate PTSD,
depersonalisation/derealisation and alcohol (17.77%); (3) high probability of all mental health difficulties and moderate alcohol (23.80%); (4) no mental health difficulties with a moderate probability of risky alcohol use (54.71%)
Discussion
Just under half the sample reported comorbid mental health difficulties but the nature of the comorbidities was different depending on class membership. It is concerning that many seem to suffer a range of difficulties, particularly complex PTSD. Of further concern is that a probability of being a risky drinker appeared in each class, at least at a moderate level. Findings suggest a one-size-fits-all approach to NI veteran mental health cannot be taken due to the nuanced nature of their comorbidities. Special attention also needs to be given to this population to help reduce risky drinking for all.
It is understood that many veterans in the UK suffer mental health difficulties and those difficulties are comorbid, however, little is known about Northern Ireland (NI) veteran mental health specifically. This is of concern as mental health seems to be particularly poor across NI generally and support services for all are limited. A deeper understanding of NI veteran mental health is needed to provide adequate support.
Methods
Latent class analysis (LCA) was conducted on NI Veteran Health and Wellbeing Survey data (n=647). Six dichotomous indicator variables were used: PTSD, complex PTSD, depersonalisation/derealisation, anxiety, depression and alcohol.
Results
Four latent classes were identified: (1) high probability of PTSD, alcohol and anxiety comorbidity (3.71%); (2) high probability of complex PTSD with moderate PTSD,
depersonalisation/derealisation and alcohol (17.77%); (3) high probability of all mental health difficulties and moderate alcohol (23.80%); (4) no mental health difficulties with a moderate probability of risky alcohol use (54.71%)
Discussion
Just under half the sample reported comorbid mental health difficulties but the nature of the comorbidities was different depending on class membership. It is concerning that many seem to suffer a range of difficulties, particularly complex PTSD. Of further concern is that a probability of being a risky drinker appeared in each class, at least at a moderate level. Findings suggest a one-size-fits-all approach to NI veteran mental health cannot be taken due to the nuanced nature of their comorbidities. Special attention also needs to be given to this population to help reduce risky drinking for all.
Original language | English |
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Publication status | Unpublished - 01 Oct 2020 |
Event | Society for the Study of Addiction : Annual Event - Virtual Duration: 05 Nov 2020 → 06 Nov 2020 https://www.addiction-ssa.org/annual-conference/annual-conference-2020/ |
Conference
Conference | Society for the Study of Addiction |
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Period | 05/11/2020 → 06/11/2020 |
Internet address |
Fingerprint
Dive into the research topics of 'Latent class analysis of Northern Ireland veterans regarding mental health and alcohol issues'. Together they form a unique fingerprint.Student theses
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The relationship between trauma, mental health, alcohol and help-seeking, for military veterans residing in Northern Ireland
Hitch, C. (Author), Armour, C. (Supervisor) & Toner, P. (Supervisor), Dec 2022Student thesis: Doctoral Thesis › Doctor of Philosophy