Aims

1
Social Well-being Predictors of Positive Mental
Health in the Irish Adult Population

• Van Lente, E.1, Barry, M. M.1, Molcho M.1, on
  behalf of the SLÁN 2007 consortium2
• Health Promotion Research Centre, Department of
  Health Promotion, National University of Ireland,
  Galway
• PIs RCSI (Professor H. McGee), NUIG (Professor
  M. Barry), UCC (Professor I. Perry), ESRI (Dr. D.
  Watson). Funding Department of Health Children.

• Socio-demographic variables
• With the exception of community involvement,
  coefficients and odds ratios were weaker for
  socio-demographic variables than for social
  well-being variables.
• Men were more likely to experience higher levels
  of energy and vitality, with women being more
  than a third less likely to be in the top 5 of
  scores.
• Age only seems to matter for predicting the
  highest levels of energy and vitality, which tend
  to be among those aged 18-29.
• Other than gender, age, and residential location,
  being in the top 5 is not associated with other
  socio-demographic variables. On the other hand,
  in the linear regression model, education,
  medical card status and employment (but not
  residential location) are significant predictors.
  

Aims

• Predictors of mental health problems have been
  extensively reported, but fewer studies have
  examined predictors of positive mental health
  (e.g. Barry Friedli, 2008 Keyes 2005) at a
  population level. The SLÁN 2007 survey of
  lifestyle, attitudes and nutrition (see e.g.
  Barry, Van Lente, Molcho et al., 2009 Morgan et
  al., 2007) provides an opportunity to establish
  predictors of positive mental health in the Irish
  population. The aims of this study are to examine
  the relationships between positive mental health
  and social well-being variables in general, and
  specifically for those with very high levels of
  positive mental health.

Methods
Conclusions

• SLÁN 2007 collected data from a representative
  sample of 10,364 adults (18 years and over)
  through face-to-face interviews (62 response
  rate). Information was obtained on the following
  variables (see also Barry, Van Lente, Molcho et
  al., 2009)
• Positive mental health
• The Energy and Vitality scale from the SF-36
  health survey (Ware, 1993) has earlier been used
  as a positive mental health scale (e.g. Lehtinen,
  2002 Lavikainen, 2006).
• Socio-demographic measures
• Gender and age (18-29, 30-44, 45-64, 65)
• Social class SC1 'Service class', SC2
  'Intermediate technical', SC3 'Self-employed',
  SC4 'Working class based on ESeC (Harrison ,
  2006) Education (3 categories)
• Marital status (3 categories) Income level
  (equivalised household 5 categories)
• Residential location urban vs. rural Medical
  card status have/dont have
• Employment status in paid employment / not in
  paid employment
• Social Well-being measures
• Perceived social support Oslo Social Support
  Scale 3 items cover people you can count on,
  people who take an interest in what you do, ease
  of getting practical help
• Loneliness RCSI Loneliness item Have you often
  felt lonely in the last 4 weeks?
• Involvement in community activities 6 West of
  Scotland Twenty-07 study items
• Neighbourhood problems 6 items from Millennium
  Cohort Study
• Linear and logistic regression models were
  created. In the linear regression model
  continuous versions of age, level of education,
  equivalized household income were used to predict
  the continuous dependent variable - energy and
  vitality score. The logistic regression predicted
  which respondents were in the top 5 of energy
  and vitality scores vs those who were in the
  bottom 95. Hierarchical regression models were
  constructed with non-significant (pgt0.05)
  variables removed at each stage.
  Socio-demographic variables were added first
  followed by social well-being variables. The
  final models are presented.

• Low levels of loneliness, high social support and
  fewer neighbourhood problems emerge as being
  protective of positive mental health. While the
  highest levels of energy and vitality are less
  associated with socio-demographic variables, the
  relationship with these social well-being
  variables is consistent in both models.
• These findings point to the need for policy-level
  interventions that address the social-determinants
  of mental health, as well as the more
  individual-level determinants (see e.g. DOHC,
  2006). In particular - policy that leads to
  reductions in levels of loneliness, increases in
  social support and reductions in problems in the
  neighbourhood may lead to increases in positive
  mental health, even at the highest levels. Policy
  promoting community involvement may also increase
  positive mental health.
• Further refinement of measures of positive mental
  health in the general population is required
  (e.g. Bartlett, 1998) in order to examine the
  determinants of positive mental health among
  different population groups and the nature of
  their relationship to other indicators of health
  and well-being.

References

• Barry, M. M., Friedli, L. (2008). Foresight
  Mental Capital and Well-Being Project.
  State-of-Science Review SR-B3. The Influence of
  Social, Demographic and Physical Factors on
  Positive Mental Health in Children, Adults and
  Older People. London, UK Government Office for
  Science
• Bartlett, C. J., Coles, E. C. (1998).
  Psychological health and well-being why and how
  should public health specialists measure it? Part
  2 Stress, subjective well-being and overall
  conclusions. Journal of Public Health Medicine,
  20(3), 288-294.
• Department of Health and Children (2006). A
  Vision for Change Report of the Expert Group on
  Mental Health Policy. Dublin Stationery Office.
• Keyes, C. L. M. (2005). Mental Illness and/or
  Mental Health? Investigating Axioms of the
  Complete State Model of Health. Journal of
  Consulting and Clinical Psychology, 73(3),
  539-548.
• Harrison E, Rose D (2006). The European
  Socio-economic Classification (ESeC) User Guide.
  Institute for Social and Economic Research
• Lavikainen, J., Fryers, T., Lehtinen, V.
  (Eds.). (2006). Improving mental health
  information in Europe. Proposal of the MINDFUL
  project. Helsinki STAKES.
• Lehtinen, V., Sohlman, B., Kovess-Masfety, V.
  (2005). Level of positive mental health in the
  European Union Results from the Eurobarometer
  2002 survey. Clinical Practice Epidemiology in
  Mental Health, 1(1), 9.
• Morgan, K., McGee, H., Watson, D., Perry, I.,
  Barry, M., Shelley, E., et al. (2008). SLÁN 2007
  Survey of Lifestyle, Attitudes Nutrition in
  Ireland. Main Report.
• Parkinson, J. E. (2007). Review of scales of
  positive mental health validated for use with
  adults in the UK Technical report. Glasgow NHS
  Health Scotland.
• Ware, J.E., Snow, K.K., Kosinski, M. and Gandek,
  B. (1993) SF-36 Health Survey Manual and
  Interpretation Guide, The Health Institute.
  Boston New England Medical Center.

Results

• Social Well-being
• In both linear (Table 1) and logistic regression
  (Table 2) models, (poor) social support,
  loneliness and neighbourhood problems were
  significant predictors of energy and vitality.
  Coefficients and odds ratios were highest for
  loneliness, followed by (poor) social support. A
  respondent who is lonely is about a third as
  likely to be in the top 5 of energy and vitality
  scores, whereas a respondent who has poor social
  support is less than half as likely.
• Community involvement was not significant
  predictor of the top 5 of energy and vitality
  scores. It was also the weakest social well-being
  predictor in the linear model.