June 24, 2024

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What is the association between relationship factors, financial difficulties, and socio-demographic factors with mental health?

3 min read

In a recent study published in PLoS ONE, a group of researchers investigated the impact of marital/relationship perceptions, financial difficulties, and socio-demographic factors on the mental health of Australian adults, using data from the Household, Income and Labour Dynamics in Australia (HILDA) survey.

Study: Associations of nuptiality perceptions, financial difficulties, and socio-demographic factors with mental health status in Australian adults: Analysis of the Household, Income and Labour Dynamics in Australia (HILDA) survey. Image Credit: SewCreamStudio/Shutterstock.comStudy: Associations of nuptiality perceptions, financial difficulties, and socio-demographic factors with mental health status in Australian adults: Analysis of the Household, Income and Labour Dynamics in Australia (HILDA) survey. Image Credit: SewCreamStudio/Shutterstock.com

Background 

Mental health is crucial for individual well-being, defined as managing life’s stresses, realizing one’s potential, working productively, and contributing to the community.

It is influenced by various life challenges, including financial hardship, employment struggles, and domestic violence, which can significantly increase mortality risk during hospital admissions.

Recent research highlights the impact of social determinants on mental health, revealing differences across gender, age, and socioeconomic factors. In Australia, mental health issues affect one in five people.

Further research is needed to unravel the complex interactions between socio-demographic factors, nuptiality, financial stress, and mental health, informing targeted interventions and policies.

About the study 

The present study utilized data from the HILDA survey, a comprehensive source that began in 2001 and includes information on wealth, labor market outcomes, household and family dynamics, health, and education.

Employing a multistage sampling strategy, it started with selecting Census Collection Districts, followed by households within these districts, ensuring a broad representation of the Australian population.

Over the years, the survey has adapted to include new household members and children of respondents, maintaining a dynamic and growing dataset. For this analysis, the latest available wave (wave 19) was used, focusing specifically on mental health variables and excluding incomplete records, leading to a final sample of 6,846 participants.

Mental health status was gauged using the mental component summary (MCS) subscale of the Short-Form (SF)-36 health survey, a widely recognized tool for measuring the quality of life-related to mental health, with a scoring system that converts responses into a composite score indicative of mental health status.

Financial difficulties were assessed through direct questions about participants’ ability to meet essential payments and needs. At the same time, nuptiality and relationship perceptions were measured through questions about marital status, relationship quality, and satisfaction.

The analysis employed hierarchical multiple linear regression to explore the impact of socio-demographic factors, financial difficulties, and nuptiality/relationship perceptions on mental health, with a systematic approach that first considered the influence of socio-demographic characteristics before introducing financial and nuptiality variables.

This methodological framework allowed for a better understanding of the relative contributions of these factors to mental health outcomes.

Ethical considerations were thoroughly addressed, with data access granted to authorized researchers under strict guidelines to ensure confidentiality and consent. 

Study results 

In the study, 6,846 individuals were analyzed to understand the relationship between socio-demographic factors, nuptiality/relationship perceptions, financial difficulties, and mental health status among Australian adults.

The demographic profile of participants indicated a predominance of individuals over 42 years (60.9%), with females making up 51.4% of the sample.

The majority were born in Australia (77.5%) and were married (78.2%). Educationally, 27.7% had a year 11 certificate or lower, and about 70% were employed.

The average MCS score, which measures mental health status, was 76.4, with a standard deviation 15.8, indicating generally good mental health among participants. However, 7.1% of participants were identified with poor mental health (MCS score less than 50).

The analysis revealed that demographic characteristics explained a small portion (2.1%) of the variance in mental health scores. Older participants (aged 60 and above) demonstrated higher mental health scores compared to the youngest cohort (less than 25 years), suggesting better mental health with age.

Conversely, being female, born outside of Australia, retired, or a student were factors associated with lower mental health scores. Financial difficulties significantly impacted mental health, accounting for an additional 3.2% of the variance in MCS scores.

Challenges such as difficulty paying bills, needing to pawn or sell belongings, and seeking financial assistance from friends, family, or welfare/community organizations were linked to lower mental health scores.

Nuptial and relationship factors were notably influential, explaining 9.8% of the variance in mental health scores. Positive perceptions of one’s relationship quality and the extent to which it met original expectations were associated with better mental health.

On the contrary, negative aspects such as frequently wishing not to have been married or in a relationship, problems within the relationship, and the intensity of love for a spouse or partner correlated with lower mental health scores. 

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