A latent class analysis to identify socio-economic and health risk profiles among mothers of young children predicting longitudinal risk of food insecurity

Daundasekara, Sajeevika S.; Schuler, Brittany R.; Hernandez, Daphne C.

doi:10.1371/journal.pone.0272614

Cited by 3 publications

(2 citation statements)

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“… 10 , 11 , 17 Previous LCA studies also led to classes of pregnant women with different health behaviours, psychosocial or socioeconomic characteristics that show differences in outcomes, although these studies included less factors and domains, and other populations in comparison to our study. 17 , 32 , 33 The findings do not inform us on how risk factors interplay and lead to adverse health outcomes. The syndemic model provides a perspective on this interplay by describing how co-occurring health adversities are fuelled by different social and contextual factors that interact and increase the health burden of both mental and physical illness.…”

Section: Discussionmentioning

confidence: 90%

Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis

Molenaar

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Bertens

et al. 2022

European Journal of Public Health

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Background Early detection of vulnerability during or before pregnancy can contribute to optimizing the first 1000 days, a crucial period for children’s development and health. We aimed to identify classes of vulnerability among pregnant women in the Netherlands using pre-pregnancy data on a wide range of social risk and protective factors, and validate these classes against the risk of adverse outcomes. Methods We conducted a latent class analysis based on 42 variables derived from nationwide observational data sources and self-reported data. Variables included individual, socioeconomic, lifestyle, psychosocial and household characteristics, self-reported health, healthcare utilization, life-events and living conditions. We compared classes in relation to adverse outcomes using logistic regression analyses. Results In the study population of 4172 women, we identified five latent classes. The largest ‘healthy and socioeconomically stable’-class [n = 2040 (48.9%)] mostly shared protective factors, such as paid work and positively perceived health. The classes ‘high care utilization’ [n = 485 (11.6%)], ‘socioeconomic vulnerability’ [n = 395 (9.5%)] and ‘psychosocial vulnerability’ [n = 1005 (24.0%)] were characterized by risk factors limited to one specific domain and protective factors in others. Women classified into the ‘multidimensional vulnerability’-class [n = 250 (6.0%)] shared multiple risk factors in different domains (psychosocial, medical and socioeconomic risk factors). Multidimensional vulnerability was associated with adverse outcomes, such as premature birth and caesarean section. Conclusions Co-existence of multiple risk factors in various domains is associated with adverse outcomes for mother and child. Early detection of vulnerability and strategies to improve parental health and well-being might benefit from focussing on different domains and combining medical and social care and support.

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Section: Discussionmentioning

confidence: 90%

Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis

Molenaar

Meer

Bertens

et al. 2022

European Journal of Public Health

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“…Similarly, Hoogstoel et al used LCA to identify different profiles from a list of risky behaviors among adolescents in Mauritius and then determined the associations of suicidality among adolescents with these profiles [25]. Daundasekara et al (2022) likewise used LCA to identify socio-economic and health risk profiles among mothers of young children to predict longitudinal risk of food insecurity [26]. An Australian National University (ANU) report revealed a greater stratification in Australian society.…”

Section: Introductionmentioning

confidence: 99%

A latent class analysis of the socio-demographic factors and associations with mental and behavioral disorders among Australian children and adolescents

2023

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Background Previous studies have shown a relationship between socio-demographic variables and the mental health of children and adolescents. However, no research has been found on a model-based cluster analysis of socio-demographic characteristics with mental health. This study aimed to identify the cluster of the items representing the socio-demographic characteristics of Australian children and adolescents aged 11–17 years by using latent class analysis (LCA) and examining the associations with their mental health. Methods Children and adolescents aged 11–17 years (n = 3152) were considered from the 2013–2014 Young Minds Matter: The Second Australian Child and Adolescent Survey of Mental Health and Wellbeing. LCA was performed based on relevant socio-demographic factors from three levels. Due to the high prevalence of mental and behavioral disorders, the generalized linear model with log-link binomial family (log-binomial regression model) was used to examine the associations between identified classes, and the mental and behavioral disorders of children and adolescents. Results This study identified five classes based on various model selection criteria. Classes 1 and 4 presented the vulnerable class carrying the characteristics of “lowest socio-economic status and non-intact family structure” and “good socio-economic status and non-intact family structure” respectively. By contrast, class 5 indicated the most privileged class carrying the characteristics of “highest socio-economic status and intact family structure”. Results from the log-binomial regression model (unadjusted and adjusted models) showed that children and adolescents belonging to classes 1 and 4 were about 1.60 and 1.35 times more prevalent to be suffering from mental and behavioral disorders compared to their class 5 counterparts (95% CI of PR [prevalence ratio]: 1.41–1.82 for class 1; 95% CI of PR [prevalence ratio]: 1.16–1.57 for class 4). Although children and adolescents from class 4 belong to a socio-economically advantaged group and shared the lowest class membership (only 12.7%), the class had a greater prevalence (44.1%) of mental and behavioral disorders than did class 2 (“worst education and occupational attainment and intact family structure”) (35.2%) and class 3 (“average socio-economic status and intact family structure”) (32.9%). Conclusions Among the five latent classes, children and adolescents from classes 1 and 4 have a higher risk of developing mental and behavioral disorders. The findings suggest that health promotion and prevention as well as combating poverty are needed to improve mental health in particular among children and adolescents living in non-intact families and in families with a low socio-economic status.

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The association between hunger-coping economic tradeoffs and food insecurity among female recipients of charitable food assistance

Hernandez,

Kim,

Brooks

et al. 2025

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A latent class analysis to identify socio-economic and health risk profiles among mothers of young children predicting longitudinal risk of food insecurity

Cited by 3 publications

References 70 publications

Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis

Defining vulnerability subgroups among pregnant women using pre-pregnancy information: a latent class analysis

A latent class analysis of the socio-demographic factors and associations with mental and behavioral disorders among Australian children and adolescents

The association between hunger-coping economic tradeoffs and food insecurity among female recipients of charitable food assistance

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