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Background
The purpose of the current study was to use a social determinants of health (SDOH) framework and latent class analysis (LCA) to identify risk classes among mothers with young children. The risk classes were then used to predict food insecurity severity and stability/change of food insecurity over time.
Method
The secondary data from the...
Citations
... Addressing the determinants of food insecurity is one approach to reducing these 2 negative health outcomes. While previous research has focused on various social determinants of health as risk factors to food insecurity (Banks et al., 2021;Daundasekara et al., 2022;Hernandez, 2015;Norris et al., 2023), there has been comparatively less emphasis on modifiable behaviors, such as the role of self-efficacy. As defined by the Social Cognitive Theory, self-efficacy is a person's confidence in their capacity to carry out particular behaviors (Bandura, 2004). ...
... To be included as part of the overarching scoping review, articles had to 1) involve multi-domain social screening (ie, screen for 2 of more domains related to socioeconomic circumstances, such as housing stability, food security, transportation access, utilities security, or financial strain); 2) be based in a US health care setting; 3) be an original research study published in the academic peerreviewed literature between 1/1/2011-2/17/2022. Our focus was on multi-domain screening given the interdependence of screening domains 16,17 and national policy measures/professional society recommendations on multi-domain screening. [18][19][20][21][22][23] To be included in the implementation outcomes review, specifically, studies also had to describe 1 or more outcomes related to screening reach, adoption, implementation, and/or maintenance of screening practices, based on the implementation science RE-AIM framework. ...
Purpose:
Though a growing crop of health care reforms aims to encourage health care-based social screening, no literature has synthesized existing social screening implementation research to inform screening practice and policymaking.
Methods:
Systematic scoping review of peer-reviewed literature on social screening implementation published 1/1/2011-2/17/2022. We applied a 2-concept search (health care-based screening; social risk factors) to PubMed and Embase. Studies had to explore the implementation of health care-based multi-domain social screening and describe 1+ outcome related to the reach, adoption, implementation, and/or maintenance of screening. Two reviewers extracted data related to key study elements, including sample, setting, and implementation outcomes.
Results:
Forty-two articles met inclusion criteria. Reach (n = 7): We found differences in screening rates by patient race/ethnicity; findings varied across studies. Patients who preferred Spanish had lower screening rates than English-preferring patients. Adoption (n = 13): Workforce education and dedicated quality improvement projects increased screening adoption. Implementation (n = 32): Time was the most cited barrier to screening; administration time differed by tool/workforce/modality. Use of standardized screening tools/workflows improved screening integration. Use of community health workers and/or technology improved risk disclosure and facilitated screening in resource-limited settings. Maintenance (n = 1): Only 1 study reported on maintenance; results showed a drop in screening over 21 months.
Conclusions:
Critical evidence gaps in social screening implementation persist. These include gaps in knowledge about effective strategies for integrating social screening into clinical workflows and ways to maximize screening equity. Future research should leverage the rapidly increasing number of screening initiatives to elevate and scale best practices.
... 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. ...
... 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. ...
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.
... Defining vulnerability subgroups among pregnant women using pre-pregnancy information 5 of 10 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. ...
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.
