Do family and maternal background matter? A multilevel approach to modelling mental health status of Australian youth using longitudinal data

PLoS ONE, Volume 17, No. 4 April, Year 2022

Interpretation

Purpose Most previous research place great importance on the influence of family and maternal background on child and adolescents’ mental health. However, age of onset studies indicates that the majority of the mental health disease prevalence occurs during the youth years. This study investigates the relationship of family and maternal background, as well as individual circumstance on youth mental health status. Method Data from 975 participants and 4632 observations of aged cohort 15 to 19 years in the Household, Income and Labour Dynamics in Australia (HILDA) longitudinal study were followed for 10 years (2007–2017). Multilevel logistic regression models were used to analyse the impact of youth circumstances on mental health status. Results The findings suggests that not all dimensions of family and maternal background (especially maternal education) have impacts on youth mental health. We found low household income (AOR: 1.572, 95% CI: 1.017–2.43) and adverse living arrangement (AOR: 1.586, 95% CI: 1.097–2.294) significantly increases mental disorder odds whereas maternal education or occupation fixed effects were not significant. Individual level circumstances have much stronger impact on youth mental health. We found financial shock (AOR: 1.412, 95% CI: 1.277–1.561), life event shock (AOR: 1.157, 95% CI: 1.01–1.326), long term health conditions (AOR: 2.855, 95% CI: 2.042–3.99), smoking (AOR: 1.676, 95% CI: 1.162–2.416), drinking (AOR: 1.649, 95% CI: 1.286–2.114) and being female (AOR: 2.021, 95% CI: 1.431–2.851) have significant deteriorating effects on youth mental health. Conclusions Our finding is in contrast to the majority of studies in the literature which give a preeminent role to maternal characteristics in child and youth mental health status. Mental health interventions should consider heterogeneity of adverse youth circumstances and health-related behaviours. All our analyses are based on sample data from the Household, Income and Labour Dynamics in Australia (HILDA) panel survey [16]. This nationally representative household survey has been carried out annually from 2001 through 2018 (waves 1–18). It interviews and subsequently reinterviews all members aged 15 years and over of the same selected household every year. More than 30,000 individuals (40,000+ enumerated) have participated in the survey over the years and on average 15,000 individuals have been interviewed every year. A 90% wave on wave response rates of HILDA survey are comparable with other large longitudinal surveys like the British Household Panel Study (BHPS) or Panel Study of Income Dynamics (PSID) [17]. Details of HILDA sample design, survey response rates and attrition rates can be found elsewhere [17]. For the purpose of this study, we limit the sample to young Australians aged 15–19 years (late adolescent period) at the baseline wave (wave 7) and then followed the participants for 10 years (up to six measurement points) which covers youth (20–24 years) and transition to adulthood phase (25–29 years) in the follow up. We chose to start from wave 7, because HILDA survey did not start to collect Kessler Psychological Distress Scale (K10) scores (our main outcome of interest) in earlier waves and it provides the score subsequently in every odd wave (every two years) thereafter. Thus, we constructed an unbalanced panel data using wave 7, 9, 11, 13, 15 and 17. To be included in the analyses, the participants had to be interviewed in the baseline wave 7 and has to appear in at least one of the follow-up waves. Our final sample contains 975 participants across the six waves with a total of 4,632 observations. The 15–19 age cohort was thus followed up to 25–29 years with an average of 5.18 observations per person. The participant flow into the sample is shown in Fig 1. This study uses the Kessler Psychological Distress Scale (K10) as the measure of mental health outcomes and is the main dependent variable for analyses [18]. In clinical practice, the scale is used to assess the likelihood of having a mental disorder; for example, a person with a score of 10–15 has a low risk of having a mental disorder whereas a person with a score of 20–24 is likely to have a mild mental disorder, a score of 25–30 would indicate a likely moderate mental disorder and a person with a score of 30–50 is likely to have a severe mental disorder [19]. In the analyses, we use a dichotomous K10 variable (where a score of greater than 20 depict the likelihood of a mental disorder) as measures of our dependent variable for mental health performance [20]. Following Roemer’s equality of opportunity theory [21, 22] we classify all our exposure variables into two types: i) circumstances category and ii) effort category. The theory of equality of opportunity revolves around the goal of compensating for ‘negative’ circumstances (such as parental background) on health outcomes while controlling the health inequalities generated by effort category variables (such as lifestyle or health habits) that can be attributed to the behaviour of an individual. We use the biological mothers’ education level and occupational status, household income and family living arrangements (whether the participant lived with both parents at the age of 14 years old) to determine the family and maternal background status as a group level characteristic of the circumstances category. We define maternal education level as low if the highest qualification level obtained by the mother is secondary level or lower. We use the Australian Socioeconomic Index 2006 (AUSEI06) occupational status scale as the measure of the occupational status of mother [23]. We assign occupational status as low if the value range falls in the lowest quintile. Similarly, we assign household income as low if the equivalised household income range falls in the lowest quintile. Using household income, family living arrangement, maternal education and occupational status we have constructed 16 (2x2x2x2) different types of family and maternal background history groups for the multilevel analyses. We use the number of financial shocks, number of life event shocks and long-term health conditions in the individual level circumstances category [12]. The number of financial shock variable shows the number of adverse financial events the study participant has experienced (for example: went without meals or asked for financial help from friends or family). Similarly, life event shock variable shows the number of life events related to grief, loss or injury the study participant has suffered (for example: death of a family member or serious personal injury). The list of events that constitutes financial and life event shocks are given in the S1 Appendix. We use negative health habits such as being obese (as a proxy of unhealthy eating and lack of exercising), being a daily smoker and regular drinker (drinks more than four standard drink/day), and positive health habits such as being an active member of a sporting/hobby/community-based club or association as an effort type of variables. This study also included gender and rural residency as demographic covariates in the analyses based on past literature [24]. In addition, we construct our time variable by setting zero at the baseline wave 7 and subsequently adding two for each additional measurement point (since between wave time is two years and there are up to six measurement points) to get a ten-year follow-up at wave 17 (t = 0,2,4,6,8, and 10). The authors constructed an unbalanced longitudinal data set of the youth cohort by linking an individual’s record who participated in the baseline (wave 7) at age 15–19 years and in one of the follow-up waves (9, 11, 13, 15 and 17). Descriptive statistics and mental health opportunity profile were summarized to understand the impact of family and maternal background group characteristics on youth mental health. Visual trends of psychological distress scale were analysed for group level characteristics. Traditional single level regression analysis such as logistic regression model only assumes fixed-effect impacts of dependent variables and does not allow for random effects of intercepts and slopes for individual and group level characteristics. However, data structure can be nested or clustered by some observable characteristics that creates similarity between individuals and ignoring these phenomena can violate the independence assumption of regression analysis. Multi-level models allow for a nested data structure (i.e., repeated measures) and make it possible to study sources of variance at different levels of an outcome variable [25]. The nested data structure is illustrated in Fig 2. In our analyses, we used both single level logistic regression and multilevel logistic regression models. we have nested our data structure into three levels: i) time, ii) individual, and iii) family and maternal background history types (a total of 16 different background history types; for example a background history type could be: household income- high; from two types: ‘high’ and ‘low’, mothers education- low; from two types: ‘high’ and ‘low’, mothers occupation- low; from two types: ‘high’ and ‘low’ and family living arrangement—whether not lived with both biological parents- yes; from two types: ‘yes’ and ‘no’. Thus, we have 2x2x2x2 = 16 types. A full combination of 16 types can be seen in Table 2’s opportunity profile). We assigned unique identifiers (From 1 to 16, see Table 2’s opportunity profile’s rank number for identifiers) for each group for the analysis. We control for individual fixed effects characteristics like circumstances and effort covariates in level 2 and group level fixed effects characteristics like various family and maternal background group characteristics in level 3. All statistical analyses were conducted using Stata 15. The HILDA study was approved by the Human Research Ethics Committee of the University of Melbourne. The study used only de-identified existing unit record data from the HILDA survey. The authors completed and signed a confidentiality agreement with NCLD (ua.vog.ssd@hcraeserdlcn) and obtained database access from the Australian Data Archive (ua.ude.una@ada) following application acceptance. Thus, the dataset studied during this work were subject to the signed confidentiality agreement.

AI Digest

Study Justification:

This study aims to investigate the relationship between family and maternal background, individual circumstances, and mental health status among Australian youth. Previous research has focused on the influence of family and maternal background on child and adolescent mental health, but this study focuses specifically on the youth years when the majority of mental health disorders occur. By understanding the factors that contribute to youth mental health, interventions can be developed to address these issues and improve outcomes for young people.

Highlights:

– The study analyzed data from the Household, Income and Labour Dynamics in Australia (HILDA) longitudinal study, which is a nationally representative survey conducted annually from 2001 to 2018.
– The sample included 975 participants aged 15 to 19 years at the baseline wave, and they were followed for 10 years.
– Multilevel logistic regression models were used to analyze the impact of family and maternal background, as well as individual circumstances, on mental health status.
– The findings suggest that not all dimensions of family and maternal background have impacts on youth mental health. Instead, individual level circumstances, such as financial shock, life event shock, long-term health conditions, smoking, drinking, and being female, have a stronger impact on mental health.
– The study challenges the preeminent role given to maternal characteristics in previous research and highlights the importance of considering heterogeneity of adverse youth circumstances and health-related behaviors in mental health interventions.

Recommendations:

– Mental health interventions should consider the individual circumstances and health-related behaviors that have a significant impact on youth mental health.
– Policies and programs should focus on addressing financial shocks, life event shocks, long-term health conditions, unhealthy habits such as smoking and drinking, and gender disparities in mental health.
– Interventions should also consider the specific needs and circumstances of different youth populations, taking into account factors such as household income, family living arrangements, and maternal education and occupation.

Key Role Players:

– Researchers and academics in the field of mental health and youth development
– Mental health professionals, including psychologists, psychiatrists, and counselors
– Policy makers and government officials responsible for mental health policies and programs
– Non-governmental organizations (NGOs) and community-based organizations working with youth
– Schools and educational institutions
– Parents and families of young people

Cost Items for Planning Recommendations:

– Research funding for further studies and evaluations of mental health interventions
– Funding for the development and implementation of mental health programs and services
– Training and professional development for mental health professionals
– Resources and materials for mental health education and awareness campaigns
– Infrastructure and technology for delivering mental health services, such as telehealth platforms
– Monitoring and evaluation of mental health programs and outcomes
– Collaboration and coordination between different stakeholders and organizations involved in youth mental health

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a clear description of the study’s purpose, method, and results. However, there is room for improvement in terms of providing more specific details about the sample size, statistical analysis methods, and limitations of the study. To improve the evidence, the authors could include information about the statistical significance of the findings, potential confounding factors, and any limitations in the study design or data collection methods.

Abstract

All our analyses are based on sample data from the Household, Income and Labour Dynamics in Australia (HILDA) panel survey [16]. This nationally representative household survey has been carried out annually from 2001 through 2018 (waves 1–18). It interviews and subsequently reinterviews all members aged 15 years and over of the same selected household every year. More than 30,000 individuals (40,000+ enumerated) have participated in the survey over the years and on average 15,000 individuals have been interviewed every year. A 90% wave on wave response rates of HILDA survey are comparable with other large longitudinal surveys like the British Household Panel Study (BHPS) or Panel Study of Income Dynamics (PSID) [17]. Details of HILDA sample design, survey response rates and attrition rates can be found elsewhere [17]. For the purpose of this study, we limit the sample to young Australians aged 15–19 years (late adolescent period) at the baseline wave (wave 7) and then followed the participants for 10 years (up to six measurement points) which covers youth (20–24 years) and transition to adulthood phase (25–29 years) in the follow up. We chose to start from wave 7, because HILDA survey did not start to collect Kessler Psychological Distress Scale (K10) scores (our main outcome of interest) in earlier waves and it provides the score subsequently in every odd wave (every two years) thereafter. Thus, we constructed an unbalanced panel data using wave 7, 9, 11, 13, 15 and 17. To be included in the analyses, the participants had to be interviewed in the baseline wave 7 and has to appear in at least one of the follow-up waves. Our final sample contains 975 participants across the six waves with a total of 4,632 observations. The 15–19 age cohort was thus followed up to 25–29 years with an average of 5.18 observations per person. The participant flow into the sample is shown in Fig 1. This study uses the Kessler Psychological Distress Scale (K10) as the measure of mental health outcomes and is the main dependent variable for analyses [18]. In clinical practice, the scale is used to assess the likelihood of having a mental disorder; for example, a person with a score of 10–15 has a low risk of having a mental disorder whereas a person with a score of 20–24 is likely to have a mild mental disorder, a score of 25–30 would indicate a likely moderate mental disorder and a person with a score of 30–50 is likely to have a severe mental disorder [19]. In the analyses, we use a dichotomous K10 variable (where a score of greater than 20 depict the likelihood of a mental disorder) as measures of our dependent variable for mental health performance [20]. Following Roemer’s equality of opportunity theory [21, 22] we classify all our exposure variables into two types: i) circumstances category and ii) effort category. The theory of equality of opportunity revolves around the goal of compensating for ‘negative’ circumstances (such as parental background) on health outcomes while controlling the health inequalities generated by effort category variables (such as lifestyle or health habits) that can be attributed to the behaviour of an individual. We use the biological mothers’ education level and occupational status, household income and family living arrangements (whether the participant lived with both parents at the age of 14 years old) to determine the family and maternal background status as a group level characteristic of the circumstances category. We define maternal education level as low if the highest qualification level obtained by the mother is secondary level or lower. We use the Australian Socioeconomic Index 2006 (AUSEI06) occupational status scale as the measure of the occupational status of mother [23]. We assign occupational status as low if the value range falls in the lowest quintile. Similarly, we assign household income as low if the equivalised household income range falls in the lowest quintile. Using household income, family living arrangement, maternal education and occupational status we have constructed 16 (2x2x2x2) different types of family and maternal background history groups for the multilevel analyses. We use the number of financial shocks, number of life event shocks and long-term health conditions in the individual level circumstances category [12]. The number of financial shock variable shows the number of adverse financial events the study participant has experienced (for example: went without meals or asked for financial help from friends or family). Similarly, life event shock variable shows the number of life events related to grief, loss or injury the study participant has suffered (for example: death of a family member or serious personal injury). The list of events that constitutes financial and life event shocks are given in the S1 Appendix. We use negative health habits such as being obese (as a proxy of unhealthy eating and lack of exercising), being a daily smoker and regular drinker (drinks more than four standard drink/day), and positive health habits such as being an active member of a sporting/hobby/community-based club or association as an effort type of variables. This study also included gender and rural residency as demographic covariates in the analyses based on past literature [24]. In addition, we construct our time variable by setting zero at the baseline wave 7 and subsequently adding two for each additional measurement point (since between wave time is two years and there are up to six measurement points) to get a ten-year follow-up at wave 17 (t = 0,2,4,6,8, and 10). The authors constructed an unbalanced longitudinal data set of the youth cohort by linking an individual’s record who participated in the baseline (wave 7) at age 15–19 years and in one of the follow-up waves (9, 11, 13, 15 and 17). Descriptive statistics and mental health opportunity profile were summarized to understand the impact of family and maternal background group characteristics on youth mental health. Visual trends of psychological distress scale were analysed for group level characteristics. Traditional single level regression analysis such as logistic regression model only assumes fixed-effect impacts of dependent variables and does not allow for random effects of intercepts and slopes for individual and group level characteristics. However, data structure can be nested or clustered by some observable characteristics that creates similarity between individuals and ignoring these phenomena can violate the independence assumption of regression analysis. Multi-level models allow for a nested data structure (i.e., repeated measures) and make it possible to study sources of variance at different levels of an outcome variable [25]. The nested data structure is illustrated in Fig 2. In our analyses, we used both single level logistic regression and multilevel logistic regression models. we have nested our data structure into three levels: i) time, ii) individual, and iii) family and maternal background history types (a total of 16 different background history types; for example a background history type could be: household income- high; from two types: ‘high’ and ‘low’, mothers education- low; from two types: ‘high’ and ‘low’, mothers occupation- low; from two types: ‘high’ and ‘low’ and family living arrangement—whether not lived with both biological parents- yes; from two types: ‘yes’ and ‘no’. Thus, we have 2x2x2x2 = 16 types. A full combination of 16 types can be seen in Table 2’s opportunity profile). We assigned unique identifiers (From 1 to 16, see Table 2’s opportunity profile’s rank number for identifiers) for each group for the analysis. We control for individual fixed effects characteristics like circumstances and effort covariates in level 2 and group level fixed effects characteristics like various family and maternal background group characteristics in level 3. All statistical analyses were conducted using Stata 15. The HILDA study was approved by the Human Research Ethics Committee of the University of Melbourne. The study used only de-identified existing unit record data from the HILDA survey. The authors completed and signed a confidentiality agreement with NCLD (ua.vog.ssd@hcraeserdlcn) and obtained database access from the Australian Data Archive (ua.ude.una@ada) following application acceptance. Thus, the dataset studied during this work were subject to the signed confidentiality agreement.

Materials

Innovations

Based on the provided description, it seems that the focus of the study is on analyzing the impact of family and maternal background, as well as individual circumstances, on youth mental health status. The study utilizes data from the Household, Income and Labour Dynamics in Australia (HILDA) longitudinal study.

In terms of innovations to improve access to maternal health, it is important to note that the provided description does not directly address this topic. However, based on the information provided, here are some potential recommendations for innovations that could be used to improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals, allowing pregnant women to receive prenatal care and consultations without the need for in-person visits.

2. Mobile health applications: Developing mobile applications that provide educational resources, appointment reminders, and personalized health information can empower pregnant women to take an active role in their own healthcare and access information conveniently.

3. Community health workers: Training and deploying community health workers who can provide maternal health education, support, and referrals in underserved areas can help improve access to care for pregnant women who may face barriers to accessing healthcare facilities.

4. Maternal health clinics: Establishing dedicated maternal health clinics in areas with limited access to healthcare facilities can ensure that pregnant women have access to comprehensive prenatal care, including regular check-ups, screenings, and vaccinations.

5. Transportation support: Providing transportation services or subsidies for pregnant women who face challenges in accessing healthcare facilities can help overcome geographical barriers and ensure timely access to maternal health services.

6. Maternal health hotlines: Setting up helplines or hotlines staffed by healthcare professionals who can provide guidance, answer questions, and offer support to pregnant women can improve access to information and address concerns in a timely manner.

7. Maternal health awareness campaigns: Conducting targeted awareness campaigns to educate communities about the importance of maternal health and the available resources can help increase awareness and encourage pregnant women to seek timely care.

It is important to note that these recommendations are based on general knowledge and may not directly align with the specific focus of the provided description. Further research and analysis would be needed to determine the most effective innovations for improving access to maternal health in the context of the study.

AI Innovations Description

The provided description is about a research study that investigates the relationship between family and maternal background, individual circumstances, and mental health status among Australian youth. The study uses data from the Household, Income and Labour Dynamics in Australia (HILDA) longitudinal study, which is a nationally representative household survey conducted annually from 2001 to 2018.

The study found that not all dimensions of family and maternal background have significant impacts on youth mental health. Factors such as low household income and adverse living arrangements were found to increase the odds of mental disorders, while maternal education or occupation did not show significant effects. On the other hand, individual-level circumstances, such as financial shocks, life event shocks, long-term health conditions, smoking, drinking, and being female, had a stronger impact on youth mental health.

The study suggests that mental health interventions should consider the heterogeneity of adverse youth circumstances and health-related behaviors. It highlights the importance of understanding individual circumstances and efforts in addressing mental health issues among youth.

It is important to note that the study used the Kessler Psychological Distress Scale (K10) as the measure of mental health outcomes. The scale assesses the likelihood of having a mental disorder based on the score obtained. The study also classified exposure variables into two types: circumstances category (related to family and maternal background) and effort category (related to individual-level circumstances and behaviors).

The study used both single-level logistic regression and multilevel logistic regression models to analyze the data. The multilevel models allowed for the consideration of nested data structure and the examination of variance sources at different levels.

The research study obtained approval from the Human Research Ethics Committee of the University of Melbourne and used de-identified existing unit record data from the HILDA survey.

AI Innovations Methodology

Based on the provided description, it seems that you are looking for innovations to improve access to maternal health and a methodology to simulate the impact of these recommendations. Unfortunately, the provided text does not explicitly mention maternal health or access to maternal health. However, I can provide you with some general innovations and a methodology to simulate their impact on improving access to maternal health.

Innovations to Improve Access to Maternal Health:

Methodology to Simulate the Impact of Recommendations:

1. Define the Objectives: Clearly define the objectives of the simulation, such as measuring the impact of specific innovations on access to maternal health.
2. Identify Key Variables: Identify the key variables that will be used to measure access to maternal health, such as the number of prenatal visits, distance to healthcare facilities, and availability of healthcare providers.
3. Collect Data: Gather relevant data on the identified variables, such as the number of prenatal visits from healthcare records, distance to healthcare facilities from geographic information systems, and availability of healthcare providers from surveys or official records.
4. Build a Simulation Model: Develop a simulation model that incorporates the identified variables and their relationships. This model should simulate the impact of the recommended innovations on access to maternal health.
5. Validate the Model: Validate the simulation model by comparing its outputs with real-world data or expert opinions to ensure its accuracy and reliability.
6. Run Simulations: Run simulations using the model to estimate the impact of the recommended innovations on access to maternal health. Vary the input parameters to assess different scenarios and their potential outcomes.
7. Analyze Results: Analyze the simulation results to understand the potential impact of the recommended innovations on access to maternal health. Identify any barriers or challenges that may arise and propose strategies to address them.
8. Refine and Iterate: Refine the simulation model based on the analysis of results and feedback from experts. Iterate the simulation process to further explore different scenarios and improve the accuracy of the simulations.

Please note that the provided methodology is a general framework and may need to be adapted based on the specific context and data availability for your study on improving access to maternal health.

Authors & Co-Authors

Statistics:

Citations: 1

Authors: 4

Identifiers:

DOI: 10.1371/journal.pone.0267191

Research Areas:

Community Interventions, Disparities, Genetics and Genomics, Health System and Policy, Maternal Access, Maternal and Child Health, Mental Health, Quality of Care, Social Determinants, Violence and Injury

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female