Trends, risk factors and interventions for some mental health problems in the US children and adolescents: evidence from the National Survey of Children’s Health, 2016-2022

Purpose The purpose of this study is threefold: Determine recent trends in several mental health problems in the USA, identify risk factors that may be responsible for the trends and evaluate intervention policies to reduce the consequences of these problems. Design/methodology/approach This study used data from the National Survey of Children's Health (NSCH), a nationally representative survey of children under the age of 17 that was conducted between 2016 and 2022. Prevalence rates in the data take into account the probability of selection and nonresponse. Because of the possible correlation in the longitudinal responses in the data, an appropriate extension of the generalized linear models (the marginal models) was used. Marginal models, also known as population-average models, do not require distributional assumptions for the observations, only a regression model for the mean response. The avoidance of distributional assumptions leads to the use of the generalized estimating equations (GEE) method. Findings The author found that the odds of children and adolescents experiencing mental health problems in the USA changed over a seven-year period, from 2016 to 2022. Anxiety and depression, in particular, have both increased, with anxiety increasing faster than depression; however, behavioral issues and attention deficit disorder/attention deficit hyperactivity disoder (ADD/ADHD) remained stable until 2020 (the start of COVID-19), when they began to rise. This paper also found a link between increased social media use and increased mental health problems, and bullying has a negative impact on the mental health of children and adolescents. Originality/value The NSCH, an annual representative survey, was used in this study to assess mental health problems among children and adolescents in the USA. Marginal models, which enable the capture of potential correlations among observations of the same subject, were used in conjunction with the GEE method. This study differs from previous research, which used other surveys, pre-COVID-19 data points and logistic regressions that assumed independence in repeated observations.