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Predicting the Risk Of ADHA among Children

Authors

Guo Aojie

Rubric:Psychology
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Objective: This study aims to 1) examine the predictors of ADHA 2) build a predictive model for ADHA using logistic regression model. 

Methods: 2017 National Survey of Children’s Health data was used for this study. The National Survey of Children’s Health (NSCH) is being conducted by the U.S. Census Bureau for the U.S. Department of Health and Human Services’ (HHS) Health Resources and Services Administration’s (HRSA) Maternal and Child Health Bureau (MCHB). It is designed to provide national and state-level information about the physical and emotional health and wellbeing of children under the age of 18 living in mailable residential housing units in the United States, their families and their communities, as well as information about the prevalence and impact of children with special health care needs.

All the participants who were eligible were randomly assigned into 2 groups: training sample and testing sample. A logistic regression model was built using training sample. Receiver operating characteristic (ROC) was calculated.

Results:

About 9.38% of 19019 children had ADHA, about 12.31% among 9777 male children and 6.28% among 9242 female children.

According to the logistic regression, children born in the USA were less likely to have ADHD (OR=0.445). When the first adult had higher completed year of school, the children were more likely to have ADHA (OR=1.046). When the first adult had worse mental health, the children were more likely to have ADHA (OR=1.111).

When children’s age increased by 1 year, the children were more likely to have ADHD (OR=1.12). Female children were less likely to have ADHD (OR=0.419). Children with normal birth weight has less likelihood to have ADHD (OR=0.619).

Children in a family which is hard to cover basics like food or housing were more likely to have ADHD (OR=1.301). Children who experienced parent or guardian divorced were less likely to have ADHD (OR=0.772). Children who had parent or guardian spent time in jail were less likely to have ADHD (OR=0.680). Children who were victims of violence were less likely to have ADHD (OR=0.638). Children who lived with mentally ill were less likely to have ADHD (OR=0.656).

The area under curve was 0.764. The optional cutoff time is 0.648. The mis-classification error was 0.095. The sensitivity rate is about 0.6% and the specificity is 99.9%.

Conclusions: In this study, we identified important of predictors of ADHD among children, for example children age, sex, mental health of adults.

Keywords

odds ratio
predictive model
logistic regression
ADHD
ROC

References:

[1] NSCH 2003-2011: National Survey of Children’s Health, telephone survey data; estimate includes children 4-17 years of age

[2] Danielson ML, Bitsko RH, Ghandour RM, Holbrook JR, Kogan MD, Blumberg SJ. Prevalence of parent-reported ADHD diagnosis and associated treatment among U.S. children and adolescents, 2016. Journal of Clinical Child and Adolescent Psychology. 2018, 47:2, 199-212.

[3] Nøvik TS, Hervas A, Ralston SJ, et al. Influence of gender on attention-deficit/hyperactivity disorder in Europe–ADORE. Eur Child Adolesc Psychiatry 2006; 15(Suppl 1): I15-I24.

[4] Willcutt EG. The prevalence of DSM-IV attention-deficit/hyperactivity disorder: a meta-analytic review. Neurotherapeutics 2012; 9: 490-499.

[5] StatSoft, Electronic Statistics Textbook, http://www.statsoft.com/textbook/stathome.html.

[6] Stokes, M., Davis, C. S. Categorical Data Analysis Using the SAS System, SAS Institute

Inc., 1995.

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