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      Association of Attention-Deficit/Hyperactivity Disorder Diagnosis With Adolescent Quality of Life

      research-article
      , MPH 1 , , , PhD 1 , , PhD 1 , , PhD 2 , , PhD 1
      JAMA Network Open
      American Medical Association

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          Key Points

          Question

          Is an attention-deficit/hyperactivity disorder (ADHD) diagnosis in childhood or early adolescence associated with quality of life in adolescents compared with age-, sex-, and behavior-matched individuals without a diagnosis?

          Findings

          In this cohort study, 393 adolescents with an ADHD diagnosis reported similar quality of life overall and on 3 subdomains but significantly worse outcomes for 5 other aspects of quality of life compared with 393 matched adolescents with similar levels of hyperactive/inattentive behaviors but no ADHD diagnosis.

          Meaning

          These findings suggest that childhood ADHD diagnosis may not result in any improvements in quality of life measures in adolescents and may negatively impact some outcomes, such as risk of self-harm.

          Abstract

          This cohort study examines quality of life in adolescents who have received a diagnosis of attention-deficit/hyperactivity disorder compared with peers with matched hyperactive/inattentive behaviors but who have not received a diagnosis.

          Abstract

          Importance

          Appropriate diagnosis of attention-deficit/hyperactivity disorder (ADHD) can improve some short-term outcomes in children and adolescents, but little is known about the association of a diagnosis with their quality of life (QOL).

          Objective

          To compare QOL in adolescents with and without an ADHD diagnosis.

          Design, Setting, and Participants

          This cohort study followed an emulated target trial design using prospective, observational data from the Longitudinal Study of Australian Children, a representative, population-based prospective cohort study with biennial data collection from 2006 to 2018 with 8 years of follow-up (ages 6-7 to 14-15 years). Propensity score matching was used to ensure children with and without ADHD diagnosis were well matched on a wide range of variables, including hyperactive/inattentive (H/I) behaviors. Eligible children were born in 1999 to 2000 or 2003 to 2004 and did not have a previous ADHD diagnosis. All incident ADHD cases were matched with controls. Data were analyzed from July 2021 to January 2022.

          Exposures

          Incident parent-reported ADHD diagnosis at age 6 to 7, 8 to 9, 10 to 11, 12 to 13, or 14 to 15.

          Main Outcomes and Measures

          Quality of life at age 14 to 15 was measured with Child Health Utility 9D (CHU9D) and 8 other prespecified, self-reported measures mapped to the World Health Organization’s QOL domains. Pooled regression models were fitted for each outcome, with 95% CIs and P values calculated using bootstrapping to account for matching and repeat observations.

          Results

          Of 8643 eligible children, a total of 393 adolescents had an ADHD diagnosis (284 [72.2%] boys; mean [SD] age, 10.03 [0.30] years; mean [SD] H/I Strengths and Difficulties Questionnaire score, 5.05 [2.29]) and were age-, sex-, and H/I score–matched with 393 adolescents without ADHD diagnosis at time zero. Compared with adolescents without diagnosis, those with an ADHD diagnosis reported similar QOL on CHU9D (mean difference, −0.03; 95% CI, −0.07 to 0.01; P = .10), general health (mean difference, 0.11; 95% CI, −0.04 to 0.27; P = .15), happiness (mean difference, −0.18; 95% CI, −0.37 to 0.00; P = .05), and peer trust (mean difference, 0.65; 95% CI, 0.00 to 1.30; P = .05). Diagnosed adolescents had worse psychological sense of school membership (mean difference, −2.58; 95% CI, −1.13 to −4.06; P < .001), academic self-concept (mean difference, −0.14; 95% CI, −0.02 to −0.26; P = .02), and self-efficacy (mean difference, −0.20; 95% CI, −0.05 to −0.33; P = .007); displayed more negative social behaviors (mean difference, 1.56; 95% CI, 0.55 to 2.66; P = .002); and were more likely to harm themselves (odds ratio, 2.53; 95% CI, 1.49 to 4.37; P < .001) than adolescents without diagnosis.

          Conclusions and Relevance

          In this cohort study, ADHD diagnosis was not associated with any self-reported improvements in adolescents’ QOL compared with adolescents with similar levels of H/I behaviors but no ADHD diagnosis. ADHD diagnosis was associated with worse scores in some outcomes, including significantly increased risk of self-harm. A large, randomized clinical trial with long-term follow-up is needed.

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          Most cited references36

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          Diagnostic and Statistical Manual of Mental Disorders

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            Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

            The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
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              Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

              Ideally, questions about comparative effectiveness or safety would be answered using an appropriately designed and conducted randomized experiment. When we cannot conduct a randomized experiment, we analyze observational data. Causal inference from large observational databases (big data) can be viewed as an attempt to emulate a randomized experiment-the target experiment or target trial-that would answer the question of interest. When the goal is to guide decisions among several strategies, causal analyses of observational data need to be evaluated with respect to how well they emulate a particular target trial. We outline a framework for comparative effectiveness research using big data that makes the target trial explicit. This framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                13 October 2022
                October 2022
                13 October 2022
                : 5
                : 10
                : e2236364
                Affiliations
                [1 ]Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
                [2 ]Institute for Evidence-Based Healthcare, Bond University, Robina, Australia
                Author notes
                Article Information
                Accepted for Publication: August 25, 2022.
                Published: October 13, 2022. doi:10.1001/jamanetworkopen.2022.36364
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Kazda L et al. JAMA Network Open.
                Corresponding Author: Luise Kazda, MPH, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia ( luise.kazda@ 123456sydney.edu.au ).
                Author Contributions: Ms Kazda had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Kazda, Bell, Thomas, Barratt.
                Acquisition, analysis, or interpretation of data: Kazda, McGeechan, Bell, Barratt.
                Drafting of the manuscript: Kazda, Barratt.
                Critical revision of the manuscript for important intellectual content: All authors.
                Statistical analysis: Kazda, McGeechan.
                Obtained funding: Barratt.
                Administrative, technical, or material support: Kazda.
                Supervision: Bell, Thomas, Barratt.
                Conflict of Interest Disclosures: None reported.
                Funding/Support: This study was funded by the National Health and Medical Research Council (NHMRC) Program Grant No. 1113532 and CRE Grant No. 1104136. Dr Bell receives funding from an NHMRC Investigator Grant (grant No. 1174523).
                Role of the Funder/Sponsor: The funder had no role in the study design, in the collection, analysis and interpretation of data, in the writing of the report or in the decision to submit the article for publication. We confirm the independence of researchers from funders.
                Additional Contributions: We thank the Australian Government Department of Social Services for providing access to the Longitudinal Study of Australian Children (LSAC) data and all the families participating in the LSAC. Mark Jones, PhD (Institute of Evidence-Based Healthcare, Bond University), provided statistical support and guidance and was not compensated for this work.
                Article
                zoi221028 zoi221028
                10.1001/jamanetworkopen.2022.36364
                9561944
                36227598
                1bf53906-c5f9-47d7-bc33-229ce93d00f6
                Copyright 2022 Kazda L et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 1 May 2022
                : 25 August 2022
                Categories
                Research
                Original Investigation
                Online Only
                Pediatrics

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