Comparative efficacy and tolerability of medications for attention-deficit hyperactivity disorder in children, adolescents, and adults: a systematic review and network meta-analysis

In spite of the growing literature about adult attention-deficit hyperactivity disorder (ADHD), relatively little is known about the prevalence and correlates of this disorder. To estimate the prevalence of adult ADHD and to identify its demographic correlates using meta-regression analysis. We used the MEDLINE, PsycLit and EMBASE databases as well as hand-searching to find relevant publications. The pooled prevalence of adult ADHD was 2.5% (95% CI 2.1-3.1). Gender and mean age, interacting with each other, were significantly related to prevalence of ADHD. Meta-regression analysis indicated that the proportion of participants with ADHD decreased with age when men and women were equally represented in the sample. Prevalence of ADHD in adults declines with age in the general population. We think, however, that the unclear validity of DSM-IV diagnostic criteria for this condition can lead to reduced prevalence rates by underestimation of the prevalence of adult ADHD.

This practice parameter describes the assessment and treatment of children and adolescents with attention-deficit/hyperactivity disorder (ADHD) based on the current scientific evidence and clinical consensus of experts in the field. This parameter discusses the clinical evaluation for ADHD, comorbid conditions associated with ADHD, research on the etiology of the disorder, and psychopharmacological and psychosocial interventions for ADHD.

The assumption of consistency, defined as agreement between direct and indirect sources of evidence, underlies the increasingly popular method of network meta-analysis. No evidence exists so far regarding the extent of inconsistency in full networks of interventions or the factors that control its statistical detection. In this paper we assess the prevalence of inconsistency from data of 40 published networks of interventions involving 303 loops of evidence. Inconsistency is evaluated in each loop by contrasting direct and indirect estimates and by employing an omnibus test of consistency for the entire network. We explore whether different effect measures for dichotomous outcomes are associated with differences in inconsistency, and evaluate whether different ways to estimate heterogeneity affect the magnitude and detection of inconsistency. Inconsistency was detected in from 2% to 9% of the tested loops, depending on the effect measure and heterogeneity estimation method. Loops that included comparisons informed by a single study were more likely to show inconsistency. About one-eighth of the networks were found to be inconsistent. The proportions of inconsistent loops do not materially change when different effect measures are used. Important heterogeneity or the overestimation of heterogeneity was associated with a small decrease in the prevalence of statistical inconsistency. The study suggests that changing the effect measure might improve statistical consistency, and that an analysis of sensitivity to the assumptions and an estimator of heterogeneity might be needed before reaching a conclusion about the absence of statistical inconsistency, particularly in networks with few studies.