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Aim: To determine if the benefit of early confirmation of permanent childhood hearing impairment (PCHI) on children's receptive language development is associated with fewer behavioural problems. Method: Follow-up of a total population cohort of 120 children with PCHI of moderate or greater severity (greater than or equal to 40 decibels relative to hearing threshold level) (67 males, 53 females; mean age 7y 11mo, range 5y 5mo-11y 8mo) and 63 hearing children (37 males, 26 females; mean age 8y 1mo, range 6y 4mo-9y 10mo). The main outcome measures were the Strengths and Difficulties Questionnaire (SDQ) completed by teachers and parents and the Vineland Adaptive Behaviour Scales (VABS) which are completed on the basis of a parental interview. Results: Children with PCHI had lower standard scores than hearing children on the Daily Living Skills (p = 0.001) and the Socialisation (p = 0.001) scales of the VABS. They had significantly higher Total Behaviour Problem scores on the parent-rated (p = 0.002) and teacher-rated SDQ (p = 0.03). Children for whom PCHI was confirmed by 9 months did not have significantly fewer problems on the behavioural measures than those confirmed after that age (p = 0.635 and p = 0.196). Interpretation: Early confirmation has a beneficial effect on receptive language development but no significant impact in reducing behavioural problems in children with PCHI.

Introduction: The reliability of cost–utility analyses depends on the quality of health state utility values (HSUVs). Given the increasing number of studies eliciting HSUVs, systematic reviews (SRs) are vital to economic evaluations. Nevertheless, a universally acceptable quality appraisal (QA) tool specific to the SRs of HSUV studies is lacking—this study aimed to develop one and fill this gap. Methods: We employed a mixed-method approach, starting with a rapid review to identify QA dimensions, QA items, and terminology in the SRs of HSUV-eliciting studies. This informed a modified Delphi process with a seven-member international expert panel, aiming to define key terms, refine the QA tool dimensions, and establish relevant signaling questions. The experts participated in two anonymous online survey rounds interspersed with structured feedback, enabling iterative refinement of their views. Following these surveys, a virtual face-to-face meeting was held to resolve outstanding issues. Consensus was defined a priori at all stages of the modified Delphi process. Results: The rapid review identified three QA dimensions and 16 initial items, noting the diverse terminologies in defining QA. Response rates to the first- and second-round questionnaires and the virtual consensus meeting were 100, 86, and 71 percent, leading to a consensus on the definitions of scientific quality, QA, the three QA dimensions (reporting, methodological limitation, and risk of bias and relevance), and scope of the QA tool. The number of QA items was refined to 14: all relevant to reporting, six to relevance, and 11 to methodological limitations and bias risk dimensions. The QA tool underscores distinct evaluations for each dimension. Conclusions: We present the first version of a QA checklist designed to provide SR authors with a tool to appraise the quality of HSUV-eliciting studies comprehensively. The QA tool aims to (i) facilitate QA in SRs of HSUV elicitation studies, (ii) promote consistency in the appraisal process, and (iii) emphasize the importance of differentiating between reporting quality, methodology, and relevance. [ABSTRACT FROM AUTHOR]

Introduction: Progress and innovation in artificial intelligence (AI)-based healthcare interventions continue to develop rapidly. However, there are limitations in the published health economic evaluations (HEEs) of AI interventions, including limited reporting on characteristics and development of algorithms. We developed an extension to the existing Consolidated Health Economic Evaluation Reporting Standards (CHEERS) to improve consistency, transparency, and reliability of the reporting of HEEs of AI interventions. Methods: The Delphi method was used, following a prespecified study protocol. A steering group with expert oversight was formed to guide the development process. A long list of potential items was defined based on two recent systematic reviews of HEEs of AI-based interventions. The steering group identified and invited 119 experts to the three-stage survey. Participants were asked to score each item on a nine-point Likert scale, and they were also able to provide free-text comments. The final checklist was piloted on a random sample of nine HEEs of AI-based interventions. Results: Three stages of the Delphi survey were completed by 58, 42, and 31 multidisciplinary respondents, respectively, including HTA specialists, health economists, AI experts, and patient representatives. The CHEERS-AI extension includes 18 AI-specific reporting items. Ten are entirely new items, including considerations about user autonomy, validation of the AI component, and AI-specific uncertainty. In addition, elaborations on eight existing CHEERS items were added to emphasize important AI-specific nuances. Some participants highlighted that CHEERS-AI can provide key benefits; for example, it could clarify the misconception that the predictive algorithms supporting AI-driven healthcare interventions are available for use without cost. Conclusions: CHEERS-AI can aid in improved reporting quality for researchers, editors, and reviewers conducting or assessing HEEs of AI interventions. [ABSTRACT FROM AUTHOR]