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A formal evaluation of the health equity impact of a new intervention is hardly ever performed as part of a health technology assessment to understand its value. This should change, in our view. An evidence-based quantitative assessment of the health equity impact can help decision makers develop coverage policies, programme designs, and quality initiatives focused on optimizing both total health and health equity given the treatment options available. We outline the conceptual basis of how a new intervention can impact health equity and adopt distributional cost-effectiveness analysis based on decision-analytic models to assess this quantitatively, using a newly US FDA-approved drug for Alzheimer's disease (aducanumab) as an example. We argue that gaps in the evidence base for the new intervention, for example, due to limited clinical research participation among racial and ethnic minority groups, do not preclude such an evaluation. Understanding these uncertainties has implications for fair pricing, decision making, and future research. If we are serious about population-level decision making that not only is focused on improving total health but also aims to improve health equity, we should consider routinely assessing the health equity impact of new interventions.

Increasing awareness of the emotional impact of an Intensive Care Unit (ICU) hospitalization on patients and their families has led to a rise in studies seeking to mitigate Post Intensive Care Syndrome (PICS) for both groups. In efforts to decrease symptoms of anxiety and depression, ICUs have implemented a variety of programs to reduce family distress.

Objectives. Cognitive-Behaviorally Based Interventions (CBIs) are evidence-based treatments for alcohol and other drug (AOD) use with potential variable effectiveness by population sub-groups. This study used evidence synthesis to examine treatment effect by demographic and study context factors in clinical trials of CBI for AOD. Methods. Studies were systematically identified, and their characteristics and outcome data were extracted and summarized. Standardized mean differences were calculated for within- and between-condition effects on substance use outcomes. Demographic and study context moderators were identified during data acquisition and several sensitivity analyses were conducted. Results. The sample included K = 29 trials and a total of 15 study-level moderators were examined. Information on participants' age, biological sex, and race were reported in at least 26 trials, but information on gender identity, sexual orientation, and ethnicity were reported infrequently or in non-inclusive ways. The mean between-condition effect size was small and moderately heterogenous (d = 0.158, 95% CI = 0.079, 0.238, I2 = 46%) and the mean within-condition effect size was large and showed high heterogeneity (dz = 1.147, 95% CI = 0.811, 1.482, - I2 = 96%). The specific drug targeted in the study and whether biological assay-based outcomes were used moderated between-condition CBI efficacy and the inclusion of co-occurring mental health conditions and study publication date moderated within-condition CBI effects. Conclusions. Results provide preliminary data on study context factors associated with effect estimates in United States based clinical trials of CBI for AOD. [ABSTRACT FROM AUTHOR]

The use of seclusion to manage conflict behaviours in psychiatric inpatient settings is increasingly viewed as an intervention of last resort. Many protocols have, thus, been developed to reduce the practice. We conducted a systematic review to determine the effectiveness of protocols to reduce seclusion on process outcomes (e.g., seclusion, restraint), patient outcomes (e.g., injuries, aggressive incidents, satisfaction), and staff outcomes (e.g., injuries, satisfaction). We searched Medline, Embase, the Cochrane Register of Clinical Trials, PsycINFO, CINAHL, cairn.info, and ClinicalTrials.gov for protocols to reduce seclusion practices for adult patients on inpatient mental health units (from inception to September 6, 2022). We summarised and categorised reported elements of the protocols designed to reduce seclusion using the Behaviour Change Wheel Intervention Functions and resources needed to implement the protocol in psychiatric units. We assessed risk of bias and determined certainty of evidence using GRADE. Forty‐eight reports addressed five approaches to reduce seclusion: hospital/unit restructuring (N = 4), staff education/training (N = 3), sensory modulation rooms (N = 7), risk assessment and management protocols (N = 7), and comprehensive/mixed interventions (N = 22; N = 6 without empirical data). The relationship between the various protocols and outcomes was mixed. Psychiatric units that implement architecturally positive designs, sensory rooms, the Brøset Violence Checklist, and various multi‐component comprehensive interventions may reduce seclusion events, though our certainty in these findings is low due to studies' methodological limitations. Future research and practice may benefit from standardised reporting of process and outcome measures and analyses that account for confounders. [ABSTRACT FROM AUTHOR]

Does treatment order matter for metastatic melanoma? The long-term results of the phase II SECOMBIT trial, now published in NEJM Evidence, surely suggest so.1 The SECOMBIT trial randomly assigned 206 patients with B-type Raf kinase (BRAF)V600-mutated metastatic melanoma into equal treatment groups: the first treatment group started with dual targeted therapy (encorafenib plus binimetinib), switching to immunotherapy (ipilimumab plus nivolumab) after progressive disease was observed; the second reversed that sequence; and the third prepended an induction phase of targeted therapy to the second group's treatment strategy (an addition that might help those whose disease progresses too fast for immunotherapy). [ABSTRACT FROM AUTHOR]

Online physician reviews are a massive and potentially rich source of information capturing patient sentiment regarding healthcare. We analyze a corpus comprising nearly 60,000 such reviews with a state-of-the-art probabilistic model of text. We describe a probabilistic generative model that captures latent sentiment across aspects of care (eg, interpersonal manner). We target specific aspects by leveraging a small set of manually annotated reviews. We perform regression analysis to assess whether model output improves correlation with state-level measures of healthcare. We report both qualitative and quantitative results. Model output correlates with state-level measures of quality healthcare, including patient likelihood of visiting their primary care physician within 14 days of discharge (p=0.03), and using the proposed model better predicts this outcome (p=0.10). We find similar results for healthcare expenditure. Generative models of text can recover important information from online physician reviews, facilitating large-scale analyses of such reviews.

The article focuses on evaluating the effectiveness of COVID-19 mitigation policies in Rhode Island, particularly examining the impact of lockdown measures and restrictions on social gatherings. Topics include the implementation of strict interventions, comparison with hypothetical scenarios, and the importance of reducing population density to control disease transmission.

Systematic reviews are increasingly used to inform health care decisions, but are expensive to produce. We explore the use of "crowdsourcing" (distributing tasks to untrained workers via the web) to reduce the cost of screening citations. We used Amazon Mechanical Turk as our platform and 4 previously conducted systematic reviews as examples. For each citation, workers answered 4 or 5 questions that were equivalent to the eligibility criteria. We aggregated responses from multiple workers into an overall decision to include or exclude the citation using 1 of 9 algorithms and compared the performance of these algorithms to the corresponding decisions of trained experts. The most inclusive algorithm (designating a citation as relevant if "any" worker did) identified 95% to 99% of the citations that were ultimately included in the reviews while excluding 68% to 82% of irrelevant citations. Other algorithms increased the fraction of irrelevant articles excluded at some cost to the inclusion of relevant studies. Crowdworkers completed screening in 4 to 17 days, costing $460 to $2220, a cost reduction of up to 88% compared to trained experts. Crowdsourcing may represent a useful approach to reducing the cost of identifying literature for systematic reviews.

A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data. [ABSTRACT FROM AUTHOR]

Existing methods for meta-analysis of diagnostic test accuracy focus primarily on a single index test. We propose models for the joint meta-analysis of studies comparing multiple index tests on the same participants in paired designs. These models respect the grouping of data by studies, account for the within-study correlation between the tests' true-positive rates (TPRs) and between their false-positive rates (FPRs) (induced because tests are applied to the same participants), and allow for between-study correlations between TPRs and FPRs (such as those induced by threshold effects). We estimate models in the Bayesian setting. We demonstrate using a meta-analysis of screening for Down syndrome with two tests: shortened humerus (arm bone), and shortened femur (thigh bone). Separate and joint meta-analyses yielded similar TPR and FPR estimates. For example, the summary TPR for a shortened humerus was 35.3% (95% credible interval (CrI): 26.9, 41.8%) versus 37.9% (27.7, 50.3%) with joint versus separate meta-analysis. Joint meta-analysis is more efficient when calculating comparative accuracy: the difference in the summary TPRs was 0.0% (-8.9, 9.5%; TPR higher for shortened humerus) with joint versus 2.6% (-14.7, 19.8%) with separate meta-analyses. Simulation and empirical analyses are needed to refine the role of the proposed methodology.

Background Imaging tests are one of the most frequently used diagnostic modalities in healthcare, but the benefits of their direct impacts on clinical decisionmaking have been countered by concerns that they can be overused. Assessing the relative value of imaging tests has largely focused on measures of test accuracy, which overlooks more comprehensive benefits and risks of imaging tests, particularly their impact on patient-centred outcomes (PCOs). We present the findings of the Patient Reported Outcomes of Diagnostics (PROD) research study in response to a methodological gap in the area of diagnostic test comparative effectiveness research. Methods Over a 3-year period, the PROD Study engaged with multiple stakeholders to identify existing conceptual models related to PCOs for imaging testing, conducted primary research and evidence synthesis, and developed consensus recommendations to describe and categorise PCOs related to imaging testing. Results The PROD framework categorises PCOs from imaging studies within four main domains: information or knowledge yielded, physical impact, emotional outcomes and test burden. PCOs interact with each other and influence effects across domains, and can be modified by factors related to the patient, clinical situation, healthcare team and the testing environment. Conclusions Using PCOs to inform healthcare decisionmaking will require ways of collating and presenting information on PCOs in ways that can inform patient- provider decision-making, and developing methods to determine the relative importance of outcomes (including test accuracy) to one another. [ABSTRACT FROM AUTHOR]

We develop a Bayesian multinomial network meta-analysis model for unordered (nominal) categorical outcomes that allows for partially observed data in which exact event counts may not be known for each category. This model properly accounts for correlations of counts in mutually exclusive categories and enables proper comparison and ranking of treatment effects across multiple treatments and multiple outcome categories. We apply the model to analyze 17 trials, each of which compares two of three treatments (high and low dose statins and standard care/control) for three outcomes for which data are complete: cardiovascular death, non-cardiovascular death and no death. We also analyze the cardiovascular death category divided into the three subcategories (coronary heart disease, stroke and other cardiovascular diseases) that are not completely observed. The multinomial and network representations show that high dose statins are effective in reducing the risk of cardiovascular disease.