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Abstract Background Hypertension (HT) is an orchestrator of atherosclerotic cardiovascular disease (ASCVD) in people living with type 2 diabetes (T2D). Control of systolic blood pressure (SBP) and HT as a whole is suboptimal in diabetes, partly due to the scarcity of doctors. While nurse-led interventions are pragmatic and cost-effective in the control of HT in primary health care, their effectiveness on SBP control among patients with T2D in Uganda is scantly known. Aim We evaluated the effectiveness of a nurse-led management intervention on SBP among T2D patients with a high ASCVD risk in Uganda. Methods A two-armed cluster randomized controlled trial compared the nurse-led management intervention with usual doctor-led care. The intervention involved training nurses to provide structured health education, protocol-based HT/CVD management, 24-h phone calls, and 2-monthly text messages for 6 months. The primary outcome was the mean difference in SBP change among patients with T2D with a high ASCVD risk in the intervention and control groups after 6 months. The secondary outcome was the absolute difference in the number of patients at target for SBP, total cholesterol (TC), fasting blood glucose (FBG), glycated hemoglobin (HbA1C), low-density lipoprotein (LDL), triglycerides (TG), and body mass index (BMI) after the intervention. The study was analyzed according to the intention-to-treat principle. Generalized estimating equations were used to assess intra-cluster effect modifiers. Statistical significance was set at 0.05 for all analyses. Results Eight clinics (n = 388 patients) were included (intervention 4 clinics; n = 192; control 4 clinics; n = 196). A nurse-led intervention reduced SBP by -11.21 ± 16.02 mmHg with a mean difference between the groups of -13.75 mmHg (95% CI -16.48 to -11.02, p

The "1,2,3,emoció!" is a universal preschool program of emotional education for children aged 3–5 years that has positive effect on emotional competence. The objective of this study was to evaluate the effectiveness of this program specifically in 3- to 5-year-old children with autism spectrum conditions. The effectiveness was evaluated during the first year of implementation (school year 2018–2019) in Barcelona. We conducted a cluster randomized community trial, with schools as units of randomization. The children's level of emotional competence was evaluated with the validated Emotional Competence Assessment Questionnaire (score range from 30 to 180). We designed a linear regression model for paired measures. The dependent variable was the final Emotional Competence Assessment Questionnaire score adjusted by the initial score, sex, and grade. The sample consisted of 49 children with autism spectrum conditions, of which 11 were girls. Children in the intervention group showed an increase from the initial score to the final score that was almost three times higher (16.1) than the increase observed in children in the comparison group (6.6). The difference between the two groups was statistically significant, with 8.9 points (95% confidence interval: 1.2–16.6) attributed to the intervention. We conclude that the "1,2,3,emoció!" universal program has the potential to be effective in children with autism spectrum conditions. There are numerous programs and interventions to improve social and emotional skills that expressly target children with autism spectrum conditions. However, there is less knowledge about the impact of universal (directed to all children) school programs of emotional education specifically on these children. In this context, the aim of our study was to evaluate the impact of the "1,2,3,emoció!" universal school program on them. The results show that children with autism spectrum conditions participating in the program improved their emotional competence more than the ones who did not participate. Therefore, we conclude that the "1,2,3,emoció!" universal program is effective in children with autism spectrum conditions. This promising outcome gives the opportunity to these children to benefit from group learning in an inclusive school setting, by complementing individual interventions with a universal program. [ABSTRACT FROM AUTHOR]

Randomized controlled trials can be used to generate evidence on the efficacy and safety of new treatments in eating disorders research. Many of the trials previously conducted in this area have been deemed to be of low quality, in part due to a number of practical constraints. This article provides an overview of established and more innovative clinical trial designs, accompanied by pertinent examples, to highlight how design choices can enhance flexibility and improve efficiency of both resource allocation and participant involvement. Trial designs include individually randomized, cluster randomized, and designs with randomizations at multiple time points and/or addressing several research questions (master protocol studies). Design features include the use of adaptations and considerations for pragmatic or registry‐based trials. The appropriate choice of trial design, together with rigorous trial conduct, reporting and analysis, can establish high‐quality evidence to advance knowledge in the field. It is anticipated that this article will provide a broad and contemporary introduction to trial designs and will help researchers make informed trial design choices for improved testing of new interventions in eating disorders. Public Significance: There is a paucity of high quality randomized controlled trials that have been conducted in eating disorders, highlighting the need to identify where efficiency gains in trial design may be possible to advance the eating disorder research field. We provide an overview of some key trial designs and features which may offer solutions to practical constraints and increase trial efficiency. [ABSTRACT FROM AUTHOR]

ObjectiveWe examine the efficacy of short message service (SMS) and SMS with peer navigation (SMS + PN) in improving linkage to HIV care and initiation of antiretroviral therapy (ART).DesignI-Care was a cluster randomized trial conducted in primary care facilities in North West Province, South Africa. The primary study outcome was retention in HIV care; this analysis includes secondary outcomes: linkage to care and ART initiation.MethodsEighteen primary care clinics were randomized to automated SMS ( n  = 7), automated and tailored SMS + PN ( n  = 7), or standard of care (SOC; n  = 4). Recently HIV diagnosed adults ( n  = 752) were recruited from October 2014 to April 2015. Those not previously linked to care ( n  = 352) contributed data to this analysis. Data extracted from clinical records were used to assess the days that elapsed between diagnosis and linkage to care and ART initiation. Cox proportional hazards models and generalized estimating equations were employed to compare outcomes between trial arms, overall and stratified by sex and pregnancy status.ResultsOverall, SMS ( n  = 132) and SMS + PN ( n  = 133) participants linked at 1.28 [95% confidence interval (CI): 1.01-1.61] and 1.60 (95% CI: 1.29-1.99) times the rate of SOC participants ( n  = 87), respectively. SMS + PN significantly improved time to ART initiation among non-pregnant women (hazards ratio: 1.68; 95% CI: 1.25-2.25) and men (hazards ratio: 1.83; 95% CI: 1.03-3.26) as compared with SOC.ConclusionResults suggest SMS and peer navigation services significantly reduce time to linkage to HIV care in sub-Saharan Africa and that SMS + PN reduced time to ART initiation among men and non-pregnant women. Both should be considered candidates for integration into national programs.Trial registrationNCT02417233, registered 12 December 2014; closed to accrual 17 April 2015.

Background: We evaluated the impact of the CHORUS™ app on adherence to the cabotegravir and rilpivirine long-acting injectable (CAB + RPV LAI) monthly injections schedule. Methods: Healthcare centers (HCCs) were randomized to access CHORUS™ CAB + RPV LAI features (intervention) or not (control) from 01OCT2021-31JAN2022. Target window adherence (maintenance injections ≤7 days before/after target day) was assessed with multivariate logistic regression (generalized estimating equations). Results: CAB + RPV LAI was administered to 188 and 79 individuals at intervention and control HCCs, respectively. Intervention was not associated with improved target window adherence (adjusted odds ratio: 0.61 [95% CI: 0.30-1.25]). However, app use was associated with increased odds of adherence compared to no app use among all intervention HCCs (2.98 [1.26-7.06]) and at smaller HCCs (3.58 [1.31-9.80]). Conclusions: While access to CHORUS™ CAB + RPV LAI features did not improve target window adherence, app use did, especially at smaller HCCs which may not have established LAI management procedures. Trial registration: https://clinicaltrials.gov/show/NCT04863261. Plain Language Summary: Evaluation of a mobile app and web portal to help with the timely injections of cabotegravir + rilpivirine long-acting injections Cabotegravir + rilpivirine long-acting injectable (CAB+RPV LAI) is the first long-acting regimen for HIV treatment, which was approved in the US in 2021. CAB+RPV LAI should be administered ≤7 days before/after the target date. We conducted a trial to evaluate the impact of the CHORUS™ app and web portal on the timing of monthly CAB+RPV injections. The intervention clinics had access to features designed to help with CAB+RPV LAI management, including flagging delayed/missed injections and appointment scheduling status. Control clinics did not have access to these features and managed CAB+RPV LAI administration on their own. Access to the app and web portal features for intervention clinics had no impact on timing of injections compared to control clinics. However, intervention clinics who actively used the app were close to three times more likely to give injections on-time than intervention clinics who did not use the app. The effect of app use was seen specifically among smaller clinics caring for <1000 people with HIV: smaller clinics that actively used the app were 3.58 times more likely to give injections on-time than those who did not use the app. In conclusion, while access to CHORUS™ CAB+RPV LAI features in the app and the web portal did not improve the likelihood of on time injections, actively using the app did make a difference, especially at smaller clinics which may not have established injection management procedures. [ABSTRACT FROM AUTHOR]

Generalized estimating equations (GEEs) provide a useful framework for estimating marginal regression parameters based on data from cluster randomized trials (CRTs), but they can result in inaccurate parameter estimates when some outcomes are informatively missing. Existing techniques to handle missing outcomes in CRTs rely on correct specification of a propensity score model, a covariate‐conditional mean outcome model, or require at least one of these two models to be correct, which can be challenging in practice. In this article, we develop new weighted GEEs to simultaneously estimate the marginal mean, scale, and correlation parameters in CRTs with missing outcomes, allowing for multiple propensity score models and multiple covariate‐conditional mean models to be specified. The resulting estimators are consistent provided that any one of these models is correct. An iterative algorithm is provided for implementing this more robust estimator and practical considerations for specifying multiple models are discussed. We evaluate the performance of the proposed method through Monte Carlo simulations and apply the proposed multiply robust estimator to analyze the Botswana Combination Prevention Project, a large HIV prevention CRT designed to evaluate whether a combination of HIV‐prevention measures can reduce HIV incidence. [ABSTRACT FROM AUTHOR]

In a cluster randomized trial clusters of persons, for instance, schools or health centers, are assigned to treatments, and all persons in the same cluster get the same treatment. Although less powerful than individual randomization, cluster randomization is a good alternative if individual randomization is impossible or leads to severe treatment contamination (carry-over). Focusing on cluster randomized trials with a pretest and post-test of a quantitative outcome, this paper shows the equivalence of four methods of analysis: a three-level mixed (multilevel) regression for repeated measures with as levels cluster, person, and time, and allowing for unstructured between-cluster and within-cluster covariance matrices; a two-level mixed regression with as levels cluster and person, using change from baseline as outcome; a two-level mixed regression with as levels cluster and time, using cluster means as data; a one-level analysis of cluster means of change from baseline. Subsequently, similar equivalences are shown between a constrained mixed model and methods using the pretest as covariate. All methods are also compared on a cluster randomized trial on mental health in children. From these equivalences follows a simple method to calculate the sample size for a cluster randomized trial with baseline measurement, which is demonstrated step-by-step. [ABSTRACT FROM AUTHOR]

Abstract Background The Large-scale Assessment of the Key health-promoting Activities of two New mass drug administration regimens with Azithromycin (LAKANA) trial in Mali aims to evaluate the efficacy and safety of azithromycin (AZI) mass drug administration (MDA) to 1–11-month-old infants as well as the impact of the intervention on antimicrobial resistance (AMR) and mechanisms of action of azithromycin. To improve the transparency and quality of this clinical trial, we prepared this statistical analysis plan (SAP). Methods/design LAKANA is a cluster randomized trial that aims to address the mortality and health impacts of biannual and quarterly AZI MDA. AZI is given to 1–11-month-old infants in a high-mortality setting where a seasonal malaria chemoprevention (SMC) program is in place. The participating villages are randomly assigned to placebo (control), two-dose AZI (biannual azithromycin-MDA), and four-dose AZI (quarterly azithromycin-MDA) in a 3:4:2 ratio. The primary outcome of the study is mortality among the intention-to-treat population of 1–11-month-old infants. We will evaluate relative risk reduction between the study arms using a mixed-effects Poisson model with random intercepts for villages, using log link function with person-years as an offset variable. We will model outcomes related to secondary objectives of the study using generalized linear models with considerations on clustering. Conclusion The SAP written prior to data collection completion will help avoid reporting bias and data-driven analysis for the primary and secondary aims of the trial. If there are deviations from the analysis methods described here, they will be described and justified in the publications of the trial results. Trial registration ClinicalTrials.gov ID NCT04424511 . Registered on 11 June 2020.

Comprehensive evaluation of treatment effects is aided by considerations for moderated effects. In educational research, the combination of natural hierarchical structures and prevalence of group-administered or shared facilitator treatments often produces three-level partially nested data structures. Literature details planning strategies for a variety of experimental designs when moderation effects are of interest but has yet to establish power formulas for detecting moderation effects in three-level partially nested designs. To address this gap, we derive and assess the accuracy of power formulas for detecting the different types of moderation effects possible in these designs. Using Monte Carlo simulation studies, we probe power rates and adequate sample sizes for detecting the different moderation effects while varying common influential factors including variance in the outcome explained by covariates, magnitude of the moderation effect, and sample sizes. The power formulas developed improve the planning of experimental studies with partial nesting and encourage the inclusion of moderator variables to capture for whom and under what conditions a treatment is effective. Educational researchers also have some initial guidance regarding adequate sample sizes and the factors that influence detecting moderation effects in three-level partially nested designs. [ABSTRACT FROM AUTHOR]

Background: Ongoing high neonatal mortality rates (NMRs) represent a global challenge. In 2021, of the 5 million deaths reported worldwide for children under five years of age, 47% were newborns. Pakistan has one of the five highest national NMRs in the world, with an estimated 39 neonatal deaths per 1,000 live births. Reducing newborn deaths requires sustainable, evidence-based, and cost-effective interventions that can be integrated within existing community healthcare infrastructure across regions with high NMR. Methods: This pragmatic, community-based, parallel-arm, open-label, cluster randomized controlled trial aims to estimate the effect of Lady Health Workers (LHWs) providing an integrated newborn care kit (iNCK) with educational instructions to pregnant women in their third trimester, compared to the local standard of care in Gilgit-Baltistan, Pakistan, on neonatal mortality and other newborn and maternal health outcomes. The iNCK contains a clean birth kit, 4% chlorhexidine topical gel, sunflower oil emollient, a ThermoSpot™ temperature monitoring sticker, a fleece blanket, a click-to-heat reusable warmer, three 200 μg misoprostol tablets, and a pictorial instruction guide and diary. LHWs are also provided with a handheld scale to weigh the newborn. The primary study outcome is neonatal mortality, defined as a newborn death in the first 28 days of life. Discussion: This study will generate policy-relevant knowledge on the effectiveness of integrating evidence-based maternal and newborn interventions and delivering them directly to pregnant women via existing community health infrastructure, for reducing neonatal mortality and morbidity, in a remote, mountainous area with a high NMR. Trial registration: NCT04798833, March 15, 2021. [ABSTRACT FROM AUTHOR]

The meaning of randomization tests has become obscure in statistics education and practice over the last century. This article makes a fresh attempt at rectifying this core concept of statistics. A new term—"quasi-randomization test"—is introduced to define significance tests based on theoretical models and distinguish these tests from the "randomization tests" based on the physical act of randomization. The practical importance of this distinction is illustrated through a real stepped-wedge cluster-randomized trial. Building on the recent literature on randomization inference, a general framework of conditional randomization tests is developed and some practical methods to construct conditioning events are given. The proposed terminology and framework are then applied to understand several widely used (quasi-)randomization tests, including Fisher's exact test, permutation tests for treatment effect, quasi-randomization tests for independence and conditional independence, adaptive randomization, and conformal prediction. for this article are available online. [ABSTRACT FROM AUTHOR]

Background: The Large-scale Assessment of the Key health-promoting Activities of two New mass drug administration regimens with Azithromycin (LAKANA) trial in Mali aims to evaluate the efficacy and safety of azithromycin (AZI) mass drug administration (MDA) to 1–11-month-old infants as well as the impact of the intervention on antimicrobial resistance (AMR) and mechanisms of action of azithromycin. To improve the transparency and quality of this clinical trial, we prepared this statistical analysis plan (SAP). Methods/design: LAKANA is a cluster randomized trial that aims to address the mortality and health impacts of biannual and quarterly AZI MDA. AZI is given to 1–11-month-old infants in a high-mortality setting where a seasonal malaria chemoprevention (SMC) program is in place. The participating villages are randomly assigned to placebo (control), two-dose AZI (biannual azithromycin-MDA), and four-dose AZI (quarterly azithromycin-MDA) in a 3:4:2 ratio. The primary outcome of the study is mortality among the intention-to-treat population of 1–11-month-old infants. We will evaluate relative risk reduction between the study arms using a mixed-effects Poisson model with random intercepts for villages, using log link function with person-years as an offset variable. We will model outcomes related to secondary objectives of the study using generalized linear models with considerations on clustering. Conclusion: The SAP written prior to data collection completion will help avoid reporting bias and data-driven analysis for the primary and secondary aims of the trial. If there are deviations from the analysis methods described here, they will be described and justified in the publications of the trial results. Trial registration: ClinicalTrials.gov ID NCT04424511. Registered on 11 June 2020. [ABSTRACT FROM AUTHOR]

Abstract Background Physical activity behavioral interventions to change individual-level drivers of activity, like motivation, attitudes, and self-efficacy, are often not sustained beyond the intervention period. Interventions at both environmental and individual levels might facilitate durable change. This community-based study seeks to test a multilevel, multicomponent intervention to increase moderate intensity physical activity among people with low incomes living in U.S. public housing developments, over a 2 year period. Methods The study design is a prospective, cluster randomized controlled trial, with housing developments (n=12) as the units of randomization. In a four-group, factorial trial, we will compare an environmental intervention (E) alone (3 developments), an individual intervention (I) alone (3 developments), an environmental plus individual (E+I) intervention (3 developments), against an assessment only control group (3 developments). The environmental only intervention consists of community health workers leading walking groups and indoor activities, a walking advocacy program for residents, and provision of walking maps/signage. The individual only intervention consists of a 12-week automated telephone program to increase physical activity motivation and self-efficacy. All residents are invited to participate in the intervention activities being delivered at their development. The primary outcome is change in moderate intensity physical activity measured via an accelerometer-based device among an evaluation cohort (n=50 individuals at each of the 12 developments) from baseline to 24-month follow up. Mediation (e.g., neighborhood walkability, motivation) and moderation (e.g., neighborhood stress) of our interventions will be assessed. Lastly, we will interview key informants to assess factors from the Consolidated Framework for Implementation Research domains to inform future implementation. Discussion We hypothesize participants living in developments in any of the three intervention groups (E only, I only, and E+I combined) will increase minutes of moderate intensity physical activity more than participants in control group developments. We expect delivery of an intervention package targeting environmental and social factors to become active, combined with the individual level intervention, will improve overall physical activity levels to recommended guidelines at the development level. If effective, this trial has the potential for implementation through other federal and state housing authorities. Trial registration Clinical Trails.gov PRS Protocol Registration and Results System, NCT05147298 . Registered 28 November 2021.

Lead exposure is harmful at any time in life, but pre-natal and early childhood exposures are particularly detrimental to cognitive development. In Bangladesh, multiple household-level lead exposures pose risks, including turmeric adulterated with lead chromate and food storage in lead-soldered cans. We developed and evaluated an intervention to reduce lead exposure among children and their caregivers in rural Bangladesh. We conducted formative research to inform theory-based behavioral recommendations. Lead exposure was one of several topics covered in the multi-component intervention focused on early child development. Community health workers (CHWs) delivered the lead component of the intervention during group sessions with pregnant women and mother-child dyads (

Background: Deimplementing overused health interventions is essential to maximizing quality and value while minimizing harm, waste, and inefficiencies. Three national guidelines discourage continuous pulse oximetry (SpO2) monitoring in children who are not receiving supplemental oxygen, but the guideline-discordant practice remains prevalent, making it a prime target for deimplementation. This paper details the statistical analysis plan for the Eliminating Monitor Overuse (EMO) SpO2 trial, which compares the effect of two competing deimplementation strategies (unlearning only vs. unlearning plus substitution) on the sustainment of deimplementation of SpO2 monitoring in children with bronchiolitis who are in room air. Methods: The EMO Trial is a hybrid type 3 effectiveness-deimplementation trial with a longitudinal cluster-randomized design, conducted in Pediatric Research in Inpatient Settings Network hospitals. The primary outcome is deimplementation sustainment, analyzed as a longitudinal difference-in-differences comparison between study arms. This analysis will use generalized hierarchical mixed-effects models for longitudinal clustering outcomes. Secondary outcomes include the length of hospital stay and oxygen supplementation duration, modeled using linear mixed-effects regressions. Using the well-established counterfactual approach, we will also perform a mediation analysis of hospital-level mechanistic measures on the association between the deimplementation strategy and the sustainment outcome. Discussion: We anticipate that the EMO Trial will advance the science of deimplementation by providing new insights into the processes, mechanisms, and likelihood of sustained practice change using rigorously designed deimplementation strategies. This pre-specified statistical analysis plan will mitigate reporting bias and support data-driven approaches. Trial registration: ClinicalTrials.gov NCT05132322. Registered on 24 November 2021.

For time-to-event outcomes, the difference in restricted mean survival time is a measure of the intervention effect, an alternative to the hazard ratio, corresponding to the expected survival duration gain due to the intervention up to a predefined time t *. We extended two existing approaches of restricted mean survival time estimation for independent data to clustered data in the framework of cluster randomized trials: one based on the direct integration of Kaplan-Meier curves and the other based on pseudo-values regression. Then, we conducted a simulation study to assess and compare the statistical performance of the proposed methods, varying the number and size of clusters, the degree of clustering, and the magnitude of the intervention effect under proportional and non-proportional hazards assumption. We found that the extended methods well estimated the variance and controlled the type I error if there was a sufficient number of clusters (≥ 50) under both proportional and non-proportional hazards assumption. For cluster randomized trials with a limited number of clusters (< 50), a permutation test for pseudo-values regression was implemented and corrected the type I error. We also provided a procedure to estimate permutation-based confidence intervals which produced adequate coverage. All the extended methods performed similarly, but the pseudo-values regression offered the possibility to adjust for covariates. Finally, we illustrated each considered method with a cluster randomized trial evaluating the effectiveness of an asthma-control education program. [ABSTRACT FROM AUTHOR]

In this article, we derive and compare methods to derive P‐values and sets of confidence intervals with strong control of the family‐wise error rates and coverage for estimates of treatment effects in cluster randomized trials with multiple outcomes. There are few methods for P‐value corrections and deriving confidence intervals, limiting their application in this setting. We discuss the methods of Bonferroni, Holm, and Romano and Wolf and adapt them to cluster randomized trial inference using permutation‐based methods with different test statistics. We develop a novel search procedure for confidence set limits using permutation tests to produce a set of confidence intervals under each method of correction. We conduct a simulation‐based study to compare family‐wise error rates, coverage of confidence sets, and the efficiency of each procedure in comparison to no correction using both model‐based standard errors and permutation tests. We show that the Romano‐Wolf type procedure has nominal error rates and coverage under non‐independent correlation structures and is more efficient than the other methods in a simulation‐based study. We also compare results from the analysis of a real‐world trial. [ABSTRACT FROM AUTHOR]

Cluster randomized trials (CRTs) are studies where treatment is randomized at the cluster level but outcomes are typically collected at the individual level. When CRTs are employed in pragmatic settings, baseline population characteristics may moderate treatment effects, leading to what is known as heterogeneous treatment effects (HTEs). Pre‐specified, hypothesis‐driven HTE analyses in CRTs can enable an understanding of how interventions may impact subpopulation outcomes. While closed‐form sample size formulas have recently been proposed, assuming known intracluster correlation coefficients (ICCs) for both the covariate and outcome, guidance on optimal cluster randomized designs to ensure maximum power with pre‐specified HTE analyses has not yet been developed. We derive new design formulas to determine the cluster size and number of clusters to achieve the locally optimal design (LOD) that minimizes variance for estimating the HTE parameter given a budget constraint. Given the LODs are based on covariate and outcome‐ICC values that are usually unknown, we further develop the maximin design for assessing HTE, identifying the combination of design resources that maximize the relative efficiency of the HTE analysis in the worst case scenario. In addition, given the analysis of the average treatment effect is often of primary interest, we also establish optimal designs to accommodate multiple objectives by combining considerations for studying both the average and heterogeneous treatment effects. We illustrate our methods using the context of the Kerala Diabetes Prevention Program CRT, and provide an R Shiny app to facilitate calculation of optimal designs under a wide range of design parameters. [ABSTRACT FROM AUTHOR]

Background: A significant and growing portion of the global burden of diseases is caused by neurological disorders. Tele-neurology has the potential to improve access to health care services and the quality of care, particularly in rural and underserved areas. The economic evaluation of the stepped wedge randomised controlled trial NeTKoH aims to ascertain the cost-effectiveness and cost-utility regarding the effects of a tele-neurologic intervention in primary care in a rural area in Germany. Methods: This protocol outlines the methods used when conducting the trial-based economic evaluation of NeTKoH. The outcomes used in our economic analysis are all prespecified endpoints of the NeTKoH trial. Outcomes considered for the cost-utility and cost-effectiveness analyses will be quality-adjusted life years (QALYs) derived from the EQ-5D-5L, proportion of neurologic problems being solved at the GP's office (primary outcome), hospital length-of-stay and number of hospital stays. Costs will be prospectively collected during the trial by the participating statutory health insurances, and will be analysed from a statutory health insurance perspective within the German health care system. This economic evaluation will be reported complying with the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. Discussion: This within-trial economic evaluation relaying the costs and outcomes of an interdisciplinary tele-consulting intervention will provide high-quality evidence for cost-effectiveness and policy implications of a tele-neurological programme, including the potential for application in other rural areas in Germany or other jurisdictions with a comparable health system. Trial registration: German Clinical Trials Register (DRKS00024492), date registered: September 28, 2021. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new command, clan, that conducts a cluster-level analysis of cluster randomized trials. The command simplifies adjusting for individual- and cluster-level covariates and can also account for a stratified design. It can be used to analyze a continuous, binary, or rate outcome. [ABSTRACT FROM AUTHOR]

On average, randomization achieves balance in covariate distributions between treatment groups; yet in practice, chance imbalance exists post randomization, which increases the error in estimating treatment effects. This is an important issue, especially in cluster randomized trials, where the experimental units (the clusters) are highly heterogeneous and relatively few in number. To address this, several restricted randomization designs have been proposed to balance on a few covariates of particular interest. More recently, approaches involving rerandomization have been proposed that aim to achieve simultaneous balance on several important prognostic factors. In this article, we comment on some properties of rerandomized designs and propose a new design for comparing two or more treatments. This design combines optimal nonbipartite matching of the subjects together with rerandomization, both aimed at minimizing a measure of distance between elements in blocks to achieve reductions in the mean squared error of estimated treatment effects. Compared with the existing alternatives, the proposed design can substantially reduce the mean squared error of the estimated treatment effect. This enhanced efficiency is evaluated both theoretically and empirically, and robustness properties are also noted. The design is generalized to three or more treatment arms. [ABSTRACT FROM AUTHOR]

Influenza is a major cause of morbidity and mortality. The protective effect of a trivalent influenza vaccine (TIV) is undetermined in military personnel. We conducted an open-label, cluster randomized trial on active-duty servicemen of Beijing, Tianjin, and Shijiazhuang, who were randomly assigned to receive either a single dose of TIV or no treatment, according to cluster randomized sampling. The subjects were then followed for a maximum of six months to assess the incidence of laboratory-confirmed influenza. A total of 5445 subjects in 114 clusters received one dose of TIV before the 2015/2016 influenza season. Laboratory-confirmed influenza was identified in 18 in the vaccine group compared with 87 in the control group (6031 subjects in 114 clusters), resulting in a vaccine effectiveness (VE) of 76.4% (95%CI: 60.7 to 85.8) against laboratory-confirmed influenza. Influenza-like illness was diagnosed in 132 in the vaccine group compared with 420 in the control group, resulting in a VE of 64.1% (95%CI: 56.2 to 70.6). The estimated VE against influenza B viruses was 80.5% (95%CI: 65.6 to 88.9) and 8.6% (95%CI: −241 to 75.5) against influenza A viruses. In conclusion, the trivalent influenza vaccine is moderately effective, highly immunogenic, and generally safe to use in healthy male military servicemen. [ABSTRACT FROM AUTHOR]

An important consideration in the design and analysis of randomized trials is the need to account for outcome observations being positively correlated within groups or clusters. Two notable types of designs with this consideration are individually randomized group treatment trials and cluster randomized trials. While sample size methods for testing the average treatment effect are available for both types of designs, methods for detecting treatment effect modification are relatively limited. In this article, we present new sample size formulas for testing treatment effect modification based on either a univariate or multivariate effect modifier in both individually randomized group treatment and cluster randomized trials with a continuous outcome but any types of effect modifier, while accounting for differences across study arms in the outcome variance, outcome intracluster correlation coefficient (ICC) and the cluster size. We consider cases where the effect modifier can be measured at either the individual level or cluster level, and with a univariate effect modifier, our closed‐form sample size expressions provide insights into the optimal allocation of groups or clusters to maximize design efficiency. Overall, our results show that the required sample size for testing treatment effect heterogeneity with an individual‐level effect modifier can be affected by unequal ICCs and variances between arms, and accounting for such between‐arm heterogeneity can lead to more accurate sample size determination. We use simulations to validate our sample size formulas and illustrate their application in the context of two real trials: an individually randomized group treatment trial (the AWARE study) and a cluster randomized trial (the K‐DPP study). [ABSTRACT FROM AUTHOR]

Background: Physical activity behavioral interventions to change individual-level drivers of activity, like motivation, attitudes, and self-efficacy, are often not sustained beyond the intervention period. Interventions at both environmental and individual levels might facilitate durable change. This community-based study seeks to test a multilevel, multicomponent intervention to increase moderate intensity physical activity among people with low incomes living in U.S. public housing developments, over a 2 year period. Methods: The study design is a prospective, cluster randomized controlled trial, with housing developments (n=12) as the units of randomization. In a four-group, factorial trial, we will compare an environmental intervention (E) alone (3 developments), an individual intervention (I) alone (3 developments), an environmental plus individual (E+I) intervention (3 developments), against an assessment only control group (3 developments). The environmental only intervention consists of community health workers leading walking groups and indoor activities, a walking advocacy program for residents, and provision of walking maps/signage. The individual only intervention consists of a 12-week automated telephone program to increase physical activity motivation and self-efficacy. All residents are invited to participate in the intervention activities being delivered at their development. The primary outcome is change in moderate intensity physical activity measured via an accelerometer-based device among an evaluation cohort (n=50 individuals at each of the 12 developments) from baseline to 24-month follow up. Mediation (e.g., neighborhood walkability, motivation) and moderation (e.g., neighborhood stress) of our interventions will be assessed. Lastly, we will interview key informants to assess factors from the Consolidated Framework for Implementation Research domains to inform future implementation. Discussion: We hypothesize participants living in developments in any of the three intervention groups (E only, I only, and E+I combined) will increase minutes of moderate intensity physical activity more than participants in control group developments. We expect delivery of an intervention package targeting environmental and social factors to become active, combined with the individual level intervention, will improve overall physical activity levels to recommended guidelines at the development level. If effective, this trial has the potential for implementation through other federal and state housing authorities. Trial registration: Clinical Trails.gov PRS Protocol Registration and Results System, NCT05147298. Registered 28 November 2021. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Current randomized trial evidence on the effect of physical activity (PA) intervention on cognitive function is scarce and mainly found in well‐educated populations, limiting its generalizability. Furthermore, the modification effect of education levels remains understudied. We conducted a cluster randomized trial to evaluate the effects of PA intervention on cognitive function in a general older population, and whether education modifies such effects. METHODS: Eight villages were randomized to the intervention (four villages, n = 240) or the control (four villages, n = 271). The intervention group received an 8‐week multilevel PA intervention based on a socio‐ecological model, while the control did not. The intervention has been condensed to three levels with activities occurring at the individual (telephone counseling, printed material, and training sessions), interpersonal (peer group), and community levels (group sharing and coaching). The primary outcome was changes in global cognition (overall cognitive function) measured by the Telephone Interview for Cognitive Status (TICS‐10) at 12‐month follow‐up. Repeated measurements were modeled using the linear mixed model, which assumed that the missing values were missing at random. RESULTS: The mean age was 70.94 years (standard deviation 5.71) and 55.6% were women. Compared to the control, the orientation improved from baseline at 12‐month follow‐up (0.24 points [95% confidence interval (CI), 0.03 to 0.46 points; P = 0.03]). Stratified analysis showed that the global cognition and orientation improved at 12‐month follow‐up among illiterate participants in the intervention (for global cognition, mean difference, 0.57 points [95% CI, 0.03 to 1.10], P = 0.04; for orientation, mean difference, 0.31 points [95% CI, 0.04 to 0.58], P = 0.03). DISCUSSION: These results confirm the improvements in orientation in older adults, as well as in global cognition and orientation in the illiterate at 12‐month follow‐up. [ABSTRACT FROM AUTHOR]

Stepped wedge cluster randomized trials (SWCRT) are increasingly used for the evaluation of complex interventions in health services research. They randomly allocate treatments to clusters that switch to intervention under investigation at variable time points without returning to control condition. The resulting unbalanced allocation over time periods and the uncertainty about the underlying correlation structures at cluster‐level renders designing and analyzing SWCRTs a challenge. Adjusting for time trends is recommended, appropriate parameterizations depend on the particular context. For sample size calculation, the covariance structure and covariance parameters are usually assumed to be known. These assumptions greatly affect the influence single cluster‐period cells have on the effect estimate. Thus, it is important to understand how cluster‐period cells contribute to the treatment effect estimate. We therefore discuss two measures of cell influence. These are functions of the design characteristics and covariance structure only and can thus be calculated at the planning stage: the coefficient matrix as discussed by Matthews and Forbes and information content (IC) as introduced by Kasza and Forbes. The main result is a new formula for IC that is more general and computationally more efficient. The formula applies to any generalized least squares estimator, especially for any type of time trend adjustment or nonblock diagonal matrices. We further show a functional relationship between IC and the coefficient matrix. We give two examples that tie in with current literature. All discussed tools and methods are implemented in the R package SteppedPower. [ABSTRACT FROM AUTHOR]

In low- and middle-income countries, children experience multiple risks for delayed development. We evaluated a multicomponent, group-based early child development intervention including behavioural recommendations on responsive stimulation, nutrition, water, sanitation, hygiene, mental health and lead exposure prevention. We conducted a 9-month, parallel, multiarm, cluster-randomised controlled trial in 31 rural villages in Kishoreganj District, Bangladesh. Villages were randomly allocated to: group sessions ('group'); alternating groups and home visits ('combined'); or a passive control arm. Sessions were delivered fortnightly by trained community members. The primary outcome was child stimulation (Family Care Indicators); the secondary outcome was child development (Ages and Stages Questionnaire Inventory, ASQi). Other outcomes included dietary diversity, latrine status, use of a child potty, handwashing infrastructure, caregiver mental health and knowledge of lead. Analyses were intention to treat. Data collectors were independent from implementers. In July-August 2017, 621 pregnant women and primary caregivers of children

BackgroundDelays in diagnosis and treatment of tuberculosis (TB) remain common in high-burden countries. To improve case detection, substantial investments have been made to scale-up Xpert MTB/RIF (Xpert), a cartridge-based nucleic acid amplification test that can detect TB within 2 hours, as a replacement for sputum smear microscopy. However, the optimal strategy for implementation of Xpert testing remains unclear.MethodsThe Xpert Performance Evaluation for Linkage to Tuberculosis Care (XPEL-TB) trial uses an ultra-pragmatic, hybrid type II effectiveness-implementation design to assess the effectiveness and implementation of a streamlined strategy for delivery of Xpert testing in real-world settings. Twenty health centers with TB microscopy units were selected to participate in the trial, with ten health centers randomized to the intervention strategy (onsite molecular testing using GeneXpert Edge, process redesign to facilitate same-day TB diagnosis and treatment, and performance feedback) or routine care (onsite sputum smear microscopy plus referral of sputum samples to Xpert testing sites). The primary outcome is the number of patients with microbiologically confirmed TB who were initiated on treatment within 14 days of presentation to the health center, which reflects successful completion of the TB diagnostic evaluation process. Secondary outcomes include health outcomes (6-month vital status), as well as measures of the reach, adoption, and implementation of the intervention strategy.DiscussionThe design elements and implementation approach for the XPEL-TB trial were intentionally selected to minimize disruptions to routine care procedures, with the goal of limiting their influence on key primary and secondary outcomes. Trial findings may result in increased support and funding for rapid, onsite molecular testing as the standard-of-care for all patients being evaluated for TB.Trial registrationUS National Institutes of Health's ClinicalTrials.gov, NCT03044158. Registered 06 February 2017. Pan African Clinical Trials Registry, PACTR201610001763265. Registered 03 September 2016.

BackgroundAntiretroviral therapy (ART) for HIV, taken daily, is an effective strategy to clinically suppress the virus, providing the dual benefit of improved survival and vastly decreasing the risk of transmission. However, this highly effective intervention has not yet reached all who could benefit. Cash transfers are increasingly recognized as an effective strategy to motivate behavior change and improve HIV care and treatment outcomes, including engagement in HIV care and adherence to ART. Despite a growing evidence base and strong theoretical foundation for the cash transfer approach, key questions remain. To address these questions and begin to bridge the "know-do gap" with respect to cash transfers, our team is employing an implementation science approach to iterative development of an incentive-based intervention to promote ART uptake and adherence among people living with HIV (PLHIV) in the Lake Zone region, Tanzania.MethodsWe will conduct a type I hybrid implementation-effectiveness trial to test the effectiveness of a cash transfer intervention on the outcome of HIV viral suppression, and concurrently examine the potential for real-world implementation with a mobile health technology (mHealth) system. Specifically, our team will expand the intervention to 32 clinics and enroll 1984 PLHIV to (a) evaluate its effectiveness by conducting a cluster randomized controlled trial with clinics as the unit of randomization and 12-month viral suppression as the primary outcome and (b) evaluate the implementation challenges and successes at multiple levels (patient, provider, clinic).DiscussionThis trial will provide evidence not only about the real-world effectiveness of cash transfers for retention in HIV care and viral suppression, but also on the implementation challenges and successes that will facilitate or hinder wider scale-up within Tanzania and beyond.Trial registrationClinicalTrials.gov NCT04201353 . Registered on December 17, 2019.

BackgroundThinness and stunting are the most severe public health problems among adolescent girls in Ethiopia. An inadequate intake of protein-source foods is the most critical cause, mainly due to the non-affordability of animal-origin foods. However, research into what extent improving pulses-based food consumption could contribute to decreasing the magnitude of protein-energy undernutrition is limited.ObjectiveThis trial aimed to evaluate the effectiveness of pulses-based nutrition education in reducing the proportion of thinness among adolescent girls.MethodsA two-arm cluster randomized controlled trial was conducted among adolescent girls in Northwest Ethiopia from December 2021 to June 2022. A total of 602 adolescent girls from four schools were enrolled in the trial. Schools were assigned to intervention and control groups using the stratified cluster randomization method. Pulses-based nutrition education was the intervention, whereas the usual dietary practice of adolescent girls was the comparator. The education was delivered over 4 weeks on a 45–60-min session per week basis. Thinness was the primary outcome of the trial, measured by anthropometry. An intention-to-treat analysis method was used. A log-binomial regression model was fitted to the data. Relative risk with the respective confidence interval and value of p was calculated. A value of p

Cluster randomized trial design may raise financial concerns because the cost to recruit an additional cluster is much higher than to enroll an additional subject in subject-level randomized trials. Therefore, it is desirable to develop an optimal design. For local optimal designs, optimization means the minimum variance of the estimated treatment effect under the total budget. The local optimal design derived from the variance needs the input of an association parameter ρ in terms of a "working" correlation structure R (ρ) in the generalized estimating equation models. When the range of ρ instead of an exact value is available, the parameter space is defined as the range of ρ and the design space is defined as enrollment feasibility, for example, the number of clusters or cluster size. For any value ρ within the range, the optimal design and relative efficiency for each design in the design space is obtained. Then, for each design in the design space, the minimum relative efficiency within the parameter space is calculated. MaxiMin design is the optimal design that maximizes the minimum relative efficiency among all designs in the design space. Our contributions are threefold. First, for three common measures (risk difference, risk ratio, and odds ratio), we summarize all available local optimal designs and MaxiMin designs utilizing generalized estimating equation models when the group allocation proportion is predetermined for two-level and three-level parallel cluster randomized trials. We then propose the local optimal designs and MaxiMin designs using the same models when the group allocation proportion is undecided. Second, for partially nested designs, we develop the optimal designs for three common measures under the setting of equal number of subjects per cluster and exchangeable working correlation structure in the intervention group. Third, we create three new Statistical Analysis System (SAS) macros and update two existing SAS macros for all the optimal designs. We provide two examples to illustrate our methods. [ABSTRACT FROM AUTHOR]

Cost-effectiveness analysis of two 12-week contingency management (CM) schedules targeting heroin abstinence or attendance at weekly keyworker appointments for opioid agonist treatment compared with treatment as usual (TAU). A cost-effectiveness analysis was conducted alongside a cluster randomized trial of 552 patients from 34 clusters (drug treatment clinics) randomly allocated 1:1:1 to opioid agonist treatment plus weekly keyworker appointments with (1) CM targeted at heroin abstinence (CM abstinence), (2) CM targeted at on-time attendance at weekly appointments (CM attendance), or (3) no CM (TAU). The primary cost-effectiveness analysis at 24 weeks after randomization took a societal cost perspective with effects measured in heroin-negative urine samples. At 24 weeks, mean differences in weekly heroin-negative urine results compared with TAU were 0.252 (95% confidence interval [CI] −0.397 to 0.901) for CM abstinence and 0.089 (95% CI −0.223 to 0.402) for CM attendance. Mean differences in costs were £2562 (95% CI £32-£5092) for CM abstinence and £317 (95% CI −£882 to £1518) for CM attendance. Incremental cost-effectiveness ratios were £10 167 per additional heroin-free urine for CM abstinence and £3562 for CM attendance with low probabilities of cost-effectiveness of 3.5% and 36%, respectively. Results were sensitive to timing of follow-up for CM attendance, which dominated TAU (better outcomes, lower costs) at 12 weeks, with an 88.4% probability of being cost-effective. Probability of cost-effectiveness remained low for CM abstinence (8.6%). Financial incentives targeted toward heroin abstinence and treatment attendance were not cost-effective over the 24-week follow-up. Nevertheless, CM attendance was cost-effective over the treatment period (12 weeks), when participants were receiving keyworker appointments and incentives. • There is evidence that contingency management (CM) may be an effective strategy in heroin treatment; nevertheless, there is limited evidence of cost-effectiveness. • CM is not a cost-effective strategy for promoting heroin abstinence and treatment engagement in England. • Further research into the duration and the target of incentives is needed before CM could be considered value for money in English drug treatment services. [ABSTRACT FROM AUTHOR]

阶梯整群随机试验（stepped wedge cluster randomized trial，SW-CRT）是一种新型的研究设计，通常用于评价整群的服务或政策干预效果。该设计不需要单独设立对照，在设计初始阶段，所有整群均不暴露于干预，随后每一个观察期随机一组整群接受干预，进入干预组，直到所有整群均接受干预。本文从SW-CRT的基本原理、类别、研究设计、样本量计算、统计学方法和报告规范等方面进行介绍，为开展SW-CRT研究提供参考。 Abstract: The stepped wedge cluster randomized trial (SW-CRT) is a kind of new cluster randomized controlled trial design, mostly applied in the field of service delivery and policy evaluation. No control groups are specifically established in SW-CRTs. At the initial time of SW-CRT, no cluster is exposed to the intervention; whereafter, one random cluster will receive the intervention at regular observation intervals until all the clusters cross to the intervention groups. In this review, we summarized the rationale, types, study design, sample size calculation, statistical methods and the reporting standards of the SW-CRT, to provide reference for carrying out SW-CRTs in the future.

Abstract Background It has been more than 20 years since the malaria epidemiologic shift to school-aged children was noted. In the meantime, school-aged children (5–15 years) have become increasingly more vulnerable with asymptomatic malaria prevalence reaching up to 70%, making them reservoirs for subsequent transmission of malaria in the endemic communities. Intermittent Preventive Treatment of malaria in schoolchildren (IPTsc) has proven to be an effective tool to shrink this reservoir. As of 3rd June 2022, the World Health Organization recommends IPTsc in moderate and high endemic areas. Even so, for decision-makers, the adoption of scientific research recommendations has been stifled by real-world implementation challenges. This study presents methodology, challenges faced, and mitigations used in the evaluation of the implementation of IPTsc using dihydroartemisinin-piperaquine (DP) in three councils (Handeni District Council (DC), Handeni Town Council (TC) and Kilindi DC) of Tanga Region, Tanzania so as to understand the operational feasibility and effectiveness of IPTsc on malaria parasitaemia and clinical malaria incidence. Methods The study deployed an effectiveness-implementation hybrid design to assess feasibility and effectiveness of IPTsc using DP, the interventional drug, against standard of care (control). Wards in the three study councils were the randomization unit (clusters). Each ward was randomized to implement IPTsc or not (control). In all wards in the IPTsc arm, DP was given to schoolchildren three times a year in four-month intervals. In each council, 24 randomly selected wards (12 per study arm, one school per ward) were chosen as representatives for intervention impact evaluation. Mixed design methods were used to assess the feasibility and acceptability of implementing IPTsc as part of a more comprehensive health package for schoolchildren. The study reimagined an existing school health programme for Neglected Tropical Diseases (NTD) control include IPTsc implementation. Results The study shows IPTsc can feasibly be implemented by integrating it into existing school health and education systems, paving the way for sustainable programme adoption in a cost-effective manner. Conclusions Through this article other interested countries may realise a feasible plan for IPTsc implementation. Mitigation to any challenge can be customized based on local circumstances without jeopardising the gains expected from an IPTsc programme. Trial registration clinicaltrials.gov, NCT04245033. Registered 28 January 2020, https://clinicaltrials.gov/ct2/show/NCT04245033

整群随机试验在临床研究中应用较为广泛，其以“群”为随机单位，一个群中的所有个体接受相同的干预，因此与以个体为随机单位的随机对照试验相比，整群随机试验能够减少“沾染”，组织实施更为简便，更适合公共卫生措施评价和医疗质量改进研究。然而，整群随机试验组间可比性欠佳，存在设计效应，因此需要较大的样本量。本文将从整群随机试验的历史渊源、基本概念、研究设计、应用场景和案例解读、报告规范等方面进行全面总结，以期帮助临床研究者认识、了解、掌握此类研究设计。 Abstract: Cluster randomized trials are widely used in clinical research, which use clusters as random unit, and all individuals in a cluster receive the same intervention. Therefore, compared with randomized controlled trials with individuals as random unit, cluster randomized trials can reduce "contamination", which is easier to organize, and is very suitable for public health measures evaluation and medical quality improvement research. However, cluster randomized trials have a design effect and poor comparability between clusters, and need a larger sample size. This article provided a comprehensive overview of cluster randomized trials in terms of history, basic concept, study design, application scenarios, cases analysis, and reporting standards, in order to help clinical researchers understand and master this type of study design.

Abstract Background Healthcare costs are rising, and a substantial proportion of medical care is of little value. De-implementation of low-value practices is important for improving overall health outcomes and reducing costs. We aimed to identify and synthesize randomized controlled trials (RCTs) on de-implementation interventions and to provide guidance to improve future research. Methods MEDLINE and Scopus up to May 24, 2021, for individual and cluster RCTs comparing de-implementation interventions to usual care, another intervention, or placebo. We applied independent duplicate assessment of eligibility, study characteristics, outcomes, intervention categories, implementation theories, and risk of bias. Results Of the 227 eligible trials, 145 (64%) were cluster randomized trials (median 24 clusters; median follow-up time 305 days), and 82 (36%) were individually randomized trials (median follow-up time 274 days). Of the trials, 118 (52%) were published after 2010, 149 (66%) were conducted in a primary care setting, 163 (72%) aimed to reduce the use of drug treatment, 194 (85%) measured the total volume of care, and 64 (28%) low-value care use as outcomes. Of the trials, 48 (21%) described a theoretical basis for the intervention, and 40 (18%) had the study tailored by context-specific factors. Of the de-implementation interventions, 193 (85%) were targeted at physicians, 115 (51%) tested educational sessions, and 152 (67%) multicomponent interventions. Missing data led to high risk of bias in 137 (60%) trials, followed by baseline imbalances in 99 (44%), and deficiencies in allocation concealment in 56 (25%). Conclusions De-implementation trials were mainly conducted in primary care and typically aimed to reduce low-value drug treatments. Limitations of current de-implementation research may have led to unreliable effect estimates and decreased clinical applicability of studied de-implementation strategies. We identified potential research gaps, including de-implementation in secondary and tertiary care settings, and interventions targeted at other than physicians. Future trials could be improved by favoring simpler intervention designs, better control of potential confounders, larger number of clusters in cluster trials, considering context-specific factors when planning the intervention (tailoring), and using a theoretical basis in intervention design. Registration OSF Open Science Framework hk4b2

Abstract Background Among rural Chinese patients with non-communicable diseases (NCDs), low socioeconomic status increases the risk of developing NCDs and associated financial burdens in paying for medicines and treatments. Despite the chronic disease medicine reimbursement policy of the local government in Nantong City, China, various barriers prevent patients from registering for and benefitting from the policy. This study aims to develop a behavior science-based intervention program for promoting the adoption of the policy and to evaluate the effectiveness of the program compared with usual practices. Methods Barriers and opportunities affecting stakeholders in adopting the policy were identified through contextual research and summarized through behavior mapping. The intervention is designed to target these barriers and opportunities through behavior science theories and will be evaluated through a 6-month cluster randomized controlled trial in Tongzhou District, Nantong, China. A total of 30 villages from two townships are randomized in a 1:1 ratio to either the intervention or the control arm (usual practices). Village doctors in the intervention arm (1) receive systematic training on policy details, registration procedures, and intervention protocol, (2) promote the policy and encourage registration, (3) follow up with patients in the first, third, and sixth months after the intervention, and (4) receive financial incentives based on performance. The primary outcome is policy registration rate and the secondary outcomes include the number of patients registering for the policy, medical costs saved, frequency of village doctor visits, and health measures such as blood pressure and glucose levels. Discussion This study is one of very few that aims to promote adoption of NCDs outpatient medication reimbursement policies, and the first study to evaluate the impact of these policies on patients’ financial and physical wellbeing in China. The simple, feasible, and scalable intervention is designed based on the theories of behavior science and is applicable to similar low-income regions nationwide where outpatient medical costs remain a financial burden for patients. Trial registration ClinicalTrials.gov NCT04731194 , registered on 29 January 2021; Chinese Clinical Trial Registry ChiCTR2100042152 , registered on 14 January 14 2021.

Background: In out-of-hours urgent care practices in Germany, physicians of different specialties care for a large number of patients, most of all unknown to them, resulting in a high workload and challenging diagnostic decision-making. As there is no common patient file, physicians have no information about patients' previous conditions or received treatments. In this setting, a digital tool for medical history taking could improve the quality of medical care. This study aims to implement and evaluate a software application (app) that takes a structured symptom-oriented medical history from patients in urgent care settings. Methods: We conduct a time-cluster-randomized trial in two out-of-hours urgent care practices in Germany for 12 consecutive months. Each week during the study defines a cluster. We will compare participants with (intervention group) and without app use (control group) prior to consultation and provision of the self-reported information for the physician. We expect the app to improve diagnostic accuracy (primary outcome), reduce physicians' perceived diagnostic uncertainty, and increase patients' satisfaction and the satisfaction with communication of both physician and patient (secondary outcomes). Discussion: While similar tools have only been subject to small-scale pilot studies surveying feasibility and usability, the present study uses a rigorous study design to measure outcomes that are directly associated with the quality of delivered care. Trial registration: The study was registered at the German Clinical Trials Register (No. DRKS00026659 registered Nov 03 2021. World Health Organization Trial Registration Data Set, https://trialsearch.who.int/Trial2.aspx? TrialID = DRKS00026659. [ABSTRACT FROM AUTHOR]

Background: Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming missing completely at random or missing at random have been previously developed, the impact of attrition on the power for detecting heterogeneous treatment effects in cluster randomized trials remains unknown. Methods: We provide a sample size formula for testing for a heterogeneous treatment effect assuming the outcome is missing completely at random. We also propose an efficient Monte Carlo sample size procedure for assessing heterogeneous treatment effect assuming covariate-dependent outcome missingness (missing at random). We compare our sample size methods with the direct inflation method that divides the estimated sample size by the mean follow-up rate. We also evaluate our methods through simulation studies and illustrate them with a real-world example. Results: Simulation results show that our proposed sample size methods under both missing completely at random and missing at random provide sufficient power for assessing heterogeneous treatment effect. The proposed sample size methods lead to more accurate sample size estimates than the direct inflation method when the missingness rate is high (e.g., ≥ 30%). Moreover, sample size estimation under both missing completely at random and missing at random is sensitive to the missingness rate, but not sensitive to the intracluster correlation coefficient among the missingness indicators. Conclusion: Our new sample size methods can assist in planning cluster randomized trials that plan to assess a heterogeneous treatment effect and participant attrition is expected to occur. [ABSTRACT FROM AUTHOR]

The stepped wedge cluster randomized trial (SW‐CRT) is an increasingly popular design for evaluating health service delivery or policy interventions. An essential consideration of this design is the need to account for both within‐period and between‐period correlations in sample size calculations. Especially when embedded in health care delivery systems, many SW‐CRTs may have subclusters nested in clusters, within which outcomes are collected longitudinally. However, existing sample size methods that account for between‐period correlations have not allowed for multiple levels of clustering. We present computationally efficient sample size procedures that properly differentiate within‐period and between‐period intracluster correlation coefficients in SW‐CRTs in the presence of subclusters. We introduce an extended block exchangeable correlation matrix to characterize the complex dependencies of outcomes within clusters. For Gaussian outcomes, we derive a closed‐form sample size expression that depends on the correlation structure only through two eigenvalues of the extended block exchangeable correlation structure. For non‐Gaussian outcomes, we present a generic sample size algorithm based on linearization and elucidate simplifications under canonical link functions. For example, we show that the approximate sample size formula under a logistic linear mixed model depends on three eigenvalues of the extended block exchangeable correlation matrix. We provide an extension to accommodate unequal cluster sizes and validate the proposed methods via simulations. Finally, we illustrate our methods in two real SW‐CRTs with subclusters. [ABSTRACT FROM AUTHOR]

Multivariate outcomes are common in pragmatic cluster randomized trials. While sample size calculation procedures for multivariate outcomes exist under parallel assignment, none have been developed for a stepped wedge design. In this article, we present computationally efficient power and sample size procedures for stepped wedge cluster randomized trials (SW‐CRTs) with multivariate outcomes that differentiate the within‐period and between‐period intracluster correlation coefficients (ICCs). Under a multivariate linear mixed model, we derive the joint distribution of the intervention test statistics which can be used for determining power under different hypotheses and provide an example using the commonly utilized intersection‐union test for co‐primary outcomes. Simplifications under a common treatment effect and common ICCs across endpoints and an extension to closed‐cohort designs are also provided. Finally, under the common ICC across endpoints assumption, we formally prove that the multivariate linear mixed model leads to a more efficient treatment effect estimator compared to the univariate linear mixed model, providing a rigorous justification on the use of the former with multivariate outcomes. We illustrate application of the proposed methods using data from an existing SW‐CRT and present extensive simulations to validate the methods. [ABSTRACT FROM AUTHOR]

Objectives: Responsible workplaces strive to minimize the harmful effects of alcohol and drug abuse. However, gambling is still a neglected area in workplace research. This study describes workplace gambling and investigates variables associated with at-risk and problem gambling (ARPG) and knowing about colleagues who gambles during work, using cross-sectional data from a cluster-randomized controlled trial on gambling prevention in the workplace (N=3,629). Methods: Measures included ARPG and knowledge about colleagues who gamble during work. Results: Of the respondents, 168 (4.7%) knew of someone who gambles at work. Knowing about a colleague who gambles during work was more common among employees who were men (odds ratio [OR] 2.98; 95% confidence interval [CI], 2.07–4.29), aged 16–34 years (OR 1.97; 95% CI, 1.19–3.28), knew about a gambling policy (OR 1.57; 95% CI, 1.03–2.39), and who themselves were classified as ARPGs (OR 2.95; 95% CI, 1.60–5.35). Similarly, being classified as an ARPG was significantly associated with being a man (OR 2.14; 95% CI, 1.43–3.20), aged 16–34 (OR 2.35; 95% CI, 1.21–4.54) or 35–44 (OR 2.36; 95% CI, 1.30–4.27) years, being a subordinate (OR 2.53; 95% CI, 1.02–6.30), and knowing about a colleague who gambles during work (OR 4.02; 95% CI, 2.38–6.79). Conclusions: Gambling during work is a prevalent phenomenon. Organizations should consider implementing gambling policies that facilitate helping workers who are problem gamblers. To determine policy contents and measures to implement, the type of gambling and its effect on employees should be explored.

IntroductionCommunity health workers (CHWs)-shown to improve access to care and reduce maternal, newborn, and child morbidity and mortality-are re-emerging as a key strategy to achieve health-related Sustainable Development Goals (SDGs). However, recent evaluations of national programmes for CHW-led integrated community case management (iCCM) of common childhood illnesses have not found benefits on access to care and child mortality. Developing innovative ways to maximise the potential benefits of iCCM is critical to achieving the SDGs.Methods and analysisAn unblinded, cluster randomised controlled trial in rural Mali aims to test the efficacy of the addition of door-to-door proactive case detection by CHWs compared with a conventional approach to iCCM service delivery in reducing under-five mortality. In the intervention arm, 69 village clusters will have CHWs who conduct daily proactive case-finding home visits and deliver doorstep counsel, care, referral and follow-up. In the control arm, 68 village clusters will have CHWs who provide the same services exclusively out of a fixed community health site. A baseline population census will be conducted of all people living in the study area. All women of reproductive age will be enrolled in the study and surveyed at baseline, 12, 24 and 36 months. The survey includes a life table tracking all live births and deaths occurring prior to enrolment through the 36 months of follow-up in order to measure the primary endpoint: under-five mortality, measured as deaths among children under 5 years of age per 1000 person-years at risk of mortality.Ethics and disseminationThe trial has received ethical approval from the Ethics Committee of the Faculty of Medicine, Pharmacy and Dentistry, University of Bamako. The results will be disseminated through peer-reviewed publications, national and international conferences and workshops, and media outlets.Trial registration numberNCT02694055; Pre-results.

Background: India contributes 15% of the total global maternal mortality burden. An increasing proportion of these deaths are due to Pregnancy Induced Hypertension (PIH), Gestational Diabetes Mellitus (GDM), and anaemia. This study aims to evaluate the effectiveness of a tablet-based electronic decision-support system (EDSS) to enhance routine antenatal care (ANC) and improve the screening and management of PIH, GDM, and anaemia in pregnancy in primary healthcare facilities of Telangana, India. The EDSS will work at two levels of primary health facilities and is customized for three cadres of healthcare providers – Auxiliary Nurse Midwifes (ANMs), staff nurses, and physicians (Medical Officers). Methods: This will be a cluster randomized controlled trial involving 66 clusters with a total of 1320 women in both the intervention and control arms. Each cluster will include three health facilities—one Primary Health Centre (PHC) and two linked sub-centers (SC). In the facilities under the intervention arm, ANMs, staff nurses, and Medical Officers will use the EDSS while providing ANC for all pregnant women. Facilities in the control arm will continue to provide ANC services using the existing standard of care in Telangana. The primary outcome is ANC quality, measured as provision of a composite of four selected ANC components (measurement of blood pressure, blood glucose, hemoglobin levels, and conducting a urinary dipstick test) by the healthcare providers per visit, observed over two visits. Trained field research staff will collect outcome data via an observation checklist. Discussion: To our knowledge, this is the first trial in India to evaluate an EDSS, targeted to enhance the quality of ANC and improve the screening and management of PIH, GDM, and anaemia, for multiple levels of health facilities and several cadres of healthcare providers. If effective, insights from the trial on the feasibility and cost of implementing the EDSS can inform potential national scale-up. Lessons learned from this trial will also inform recommendations for designing and upscaling similar mHealth interventions in other low and middle-income countries. Trial Registration. ClinicalTrials.gov, NCT03700034, registered 9 Oct 2018, https://www.clinicaltrials.gov/ct2/show/NCT03700034 CTRI, CTRI/2019/01/016857, registered on 3 Mar 2019, http://www.ctri.nic.in/Clinicaltrials/pdf_generate.php?trialid=28627&EncHid=&modid=&compid=%27,%2728627det%27 [ABSTRACT FROM AUTHOR]