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Background: Globally alcohol consumption is a leading risk factor for premature death and disability and is associated with crime, social and economic consequences. Local communities may be able to play a role in addressing alcohol-related issues in their area. Objectives: To evaluate the effectiveness and cost–benefit of an asset-based community development approach to reducing alcohol-related harm and understand the context and factors that enable or hinder its implementation. Design: A mixed-methods evaluation. Area-level quasi-experimental trial analysed using four different evaluation methods (a stepped-wedge design where each area was a control until it entered the intervention, comparison to matched local/national controls and comparison to synthetic controls), alongside process and economic evaluations. Setting: Ten local authorities in Greater Manchester, England. Participants: The outcomes evaluation was analysed at an area level. Ninety-three lay persons representing nineareas completed questionnaires, with 12 follow-up interviews in five areas; 20 stakeholders representing ten areas were interviewed at baseline, with 17 follow-up interviews in eight areas and 26 members of the public from two areas attended focus groups. Interventions: Professionals in a co-ordinator role recruited and supported lay volunteers who were trained to become alcohol health champions. The champion's role was to provide informal, brief alcohol advice to the local population and take action to strengthen restrictions on alcohol availability. Main outcome measures: Numbers of alcohol-related hospital admissions, accident and emergency attendances, ambulance call-outs, street-level crime and antisocial behaviour in the intervention areas (area size: 1600–5500 residents). Set-up and running costs were collected alongside process evaluation data exploring barriers and facilitators. Data sources: Routinely collected quantitative data on outcome measures aggregated at the intervention area and matched control and synthetic control areas. Data from policy documents, licensing registers, meeting notes, invoices, time/cost diaries, training registers, questionnaires, interviews, reflective diaries and focus groups. Results: The intervention rolled out in nine out of ten areas, seven of which ran for a full 12 months. Areas with better-established infrastructure at baseline were able to train more champions. In total, 123 alcohol health champions were trained (95 lay volunteers and 28 professionals): lay volunteers self-reported positive impact. Champions engaged in brief advice conversations more readily than taking action on alcohol availability. There were no consistent differences in the health and crime area-level indicators between intervention areas and controls, as confirmed by using three different analysis methods for evaluating natural experiments. The intervention was not found to be cost-beneficial. Limitations: Although the sequential roll-out order of the intervention was randomised, the selection of the intervention areas was not. Self-reported impact may have been subject to social desirability bias due to the project's high profile. Conclusions: There was no measurable impact on health and crime outcomes. Possible explanations include too few volunteers trained, volunteers being unwilling to get involved in licensing decisions, or that the intervention has no direct impact on the selected outcomes. Future work: Future similar interventions should use a coproduced community outcomes framework. Other natural experiment evaluations should use methodological triangulation to strengthen inferences about effectiveness. Trial registration: This trial is registered as ISRCTN81942890. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 15/129/03) and is published in full in Public Health Research; Vol. 12, No. 9. See the NIHR Funding and Awards website for further award information. Plain language summary: What was the question?: Alcohol consumption puts an individual's health and social relationships at risk of harm. The more a person drinks, the more harmful it is. The harmful effects can place a burden on emergency services and hospitals. We wanted to find out whether community members can make a difference by taking action to address alcohol harm in their local area. What did we do?: Local councils in Greater Manchester developed a project called Communities in Charge of Alcohol, where volunteers in targeted local areas were trained to become 'alcohol health champions'. Alcohol health champions gave alcohol-related brief advice to people to help them drink less. They had a say about when, where and how alcohol is sold by reporting issues to their local council. We compared numbers of alcohol-related hospital admissions, accident and emergency attendances, ambulance call-outs and reports of crime and antisocial behaviour between areas that had alcohol health champions with other similar areas in England that did not. We calculated how much it costs to run and whether Communities in Charge of Alcohol could save society money. What did we find?: Not as many volunteers came forward to become an alcohol health champion as hoped for. Those who did give alcohol-related brief advice to people. They preferred not to report issues about alcohol sales to their local council, either because it was too complicated or because they did not want to be called a 'grass'. We did not find levels of alcohol harm changed in the Communities in Charge of Alcohol areas. Because of this, we could not demonstrate that Communities in Charge of Alcohol could save society money. What does this mean?: Getting involved in alcohol licensing decisions needs to be made easier for communities, with more anonymity, through the support of professionals. More work needs to be done to understand whether giving brief advice can reduce alcohol harm in whole communities. Scientific summary: Background: The availability of, and access to, alcohol is a key determinant of alcohol harm, and powers exist within the Licensing Act 2003 of England and Wales that enable the public to influence the local sale and supply of alcohol. At an individual level, implementing programmes to promote early risk identification and brief advice have the potential to reduce alcohol consumption and prevent alcohol-related harm, but there is a dearth of evidence to determine the feasibility and effectiveness of lay people having a role. The Greater Manchester (GM) Combined Authority (GMCA) and Greater Manchester Health and Social Care Partnership (GMHSCP) designed a new programme, 'Communities in Charge of Alcohol' (CICA) in 2017 to train lay volunteers to become alcohol health champions (AHCs), who would be able to (1) give alcohol-related brief advice to individuals and (2) help communities influence alcohol availability and strengthen restrictions in alcohol risk environments. Overall aims: To evaluate the effectiveness and cost–benefit of implementing a locally delivered AHC intervention and understand the context and factors that enable or hinder the intervention. Objectives: Relating to the outcome evaluation: to determine the effect on area-level key health performance indicators: alcohol-related hospital admissions (narrow measure), weekend accident and emergency (A&E) attendances (Friday 3 p.m.–Sunday 3 p.m.), weekday A&E attendances (Monday–Friday 9 a.m.–5 p.m. each day), weekend ambulance call-outs (Friday 3 p.m.–Sunday 3 p.m.), weekday ambulance call-outs (Monday–Friday 9 a.m.–5 p.m. each day) to determine the effect on key crime indicators (street-level crime data) at the weekend (Friday 3 p.m.–Sunday 3 p.m.) and weekday (Monday–Friday 9 a.m.–5 p.m. each day) to determine the effect on key antisocial behaviour (ASB) indicators (police recorded calls for service were classified as ASB according to the National Standard for Incident Recording). Relating to the process evaluation: to explore policy context and variation in licensing practice, including any impact of devolution in GM to explore barriers and facilitators at key stages of the implementation of the intervention: recruitment of AHCs to initial training and cascade training, delivery of initial training and cascade training, use of skills beyond the training in AHC activity, retention of AHCs to explore responses to AHC training, modelling of health behaviours, perceptions of community cohesion and development to determine numbers of brief interventions applied and community awareness events organised/participated in to examine and quantify the amount and success of community involvement in licensing issues to determine whether there was a change in composite measures of alcohol availability. Relating to the economic evaluation: to identify set-up and running costs using a standardised costing exercise to resolve costs by sector (health, ambulance and police) before, during and after set-up to quantify benefits due to reduced hospital admissions, ambulance call-outs, A&E use, crime and ASB. Methods: Communities in Charge of Alcohol was a complex intervention already planned by the GMCA in 10 small local authority areas (~1600–5500 residents each) and was outside direct researcher control. Intervention areas were chosen by each local authority public health team as having high levels of alcohol harm in comparison to the rest of the local authority. All intervention areas were at the bottom of the deprivation index, in deprivation decile 1 or 2. Using a stepped-wedge design, researchers were able to randomise the order of roll-out of intervention areas to bring a quasi-experimental approach to the evaluation. Two additional evaluation designs were used in order to triangulate findings, namely comparison to matched controls and comparison to synthetic controls. Ethical approval was received from the University of Salford Research Ethics Committee on 17 May 2017 (reference number: HSR1617–135) and obtained from the University of Bristol on 16 May 2019 (reference number: 82762). Outcome evaluation data sources: Routinely collected data at the lower-layer super output area (LSOA) level for intervention areas was compared with control areas. Data sources were as follows: alcohol-related hospital admissions data – Public Health England (now Office for Health Improvement and Disparities) accident and emergency department attendance data – NHS Digital ambulance call-out data – North West Ambulance Service reported crime and ASB data – GM Police. Analysis spanned a 10-year period (7 years pre intervention and a maximum of 3 years post implementation), ending in January 2020. Data were analysed using log-rate growth models and time series analyses to quantify the effect of the CICA programme on key performance indicators. Process evaluation data sources: Pre-implementation phase document review of local Statements of Licensing Policy (n = 9), alcohol availability composite score of on- and off-licensed premises (n = 9 areas), roll-out preparatory meeting notes. Train-the-Trainer attendance registers by area (n = 5, representing 9 areas, attended by 48 lay participants and 25 professional participants). Cascade training attendance registers by area (n = 11, representing 7 areas, attended by 47 lay participants and 3 professional participants). Pre- and post-training questionnaires (n = 93 lay people, 98% response rate). Baseline interviews with stakeholders (n = 20) (initial implementation phase); interviews with lay volunteer AHCs (n = 5, representing 3 areas) within 3–6 months of initial training (implementation phase). Follow-up interviews (12 months) with stakeholders (n = 11, representing eight areas), licensing officers (n = 6, representing six areas) and lay volunteer AHCs (n = 7, representing four areas); follow-up questionnaires with AHCs (n = 11); focus groups with local residents (n = 3 groups, representing two areas, with a total of 26 participants). Economic evaluation data sources: Time and transportation costs incurred during project meetings aggregated from meeting minutes and stakeholder time/cost diaries. Invoices for the design, development and delivery of training packages and accreditation fees. Invoices for the qualification and assessment fees, time and miscellaneous (transportation, room hire, catering, printing costs). AHC time and costs aggregated from volunteer diaries and stakeholder time/cost diaries. Invoices for engagement materials (AUDIT-C scratchcards). Greater Manchester Combined Authority research team's (formerly New Economy) cost–benefit analysis (CBA) tool was populated using aggregated outcome evaluation data to resolve costs for each key performance indicator before, during and after CICA setup. Results: Nine out of 10 local authorities rolled out the CICA intervention on their given start date: seven areas completed a full year, carrying out cascade training in the first 12 months, two areas withdrew at 6 and 9 months and one area withdrew in the pre-implementation phase. There were 118 [interquartile range (IQR): 60–205] alcohol-related hospital admissions per month per pre-intervention LSOA , rising to 134 (IQR : 67–203) post intervention. Some outcomes had very small counts at the LSOA level; notably, there were only 2 (IQR : 1–3) weekend crimes per month pre-intervention and 4 (IQR : 2–6) post intervention. The primary, stepped-wedge analyses provide weak evidence of an average increase in alcohol-related hospital admissions following the implementation of CICA of about 13%, corresponding to about 20 admissions weekly, although the confidence interval was wide, −1.98 to 31.39, and not statistically significant (p~0.09), while the analysis that made use of local controls suggested a bigger effect, at 16.4% (7.33–26.16, p < 0.001). However, the other methods of analysis (national controls and counterfactuals) indicated much smaller effect sizes and confidence intervals, including unity: 3.42% (−4.56 to 12.07; p = 0.41) and 7.14% (−9.76 to 24.04; p = 0.41), respectively. The other quantitative outcomes (A&E admissions, or ambulance call-outs to the area, nor to reported crimes or reported ASB incidents) showed some were individual statistically significant effects (with some methods and some indicators), but these were more often not in favour of the intervention. Triangulation of three methods of analysis did not indicate any consistent differences between control and intervention areas. The pre-implementation phase was defined as the period leading up to the roll-out of the first Train-the-Trainer event. Several facilitating contextual factors were identified a priori. The total number of these factors in place at baseline correlated with numbers trained in the first year (Rs = 0.77, p = 0.01). Specifically, areas with a healthcare provider to co-ordinate the intervention (p = 0.02); a pool of other volunteers to recruit from (p = 0.02); a contract in place with a commissioned service (p = 0.02); and formal volunteer arrangements (p = 0.03) trained more AHCs. Across all nine areas, fewer volunteers were recruited than the anticipated target of 35 per area. In total, 123 AHCs were trained and gained a Royal Society for Public Health Level 2 qualification. Of these, 95 were lay volunteers from the intervention areas. The majority identified as white (70%), with ages ranging from 18 to 65+, and almost half aged between 41 and 60 (48%). Slightly more women volunteered compared to men (61%). Most AHCs self-reported being in the lower-risk drinking category (66% scored 0–4 on AUDIT-C). Post-training questionnaires suggested that volunteers felt more confident to talk about the harms associated with alcohol and give alcohol-related brief advice than they did pre training (91.4% compared with 79.6%, p < 0.001) and that they felt more confident to raise issues about venues selling alcohol (90.3% compared with 74.2% pre training, p < 0.001). Intervention areas that recorded AHC activity (n = 5) captured 1100 conversations, 251 AUDIT-Cs completed and 65 community events attended. Time/cost diaries suggested that AHCs spent on average 11 minutes having an informal brief advice conversation. Interviews with AHCs on self-reported activity were consistent with time/cost and reflective diaries: AHCs put into practice brief advice conversations more than community action to influence licensing. Interviews with stakeholders indicated several enabling factors to consider when rolling out and embedding an AHC intervention: a clear understanding of the place-based focus; commissioned provider services needing a clear understanding of own roles and the anticipated outcomes; and co-ordinators having the skills and capacity to support inexperienced volunteer groups. The importance of infrastructure was reflected in interviews, recognising that preparing the ground at a hyperlocal level takes time, needing to be well in advance of the formal implementation period. Recruitment strategies need to be multipronged, and stakeholders need to be prepared to provide a sustained period of support for new and existing champions. In the follow-up phase, 12 months post intervention, interviews with stakeholders revealed how the local co-ordinators' ability to operationalise and sustain CICA was significantly impacted by their lack of capacity to sustain the levels of support, as well as the complexity of skills required of the role. After 12 months, there was strong consistency in findings from follow-up interviews with stakeholders, AHCs and focus groups with members of the public. Communities in Charge of Alcohol's intended mechanism of action was to reduce alcohol-related harm through secondary prevention interventions; however perceptions of those most in need of an AHC conversation were 'problem drinkers' to signpost into tertiary interventions. Quality of local alcohol treatment services was considered to be poor, with multiple barriers to help and information seeking. Conclusions: To our knowledge, CICA is the first alcohol-focused champion role of its kind to be investigated and across multiple intervention sites. It is also the first evaluation of a health champion programme that uses methodological triangulation to strengthen inferences about effectiveness. There was evidence that lay people trained as AHCs were able to have conversations with members of their communities who were consuming alcohol at higher risk levels, demonstrating a level of skill and confidence that can sometimes be absent in health professionals. AHCs, their co-ordinators and the public valued the role. However, significant infrastructure is needed to support an intervention such as CICA , and time is needed to develop and embed a group of volunteers. Despite evidence (from the process evaluation) of the planned activity taking place at local level, we were not able to show that CICA was effective, nor was it cost-effective. There are likely to be two main reasons for this. Firstly, the number of champions trained was too small to have a sufficient number of brief advice conversations to generate any measurable effect at the area level of analysis. Moreover, brief interventions may not directly impact on primary health and crime outcomes at a population level. Secondly, the AHCs were less willing (and had less opportunity) to get involved with alcohol licensing decisions. Since licensing policies theoretically have an impact at an area level, this theoretically would have been more likely to generate a significant area-level effect. The evidence from this study is that communities continue to struggle to influence statutory processes that affect alcohol availability where they live, and further consideration of how to enable increased community engagement is necessary. Recommendations for future research (numbered in priority order): natural experiment evaluations should include methodological triangulation to guard against overinterpretation of spurious results investigate coproducing a community outcomes framework to measure reductions in alcohol harm evaluate whether rebalancing local licensing policy to be community-centred might increase community engagement in the local licensing decision-making process investigate the effectiveness of CICA in areas where indicators of alcohol harm are increasing but are not at the highest level of harm within the local authority a wider range of external contextual factors could be tested in the pre-implementation phase of a complex intervention, incorporating methods to combine factors into mean scores, to test relationships between process indicators and outcomes. Trial registration: This trial is registered as ISRCTN81942890. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 15/129/03) and is published in full in Public Health Research; Vol. 12, No. 9. See the NIHR Funding and Awards website for further award information. [ABSTRACT FROM AUTHOR]

Background: The Bayesian group sequential design has been applied widely in clinical studies, especially in Phase II and III studies. It allows early termination based on accumulating interim data. However, to date, there lacks development in its application to stepped-wedge cluster randomized trials, which are gaining popularity in pragmatic trials conducted by clinical and health care delivery researchers. Methods: We propose a Bayesian adaptive design approach for stepped-wedge cluster randomized trials, which makes adaptive decisions based on the predictive probability of declaring the intervention effective at the end of study given interim data. The Bayesian models and the algorithms for posterior inference and trial conduct are presented. Results: We present how to determine design parameters through extensive simulations to achieve desired operational characteristics. We further evaluate how various design factors, such as the number of steps, cluster size, random variability in cluster size, and correlation structures, impact trial properties, including power, type I error, and the probability of early stopping. An application example is presented. Conclusion: This study presents the incorporation of Bayesian adaptive strategies into stepped-wedge cluster randomized trials design. The proposed approach provides the flexibility to stop the trial early if substantial evidence of efficacy or futility is observed, improving the flexibility and efficiency of stepped-wedge cluster randomized trials. [ABSTRACT FROM AUTHOR]

Background Globally alcohol consumption is a leading risk factor for premature death and disability and is associated with crime, social and economic consequences. Local communities may be able to play a role in addressing alcohol-related issues in their area. Objectives To evaluate the effectiveness and cost–benefit of an asset-based community development approach to reducing alcohol-related harm and understand the context and factors that enable or hinder its implementation. Design A mixed-methods evaluation. Area-level quasi-experimental trial analysed using four different evaluation methods (a stepped-wedge design where each area was a control until it entered the intervention, comparison to matched local/national controls and comparison to synthetic controls), alongside process and economic evaluations. Setting Ten local authorities in Greater Manchester, England. Participants The outcomes evaluation was analysed at an area level. Ninety-three lay persons representing nineareas completed questionnaires, with 12 follow-up interviews in five areas; 20 stakeholders representing ten areas were interviewed at baseline, with 17 follow-up interviews in eight areas and 26 members of the public from two areas attended focus groups. Interventions Professionals in a co-ordinator role recruited and supported lay volunteers who were trained to become alcohol health champions. The champion’s role was to provide informal, brief alcohol advice to the local population and take action to strengthen restrictions on alcohol availability. Main outcome measures Numbers of alcohol-related hospital admissions, accident and emergency attendances, ambulance call-outs, street-level crime and antisocial behaviour in the intervention areas (area size: 1600–5500 residents). Set-up and running costs were collected alongside process evaluation data exploring barriers and facilitators. Data sources Routinely collected quantitative data on outcome measures aggregated at the intervention area and matched control and synthetic control areas. Data from policy documents, licensing registers, meeting notes, invoices, time/cost diaries, training registers, questionnaires, interviews, reflective diaries and focus groups. Results The intervention rolled out in nine out of ten areas, seven of which ran for a full 12 months. Areas with better-established infrastructure at baseline were able to train more champions. In total, 123 alcohol health champions were trained (95 lay volunteers and 28 professionals): lay volunteers self-reported positive impact. Champions engaged in brief advice conversations more readily than taking action on alcohol availability. There were no consistent differences in the health and crime area-level indicators between intervention areas and controls, as confirmed by using three different analysis methods for evaluating natural experiments. The intervention was not found to be cost-beneficial. Limitations Although the sequential roll-out order of the intervention was randomised, the selection of the intervention areas was not. Self-reported impact may have been subject to social desirability bias due to the project’s high profile. Conclusions There was no measurable impact on health and crime outcomes. Possible explanations include too few volunteers trained, volunteers being unwilling to get involved in licensing decisions, or that the intervention has no direct impact on the selected outcomes. Future work Future similar interventions should use a coproduced community outcomes framework. Other natural experiment evaluations should use methodological triangulation to strengthen inferences about effectiveness. Trial registration This trial is registered as ISRCTN81942890. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 15/129/03) and is published in full in Public Health Research; Vol. 12, No. 9. See the NIHR Funding and Awards website for further award information. Plain language summary What was the question? Alcohol consumption puts an individual’s health and social relationships at risk of harm. The more a person drinks, the more harmful it is. The harmful effects can place a burden on emergency services and hospitals. We wanted to find out whether community members can make a difference by taking action to address alcohol harm in their local area. What did we do? Local councils in Greater Manchester developed a project called Communities in Charge of Alcohol, where volunteers in targeted local areas were trained to become ‘alcohol health champions’. Alcohol health champions gave alcohol-related brief advice to people to help them drink less. They had a say about when, where and how alcohol is sold by reporting issues to their local council. We compared numbers of alcohol-related hospital admissions, accident and emergency attendances, ambulance call-outs and reports of crime and antisocial behaviour between areas that had alcohol health champions with other similar areas in England that did not. We calculated how much it costs to run and whether Communities in Charge of Alcohol could save society money. What did we find? Not as many volunteers came forward to become an alcohol health champion as hoped for. Those who did give alcohol-related brief advice to people. They preferred not to report issues about alcohol sales to their local council, either because it was too complicated or because they did not want to be called a ‘grass’. We did not find levels of alcohol harm changed in the Communities in Charge of Alcohol areas. Because of this, we could not demonstrate that Communities in Charge of Alcohol could save society money. What does this mean? Getting involved in alcohol licensing decisions needs to be made easier for communities, with more anonymity, through the support of professionals. More work needs to be done to understand whether giving brief advice can reduce alcohol harm in whole communities. Scientific summary Background The availability of, and access to, alcohol is a key determinant of alcohol harm, and powers exist within the Licensing Act 2003 of England and Wales that enable the public to influence the local sale and supply of alcohol. At an individual level, implementing programmes to promote early risk identification and brief advice have the potential to reduce alcohol consumption and prevent alcohol-related harm, but there is a dearth of evidence to determine the feasibility and effectiveness of lay people having a role. The Greater Manchester (GM) Combined Authority (GMCA) and Greater Manchester Health and Social Care Partnership (GMHSCP) designed a new programme, ‘Communities in Charge of Alcohol’ (CICA) in 2017 to train lay volunteers to become alcohol health champions (AHCs), who would be able to (1) give alcohol-related brief advice to individuals and (2) help communities influence alcohol availability and strengthen restrictions in alcohol risk environments. Overall aims To evaluate the effectiveness and cost–benefit of implementing a locally delivered AHC intervention and understand the context and factors that enable or hinder the intervention. Objectives Relating to the outcome evaluation: to determine the effect on area-level key health performance indicators: alcohol-related hospital admissions (narrow measure), weekend accident and emergency (A&E) attendances (Friday 3 p.m.–Sunday 3 p.m.), weekday A&E attendances (Monday–Friday 9 a.m.–5 p.m. each day), weekend ambulance call-outs (Friday 3 p.m.–Sunday 3 p.m.), weekday ambulance call-outs (Monday–Friday 9 a.m.–5 p.m. each day) to determine the effect on key crime indicators (street-level crime data) at the weekend (Friday 3 p.m.–Sunday 3 p.m.) and weekday (Monday–Friday 9 a.m.–5 p.m. each day) to determine the effect on key antisocial behaviour (ASB) indicators (police recorded calls for service were classified as ASB according to the National Standard for Incident Recording). Relating to the process evaluation: to explore policy context and variation in licensing practice, including any impact of devolution in GM to explore barriers and facilitators at key stages of the implementation of the intervention: recruitment of AHCs to initial training and cascade training, delivery of initial training and cascade training, use of skills beyond the training in AHC activity, retention of AHCs to explore responses to AHC training, modelling of health behaviours, perceptions of community cohesion and development to determine numbers of brief interventions applied and community awareness events organised/participated in to examine and quantify the amount and success of community involvement in licensing issues to determine whether there was a change in composite measures of alcohol availability. Relating to the economic evaluation: to identify set-up and running costs using a standardised costing exercise to resolve costs by sector (health, ambulance and police) before, during and after set-up to quantify benefits due to reduced hospital admissions, ambulance call-outs, A&E use, crime and ASB. Methods Communities in Charge of Alcohol was a complex intervention already planned by the GMCA in 10 small local authority areas (~1600–5500 residents each) and was outside direct researcher control. Intervention areas were chosen by each local authority public health team as having high levels of alcohol harm in comparison to the rest of the local authority. All intervention areas were at the bottom of the deprivation index, in deprivation decile 1 or 2. Using a stepped-wedge design, researchers were able to randomise the order of roll-out of intervention areas to bring a quasi-experimental approach to the evaluation. Two additional evaluation designs were used in order to triangulate findings, namely comparison to matched controls and comparison to synthetic controls. Ethical approval was received from the University of Salford Research Ethics Committee on 17 May 2017 (reference number: HSR1617–135) and obtained from the University of Bristol on 16 May 2019 (reference number: 82762). Outcome evaluation data sources Routinely collected data at the lower-layer super output area (LSOA) level for intervention areas was compared with control areas. Data sources were as follows: alcohol-related hospital admissions data – Public Health England (now Office for Health Improvement and Disparities) accident and emergency department attendance data – NHS Digital ambulance call-out data – North West Ambulance Service reported crime and ASB data – GM Police. Analysis spanned a 10-year period (7 years pre intervention and a maximum of 3 years post implementation), ending in January 2020. Data were analysed using log-rate growth models and time series analyses to quantify the effect of the CICA programme on key performance indicators. Process evaluation data sources Pre-implementation phase document review of local Statements of Licensing Policy (n = 9), alcohol availability composite score of on- and off-licensed premises (n = 9 areas), roll-out preparatory meeting notes. Train-the-Trainer attendance registers by area (n = 5, representing 9 areas, attended by 48 lay participants and 25 professional participants). Cascade training attendance registers by area (n = 11, representing 7 areas, attended by 47 lay participants and 3 professional participants). Pre- and post-training questionnaires (n = 93 lay people, 98% response rate). Baseline interviews with stakeholders (n = 20) (initial implementation phase); interviews with lay volunteer AHCs (n = 5, representing 3 areas) within 3–6 months of initial training (implementation phase). Follow-up interviews (12 months) with stakeholders (n = 11, representing eight areas), licensing officers (n = 6, representing six areas) and lay volunteer AHCs (n = 7, representing four areas); follow-up questionnaires with AHCs (n = 11); focus groups with local residents (n = 3 groups, representing two areas, with a total of 26 participants). Economic evaluation data sources Time and transportation costs incurred during project meetings aggregated from meeting minutes and stakeholder time/cost diaries. Invoices for the design, development and delivery of training packages and accreditation fees. Invoices for the qualification and assessment fees, time and miscellaneous (transportation, room hire, catering, printing costs). AHC time and costs aggregated from volunteer diaries and stakeholder time/cost diaries. Invoices for engagement materials (AUDIT-C scratchcards). Greater Manchester Combined Authority research team’s (formerly New Economy) cost–benefit analysis (CBA) tool was populated using aggregated outcome evaluation data to resolve costs for each key performance indicator before, during and after CICA setup. Results Nine out of 10 local authorities rolled out the CICA intervention on their given start date: seven areas completed a full year, carrying out cascade training in the first 12 months, two areas withdrew at 6 and 9 months and one area withdrew in the pre-implementation phase. There were 118 [interquartile range (IQR): 60–205] alcohol-related hospital admissions per month per pre-intervention LSOA, rising to 134 (IQR: 67–203) post intervention. Some outcomes had very small counts at the LSOA level; notably, there were only 2 (IQR: 1–3) weekend crimes per month pre-intervention and 4 (IQR: 2–6) post intervention. The primary, stepped-wedge analyses provide weak evidence of an average increase in alcohol-related hospital admissions following the implementation of CICA of about 13%, corresponding to about 20 admissions weekly, although the confidence interval was wide, −1.98 to 31.39, and not statistically significant (p~0.09), while the analysis that made use of local controls suggested a bigger effect, at 16.4% (7.33–26.16, p

Abstract Background The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes. Methods We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods. Results Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD. Conclusions We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE.

Background: The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes. Methods: We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods. Results: Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD. Conclusions: We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE. [ABSTRACT FROM AUTHOR]

Abstract Background The Perinatal Center of the University Hospital Carl Gustav Carus Dresden has initiated the telemedical healthcare network “SAFE BIRTH” to coordinate and improve specialized care in non-metropolitan regions for pregnant women and newborns. The network incorporates five intervention bundles (IB): (1) Multi-professional, inter-disciplinary prenatal care plan; (2) Neonatal resuscitation; (3) Neonatal antibiotic stewardship; (4) Inter-facility transfer of premature and sick newborns; (5) Psycho-social support for parents. We evaluate if the network improves care close to home for pregnant women, premature and sick newborns. Methods To evaluate the complex healthcare intervention “SAFE BIRTH” we will conduct a cluster-randomized controlled stepped-wedge trial in five prenatal medical outpatient offices and eight non-metropolitan hospitals in Saxony, Germany. The offices and hospitals will be randomly allocated to five respectively eight sequential steps over a 30-month period to implement the telemedical IB. We define one specific primary process outcome for each IB (for instance IB#1: “Proportion of patients with inclusion criterion IB#1 who have a prenatal care plan and psychosocial counseling within one week”). We estimated a separate multilevel logistic regression model for each primary process outcome using the intervention status as a regressor (control or intervention group). Across all IB, a total of 1,541 and 1,417 pregnant women or newborns need to be included in the intervention and control group, respectively, for a power above 80% for small to medium intervention effects for all five hypothesis tests. Additionally, we will assess job satisfaction and sense of safety of health professionals caring for newborns (questionnaire survey) and we will assess families’ satisfaction, resilience, quality of life and depressive, anxiety and stress symptoms (questionnaire surveys). We will also evaluate the cost-effectiveness of ”SAFE BIRTH” (statutory health insurance routine data, process data) and barriers to its implementation (semi-structured interviews). We use multilevel regression models adjusting for relevant confounders (e.g. socioeconomic status, age, place of residence), as well as descriptive analyses and qualitative content analyses. Discussion If the telemedical healthcare network “SAFE BIRTH” proves to be effective and cost-efficient, strategies for its translation into routine care should be developed. Trial registration German clinical trials register. DRKS-ID: DRKS00031482.

Background: The Perinatal Center of the University Hospital Carl Gustav Carus Dresden has initiated the telemedical healthcare network "SAFE BIRTH" to coordinate and improve specialized care in non-metropolitan regions for pregnant women and newborns. The network incorporates five intervention bundles (IB): (1) Multi-professional, inter-disciplinary prenatal care plan; (2) Neonatal resuscitation; (3) Neonatal antibiotic stewardship; (4) Inter-facility transfer of premature and sick newborns; (5) Psycho-social support for parents. We evaluate if the network improves care close to home for pregnant women, premature and sick newborns. Methods: To evaluate the complex healthcare intervention "SAFE BIRTH" we will conduct a cluster-randomized controlled stepped-wedge trial in five prenatal medical outpatient offices and eight non-metropolitan hospitals in Saxony, Germany. The offices and hospitals will be randomly allocated to five respectively eight sequential steps over a 30-month period to implement the telemedical IB. We define one specific primary process outcome for each IB (for instance IB#1: "Proportion of patients with inclusion criterion IB#1 who have a prenatal care plan and psychosocial counseling within one week"). We estimated a separate multilevel logistic regression model for each primary process outcome using the intervention status as a regressor (control or intervention group). Across all IB, a total of 1,541 and 1,417 pregnant women or newborns need to be included in the intervention and control group, respectively, for a power above 80% for small to medium intervention effects for all five hypothesis tests. Additionally, we will assess job satisfaction and sense of safety of health professionals caring for newborns (questionnaire survey) and we will assess families' satisfaction, resilience, quality of life and depressive, anxiety and stress symptoms (questionnaire surveys). We will also evaluate the cost-effectiveness of "SAFE BIRTH" (statutory health insurance routine data, process data) and barriers to its implementation (semi-structured interviews). We use multilevel regression models adjusting for relevant confounders (e.g. socioeconomic status, age, place of residence), as well as descriptive analyses and qualitative content analyses. Discussion: If the telemedical healthcare network "SAFE BIRTH" proves to be effective and cost-efficient, strategies for its translation into routine care should be developed. Trial registration: German clinical trials register. DRKS-ID: DRKS00031482. [ABSTRACT FROM AUTHOR]

Abstract Background Vasovagal reactions (VVRs) are the most common acute complications of blood donation. Responsible for substantial morbidity, they also reduce the likelihood of repeated donations and are disruptive and costly for blood services. Although blood establishments worldwide have adopted different strategies to prevent VVRs (including water loading and applied muscle tension [AMT]), robust evidence is limited. The Strategies to Improve Donor Experiences (STRIDES) trial aims to reliably assess the impact of four different interventions to prevent VVRs among blood donors. Methods STRIDES is a cluster-randomised cross-over/stepped-wedge factorial trial of four interventions to reduce VVRs involving about 1.4 million whole blood donors enrolled from all 73 blood donation sites (mobile teams and donor centres) of National Health Service Blood and Transplant (NHSBT) in England. Each site (“cluster”) has been randomly allocated to receive one or more interventions during a 36-month period, using principles of cross-over, stepped-wedge and factorial trial design to assign the sequence of interventions. Each of the four interventions is compared to NHSBT’s current practices: (i) 500-ml isotonic drink before donation (vs current 500-ml plain water); (ii) 3-min rest on donation chair after donation (vs current 2 min); (iii) new modified AMT (vs current practice of AMT); and (iv) psychosocial intervention using preparatory materials (vs current practice of nothing). The primary outcome is the number of in-session VVRs with loss of consciousness (i.e. episodes involving loss of consciousness of any duration, with or without additional complications). Secondary outcomes include all in-session VVRs (i.e. with and without loss of consciousness), all delayed VVRs (i.e. those occurring after leaving the venue) and any in-session non-VVR adverse events or reactions. Discussion The STRIDES trial should yield novel information about interventions, singly and in combination, for the prevention of VVRs, with the aim of generating policy-shaping evidence to help inform blood services to improve donor health, donor experience, and service efficiency. Trial registration ISRCTN: 10412338. Registration date: October 24, 2019.

Background: Vasovagal reactions (VVRs) are the most common acute complications of blood donation. Responsible for substantial morbidity, they also reduce the likelihood of repeated donations and are disruptive and costly for blood services. Although blood establishments worldwide have adopted different strategies to prevent VVRs (including water loading and applied muscle tension [AMT]), robust evidence is limited. The Strategies to Improve Donor Experiences (STRIDES) trial aims to reliably assess the impact of four different interventions to prevent VVRs among blood donors. Methods: STRIDES is a cluster-randomised cross-over/stepped-wedge factorial trial of four interventions to reduce VVRs involving about 1.4 million whole blood donors enrolled from all 73 blood donation sites (mobile teams and donor centres) of National Health Service Blood and Transplant (NHSBT) in England. Each site ("cluster") has been randomly allocated to receive one or more interventions during a 36-month period, using principles of cross-over, stepped-wedge and factorial trial design to assign the sequence of interventions. Each of the four interventions is compared to NHSBT's current practices: (i) 500-ml isotonic drink before donation (vs current 500-ml plain water); (ii) 3-min rest on donation chair after donation (vs current 2 min); (iii) new modified AMT (vs current practice of AMT); and (iv) psychosocial intervention using preparatory materials (vs current practice of nothing). The primary outcome is the number of in-session VVRs with loss of consciousness (i.e. episodes involving loss of consciousness of any duration, with or without additional complications). Secondary outcomes include all in-session VVRs (i.e. with and without loss of consciousness), all delayed VVRs (i.e. those occurring after leaving the venue) and any in-session non-VVR adverse events or reactions. Discussion: The STRIDES trial should yield novel information about interventions, singly and in combination, for the prevention of VVRs, with the aim of generating policy-shaping evidence to help inform blood services to improve donor health, donor experience, and service efficiency. Trial registration: ISRCTN: 10412338. Registration date: October 24, 2019. [ABSTRACT FROM AUTHOR]

BackgroundQuality of cardiopulmonary resuscitation (CPR) is associated with survival, but recommended guidelines are often not met, and less than half the children with an in-hospital arrest will survive to discharge. A single-center before-and-after study demonstrated that outcomes may be improved with a novel training program in which all pediatric intensive care unit staff are encouraged to participate in frequent CPR refresher training and regular, structured resuscitation debriefings focused on patient-centric physiology.Methods/designThis ongoing trial will assess whether a program of structured debriefings and point-of-care bedside practice that emphasizes physiologic resuscitation targets improves the rate of survival to hospital discharge with favorable neurologic outcome in children receiving CPR in the intensive care unit. This study is designed as a hybrid stepped-wedge trial in which two of ten participating hospitals are randomly assigned to enroll in the intervention group and two are assigned to enroll in the control group for the duration of the trial. The remaining six hospitals enroll initially in the control group but will transition to enrolling in the intervention group at randomly assigned staggered times during the enrollment period.DiscussionTo our knowledge, this is the first implementation of a hybrid stepped-wedge design. It was chosen over a traditional stepped-wedge design because the resulting improvement in statistical power reduces the required enrollment by 9 months (14%). However, this design comes with additional challenges, including logistics of implementing an intervention prior to the start of enrollment. Nevertheless, if results from the single-center pilot are confirmed in this trial, it will have a profound effect on CPR training and quality improvement initiatives.Trial registrationClinicalTrials.gov, NCT02837497 . Registered on July 19, 2016.

Background: Chronic pancreatitis (CP) is an inflammatory disease that may be complicated by abdominal pain, pancreatic dysfunction, nutritional deficiencies, and diminished bone density. Importantly, it is also associated with a substantially impaired quality of life and reduced life expectancy. This may partly be explained by suboptimal treatment, in particular the long-term management of this chronic condition, despite several national and international guidelines. Standardization of care through a structured implementation of guideline recommendations may improve the level of care and lower the complication rate of these patients. Therefore, the aim of the present study is to evaluate to what extent patient education and standardization of care, through the implementation of an evidence-based integrated management algorithm, improve quality of life and reduce pain severity in patients with CP. Methods: The COMBO trial is a nationwide stepped-wedge cluster-randomized controlled trial. In a stepwise manner, 26 centers, clustered in 6 health regions, cross-over from current practice to care according to an evidence-based integrated management algorithm. During the current practice phase, study participants are recruited and followed longitudinally through questionnaires. Individual patients contribute data to both study periods. Co-primary study endpoints consist of quality of life (assessed by the PANQOLI score) and level of pain (assessed by the Izbicki questionnaire). Secondary outcomes include process measure outcomes, clinical outcomes (e.g., pancreatic function, nutritional status, bone health, interventions, medication use), utilization of healthcare resources, (in) direct costs, and the level of social participation. Standard follow-up is 35 months from the start of the trial. Discussion: This is the first stepped-wedge cluster-randomized controlled trial to investigate whether an evidence-based integrated therapeutic approach improves quality of life and pain severity in patients with CP as compared with current practice. Trial registration: ISRCTN, ISRCTN13042622. Registered on 5 September 2020. [ABSTRACT FROM AUTHOR]

Abstract Background There are currently no methodological studies on the performance of the statistical models for estimating intervention effects based on the time-to-recurrent-event (TTRE) in stepped wedge cluster randomised trial (SWCRT) using an open cohort design. This study aims to address this by evaluating the performance of these statistical models using an open cohort design with the Monte Carlo simulation in various settings and their application using an actual example. Methods Using Monte Carlo simulations, we evaluated the performance of the existing extended Cox proportional hazard models, i.e., the Andersen-Gill (AG), Prentice-Williams-Peterson Total-Time (PWP-TT), and Prentice-Williams-Peterson Gap-time (PWP-GT) models, using the settings of several event generation models and true intervention effects, with and without stratification by clusters. Unidirectional switching in SWCRT was represented using time-dependent covariates. Results Using Monte Carlo simulations with the various described settings, in situations where inter-individual variability do not exist, the PWP-GT model with stratification by clusters showed the best performance in most settings and reasonable performance in the others. The only situation in which the performance of the PWP-TT model with stratification by clusters was not inferior to that of the PWP-GT model with stratification by clusters was when there was a certain amount of follow-up period, and the timing of the trial entry was random within the trial period, including the follow-up period. In situations where inter-individual variability existed, the PWP-GT model consistently underperformed compared to the PWP-TT model. The AG model performed well only in a specific setting. By analysing actual examples, it was found that almost all the statistical models suggested that the risk of events during the intervention condition may be somewhat higher than in the control, although the difference was not statistically significant. Conclusions When estimating the TTRE-based intervention effects of SWCRT in various settings using an open cohort design, the PWP-GT model with stratification by clusters performed most reasonably in situations where inter-individual variability was not present. However, if inter-individual variability was present, the PWP-TT model with stratification by clusters performed best.

Introduction: Randomized controlled trials (RCTs) yield the highest level of evidence but are notoriously difficult to perform in surgery. Surgical RCTs may be hampered by slow accrual, the surgical learning curve, and lack of financial support. Alternative RCT designs such as stepped-wedge randomized controlled trials (SW-RCTs), registry-based randomized controlled trials (RB-RCTs), and trials-within-cohorts (TwiCs) may overcome several of these difficulties. This review provides an overview of alternative RCT designs used in surgical research. Methods: We systematically searched PubMed, EMBASE, and Cochrane Central for surgical SW-RCTs, RB-RCTs, and TwiCs. A surgical RCT was defined as a randomized trial that studied interventions in patients undergoing general surgery, regardless of the affiliation of the corresponding author. Exponential regression analysis was performed to assess time trends. Results: Overall, 41 surgical RCTs using alternative designs were identified, including 17 published final RCT reports and 24 published protocols of ongoing RCTs. These included 25 SW-RCTs (61%), 13 RB-RCTs (32%), and 3 TwiCs (7%). Most of these RCTs were performed in Europe (63%) and within gastrointestinal/oncological surgery (41%). The total number of RCTs using alternative designs exponentially increased over the last 7 years (P <0.01), with 95% (n=39/41) of the total number published within this time frame. The most reported reasons for using alternative RCT designs were avoidance of contamination for SW-RCTs and generalizability of the trial population for RB-RCTs and TwiCs. Conclusions: Alternative RCT designs are increasingly used in surgical research, mostly in Europe and within gastrointestinal/oncological surgery. When adequately used, these alternative designs may overcome several difficulties associated with surgical RCTs. [ABSTRACT FROM AUTHOR]

Abstract Background Neonatal mortality remains unacceptably high. Many studies successful at reducing neonatal mortality have failed to realise similar gains at scale. Effective implementation and scale-up of interventions designed to tackle neonatal mortality is a global health priority. Multifaceted programmes targeting the continuum of neonatal care, with sustainability and scalability built into the design, can provide practical insights to solve this challenge. Cambodia has amongst the highest neonatal mortality rates in South-East Asia, with rural areas particularly affected. The primary objective of this study is the design, implementation, and assessment of the Saving Babies’ Lives programme, a package of interventions designed to reduce neonatal mortality in rural Cambodia. Methods This study is a five-year stepped-wedge cluster-randomised trial conducted in a rural Cambodian province with an estimated annual delivery rate of 6615. The study is designed to implement and evaluate the Saving Babies’ Lives programme, which is the intervention. The Saving Babies’ Lives programme is an iterative package of neonatal interventions spanning the continuum of care and integrating into the existing health system. The Saving Babies’ Lives programme comprises two major components: participatory learning and action with community health workers, and capacity building of primary care facilities involving facility-based mentorship. Standard government service continues in control arms. Data collection covering the whole study area includes surveillance of all pregnancies, verbal and social autopsies, and quality of care surveys. Mixed methods data collection supports iteration of the complex intervention, and facilitates impact, outcome, process and economic evaluation. Discussion Our study uses a robust study design to evaluate and develop a holistic, innovative, contextually relevant and sustainable programme that can be scaled-up to reduce neonatal mortality. Trial registration ClinicalTrials.gov: NCT04663620 . Registered on 11th December 2020, retrospectively registered.

Background: There are currently no methodological studies on the performance of the statistical models for estimating intervention effects based on the time-to-recurrent-event (TTRE) in stepped wedge cluster randomised trial (SWCRT) using an open cohort design. This study aims to address this by evaluating the performance of these statistical models using an open cohort design with the Monte Carlo simulation in various settings and their application using an actual example.Methods: Using Monte Carlo simulations, we evaluated the performance of the existing extended Cox proportional hazard models, i.e., the Andersen-Gill (AG), Prentice-Williams-Peterson Total-Time (PWP-TT), and Prentice-Williams-Peterson Gap-time (PWP-GT) models, using the settings of several event generation models and true intervention effects, with and without stratification by clusters. Unidirectional switching in SWCRT was represented using time-dependent covariates.Results: Using Monte Carlo simulations with the various described settings, in situations where inter-individual variability do not exist, the PWP-GT model with stratification by clusters showed the best performance in most settings and reasonable performance in the others. The only situation in which the performance of the PWP-TT model with stratification by clusters was not inferior to that of the PWP-GT model with stratification by clusters was when there was a certain amount of follow-up period, and the timing of the trial entry was random within the trial period, including the follow-up period. In situations where inter-individual variability existed, the PWP-GT model consistently underperformed compared to the PWP-TT model. The AG model performed well only in a specific setting. By analysing actual examples, it was found that almost all the statistical models suggested that the risk of events during the intervention condition may be somewhat higher than in the control, although the difference was not statistically significant.Conclusions: When estimating the TTRE-based intervention effects of SWCRT in various settings using an open cohort design, the PWP-GT model with stratification by clusters performed most reasonably in situations where inter-individual variability was not present. However, if inter-individual variability was present, the PWP-TT model with stratification by clusters performed best. [ABSTRACT FROM AUTHOR]

BackgroundWe designed a pragmatic stepped-wedge cluster randomized controlled trial in order to evaluate provider-initiated evaluation of household contacts (HCs) of smear positive tuberculosis (TB) cases within a routine TB program in Lima, Peru.Methods/designNational TB program (NTP) officers of San Juan de Lurigancho District (Lima, Peru) and university-based researchers jointly designed a pragmatic stepped-wedge cluster randomized trial design in order to evaluate a planned active case finding (ACF) program for all HCs of smear-positive TB cases in 34 district healthcare centres. Randomization of time to intervention initiation was stratified by health centre TB case rate. The ACF intervention included provider-initiated home visits of all new sputum smear positive TB patients in order to evaluate household contacts for active TB. Active TB was diagnosed using symptom screening, sputum screening, chest x-ray and clinical evaluation. Once initiated, ACF was provided by NTP staff and integrated into the routine DOTS TB program activities.DiscussionThis study protocol describes the pragmatic stepped-wedge cluster randomized trial of active household contact evaluations within an NTP. The stepped-wedge design met overlapping needs of local TB programmers and researchers to adequately evaluate the large-scale roll out of a new control program in a TB endemic setting. Multiple planning meetings were required to develop the necessary networks and in order to understand the operations, needs and goals of the NTP staff and researchers collaborating on this project. The advantages and challenges of using this study design in practice and within existing routine TB programs in a middle-income country context are discussed.Trial registrationClinicalTrials.gov NCT02174380. Registered 24 Jun 2014.

Background: Early warning scores (EWS) are routinely used in hospitals to assess a patient's risk of deterioration. EWS are traditionally recorded on paper observation charts but are increasingly recorded digitally. In either case, evidence for the clinical effectiveness of such scores is mixed, and previous studies have not considered whether EWS leads to changes in how deteriorating patients are managed., Objective: This study aims to examine whether the introduction of a digital EWS system was associated with more frequent observation of patients with abnormal vital signs, a precursor to earlier clinical intervention., Methods: We conducted a 2-armed stepped-wedge study from February 2015 to December 2016, over 4 hospitals in 1 UK hospital trust. In the control arm, vital signs were recorded using paper observation charts. In the intervention arm, a digital EWS system was used. The primary outcome measure was time to next observation (TTNO), defined as the time between a patient's first elevated EWS (EWS ≥3) and subsequent observations set. Secondary outcomes were time to death in the hospital, length of stay, and time to unplanned intensive care unit admission. Differences between the 2 arms were analyzed using a mixed-effects Cox model. The usability of the system was assessed using the system usability score survey., Results: We included 12,802 admissions, 1084 in the paper (control) arm and 11,718 in the digital EWS (intervention) arm. The system usability score was 77.6, indicating good usability. The median TTNO in the control and intervention arms were 128 (IQR 73-218) minutes and 131 (IQR 73-223) minutes, respectively. The corresponding hazard ratio for TTNO was 0.99 (95% CI 0.91-1.07; P=.73)., Conclusions: We demonstrated strong clinical engagement with the system. We found no difference in any of the predefined patient outcomes, suggesting that the introduction of a highly usable electronic system can be achieved without impacting clinical care. Our findings contrast with previous claims that digital EWS systems are associated with improvement in clinical outcomes. Future research should investigate how digital EWS systems can be integrated with new clinical pathways adjusting staff behaviors to improve patient outcomes., (©David Chi-Wai Wong, Timothy Bonnici, Stephen Gerry, Jacqueline Birks, Peter J Watkinson. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.06.2024.)

Background: Neonatal mortality remains unacceptably high. Many studies successful at reducing neonatal mortality have failed to realise similar gains at scale. Effective implementation and scale-up of interventions designed to tackle neonatal mortality is a global health priority. Multifaceted programmes targeting the continuum of neonatal care, with sustainability and scalability built into the design, can provide practical insights to solve this challenge. Cambodia has amongst the highest neonatal mortality rates in South-East Asia, with rural areas particularly affected. The primary objective of this study is the design, implementation, and assessment of the Saving Babies' Lives programme, a package of interventions designed to reduce neonatal mortality in rural Cambodia.Methods: This study is a five-year stepped-wedge cluster-randomised trial conducted in a rural Cambodian province with an estimated annual delivery rate of 6615. The study is designed to implement and evaluate the Saving Babies' Lives programme, which is the intervention. The Saving Babies' Lives programme is an iterative package of neonatal interventions spanning the continuum of care and integrating into the existing health system. The Saving Babies' Lives programme comprises two major components: participatory learning and action with community health workers, and capacity building of primary care facilities involving facility-based mentorship. Standard government service continues in control arms. Data collection covering the whole study area includes surveillance of all pregnancies, verbal and social autopsies, and quality of care surveys. Mixed methods data collection supports iteration of the complex intervention, and facilitates impact, outcome, process and economic evaluation.Discussion: Our study uses a robust study design to evaluate and develop a holistic, innovative, contextually relevant and sustainable programme that can be scaled-up to reduce neonatal mortality.Trial Registration: ClinicalTrials.gov: NCT04663620 . Registered on 11th December 2020, retrospectively registered. [ABSTRACT FROM AUTHOR]

Background: For patients with early breast cancer considered at very-low risk of local relapse, risks of radiotherapy may outweigh the benefits. Decisions regarding treatment omission can lead to patient uncertainty (decisional conflict), which may be lessened with patient decision aids (PDA). PRIMETIME (ISRCTN 41579286) is a UK-led biomarker-directed study evaluating omission of adjuvant radiotherapy in breast cancer; an embedded Study Within A Trial (SWAT) investigated whether PDA reduces decisional conflict using a cluster stepped-wedge trial design.Methods: PDA diagrams and a video explaining risks and benefits of radiotherapy were developed in close collaboration between patient advocates and PRIMETIME trialists. The SWAT used a cluster stepped-wedge trial design, where each cluster represented the radiotherapy centre and referring peripheral centres. All clusters began in the standard information group (patient information and diagrams) and were randomised to cross-over to the enhanced information group (standard information plus video) at 2, 4 or 6 months. Primary endpoint was the decisional conflict scale (0-100, higher scores indicating greater conflict) which was assessed on an individual participant level. Multilevel mixed effects models used a random effect for cluster and a fixed effect for each step to adjust for calendar time and clustering. Robust standard errors were also adjusted for the clustering effect.Results: Five hundred twenty-one evaluable questionnaires were returned from 809 eligible patients (64%) in 24 clusters between April 2018 and October 2019. Mean decisional conflict scores in the standard group (N = 184) were 10.88 (SD 11.82) and 8.99 (SD 11.82) in the enhanced group (N = 337), with no statistically significant difference [mean difference - 1.78, 95%CI - 3.82-0.25, p = 0.09]. Compliance with patient information and diagrams was high in both groups although in the enhanced group only 121/337 (36%) reported watching the video.Conclusion: The low levels of decisional conflict in PRIMETIME are reassuring and may reflect the high-quality information provision, such that not everyone required the video. This reinforces the importance of working with patients as partners in clinical trials especially in the development of patient-centred information and decision aids. [ABSTRACT FROM AUTHOR]

Abstract Background Cluster randomised trials with unequal sized clusters often have lower precision than with clusters of equal size. To allow for this, sample sizes are inflated by a modified version of the design effect for clustering. These inflation factors are valid under the assumption that randomisation is stratified by cluster size. We investigate the impact of unequal cluster size when that constraint is relaxed, with particular focus on the stepped-wedge cluster randomised trial, where this is more difficult to achieve. Methods Assuming a multi-level mixed effect model with exchangeable correlation structure for a cross-sectional design, we use simulation methods to compare the precision for a trial with clusters of unequal size to a trial with clusters of equal size (relative efficiency). For a range of scenarios we illustrate the impact of various design features (the cluster-mean correlation – a function of the intracluster correlation and the cluster size, the number of clusters, number of randomisation sequences) on the average and distribution of the relative efficiency. Results Simulations confirm that the average reduction in precision, due to varying cluster sizes, is smaller in a stepped-wedge trial compared to the parallel trial. However, the variance of the distribution of the relative efficiency is large; and is larger under the stepped-wedge design compared to the parallel design. This can result in large variations in actual power, depending on the allocation of clusters to sequences. Designs with larger variations in cluster sizes, smaller number of clusters and studies with smaller cluster-mean correlations (smaller cluster sizes or smaller intra-cluster correlation) are particularly at risk. Conclusion The actual realised power in a stepped-wedge trial might be substantially higher or lower than that estimated. This is particularly important when there are a small number of clusters or the variability in cluster sizes is large. Constraining the randomisation on cluster size, where feasible, might mitigate this effect.

Background: The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes., Methods: We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods., Results: Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD., Conclusions: We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE., (© 2024. The Author(s).)

The stepped-wedge cluster randomized trial (SW-CRT) involves the sequential transition of clusters (such as hospitals, public health units or communities) from control to intervention conditions in a randomized order. The use of the SW-CRT is growing rapidly. Yet the SW-CRT is at greater risks of bias compared with the conventional parallel cluster randomized trial (parallel-CRT). For this reason, the CONSORT extension for SW-CRTs requires that investigators provide a clear justification for the choice of study design. In this paper, we argue that all other things being equal, the SW-CRT is at greater risk of bias due to misspecification of the secular trends at the analysis stage. This is particularly problematic for studies randomizing a small number of heterogeneous clusters. We outline the potential conditions under which an SW-CRT might be an appropriate choice. Potentially appropriate and often overlapping justifications for conducting an SW-CRT include: (i) the SW-CRT provides a means to conduct a randomized evaluation which otherwise would not be possible; (ii) the SW-CRT facilitates cluster recruitment as it enhances the acceptability of a randomized evaluation either to cluster gatekeepers or other stakeholders; (iii) the SW-CRT is the only feasible design due to pragmatic and logistical constraints (for example the roll-out of a scare resource); and (iv) the SW-CRT has increased statistical power over other study designs (which will include situations with a limited number of clusters). As the number of arguments in favour of an SW-CRT increases, the likelihood that the benefits of using the SW-CRT, as opposed to a parallel-CRT, outweigh its risks also increases. We argue that the mere popularity and novelty of the SW-CRT should not be a factor in its adoption. In situations when a conventional parallel-CRT is feasible, it is likely to be the preferred design. [ABSTRACT FROM AUTHOR]

The use of cluster randomized trial design to answer research questions is increasing. This design and associated variants such as the cluster randomized crossover and stepped wedge are useful to assess complex interventions in a pragmatic way but when adopting such designs, one may face specific implementation challenges. This article summarizes common challenges faced when conducting cluster randomized trials, cluster randomized crossover trials, and stepped wedge trials, and provides recommendations., Competing Interests: Declaration of competing interest The authors declare they have no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Numerous publications have now addressed the principles of designing, analyzing, and reporting the results of stepped-wedge cluster randomized trials. In contrast, there is little research available pertaining to the design and analysis of multiarm stepped-wedge cluster randomized trials, utilized to evaluate the effectiveness of multiple experimental interventions. In this paper, we address this by explaining how the required sample size in these multiarm trials can be ascertained when data are to be analyzed using a linear mixed model. We then go on to describe how the design of such trials can be optimized to balance between minimizing the cost of the trial and minimizing some function of the covariance matrix of the treatment effect estimates. Using a recently commenced trial that will evaluate the effectiveness of sensor monitoring in an occupational therapy rehabilitation program for older persons after hip fracture as an example, we demonstrate that our designs could reduce the number of observations required for a fixed power level by up to 58%. Consequently, when logistical constraints permit the utilization of any one of a range of possible multiarm stepped-wedge cluster randomized trial designs, researchers should consider employing our approach to optimize their trials efficiency. [ABSTRACT FROM AUTHOR]

Abstract: The ability to accurately estimate the sample size required by a stepped‐wedge (SW) cluster randomized trial (CRT) routinely depends upon the specification of several nuisance parameters. If these parameters are misspecified, the trial could be overpowered, leading to increased cost, or underpowered, enhancing the likelihood of a false negative. We address this issue here for cross‐sectional SW‐CRTs, analyzed with a particular linear‐mixed model, by proposing methods for blinded and unblinded sample size reestimation (SSRE). First, blinded estimators for the variance parameters of a SW‐CRT analyzed using the Hussey and Hughes model are derived. Following this, procedures for blinded and unblinded SSRE after any time period in a SW‐CRT are detailed. The performance of these procedures is then examined and contrasted using two example trial design scenarios. We find that if the two key variance parameters were underspecified by 50%, the SSRE procedures were able to increase power over the conventional SW‐CRT design by up to 41%, resulting in an empirical power above the desired level. Thus, though there are practical issues to consider, the performance of the procedures means researchers should consider incorporating SSRE in to future SW‐CRTs. [ABSTRACT FROM AUTHOR]

Cluster randomized trials are frequently used in health service evaluation. It is common practice to use an analysis model with a random effect to allow for clustering at the analysis stage. In designs where clusters are exposed to both control and treatment conditions, it may be of interest to examine treatment effect heterogeneity across clusters. In designs where clusters are not exposed to both control and treatment conditions, it can also be of interest to allow heterogeneity in the degree of clustering between arms. These two types of heterogeneity are related. It has been proposed in both parallel cluster trials, stepped-wedge, and other cross-over designs that this heterogeneity can be allowed for by incorporating additional random effect(s) into the model. Here, we show that the choice of model parameterization needs careful consideration as some parameterizations for additional heterogeneity induce unnecessary or implausible assumptions. We suggest more appropriate parameterizations, discuss their relative advantages, and demonstrate the implications of these model choices using a real example of a parallel cluster trial and a simulated stepped-wedge trial. [ABSTRACT FROM AUTHOR]

Background: A personal health record (PHR) is an online application through which individuals can access, manage, and share their health information in a private, secure, and confidential environment. Personal health records empower patients, facilitate collaboration among healthcare professionals, and improve health outcomes. Given these anticipated positive effects, we want to implement a PHR, named MyPregn@ncy, in a Dutch maternity care setting and to evaluate its effects in routine care. This paper presents the study protocol.Methods/design: The effects of implementing a PHR in maternity care on patients and professionals will be identified in a stepped-wedge, cluster-randomised, controlled trial. The study will be performed in the region of Nijmegen, a Dutch area with an average of 4,500 births a year and more than 230 healthcare professionals involved in maternity care. Data analyses will describe the effects of MyPregn@ncy on health outcomes in maternity care, quality of care from the patients' perspectives, and collaboration among healthcare professionals. Additionally, a process evaluation of the implementation of MyPregn@ncy will be performed. Data will be collected using data from the Dutch perinatal registry, questionnaires, interviews, and log data.Discussion: The study is expected to yield new information about the effects, strengths, possibilities, and challenges to the implementation and usage of a PHR in routine maternal care settings. Results may lead to new insights and improvements in the quality of maternal and perinatal care.Trial Registration: Netherlands Trial Register: NTR4063. [ABSTRACT FROM AUTHOR]

Background: An Early Warning Score is a clinical risk score based upon vital signs intended to aid recognition of patients in need of urgent medical attention. The use of an escalation of care policy based upon an Early Warning Score is mandated as the standard of practice in British hospitals. Electronic systems for recording vital sign observations and Early Warning Score calculation offer theoretical benefits over paper-based systems. However, the evidence for their clinical benefit is limited. Previous studies have shown inconsistent results. The majority have employed a "before and after" study design, which may be strongly confounded by simultaneously occurring events. This study aims to examine how the implementation of an electronic early warning score system, System for Notification and Documentation (SEND), affects the recognition of clinical deterioration occurring in hospitalised adult patients.Methods: This study is a non-randomised stepped wedge evaluation carried out across the four hospitals of the Oxford University Hospitals NHS Trust, comparing charting on paper and charting using SEND. We assume that more frequent monitoring of acutely ill patients is associated with better recognition of patient deterioration. The primary outcome measure is the time between a patient's first observations set with an Early Warning Score above the alerting threshold and their subsequent set of observations. Secondary outcome measures are in-hospital mortality, cardiac arrest and Intensive Care admission rates, hospital length of stay and system usability measured using the System Usability Scale. We will also measure Intensive Care length of stay, Intensive Care mortality, Acute Physiology and Chronic Health Evaluation (APACHE) II acute physiology score on admission, to examine whether the introduction of SEND has any effect on Intensive Care-related outcomes.Discussion: The development of this protocol has been informed by guidance from the Agency for Healthcare Research and Quality (AHRQ) Health Information Technology Evaluation Toolkit and Delone and McLeans's Model of Information System Success. Our chosen trial design, a stepped wedge study, is well suited to the study of a phased roll out. The choice of primary endpoint is challenging. We have selected the time from the first triggering observation set to the subsequent observation set. This has the benefit of being easy to measure on both paper and electronic charting and having a straightforward interpretation. We have collected qualitative measures of system quality via a user questionnaire and organisational descriptors to help readers understand the context in which SEND has been implemented. [ABSTRACT FROM AUTHOR]

The public health response to HIV/AIDS has turned its focus onto optimizing health care system delivery to maximize case identification, access and sustained engagement in antiretroviral treatment (ART). Opioid Agonist Treatment (OAT) provides a critical opportunity for HIV testing and linkage to ART. The EHOST study is a cluster-randomized, stepped-wedge trial to evaluate a prescriber-focused intervention to increase HIV testing rates, and optimize ART engagement and retention outcomes among individuals engaged in OAT. The study will encompass all drug treatment clinics currently admitting patients for the treatment of opioid use disorder across the province of British Columbia, encompassing an estimated 90% of the OAT caseload. The trial will be executed over a 24-month period, with groups of clinics receiving the intervention in 6-month intervals. Evaluation of the proposed intervention's effectiveness will focus on three primary outcomes: (i) the HIV testing rate among those not known to be HIV positive; (ii) the rate of ART initiation among those not on ART; and (iii) the rate of ART continuation among those on ART. A difference-in-differences analytical framework will be applied to estimate the intervention's effect. This approach will assess site-specific changes in primary outcomes across clusters while adjusting for potential residual heterogeneity in patient case mix, volume, and quality of care across clinics. Statistical analysis of outcomes will be conducted entirely with linked population-level administrative health datasets. Facilitated by established collaborations between key stakeholders across the province, the EHOST intervention promises to optimize HIV testing and care within a marginalized and hard-to-reach population. [ABSTRACT FROM AUTHOR]

Stepped-wedge cluster randomised trials (SW-CRTs) are being used with increasing frequency in health service evaluation. Conventionally, these studies are cross-sectional in design with equally spaced steps, with an equal number of clusters randomised at each step and data collected at each and every step. Here we introduce several variations on this design and consider implications for power. One modification we consider is the incomplete cross-sectional SW-CRT, where the number of clusters varies at each step or where at some steps, for example, implementation or transition periods, data are not collected. We show that the parallel CRT with staggered but balanced randomisation can be considered a special case of the incomplete SW-CRT. As too can the parallel CRT with baseline measures. And we extend these designs to allow for multiple layers of clustering, for example, wards within a hospital. Building on results for complete designs, power and detectable difference are derived using a Wald test and obtaining the variance-covariance matrix of the treatment effect assuming a generalised linear mixed model. These variations are illustrated by several real examples. We recommend that whilst the impact of transition periods on power is likely to be small, where they are a feature of the design they should be incorporated. We also show examples in which the power of a SW-CRT increases as the intra-cluster correlation (ICC) increases and demonstrate that the impact of the ICC is likely to be smaller in a SW-CRT compared with a parallel CRT, especially where there are multiple levels of clustering. Finally, through this unified framework, the efficiency of the SW-CRT and the parallel CRT can be compared. © 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

• This R-package calculates the trial powers for different random allocations. • The package calculates the risk that a trial design will yield inadequate power. • The package covers cluster-randomized trial designs with unequal cluster sizes. • The package allows for continuous, binary, and count outcomes. • For non-R users, a Shiny app is also available. The attained power, calculated conditional on the realized allocation, of a clinical trial may differ from the expected power, obtained pre-randomization through averaging over all potential allocations that could be generated by the randomization algorithm (RA). For example, a two-arm trial using a RA that is expected to allocate 20 participants to each arm will attain less than the expected power if by chance it allocates 25 and 15 participants to the arms. Cluster randomized trials with unequal cluster sizes have elevated risk of realizing an allocation that yields an attained power much lower than the expected power when modest numbers of clusters are randomized. We developed the R package CRTpowerdist, which implements both simulations and approximate analytic formulae to calculate the attained powers associated with different realized allocations and constructs the pre-randomization power distribution associated with the RA to facilitate assessing the risk of obtaining inadequate power. The package covers unequal cluster-size, cross-sectional stepped-wedge and parallel cluster randomized trials, with or without stratification. Allowed outcome types are: continuous (Gaussian), binary (Binomial) and count (Poisson). The analytic formulae-based calculations are also implemented in a Shiny app. The functionality of the CRTpowerdist is illustrated for each type of trial design. The examples show how to obtain the attained power, the power distribution, and the risk of low attained power, using both simulation and analytic formulae. For cluster randomized trials with unequal cluster sizes, the CRTpowerdist package can assist users in identifying an appropriate randomization algorithm by enabling the user to assess the risk that a randomization algorithm will lead to an allocation with inadequate attained power. The Shiny app makes these assessments accessible to researchers who are unable or do not wish to use the CRTpowerdist package. [ABSTRACT FROM AUTHOR]