Estimates Of Intervention Effects Research Articles

A framework for local-level economic evaluation to inform implementation decisions: health service interventions to prevent hospital-acquired hypoglycemia.

Published evidence on health service interventions should inform decision-making in local health services, but primary effectiveness studies and cost-effectiveness analyses are unlikely to reflect contexts other than those in which the evaluations were undertaken. A ten-step framework was developed and applied to use published evidence as the basis for local-level economic evaluations that estimate the expected costs and effects of new service intervention options in specific local contexts. Working with a multidisciplinary group of local clinicians, the framework was applied to evaluate intervention options for preventing hospital-acquired hypoglycemia. The framework included: clinical audit and analyses of local health systems data to understand the local context and estimate baseline event rates; pragmatic literature review to identify evidence on relevant intervention options; expert elicitation to adjust published intervention effect estimates to reflect the local context; and modeling to synthesize and calibrate data derived from the disparate data sources. From forty-seven studies identified in the literature review, the working group selected three interventions for evaluation. The local-level economic evaluation generated estimates of intervention costs and a range of cost, capacity and patient outcome-related consequences, which informed working group recommendations to implement two of the interventions. The applied framework for modeled local-level economic evaluation was valued by local stakeholders, in particular the structured, formal approach to identifying and interpreting published evidence alongside local data. Key methodological issues included the handling of alternative reported outcomes and the elicitation of the expected intervention effects in the local context.

Intervention effect estimates in randomised controlled trials conducted in primary care versus secondary or tertiary care settings: a meta-epidemiological study

BackgroundMany clinical practice guidelines are based on randomised controlled trials conducted in secondary or tertiary care setting and general practitioners frequently question their relevance for primary care patients. Our aim was to compare the intervention effect estimates between primary care setting randomised controlled trials (PC-RCTs) and secondary or tertiary care setting randomised controlled trials (ST-RCTs).MethodsMeta-epidemiological study of meta-analyses (MAs) of a binary outcome including at least one PC-RCT and one ST-RCT. PC-RCTs were defined as trials recruiting patients in general practices, primary care practices, family practices, community centers or community pharmacies. ST-RCTs were defined as trials recruiting in hospitals, including hospitalized patients, hospital outpatients and patients from emergency departments. For each MA, we estimated a ratio of odds ratio (ROR) by using random-effects meta-regression, with an ROR less than 1 indicating lower estimates of the intervention effect in PC-RCTs than ST-RCTs. Finally, we estimated a combined ROR across MAs by using a random-effects meta-analysis. We performed subgroup analyses considering the type of outcomes (objective vs subjective), type of experimental intervention (pharmacological vs non-pharmacological), and control group (active vs inactive) as well as analyses adjusted on items of the Cochrane Risk of Bias tool.ResultsAmong 1765 screened reviews, 76 MAs with 230 PC-RCTs and 384 ST-RCTs were selected. The main medical fields were pneumology (13.2%) and psychiatry or addictology (38.2%). Intervention effect estimates did not significantly differ between PC-RCTs and ST-RCTs (ROR = 0.97, 95% confidence interval 0.88 to 1.08), with moderate heterogeneity across MAs (I2 = 45%). Subgroup and adjusted analyses led to consistent results.ConclusionWe did not observe any significant difference in intervention effect estimates between PC-RCTs and ST-RCTs. Nevertheless, most of the medical fields in this meta-epidemiological study were not representative of the pathologies encountered in primary care. Further studies with pathologies more frequently encountered in primary care are needed.

Substance use prevention during adolescence: Study protocol for a large-scale cluster randomized trial of Botvin High School LifeSkills Training

BackgroundConcerns about the rise in adolescent vaping and cannabis use suggest the need for effective substance use prevention programs. Botvin LifeSkills Training (LST) has a strong evidence base at the middle school level for preventing or reducing tobacco use and related problems. A high school (grades 9–10) version of the LST program was also developed and shows promising initial evidence for reducing tobacco use in a single pilot study. However, the high school version of LST has not been sufficiently tested in an experimental trial, despite being widely implemented in high schools across the U.S. This paper outlines the study protocol for a large-scale cluster randomized trial of Botvin High School LST, with objectives of documenting the design of prospective research and promoting transparency. MethodsA total of 60 high schools in Colorado and Ohio were randomized to the 10-session, teacher-led intervention group (n = 33 schools) or business-as-usual control group (n = 27 schools). Across two cohorts of schools, 9th-grade students complete self-report surveys at pretest, immediate posttest, 1-year follow-up, and 21-month follow-up. Primary outcomes are tobacco (nicotine) use and cannabis use. Secondary outcomes are alcohol use, illicit drug use, psychosocial behaviors (e.g., violence and mental health), and academic achievement. Intent-to-treat analyses will use multilevel modeling to estimate intervention effects across assessment points. ConclusionThis independent evaluation will help to determine whether the intervention is appropriate for large-scale adoption. This trial is preregistered with the Open Science Framework (https://osf.io/dnz5q/).

Impact of a link worker social prescribing intervention on non-elective admitted patient care costs: A quasi-experimental study

Healthcare systems in many countries are enthusiastically adopting link worker social prescribing interventions that aim to tackle the social determinants of health by linking patients to voluntary and community sector activities and sources of support to address their social needs and improve condition management. Social prescribing interventions aim to improve health and reduce healthcare spending. However, despite the diversion of healthcare budgets to fund social prescribing, we still lack robust evidence for its effectiveness. In this study we evaluate whether participation in a social prescribing intervention reduced non-elective admitted patient care use and costs for 8283 patients aged between 40 and 74 years, with a diagnosis of type 2 diabetes and living in an area of high socioeconomic deprivation in north-east England. Patients were followed for a total of 6 years: 2 years pre-intervention and 4 years post intervention. Exploiting a natural experiment, we used a two-part difference-in-differences regression model to estimate costs conditional on healthcare use. We also estimated intervention effects across several intervention and control groups and sample subgroups. Participation in the intervention resulted in reductions of up to -£77.57 [95% CI: -152.30, -2.84] (for high engagement patients) per patient, per year, in non-elective care costs. Reductions were greater for patients with higher levels of engagement with the intervention. Sub-group analyses showed greater cost reductions for non-White patients, older patients, and patients without additional co-morbidities. Our findings suggest that engagement with a link worker social prescribing intervention may reduce non-elective healthcare spending, perhaps through enabling better condition management that results in fewer avoidable health crises.

Face masks to prevent transmission of respiratory infections: Systematic review and meta-analysis of randomized controlled trials on face mask use.

To examine the use of face mask intervention in mitigating the risk of spreading respiratory infections and whether the effect of face mask intervention differs in different exposure settings and age groups. Systematic review and meta-analysis. We evaluated the risk of bias using the Cochrane Risk of Bias 2 tool (ROB2). We searched PubMed, Embase, Cochrane Central Register of Controlled Trials, and Web of Science were searched for randomized controlled trials investigating the effect of face masks on respiratory infections published between 1981 and February 9, 2022. We followed the PRISMA 2020 guidelines. We included randomized controlled trials investigating the use of face mask intervention in mitigating the risk of spreading respiratory infections across different exposure settings. We identified 2,400 articles for screening. 18 articles passed the inclusion criteria for both evidence synthesis and meta-analysis. There were N = 189,145 individuals in the face mask intervention arm and N = 173,536 in the control arm, and the follow-up times ranged from 4 days to 19 months. Our results showed between-study heterogeneity (p < 0.0001). While there was no statistically significant association over all studies when the covariate unadjusted intervention effect estimates were used (RR = 0.977 [0.858-1.113], p = 0.728), our subgroup analyses revealed that a face mask intervention reduced respiratory infections in the adult subgroup (RR = 0.8795 [0.7861-0.9839], p = 0.0249) and in a community setting (RR = 0.890 [0.812-0.975], p = 0.0125). Furthermore, our leave-one-out analysis found that one study biased the results towards a null effect. Consequently, when using covariate adjusted odds ratio estimates to have a more precise effect estimates of the intervention effect to account for differences at the baseline, the results showed that a face mask intervention did reduce respiratory infections when the biasing study was excluded from the analysis (OR = 0.8892 [0.8061-0.9810], p = 0.0192). Our findings support the use of face masks particularly in a community setting and for adults. We also observed substantial between-study heterogeneity and varying adherence to protocol. Notably, many studies were subject to contamination bias thus affecting the efficacy of the intervention, that is when also some controls used masks or when the intervention group did not comply with mask use leading to a downward biased effect of treatment receipt and efficacy. PROSPERO registration number CRD42020205523.

Estimating Causal Effects of Interventions on Early-life Environmental Exposures Using Observational Data.

We discuss how epidemiologic studies have used observational data to estimate the effects of potential interventions on early-life environmental exposures. We summarize the value of posing questions about interventions, how a group of techniques known as "g-methods" can provide advantages for estimating intervention effects, and how investigators have grappled with the strong assumptions required for causal inference. We identified nine studies that estimated health effects of hypothetical interventions on early-life environmental exposures. Of these, six examined air pollution. Interventions evaluated by these studies included setting exposure levels at a specific value, shifting exposure distributions, and limiting exposure levels to less than a threshold value. Only one study linked exposure contrasts to a specific intervention on an exposure source, however. There is growing interest in estimating intervention effects of early-life environmental exposures, in part because intervention effects are directly related to possible public health actions. Future studies can build on existing work by linking research questions to specific hypothetical interventions that could reduce exposure levels. We discuss how framing questions around interventions can help overcome some of the barriers to causal inference and how advances related to machine learning may strengthen studies by sidestepping the overly restrictive assumptions of parametric regression models. By leveraging advancements in causal inference and exposure science, an intervention framework for environmental epidemiology can guide actionable solutions to improve children's environmental health.

A comparison of four quasi-experimental methods: an analysis of the introduction of activity-based funding in Ireland

BackgroundHealth services research often relies on quasi-experimental study designs in the estimation of treatment effects of a policy change or an intervention. The aim of this study is to compare some of the commonly used non-experimental methods in estimating intervention effects, and to highlight their relative strengths and weaknesses. We estimate the effects of Activity-Based Funding, a hospital financing reform of Irish public hospitals, introduced in 2016.MethodsWe estimate and compare four analytical methods: Interrupted time series analysis, Difference-in-Differences, Propensity Score Matching Difference-in-Differences and the Synthetic Control method. Specifically, we focus on the comparison between the control-treatment methods and the non-control-treatment approach, interrupted time series analysis. Our empirical example evaluated the length of stay impact post hip replacement surgery, following the introduction of Activity-Based Funding in Ireland. We also contribute to the very limited research reporting the impacts of Activity-Based-Funding within the Irish context.ResultsInterrupted time-series analysis produced statistically significant results different in interpretation, while the Difference-in-Differences, Propensity Score Matching Difference-in-Differences and Synthetic Control methods incorporating control groups, suggested no statistically significant intervention effect, on patient length of stay.ConclusionOur analysis confirms that different analytical methods for estimating intervention effects provide different assessments of the intervention effects. It is crucial that researchers employ appropriate designs which incorporate a counterfactual framework. Such methods tend to be more robust and provide a stronger basis for evidence-based policy-making.

Interventions for improving executive functions in children with foetal alcohol spectrum disorder (FASD): A systematic review.

Interventions for improving executive functions in children with foetal alcohol spectrum disorder (FASD): A systematic review.

Estimating intervention effects on infectious disease control: The effect of community mobility reduction on Coronavirus spread

Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. Susceptible–Infectious–Recovered (SIR) models are traditionally used to forecast the infection rates but do not provide insights into the causal effects of interventions. We propose a spatiotemporal model that estimates the causal effect of changes in community mobility (intervention) on infection rates. Using an approximation to the SIR model and incorporating spatiotemporal dependence, the proposed model estimates a direct and indirect (spillover) effect of intervention. Under an interference and treatment ignorability assumption, this model is able to estimate causal intervention effects, and additionally allows for spatial interference between locations. Reductions in community mobility were measured by cell phone movement data. The results suggest that the reductions in mobility decrease Coronavirus cases 4 to 7 weeks after the intervention.

Empirical evidence of study design biases in nutrition randomised controlled trials: a meta-epidemiological study

BackgroundInstruments to critically appraise randomised controlled trials (RCTs) are based on evidence from meta-epidemiological studies. We aim to conduct a meta-epidemiological study on the average bias associated with reported methodological trial characteristics such as random sequence generation, allocation concealment, blinding, incomplete outcome data, selective reporting, and compliance of RCTs in nutrition research.MethodsWe searched the Cochrane Database of Systematic Reviews, for systematic reviews of RCTs, published between 01 January 2010 and 31 December 2019. We combined the estimates of the average bias (e.g. ratio of risk ratios [RRR] or differences in standardised mean differences) in meta-analyses using the random-effects model. Subgroup analyses were conducted to investigate the potential differences among the RCTs with low versus high/unclear risk of bias with respect to the different types of interventions (e.g. micronutrients, fatty acids, dietary approach), outcomes (e.g. mortality, pregnancy outcomes), and type of outcome (objective, subjective). Heterogeneity was assessed through I2 and τ2, and prediction intervals were calculated.ResultsWe included 27 Cochrane nutrition reviews with 77 meta-analyses (n = 927 RCTs). The available evidence suggests that intervention effect estimates may not be exaggerated in RCTs with high/unclear risk of bias (versus low) judgement for sequence generation (RRR 0.97, 95% CI 0.93 to 1.02; I2 = 28%; τ2 = 0.002), allocation concealment (RRR 1.00, 95% CI 0.96 to 1.04; I2 = 27%; τ2 = 0.001), blinding of participants and personnel (RRR 0.95, 95% CI 0.91 to 1.00; I2 = 23%; τ2 = 0), selective reporting (RRR 0.97, 95% CI 0.92 to 1.02; I2 = 24%; τ2 = 0), and compliance (RRR 0.95, 95% CI 0.89 to 1.02; I2 = 0%; τ2 = 0). Intervention effect estimates seemed to be exaggerated in RCTs with a high/unclear risk of bias judgement for blinding of outcome assessment (RRR 0.81, 95% CI 0.70 to 0.94; I2 = 26%; τ2 = 0.03), which was predominately driven by subjective outcomes, and incomplete outcome data (RRR 0.92, 95% CI 0.88 to 0.97; I2 = 22%; τ2 = 0.001). For continuous outcomes, no differences were observed, except for selective reporting.ConclusionsOn average, most characteristics of nutrition RCTs may not exaggerate intervention effect estimates, but the average bias appears to be greatest in trials of subjective outcomes. Replication of this study is suggested in this field to keep this conclusion updated.

Safety and Efficacy of Inpatient Pulmonary Rehabilitation for Patients Hospitalized with an Acute Exacerbation of Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-analyses.

Rationale: Pulmonary rehabilitation (PR) during hospitalization for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) occurs during a period of disease instability for the patient, and the safety and efficacy of PR, specifically during the hospitalization period, have not been established. Objective: The purpose of this review is to determine the safety and efficacy of PR during the hospitalization phase for individuals with AECOPD. Methods: Scientific databases were searched up to August 2022 for randomized controlled trials that compared in-hospital PR with usual care. PR programs commenced during the hospitalization and included a minimum of two sessions. Titles and abstracts followed by full-text screening and data extraction were conducted independently by two reviewers. The intervention effect estimates were calculated through meta-analysis using a random-effect model. Results: A total of 27 studies were included (n = 1,317). The meta-analysis showed that inpatient PR improved the 6-minute-walk distance by 105 m (P < 0.001). Inpatient PR improved the performance on the five-repetition sit-to-stand test by -7.02 seconds (P = 0.03). Quality of life (QOL), as measured by the 5-level EuroQoL Group-5 dimension version (EQ-ED-5L) and the St. George's Respiratory Questionnaire, was significantly improved by the intervention. Inpatient PR increased lower limb muscle strength by 33.35 N (P < 0.001). There was no change in the length of stay. Only one serious adverse event related to the intervention was reported. Conclusions: This review suggests that it is safe and effective to provide PR during hospitalization for individuals with AECOPD. In-hospital PR improves functional exercise capacity, QOL, and lower limb strength without prolonging the hospital length of stay.

Battling the obesity epidemic with a school-based intervention: Long-term effects of a quasi-experimental study.

School-based health-promoting interventions are increasingly seen as an effective population strategy to improve health and prevent obesity. Evidence on the long-term effectiveness of school-based interventions is scarce. This study investigates the four-year effectiveness of the school-based Healthy Primary School of the Future (HPSF) intervention on children's body mass index z-score (BMIz), and on the secondary outcomes waist circumference (WC), dietary and physical activity (PA) behaviours. This study has a quasi-experimental design with four intervention schools, i.e., two full HPSFs (focus: diet and PA), two partial HPSFs (focus: PA), and four control schools. Primary school children (aged 4-12 years) attending the eight participating schools were invited to enrol in the study between 2015 and 2019. Annual measurements consisted of children's anthropometry (weight, height and waist circumference), dietary behaviours (child- and parent-reported questionnaires) and PA levels (accelerometers). Between 2015 and 2019, 2236 children enrolled. The average exposure to the school condition was 2·66 (SD 1·33) years, and 900 participants were exposed for the full four years (40·3%). After four years of intervention, both full (estimated intervention effect (B = -0·17 (95%CI -0·27 to -0·08) p = 0·000) and partial HPSF (B = -0·16 (95%CI-0·25 to -0·06) p = 0·001) resulted in significant changes in children's BMIz compared to control schools. Likewise, WC changed in favour of both full and partial HPSFs. In full HPSFs, almost all dietary behaviours changed significantly in the short term. In the long term, only consumption of water and dairy remained significant compared to control schools. In both partial and full HPSFs, changes in PA behaviours were mostly absent. This school-based health-promoting intervention is effective in bringing unfavourable changes in body composition to a halt in both the short and long term. It provides policy makers with robust evidence to sustainably implement these interventions in school-based routine.

Estimating effects of health policy interventions using interrupted time-series analyses: a simulation study

BackgroundA classic methodology used in evaluating the impact of health policy interventions is interrupted time-series (ITS) analysis, applying a quasi-experimental design that uses both pre- and post-policy data without randomization. In this paper, we took a simulation-based approach to estimating intervention effects under different assumptions.MethodsEach of the simulated mortality rates contained a linear time trend, seasonality, autoregressive, and moving-average terms. The simulations of the policy effects involved three scenarios: 1) immediate-level change only, 2) immediate-level and slope change, and 3) lagged-level and slope change. The estimated effects and biases of these effects were examined via three matched generalized additive mixed models, each of which used two different approaches: 1) effects based on estimated coefficients (estimated approach), and 2) effects based on predictions from models (predicted approach). The robustness of these two approaches was further investigated assuming misspecification of the models.ResultsWhen one simulated dataset was analyzed with the matched model, the two analytical approaches produced similar estimates. However, when the models were misspecified, the number of deaths prevented, estimated using the predicted vs. estimated approaches, were very different, with the predicted approach yielding estimates closer to the real effect. The discrepancy was larger when the policy was applied early in the time-series.ConclusionEven when the sample size appears to be large enough, one should still be cautious when conducting ITS analyses, since the power also depends on when in the series the intervention occurs. In addition, the intervention lagged effect needs to be fully considered at the study design stage (i.e., when developing the models).

Brief Alcohol Interventions are Effective through 6 Months: Findings from Marginalized Zero-inflated Poisson and Negative Binomial Models in a Two-step IPD Meta-analysis

To evaluate and optimize brief alcohol interventions (BAIs), it is critical to have a credible overall effect size estimate as a benchmark. Estimating such an effect size has been challenging because alcohol outcomes often represent responses from a mixture of individuals: those at high risk for alcohol misuse, occasional nondrinkers, and abstainers. Moreover, some BAIs exclusively focus on heavy drinkers, whereas others take a universal prevention approach. Depending on sample characteristics, the outcome distribution might have many zeros or very few zeros and overdispersion; consequently, the most appropriate statistical model may differ across studies. We synthesized individual participant data (IPD) from 19 studies in Project INTEGRATE (Mun et al., 2015b) that randomly allocated participants to intervention and control groups (N = 7,704 participants, 38.4% men, 74.7% White, 58.5% first-year students). We sequentially estimated marginalized zero-inflated Poisson (Long et al., 2014) or negative binomial regression models to obtain covariate-adjusted, study-specific intervention effect estimates in the first step, which were subsequently combined in a random-effects meta-analysis model in the second step. BAIs produced a statistically significant 8% advantage in the mean number of drinks at both 1–3 months (RR = 0.92, 95% CI = [0.85, 0.98]) and 6 months (RR = 0.92, 95% CI = [0.85, 0.99]) compared to controls. At 9–12 months, there was no statistically significant difference in the mean number of drinks between BAIs and controls. In conclusion, BAIs are effective at reducing the mean number of drinks through at least 6 months post intervention. IPD can play a critical role in deriving findings that could not be obtained in original individual studies or standard aggregate data meta-analyses.

Mediators of Effects on Physical Activity and Sedentary Time in an Activity Tracker and Behavior Change Intervention for Adolescents: Secondary Analysis of a Cluster Randomized Controlled Trial.

BackgroundAdolescence is a critical age where steep declines in physical activity and increases in sedentary time occur. Promoting physical activity should therefore be a priority for short- and long-term health benefits. Wearable activity trackers in combination with supportive resources have the potential to influence adolescents’ physical activity levels and sedentary behavior. Examining the pathways through which such interventions work can inform which mediators to target in future studies.ObjectiveThe aim of this paper is to examine the impact of the Raising Awareness of Physical Activity (RAW-PA) intervention on potential mediators of behavior change after intervention, and whether these mediated the intervention effects on physical activity and sedentary time at 6-month follow-up.MethodsRAW-PA was a 12-week intervention, grounded in social cognitive theory and behavioral choice theory, aimed at increasing physical activity among inactive adolescents through combining a wearable activity tracker with digital resources delivered via a private Facebook group (n=159 complete cases). The targeted potential mediators were identified from previous studies conducted in adolescents and included self-efficacy, peer support, family support, teacher support, self-regulation strategies, barriers, and enjoyment. Outcomes included sedentary time as well as light- and moderate-to-vigorous–intensity physical activity. A series of mixed linear models were used to estimate intervention effects on physical activity and sedentary behavior at follow-up and on potential mediators after intervention and to test whether there were indirect effects of the intervention on physical activity and sedentary behavior via mediators.ResultsAdolescents in the intervention group (n=75) engaged in higher sedentary time and lower light intensity at 6-month follow-up compared to the wait-list controls (n=84). There were no intervention effects for moderate-to-vigorous–intensity physical activity. The intervention group perceived more barriers to physical activity than the wait-list control group at 6-month follow-up (mean adjusted difference=1.77; 95% CI 0.19-3.34; P=.03). However, indirect effects for each outcome were not statistically significant, indicating that perceived barriers to physical activity did not mediate intervention effects for physical activity or sedentary time.ConclusionsRAW-PA did not beneficially impact hypothesized mediators in these inactive adolescents, despite strategies being designed to target them. This suggests that the lack of overall intervention effects on physical activity and sedentary time observed in the RAW-PA study could be due to the limited impact of the intervention on the targeted mediators. Future studies should consider different strategies to target theoretically informed potential mediators and identify intervention strategies that effectively target key mediators to improve physical activity among inactive adolescents. Finally, intervention effects according to level of wearable tracker use or level of engagement with the intervention should be explored. This may provide important insights for designing successful wearable activity tracker interventions.Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12616000899448; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370716&isReview=trueInternational Registered Report Identifier (IRRID)RR2-10.1186/s12889-016-3945-5

Network meta-analysis of randomized controlled trials on esophagectomies in esophageal cancer: The superiority of minimally invasive surgery.

BACKGROUNDPrevious meta-analyses, with many limitations, have described the beneficial nature of minimal invasive procedures.AIMTo compare all modalities of esophagectomies to each other from the results of randomized controlled trials (RCTs) in a network meta-analysis (NMA). METHODSWe conducted a systematic search of the MEDLINE, EMBASE, Reference Citation Analysis (https://www.referencecitationanalysis.com/) and CENTRAL databases to identify RCTs according to the following population, intervention, control, outcome (commonly known as PICO): P: Patients with resectable esophageal cancer; I/C: Transthoracic, transhiatal, minimally invasive (thoracolaparoscopic), hybrid, and robot-assisted esophagectomy; O: Survival, total adverse events, adverse events in subgroups, length of hospital stay, and blood loss. We used the Bayesian approach and the random effects model. We presented the geometry of the network, results with probabilistic statements, estimated intervention effects and their 95% confidence interval (CI), and the surface under the cumulative ranking curve to rank the interventions.RESULTSWe included 11 studies in our analysis. We found a significant difference in postoperative pulmonary infection, which favored the minimally invasive intervention compared to transthoracic surgery (risk ratio 0.49; 95%CI: 0.23 to 0.99). The operation time was significantly shorter for the transhiatal approach compared to transthoracic surgery (mean difference -85 min; 95%CI: -150 to -29), hybrid intervention (mean difference -98 min; 95%CI: -190 to -9.4), minimally invasive technique (mean difference -130 min; 95%CI: -210 to -50), and robot-assisted esophagectomy (mean difference -150 min; 95%CI: -240 to -53). Other comparisons did not yield significant differences.CONCLUSIONBased on our results, the implication of minimally invasive esophagectomy should be favored.

Impact of a Multi-Level, Multi-Component, System Intervention on HPV Vaccination in a Federally Qualified Health Center.

Human papillomavirus (HPV) vaccines can significantly reduce the burden of HPV-associated cancers, but remain underutilized. We evaluated a multi-component, system-level intervention to improve HPV vaccination in a large Federally Qualified Health Center (FQHC) that serves a primarily low income Latino population. From January 2015 through March 2017, we evaluated the effectiveness of a multi-component, system-level intervention to improve HPV vaccination rates in eight clinics randomly assigned to study condition (four intervention, four usual care). The intervention included parent reminders for HPV vaccine series completion, provider training, clinic-level audit and feedback, and workflow modifications to reduce missed opportunities for vaccination. Using a difference-in-differences approach, we compared HPV vaccination rates among patients, ages 11 to 17 during a 12-month preintervention period and a 15-month intervention period. Linear mixed models were used to estimate intervention effects on vaccine initiation and completion. The sample included approximately 15,000 adolescents each quarter (range 14,773-15,571; mean age 14 years; 51% female, 88% Latino). A significantly greater quarterly increase in HPV vaccine initiation was observed for intervention compared with usual care clinics (0.75 percentage point greater increase, P &lt; 0.001), corresponding to 114 additional adolescents vaccinated per quarter. The intervention led to a greater increase in HPV vaccine completion rates among boys (0.65 percentage point greater increase, P &lt; 0.001), but not girls. Our system-level intervention was associated with modest improvements in HPV vaccine initiation overall and completion among boys. Study findings have implications for reducing HPV-related cancers in safety net populations.

Assessment of patient perception of treatment assignment and patient-reported outcomes in a cannabis use disorder trial

ABSTRACT Background: Blinding is a cornerstone of trial methodology. Prior work indicates participant-perceived assignment may be associated with trial outcomes. Less is known about how perception changes over time and if this is associated with outcomes. Objectives: To evaluate if participants change their perception of assignment over time in a blinded trial, and if perception is associated with different types of patient-reported outcomes (PROs). Methods: This was a secondary analysis of data from the Achieving Cannabis Cessation-Evaluating N-Acetylcysteine Treatment (ACCENT) trial, which evaluated the efficacy of N-acetylcysteine (NAC) relative to placebo for treating cannabis use disorder. Participants (N = 234; 164 men, 70 women) were asked at weeks 5 and 9 what treatment (placebo or NAC) they believed they were receiving. We included PROs proximal (cannabis-associated problems, craving) and distal (anxiety) to the intervention. Analysis was by multiple linear regression and mixed models. Results: Approximately 20% of participants in both arms changed their perception over time. Relative to participants who consistently perceived assignment to placebo, participants who consistently perceived assignment to NAC did not always have comparatively better average scores (coefficient −3.3 [95% CI: −7.0, 0.5]). In some analyses, participants who switched to guessing NAC from placebo had comparatively better average scores (coefficient −3.0 [95% CI: −9.3, 3.4]), but this was inconsistent across outcomes or strata defined by actual assignment or guess accuracy. Conclusion: The study suggests that the proportion of individuals who switch their perception over time is modest. However, this group may influence the estimates of intervention effects on some PROs.

Effectiveness and Implementation Outcome Measures of Mental Health Curriculum Intervention Using Social Media to Improve the Mental Health Literacy of Adolescents.

PurposeGlobally, adolescent mental health problems reportedly manifest more severely in individuals with lower mental health literacy. Mental health curriculum interventions using social media have been associated with positive implementation outcomes. This study aimed to investigate whether such an intervention significantly improves adolescent mental health literacy and is associated with positive implementation outcome measures.MethodsThis study employed a quasi-experimental design, comparing the before-and-after difference between the intervention group (n1=77) and the control group (n1=76). A mental health literacy module was delivered using social media. Data were collected using validated questionnaires. Effect size and difference-in-differences model calculations (and associated 95% confidence intervals, CI) were conducted alongside other descriptive analyses.ResultsThe estimated intervention effect size and difference-in-differences estimates were greater than zero. The effect was greater in the intervention than in the control group (p<0.05) despite sex and age differences. The effect size estimate provided by Cohen’s d and Hedges’ g values was medium-to-large (d/g=0.429–0.767, p<0.05). The difference-in-differences reflected a significant effect (DID=0.348, CI: 0.154–0.542, p<0.001) in increasing mental health literacy despite differences associated with sex, age, and school grade. The intervention program was acceptable, appropriate, feasible, and satisfactory, and more than 80% of participants said they agree to completely agree with these implementation outcomes. Resources and personal and family-related factors were among the perceived influencing factors determining the effectiveness and implementation outcome measures.ConclusionThe intervention program effectively improved the mental health literacy of adolescents. It has been highlighted that social media interventions for mental health promotion could be practical and scalable; however, there is a need to take into account socio-demographic differences and barriers to inclusion/compliance.

Comparison of statistical models for estimating intervention effects based on time-to-recurrent-event in stepped wedge cluster randomized trial using open cohort design

BackgroundThere 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.MethodsUsing 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.ResultsUsing 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.ConclusionsWhen 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.