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Background: Synthesized evidence of analgesic effects of breastfeeding, skin-to-skin care, and sucrose for newborns undergoing painful procedures exists; however, these interventions are inconsistently used in practice. Although a parent-targeted video demonstrating these interventions is publicly accessible, its implementation has not been rigorously evaluated. Purpose: To evaluate clinical nurses' and nurse unit managers’ perceptions of the video implementation which portrays three pain-reducing interventions, and barriers and facilitators to implementing the video and interventions in practice. Methods: This study was part of a larger multisite s study in eight maternal-newborn centres in Ontario, Canada. This current study used online cross-sectional surveys with open and closed-ended questions administered following a 6-month intervention period where the parent-targeted video was made available for staff to share with parents before blood sampling for newborn screening. Two surveys were used: one for clinical nurses and one for nurse unit managers from participating maternal/newborn units. Participants provided their perceptions of the video's implementation and barriers and facilitators of using the pain management strategies. Descriptive statistics were used for quantitative data, and reflexive content analysis was applied to qualitative data. Results: Thirty of 205 (15 %) eligible nurses and nine unit managers from the eight participating sites completed the surveys. Eighteen (60 %) nurses and five managers (55 %) perceived the video was offered to less than half of the parents, highlighting the video was not implemented into their units as planned. Key barriers reported by both groups were being too busy to show the video with parents in the post-natal period and a preference for not involving parents in pain treatments. Facilitators included working towards Baby Friendly Hospital Initiatives endorsement and single-room units. Implications: Increasing pare

Background: Health behaviours such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease. Better understanding which behaviours tend to co-occur (i.e., cluster together) and co-vary (i.e., are correlated) may provide novel opportunities to develop more comprehensive interventions to promote multiple health behaviour change. However, whether co-occurrence or co-variation based approaches are better suited for this task remains relatively unknown. Purpose: To compare the utility of co-occurrence vs co-variation based approaches for understanding the interconnectedness between multiple health impacting behaviours. Methods: Using baseline and follow-up data (N=40,268) from the Canadian Longitudinal Study of Aging, we examined the co-occurrence and co-variation of health behaviours. We used cluster analysis to group individuals based on their behavioural tendencies across multiple behaviours and to examine how these clusters are associated with demographic characteristics and health indicators. We compared outputs from cluster analysis to behavioural correlations and compared regression analyses of clusters and individual behaviours predicting future health outcomes. Results: Seven clusters were identified, with clusters differentiated by six of the seven health behaviours included in the analysis. Sociodemographic characteristics varied across several clusters. Correlations between behaviours were generally small. In regression analyses individual behaviours accounted for more variance in health outcomes than clusters.

Background: Health behaviours such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease. Better understanding which behaviours tend to co-occur (i.e., cluster together) and co-vary (i.e., are correlated) may provide novel opportunities to develop more comprehensive interventions to promote multiple health behaviour change. However, whether co-occurrence or co-variation based approaches are better suited for this task remains relatively unknown. Purpose: To compare the utility of co-occurrence vs co-variation based approaches for understanding the interconnectedness between multiple health impacting behaviours. Methods: Using baseline and follow-up data (N=40,268) from the Canadian Longitudinal Study of Aging, we examined the co-occurrence and co-variation of health behaviours. We used cluster analysis to group individuals based on their behavioural tendencies across multiple behaviours and to examine how these clusters are associated with demographic characteristics and health indicators. We compared outputs from cluster analysis to behavioural correlations and compared regression analyses of clusters and individual behaviours predicting future health outcomes. Results: Seven clusters were identified, with clusters differentiated by six of the seven health behaviours included in the analysis. Sociodemographic characteristics varied across several clusters. Correlations between behaviours were generally small. In regression analyses individual behaviours accounted for more variance in health outcomes than clusters.

OBJECTIVES: Interrupted Time Series (ITS) are a type of nonrandomized design commonly used to evaluate public health policy interventions, and the impact of exposures, at the population level. Meta-analysis may be used to combine results from ITS across studies (in the context of systematic reviews) or across sites within the same study. We aimed to examine the statistical approaches, methods, and completeness of reporting in reviews that meta-analyze results from ITS. STUDY DESIGN AND SETTINGS: Eight electronic databases were searched to identify reviews (published 2000-2019) that meta-analyzed at least two ITS. Characteristics of the included reviews, the statistical methods used to analyze the ITS and meta-analyze their results, effect measures, and risk of bias assessment tools were extracted. RESULTS: Of the 4213 identified records, 54 reviews were included. Nearly all reviews (94%) used two-stage meta-analysis, most commonly fitting a random effects model (69%). Among the 41 reviews that re-analyzed the ITS, linear regression (39%) and ARIMA (20%) were most commonly used; 38% adjusted for autocorrelation. The most common effect measure meta-analyzed was an immediate level-change (46/54). Reporting of the statistical methods and ITS characteristics was often incomplete. CONCLUSION: Improvement is needed in the conduct and reporting of reviews that meta-analyze results from ITS.

Contains fulltext : 283114.pdf (Publisher’s version ) (Open Access), INTRODUCTION: Children with inherited metabolic diseases (IMDs) often have complex and intensive healthcare needs and their families face challenges in receiving high-quality, family centred health services. Improvement in care requires complex interventions involving multiple components and stakeholders, customised to specific care contexts. This study aims to comprehensively understand the healthcare experiences of children with IMDs and their families across Canada. METHODS AND ANALYSIS: A two-stage explanatory sequential mixed methods design will be used. Stage 1: quantitative data on healthcare networks and encounter experiences will be collected from 100 parent/guardians through a care map, 2 baseline questionnaires and 17 weekly diaries over 5-7 months. Care networks will be analysed using social network analysis. Relationships between demographic or clinical variables and ratings of healthcare experiences across a range of family centred care dimensions will be analysed using generalised linear regression. Other quantitative data related to family experiences and healthcare experiences will be summarised descriptively. Ongoing analysis of quantitative data and purposive, maximum variation sampling will inform sample selection for stage 2: a subset of stage 1 participants will participate in one-on-one videoconference interviews to elaborate on the quantitative data regarding care networks and healthcare experiences. Interview data will be analysed thematically. Qualitative and quantitative data will be merged during analysis to arrive at an enhanced understanding of care experiences. Quantitative and qualitative data will be combined and presented narratively using a weaving approach (jointly on a theme-by-theme basis) and visually in a side-by-side joint display. ETHICS AND DISSEMINATION: The study protocol and procedures were approved by the Children's Hospital of Eastern Ontario's Research Ethics Board, the University of Ottawa Research Ethics Board and t

INTRODUCTION: Interrupted Time Series (ITS) studies may be used to assess the impact of an interruption, such as an intervention or exposure. The data from such studies are particularly amenable to visual display and, when clearly depicted, can readily show the short- and long-term impact of an interruption. Further, well-constructed graphs allow data to be extracted using digitizing software, which can facilitate their inclusion in systematic reviews and meta-analyses. AIM: We provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. METHODS AND RESULTS: Graphing recommendations from seminal data visualization resources were adapted for use with ITS studies. The adapted recommendations cover plotting of data points, trend lines, interruptions, additional lines and general graph components. We assessed whether 217 graphs from recently published (2013-2017) ITS studies met our recommendations and found that 130 graphs (60%) had clearly distinct data points, 100 (46%) had trend lines, and 161 (74%) had a clearly defined interruption. Accurate data extraction (requiring distinct points that align with axis tick marks and labels that allow the points to be interpreted) was possible in only 72 (33%) graphs. CONCLUSION: We found that many ITS graphs did not meet our recommendations and could be improved with simple changes. Our proposed recommendations aim to achieve greater standardization and improvement in the display of ITS data, and facilitate re-use of the data in systematic reviews and meta-analyses.

BACKGROUND: Interrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. However, examination of the performance of statistical methods for this design has received relatively little attention. METHODS: We simulated continuous data to compare the performance of a set of statistical methods under a range of scenarios which included different level and slope changes, varying lengths of series and magnitudes of lag-1 autocorrelation. We also examined the performance of the Durbin-Watson (DW) test for detecting autocorrelation. RESULTS: All methods yielded unbiased estimates of the level and slope changes over all scenarios. The magnitude of autocorrelation was underestimated by all methods, however, restricted maximum likelihood (REML) yielded the least biased estimates. Underestimation of autocorrelation led to standard errors that were too small and coverage less than the nominal 95%. All methods performed better with longer time series, except for ordinary least squares (OLS) in the presence of autocorrelation and Newey-West for high values of autocorrelation. The DW test for the presence of autocorrelation performed poorly except for long series and large autocorrelation. CONCLUSIONS: From the methods evaluated, OLS was the preferred method in series with fewer than 12 points, while in longer series, REML was preferred. The DW test should not be relied upon to detect autocorrelation, except when the series is long. Care is needed when interpreting results from all methods, given confidence intervals will generally be too narrow. Further research is required to develop better performing methods for ITS, especially for short series.

BACKGROUND: The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets. METHODS: A random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods. RESULTS: From the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4 to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series. CONCLUSIONS: The choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.

Background: Systematic reviews are used to inform healthcare decision making. In reviews that aim to examine the effects of organisational, policy change or public health interventions, or exposures, evidence from interrupted time series (ITS) studies may be included. A core component of many systematic reviews is meta-analysis, which is the statistical synthesis of results across studies. There is currently a lack of guidance informing the choice of meta-analysis methods for combining results from ITS studies, and there have been no studies examining the meta-analysis methods used in practice. This study therefore aims to describe current meta-analysis methods used in a cohort of reviews of ITS studies. Methods: We will identify the 100 most recent reviews (published between 1 January 2000 and 11 October 2019) that include meta-analyses of ITS studies from a search of eight electronic databases covering several disciplines (public health, psychology, education, economics). Study selection will be undertaken independently by two authors. Data extraction will be undertaken by one author, and for a random sample of the reviews, two authors. From eligible reviews we will extract details at the review level including discipline, type of interruption and any tools used to assess the risk of bias / methodological quality of included ITS studies; at the meta-analytic level we will extract type of outcome, effect measure(s), meta-analytic methods, and any methods used to re-analyse the individual ITS studies. Descriptive statistics will be used to summarise the data. Conclusions: This review will describe the methods used to meta-analyse results from ITS studies. Results from this review will inform future methods research examining how different meta-analysis methods perform, and ultimately, the development of guidance.

Item does not contain fulltext, OBJECTIVES: The Consolidated Standards of Reporting Trials extension for the stepped-wedge cluster randomized trial (SW-CRT) is a recently published reporting guideline for SW-CRTs. We assess the quality of reporting of a recent sample of SW-CRTs. STUDY DESIGN AND SETTING: Quality of reporting was asssessed according to the 26 items in the new guideline using a novel crowd sourcing methodology conducted independently and in duplicate, with random assignment, by 50 reviewers. We assessed reliability of the quality assessments, proposing this as a novel way to assess robustness of items in reporting guidelines. RESULTS: Several items were well reported. Some items were very poorly reported, including several items that have unique requirements for the SW-CRT, such as the rationale for use of the design, description of the design, identification and recruitment of participants within clusters, and concealment of cluster allocation (not reported in more than 50% of the reports). Agreement across items was moderate (median percentage agreement was 76% [IQR 64 to 86]). Agreement was low for several items including the description of the trial design and why trial ended or stopped for example. CONCLUSIONS: When reporting SW-CRTs, authors should pay particular attention to ensure clear reporting on the exact format of the design with justification, as well as how clusters and individuals were identified for inclusion in the study, and whether this was done before or after randomization of the clusters, which are crucial for risk of bias assessments. Some items, including why the trial ended, might either not be relevant to SW-CRTs or might be unclearly described in the statement.

INTRODUCTION: An interrupted time series (ITS) design is an important observational design used to examine the effects of an intervention or exposure. This design has particular utility in public health where it may be impracticable or infeasible to use a randomised trial to evaluate health system-wide policies, or examine the impact of exposures (such as earthquakes). There have been relatively few studies examining the design characteristics and statistical methods used to analyse ITS designs. Further, there is a lack of guidance to inform the design and analysis of ITS studies.This is the first study in a larger project that aims to provide tools and guidance for researchers in the design and analysis of ITS studies. The objectives of this study are to (1) examine and report the design characteristics and statistical methods used in a random sample of contemporary ITS studies examining public health interventions or exposures that impact on health-related outcomes, and (2) create a repository of time series data extracted from ITS studies. Results from this study will inform the remainder of the project which will investigate the performance of a range of commonly used statistical methods, and create a repository of input parameters required for sample size calculation. METHODS AND ANALYSIS: We will collate 200 ITS studies evaluating public health interventions or the impact of exposures. ITS studies will be identified from a search of the bibliometric database PubMed between the years 2013 and 2017, combined with stratified random sampling. From eligible studies, we will extract study characteristics, details of the statistical models and estimation methods, effect metrics and parameter estimates. Further, we will extract the time series data when available. We will use systematic review methods in the screening, application of inclusion and exclusion criteria, and extraction of data. Descriptive statistics will be used to summarise the data. ETHICS AND DISSEMIN

Audit and feedback (A&F) is a commonly used quality improvement (QI) approach. A Cochrane review indicates that A&F is generally effective and leads to modest improvements in professional practice but with considerable variation in the observed effects. While we have some understanding of factors that enhance the effects of A&F, further research needs to explore when A&F is most likely to be effective and how to optimise it. To do this, we need to move away from two-arm trials of A&F compared with control in favour of head-to-head trials of different ways of providing A&F. This paper describes implementation laboratories involving collaborations between healthcare organisations providing A&F at scale, and researchers, to embed head-to-head trials into routine QI programmes. This can improve effectiveness while producing generalisable knowledge about how to optimise A&F. We also describe an international meta-laboratory that aims to maximise cross-laboratory learning and facilitate coordination of A&F research.