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Background Randomised controlled trials (RCTs) are considered the gold standard when evaluating the causal effects of healthcare interventions. When RCTs cannot be used (e.g. ethically difficult), the interrupted time series (ITS) design is a possible alternative. ITS is one of the strongest quasi-experimental designs. The aim of this methodological study was to describe how ITS designs were being used, the design characteristics, and reporting in the healthcare setting. Methods We searched MEDLINE for reports of ITS designs published in 2015 which had a minimum of two data points collected pre-intervention and one post-intervention. There was no restriction on participants, language of study, or type of outcome. Data were summarised using appropriate summary statistics. Results One hundred and sixteen studies were included in the study. Interventions evaluated were mainly programs 41 (35%) and policies 32 (28%). Data were usually collected at monthly intervals, 74 (64%). Of the 115 studies that reported an analysis, the most common method was segmented regression (78%), 55% considered autocorrelation, and only seven reported a sample size calculation. Estimation of intervention effects were reported as change in slope (84%) and change in level (70%) and 21% reported long-term change in levels. Conclusions This methodological study identified problems in the reporting of design features and results of ITS studies, and highlights the need for future work in the development of formal reporting guidelines and methodological work.

In response to public concerns and campaigns, some United Kingdom supermarkets have implemented policies to reduce less-healthy food at checkouts. We explored the effects of these policies on purchases of less-healthy foods commonly displayed at checkouts. We used a natural experimental design and two data sources providing complementary and unique information. We analysed data on purchases of small packages of common, less-healthy, checkout foods (sugary confectionary, chocolate, and potato crisps) from 2013 to 2017 from nine UK supermarkets (Aldi, Asda, Co-op, Lidl, M&S, Morrisons, Sainsbury's, Tesco, and Waitrose). Six supermarkets implemented a checkout food policy between 2013 and 2017 and were considered intervention stores; the remainder were comparators. Firstly, we studied the longitudinal association between implementation of checkout policies and purchases taken home. We used data from a large (n ≈ 30,000) household purchase panel of food brought home to conduct controlled interrupted time series analyses of purchases of less-healthy common checkout foods from 12 months before to 12 months after implementation. We conducted separate analyses for each intervention supermarket, using others as comparators. We synthesised results across supermarkets using random effects meta-analyses. Implementation of a checkout food policy was associated with an immediate reduction in four-weekly purchases of common checkout foods of 157,000 (72,700-242,800) packages per percentage market share-equivalent to a 17.3% reduction. This decrease was sustained at 1 year with 185,100 (121,700-248,500) fewer packages purchased per 4 weeks per percentage market share-equivalent to a 15.5% reduction. The immediate, but not sustained, effect was robust to sensitivity analysis. Secondly, we studied the cross-sectional association between checkout food policies and purchases eaten without being taken home. We used data from a smaller (n ≈ 7,500) individual purchase panel of food bought and eaten 'on the go'. We conducted cross-sectional analyses comparing purchases of common checkout foods in 2016-2017 from supermarkets with and without checkout food policies. There were 76.4% (95% confidence interval 48.6%-89.1%) fewer annual purchases of less-healthy common checkout foods from supermarkets with versus without checkout food policies. The main limitations of the study are that we do not know where in the store purchases were selected and cannot determine the effect of changes in purchases on consumption. Other interventions may also have been responsible for the results seen. There is a potential impact of checkout food polices on purchases. Voluntary supermarket-led activities may have public health benefits.

We evaluated the impact of the pharmacist-led Safety Medication dASHboard (SMASH) intervention on medication safety in primary care. SMASH comprised (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedback; and (3) pharmacists reviewing individual at-risk patients, and initiating remedial actions or advising general practitioners on doing so. It was implemented in 43 general practices covering a population of 235,595 people in Salford (Greater Manchester), UK. All practices started receiving the intervention between 18 April 2016 and 26 September 2017. We used an interrupted time series analysis of rates (prevalence) of potentially hazardous prescribing and inadequate blood-test monitoring, comparing observed rates post-intervention to extrapolations from a 24-month pre-intervention trend. The number of people registered to participating practices and having 1 or more risk factors for being exposed to hazardous prescribing or inadequate blood-test monitoring at the start of the intervention was 47,413 (males: 23,073 [48.7%]; mean age: 60 years [standard deviation: 21]). At baseline, 95% of practices had rates of potentially hazardous prescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% CI 20.3% to 36.8%, p < 0.001) at 24 weeks and by 40.7% (95% CI 29.1% to 54.2%, p < 0.001) at 12 months after introduction of SMASH. The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI 0.2% to 50.7%, p = 0.046) at 24 weeks; the change at 12 months (23.5%) was no longer significant (95% CI -4.5% to 61.6%, p = 0.127). After 12 months, 95% of practices had rates of potentially hazardous prescribing between 0.74% and 3.02%. Study limitations include the fact that practices were not randomised, and therefore unmeasured confounding may have influenced our findings. The SMASH intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices. This reduction was sustained over 12 months after the start of the intervention for prescribing but not for monitoring of medication. There was a marked reduction in the variation in rates of hazardous prescribing between practices.

Long-acting reversible contraception (LARC) is among the most effective contraceptive methods, but uptake remains low even in high-income settings. In 2009/2010, a target-based pay-for-performance (P4P) scheme in Britain was introduced for primary care physicians (PCPs) to offer advice about LARC methods to a specified proportion of women attending for contraceptive care to improve contraceptive choice. We examined the impact and equity of this scheme on LARC uptake and abortions. We examined records of 3,281,667 women aged 13 to 54 years registered with a primary care clinic in Britain (England, Wales, and Scotland) using Clinical Practice Research Datalink (CPRD) from 2004/2005 to 2013/2014. We used interrupted time series (ITS) analysis to examine trends in annual LARC and non-LARC hormonal contraception (NLHC) uptake and abortion rates, stratified by age and deprivation groups, before and after the P4P was introduced in 2009/2010. Between 2004/2005 and 2013/2014, crude LARC uptake rates increased by 32.0% from 29.6 per 1,000 women to 39.0 per 1,000 women, compared with 18.0% decrease in NLHC uptake. LARC uptake among women of all ages increased immediately after the P4P with step change of 5.36 per 1,000 women (all values are per 1,000 women unless stated, 95% CI 5.26-5.45, p < 0.001). Women aged 20 to 24 years had the largest step change (8.40, 8.34-8.47, p < 0.001) and sustained trend increase (3.14, 3.08-3.19, p < 0.001) compared with other age groups. NLHC uptake fell in all women with a step change of -22.8 (-24.5 to -21.2, p < 0.001), largely due to fall in combined hormonal contraception (CHC; -15.0, -15.5 to -14.5, p < 0.001). Abortion rates in all women fell immediately after the P4P with a step change of -2.28 (-2.98 to -1.57, p = 0.002) and sustained decrease in trend of -0.88 (-1.12 to -0.63, p < 0.001). The largest falls occurred in women aged 13 to 19 years (step change -5.04, -7.56 to -2.51, p = 0.011), women aged 20 to 24 years (step change -4.52, -7.48 to -1.57, p = 0.030), and women from the most deprived group (step change -4.40, -6.89 to -1.91, p = 0.018). We estimate that by 2013/2014, the P4P scheme resulted in an additional 4.53 LARC prescriptions per 1,000 women (relative increase of 13.4%) more than would have been expected without the scheme. There was a concurrent absolute reduction of -5.31 abortions per 1,000 women, or -38.3% relative reduction. Despite universal coverage of healthcare, some women might have obtained contraception elsewhere or had abortion procedure that was not recorded on CPRD. Other policies aiming to increase LARC use or reduce unplanned pregnancies around the same time could also explain the findings. In this study, we found that LARC uptake increased and abortions fell in the period after the P4P scheme in British primary care, with additional impact for young women aged 20-24 years and those from deprived backgrounds.

Background Cardiovascular disease (CVD) is the main cause of morbidity and mortality in Sweden. This study aims to assess the impact of a CVD intervention implemented in 1993 in northern Sweden on the reduction of premature ischemic heart disease (IHD) morbidity and mortality in women and men during the period 1987–2013. Methods An ecological controlled interrupted time series design, with pre-intervention period defined as 1987–1993 and post-intervention period 1994–2013 was carried out. For each year, IHD events, stratified by sex, were retrieved from national registers. Results Impressive reductions on IHD premature morbidity and mortality were observed to a similar degree in both the intervention county and the other comparison counties across the last 27 years. Significant differences in the pre-post intervention trends indicating the intervention group had smaller reductions than expected from its pre-intervention trend and the trend of control counties were found among men for both IHD morbidity and mortality. A similar pattern was observed among women but without significant differences. Conclusions Taken together, the data do not support that the intervention has contributed to an additional reduction on IHD morbidity and mortality, above and beyond that which is already seen in neighbouring counties without similar programs.

Interrupted time series (ITS) is a powerful and increasingly popular design for evaluating public health and health service interventions. The design involves analyzing trends in the outcome of interest and estimating the change in trend following an intervention relative to the counterfactual (the expected ongoing trend if the intervention had not occurred). There are two key components to modeling this effect: first, defining the counterfactual; second, defining the type of effect that the intervention is expected to have on the outcome, known as the impact model. The counterfactual is defined by extrapolating the underlying trends observed before the intervention to the postintervention period. In doing this, authors must consider the preintervention period that will be included, any time-varying confounders, whether trends may vary within different subgroups of the population and whether trends are linear or nonlinear. Defining the impact model involves specifying the parameters that model the intervention, including for instance whether to allow for an abrupt level change or a gradual slope change, whether to allow for a lag before any effect on the outcome, whether to allow a transition period during which the intervention is being implemented, and whether a ceiling or floor effect might be expected. Inappropriate model specification can bias the results of an ITS analysis and using a model that is not closely tailored to the intervention or testing multiple models increases the risk of false positives being detected. It is important that authors use substantive knowledge to customize their ITS model a priori to the intervention and outcome under study. Where there is uncertainty in model specification, authors should consider using separate data sources to define the intervention, running limited sensitivity analyses or undertaking initial exploratory studies.

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 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.

Worldwide nearly 3 million people die from chronic obstructive pulmonary disease (COPD) every year. Integrated disease management (IDM) improves quality of life for COPD patients and can reduce hospitalization. Self-management of COPD through eHealth is an effective method to improve IDM and clinical outcomes. The objective of this implementation study was to investigate the effect of 3 chronic obstructive pulmonary disease eHealth programs applied in primary care on health status. The e-Vita COPD study compares different levels of integration of Web-based self-management platforms in IDM in 3 primary care settings. Patient health status is examined using the Clinical COPD Questionnaire (CCQ). The parallel cohort design includes 3 levels of integration in IDM (groups 1, 2, 3) and randomization of 2 levels of personal assistance for patients (group A, high assistance, group B, low assistance). Interrupted time series (ITS) design was used to collect CCQ data at multiple time points before and after intervention, and multilevel linear regression modeling was used to analyze CCQ data. Of the 702 invited patients, 215 (30.6%) registered to a platform. Of these, 82 participated in group 1 (high integration IDM), 36 in group 1A (high assistance), and 46 in group 1B (low assistance); 96 participated in group 2 (medium integration IDM), 44 in group 2A (high assistance) and 52 in group 2B (low assistance); also, 37 participated in group 3 (no integration IDM). In the total group, no significant difference was found in change in CCQ trend (P=.334) before (-0.47% per month) and after the intervention (-0.084% per month). Also, no significant difference was found in CCQ changes before versus after the intervention between the groups with high versus low personal assistance. In all subgroups, there was no significant change in the CCQ trend before and after the intervention (group 1A, P=.237; 1B, P=.991; 2A, P=.120; 2B, P=.166; 3, P=.945). The e-Vita eHealth-supported COPD programs had no beneficial impact on the health status of COPD patients. Also, no differences were found between the patient groups receiving different levels of personal assistance. Netherlands Trial Registry NTR4098; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4098 (Archived by WebCite at http://www.webcitation.org/6sbM5PayG).

To assess and compare the immediate and long-term change in reporting quality of randomized controlled trial (RCT) abstracts published in Pediatrics, The Journal of Pediatrics, and JAMA Pediatrics before and after the publication of Consolidated Standards of Reporting Trial (CONSORT)-abstract statement. Study had “Interrupted time-series” design. Eligible RCT abstracts were retrieved by PubMed search in two study periods from January 2003 to December 2007 (pre-CONSORT) and January 2010 to December 2014 (post-CONSORT). These abstracts were matched with the CONSORT checklist for abstracts. The primary outcome measure was CONSORT-abstract score defined as number of CONSORT items correctly reported divided by 18 and expressed as percentage. The mean percentage scores were used to compare reporting quality between pre- and post-CONSORT using segmented linear regression. A total of 424 RCT abstracts in pre-CONSORT and 467 in post-CONSORT were analyzed. A significant change in slope of regression line between two time periods (0.151 [confidence interval CI, 0.004–0.298], P = 0.044) was observed. Intercepts did not show a significant difference (−2.39 [CI, 4.93–0.157], P = 0.065). The overall reporting quality of RCT abstracts in the high-impact pediatrics journals was suboptimal (<50%); however, it improved when assessed over a 5-year period, implying slow but gradual adoption of guideline.