Impact of a six-month yoga intervention on cognitive performance among desk-based workers: An interrupted time-series design

BackgroundVarious interacting and interdependent components comprise complex interventions. These components create difficulty in assessing the true impact of interventions designed to improve patient-centered outcomes. Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. While ITS designs are aptly situated for studying the impacts of large-scale public health policies, existing ITS software implement rigid ITS methodology that often assume the pre- and post-intervention phases are fully differentiated (by a known change-point or set of time points) and do not allow for changes in both the mean functions and correlation structure.ResultsThis article describes the Robust Interrupted Time Series (RITS) toolbox, a stand-alone user-friendly application researchers can use to implement flexible ITS models that estimate the lagged effect of an intervention on an outcome, level and trend changes, and post-intervention changes in the correlation structure, for single and multiple ITS. The RITS toolbox incorporates a formal test for the existence of a change in the outcome and estimates a change-point over a set of possible change-points defined by the researcher. In settings with multiple ITS, RITS provides a global over-all units change-point and allows for unit-specific changes in the mean functions and correlation structures.ConclusionsThe RITS toolbox is the first piece of software that allows researchers to use flexible ITS models that test for the existence of a change-point, estimate the change-point (if estimation is desired), and allow for changes in both the mean functions and correlation structures at the change point. RITS does not require any knowledge of a statistical (or otherwise) programming language, is freely available to the community, and may be downloaded and used on a local machine to ensure data protection.

13 - Time-series Designs and Analyses

Objectives The rapid increase both in daily cases and daily deaths made the second wave of COVID-19 pandemic in India more lethal than the first wave. Record number of infections and casualties were reported all over India during this period. Delhi and Maharashtra are the two most affected places in India during the second wave. So in response to this, the Indian government implemented strict intervention policies (“lockdowns”, “social distancing” and “vaccination drive”) in every state during this period to prohibit the spread of this virus. The objective of this article is to conduct an interrupted time series (ITS) analysis to study the impact of the interventions on the daily cases and deaths. Methods We collect daily data for Delhi and Maharashtra before and after the intervention points with a 14-day (incubation period of COVID-19) observation window. A segmented linear regression analysis is done to study the post-intervention slopes as well as whether there were any immediate changes after the interventions or not. We also add the counterfactuals and delayed time effects in the analysis to investigate the significance of our ITS design. Results Here, we observe the post-intervention trends to be statistically significant and negative for both the daily cases and the daily deaths. We also find that there is no immediate change in trend after the start of intervention, and hence we study some delayed time effects which display how changes in the trends happened over time. And from the Counterfactuals in our study, we can have an idea what would have happened to the COVID scenario had the interventions not been implemented. Conclusions We statistically try to figure out different circumstances of COVID scenario for both Delhi and Maharashtra by exploring all possible ingredients of ITS design in our analysis in order to present a feasible design to show the importance of implementation of proper intervention policies for tackling this type of pandemic which can have various highly contagious variants.

ObjectivesTo evaluate the impact of pay-for-performance on antimicrobial consumption and antimicrobial expenditure in a large teaching hospital in Guangzhou, China. MethodsWe collected data from hospital information system from January 2018 through September 2022 in the inpatient wards. Antimicrobial consumption was evaluated using antibiotic use density (AUD) and antibiotic use rate (AUR). The economic impact of intervention was assessed by antimicrobial expenditure percentage. The data was analyzed using interrupted time series (ITS) analysis. ResultsFollowing the implementation of the intervention, immediate decreases in the level of AUD were observed in Department of Hematology Unit 3 (β = −66.93 DDDs/100PD, P = 0.002), Urology (β = −32.80 DDDs/100PD, P < 0.001), Gastrointestinal Surgery Unit 3 (β = −11.44 DDDs/100PD, P = 0.03), Cardiac Surgery (β = −14.30 DDDs/100PD, P = 0.01), ICU, Unit 2 (β = −81.91 DDDs/100PD, P = 0.02) and Cardiothoracic Surgery ICU (β = −41.52 DDDs/100PD, P = 0.05). Long-term downward trends in AUD were also identified in Organ Transplant Unit (β = −1.64 DDDs/100PD, P = 0.02). However, only Urology (β = −6.56 DDDs/100PD, P = 0.02) and Gastrointestinal Surgery Unit 3 (β = −8.50 %, P = 0.01) showed an immediate decrease in AUR, and long-term downward trends in AUR were observed in Pediatric ICU (β = −1.88 %, P = 0.05) and ICU Unit 1 (β = −0.55 %, P = 0.02). ConclusionThis study demonstrates that the adoption of pay-for-performance effectively reduces antibiotic consumption in specific departments of a hospital in Guangzhou in the short term. However, it is important to recognize that the long-term impact of such interventions is often limited. Additionally, it should be noted that the overall effectiveness of the intervention across the entire hospital was not significant.

Health promotion is essential for influencing behaviour change to prevent birth defects and achieve the Sustainable Development Goal of reducing neonatal and under-five mortality by 2030. Public broadcasting has the potential to deliver health messages widely, particularly in underserved communities, but evidence from interrupted time series (ITS) studies remains limited. This study aims to evaluate the impact of public broadcast interventions on knowledge of preconception folic acid intake for birth defect prevention in Malaysian using an interrupted time series (ITS) design. This study used an ITS design. Data were collected fortnightly at six time points over 12 weeks from 2,832 adults aged 18–64 years, recruited via convenience sampling. Participants were equally divided between an intervention group in Kelantan (n = 1,416), where targeted radio and television messages were broadcast, and a control group in Terengganu (n = 1,416), which received no intervention. Knowledge was measured using a standardised questionnaire. Segmented regression analysis showed a descriptive increase in mean knowledge scores post-intervention in the intervention group. However, no statistically significant changes were observed in trend (slope change = 0.0006) or level (intercept change (&lt;–0.01) between pre- and post-intervention phases. While statistical significance was not achieved, the findings indicate that public broadcasting is a promising medium for large-scale health promotion, capable of reaching broad audiences and addressing knowledge gaps. These results provide baseline evidence for designing future national-level broadcast interventions, which may require longer exposure periods or intensified messaging to achieve significant and sustained improvements in public health knowledge.

Assessing the impact of complex interventions on measurable health outcomes is a growing concern in health care and health policy. Interrupted time series (ITS) designs borrow from traditional case-crossover designs and function as quasi-experimental methodology able to retrospectively analyze the impact of an intervention. Statistical models used to analyze ITS designs primarily focus on continuous-valued outcomes. We propose the "Generalized Robust ITS" (GRITS) model appropriate for outcomes whose underlying distribution belongs to the exponential family of distributions, thereby expanding the available methodology to adequately model binary and count responses. GRITS formally implements a test for the existence of a change point in discrete ITS. The methodology proposed is able to test for the existence of and estimate the change point, borrow information across units in multi-unit settings, and test for differences in the mean function and correlation pre- and post-intervention. The methodology is illustrated by analyzing patient falls from a hospital that implemented and evaluated a new care delivery model in multiple units.

ObjectivesInterrupted time series (ITS) design involves collecting data across multiple time points before and after the implementation of an intervention to assess the effect of the intervention on an outcome....

The need for drug-related safety warnings is undisputed, and their impact should also be evaluated. This systematic review investigates and assesses the impact of safety warnings on drug therapy. Studies published in English between January 1998 and December 2018 were searched in EMBASE and MEDLINE, complemented by manual search. Randomised controlled trials, cohort studies with a before/after component, and case-control studies were included, selected to predefined criteria, and assessed for their reporting and methodological quality. Out of 7454 references identified, 72 studies were included. A total of 28/72 (39%) studies described the impact of safety warnings on drug therapy as being effective, whereas 12/72 (17%) studies did not. Further, 26/72 (36%) studies described a partial implementation of the warnings (one part of the warning had an impact on drug therapy and another did not). Unintended effects were investigated in 6/72 (8%) studies. While 34 (47%) studies examined safety warnings on psychotropic drugs using an interrupted time series (ITS) design (53%), a before/after (26%), and a time series design (21%), 38 (53%) studied other substances using an ITS design (34%), a before/after (40%), and a time series design (26%). The proportion of an effective impact on drug therapy was lower in the "psychotropic drugs" group (23%) than in the "others" group (53%). Drug-related safety warnings induce intended and unintended effects. The included studies are of broadly varying methodological quality. To better compare their effectiveness, studies should be conducted using standardised procedures.

COVID-19 related non-pharmaceutical interventions (NPIs) disrupted global healthcare utilisation, with notable declines in infection related paediatric hospitalisations. We aimed to identify non-infectious paediatric conditions for which the incidence of hospital admissions increased during the introduction and alleviation of NPIs in 2020. We examined anonymous hospitalisation data from Perth's sole tertiary paediatric hospital (Jan 2015-Dec 2020), according to pre-defined age groups (0-4 years, 5-9 years, 10-16 years and 0-16). We identified the most frequent non-infectious diagnoses in these age groups over five different NPI periods. We quantified the impact of NPIs on admissions for the most frequent diagnosis groups (perinatal disorders, mental disorders) using interrupted time series (ITS) analysis. Following implementation of NPIs, admission rates for perinatal disorders amongst 0-4 year-olds increased by 18% (IRR = 1.18 [95% CI: 1.04-1.35]). ITS analysis revealed non-significant changes in admissions for mental disorders in 10-16 year-olds (IRR = 0.91 [95% CI: 0.79-1.06]). The incidence of eating disorders, however, increased significantly following the introduction of NPIs (IRR = 1.60 [95% CI: 1.14-2.25]). Changes in admissions for perinatal disorders and eating disorders highlight the unintended impact of COVID-19 associated NPIs on paediatric health. Amongst mental disorders more generally, it is possible that admission rates in 2020 may have been greater had COVID-19 associated NPIs not disrupted pre-pandemic trends. These findings aid our understanding of social factors mediating paediatric disease and may improve healthcare delivery in a post-pandemic era.

ObjectiveHepatitis C presents a profound global health challenge. The impact of COVID-19 on hepatitis C, however, remain uncertain. This study aimed to ascertain the influence of COVID-19 on the hepatitis C epidemic trend in Henan Province.MethodsWe collated the number of monthly diagnosed cases in Henan Province from January 2013 to September 2022. Upon detailing the overarching epidemiological characteristics, the interrupted time series (ITS) analysis using autoregressive integrated moving average (ARIMA) models was employed to estimate the hepatitis C diagnosis rate pre and post the COVID-19 emergence. In addition, we also discussed the model selection process, test model fitting, and result interpretation.ResultsBetween January 2013 and September 2022, a total of 267,968 hepatitis C cases were diagnosed. The yearly average diagnosis rate stood at 2.42/100,000 persons. While 2013 witnessed the peak diagnosis rate at 2.97/100,000 persons, 2020 reported the least at 1.7/100,000 persons. The monthly mean hepatitis C diagnosed numbers culminated in 2291 cases. The optimal ARIMA model chosen was ARIMA (0,1,1) (0,1,1)12 with AIC = 1459.58, AICc = 1460.19, and BIC = 1472.8; having coefficients MA1=-0.62 (t=-8.06, P < 0.001) and SMA1=-0.79 (t=-6.76, P < 0.001). The final model’s projected step change was − 800.0 (95% confidence interval [CI] -1179.9 ~ -420.1, P < 0.05) and pulse change was 463.40 (95% CI 191.7 ~ 735.1, P < 0.05) per month.ConclusionThe measures undertaken to curtail COVID-19 led to a diminishing trend in the diagnosis rate of hepatitis C. The ARIMA model is a useful tool for evaluating the impact of large-scale interventions, because it can explain potential trends, autocorrelation, and seasonality, and allow for flexible modeling of different types of impacts.

Influence of Telemedicine-first Intervention on Patient Visit Choice, Postvisit Care, and Patient Satisfaction in Gastroenterology

This paper aims to promote the use of simple interrupted time series (ITS) analyses of routine data as a responsive feedback tool to improve public health programs. Although advanced ITS techniques exist, their use is often not feasible due to limitations in funding or research capacity. We propose an Excel-based analysis that requires minimal resources or statistical expertise, and illustrate it by measuring the effect of a radio campaign to promote a family planning call center in Nigeria on the demand for family planning information. We used a single group interrupted time series design (ITS) as a responsive feedback mechanism to determine whether the radio campaign influenced use of the Honey&Banana call center. ITS is ideal when there is no control group. ITS uses the pre-intervention trend to predict what would have happened if the intervention were absent. After conducting ITS analyses, the results show that the number of calls requesting family planning information increased throughout the campaign period, with a gain of about 500 additional calls per month, and then decreased after the campaign ended. However, the number of calls gained from the campaign was substantially lower than anticipated. While end-of-project impact evaluations are necessary, there should be regular feedback system to provide program implementers with information about the status of the project, such as failures, successes, and areas of improvements. This would allow implementers to make necessary adjustments as needed throughout the intervention period. The finding that the radio campaign was not living up to expectations helped Honey&Banana program implementers to end the campaign prematurely and re-allocate resources to a more promising activity. Our research shows that basic Excel-based ITS analysis of routine data can be a useful tool for receiving regular feedback to guide programming improvements for organizations that have limited resources and/or research capacity.

ObjectivesThe interrupted time series (ITS) design is commonly used to investigate the impact of an intervention or exposure in public health. There are many statistical methods that can be used to analyse ITS data and to meta-analyse their results. We undertook two empirical studies to investigate: (i) how effect estimates (and associated statistics) compared when six statistical methods were applied to 190 real-world datasets; and (ii) how meta-analysis effect estimates (and associated statistics) compared when the combinations of two ITS analysis methods and five meta-analysis methods were applied to 17 real-world meta-analyses including 283 ITS datasets. Here we present a curated repository of a subset of ITS datasets from these studies.Data descriptionThe repository includes 430 ITS datasets curated from the two empirical studies. The datasets are diverse in the populations, interruptions and outcomes examined, and are methodologically diverse in the outcome types, aggregation time intervals, number of timepoints and segments. Most of the datasets are from public health. For each dataset, we provide the outcome value at each timepoint and the segment (indicating different interruptions), along with characteristics of the dataset. This repository may be of value for future research of ITS studies, and as a source of examples of ITS for use in teaching.

Interrupted time series (ITS) designs are increasingly used for estimating the effect of shocks in natural experiments. Currently, ITS designs are often used in scenarios with many time points and simple data structures. This research investigates the performance of ITS designs when the number of time points is limited and with complex data structures. Using a Monte Carlo simulation study, we empirically derive the performance-in terms of power, bias and precision- of the ITS design. Scenarios are considered with multiple interventions, a low number of time points and different effect sizes based on a motivating example of the learning loss due to COVID school closures. The results of the simulation study show the power of the step change depends mostly on the sample size, while the power of the slope change depends on the number of time points. In the basic scenario, with both a step and a slope change and an effect size of 30% of the pre-intervention slope, the required sample size for detecting a step change is 1,100 with a minimum of twelve time points. For detecting a slope change the required sample size decreases to 500 with eight time points. To decide if there is enough power researchers should inspect their data, hypothesize about effect sizes and consider an appropriate model before applying an ITS design to their research. This paper contributes to the field of methodology in two ways. Firstly, the motivation example showcases the difficulty of employing ITS designs in cases which do not adhere to a single intervention. Secondly, models are proposed for more difficult ITS designs and their performance is tested.

BackgroundRandomised 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.MethodsWe 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.ResultsOne 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.ConclusionsThis 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.