Methods, applications, interpretations and challenges of interrupted time series (ITS) data: protocol for a scoping review

Trends in Incident Prescriptions for Behavioral Health Medications in the US, 2018-2022

Importance

The COVID-19 pandemic reportedly increased behavioral health needs and impacted treatment access.

Objective

To assess changes in incident prescriptions dispensed for medications commonly used to treat depression, anxiety, attention-deficit/hyperactivity disorder (ADHD), and opioid use disorder (OUD), before and during the COVID-19 pandemic.

Design, Setting, and Participants

This was a cross-sectional study using comprehensive, population-level, nationally projected data from IQVIA National Prescription Audit on incident prescriptions (prescriptions dispensed to patients with no prior dispensing from the same drug class in the previous 12 months) dispensed for antidepressants, benzodiazepines, Schedule II (C-II) stimulants, nonstimulant medications for ADHD, and buprenorphine-containing medication for OUD (MOUD), from US outpatient pharmacies. Data were analyzed from April 2018 to March 2022.

Exposure

Incident prescriptions by drug class (by prescriber specialty, patient age, and sex) and drug.

Main Outcomes and Measures

Interrupted time-series analysis to compare changes in trends in the monthly incident prescriptions dispensed by drug class and percentage changes in aggregate incident prescriptions dispensed between April 2018 and March 2022.

Results

Incident prescriptions dispensed for the 5 drug classes changed from 51 500 321 before the COVID-19 pandemic to 54 000 169 during the pandemic. The largest unadjusted percentage increase in incident prescriptions by prescriber specialty was among nurse practitioners across all drug classes ranging from 7% (from 1 811 376 to 1 944 852; benzodiazepines) to 78% (from 157 578 to 280 925; buprenorphine MOUD), whereas for patient age and sex, the largest increases were within C-II stimulants and nonstimulant ADHD drugs among patients aged 20 to 39 years (30% [from 1 887 017 to 2 455 706] and 81% [from 255 053 to 461 017], respectively) and female patients (25% [from 2 352 095 to 2 942 604] and 59% [from 395 678 to 630 678], respectively). Trends for C-II stimulants and nonstimulant ADHD drugs (slope change: 4007 prescriptions per month; 95% CI, 1592-6422 and 1120 prescriptions per month; 95% CI, 706-1533, respectively) significantly changed during the pandemic, exceeding prepandemic trends after an initial drop at the onset of the pandemic (level changes: -50 044 prescriptions; 95% CI, -80 202 to -19 886 and -12 876 prescriptions; 95% CI, -17 756 to -7996, respectively). Although buprenorphine MOUD dropped significantly (level change: -2915 prescriptions; 95% CI, -5513 to -318), trends did not significantly change for buprenorphine MOUD, antidepressants, or benzodiazepines.

Conclusions and Relevance

Incident use of many behavioral health medications remained relatively stable during the COVID-19 pandemic in the US, whereas ADHD medications, notably C-II stimulants, sharply increased. Additional research is needed to differentiate increases due to unmet need vs overprescribing, highlighting the need for further ADHD guideline development to define treatment appropriateness.

The trend of poisonings before and after the COVID-19 pandemic

The COVID-19 pandemic has substantially affected people and healthcare systems. One of the main challenges was the reduction and change in the pattern of non-COVID-19 diseases and conditions. Moreover, due to the mental burden of the pandemic, the trend of poisonings and abuses changed. In this study, we aimed to assess the trends of poisonings from different agents before and during the COVID-19 pandemic using the interrupted time series method. This study was conducted at one of the main Tehran referral centers for poisoning, Baharloo Hospital. Pre-COVID-19 period was defined as April 2018 to January 2020 while the COVID-19 time was from February 2020 to March 2022. The total number of monthly poisoning cases in addition to eight categories of drugs/substances/agents were identified, including drugs (such as psychiatric drugs, cardiovascular drugs, and analgesics), opioids, stimulants, methanol, ethanol, cannabis, pesticides, and carbon monoxide. Interrupted time series analysis was performed to compare the pre-pandemic trend of total monthly cases from each category in addition to the proportion (%) of each one. In total, 13,020 cases were poisoned during the study period, among which 6088 belonged to the pre-pandemic period and 6932 were admitted during the COVID-19 era. There was no significant difference in terms of demographic characteristics of patients before and during the pandemic (p-value > 0.05). At the beginning of the pandemic, there was a sudden fall in the number of poisoning patients (- 77.2 cases/month, p-value = 0.003), however, there was a significant increasing trend during the COVID time (3.9 cases/month, p-value = 0.006). Most of the categories had a sharp decrease at the beginning of the pandemic except for methanol and ethanol which had increases, although not significant. Cannabis also had a significant change in slope (- 0.6 cases/month, p-value = 0.016), in addition to the sudden decrease at the beginning of the pandemic (- 10 cases/month, p-value = 0.007). Regarding the proportion of each category from total monthly poisoning cases, methanol, and ethanol had immediate rises of 4.2% per month and 10.1% per month, respectively (both significant). The pandemic had significant effects on the pattern of poisonings from different agents in Iran, the most important of which were alcohol (ethanol and methanol). These differences had policy implications that can be helpful for policymakers and healthcare systems in combating similar situations in the future.

Optimized segmented regression models for the transition period of intervention effects

BACKGROUND

The interrupted time series (ITS) design is a widely used approach to examine the effects of interventions. However, the classic segmented regression (CSR) method, the most popular statistical technique for analyzing ITS data, may not be adequate when there is a transitional period between the pre- and post-intervention phases.

METHODS

To address this issue and better capture the distribution patterns of intervention effects during the transition period, we propose using different cumulative distribution functions in the CSR model and developing corresponding optimized segmented regression (OSR) models. This study illustrates the application of OSR models to estimate the long-term impact of a national free delivery service policy intervention in Ethiopia.

RESULTS

Regardless of the choice of transition length ([Formula: see text]) and distribution patterns of intervention effects, the OSR models outperformed the CSR model in terms of mean square error (MSE), indicating the existence of a transition period and the validity of our model's assumptions. However, the estimates of long-term impacts using OSR models are sensitive to the selection of L, highlighting the importance of reasonable parameter specification. We propose a data-driven approach to select the transition period length to address this issue.

CONCLUSIONS

Overall, our OSR models provide a powerful tool for modeling intervention effects during the transition period, with a superior model fit and more accurate estimates of long-term impacts. Our study highlights the importance of appropriate statistical methods for analyzing ITS data and provides a useful framework for future research.

Impact of COVID-19 on poisoning-related mortality in Iran: An interrupted time series study

BACKGROUND

The COVID-19 pandemic had many negative effects worldwide. These effects involved mental health status issues such as suicide, depression, and the pattern of death associated with drug/poisonings. One of the major concerns of the healthcare community during the pandemic was mortality from poisonings. This study aimed to investigate the trends of mortality from different types of poisonings before and after COVID-19.

METHODS

The patients who died from six different categories of drugs or poisons were identified by forensic analysis of body fluids/tissues in Tehran, Iran. The pandemic was separated into the pre-COVID-19 period (April 2018 to January 2020), and the COVID pandemic (February 2020-April 2022). Demographic characteristics were collected from each victim, and comparisons of death trends before and after the pandemic were conducted using the interrupted time series analysis. The absolute number of deaths and proportion of deaths from each type of drug/poisoning were used for the analyses.

RESULTS

A total of 6,316 deaths from drugs/poisoning were identified between April 2018-Mar 2022). During this period, 2,485 deaths occurred pre-COVID, and 3,831 were during the COVID-19 era. There were no statistical differences in terms of demographic characteristics before and after the pandemic, except for job status. There was a sharp increase in proportion of methanol death among all poisonings after the start of the pandemic (16.5%, p-value = 0.025), while there was a decreasing trend during the pandemic (-0.915 deaths monthly, p-value = 0.027). The trends for opioids, stimulants, and drug-related deaths changed from decreasing to increasing. No change was seen in the trends for ethanol and volatile substance deaths. This pattern was mirrored in the proportion of each type of poisoning relative to the total number.

CONCLUSION

Changes in poisoning-related mortality patterns showed dramatic changes after the start of the pandemic, especially deaths from methanol. Other poisonings such as opioids, stimulants, and drugs should also be addressed as there was an increasing trend during the COVID-19 period, compared to the pre-COVID data.

Impact of the COVID-19 Pandemic on Opioid Overdose Deaths: a Spatiotemporal Analysis

The effects of the opioid crisis have varied across diverse and socioeconomically defined urban communities, due in part to widening health disparities. The onset of the COVID-19 pandemic has coincided with a spike in drug overdose deaths in the USA. However, the extent to which the impact of the pandemic on overdose deaths has varied across different demographics in urban neighborhoods is unclear. We examine the influence of COVID-19 pandemic on opioid overdose deaths through spatiotemporal analysis techniques. Using Milwaukee County, Wisconsin as a study site, we used georeferenced opioid overdose data to examine the locational and demographic differences in overdose deaths over time (2017-2020). We find that the pandemic significantly increased the monthly overdose deaths. The worst effects were seen in the poor, urban neighborhoods, affecting Black and Hispanic communities. However, more affluent, suburban White communities also experienced a rise in overdose deaths. A better understanding of contributing factors is needed to guide interventions at the local, regional, and national scales.

What Will Be the Economic Impact of the New Medical Device Regulation? An Interrupted Time-Series Analysis of Foreign Trade Data

OBJECTIVES

To date, the economic effects of medical device directives compared with the clinical and regulatory effects have not been sufficiently studied. We believe that the financial obligations and responsibilities imposed on the stakeholders by the legal regulations have a corresponding economic indicator in the medical device market.

METHODS

In this study, we selected 2 crucial legal regulations on medical devices in Turkey: the Medical Device Directive that has been harmonized from the European Union and the Medical Device Sales, Advertising, and Promotion Directive. The impacts of these regulations on foreign trade were investigated using interrupted time-series analysis. Turkey's monthly medical device export and import data from 2008 to 2019 were obtained under 56 6-digit custom codes.

RESULTS

The results show that the selected directives have significantly affected the foreign trade trend level, and they may be breakpoints in the Turkey's foreign trade trend curve. We also reported the significant differences between the Medical Device Directive and the European Union's new medical device regulation (MDR) (2017/745 MDR).

CONCLUSIONS

We concluded that the MDR, which has more strict requirements, will result in increased costs for economic operators in comparison with the current directive and would further increase the import of medical devices by the importing countries. To prevent this, Turkey should aim to have a manufacturer's position by improving their clinical trial capabilities and manufacturing infrastructures with innovation-based approaches.

The Impact of the COVID-19 Pandemic on Antibiotic Prescribing Trends in Outpatient Care: A Nationwide, Quasi-Experimental Approach

Coronavirus disease 2019 (COVID-19) has spread globally and is currently having a damaging impact on nearly all countries in the world. The implementation of stringent measures to stop COVID-19 dissemination had an influence on healthcare services and associated procedures, possibly causing antibiotic consumption fluctuations. This paper aims to evaluate the immediate and long-term impact of the COVID-19 pandemic on antibiotic prescribing trends in outpatient care of the Portuguese public health sector, including in primary healthcare centers and hospitals, as well as on specific antibiotic groups known to be closely associated with increased resistance. Segmented regression analysis with interrupted time series data was used to analyze whether the COVID-19 pandemic had an impact in antibiotic prescribing tendencies at a national level. The outcomes from this quasi-experimental approach demonstrate that, at the beginning of the pandemic, a significant, immediate decrease in the overall antibiotic prescribing trends was noticed in the context of outpatient care in Portugal, followed by a statistically non-significant fall over the long term. The data also showed a significant reduction in the prescription of particular antibiotic classes (antibiotics from the Watch group, 3rd-generation cephalosporins, fluoroquinolones, and clarithromycin) upon COVID-19 emergence. These findings revealed an important disruption in antibiotics prescribing caused by the current public health emergency.

The value of intentional self-care practices: The effects of mindfulness on improving job satisfaction, teamwork, and workplace environments

BACKGROUND

Burnout rates among nurses have detrimental impact on job satisfaction, teamwork, and patient care. This costs millions of dollars in the healthcare system and challenges nurse leaders to address in order to keep up with the healthcare demands. Furthermore, burnout is especially relevant in our current healthcare climate, as frontline nurses have increased workload and multiple psychosocial stressors during the coronavirus disease (COVID-19) pandemic (Sultana, Sharma, Hossain, Bhattacharya, & Purohit, 2019). Literature also suggests that mindful self-care practices need to be reinforced in order to impact burnout long term (Chamorro-Premuzic & Lusk, 2017). Project7 Mindfulness Pledge© is an accessible and voluntary mindfulness tool that nurses can utilize in their individual practice to reduce burnout and does not require significant time commitment.

OBJECTIVE

To evaluate the effectiveness of intentional self-care practices on nurse burnout and workplace environment by measuring job satisfaction and teamwork among nurses.

METHODS

Comparisons between inpatient units on data from the National Database of Nursing Quality Indicators (NDNQI) with the Practice Environment Scale (PES), specifically on job enjoyment and teamwork, were done utilizing ANOVA.

RESULTS

Results show that nurses in an inpatient unit that implemented Project7 has significantly higher job satisfaction as compared to units that did not implement Project7.

CONCLUSIONS

This suggests that this tool provides an effective and accessible mindfulness framework managers and directors can utilize to improve job satisfaction, teamwork, and thereby reduce burnout to create healthier work environments.

24 hour consultant obstetrician presence on the labour ward and intrapartum outcomes in a large unit in England: A time series analysis

Objectives

To explore the effect of introducing 24/7 resident labour ward consultant presence on neonatal and maternal outcomes in a large obstetric unit in England.

Design

Retrospective time sequence analysis of routinely collected data.

Setting

Obstetric unit of large teaching hospital in England.

Participants

Women and babies delivered between1 July 2011 and 30 June 2017. Births <24 weeks gestation or by planned caesarean section were excluded.

Main outcome measures

The primary composite outcome comprised intrapartum stillbirth, neonatal death, babies requiring therapeutic hypothermia, or admission to neonatal intensive care within three hours of birth. Secondary outcomes included markers of neonatal and maternal morbidity. Planned subgroup analyses investigated gestation (<34 weeks; 34–36 weeks; ≥37 weeks) and time of day.

Results

17324 babies delivered before and 16110 after 24/7 consultant presence. The prevalence of the primary outcome increased by 0.65%, from 2.07% (359/17324) before 24/7 consultant presence to 2.72% (438/16110, P < 0.001) after 24/7 consultant presence which was consistent with an upward trend over time already well established before 24/7 consultant presence began (OR 1.09 p.a.; CI 1.04 to 1.13). Overall, there was no change in this trend associated with the transition to 24/7. However, in babies born ≥37 weeks gestation, the upward trend was reversed after implementation of 24/7 (OR 0.67 p.a.; CI 0.49 to 0.93; P = 0.017). No substantial differences were shown in other outcomes or subgroups.

Conclusions

Overall, resident consultant obstetrician presence 24/7 on labour ward was not associated with a change in a pre-existing trend of increasing adverse infant outcomes. However, 24/7 presence was associated with a reversal in increasing adverse outcomes for term babies.

Assessing the Effect of Unit Champion-Initiated Audits on Fall Rates: Improving Awareness

BACKGROUND

Inpatient falls remain challenging with repercussions that can include patient injury and increased hospital expense. Fall rates were consistently above the national benchmark. An initiative to reduce fall rates was use of Fall Champion Audits (FCAs).

PURPOSE

The aim of this study was to assess the effect of FCAs on patient fall rates.

METHODS

FCAs were piloted on a medical-oncology unit. An interrupted time series design was used to assess the effect of FCAs on fall rates.

INTERVENTION

FCA is an audit conducted by the unit fall champion that assesses fall risk, interventions, and barriers among staff and patients.

RESULTS

Analysis suggested a significant decrease in fall rates from pre- (3.75) to postimplementation (1.62). FCAs worked in conjunction with a division-wide fall program in reducing fall rate.

CONCLUSIONS

FCAs, in conjunction with a fall program, are a feasible intervention in reducing fall rates.

Introducing an innovative model of acute paediatric mental health and addictions care to paediatric emergency departments: a protocol for a multicentre prospective cohort study

INTRODUCTION

Children and youth with mental health and addiction crises are a vulnerable patient group that often are brought to the hospital for emergency department care. We propose to evaluate the effect of a novel, acute care bundle that standardises a patient-centred approach to care.

METHODS AND ANALYSIS

Two paediatric emergency departments in Alberta, Canada are involved in this prospective, pragmatic, 29-month interventional quasi-experimental study. The acute care bundle comprises three components, applied when appropriate: (1) assessing self-harm risk at triage using the Ask Suicide-Screening Questionnaire (ASQ) to standardise the questions administered, enabling risk stratification; (2) use of the HEADS-ED (Home, Education, Activities/peers, Drug/alcohol, Suicidality, Emotions and behaviour, Discharge Resources) to focus mental health evaluations for those who screen high risk on the ASQ; and (3) implementation of a Choice And Partnership Approach to enable shared decision making in care following the emergency department visit. The overarching goal is to deliver the right care at the right place and time for the patients. The study design involves a longitudinal collection of data 12 months before and after the introduction of the bundle and the use of quality improvement strategies such as Plan-Do-Study-Act cycles during a 5-month run-in period to test and implement changes. The primary study end-point is child/youth well-being 1 month after the emergency department visit. Secondary outcomes include family functioning, child/youth well-being at 3 and 6 months, satisfaction with emergency department care, and health system outcomes (hospital admissions, length of emergency department stays, emergency department revisits).

ETHICS AND DISSEMINATION

The study is registered at www.ClinicalTrials.gov and has received ethics and operational approvals from study sites. The results of the study will be reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology statement. Results will be shared broadly with key policy and decision makers and disseminated in peer-reviewed academic journals and presentations at conferences.

TRIAL REGISTRATION NUMBER

NCT04292379.

Maternity iron, anaemia and blood management in South Australia: a practice-based evidence for clinical practice improvement

BACKGROUND

Anaemia at delivery is a strong modifiable risk factor for transfusion in women with a postpartum haemorrhage (PPH). A Maternity Patient Blood Management (PBM) Practice Based Evidence Clinical Practice Improvement (CPI) was conducted to optimize antenatal haemoglobin and iron stores prior to delivery.

METHODS

Australian maternity PBM CPI resources (featuring algorithms on diagnosing iron deficiency with both haemoglobin and ferritin screening, as well as information on oral iron therapy for maternity patients) were introduced at a major tertiary hospital from November 2016 to March 2017. To assess the effectiveness of these resources on haemoglobin and iron stores, an interrupted time series (ITS) analysis was conducted for 11,263 deliveries from January 2016 to June 2018. The evaluation timeframe was divided into baseline (pre-CPI), pilot (during CPI) and post-pilot (post-CPI).

RESULTS

In 1550 patients with haemoglobin and ferritin in the first trimester, non-anaemic iron deficiency was detected in 416 women (26·8%) and iron deficiency anaemia (IDA) in 239 women (15·41%) throughout the whole study period. The number of women with IDA increases as pregnancy progresses but applying PBM CPI shows a reduction of IDA rate in all trimesters and reduction in anaemia at delivery in the post-pilot period from baseline. More anaemic episodes were observed in the postpartum period compared to the first trimester. ITS analysis for the whole study period showed a clinically significant increase in the monthly average predelivery haemoglobin of 0·9 g/l (P = 0·16). This corresponded with a reduction in the monthly rate of anaemic patients by 18% (P = 0·12). There was a significant decrease in the rates of anaemia at delivery and decrease in red cell transfusion in anaemic women, even though the number of women with PPH was stable. The factors associated with red cell transfusion are anaemia at delivery (P < 0·001) and the incidence of PPH (P < 0·001).

CONCLUSIONS

The maternity PBM CPI resources had a clinically relevant but not statistically significant effect in optimizing antenatal haemoglobin and decreasing the risk of predelivery anaemia. This study demonstrates how a CPI can modify one risk factor for blood loss, which is the anaemia at delivery, and subsequent transfusion in the perinatal period.

MultiCenter Interrupted Time Series Analysis: Incorporating Within and Between-Center Heterogeneity

Background

Segmented regression (SR) is the most common statistical method used in the analysis of interrupted time series (ITS) data. However, this modeling strategy is indicated to produce spurious results when applied to aggregated data. For multicenter ITS studies, data at a given time point are often aggregated across different participants and settings; thus, conventional segmented regression analysis may not be an optimal approach. Our objective is to provide a robust method for analysis of ITS data, while accounting for two sources of heterogeneity, between participants and across sites.

Methods

We present a methodological framework within the segmented regression modeling strategy, where we introduced weights to account for between-participant variation and the differences across multiple sites. We empirically compared the proposed weighted segmented regression (wSR) with the conventional SR as well as with a previously published pooled analysis method using data from the Mobility of Vulnerable Elders in Ontario (MOVE-ON) project, a multisite ITS study.

Results

Overall, the wSR produced the most precise estimates, where they had the narrowest 95% CI, while the conventional SR method resulted in the least precise estimates. Our method also resulted in increased power. The pooled analysis method and the wSR had comparable results when there were ≤4 sites included in the overall analysis and when there was moderate to high between-site heterogeneity as measured by the I² statistic.

Conclusion

Incorporating participant-level and site-level variability led to estimates that were more precise and accurate in determining the magnitude of the effect of an intervention and led to increased statistical power. This underscores the importance of accounting for the inherent variability in aggregated data. Extensive simulations are required to further assess the methods in a wide range of scenarios and outcome types.

A reliable time-series method for predicting arthritic disease outcomes: New step from regression toward a nonlinear artificial intelligence method

BACKGROUND AND OBJECTIVE

The interrupted time-series (ITS) concept is performed using linear regression to evaluate the impact of policy changes in public health at a specific time. Objectives of this study were to verify, with an artificial intelligence-based nonlinear approach, if the estimation of ITS data could be facilitated, in addition to providing a computationally explicit equation.

METHODS

Dataset were from a study of Hawley et al. (2018) in which they evaluated the impact of UK National Institute for Health and Care Excellence (NICE) approval of tumor necrosis factor inhibitor therapies on the incidence of total hip (THR) and knee (TKR) replacement in rheumatoid arthritis patients. We used the newly developed Generalized Structure Group Method of Data Handling (GS-GMDH) model, a nonlinear method, for the prediction of THR and TKR incidence in the abovementioned population.

RESULTS

In contrast to linear regression, the GS-GMDH yields for both THR and TKR prediction values that almost fitted with the measured ones. These models demonstrated a low mean absolute relative error (0.10 and 0.09 respectively) and high correlation coefficient values (0.98 and 0.78). The GS-GMDH model for THR demonstrated 6.4/1000 person years (PYs) at the mid-point of the linear regression line post-NICE, whereas at the same point linear regression is 4.12/1000 PYs, a difference of around 35%. Similarly for the TKR, the linear regression to the datasets post-NICE was 9.05/1000 PYs, which is lower by about 27% than the GS-GMDH values of 12.47/1000 PYs. Importantly, with the GS-GMDH models, there is no need to identify the change point and intervention lag time as they simulate ITS continually throughout modelling.

CONCLUSIONS

The results demonstrate that in the medical field, when looking at the estimation of the impact of a new drug using ITS, a nonlinear GS-GMDH method could be used as a better alternative to regression-based methods data processing. In addition to yielding more accurate predictions and requiring less time-consuming experimental measurements, this nonlinear method addresses, for the first time, one of the most challenging tasks in ITS modelling, i.e. avoiding the need to identify the change point and intervention lag time.

Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review

Objective

Interrupted time series (ITS) designs are robust quasi-experimental designs commonly used to evaluate the impact of interventions and programs implemented in healthcare settings. This scoping review aims to 1) identify and summarize existing methods used in the analysis of ITS studies conducted in health research, 2) elucidate their strengths and limitations, 3) describe their applications in health research and 4) identify any methodological gaps and challenges.

Design

Scoping review.

Data Sources

Searches were conducted in MEDLINE, JSTOR, PUBMED, EMBASE, CINAHL, Web of Science and the Cochrane Library from inception until September 2017.

Study Selection

Studies in health research involving ITS methods or reporting on the application of ITS designs.

Data Extraction

Screening of studies was completed independently and in duplicate by two reviewers. One reviewer extracted the data from relevant studies in consultations with a second reviewer. Results of the review were presented with respect to methodological and application areas, and data were summarized using descriptive statistics.

Results

A total of 1389 articles were included, of which 98.27% (N=1365) were application papers. Segmented linear regression was the most commonly used method (26%, N=360). A small percentage (1.73%, N=24) were methods papers, of which 11 described either the development of novel methods or improvement of existing methods, 7 adapted methods from other areas of statistics, while 6 provided comparative assessment of conventional ITS methods.

Conclusion

A significantly increasing trend in ITS use over time is observed, where its application in health research almost tripled within the last decade. Several statistical methods are available for analyzing ITS data. Researchers should consider the types of data and validate the required assumptions for the various methods. There is a significant methodological gap in ITS analysis involving aggregated data, where analyses involving such data did not account for heterogeneity across patients and hospital settings.

Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review

OBJECTIVES

Interrupted time series (ITS) designs are frequently used in public health to examine whether an intervention or exposure has influenced health outcomes. Few reviews have been undertaken to examine the design characteristics, statistical methods, and completeness of reporting of published ITS studies.

STUDY DESIGN AND SETTING

We used stratified random sampling to identify 200 ITS studies that evaluated public health interventions or exposures from PubMed (2013-2017). Study characteristics, details of statistical models and estimation methods used, effect metrics, and parameter estimates were extracted. From the 200 studies, 230 time series were examined.

RESULTS

Common statistical methods used were linear regression (31%, 72/230) and autoregressive integrated moving average (19%, 43/230). In 17% (40/230) of the series, we could not determine the statistical method used. Autocorrelation was acknowledged in 63% (145/230) of the series. An estimate of the autocorrelation coefficient was given for only 1% of the series (3/230). Measures of precision were reported for 63% of effect measures (541/852).

CONCLUSION

Many aspects of the design, methods, analysis, and reporting of ITS studies can be improved, particularly description of the statistical methods and approaches to adjust for and estimate autocorrelation. More guidance on the conduct and reporting of ITS studies is needed to improve this study design.

Types of Interventions Targeting Dietary, Physical Activity, and Weight-Related Outcomes among University Students: A Systematic Review of Systematic Reviews

A plethora of studies aiming to improve dietary, physical activity (PA), and weight-related (WR) outcomes among university students have been implemented and summarized in a series of systematic reviews, with unclear conclusions regarding their effectiveness. This overview aims to identify systematic reviews and meta-analyses of studies aiming to improve health outcomes in university students, to assess their methodological quality, to identify the different types of interventions used and outcomes assessed, and to estimate their overall effect. Four electronic databases were searched until 19 March, 2018 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The identified reviews were described and their methodological quality was rated. The studies of reviews were investigated to identify the different types of interventions used and outcomes assessed. Effectiveness was assessed by measuring the overall number of improved outcomes out of the total number of outcomes reported. As a result, 8 reviews were identified targeting food sales (n = 2), dietary (n = 3), PA (n = 1), WR (n = 1), or all outcomes (n = 1). The methodological quality of the reviews was moderate (n = 5) to low (n = 3). In all, the reviews included 122 studies, of which 36 used an environmental, 51 a face-to-face, 30 an e-intervention, and 5 a combined approach. Environmental interventions improved a moderate number of food sales (32 of 61) and dietary intake (22 of 47) outcomes. Face-to-face interventions improved a high number of dietary cognitive outcomes (15 of 18), a moderate number of dietary intake (28 of 65) and WR (11 of 18) outcomes, and a low number of PA behavioral (22 of 69) and cognitive (2 of 14) outcomes. E-interventions improved a high number of dietary cognitive variables (11 of 16) but had a low effect (≤33%) on the other types of outcomes. In conclusion, face-to-face and e-interventions improved cognitive variables toward diet or PA but were less effective in changing actual behaviors. Environmental interventions favorably changed food sales. Face-to-face and e-interventions moderately affected WR outcomes. Future research should focus on long-term studies.

Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study

Interrupted time series (ITS) analysis is being increasingly used in epidemiology. Despite its growing popularity, there is a scarcity of guidance on power and sample size considerations within the ITS framework. Our aim of this study was to assess the statistical power to detect an intervention effect under various real-life ITS scenarios. ITS datasets were created using Monte Carlo simulations to generate cumulative incidence (outcome) values over time. We generated 1,000 datasets per scenario, varying the number of time points, average sample size per time point, average relative reduction post intervention, location of intervention in the time series, and reduction mediated via a 1) slope change and 2) step change. Performance measures included power and percentage bias. We found that sample size per time point had a large impact on power. Even in scenarios with 12 pre-intervention and 12 post-intervention time points with moderate intervention effect sizes, most analyses were underpowered if the sample size per time point was low. We conclude that various factors need to be collectively considered to ensure adequate power for an ITS study. We demonstrate a means of providing insight into underlying sample size requirements in ordinary least squares (OLS) ITS analysis of cumulative incidence measures, based on prespecified parameters and have developed Stata code to estimate this.

Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: protocol for a review

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 DISSEMINATION

Ethics approval is not required since information will only be extracted from published studies. Dissemination of the results will be through peer-reviewed publications and presentations at conferences. A repository of data extracted from the published ITS studies will be made publicly available.

Effects on the medical revenue of comprehensive pricing reform in Chinese urban public hospitals after removing drug markups: case of Nanjing

AIMS

The State Council of China requires that all urban public hospitals must eliminate drug markups by September 2017, and that hospital drugs must be sold at the purchase price. Nanjing-one of the first provincial capital cities to implement the reform-is studied to evaluate the effects of the comprehensive reform on drug prices in public hospitals, and to explore differential compensation plans.

METHODS

Sixteen hospitals were selected, and financial data were collected over the 48-month period before the reform and for 12 months after the reform. An analysis was carried out using a simple linear interrupted time series model.

RESULTS

The average difference ratio of drug surplus fell 13.39% after the reform, and the drug markups were basically eliminated. Revenue from medical services showed a net growth of 28.25%. The overall compensation received from government financial budget and medical service revenue growth was 103.69% for the loss from policy-permitted 15% markup sales, and 116.48% for the net loss. However, there were large differences in compensation levels at different hospitals, ranging from -21.92% to 413.74% by medical services revenue growth, causing the combined rate of both financial and service compensation to vary from 28.87-413.74%, There was a significant positive correlation between the services compensation rate and the proportion of medical service revenue (p < .001), and the compensation rate increased by 8% for every 1% increase in the proportion of services revenue.

DISCUSSION

Nanjing's pricing and compensation reform has basically achieved the policy targets of eliminating the drug markups, promoting the growth of medical services revenue, and adjusting the structure of medical revenue. However, the growth rate of service revenue of hospitals varied significantly from one another.

CONCLUSIONS

Nanjing's reform represents successful pricing and compensation reform in Chinese urban public hospitals. It is recommended that a differentiated and dynamic compensation plan should be established in accordance with the revenue structure of different hospitals.