Corrigendum to: Interrupted time series regression for the evaluation of public health interventions: a tutorial.
Ontology highlight
ABSTRACT:
SUBMITTER: Bernal JL
PROVIDER: S-EPMC7750921 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
| S-EPMC5407170 | biostudies-literature
Project description: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.
| S-EPMC6352832 | biostudies-literature
Project description:BACKGROUND:When randomisation is not possible, interrupted time series (ITS) design has increasingly been advocated as a more robust design to evaluating health system quality improvement (QI) interventions given its ability to control for common biases in healthcare QI. However, there is a potential risk of producing misleading results when this rather robust design is not used appropriately. We performed a methodological systematic review of the literature to investigate the extent to which the use of ITS has followed best practice standards and recommendations in the evaluation of QI interventions. METHODS:We searched multiple databases from inception to June 2018 to identify QI intervention studies that were evaluated using ITS. There was no restriction on date, language and participants. Data were synthesised narratively using appropriate descriptive statistics. The risk of bias for ITS studies was assessed using the Cochrane Effective Practice and Organisation of Care standard criteria. The systematic review protocol was registered in PROSPERO (registration number: CRD42018094427). RESULTS:Of 4061 potential studies and 2028 unique records screened for inclusion, 120 eligible studies assessed eight QI strategies and were from 25 countries. Most studies were published since 2010 (86.7%), reported data using monthly interval (71.4%), used ITS without a control (81%) and modelled data using segmented regression (62.5%). Autocorrelation was considered in 55% of studies, seasonality in 20.8% and non-stationarity in 8.3%. Only 49.2% of studies specified the ITS impact model. The risk of bias was high or very high in 72.5% of included studies and did not change significantly over time. CONCLUSIONS:The use of ITS in the evaluation of health system QI interventions has increased considerably over the past decade. However, variations in methodological considerations and reporting of ITS in QI remain a concern, warranting a need to develop and reinforce formal reporting guidelines to improve its application in the evaluation of health system QI interventions.
| S-EPMC7559052 | biostudies-literature
Project description:ObjectiveTo discuss the study design and data analysis for three-phase interrupted time series (ITS) studies to evaluate the impact of health policy, systems, or environmental interventions. Simulation methods are used to conduct power and sample size calculation for these studies.MethodsWe consider the design and analysis of three-phase ITS studies using a study funded by National Institutes of Health as an exemplar. The design and analysis of both one-arm and two-arm three-phase ITS studies are introduced.ResultsA simulation-based approach, with ready-to-use computer programs, was developed to determine the power for two types of three-phase ITS studies. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9 with various effect sizes. The power increased as the sample size or the effect size increased. The power to detect the same effect sizes varied largely, depending on testing level change, trend changes, or both.ConclusionThis article provides a convenient tool for investigators to generate sample sizes to ensure sufficient statistical power when three-phase ITS study design is implemented.
| S-EPMC7028460 | biostudies-literature
Project description:BackgroundReducing maternal mortality remains a global priority. In 2000, the United Nations Member States pledged to work towards a series of Millennium Development Goals (MDGs), in which the fifth target was to reduce maternal mortality ratio by 75% from 1990 to 2015. The Chinese government introduced Basic Public Health Service project in 2009 to the further improvement of maternal health services and reduction in maternal mortality. China had achieved the goal of MDG5 1 year ahead of the schedule in 2014, but the effects of the project on reducing maternal mortality were rarely evaluated with robust methods.MethodsWe conducted a longitudinal study on maternal mortality ratio by extracting mortality data from the National Maternal Mortality Surveillance System (1991-2016) and maternal health services measures from the China health statistic yearbook (2001-2016). We utilized the segmented linear regression model to assess changes and trends of maternal mortality ratio and maternal health services before and after the introduction of Basic Public Health Service project. Pearson correlation analysis was conducted to measure the strength of association between the maternal mortality ratio and maternal health services.ResultsThe yearly trend change of national maternal mortality ratio was -?1.76 (p?<?0.01) after the introduction of Basic Public Health Service project in 2009, while the yearly trend change of maternal health record establish rate, prenatal examination rate, postpartum visit rate was 0.77 (p?<?0.01), 0.61 (p?<?0.01) and 0.83 (p?<?0.01) separately. The negative correlations were also found between national maternal mortality ratio and prenatal examination rate (r?=?-?0.95, p?<?0.01), maternal health record establish rate (r?=?-?0.93, p?<?0.01) and postpartum visit rate (r?=?-?0.92, p?<?0.01).ConclusionsThe Basic Public Health Service project was found to be associated with the improvements in the maternal health services and reduction in maternal mortality. The design and implementation of the project may serve as a positive example for other developing countries. Continued monitoring and assessment of project effects should be stressed.
| S-EPMC6595598 | biostudies-literature
Project description:BACKGROUND:Rapid evaluation was at the heart of National Health Service England's evaluation strategy of the new models of care vanguard programme. This was to facilitate the scale and spread of successful models of care throughout the health & social care system. The aim of this paper is to compare the findings of the two evaluations of the Enhanced health in Care Homes (EHCH) vanguard in Gateshead, one using a smaller data set for rapidity and one using a larger longitudinal data set and to investigate the implications of the use of rapid evaluations using interrupted time series (ITS) methods. METHODS:A quasi-experimental design study in the form of an ITS was used to evaluate the impact of the vanguard on secondary care use. Two different models are presented differing by timeframes only. The short-term model consisted of data for 11?months data pre and 20?months post vanguard. The long-term model consisted of data for 23?months pre and 34?months post vanguard. RESULTS:The cost consequences, including the cost of running the EHCH vanguard, were estimated using both a single tariff non-elective admissions methodology and a tariff per bed day methodology. The short-term model estimated a monthly cost increase of £73,408 using a single tariff methodology. When using a tariff per bed day, there was an estimated monthly cost increase of £14,315. The long-term model had, using a single tariff for non-elective admissions, an overall cost increase of £7576 per month. However, when using a tariff per bed-days, there was an estimated monthly cost reduction of £57,168. CONCLUSIONS:Although it is acknowledged that there is often a need for rapid evaluations in order to identify "quick wins" and to expedite learning within health and social care systems, we conclude that this may not be appropriate for quasi-experimental designs estimating effect using ITS for complex interventions. Our analyses suggests that care must be taken when conducting and interpreting the results of short-term evaluations using ITS methods, as they may produce misleading results and may lead to a misallocation of resources.
| S-EPMC6911271 | biostudies-literature
Project description:Background & aimsPublic health measures introduced to limit transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), also disrupted various healthcare services in many regions worldwide, including British Columbia (BC), Canada. We assessed the impact of these measures, first introduced in BC in March 2020, on hepatitis C (HCV) testing and first-time HCV-positive diagnoses within the province.MethodsDe-identified HCV testing data for BC residents were obtained from the provincial Public Health Laboratory. Weekly changes in anti-HCV, HCV RNA and genotype testing episodes and first-time HCV-positive (anti-HCV/RNA/genotype) diagnoses from January 2018 to December 2020 were assessed and associations were determined using segmented regression models examining rates before vs after calendar week 12 of 2020, when measures were introduced.ResultsAverage weekly HCV testing and first-time HCV-positive diagnosis rates fell immediately following the imposition of public health measures by 62.3 per 100 000 population and 2.9 episodes per 1 000 000 population, respectively (P < .0001 for both), and recovered in subsequent weeks to near pre-March 2020 levels. Average weekly anti-HCV positivity rates decreased steadily pre-restrictions and this trend remained unchanged afterwards.ConclusionsReductions in HCV testing and first-time HCV-positive diagnosis rates, key drivers of progression along the HCV care cascade, occurred following the introduction of COVID-19-related public health measures. Further assessment will be required to better understand the full impact of these service disruptions on the HCV care cascade and to inform strategies for the re-engagement of people who may have been lost to care because of these measures.
| S-EPMC8662267 | biostudies-literature
Project description:BackgroundThe 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.MethodsA 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.ResultsFrom 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.ConclusionsThe 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.
| S-EPMC8235830 | biostudies-literature
Project description:In the United States, there is concern that recent state laws restricting undocumented immigrants' rights could threaten access to Medicaid and the Children's Health Insurance Program (CHIP) for citizen children of immigrant parents. Of particular concern are omnibus immigration laws, state laws that include multiple provisions increasing immigration enforcement and restricting rights for undocumented immigrants. These laws could limit Medicaid/CHIP access for citizen children in immigrant families by creating misinformation about their eligibility and fostering fear and mistrust of government among immigrant parents. This study uses nationally-representative data from the National Health Interview Survey (2005-2014; n = 70,187) and comparative interrupted time series methods to assess whether passage of state omnibus immigration laws reduced access to Medicaid/CHIP for US citizen Latino children. We found that law passage did not reduce enrollment for children with noncitizen parents and actually resulted in temporary increases in coverage among Latino children with at least one citizen parent. These findings are surprising in light of prior research. We offer potential explanations for this finding and conclude with a call for future research to be expanded in three ways: 1) examine whether policy effects vary for children of undocumented parents, compared to children whose noncitizen parents are legally present; 2) examine the joint effects of immigration-related policies at different levels, from the city or county to the state to the federal; and 3) draw on the large social movements and political mobilization literature that describes when and how Latinos and immigrants push back against restrictive immigration laws.
| S-EPMC5650910 | biostudies-literature
Project description:ObjectiveCOVID-19-associated non-pharmaceutical interventions (NPI) have disrupted respiratory viral transmission. We quantified the changes in pediatric hospital admissions in 2020 from five different NPI phases in Western Australia for acute lower respiratory infections (ALRI) in children in the context of all-cause admissions.Study design and settingWe assessed anonymised hospitalization data from Perth Children's Hospital (Jan 2015-Dec 2020) for all-cause admissions, ALRI, febrile illnesses and trauma (negative control) in those <17 years. We evaluated absolute changes in admissions and the weekly change estimated from interrupted time-series models.ResultsThe absolute number of admissions was comparable in 2020 (15,678) vs. 2015 to 2019 average (15,310). Following the introduction of strict NPIs, all-cause admissions declined by 35%, recovered to pre-pandemic levels, then increased by 24% following NPI cessation. ALRI admissions in children <5 years initially declined by 89%, which was sustained throughout the gradual easing of NPI until an increase of 579% (997% in <3 months) following the final easing of NPI. Admissions for trauma showed minimal changes in 2020 compared to preceding years.ConclusionCOVID-19-associated NPI had significant unintended consequences in health service utilization, especially for ALRI and infants <3 months, prompting the need to understand viral transmission dynamics in young children.
| S-EPMC8600916 | biostudies-literature