scholarly journals Correction to: Measuring the impact of the Afordable Care Act Medicaid expansion on access to primary care using an interrupted time series approach

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Elizabeth A. Brown ◽  
Brandi M. White ◽  
Walter J. Jones ◽  
Mulugeta Gebregziabher ◽  
Kit N. Simpson
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Interrupted Time Series ◽  
Medicaid Expansion ◽  
Time Series Approach ◽  
Access To Primary Care ◽  
The Impact

An amendment to this paper has been published and can be accessed via the original article.

THE IMPACT OF THE HOTEL ROOM TAX: AN INTERRUPTED TIME SERIES APPROACH

1992 ◽  
Vol 45 (4) ◽  
pp. 433-441 ◽  
Author(s):  
CARL BONHAM ◽  
EDWIN FUJII ◽  
ERIC IM ◽  
JAMES MAK
Keyword(s):  
Time Series ◽  
Interrupted Time Series ◽  
Hotel Room ◽  
Time Series Approach ◽  
The Impact

The Impact of a National Antimicrobial Stewardship Program on Antibiotic Prescribing in Primary Care: An Interrupted Time Series Analysis

2018 ◽  
Vol 69 (2) ◽  
pp. 227-232 ◽  
Author(s):  
Violeta Balinskaite ◽  
Alan P Johnson ◽  
Alison Holmes ◽  
Paul Aylin
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Broad Spectrum ◽  
Interrupted Time Series ◽  
Series Data ◽  
Antibiotic Prescribing ◽  
Segmented Regression ◽  
Broad Spectrum Antibiotic ◽  
Stewardship Program ◽  
The Impact

Abstract Background The Quality Premium was introduced in 2015 to financially reward local commissioners of healthcare in England for targeted reductions in antibiotic prescribing in primary care. Methods We used a national antibiotic prescribing dataset from April 2013 until February 2017 to examine the number of antibiotic items prescribed, the total number of antibiotic items prescribed per STAR-PU (specific therapeutic group age/sex-related prescribing units), the number of broad-spectrum antibiotic items prescribed, and broad-spectrum antibiotic items prescribed, expressed as a percentage of the total number of antibiotic items. To evaluate the impact of the Quality Premium on antibiotic prescribing, we used a segmented regression analysis of interrupted time series data. Results During the study period, over 140 million antibiotic items were prescribed in primary care. Following the introduction of the Quality Premium, antibiotic items prescribed decreased by 8.2%, representing 5933563 fewer antibiotic items prescribed during the 23 post-intervention months, as compared with the expected numbers based on the trend in the pre-intervention period. After adjusting for the age and sex distribution in the population, the segmented regression model also showed a significant relative decrease in antibiotic items prescribed per STAR-PU. A similar effect was found for broad-spectrum antibiotics (comprising 10.1% of total antibiotic prescribing), with an 18.9% reduction in prescribing. Conclusions This study shows that the introduction of financial incentives for local commissioners of healthcare to improve the quality of prescribing was associated with a significant reduction in both total and broad-spectrum antibiotic prescribing in primary care in England.

Interrupted Time-Series Analysis to Evaluate the Impact of a National Antimicrobial Stewardship Campaign on Antibiotic Prescribing: A Typical Practice in China’s Primary Care

2020 ◽  
Author(s):  
Hui Li ◽  
Yanhong Gong ◽  
Jing Han ◽  
Shengchao Zhang ◽  
Shanquan Chen ◽  
...  
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Antimicrobial Stewardship ◽  
Broad Spectrum ◽  
Interrupted Time Series ◽  
Antibiotic Prescribing ◽  
Interrupted Time Series Analysis ◽  
Targeted Interventions ◽  
The Impact ◽  
Outpatient Prescriptions

Abstract Background After implementing the 2011 national antimicrobial stewardship campaign, few studies focused on evaluating its effect in China’s primary care facilities. Methods We randomly selected 11 community health centers in Shenzhen, China, and collected all outpatient prescriptions of these centers from 2010–2015. To evaluate the impact of local interventions on antibiotic prescribing, we used a segmented regression model of interrupted time series to analyze seven outcomes, i.e., percentage of prescriptions with antibiotics, and percentages of prescriptions with broad-spectrum antibiotics, with parenteral antibiotics, and with two or more antibiotics in all prescriptions or antibiotics-containing prescriptions. Results Overall, 1 482 223 outpatient prescriptions were obtained. The intervention was associated with a significant immediate change (–5.2%, P=.04) and change in slope (–3.1% per month, P<.01) for the percentage of prescriptions with antibiotics, and its relative cumulative effect at the end of the study was –74.0% (95% confidence interval, –79.0% to –69.1%). After the intervention, the percentage of prescriptions with broad-spectrum, and with parenteral antibiotics decreased dramatically by 36.7% and 77.3%, respectively, but their percentages in antibiotic-containing prescriptions decreased insignificantly. Percentage of prescriptions with two or more antibiotics in all prescriptions or antibiotics-containing prescriptions only showed immediate changes, but significant changes in slope were not observed. Conclusions A typical practice in Shenzhen, China, showed that strict enforcement of antimicrobial stewardship campaign could effectively reduce antibiotic prescribing in primary care with a stable long-term effect. However, prescribing of broad-spectrum and parenteral antibiotics was still prevalent. More targeted interventions are required to promote appropriate antibiotic use.

scholarly journals 1086Methodological challenges using routine aggregated data in a primary care implementation trial on opioid prescribing

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Tracey Farragher ◽  
Sarah Alderson ◽  
Paul Carder ◽  
Tom Willis ◽  
Robbie Foy
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Routine Data ◽  
Interrupted Time Series ◽  
Data Sources ◽  
Research Database ◽  
Feedback Intervention ◽  
Opioid Prescribing ◽  
Number Of Patients ◽  
The Impact

Abstract Focus of Presentation There is international concern over rising trends in opioid prescribing, largely attributed to prescribing for chronic non-cancer pain. We conducted a controlled interrupted time series study on anonymised, aggregated practice data to evaluate the effect of the Campaign to Reduce Opioid Prescribing (CROP) in reducing the number of patients taking opioid medication in West Yorkshire UK practices targeted by the feedback intervention, compared to practices outside of West Yorkshire. We will discuss the methodological challenges addressed in the collection and analysis of these data, and the implications for using routine data in trials. Findings Primary care data sources for feedback interventions include large-scale databases (General Practice Research Database), high-level nationally gathered databases (OpenPrescribing.com) or data extracted directly from electronic health records (EHR). We will discuss the implications of the different sources of data and compare the results from each, in understanding the impact of the feedback intervention of reducing opioid prescribing over time. The consequences of the heterogeneity of the data sources on the interrupted time series analysis undertaken will also be discussed and solutions outlined. Conclusions/Implications Routine data are heterogeneous, with different purposes, structures and collection methods, which have considerable implications on their use, analysis and interpretation. Researchers need to understand that the utility of routine data sources have implications (both practically and methodologically) in conducting pragmatic trials, which should be considered when planning and conducting future studies using routine data.

scholarly journals Educational intervention to optimise serum immunoglobulin test use in Irish primary care: an interrupted time series with segmented regression analysis

2019 ◽  
Vol 70 (691) ◽  
pp. e146-e154
Author(s):  
Sharon L Cadogan ◽  
John P Browne ◽  
Colin P Bradley ◽  
Anthony P Fitzgerald ◽  
Mary R Cahill
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Regression Analysis ◽  
Laboratory Data ◽  
Interrupted Time Series ◽  
Serum Immunoglobulin ◽  
Segmented Regression ◽  
Segmented Regression Analysis ◽  
The Impact ◽  
Test Ordering

BackgroundImplementation science experts recommend that theory-based strategies, developed in collaboration with healthcare professionals, have greater chance of success.AimThis study evaluated the impact of a theory-based strategy for optimising the use of serum immunoglobulin testing in primary care.Design and settingAn interrupted time series with segmented regression analysis in the Cork–Kerry region, Ireland. An intervention was devised comprising a guideline and educational messages-based strategy targeting previously identified GP concerns relevant to testing for serum immunoglobulins.MethodInterrupted time series with segmented regression analysis was conducted to evaluate the intervention, using routine laboratory data from January 2012 to October 2016. Data were organised into fortnightly segments (96 time points pre-intervention and 26 post-intervention) and analysed using incidence rate ratios with their corresponding 95% confidence intervals.ResultsIn the most parsimonious model, the change in trend before and after the introduction of the intervention was statistically significant. In the 1-year period following the implementation of the strategy, test orders were falling at a rate of 0.42% per fortnight (P<0.001), with an absolute reduction of 0.59% per fortnight, corresponding to a reduction of 14.5% over the 12-month study period.ConclusionThe authors’ tailored guideline combined with educational messages reduced serum immunoglobulin test ordering in primary care over a 1-year period. Given the rarity of the conditions for which the test is utilised and the fact that the researchers had only population-level data, further investigation is required to examine the clinical implications of this change in test-ordering patterns.

scholarly journals Understanding the Implementation of Hierarchical Medical System Policy: An Interrupted Time Series Analysis of a Pilot Scheme in China

2021 ◽  
Author(s):  
Huajie Hu ◽  
Ruilin Wang ◽  
Huangqianyu Li ◽  
Sheng Han ◽  
Peng Shen ◽  
...  
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Healthcare System ◽  
Betweenness Centrality ◽  
Interrupted Time Series ◽  
Medical System ◽  
Patient Encounter ◽  
The Mean ◽  
The Impact ◽  
Hierarchical Medical System

Abstract Background The Chinese healthcare system faces a dilemma between its hospital-centric approach to healthcare delivery and a rapidly aging population that requires strong primary care. To improve system efficiency and continuity of care, the Hierarchical Medical System (HMS) policy package was implemented in 2015 in Zhejiang province, China. This paper investigated the impact of HMS on the local healthcare system.MethodsWe conducted a repeated cross-sectional study with quarterly data collected between 2010 and 2018 from Yinzhou district, Ningbo. The data was analyzed with an interrupted time series (ITS) design to assess the impact of HMS on the changes of three outcome variables: primary care physicians (PCPs) patient encounter ratio (i.e., the mean quarterly number of patient encounters of PCPs divided by that of all other physicians), PCP degree ratio (i.e., the mean degree of PCPs divided by that of all other physicians), PCP betweenness centrality ratio (i.e., the mean betweenness centrality of PCPs divided by that of all other physicians). Results272,267 patients visited doctors for hypertension between 2010 and 2018. Compared to the counterfactual in the fourth quarter of 2018, the PCP patient encounter ratio rose by 42.7% (95%CI: 27.1—58.2, p<0.001), the PCP degree ratio increased by 23.6% (95%CI: 8.6—38.5, p<0.01), and the PCP betweenness centrality ratio grew by 129.4% (95%CI: 87.1—171.7, p<0.001).ConclusionsThe HMS policy can incentivize patients to visit primary care facilities and enhance the centrality of PCPs within their professional network. Local policymakers should sustain HMS policy efforts to obtain long-term and large-scale benefits.

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