scholarly journals PRimary care rEsponse to domestic violence and abuse in the COvid-19 panDEmic (PRECODE): protocol of a rapid mixed-methods study in the UK

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Eszter Szilassy ◽  
Estela Capelas Barbosa ◽  
Sharon Dixon ◽  
Gene Feder ◽  
Chris Griffiths ◽  
...  
Keyword(s):  
Primary Care ◽  
Domestic Violence ◽  
Time Series ◽  
Linear Regression Analysis ◽  
Sensitivity Analyses ◽  
Series Data ◽  
The Uk ◽  
The Impact ◽  
Domestic Violence And Abuse ◽  
Care Response

Abstract Background The implementation of lockdowns in the UK during the COVID-19 pandemic resulted in a system switch to remote primary care consulting at the same time as the incidence of domestic violence and abuse (DVA) increased. Lockdown-specific barriers to disclosure of DVA reduced the opportunity for DVA detection and referral. The PRECODE (PRimary care rEsponse to domestic violence and abuse in the COvid-19 panDEmic) study will comprise quantitative analysis of the impact of the pandemic on referrals from IRIS (Identification and Referral to Improve Safety) trained general practices to DVA agencies in the UK and qualitative analysis of the experiences of clinicians responding to patients affected by DVA and adaptations they have made transitioning to remote DVA training and patient support. Methods/Design Using a rapid mixed method design, PRECODE will explore and explain the dynamics of DVA referrals and support before and during the pandemic on a national scale using qualitative data and over four years of referrals time series data. We will undertake interrupted-time series and non-linear regression analysis, including sensitivity analyses, on time series of referrals to DVA services from routinely collected data to evaluate the impact of the pandemic and associated lockdowns on referrals to the IRIS Programme, and analyse key determinants associated with changes in referrals. We will also conduct an interview- and observation-based qualitative study to understand the variation, relevance and feasibility of primary care responses to DVA before and during the pandemic and its aftermath. The triangulation of quantitative and qualitative findings using rapid analysis and synthesis will enable the articulation of multiscale trends in primary care responses to DVA and complex mechanisms by which these responses have changed during the pandemic. Discussion Our findings will inform the implementation of remote primary care and DVA service responses as services re-configure. Understanding the adaptation of clinical and service responses to DVA during the pandemic is crucial for the development of evidence-based, effective remote support and referral beyond the pandemic. Trial registration PRECODE is an observational epidemiologic study, not an intervention evaluation or trial. We will not be reporting results of an intervention on human participants.

scholarly journals Exploring primary care responses to domestic violence and abuse (DVA): operationalisation of a national initiative

2019 ◽  
Vol 21 (2) ◽  
pp. 144-154 ◽  
Author(s):  
Julie McGarry ◽  
Basharat Hussain ◽  
Kim Watts
Keyword(s):  
Primary Care ◽  
Domestic Violence ◽  
Care Setting ◽  
Primary Care Setting ◽  
Local Level ◽  
Team Approach ◽  
Content Type ◽  
The Uk ◽  
The Impact ◽  
Domestic Violence And Abuse

Purpose In the UK, the Identification and Referral to Improve Safety (IRIS) initiative has been developed for use within primary care to support women survivors of domestic violence and abuse (DVA). However, while evaluated nationally, less is known regarding impact of implementation at a local level. The purpose of this paper is to explore the effectiveness of IRIS within one locality in the UK. Design/methodology/approach A qualitative study using interviews/focus groups with primary care teams and women who had experienced DVA in one primary care setting in the UK. Interviews with 18 participants from five professional categories including: general practitioners, practice nurses, practice managers, assistant practice managers and practice receptionists. Focus group discussion/interview with seven women who had accessed IRIS. Data were collected between November 2016 and March 2017. Findings Five main themes were identified for professionals: Team role approach to training, Professional confidence, Clear pathway for referral and support, Focussed support, Somewhere to meet that is a “safe haven”. For women the following themes were identified: Longevity of DVA; Lifeline; Face to face talking to someone; Support and understood where I was coming from; A place of safety. Practical implications IRIS played a significant role in helping primary care professionals to respond effectively. For women IRIS was more proactive and holistic than traditional approaches. Originality/value This study was designed to assess the impact that a local level implementation of the national IRIS initiative had on both providers and users of the service simultaneously. The study identifies that a “whole team approach” in the primary care setting is critical to the effectiveness of DVA initiatives.

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.

scholarly journals Disruption of a primary health care domestic violence and abuse service in two London boroughs: interrupted time series evaluation

2020 ◽  
Author(s):  
Jasmina Panovska-Griffiths ◽  
Alex Hardip Sohal ◽  
Peter Martin ◽  
Estela Barbosa Capelas ◽  
Medina Johnson ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Time Series ◽  
Service Providers ◽  
Interrupted Time Series ◽  
Referral Rate ◽  
System Level ◽  
Health Concern ◽  
Service Disruption ◽  
The Impact ◽  
Domestic Violence And Abuse

Abstract Background Domestic violence and abuse (DVA) is experienced by about 1/3 of women globally and remains a major health concern worldwide. IRIS (Identification and Referral to Improve Safety of women affected by DVA) is a complex, system-level, training and support programme, designed to improve the primary healthcare response to DVA. Following a successful trial in England, since 2011 IRIS has been implemented in eleven London boroughs. In two boroughs the service was disrupted temporarily. This study evaluates the impact of that service disruption.Methods We used anonymised data on daily referrals received by DVA service providers from general practices in two IRIS implementation boroughs that had service disruption for a period of time (six and three months). In line with previous work we refer to these as boroughs B and C. The primary outcome was the number of daily referrals received by the DVA service provider across each borough over 48 months (March 2013-April 2017) in borough B and 42 months (October 2013-April 2017) in borough C. The data were analysed using interrupted-time series, non-linear regression with sensitivity analyses exploring different regression models. Incidence Rate Ratio (IRR), 95% confidence intervals and p-values associated with the disruption were reported for each borough.Results A mixed-effects negative binomial regression was the best fit model to the data. In borough B, the disruption, lasted for about six months, reducing the referral rate significantly (p=0.006) by about 70% (95%CI=(23%,87%)). In borough C, the three-month service disruption, also significantly (p=0.005), reduced the referral rate by about 49% (95% CI=(18%,68%)). Conclusions Disrupting the IRIS service substantially reduced the rate of referrals to DVA service providers. Our findings are evidence in favour of continuous funding and staffing of IRIS as a system level programme.

scholarly journals Disruption of a primary health care domestic violence and abuse service in two London boroughs: interrupted time series evaluation

2020 ◽  
Author(s):  
Jasmina Panovska-Griffiths ◽  
Alex Hardip Sohal ◽  
Peter Martin ◽  
Estela Barbosa Capelas ◽  
Medina Johnson ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Time Series ◽  
Service Providers ◽  
Interrupted Time Series ◽  
Referral Rate ◽  
System Level ◽  
Health Concern ◽  
Service Disruption ◽  
The Impact ◽  
Domestic Violence And Abuse

Abstract Background Domestic violence and abuse (DVA) is experienced by about 1/3 of women globally and remains a major health concern worldwide. IRIS (Identification and Referral to Improve Safety of women affected by DVA) is a complex, system-level, training and support programme, designed to improve the primary healthcare response to DVA. Following a successful trial in England, since 2011 IRIS has been implemented in eleven London boroughs. In two boroughs the service was disrupted temporarily. This study evaluates the impact of that service disruption.Methods We used anonymised data on daily referrals received by DVA service providers from general practices in two IRIS implementation boroughs that had service disruption for a period of time (six and three months). In line with previous work we refer to these as boroughs B and C. The primary outcome was the number of daily referrals received by the DVA service provider across each borough over 48 months (March 2013-April 2017) in borough B and 42 months (October 2013-April 2017) in borough C. The data were analysed using interrupted-time series, non-linear regression with sensitivity analyses exploring different regression models. Incidence Rate Ratios (IRRs), 95% confidence intervals and p-values associated with the disruption were reported for each borough.Results A mixed-effects negative binomial regression was the best fit model to the data. In borough B, the disruption, lasted for about six months, reducing the referral rate significantly (p=0.006) by about 70% (95%CI=(23%,87%)). In borough C, the three-month service disruption, also significantly (p=0.005), reduced the referral rate by about 49% (95% CI=(18%,68%)). Conclusions Disrupting the IRIS service substantially reduced the rate of referrals to DVA service providers. Our findings are evidence in favour of continuous funding and staffing of IRIS as a system level programme.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

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.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

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