scholarly journals Trends in inpatient care for psychiatric disorders in NHS hospitals across England, 1998/99–2019/20: an observational time series analysis

2021 ◽  
Author(s):  
Michelle Degli Esposti ◽  
Hisham Ziauddeen ◽  
Lucy Bowes ◽  
Aaron Reeves ◽  
Adam M. Chekroud ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Psychiatric Disorders ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Admission Rates ◽  
Series Analysis ◽  
Binomial Regression ◽  
Bed Days

Abstract Purpose It is unclear how hospitals are responding to the mental health needs of the population in England, against a backdrop of diminishing resources. We aimed to document patterns in hospital activity by psychiatric disorder and how these have changed over the last 22 years. Methods In this observational time series analysis, we used routinely collected data on all NHS hospitals in England from 1998/99 to 2019/20. Trends in hospital admissions and bed days for psychiatric disorders were smoothed using negative binomial regression models with year as the exposure and rates (per 1000 person-years) as the outcome. When linear trends were not appropriate, we fitted segmented negative binomial regression models with one change-point. We stratified by gender and age group [children (0–14 years); adults (15 years +)]. Results Hospital admission rates and bed days for all psychiatric disorders decreased by 28.4 and 38.3%, respectively. Trends were not uniform across psychiatric disorders or age groups. Admission rates mainly decreased over time, except for anxiety and eating disorders which doubled over the 22-year period, significantly increasing by 2.9% (AAPC = 2.88; 95% CI: 2.61–3.16; p < 0.001) and 3.4% (AAPC = 3.44; 95% CI: 3.04–3.85; p < 0.001) each year. Inpatient hospital activity among children showed more increasing and pronounced trends than adults, including an increase of 212.9% for depression, despite a 63.8% reduction for adults with depression during the same period. Conclusion In the last 22 years, there have been overall reductions in hospital activity for psychiatric disorders. However, some disorders showed pronounced increases, pointing to areas of growing need for inpatient psychiatric care, especially among children.

scholarly journals Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 480
Author(s):  
Rania Kousovista ◽  
Christos Athanasiou ◽  
Konstantinos Liaskonis ◽  
Olga Ivopoulou ◽  
George Ismailos ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Acinetobacter Baumannii ◽  
Regression Models ◽  
Tertiary Care ◽  
Antimicrobial Use ◽  
Care Hospital ◽  
Arima Models ◽  
Series Analysis ◽  
Resistance Rates

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

scholarly journals Association between weather factors and dengue in Dhaka: a time-series analysis

2021 ◽  
Vol 39 (1) ◽  
pp. 43-49
Author(s):  
Shafia Shaheen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Incidence Rate ◽  
Negative Binomial ◽  
Weather Data ◽  
Negative Binomial Regression Model ◽  
Weather Factors ◽  
Average Temperature ◽  
Series Analysis ◽  
Average Maximum

Background: There was an epidemic of dengue fever that happened  in Bangladesh  in the year of 2019. Temperature of this country has been raising which leads to changing in rainfall pattern. This study was aimed to investigate the relationship of weather factors and dengue incidence in Dhaka. Methods: A time series analysis was carried out by using 10 years weather data as average , maximum and minimum monthly temperature, average monthly humidity and average and cumulative monthly rainfall. Reported number of dengue cases was extracted from January 2009 to July 2019. Firstly, dengue incidence rate was  calculated. Correlation analysis and negative binomial regression model was developed. Results: Dengue incidence rate had sharp upward trend. Dengue incidence and mean, maximum and minimum average temperature showed statistically significant negative correlation at 3 months' lag. Highest incidence Rate Ratio (IRR) of dengue was found at minimum average temperature at 0 and I-month lag. Average humidity showed positive and significant correlation with dengue incidence at 0-month lag. Average and cumulative rainfall also showed negative and significant correlation only at 3-months lag period. Conclusion: Weather variability influences dengue incidence and the association between the weather factors are non­ linear and not consistent. So the study findings should be evaluated area basis with other local factors to develop early warning for dengue epidemic prediction. JOPSOM 2020; 39(1): 43-49

Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

2018 ◽  
Vol 37 (20) ◽  
pp. 3012-3026 ◽  
Author(s):  
Saptarshi Chatterjee ◽  
Shrabanti Chowdhury ◽  
Himel Mallick ◽  
Prithish Banerjee ◽  
Broti Garai
Keyword(s):  
Health Care ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Health Care Demand ◽  
Binomial Regression

What Do We Know About Changing Economic Activity of Firms?

2019 ◽  
pp. 232102221886979
Author(s):  
Radhika Pandey ◽  
Amey Sapre ◽  
Pramod Sinha
Keyword(s):  
Economic Activity ◽  
Regression Models ◽  
Statistical Approach ◽  
Negative Binomial ◽  
Large Data ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Binomial Regression ◽  
History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

Traffic accident modeling: some statistical issues

2006 ◽  
Vol 33 (9) ◽  
pp. 1115-1124 ◽  
Author(s):  
Z Sawalha ◽  
T Sayed
Keyword(s):  
Road Safety ◽  
Regression Models ◽  
Model Building ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Outlier Analysis ◽  
Accident Prediction ◽  
Binomial Regression ◽  
Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

The Effect of Universal Decolonization With Screening in Critical Care to Reduce MRSA Across an Entire Hospital

2017 ◽  
Vol 38 (4) ◽  
pp. 430-435 ◽  
Author(s):  
Craig W. Bradley ◽  
Martyn A. C. Wilkinson ◽  
Mark I. Garvey
Keyword(s):  
Time Series ◽  
United Kingdom ◽  
Time Series Analysis ◽  
Regression Models ◽  
Kernel Regression ◽  
Break Point ◽  
University Hospitals ◽  
Series Analysis ◽  
The United Kingdom ◽  
Break Points

OBJECTIVETo describe the effect of universal methicillin-resistant Staphylococcus aureus (MRSA) decolonization therapy in a large intensive care unit (ICU) on the rates of MRSA cases and acquisitions in a UK hospital.DESIGNDescriptive study.SETTINGUniversity Hospitals Birmingham (UHB) NHS Foundation Trust is a tertiary referral teaching hospital in Birmingham, United Kingdom, that provides clinical services to nearly 1 million patients every year.METHODSA break-point time series analysis and kernel regression models were used to detect significant changes in the cumulative monthly numbers of MRSA bacteremia cases and acquisitions from April 2013 to August 2016 across the UHB system.RESULTSPrior to 2014, all ICU patients at UHB received universal MRSA decolonization therapy. In August 2014, UHB discontinued the use of universal decolonization due to published reports in the United Kingdom detailing the limited usefulness and cost-effectiveness of such an intervention. Break-point time series analysis of MRSA acquisition and bacteremia data indicated that break points were associated with the discontinuation and subsequent reintroduction of universal decolonization. Kernel regression models indicated a significant increase (P<.001) in MRSA acquisitions and bacteremia cases across UHB during the period without universal decolonization.CONCLUSIONWe suggest that routine decolonization for MRSA in a large ICU setting is an effective strategy to reduce the spread and incidence of MRSA across the whole hospital.Infect Control Hosp Epidemiol 2017;38:430–435

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