scholarly journals Trends, variations and prediction of staff sickness absence rates among NHS ambulance services in England: a time series study

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e053885
Author(s):  
Zahid B Asghar ◽  
Paresh Wankhade ◽  
Fiona Bell ◽  
Kristy Sanderson ◽  
Kelly Hird ◽  
...  
Keyword(s):  
Time Series ◽  
Sickness Absence ◽  
Moving Average ◽  
Clinical Staff ◽  
Full Time ◽  
Full Time Equivalent ◽  
Service Staff ◽  
Individual Participant ◽  
Enabling Services ◽  
Over Time

ObjectivesOur aim was to measure ambulance sickness absence rates over time, comparing ambulance services and investigate the predictability of rates for future forecasting.SettingAll English ambulance services, UK.DesignWe used a time series design analysing published monthly National Health Service staff sickness rates by gender, age, job role and region, comparing the 10 regional ambulance services in England between 2009 and 2018. Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) models were developed using Stata V.14.2 and trends displayed graphically.ParticipantsIndividual participant data were not available. The total number of full-time equivalent (FTE) days lost due to sickness absence (including non-working days) and total number of days available for work for each staff group and level were available. In line with The Data Protection Act, if the organisation had less than 330 FTE days available during the study period it was censored for analysis.ResultsA total of 1117 months of sickness absence rate data for all English ambulance services were included in the analysis. We found considerable variation in annual sickness absence rates between ambulance services and over the 10-year duration of the study in England. Across all the ambulance services the median days available were 1 336 888 with IQR of 548 796 and 73 346 median days lost due to sickness absence, with IQR of 30 551 days. Among clinical staff sickness absence varied seasonally with peaks in winter and falls over summer. The winter increases in sickness absence were largely predictable using seasonally adjusted (SARIMA) time series models.ConclusionSickness rates for clinical staff were found to vary considerably over time and by ambulance trust. Statistical models had sufficient predictive capability to help forecast sickness absence, enabling services to plan human resources more effectively at times of increased demand.

Earnings trends among older employees in England and Wales, 1972–2001

2003 ◽  
Vol 23 (3) ◽  
pp. 363-374 ◽  
Author(s):  
TONY MALLIER ◽  
DAVID MORRIS
Keyword(s):  
Time Series ◽  
Great Britain ◽  
Occupational Group ◽  
Full Time ◽  
Male And Female ◽  
Older Employees ◽  
Female Employees ◽  
Earnings Trends ◽  
Full Time Employment ◽  
Over Time

This article considers the hypothesis that ‘older people in full-time employment normally receive earnings below the level previously enjoyed’, by examining the money and real earnings of older British full-time employees as they age. After a review of the factors that influence earnings, data from the New Earnings Survey of Great Britain are used to estimate average gross weekly money and real earnings of two cohorts of manual and non-manual workers as they age. The two cohorts were born respectively in 1927 and 1937, and male and female employees are considered separately. The estimates are used to develop time series age-earnings profiles of real earnings. These suggest that the average full-time older employee normally benefits over time from rising real earnings as a consequence of increases in national prosperity, although the increases vary by gender, occupational group and cohort. Older female employees benefited more than males from significantly higher percentage increases in their average real earnings, and between 1981–2000 average real earnings in non-manual occupations rose relative to manual workers' earnings.

PP13 Understanding sickness absence in the ambulance service

2019 ◽  
Vol 36 (10) ◽  
pp. e6.3-e7
Author(s):  
Laura Simmons ◽  
Graham Law ◽  
Zahid Asghar ◽  
Ruth Gaunt ◽  
A Niroshan Siriwardena
Keyword(s):  
Sickness Absence ◽  
Coping Styles ◽  
Well Being ◽  
Health Condition ◽  
Ambulance Service ◽  
Stress Coping ◽  
Full Time ◽  
Online Questionnaire ◽  
Daily Stress ◽  
Service Staff

BackgroundAmbulance service employees have high sickness absence rates compared to other National Health Service (NHS) occupations. The aim of this study was to understand factors linked to sickness absence in front-line ambulance service staff by determining whether there was an association between work and daily (non-work-related) stress, coping styles, demographic variables (health conditions, overtime hours, length of time in service, shift pattern, age and sex) and sickness absence.MethodsWe used a cross-sectional design. An opportunity sampling method was utilised to recruit full-time clinical and management employees from a UK ambulance service to complete an online questionnaire. Multiple linear regression was used to determine whether and to what extent variation in sickness absence could be explained by the independent variables of interest listed.ResultsA total of 101 participants, including paramedics, team leaders and ambulance technicians, completed the questionnaire. Participants were aged 24 to 62 years (Mean [M]=45.29, Standard Deviation [SD]=9.97) with an average 13.8 years in the service (SD=9.67). Sickness absence rates ranged from 0 to 83.3% (M=8.92, SD=14.99). Work and daily stress, coping styles, overtime hours and the presence of a health condition accounted for 17.5% of the variance in sickness absence with adjusted R2=13.2%. Work and daily stress, coping styles, overtime hours and the presence of a health condition significantly predicted sickness absence, F(5, 95) =4.039, p=0.002. Those with a health condition were 9.46 times more likely, on average, to have a leave of sickness absence.ConclusionsOur findings suggest that the presence of a health condition may affect sickness absence more than stress and coping styles. When designing interventions, it may be important to consider preventative measures that improve staff well-being and health status while also reducing sickness absence rates.

scholarly journals Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

2019 ◽  
Vol 34 (5) ◽  
pp. 971-994 ◽  
Author(s):  
Eric P. Thelin ◽  
Rahul Raj ◽  
Bo-Michael Bellander ◽  
David Nelson ◽  
Anna Piippo-Karjalainen ◽  
...  
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Moving Average ◽  
Cerebrovascular Reactivity ◽  
Reactivity Index ◽  
Mean Values ◽  
Similar Information ◽  
Statistical Time ◽  
Map Data ◽  
Over Time

Abstract Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p < 0.0001 for each patient). Thus, these particular L-PRx variants appear closest in nature to standard PRx. ICP and MAP derived via 10-s or minute based averaging display similar statistical time-series structure and co-variance patterns. PRx and L-PRx based on shorter windows also behave similarly over time. These results imply certain L-PRx variants may carry similar information to PRx in TBI.

A threshold mixed count time series model: estimation and application

2019 ◽  
Vol 24 (2) ◽  
Author(s):  
Mardi Dungey ◽  
Vance L. Martin ◽  
Chrismin Tang ◽  
Andrew Tremayne
Keyword(s):  
Time Series ◽  
Global Financial Crisis ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Efficient Method Of Moments ◽  
The Great Depression ◽  
Autoregressive Moving Average Model ◽  
Global Banking ◽  
Method Of Moments Estimator ◽  
Over Time

AbstractA new class of integer time series models is proposed to capture the dynamic transmission of count processes over time. The approach extends existing integer mixed autoregressive-moving average models (INARMA) by allowing for shifts in the dynamics of the count process through regime changes, referred to as a threshold integer autoregressive-moving average model (TINARMA). An efficient method of moments estimator is proposed, with standard errors based on subsampling, as maximum likelihood methods are infeasible for TINARMA processes. Applying the framework to global banking crises over 200 years of data, the empirical results show strong evidence of autoregressive and moving average dynamics which vary across systemic and nonsystemic regimes over time. Coherent forecast distributions are also produced with special attention given to the Great Depression and the more recent Global Financial Crisis.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

ANALISIS BEBAN KERJA DENGAN METODE FULL TIME QUIVALENT UNTUK MENGOPTIMALKAN KINERJA PADA BAGIAN PRODUKSI DI ERLANGGA KONVEKSI, MALANG

2020 ◽  
Vol 10 (2) ◽  
pp. 55-59
Author(s):  
Irgi Achmad Fahrezy ◽  
ST. Salmia L. A ◽  
Soemanto Soemanto
Keyword(s):  
Quality Control ◽  
Full Time ◽  
Full Time Equivalent

Pertumbuhanydan permintaan akan sandang yangysemakin meningkat menuntutbperusahan konveksi untuk memiliki tingkat produktifitas yang tinggi, dimana proses ini dapat dilakukan dengan cara meningkatkanbproduktifitas karyawannya. Erlangga Konveksi adalah salah satu perusahaan konveksi yang berdiri tahun 2010. Masalah yang terjadi di Erlangga Konveksi adalah tidak seimbangnya waktu proses produksi pada tiap stasiun kerja yang menyebabkan terjadinya penambahan jumlahpwaktu kerja dan menyebabkan penumpukanfdan banyak kegiatan dari operator yang menghabiskantwaktu dimana operator banyak melakukan kegiatan di luar dari stasiun kerja mereka sendiri untuk membantu operator di stasiun kerja lainya. Untuk itu perlu dilakukan pengukuran beban kerja sebagai dasar perhitungan kebutuhan tenaga kerja yang sesuai pada bagian produksi. Metode yang digunakan dalam penelitian ini adalah metode Full Time Equivalent. Hasil pengukuran menunjukkan beban kerja adalah sebesar 0,33 pada operator gambar pola; 0,29 pada operator  pemotongan 1; 0,31 pada operator pemotongan 2; 0,21 pada operator sablon 1 dan 2; 0,22 pada operator press sablon; 1,24 pada operator jahit obras 1; 1,27 padaooperator jahit obras 2; 0,34 pada operator jahit rantai; 0,25 pada operator cutting sebelumnoverdeck; 0,55 pada operator overdeck 1 dan 2; 0,57 pada operator overdeck 3; 0,18 pada operator quality control 1 dan 2; 0,14 pada operator steam; 0,42 pada operatorpsetrika dan 0,20 pada operator packaging. Berdasarkan beban kerja yang telah dihitung pada masing-masing operatorybagian produksi Erlangga Konveksi, Malang, jumlah tenaga optimal pada bagian produksi adalah sebanyak 7 orang yang terbagi ke dalam 7 stasiun kerja.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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