seasonal effect

Introduction: Change point analysis is a technique for detecting change in trend over time. Segmented regression analysis and joinpoint analysis are used to try to determine the most important interventions which impact rates of healthcare associated infections (HAIs). AiMs: Use change point analysis to determine which interventions reduce rates of HAIs. Data and Methods: The data on HAIs was monitored by Health Protection Scotland (HPS) and includes staphylococcus aureus bacteraemia (SAB) and clostridium difficile infection (CDI). Since 2004, several interventions were implemented by NHS to reduce the rates and these were reported by Health Protection Scotland (HPS). Segmented regression and joinpoint models are investigated and developed to detect times when the indection rates changed significantly. Profile likelihood and bootstrapping are used to construct confidence intervals for the joinpoints. Results: Interventions which took place from was a significant change in the trend of MRSA bacteraemia. The trend of MSSA bacteraemia did change significantly from January-March 2011, however, joinpoint analysis did not indicate any change in MSSA bacteraemia. A segmented model suggested interventions around October-December 2009 had an impact on the rates of CDI in patients over 65 years and joinpoint analysis showed from April-June 2008 and January-March 2011 the rates changed. Conclusion: Segmented regression analysis detects changes in rates associated with specific interventions. It appears from this that training of hospital staff in dealing with infection outbreaks and the implementation of an antibiotic policy are associated with an observed reduction in the rates of SAB and CDI in healthcare settings. Joinpoint analysis gives the most robust modelling to estimate significant change. Keywords: Segmented regression; joinpoint analysis; seasonal effect; bootstrap confidence interval; profile likelihood confidence interval; healthcare associated infections; MRSA; MSSA; CDI. I. INTRODUCTION Everything in the world tends to change over time such as the economy, education and epidemiology. It is of interest to know when changes happened to determine the cause. Change point analysis aims to detect a time point when a trend in data changes. A trend in data may have multiple change points. The change point analysis addresses two aspects in the analysis of a trend-a hypothesis testing approach to decide if there are any change points and an estimation approach to locate the change. Detecting change points was investigated using different methods. Segmented regression analysis was used to discover change points where the rates change significantly after a specific intervention and thereby help improve the quality of the interventions [Wagner et al. (2002)]. Joinpoint analysis was used to identify change points at unknown times and to estimate the location of them. Using a joinpoint statistical software package [NCISR (2020)] the simplest joinpoint model (which in includes only a time variable) is fitted to the data. The minimum and maximum number of joinpoints is determined. The program starts with the minimum number of joinpoints and tests whether additional joinpoints are statistically significant using a permutation method. These are added to the model up to the maximum number. A grid search method has a discrete number of locations that are tested to find the best fitted model and joinpoints occur exactly at one or more of these locations (observed rate) [Kim (2001)].

A study was conducted in 2001 and 2002 to identify genotypic attributes relevant to performance and adaptation of ten maize cultivars grown on the field in the rainy and in the late seasons (terminal drought situation) in a humid zone of Nigeria. Data were collected on shoot biomass and seed yield and on leaf tissue concentrations of chlorophyll a and

Lactoferrin (Lf) and IgG were estimated in camel's milk from Kazakhstan, where 2 species of camels (Camelus bactrianus, Camelus dromedarius) and their hybrids cohabit. The concentrations of Lf and IgG were determined according to 3 variation factors: region (n = 4), season (n = 4), and species (n = 5; sample 4 was mixed milk and sample 5 was of unknown origin). The mean values in raw camel's milk were 0.229 +/- 0.135 mg/mL for Lf concentration and 0.718 +/- 0.330 mg/mL for IgG concentration. The seasonal effect was the only significant variation factor observed, with the highest values in the spring for Lf and in the winter for IgG. The Lf concentration varied in 1-wk postpartum milk from 1.422 to 0.586 mg/mL. The range in IgG concentration was wide and decreased from 132 to 4.75 mg/mL throughout the 7 d postpartum, with an important drop after parturition. In fermented milk, the lactoproteins are generally hydrolyzed. For milk samples from undefined species, discriminant an...

... DOI: 10.1080/00103620600585914 Zafar Iqbal Khan a , Muhammad Ashraf a , Altaf Hussain a & LR McDowell b * pages 1471-1483. ... (1979)4. Fick, KR, McDowell, LR, Miles, PH, Wilkinson, NS, Funk, JD and Conrad, JH 1979. ...

The effects of seasonal temperature change on the quality of easily degradable organic matter in municipal solid waste was monitored and the related influence on the anaerobic digester performance examined. It was verified that increased external temperature caused the transformation of the ethanol contained in the source-sorted organic fraction of municipal solid waste fed to the digester, into acetate. This transformation has been associated with a remarkable effect on the process kinetics of substrate utilization. Thus, the kinetic constant for the first order substrate utilization model in the summer (external temperature greater than 18-20°C) doubled with respect to the winter, although the digester was held at 35±2°C throughout.

Estimates are made of changes in effective runoff at a high spatial resolution for the island of Ireland under different climate change scenarios. The first part of the investigation examines changes in annual and seasonal effective runoff for the whole land area of Ireland. The rainfall-runoff model HYSIM is used to carry out the hydrological simulations. The output from the

... a Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA. Received 21 September 2007; revised 27 November 2007; accepted 28 November 2007. ... Variables: A, Ann Arbor; Y, Ypsilanti; D, Dearborn; c, cold season; w, warm season. ...

The precise calibration of relative gravity meters is essential for the accuracy of gravimetric surveys. We show that in the new Zugspitze cali- bration system established in 2004 - 2005 excellent accuracies of the linear calibration factor of relative gravity meters in the range of 1-2 * 10 -5 can be obtained. The calibration system is particularly useful for the precise calibration of Scintrex linear quartz gravity meters. The key advantages of the new calibration system are (a) the large gravity range of up to 528 mGal and (b) the short transport time of instruments by cable cars between the ab- solute gravity reference stations. This allows im- proving the accuracy of the Scintrex relative gra- vimetric observations by carrying out numerous ties over large gravity differences in a day or less. We study the accuracy and repeatability of Scintrex CG3 and CG5 calibration observations and the influence of seasonal effects due to environmental mass changes based on a time series of 5...

Previously described weed competition models that use competition indices based on weed size relative to tree size (e.g., tree height divided by weed height) require models of weed growth and models of weed-free tree growth. A method is presented to model these using standard sigmoidal growth functions and to incorporate regular seasonal patterns of tree growth using Fourier series approximations. The method is tested against data from a field trial at Rotorua, New Zealand, in which Pinus radiata D. Don was grown both on its own and in competition with several common weed species. Weed and tree height were best modelled by a Weibull function, and tree ground line diameter by a Schumacher function. Seasonal fluctuations in both tree and weed growth were adequately modelled by a single-term Fourier series. All weed species showed very similar, strong seasonal fluctuations in height growth, peaking in February. Seasonal fluctuations in tree height growth, and especially diameter growth...

SUMMARY - Seasonal effects of three stocking rates on resource availability and intake of goats were evaluated in a kermes oak (Quercus coccifera L.) shrubland in Greece from 1991 to 1994. Stocking rates were: moderate (MSR: 1 goat/ha/y), heavy (HSR: 2 goatslha/y) and very heavy (VHSR: 4 goatdhdy). In an adjacent kermes oak shrubland the effects of a continuous and a rotational grazing system on forage conditions and animal performance were studied from 1992 to 1994. The stocking rate was set at 1.3 goats/ha/y. Vegetation cover (62%), forage production (1413 kg/ha) and goat liveweight gain (-2.3 kg/goat) were significantly lower and bare soil + litter higher (38%) in the VHSR compared to MSR (85%, 2905 kg/ha, 1 .O kg/goat and 15%) and HSR (80%, 2067 kg/ha, 0.5 kg/goat and 20%) while there were no significant differences (P 5 0.05) between MSR and HSR in all parameters but the total forage production. The goats' forage intake was lower in HSR and VHSR (32.9 g/kg BW0.75 and 33.3 g...

HYDRUS-1D was used to simulate water flow and leaching of fecal coliforms and bromide (Br) through six undisturbed soil lysimeters (70cm depth by 50cm diameter) under field conditions. Dairy shed effluent (DSE) spiked with Br was applied to the lysimeters, which ...

Summary Fazio, E., P. Medica & A. Ferlazzo, 2009. Seasonal patterns of circulating β-endorphin, adrenocorticotropic hormone and cortisol levels in pregnant and barren mares. Bulg. J. Vet. Med., 12, No 2, 125−135. The study was carried out to investigate the changes of circulating β-endorphin, adrenocorticotropic hormone (ACTH) and cortisol concentrations in 18 Thoroughbred mares (n=12 pregnant and n=6 barren), aged

Effects of freshwater forcing from river discharge into the Indian Ocean on oceanic vertical structure and the Indian monsoons are investigated using a fully coupled, high-resolution, regional climate model. The effect of river discharge is included in the model by restoring sea surface salinity (SSS) toward observations. The simulations with and without this effect in the coupled model reveal a highly seasonal influence of salinity and the barrier layer (BL) on oceanic vertical density stratification, which is in turn linked to changes in sea surface temperature (SST), surface winds, and precipitation. During both boreal summer and winter, SSS relaxation leads to a more realistic spatial distribution of salinity and the BL in the model. In summer, the BL in the Bay of Bengal enhances the upper-ocean stratification and increases the SST near the river mouths where the freshwater forcing is largest. However, the warming is limited to the coastal ocean and the amplitude is not large e...