scholarly journals Environmental Recovery of Nosocomial Bacteria in a Companion Animal Shelter Before and After Infection Control Procedures

2021 ◽  
Vol 7 ◽  
Author(s):  
Sara Horsman ◽  
Hester Rynhoud ◽  
Xiaoyan Zhou ◽  
Ricardo J. Soares Magalhães ◽  
Justine S. Gibson ◽  
...  
Keyword(s):  
Bacterial Contamination ◽  
Negative Binomial ◽  
Health Care Facilities ◽  
Negative Binomial Regression Model ◽  
Large Animal ◽  
Animal Shelter ◽  
Animal Movements ◽  
Veterinary Clinic ◽  
Cleaning And Disinfection ◽  
Target Locations

While the effects of cleaning and disinfection practices on the reduction of environmental nosocomial bacteria are well-established in human and large animal veterinary hospitals, how animal movements within animal health care facilities influence environmental bacterial recovery is poorly understood. During three consecutive weeks, 155 electrostatic wipes were collected from the environment pre- and post-cleaning only or following disinfection from seven target locations within an animal shelter. All samples were cultured, and isolates were identified using a matrix-assisted laser desorption ionization—time of flight mass spectrometry. Social network analysis of animal movements during the sampling period was performed to estimate the level of connectivity of the seven target locations. The relationship between bacterial levels and connectivity estimates of the target locations were investigated using a negative binomial regression model with a random effect of sampling areas. Overall, our results indicate a significant reduction in the total bacterial contamination with disinfection when compared to cleaning only [Coefficient (Coef.) = −1.72, 95% Confidence Interval (CI) = −3.09, −0.34, P = 0.015]. Higher total bacterial contamination was significantly more likely in sampled areas with less animal movement connectivity (Coef. = −0.32, 95% CI = −0.49, −0.15, P ≤ 0.001). Pseudomonas aeruginosa and ampicillin resistant Enterobacteriaceae (Escherichia coli, Enterobacter spp. and Klebsiella spp.) were present in the animal holdings and in the shelters' veterinary clinic environment at all sampling times. Our findings demonstrate that cleaning followed by disinfection practices are effective at reducing environmental bacterial levels. Areas with less animal connectivity are more likely to have a higher bacterial contamination. These areas could represent environmental reservoirs for bacterial infection and should be targeted with effective cleaning and disinfection protocols.

scholarly journals The Demand for Primary Health Care Service in Nigeria: New Evidence from Facility Determinants

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Uche Abamba Osakede
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Power Supply ◽  
Negative Binomial ◽  
Health Care Service ◽  
Cost Of Care ◽  
Health Care Facilities ◽  
Negative Binomial Regression Model ◽  
Primary Health ◽  
Number Of Visits

Despite high distribution of Primary Health Care Centres (PHC) and low cost of care provision, patronage is low in Nigeria. Findings for the determinants of health care demand are one sided with focus on patient related factors. Less attention is given to facility determinants. This study examined facility determinants of the demand for PHC service in Nigeria using basic infrastructures, equipment and staff type.  The 2013-2014 SDI data covering 11,690 primary health care facilities was used. The Negative Binomial regression model was fitted to determine the effects of facility factors on number of visits. The results show more community workers and paraprofessionals and few medical Doctors and Nurses in PHC’s. Facilities are equipped with weighing scales, sphygmomanometer and stethoscope with few thermometer. High staff absence rate, lack of improved water source and non-regular power supply are also observed.  Findings associate presence of Nurses with increased number of visits than other staff type.  Child weighing scale, regular power supply and improved water are also key positive determinants. Better use of PHC’s in Nigeria is possible with medical personnel especially nurses in employment. Improved water, regular power and instruments like child weighing scale are also vital.

scholarly journals Urinary Bisphenol A, F and S Levels and Semen Quality in Young Adult Danish Men

2021 ◽  
Vol 18 (4) ◽  
pp. 1742
Author(s):  
Thea Emily Benson ◽  
Anne Gaml-Sørensen ◽  
Andreas Ernst ◽  
Nis Brix ◽  
Karin Sørig Hougaard ◽  
...  
Keyword(s):  
Bisphenol A ◽  
Male Fertility ◽  
Parental Education ◽  
Negative Binomial ◽  
Semen Quality ◽  
Sperm Count ◽  
Smoking Status ◽  
Sperm Concentration ◽  
First Trimester ◽  
Negative Binomial Regression Model

Bisphenol A (BPA) is considered an endocrine disruptor and has been associated with deleterious effects on spermatogenesis and male fertility. Bisphenol F (BPF) and S (BPS) are structurally similar to BPA, but knowledge of their effects on male fertility remains limited. In this cross–sectional study, we investigated the associations between exposure to BPA, BPF, and BPS and semen quality in 556 men 18–20 years of age from the Fetal Programming of Semen Quality (FEPOS) cohort. A urine sample was collected from each participant for determination of BPA, BPF, and BPS concentrations while a semen sample was collected to determine ejaculate volume, sperm concentration, total sperm count, sperm motility, and sperm morphology. Associations between urinary bisphenol levels (continuous and quartile–divided) and semen characteristics were estimated using a negative binomial regression model adjusting for urine creatinine concentration, alcohol intake, smoking status, body mass index (BMI), fever, sexual abstinence time, maternal pre–pregnancy BMI, and first trimester smoking, and highest parental education during first trimester. We found no associations between urinary bisphenol of semen quality in a sample of young men from the general Danish population.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

scholarly journals Repeat Induced Abortion among Chinese Women Seeking Abortion: Two Cross Sectional Studies

2021 ◽  
Vol 18 (9) ◽  
pp. 4446
Author(s):  
Longmei Tang ◽  
Shangchun Wu ◽  
Dianwu Liu ◽  
Marleen Temmerman ◽  
Wei-Hong Zhang
Keyword(s):  
Negative Binomial ◽  
Induced Abortion ◽  
Education Level ◽  
Married Women ◽  
Negative Binomial Regression Model ◽  
Related Factors ◽  
Cross Sectional ◽  
Lower Education Level ◽  
Induced Abortions ◽  
Lower Education

Background: In China, there were about 9.76 million induced abortions in 2019, 50% of which were repeat abortions. Understanding the tendency of repeat induced abortion and identifying its related factors is needed to develop prevention strategies. Methods: Two hospital-based cross-sectional surveys were conducted from 2005–2007 and 2013–2016 in 24 and 90 hospitals, respectively. The survey included women who sought an induced abortion within 12 weeks of pregnancy. The proportion of repeat induced abortions by adjusting the covariates through propensity score matching was compared between the two surveys, and the zero-inflated negative binomial regression model was established to identify independent factors of repeat induced abortion. Results: Adjusting the age, occupation, education, marital status and number of children, the proportion of repeat induced abortions in the second survey was found to be low (60.28% vs. 11.11%), however the unadjusted proportion was high in the second survey (44.97% vs. 51.54%). The risk of repeat induced abortion was higher among married women and women with children [ORadj and 95% CI: 0.31 (0.20, 0.49) and 0.08 (0.05, 0.13)]; the risk among service industry staff was higher when compared with unemployed women [ORadj and 95% CI: 0.19 (0.07, 0.54)]; women with a lower education level were at a higher risk of a repeat induced abortion (ORadj < 1). Compared with women under the age of 20, women in other higher age groups had a higher frequency of repeat induced abortions (IRadj: 1.78, 2.55, 3.27, 4.01, and 3.93, separately); the frequency of women with lower education levels was higher than those with a university or higher education level (IRadj > 1); the repeat induced abortion frequency of married women was 0.93 (0.90, 0.98) when compared to the frequency of unmarried women, while the frequency of women with children was 1.17 (1.10, 1.25) of childless women; the induced abortion frequency of working women was about 60–95% with that of unemployed women. Conclusions: The repeat induced abortion proportion was lower than 10 years ago. Induced abortion seekers who were married, aged 20 to 30 years and with a lower education level were more likely to repeat induced abortions.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

scholarly journals Improving the Efficiency of Robust Estimators for the Generalized Linear Model

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 88-107
Author(s):  
Alfio Marazzi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Linear Model ◽  
Negative Binomial ◽  
Small Sample ◽  
Hospital Length Of Stay ◽  
Robust Estimator ◽  
Negative Binomial Regression Model ◽  
Likelihood Estimator ◽  
Density Power Divergence

The distance constrained maximum likelihood procedure (DCML) optimally combines a robust estimator with the maximum likelihood estimator with the purpose of improving its small sample efficiency while preserving a good robustness level. It has been published for the linear model and is now extended to the GLM. Monte Carlo experiments are used to explore the performance of this extension in the Poisson regression case. Several published robust candidates for the DCML are compared; the modified conditional maximum likelihood estimator starting with a very robust minimum density power divergence estimator is selected as the best candidate. It is shown empirically that the DCML remarkably improves its small sample efficiency without loss of robustness. An example using real hospital length of stay data fitted by the negative binomial regression model is discussed.

scholarly journals Quantifying the Northward Spread of Ticks (Ixodida) as Climate Warms in Northern Russia

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 233
Author(s):  
Leonid N. Vladimirov ◽  
Grigory N. Machakhtyrov ◽  
Varvara A. Machakhtyrova ◽  
Albertus S. Louw ◽  
Netrananda Sahu ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Global Climate ◽  
Indigenous Communities ◽  
Tick Bite ◽  
Negative Binomial Regression Model ◽  
Tick Abundance ◽  
Climate Parameters ◽  
Average Annual Temperature ◽  
The Republic ◽  
Northern Russia

Climate change is affecting human health worldwide. In particular, changes to local and global climate parameters influence vector and water-borne diseases like malaria, dengue fever, and tick-borne encephalitis. The Republic of Sakha in northern Russia is no exception. Long-term trends of increasing annual temperatures and thawing permafrost have corresponded with the northward range expansion of tick-species in the Republic. Indigenous communities living in these remote areas may be severely affected by human and livestock diseases introduced by disease vectors like ticks. To better understand the risk of vector-borne diseases in Sakha, we aimed to describe the increase and spatial spread of tick-bite cases in the Republic. Between 2000 and 2018, the frequency of tick bite cases increased 40-fold. At the start of the period, only isolated cases were reported in southern districts, but by 2018, tick bites had been reported in 21 districts in the Republic. This trend coincides with a noticeable increase in the average annual temperature in the region since the 2000s by an average of 1 °C. Maps illustrate the northward spread of tick-bite cases. A negative binomial regression model was used to correlate the increase in cases with a number of climate parameters. Tick bite case frequency per district was significantly explained by average annual temperature, average temperature in the coldest month of the year, the observation year, as well as Selyaninov’s hydrothermal coefficient. These findings contribute to the growing literature that describe the relationship between tick abundance and spread in Northern Latitudes and changes in temperatures and moisture. Future studies might use these and similar results to map and identify areas at risk of infestation by ticks, as climates continue to change in Sakha.

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