scholarly journals Correlation of school absenteeism and laboratory results for Flu A in Alberta, Canada

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Elizabeth Birk-Urovitz ◽  
Ye Li ◽  
Steven Drews ◽  
Christopher Sikora ◽  
Deena Hinshaw ◽  
...  
Keyword(s):  
Public Health ◽  
Regression Models ◽  
Laboratory Data ◽  
Calculated Data ◽  
School Absenteeism ◽  
Linear Regression Models ◽  
Public Health Action ◽  
Percent Positivity ◽  
The Difference ◽  
Student Absenteeism

ObjectiveTo assess the correlations between weekly rates of elementaryschool absenteeism due to illness (SAi) and percent positivity forinfluenza A from laboratory testing (PPFluA) when conducted at acity level from September to December over multiple years.IntroductionRates of student absenteeism in schools have been mainly used todetect outbreaks in schools and prompt public health action to stoplocal transmission1,2. A report by Mogto et al.3stated that aggregatedcounts of school absenteeism (SAi) were correlated with PPFluA, butthe sample may have been biased. The purpose of this study was toassess the correlation between aggregated rates of SAi and PPFluAfor two cities, Calgary and Edmonton, in Alberta. In such situations,SAi could potentially be used as a proxy for PPFluA when there arenot enough samples for stable laboratory estimates.MethodsThe Alberta Real-Time Syndromic Surveillance Net (ARTSSN)4collects elementary SA data from the two major school boards intwo cities in Alberta with populations >800,000. Since reasons forSA are stated, rates of SAi can be calculated. Data were obtained forthree years, 2012 to 2014, for each city. Laboratory data on tests ofrespiratory agents using a standardized protocol were obtained fromAlberta’s Provincial Laboratory for Public Health for the same timeperiod and locations. The dates of the specimens being received bythe laboratory were used in this analysis. For each data source, therelative proportions (SAi and PPFluA) were calculated. Data forthe first week of school in September and for the last two weeks ofDecember were removed for each year due to the SAi rates beingunstable. Linear regression models were constructed, with rates ofSAi predicted by PPFluA. Separate models were run for each cityand for each year, resulting in a total of 6 models. Percent positivityfor entero-rhinoviruses (PPERV) was added to see if it improved themodel. The regression models were created using Excel and checkedin the statistical programs, SAS and R. An analysis to assess theinfluence of a lag period was assessed using R.ResultsFor each city, the provincial lab tested between 4,000 and 6,000specimens each fall and SAi rates were based on denominators ofbetween 20,000 and 36,000 children. The R2, betas, and p-valuesfor all 6 regression models are shown in Table 1. The minimumcorrelation value was 0.693 and the maximum was 0.935. Dueto the strong negative correlations between PPERV and PPFluA,PPERV was not retained in the models. Looking at the lag periods,the maximum correlations occurred at a zero week lag in two years(2012 and 2014) and at a -1 week lag in 2013. The two years with azero lag were both dominated by a H3N2 strain while the year withmainly a H1N1 strain showed a lag of -1. Only one year of H1N1 datawas available for analysis.ConclusionsWe observed strong correlations between the weekly rates ofelementary SAi and PPFluA at the city level over three years, fromSeptember to December. The reasons for the difference in lag timesbetween the H1N1 and H3N2 seasons are being investigated.

scholarly journals An outbreak of COVID-19 associated with a fitness centre in Saskatchewan: Lessons for prevention

2021 ◽  
Vol 47 (11) ◽  
pp. 485-490
Author(s):  
Maureen Anderson ◽  
Ashok Chhetri ◽  
Edith Halyk ◽  
Amanda Lang ◽  
Ryan McDonald ◽  
...  
Keyword(s):  
Public Health ◽  
High School ◽  
Laboratory Data ◽  
Control Measures ◽  
Fourth Generation ◽  
Infectious Period ◽  
Epidemiological Methods ◽  
Sequencing Data ◽  
Public Health Action ◽  
Fitness Facility

Background: An outbreak of the coronavirus disease 2019 (COVID-19) occurred in Saskatchewan from September 12 to October 20, 2020. The index event, attendance at a local gym, seeded six additional clusters/outbreaks in multiple settings. These included a high school, a hospital, three workplaces (A, B and C) and several households. The overall cluster comprised 63 cases, 27 gym members and an additional 36 second, third and fourth generation cases. Methods: All outbreak-related, laboratory-confirmed cases of COVID-19 were included in the analysis. Local public health authorities interviewed all cases and contacts and conducted environmental investigations of the fitness facility. We used descriptive epidemiological methods to understand transmission dynamics of the gym-associated cluster using case investigation, contact investigation and laboratory data, including whole genome sequencing. Results: Sequencing data confirmed the unique lineage of cluster-related cases (n=32 sequenced; severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] lineage B.1.1.72). In addition to gym attendance, infectious cases attended high school and were involved in other activities. Despite ongoing transmission in the fitness facility, no secondary cases were identified in the high school where four student belonging to the cluster attended class during their infectious period. Conclusion: We describe an outbreak of COVID-19 where the index case(s) attended a fitness facility, and further spread occurred for 38 days despite active-case finding and isolation of positive cases over this period. Due to gym attendance over time, short-term closing and cleaning may not interrupt chains of transmission. Targeted, preventive public health action in fitness facilities may be warranted. Control measures worked to limit in-school acquisition.

scholarly journals Older Adults’ Worry about COVID-19: Associations with Experiences of COVID-19 Among Social Connections

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 744-744
Author(s):  
Amanda Leggett ◽  
Hyun Jung Koo ◽  
Lindsay Kobayashi ◽  
Jessica Finlay ◽  
Hannah Lee ◽  
...  
Keyword(s):  
Public Health ◽  
Older Adults ◽  
Regression Models ◽  
Social Connections ◽  
Linear Regression Models ◽  
Physical And Mental Health ◽  
Disease Symptoms ◽  
Increased Risk ◽  
Higher Educational Attainment

Abstract The COVID-19 pandemic has challenged the physical and mental health of older adults, yet it is unknown how much older adults worry about their own exposure. As older adults are at increased risk for severe complications from COVID-19, understanding patterns of worry may inform public health guidelines and interventions for this age group. We investigated older adults’ worry about COVID-19 in the early months of the pandemic and associations with familial/friend’s diagnosis or disease symptoms. Data comes from the baseline (April/May 2020), one-month, and two-month follow-up surveys from the COVID-19 Coping Study, a national longitudinal cohort study of US adults aged ≥55. We used linear regression models to investigate the association between self-reported familial/friend diagnosis or symptoms with pandemic worry, accounting for demographic factors and individual diagnosis or experience of COVID-19 symptoms. Participants (Baseline=4379, 1 month= 2553, 2 month=2682) were 67 years old on average, 72% were female, 5.7% were non-White, and 80.5% had a college degree. At baseline, 26.6% of participants had friends or family who had been diagnosed or experienced symptoms of COVID-19. Having friends or family diagnosed or with symptoms of COVID-19 (B=0.08, SE=0.04, p<.05), being female (B=0.42, SE=0.03, p<.001), and having higher educational attainment (B=0.06, SE=0.02, p<.001) were significantly associated with greater worry about COVID-19. These associations were consistent over 3 months. Understanding if worry about the pandemic correlates with following public health guidelines is a key next step so intervention strategies can prioritize older adults and their social networks.

scholarly journals Influence of year-on-year performance on final degree classification in a chiropractic master's degree program

2016 ◽  
Vol 30 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Philip Dewhurst ◽  
Jacqueline Rix ◽  
David Newell
Keyword(s):  
Logistic Regression ◽  
Problem Solving ◽  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
The Difference ◽  
Degree Classification ◽  
Grade Performance ◽  
Final Degree

Objective: We explored if any predictors of success could be identified from end-of-year grades in a chiropractic master's program and whether these grades could predict final-year grade performance and year-on-year performance. Methods: End-of-year average grades and module grades for a single cohort of students covering all academic results for years 1–4 of the 2013 graduating class were used for this analysis. Analysis consisted of within-year correlations of module grades with end-of-year average grades, linear regression models for continuous data, and logistic regression models for predicting final degree classifications. Results: In year 1, 140 students were enrolled; 85.7% of students completed the program 4 years later. End-of-year average grades for years 1–3 were correlated (Pearson r values ranging from .75 to .87), but the end-of-year grades for years 1–3 were poorly correlated with clinic internship performance. In linear regression, several modules were predictive of end-of-year average grades for each year. For year 1, logistic regression showed that the modules Physiology and Pharmacology and Investigative Imaging were predictive of year 1 performance (odds ratio [OR] = 1.15 and 0.9, respectively). In year 3, the modules Anatomy and Histopathology 3 and Problem Solving were predictors of the difference between a pass/merit or distinction final degree classification (OR = 1.06 and 1.12, respectively). Conclusion: Early academic performance is weakly correlated with final-year clinic internship performance. The modules of Anatomy and Histopathology year 3 and Problem Solving year 3 emerged more consistently than other modules as being associated with final-year classifications.

Spatial Distance to Epicenter, Risk Perceptions, and Preventive Behaviors Against Health Risks in the Initial Stage of the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Diyi Liu ◽  
Sanmei Wen ◽  
Jing Su
Keyword(s):  
Public Health ◽  
Risk Perceptions ◽  
Regression Models ◽  
Spatial Distance ◽  
Linear Regression Models ◽  
Preventive Behaviors ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Perceived Susceptibility ◽  
Perceived Severity

BACKGROUND The rapid global spread of COVID-19 has become a monumental public health emergency. Gauging people’s psychological and behavioral reactions in an initial alerting stage is crucial for helping public health authorities to manage the epidemic. OBJECTIVE To investigate how spatial distance from the epicenter of Wuhan influenced people’s risk perceptions regarding COVID-19. Additionally, how risk perceptions, in concert with demographic variables, influenced the adoption of different preventive behaviors in the early stages of the outbreak. METHODS We conducted a national cross-sectional survey from January 21, 2020 to January 23, 2020. We assessed the association between spatial distance from the epicenter and participants’ risk perceptions using linear regression models. We used binomial logistic regression models to calculate the determinants of the adoption of six preventive behaviors against COVID-19. RESULTS Our data contain 1988 valid responses from 31 provinces in mainland China; 28.2% of respondants resided in Hubei province (n=560). Participant locations were roughly coded into five categories based on their geographical distance from the epicenter. We found that the closer people were to the initial epicenter in Wuhan, the higher susceptibility they felt (β=-.24, t=-11.12, P<.001), while their perceived severity displayed no significant variation based on location (β=-.02, t=-.93, P=.35). Compared with those in the peripheral provinces, people in Hubei and the forth-category provinces reported higher odds of wearing facemasks when going out (odds ratio [OR] 2.635 95%CI 1.33-4.17, P<.001; OR 3.19, 95%CI 1.78-5.72, P<.001, respectively). Participants with higher perceived susceptibility had a higher likelihood of wearing masks (OR 1.15, 95%CI 1.01-1.31, P=.04), however, lower odds of avoiding social gatherings (OR 0.87, 95%CI 0.77-0.99, P=.03) and avoiding visiting Wuhan (OR 0.69, 95%CI 0.61-0.77, P<.001). Participants’ perceived severity was positively associated with their engagement in washing hands and frequent ventilation (OR 1.12, 95%CI 1.00-1.24, P=.05), wearing masks in public (OR 1.39, 95%CI 1.25-1.55, P<.001), avoiding social gathering (OR 1.25, 95%CI 1.12-1.38, P<.001) and avoiding traveling to Wuhan (OR 1.13, 95%CI 1.02-1.25, P=.02). Participants’ sex was also associated with their perceived severity and the engagement of precautionary behaviors. CONCLUSIONS These results characterize an “epicenter effect” early in the pandemic. Our findings expand the understanding of perceived susceptibility and severity, which acted as two distinct dimensions of risk perception, and led to different behavioral outcomes.

A Study of the Atmospheric Weighting Mean Temperature in Northwest Plateau of China

2011 ◽  
Vol 137 ◽  
pp. 291-296
Author(s):  
Jing Jiang Zhang ◽  
Yan Li Chu ◽  
Ji Qin Zhong
Keyword(s):  
Water Vapor ◽  
Linear Regression ◽  
Inner Mongolia ◽  
Regression Models ◽  
Precipitable Water ◽  
Linear Regression Models ◽  
Mean Temperature ◽  
Tm Model ◽  
The Difference ◽  
Data Collections

The data from 11 meteorological radiosonde stations in 5 provinces including Shanxi, Shaanxi, Ningxia, Inner Mongolia and Hebei are divided into 9 different data collections which are used to deduce the linear regression models of atmospheric weighting mean temperature (Tm) for Ground-based GPS precipitable water vapor (PWV) retrieval. These 9 models, together with Bevis model, are used to retrieve the GPS PWV at station BGTY. In comparison with the correlations between the ground-based GPS PWV and radiosonde PWV at this station, the difference between these 10 different models of Tm is analyzed. The results show that the Bevis model of Tm can be used to retrieve the GPS PWV of the regions mentioned above. At the same time, the Tm model computed from the radiosonde measurements of specific regions and seasons can provide more accurate GPS PWV than the Bevis model.

Prediction of throughfall quantities under different forest stands

1982 ◽  
Vol 12 (3) ◽  
pp. 474-481 ◽  
Author(s):  
M. K. Mahendrappa ◽  
D. G. O. Kingston
Keyword(s):  
Regression Models ◽  
Balsam Fir ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Quantitative Distribution ◽  
Rain Gauges ◽  
Case Application ◽  
The Difference ◽  
And Control ◽  
The Relationship

The quantitative distribution of throughfall in fertilizer-treated and control plots under six softwood and three hardwood stands and rainfall in open areas close to these stands were intensively measured during 1977–1978. Standard rain gauges and improvised funnel-type collectors were used for measuring both throughfall and rainfall in the open. The improvised funnel-type collectors with an orifice smaller than that of the standard rain gauges were found to be suitable for accurately measuring quantities of both open rainfall and throughfall. Simple linear regression models of the form Y = b − a were sufficient to predict throughfall quantities (Y) based on rainfall (X) measurements. The relationship between the quantities of throughfall and rainfall was highly significant in each case. Application of urea at a rate of 225 kg N ha−1 did not affect either the slope (b) or the elevation (a) of the relationship. No significant differences were found between the relationships calculated for 1977 and 1978 for any species except balsam fir (Abiesbalsamea (L.) Mill) and larch (Lurixlaricina (Du Roi) K. Koch). The difference for balsam fir was probably a result of extensive defoliation by the spruce budworm (Choristoneurafumiferana (Clem.)). In most cases, values of the slopes (b) of the models for the different species were not significantly different. The nine species did, however, differ significantly from one another in terms of the minimum quantity of rain (a/b) that must fall before throughfall was measurable in the collectors.

scholarly journals True versus perturbed forest inventory plot locations for modeling: a simulation study

2006 ◽  
Vol 36 (3) ◽  
pp. 801-807 ◽  
Author(s):  
John W Coulston ◽  
Kurt H Riitters ◽  
Ronald E McRoberts ◽  
Greg A Reams ◽  
William D Smith
Keyword(s):  
Linear Regression ◽  
Mean Square Error ◽  
Forest Inventory ◽  
Regression Models ◽  
Model Development ◽  
Forest Health ◽  
Mean Square ◽  
Linear Regression Models ◽  
Coarse Spatial Resolution ◽  
The Difference

USDA Forest Service Forest Inventory and Analysis plot information is widely used for timber inventories, forest health assessments, and environmental risk analyses. With few exceptions, true plot locations are not revealed; the plot coordinates are manipulated to obscure the location of field plots and thereby preserve plot integrity. The influence of perturbed plot locations on the development and accuracy of statistical models is unknown. We tested the hypothesis that the influence is related to the spatial structure of the data used in the models. For ordinary kriging we examined the difference in mean square error based on true and perturbed plot locations across a range of spatial autocorrelations. We also examined the difference in mean square error for regression models developed with true and perturbed plot locations across a range of spatial autocorrelations and spatial resolutions. Perturbing plot locations did not significantly influence the accuracy of kriging estimates, but in some situations linear regression model development and accuracy were significantly influenced. Unless the independent variable has high spatial autocorrelation, only coarse spatial resolution data should be used to develop linear regression models.

scholarly journals The Impact of a Good Season Start on Team Performance in Elite Handball

2016 ◽  
Vol 50 (1) ◽  
pp. 195-202 ◽  
Author(s):  
Carlos Lago-Peñas ◽  
María A. Fernández-Villarino ◽  
Iván González-García ◽  
Patricio Sánchez-Fernández ◽  
Jaime Sampaio
Keyword(s):  
Team Performance ◽  
Regression Models ◽  
Fine Tuning ◽  
Final Position ◽  
Linear Regression Models ◽  
Annual Budget ◽  
The Difference ◽  
The Impact ◽  
Positive Effect ◽  
High Range

AbstractThe aim of the current study was (i) to identify how important was a good season start in relation to elite handball teams’ performance, and (ii) to examine if this impact was related to the clubs’ financial budget. The match performances and annual budgets of all teams were collected from the Spanish Professional Handball League during ten seasons. The dependent variable was the difference between the ranking of each team in accordance to the annual budget and the ranking of each team at the end of the season. A k-means cluster analysis classified the clubs according to their budget as High Range Budget Clubs (HRBC), Upper-Mid Range Budget Clubs (UMRBC), Lower-Mid Range Budget Clubs (LMRBC) and Low Range Budget Clubs (LRBC). Data were examined through linear regression models. Overall, the results suggested that the better the team performance at the beginning of the season, the better the ranking at the end of the season. Each position in the ranking above expected in accordance to the budget of the teams in Rounds 3, 4 or 5 improved by 0.47, 0.50 or 0.49, respectively, in the ranking at the end of the season (p<0.05). However, the impact of the effect depended on the clubs’ annual budget. For UMRBC, LMRBC and LRBC a good start to the season had a positive effect on the final outcome (p<0.05). Nevertheless, for HRBC, a good or a bad start of the season did not explain their final position. These variables can be used to develop accurate models to estimate final rankings. UMRBC, LMRBC and LRBC can benefit from fine-tuning preseason planning in order to accelerate the acquisition of optimal performances.

FUZZY LINEAR REGRESSION ANALYSIS FROM THE POINT OF VIEW RISK

2004 ◽  
Vol 12 (05) ◽  
pp. 635-649 ◽  
Author(s):  
MOHAMMAD MODARRES ◽  
EBRAHIM NASRABADI ◽  
MOHAMMAD MEHDI NASRABADI
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Fuzzy Numbers ◽  
Linear Regression Analysis ◽  
Point Of View ◽  
Risk Averse ◽  
Linear Regression Models ◽  
Fuzzy Linear Regression ◽  
Risk Seeking ◽  
The Difference

In this paper, fuzzy linear regression models with fuzzy/crisp output, fuzzy/crisp input are considered. In this regard, we define risk-neutral, risk-averse and risk-seeking fuzzy linear regression models. In order to do that, two equality indices are applied to express the degree of equality between a pair of fuzzy numbers. We also develop three mathematical models to obtain the parameters of fuzzy linear regression models. Minimizing the difference between the total spread of the observed and estimated values is the objective of these models. The advantage of our proposed models is the simplicity in programming and computation.

scholarly journals An Analysis of the Effects of Development-Restricted Areas on Land Price Using Spatial Analysis

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 660
Author(s):  
Dongkwan Lee ◽  
Choongik Choi
Keyword(s):  
Land Use ◽  
Information System ◽  
Geographic Information System ◽  
Regression Models ◽  
Metropolitan Areas ◽  
Linear Regression Models ◽  
Land Price ◽  
Administrative Area ◽  
The Difference ◽  
Log Linear

The purpose of this study was to estimate the effects of development-restricted areas (DRAs) on land price. The study area used was Goyang city in South Korea, where DRAs occupy a large proportion of the city’s administrative area. To examine the economic impact of the DRA, this study estimated log-linear regression models and calculated the difference between the land price determined within the DRA and the land price of the developed areas within buffers created by using a geographic information system (GIS). The results showed that a designation of DRA decreased land price, and that there was a large difference in the land price between the inner and the outer DRA, with a difference of USD 871/m2 in the average land price of the study area. These results serve as a reference for policymakers regarding land use in metropolitan areas in the future.

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