scholarly journals Construction of A New Dose–Response Model for Staphylococcus aureus Considering Growth and Decay Kinetics on Skin

Pathogens ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 253 ◽  
Author(s):  
Esfahanian ◽  
Adhikari ◽  
Dolan ◽  
Mitchell
Keyword(s):  
Staphylococcus Aureus ◽  
Dose Response ◽  
Sensitivity Coefficient ◽  
Ordinary Least Squares ◽  
Exposure Dose ◽  
Human Infection ◽  
Model Parameters ◽  
Response Model ◽  
Least Squares Regression ◽  
Decay Kinetics

. In order to determine the relationship between an exposure dose of Staphylococcus aureus (S. aureus) on the skin and the risk of infection, an understanding of the bacterial growth and decay kinetics is very important. Models are essential tools for understanding and predicting bacterial kinetics and are necessary to predict the dose of organisms post-exposure that results in a skin infection. One of the challenges in modeling bacterial kinetics is the estimation of model parameters, which can be addressed using an inverse problem approach. The objective of this study is to construct a microbial kinetic model of S. aureus on human skin and use the model to predict concentrations of S. aureus that result in human infection. In order to model the growth and decay of S. aureus on skin, a Gompertz inactivation model was coupled with a Gompertz growth model. A series of analyses, including ordinary least squares regression, scaled sensitivity coefficient analysis, residual analysis, and parameter correlation analysis were conducted to estimate the parameters and to describe the model uncertainty. Based on these analyses, the proposed model parameters were estimated with high accuracy. The model was then used to develop a new dose-response model for S. aureus using the exponential dose–response model. The new S. aureus model has an optimized k parameter equivalent to 8.05 × 10−8 with 95th percentile confidence intervals between 6.46 × 10−8 and 1.00 × 10−7.

The effect of various model parameters on enamel caries lesions in a dose–response model in situ

2015 ◽  
Vol 43 (10) ◽  
pp. 1261-1267 ◽  
Author(s):  
H. Meyer-Lueckel ◽  
R.J. Wierichs ◽  
B. Gninka ◽  
P. Heldmann ◽  
C.E. Dörfer ◽  
...  
Keyword(s):  
Dose Response ◽  
Model Parameters ◽  
Response Model ◽  
Enamel Caries ◽  
Caries Lesions

Developing a polycyclic aromatic hydrocarbon exposure dose-response model for fish health and growth

2016 ◽  
Vol 109 (1) ◽  
pp. 259-266 ◽  
Author(s):  
Lindsey Dornberger ◽  
Cameron Ainsworth ◽  
Stephen Gosnell ◽  
Felicia Coleman
Keyword(s):  
Polycyclic Aromatic Hydrocarbon ◽  
Aromatic Hydrocarbon ◽  
Dose Response ◽  
Exposure Dose ◽  
Response Model ◽  
Fish Health ◽  
Polycyclic Aromatic

Comparison of three strong ion models used for quantifying the acid-base status of human plasma with special emphasis on the plasma weak acids

2005 ◽  
Vol 98 (6) ◽  
pp. 2119-2125 ◽  
Author(s):  
Chris M. Anstey
Keyword(s):  
Metabolic Stress ◽  
Ordinary Least Squares ◽  
Weak Acid ◽  
Model Parameters ◽  
Strong Ion Difference ◽  
Least Squares Regression ◽  
Weak Acids ◽  
Acid Base ◽  
Ph Titration ◽  
Significant Difference

Currently, three strong ion models exist for the determination of plasma pH. Mathematically, they vary in their treatment of weak acids, and this study was designed to determine whether any significant differences exist in the simulated performance of these models. The models were subjected to a “metabolic” stress either in the form of variable strong ion difference and fixed weak acid effect, or vice versa, and compared over the range 25 ≤ Pco2 ≤ 135 Torr. The predictive equations for each model were iteratively solved for pH at each Pco2 step, and the results were plotted as a series of log(Pco2)-pH titration curves. The results were analyzed for linearity by using ordinary least squares regression and for collinearity by using correlation. In every case, the results revealed a linear relationship between log(Pco2) and pH over the range 6.8 ≤ pH ≤ 7.8, and no significant difference between the curve predictions under metabolic stress. The curves were statistically collinear. Ultimately, their clinical utility will be determined both by acceptance of the strong ion framework for describing acid-base physiology and by the ease of measurement of the independent model parameters.

Developing an exposure–dose–response model for the acute neurotoxicity of organic solvents: overview and progress on in vitro models and dosimetry

2005 ◽  
Vol 19 (3) ◽  
pp. 607-614 ◽  
Author(s):  
Philip J. Bushnell ◽  
Timothy J. Shafer ◽  
Ambuja S. Bale ◽  
William K. Boyes ◽  
Jane Ellen Simmons ◽  
...  
Keyword(s):  
Organic Solvents ◽  
Dose Response ◽  
Exposure Dose ◽  
In Vitro Models ◽  
Response Model ◽  
Acute Neurotoxicity

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

Home Health Agencies: Empirical Evidence on the Patient-Driven Groupings Model’s Expected Effects on Agency Reimbursements

2021 ◽  
pp. 108482232199038
Author(s):  
Elizabeth Plummer ◽  
William F. Wempe
Keyword(s):  
Ordinary Least Squares ◽  
Process Quality ◽  
Home Health ◽  
Least Squares Regression ◽  
Residual Variation ◽  
Health Agencies ◽  
Dual Eligible ◽  
Home Health Agencies ◽  
Seriously Ill Patients ◽  
Seriously Ill

Beginning January 1, 2020, Medicare’s Patient-Driven Groupings Model (PDGM) eliminated therapy as a direct determinant of Home Health Agencies’ (HHAs’) reimbursements. Instead, PDGM advances Medicare’s shift toward value-based payment models by directly linking HHAs’ reimbursements to patients’ medical conditions. We use 3 publicly-available datasets and ordered logistic regression to examine the associations between HHAs’ pre-PDGM provision of therapy and their other agency, patient, and quality characteristics. Our study therefore provides evidence on PDGM’s likely effects on HHA reimbursements assuming current patient populations and service levels do not change. We find that PDGM will likely increase payments to rural and facility-based HHAs, as well as HHAs serving greater proportions of non-white, dual-eligible, and seriously ill patients. Payments will also increase for HHAs scoring higher on quality surveys, but decrease for HHAs with higher outcome and process quality scores. We also use ordinary least squares regression to examine residual variation in HHAs’ expected reimbursement changes under PDGM, after accounting for any expected changes related to their pre-PDGM levels of therapy provision. We find that larger and rural HHAs will likely experience residual payment increases under PDGM, as will HHAs with greater numbers of seriously ill, younger, and non-white patients. HHAs with higher process quality, but lower outcome quality, will similarly benefit from PDGM. Understanding how PDGM affects HHAs is crucial as policymakers seek ways to increase equitable access to safe and affordable non-facility-provided healthcare that provides appropriate levels of therapy, nursing, and other care.

scholarly journals Predicting presenteeism using measures of health status

2021 ◽  
Author(s):  
Cheryl Jones ◽  
Katherine Payne ◽  
Alexander Thompson ◽  
Suzanne M. M. Verstappen
Keyword(s):  
Health Status ◽  
Online Survey ◽  
Activity Index ◽  
Work Productivity ◽  
Rank Correlation ◽  
Ordinary Least Squares ◽  
Statistical Correlation ◽  
Least Squares Regression ◽  
Mapping Algorithm ◽  
Spearman’S Rank Correlation

Abstract Objectives To identify whether it is feasible to develop a mapping algorithm to predict presenteeism using multiattribute measures of health status. Methods Data were collected using a bespoke online survey in a purposive sample (n = 472) of working individuals with a self-reported diagnosis of Rheumatoid arthritis (RA). Survey respondents were recruited using an online panel company (ResearchNow). This study used data captured using two multiattribute measures of health status (EQ5D-5 level; SF6D) and a measure of presenteeism (WPAI, Work Productivity Activity Index). Statistical correlation between the WPAI and the two measures of health status (EQ5D-5 level; SF6D) was assessed using Spearman’s rank correlation. Five regression models were estimated to quantify the relationship between WPAI and predict presenteeism using health status. The models were specified based in index and domain scores and included covariates (age; gender). Estimated and observed presenteeism were compared using tenfold cross-validation and evaluated using Root mean square error (RMSE). Results A strong and negative correlation was found between WPAI and: EQ5D-5 level and WPAI (r = − 0.64); SF6D (r =− 0.60). Two models, using ordinary least squares regression were identified as the best performing models specifying health status using: SF6D domains with age interacted with gender (RMSE = 1.7858); EQ5D-5 Level domains and age interacted with gender (RMSE = 1.7859). Conclusions This study provides indicative evidence that two existing measures of health status (SF6D and EQ5D-5L) have a quantifiable relationship with a measure of presenteeism (WPAI) for an exemplar application of working individuals with RA. A future study should assess the external validity of the proposed mapping algorithms.

From Measures of Association to Multilevel Models: Sociology Journals and the Quantitative Literacy Gap

2020 ◽  
pp. 0092055X2098042
Author(s):  
Thomas J. Linneman
Keyword(s):  
Multilevel Models ◽  
Ordinary Least Squares ◽  
Quantitative Literacy ◽  
Least Squares Regression ◽  
Journal Articles ◽  
Measures Of Association ◽  
Chi Square ◽  
Literacy Gap ◽  
Statistics Course ◽  
Sociology Journals

While most sociology majors must take a statistics course, the content of this course varies widely across departments. Starting from the assumption that sociology students should be able to engage effectively with the sociological literature, this article examines the statistical techniques used in 2,804 journal articles—from four generalist sociology journals from 1990 to 2019 and 11 additional sociology journals from 2019—in order to assess which techniques have risen or fallen in prevalence. Although stalwarts such as ordinary least squares regression, chi-square tests, and t tests maintain strong presences, the rise of logistic regression, interaction effects, and multilevel models has been dramatic. After assessing the proportion of articles students hypothetically could understand given various levels of statistical training, the article ends with suggestions for how to revamp the statistics course to help our students become more numerate citizens, both in their sociology courses and in the world at large.

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