Analysis on the factors related to corporate expenditure on preventing industrial accidents

Thesis(Master) -- KDI School: Master of Public Policy, 2022Public demand for occupational health and safety has increased in South Korea as suggested by a number of legislations that were amended or enacted recently (i.e. amendment to the Korea Occupational Safety and Health Act in 2020, Serious Accidents Punishment Act in 2022). In light of this, the study aims to investigate different factors that could impact firms’ investment toward occupational safety and health. Fixed effect regression using panel survey data from 2015-2019 by South Korea’s Workplace Panel Survey demonstrated that only the number of prior year accidents had a positive and statistically significant association with a firm’s safety investment. Other factors including firm size, firm loss due to industrial accidents, and the fraction of temporary workers showed no statistically significant association with a firm’s investment toward occupational health and safety. Although this study had merit for investigating different factors behind firms’ behavior toward safety investment, further studies may reveal more of this nature especially during some years after the enactment of Serious Accidents Punishment ActI. Introduction II. Literature Review III. Results & Discussion IV. Conclusion V. ReferencemasterpublishedSunguk CHO

Directive 02-14: Tax Obligations of Persons Purchasing Cigarettes in Interstate Commerce for which the Massachusetts Cigarette Excise Has Not Been Paid

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.</p

The Marker State Space (MSS) Method for Classifying Clinical Samples

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications. © 2013 Fallon et al

Comparison of performance between methods.

*<p>The software did not calculate an average sensitivity and specificity for MSS in 10-fold cross validation because its does not separately calculate those parameters in each cross validation split.</p

Training set marker states and patient classifications.

<p>(A) Training set marker states. The eight possible marker states for the three indicated markers are shown, followed by the numbers of case and control samples in each state and the categorization of each state. *State 2 was unoccupied by categorized as a control state because of similarity to other control states. The lower panel shows condensed marker states, in which X indicates either 0 or 1. (B) Individual sample classifications. Each column represents an individual patient sample, and the first three rows indicate results from the indicated markers. A yellow square indicates the sample was above the threshold for that marker, and black indicates below. The blue lines indicate the state in which each sample was classified.</p

Determining optimal thresholds for a two-marker panel.

<p>(A) Scanning thresholds. Three different thresholds are depicted for Marker 1 (left) and Marker 2 (right), with the resulting conversion to 1s and 0s for each threshold, followed by the sensitivities and specificities for each marker at each threshold. (B) Determining the best combination of thresholds. All possible combinations of thresholds were assembled for the two-marker panel, resulting in nine combinations. Based on the results from panel A, the numbers of cancer and non-cancer samples that occupy each state were determined for each combination, from which the sensitivity and specificity could be calculated for each combination. The combination of thresholds giving the best performance (in this case threshold 2 for Marker 1 and threshold 2 for Marker 2) is selected.</p

Assigning patient classes and classifying marker states.

<p>(A) Thresholding the data. Representative data for 21 samples are presented, in which each point represents a patient sample measurement for Marker 1 (left) or Marker 2 (right). A threshold (dashed line) was applied to each marker. Values above the threshold are converted to 1 and values below the threshold are converted to 0. (B) Possible states. Each column represents a unique state for panels of 1, 2, or 3 markers. (C) Determining marker states for each patient. The data from both Marker 1 and Marker 2 are presented for each of the 21 patients, along with their respective thresholds (horizontal lines). The thresholded data are below the column graph. Each sample has a particular marker state (0,0; 0,1; 1,0; or 1,1). (D) State classification. Each state is classified as either case or control based on whether cancer or non-cancer samples have a greater number of occurrences in that state. The “true positives” are the cancer samples that occupy case states, and the “true negatives” are the non-cancer samples that occupy control states. These values are used to calculate the sensitivity and specificity for the panel.</p

Test set marker states and patient classifications.

<p>The same marker panel, thresholds, and classification rules as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065905#pone-0065905-g004" target="_blank">figure 4</a> were applied to the one-third of the total samples that were separated as a test set. (A) Occupancy of the marker states in the test set. (B) Individual sample classifications in the test set.</p