A comparison of worker risk of injury while working on fully automated side loaders and other residential collection trucks at a large solid waste collection company

The study aim was to determine whether using automated side loader (ASL) trucks in higher proportions compared to other types of trucks for residential waste collection results in lower injury rates (from all causes). The primary hypothesis was that the risk of injury to workers was lower for those who work with ASL trucks than for workers who work with other types of trucks used in residential waste collection. To test this hypothesis, data were collected from one of the nation’s largest companies in the solid waste management industry. Different local operating units (i.e. facilities) in the company used different types of trucks to varying degrees, which created a special opportunity to examine refuse collection injuries and illnesses and the risk reduction potential of ASL trucks.^ The study design was ecological and analyzed end-of-year data provided by the company for calendar year 2007. During 2007, there were a total of 345 facilities which provided residential services. Each facility represented one observation.^ The dependent variable – injury and illness rate, was defined as a facility’s total case incidence rate (TCIR) recorded in accordance with federal OSHA requirements for the year 2007. The TCIR is the rate of total recordable injury and illness cases per 100 full-time workers. The independent variable, percent of ASL trucks, was calculated by dividing the number of ASL trucks by the total number of residential trucks at each facility.^ Multiple linear regression models were estimated for the impact of the percent of ASL trucks on TCIR per facility. Adjusted analyses included three covariates: median number of hours worked per week for residential workers; median number of months of work experience for residential workers; and median age of residential workers. All analyses were performed with the statistical software, Stata IC (version 11.0).^ The analyses included three approaches to classifying exposure, percent of ASL trucks. The first approach included two levels of exposure: (1) 0% and (2) >0 - <100%. The second approach included three levels of exposure: (1) 0%, (2) ≥ 1 - < 100%, and (3) 100%. The third approach included six levels of exposure to improve detection of a dose-response relationship: (1) 0%, (2) 1 to <25%, (3) 25 to <50%, (4) 50 to <75%, (5) 75 to <100%, and (6) 100%. None of the relationships between injury and illness rate and percent ASL trucks exposure levels was statistically significant (i.e., p<0.05), even after adjustment for all three covariates.^ In summary, the present study shows that there is some risk reduction impact of ASL trucks but not statistically significant. The covariates demonstrated a varied yet more modest impact on the injury and illness rate but again, none of the relationships between injury and illness rate and the covariates were statistically significant (i.e., p<0.05). However, as an ecological study, the present study also has the limitations inherent in such designs and warrants replication in an individual level cohort design. Any stronger conclusions are not suggested.^

Evaluation of a wellness program at a large private sector petrochemical company in Texas

The study purpose was to analyze the effects Integrated Health Solutions (IHS), an employee wellness program that has been implemented for one year on the corporate campus of a major private sector petrochemical company in Houston, TX, has on employee health. ^ Chronic diseases are the leading causes of morbidity and mortality in the United States and are the most preventable of all health problems. The costs of chronic diseases in the working-age adult population include not only health problems and a decrease in quality of life, but also an increase the cost of health care and costs to businesses and employers, both directly and indirectly. These emerging costs to employers as well as the fact that adults now spend the majority of waking hours at the office have increased the interest in worksite health promotion programs that address many of the behavioral factors that lead to chronic conditions. Therefore, implementing and evaluating programs that are aimed at promoting health and decreasing the prevalence of chronic diseases at worksites is very important. ^ Data came from existing data that were collected by IHS staff during employee biometric screenings at the company in 2010 and 2011. Data from employees who participated in screenings in both 2010 and 2011 were grouped into a cohort by IHS staff. ^ One-tailed t-tests were conducted to determine if there were significant improvements in the biometric measures of body fat percentage, BMI, waist circumference, systolic and diastolic blood pressures, total, HDL, and LDL cholesterol levels, triglycerides, blood glucose levels, and cardiac risk ratios. Sensitivity analysis was conducted to determine if there were differences in program outcomes when stratified by age, gender, job type, and time between screenings. ^ Mean differences for the variables from 2010 to 2011 were small and not always in the desired direction for health improvement indicators. Through conducting t-tests, it was found that there were significant improvements in HDL, cardiac risk ratio, and glucose levels. There were significant increases in cholesterol, LDL, and diastolic blood pressures. For the IHS program, it appears that gender, job type, and time between screenings were possible modifiers of program effectiveness. When program outcome measures were stratified by these factors, results suggest that corporate employees had better outcomes than field employees, males had better outcomes overall than females, and more positive program effects were seen for employees with less time between their two screenings. ^ Recommendations for the program based on the results include ensuring validity of instruments and initial and periodic training of measurement procedures and equipment handling, using normative data or benchmarks to decrease chances for biased estimates of program effectiveness, measuring behaviors as well as biometric and physiologic statuses and changes, and collecting level of engagement data.^