Bacterial Foodborne Disease: Medical Costs and Productivity Losses

Microbial pathogens in food cause an estimated 6.5-33 million cases of human illness and up to 9,000 deaths in the United States each year. Over 40 different foodborne microbial pathogens, including fungi, viruses, parasites, and bacteria, are believed to cause human illnesses. For six bacterial pathogens, the costs of human illness are estimated to be $9.3-$12.9 billion annually. Of these costs, $2.9-$6.7 billion are attributed to foodborne bacteria. These estimates were developed to provide analytical support for USDA's Hazard Analysis and Critical Control Point (HACCP) systems rule for meat and poultry. (Note that the parasite Toxoplasma gondii is not included in this report.) To estimate medical costs and productivity losses, ERS uses four severity categories for acute illnesses: those who did not visit a physician, visited a physician, were hospitalized, or died prematurely. The lifetime consequences of chronic disease are included in the cost estimates for E. coli O157:H7 and fetal listeriosis.cost-of-illness, foodborne pathogens, lost productivity, medical costs, Food Consumption/Nutrition/Food Safety, Health Economics and Policy,

Stepping On Fall Prevention Project

Background: Falls are a major problem in the United States among the older adult population and provide opportunity for community outreach via student-led physical therapy projects. Objective: The purpose of this project was to investigate the relationship between fall related outcome measures and questionnaires with the completion of the Stepping On Fall Prevention program along with evaluating the benefits of Physical Therapy student development with participation in service learning projects. Methods: The research quantified the fall risk of 13 participants with assessment of: gait speed (Timed Up and Go), lower extremity strength (30-Second Chair Stand), balance (4-Stage Balance Test), and psychological factors (Stay Independent Questionnaire, Falls Efficacy Scale-International, and Geriatric Depression Scale). Results: Of the functional measures, significant improvements were observed in the Timed up and Go (TUG) (∆1.72s ± 1.66, p=0.003), the 30-second chair stand (∆4.54 ± 4.27, p= 0.002), Stage 4 of the 4-Stage Balance Test (∆3.37s ± 3.26, p= 0.003), and the Stay Independent questionnaire (∆1.77 ± 2.52, p=0.026). Conclusion: Stepping On demonstrated improvements in gait speed, strength, and balance. These improvements allow older adults to improve their overall safety in both their own homes and the community. More research is needed to evaluate the psychological benefits of completing Stepping On. Furthermore, service learning project opportunities should become more of a standard practice across physical therapy programs

Different hydrogen-bonded chains in the crystal structures of three alkyl N-[(E )-1-(2-benzylidene-1-methylhydrazinyl)-3- hydroxy-1-oxopropan-2-yl]carbamates

Peer reviewedPublisher PD

Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women.

ObjectivesIndependently, physical activity (PA), sedentary behavior (SB), and sleep are related to the development and progression of chronic diseases. Less is known about how rest-activity behaviors cluster within individuals and how rest-activity behavior profiles relate to health. In this study we aimed to investigate if adult women cluster into profiles based on how they accumulate rest-activity behavior (including accelerometer-measured PA, SB, and sleep), and if participant characteristics and health outcomes differ by profile membership.MethodsA convenience sample of 372 women (mean age 55.38 + 10.16) were recruited from four US cities. Participants wore ActiGraph GT3X+ accelerometers on the hip and wrist for a week. Total daily minutes in moderate-to-vigorous PA (MVPA) and percentage of wear-time spent in SB was estimated from the hip device. Total sleep time (hours/minutes) and sleep efficiency (% of in bed time asleep) were estimated from the wrist device. Latent profile analysis (LPA) was performed to identify clusters of participants based on accumulation of the four rest-activity variables. Adjusted ANOVAs were conducted to explore differences in demographic characteristics and health outcomes across profiles.ResultsRest-activity variables clustered to form five behavior profiles: Moderately Active Poor Sleepers (7%), Highly Actives (9%), Inactives (41%), Moderately Actives (28%), and Actives (15%). The Moderately Active Poor Sleepers (profile 1) had the lowest proportion of whites (35% vs 78-91%, p < .001) and college graduates (28% vs 68-90%, p = .004). Health outcomes did not vary significantly across all rest-activity profiles.ConclusionsIn this sample, women clustered within daily rest-activity behavior profiles. Identifying 24-hour behavior profiles can inform intervention population targets and innovative behavioral goals of multiple health behavior interventions

Automated Ecological Assessment of Physical Activity: Advancing Direct Observation.

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings

A Systems Approach and Notional Response Model for Preserving the Health System during the COVID-19 Pandemic

During any pandemic, it has long been known that local jurisdictions would need to be self-sufficient with little or no outside assistance, particularly from the federal government. While all eyes have been on California, New York, and Massachusetts, the capacities of health systems in other states have yet to be put to the test. If there are subsequent waves of COVID-19 and other jurisdictions see significant increases in disease spread, the systems used to respond will become critical.Using a review and synthesis approach, this article explores our collective experience and knowledge as it pertains to use of alternate care sites for dealing with the patient surge created by a disease outbreak. Probing the concept of alternate care site (ACS) systems reveals various types of alternate care sites that may be employed during an outbreak. The historical value of ACS models used during outbreak response are discussed. This culminates in the development of a notional response model and list of actions that should be taken by all jurisdictions as we prepare for additional waves of disease