Impacts of the Pandemic on Social Determinants of Health in an Academic Emergency Department.

INTRODUCTION: The coronavirus 2019 (COVID-19) pandemic caused significant disruptions in daily life. Given the role that social determinants of health play in the overall well-being of individuals and populations, we wanted to determine the effects of the COVID-19 pandemic on our patient population in the emergency department (ED). METHODS: We adapted the Centers for Medicare and Medicaid Services social risk assessment to assess changes to participants' social situations throughout the COVID-19 pandemic from January 2020-February 2021. The survey was administered within the ED to individuals selected by a convenience sample of patients who were stable enough to complete the form. RESULTS: We received 200 (66%) responses from the 305 patients approached. Worsened food access was reported by 8.5% (17) of respondents, while 13.6% (27) reported worsened food concern since the onset of the COVID-19 pandemic. The odds of worsened food access were higher among non-Whites (adjusted odds ratio [aOR] 19.17, 95% confidence interval [CI] 3.33-110.53) and females (aOR 9.77, CI 1.51-63.44). Non-Whites had greater odds of worsened food concern (aOR 15.31, CI 3.94-59.54). Worsened financial difficulty was reported by 24% (48) of respondents. The odds of worsened financial difficulty were higher among females (aOR 2.87, 95% CI 1.08-7.65) and non-Whites (aOR 10.53, CI 2.75-40.35). CONCLUSION: The COVID-19 pandemic has worsened many of the social determinants of health found within communities. Moreover, vulnerable communities were found to be disproportionately affected as compared to their counterparts. Understanding the challenges faced by our patient populations can serve as a guide on how to assist them more comprehensively.

Analysis of Growth Characteristics of Kimchi Cabbage Using Drone-Based Cabbage Surface Model Image

Cultivation soil is the basis for cabbage growth, and it is important to assess not only to provide information on how it affects the growth of vegetable crops but also for cultivation management. Until now, field cabbage surveys have measured size and growth variations in the field, and this method requires a lot of time and effort. Drones and sensors provide opportunities to accurately capture and utilize cabbage growth and variation data. This study aims to determine the growth stages based on drone remote estimation of the cabbage height and evaluate the impact of the soil texture on cabbage height. Time series variation according to the growth of Kimchi cabbage exhibits an S-shaped sigmoid curve. The logistic model of the growth curve indicates the height and growth variation of Kimchi cabbage, and the growth rate and growth acceleration formula of Kimchi cabbage can thus be derived. The curvature of the growth parameter can be used to identify variations in Kimchi cabbage height and its stages of growth. The main research results are as follows. (1) According to the growth curve, Kimchi cabbage growth can be divided into four stages: initial slow growth stage (seedling), growth acceleration stage (transplant and cupping), heading through slow growth, and final maturity. The three boundary points of the Kimchi cabbage growth curve are 0.2113 Gmax, 0.5 Gmax, and 0.7887 Gmax, where Gmax is the maximum height of Kimchi cabbage. The growth rate of cabbage reaches its peak at 0.5 Gmax. The growth acceleration of cabbage forms inflection points at 0.2113 Gmax and 0.7887 Gmax, and shows a variation characteristic. (2) The produced logistic growth model expresses the variation in the cabbage surface model value for each date of cabbage observation under each soil texture condition, with a high degree of accuracy. The accuracy evaluation showed that R2 was at least 0.89, and the normalized root-mean-square error (nRMSE) was 0.09 for clay loam, 0.06 for loam, and 0.07 for sandy loam, indicating a very strong regression relationship. It can be concluded that the logistic model is an important model for the phase division of cabbage growth and height variation based on cabbage growth parameters. The results obtained in this study provide a new method for understanding the characteristics and mechanisms of the growth phase transition of cabbage, and this study will be useful in the future to extract various types of information using drones and sensors from field vegetable crops.

Prediction models for severe manifestations and mortality due to COVID-19: A systematic review.

BACKGROUND: Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available. OBJECTIVE: This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19. METHODS: Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized. RESULTS: A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability. CONCLUSION: Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,15 and Knight et al.,18 which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.