Differential anchoring effects of vaccination comparator selection: characterizing a potential bias due to healthcare utilization in COVID-19 versus influenza

IntroductionObservational data enables large-scale vaccine safety surveillance but requires careful evaluation of potential sources of bias. One potential source of bias is an index date selection procedure for the unvaccinated cohort or unvaccinated comparison time. Here, we evaluate different index date selection procedures for two vaccines: COVID-19 and influenza.MethodsFor each vaccine, we extracted patient baseline characteristics on the index date and up to 450 days prior and then compared them to the characteristics of the unvaccinated patients indexed on an arbitrary date or indexed on a date of a visit. Additionally, we compared vaccinated patients indexed on the date of vaccination and the same patients indexed on a prior date or visit.ResultsCOVID-19 vaccination and influenza vaccination differ drastically from each other in terms of populations vaccinated and their status on the day of vaccination. When compared to indexing on a visit in unvaccinated population, influenza vaccination had markedly higher covariate proportions and COVID-19 vaccination had lower proportions of most covariates on the index date. In contrast, COVID-19 vaccination had similar covariate proportions when compared to an arbitrary date. These effects attenuated but were still present with a longer lookback period. The effect of day 0 was present even when patients served as their own controls.ConclusionPatient baseline characteristics are sensitive to the choice of the index date. In vaccine safety studies, unexposed index event should represent vaccination settings. Study designs previously used to assess influenza vaccination must be reassessed for COVID-19 to account for a potentially healthier population and lack of medical activity on the day of vaccination.

Pengungkapan Aspek Matematis pada Aktivitas Etnomatematika Produksi Ecoprint di Butik El Hijaaz

AbstrakButik El Hijaaz merupakan pengrajin kain ecoprint di Tulungagung yang mengembangkan teknik ecoprint menggunakan bahan alam sebagai pewarna kain dan pencetak motif yang ramah lingkungan. Penelitian dilakukan untuk mengungkap aspek matematika yang terkandung dari proses pembuatan kain ecoprint, seperti membilang, mengukur, mendesain. Tujuan dari penelitian ini adalah mengungkap aspek-aspek matematis dan aktivitas-aktivitas fundamental matematis yang terdapat dalam aktivitas ecoprint di butik El Hijaaz Tulungagung. Penelitian ini menggunakan pendekatan kualitatif dengan metode etnografi yakni observasi, wawancara, dokumentasi. Subjek penelitian yaitu karyawan butik dan pemilik Butik El Hijaaz. Lokasi penelitian yaitu di butik El Hijaaz Kabupaten Tulungagung. Hasil penelitian menunjukan bahwa: 1) aktivitas fundamental yang ditemukan pada proses pembuatan kerajinan kain ecoprint di Galery El Hijaaz antara lain counting, measuring, design, locating, playing dan explaining. 2) Konsep matematis yang dapat diungkap adalah perbandingan, konversi waktu, konversi suhu, konsep membilang, himpunan, konsep pecahan, konsep pengukuran, refleksi, kesebangunan, kongruensi, program linier, aritmatika sosial. Disclosure of Mathematics Aspects on Ethnomathematics Activities of Ecoprint Production in El Hijaaz BoutiqueAbstractButik El Hijaaz is an ecoprint fabric craftsman in Tulungagung who develops ecoprint techniques using natural materials as fabric dyes and prints environmentally friendly motifs. The research was conducted to reveal the mathematical aspects contained in the process of making ecoprint fabrics, such as counting, measuring, designing. The purpose of this research is to reveal the mathematical aspects and mathematical fundamental activities contained in the ecoprint activity at El Hijaaz boutique Tulungagung. This study uses a qualitative approach with ethnographic methods namely observation, interviews, documentation. The research subjects are boutique employees and El Hijaaz Boutique owners. The research location is the El Hijaaz boutique, Tulungagung Regency. The results of the study show that: 1) the fundamental activities found in the process of making ecoprint fabrics at the El Hijaaz Gallery include counting, measuring, design, locating, playing and explaining. 2) Mathematical concepts that can be revealed are comparison, time conversion, temperature conversion, counting concept, set, fraction concept, measurement concept, reflection, similarity, congruence, linear program, social arithmetic.

Differential anchoring effects of vaccination comparator selection: characterizing a potential bias due to healthcare utilization in COVID-19 versus influenza (Preprint)

BACKGROUND Observational data enables large-scale vaccine safety surveillance but requires careful evaluation of potential sources of bias. One potential source of bias is an index date selection procedure for the unvaccinated cohort or unvaccinated comparison time. OBJECTIVE Here, we evaluate different index date selection procedures for two vaccines: COVID-19 and influenza. METHODS For each vaccine, we extracted patient baseline characteristics on the index date and up to 450 days prior and then compared them to the characteristics of the unvaccinated patients indexed on an arbitrary date or indexed on a date of a visit. Additionally, we compared vaccinated patients indexed on the date of vaccination and the same patients indexed on a prior date or visit. RESULTS COVID-19 vaccination and influenza vaccination differ drastically from each other in terms of populations vaccinated and their status on the day of vaccination. When compared to indexing on a visit in unvaccinated population, influenza vaccination had markedly higher covariate proportions and COVID-19 vaccination had lower proportions of most covariates on the index date. In contrast, COVID-19 vaccination had similar covariate proportions when compared to an arbitrary date. These effects attenuated but were still present with a longer lookback period. The effect of day 0 was present even when patients served as their own controls. CONCLUSIONS Patient baseline characteristics are sensitive to the choice of the index date. In vaccine safety studies, unexposed index event should represent vaccination settings. Study designs previously used to assess influenza vaccination must be reassessed for COVID-19 to account for a potentially healthier population and lack of medical activity on the day of vaccination.

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

There are many research studies in the field of breast cancer prediction, but it has been observed that the time taken for prediction needs to be reduced. The problem in the existing research is space consumption by graphical content. The proposed research is supposed to minimize the prediction time and space consumption. In this paper, research has focused on the study of existing breast cancer research and techniques and eliminating their limitation. It has been observed that when the number of datasets increases, every comparison makes a huge gap in size and comparison time. This research proposes a methodology for breast cancer prediction using an edge-based CNN (convolutional neural network) algorithm. The elimination of useless content from the graphical image before applying CNN has reduced the time consumption along with space consumption. The edge detection mechanism would retail only edges from the image sample in order to detect the pattern to predict breast cancer. The proposed work is supposed to implement the proposed methodology. A comparison of the proposed methodology and algorithm with the existing algorithm is made during simulation. The proposed work is found to be more efficient compared to the existing techniques used in breast cancer prediction. The utilization of proposed in the work area of medical science is supposed to enhance the capability in case of CNN at the time of decision-making. The proposed work is supposed to be more accurate compared to the existing works. It has been observed that the proposed work is fourteen to fifteen percent more accurate. It is taking 9/4 times less space and 1.0849004/0.178971 times less time compared to the general CNN model. Accuracy might vary as per size of the image and alteration performed in dataset of the image.

BW-MaxEnt: A Novel MCDM Method for Limited Knowledge

With the development of the social economy and an enlarged volume of information, the application of multiple-criteria decision making (MCDM) has become increasingly wide and deep. As a brilliant MCDM technique, the best–worst method (BWM) has attracted many scholars’ attention because it can determine the weights of criteria with less comparison time and higher consistency between judgments than analytic hierarchy process. However, the effectiveness of the BWM is based on complete comparison information among criteria. Considering the fact that the decision makers may have limited time and energy to study all criteria, they cannot construct a complete comparison system. In this paper, we propose a novel MCDM method named BW-MaxEnt that combines BWM and the maximum entropy method (MaxEnt) to identify the weights of unfamiliar criteria with incomplete decision information. The model can be translated into a convex optimization problem that can be solved effectively and has an overall optimal solution. Finally, a practical application concerning the procurement of GPU workstations illustrates the feasibility of the proposed BW-MaxEnt method.

Knockout-Tournament Procedures for Large-Scale Ranking and Selection in Parallel Computing Environments

On one hand, large-scale ranking and selection (R&S) problems require a large amount of computation. On the other hand, parallel computing environments that provide a large capacity for computation are becoming prevalent today, and they are accessible by ordinary users. Therefore, solving large-scale R&S problems in parallel computing environments has emerged as an important research topic in recent years. However, directly implementing traditional stagewise procedures and fully sequential procedures in parallel computing environments may encounter problems because either the procedures require too many simulation observations or the procedures’ selection structures induce too many comparisons and too frequent communications among the processors. In this paper, inspired by the knockout-tournament arrangement of tennis Grand Slam tournaments, we develop new R&S procedures to solve large-scale problems in parallel computing environments. We show that no matter whether the variances of the alternatives are known or not, our procedures can theoretically achieve the lowest growth rate on the expected total sample size with respect to the number of alternatives and thus, are optimal in rate. Moreover, common random numbers can be easily adopted in our procedures to further reduce the total sample size. Meanwhile, the comparison time in our procedures is negligible compared with the simulation time, and our procedures barely request for communications among the processors.

Cross-Sectional and Longitudinal Studies

This chapter focuses on cross-sectional and longitudinal studies. cross-sectional studies involve the analysis of usually quantitative data collected at a single snapshot in time. The unit of observation might be people or countries, and those are measured only once, all at approximately the same time. In contrast, longitudinal studies (also referred to as repeated measures studies) involve analysis on multiple occasions over time, where the same individuals (or countries) — the panel — are measured on each occasion. As such, the unit of observation is occasions, and there are multiple occasions/measures of each individual. A subcategory of longitudinal studies is event-history/survival/duration analysis, where the dependent variable is binary and the focus is on causes of changes between the two states of the outcome. Note that in comparison, time series analysis typically involves fewer individuals (often only one) and a larger number of time points. A third type of study, situated in between longitudinal and cross-sectional studies, is repeated cross-sectional analysis, which involves the analysis of multiple cross-sectional data sets over time, and different individuals are measured in each wave of the survey. Here, the unit of observation is individuals, and there are multiple individuals measured in each survey wave.

MANAGING AND ORGANISING THE COVID-19 EFFECT ON ONLINE SHOPPING BEHAVIOR

These days, we are all exposed to unusual conditions due to the Coronavirus pandemic COVID-19, which exposes to unpleasant circumstances our families, friends, and also our business activities. According to this fact, it will probably increase the online shopping, because the people are spinning to the e-commerce to buy the things which in normal conditions would buy personally. The paper aims to find out how the COVID-19 pandemic has influenced on the growth of online shopping and electronic commerce, such as managing the new way of living. The authors in this paper use the methods of synthesis, comparison, time series analysis and use data from the Association of e-commerce of the Republic of North Macedonia. The authors concluded that the COVID-19 pandemic has changed the habits of the domestic buyers, who redirected the big part of the online shopping from foreign countries to domestic e-shops.

Scaling Up Software Birthmarks Using Fuzzy Hashing

To detect the software theft, software birthmarks have been proposed. Software birthmark systems extract software birthmarks, which are native characteristics of software, from binary programs, and compare them by computing the similarity between birthmarks. This paper proposes a new procedure for scaling up the birthmark systems. While conventional birthmark systems are composed of the birthmark extraction phase and the birthmark comparison phase, the proposed method adds two new phases between extraction and comparison, namely, compression phase, which employs fuzzy hashing, and pre-comparison phase, which aims to increase distinction property of birthmarks. The proposed method enables us to reduce the required time in the comparison phase, so that it can be applied to detect software theft among many larger scale software products. From an experimental evaluation, the authors found that the proposed method significantly reduces the comparison time, and keeps the distinction performance, which is one of the important properties of the birthmark. Also, the preservation performance is acceptable when the threshold value is properly set.

483. OPAT Delivery during COVID-19

Background In 2020, COVID-19 spurred unprecedented change in the delivery of routine clinical care. The UNC OPAT program staff, previously accustomed to in-person collaboration in the hospital, became geographically distant amid North Carolina’s partial shutdown starting in March 2020. Team members relied on teleworking and many OPAT clinic visits shifted to phone and video telehealth. We assessed how COVID-19 impacted our care of OPAT patients including follow-up visits and readmissions. Methods UNC’s OPAT database contains clinical and demographic information on all patients on OPAT for at least 14 days who received specialized monitoring program led by an infectious diseases (ID) pharmacist, after evaluation by an ID physician. For all OPAT courses that ended between 3/1/20 and 5/20/20 (last available data cut), we assessed the length of OPAT treatment course, readmissions, adverse events, follow-up ID clinic visits, and the method of follow up visit utilized. We compared these measurements to historical baseline data from 3/1/19 to 5/20/19. Results During the 2020 period, 73 patients completed OPAT, with median OPAT enrollment lasting 36 days, which was similar to 2019 data (70 patients; median OPAT enrollment of 35 days). During the 2019 period, 93% of patients attended a follow up visit with an infectious diseases clinician, all of which took place in person. During the 2020 (COVID-19) period, 85% of patients attended an ID follow up visit; contrary to 2019, 42% of these visits took place in person, 45% were by phone and 13% were via a telemedicine video service. Readmission rates were similar across the two time periods (16% during COVID-19 vs 14% during 2019 comparison time period, P=0.72). Conclusion UNC OPAT continued through the emergence of COVID-19 as an essential service for a high patient volume by adapting its care delivery and follow-up visit protocols to include virtual care options. Readmission rates for OPAT patients during COVID-19 were comparable to historical baseline data. Disclosures All Authors: No reported disclosures