COVID-19 in the city of Madrid and vulnerability. Analysis of the first two waves

Background: The advance of the pandemic in the large cities of the world with great virulence and the apparent heterogeneous distribution by factors of vulnerability, led us to propose this work. The objective of this study was to relate COVID-19 infection rates to the social vulnerability of the city of Madrid by district, in two different episodes, spring 2020 and summer 2020.

COVID-19 in the city of Madrid and vulnerability. Analysis of the first two waves

Background: The advance of the pandemic in the large cities of the world with great virulence and the apparent heterogeneous distribution by factors of vulnerability, led us to propose this work. The objective of this study was to relate COVID-19 infection rates to the social vulnerability of the city of Madrid by district, in two different episodes, spring 2020 and summer 2020. Methods: Analytical cross-sectional study. Taking sociodemographic data of the entire population of the city of Madrid between the months of April and October 2020, together with the cumulative incidence rates of COVID-19, a linear regression analysis, correlation and factor analysis was carried out, relating the cumulative incidence rate of COVID-19 and the vulnerability indicator of the districts of the city of Madrid. Results: The results showed important differences between the two episodes of the pandemic: on one hand, the first had more relationship with health factors, while in the second, a relationship appeared with the groups of greater social vulnerability, territorially located in the South-East of the City and related in this case to social factors rather than health. Thus we see that the TIA x 100,000 in the first episode in Chamberi and Usera-two extreme districts in vulnerability-were 896 and 843 cases respectively, while in the second they were 3,708 and 6,258 cases. Conclusions: The territorial differences in the city become evident with the arrival of a generalised, universal event such as COVID-19, where vulnerability increases for everyone, with greater repercussions in those territories where it already had an impact.

SARS-CoV-2 epidemic behavior in worker population affiliated to the Mexican Social Security Institute

Objective. To describe the behavior of SARS- CoV-2 epidemic among workers affiliated to the Mexican Social Security Institute. Materials and methods.We analyzed Short Term Disability Claims due to Respiratory Diseases (RD-STDC), associated hospitalizations and deaths (March 1 - December 31, 2020).We estimated the attack rate (AR) for SARS-CoV-2, and relative risks (RR) of TWD-RD, hospitalization and deaths. Results. 1 730 334 workers received at least one RD-STDC. AR was higher in women and in both sexes it was lower in >60 years old. RR of hospitalization and death were higher in men and increased with age. Compared with RD-STDC of 2015-2019, the states of Durango, Tamaulipas and Nuevo Leon had a higher RR of RD-STDC than the rest of the country. Conclusions. The SARS-CoV-2 epidemic impacted IMSS-affiliated workers significantly. We observed a 4.6 fold excess in RD-STDC compared to the expected frequency and nearly 1 million SARS-CoV-2 cases in this population. Our data suggest that the RD-STDC system can be used as an additional resource for epidemiological surveillance of emerging diseases.

COVID-19 in the city of Madrid and vulnerability. Analysis of the first two waves

OBJECTIVE: The advance of the pandemic in the large cities of the world with great virulence and the apparent heterogeneous distribution by factors of vulnerability, led us to propose this work. The objective of this study was to relate COVID-19 infection rates to the social vulnerability of the city of Madrid by district, in two different episodes, spring 2020 and summer 2020. METHODS: Analytical cross-sectional study. Taking sociodemographic data of the entire population of the city of Madrid between the months of April and October 2020, together with the cumulative incidence rates of COVID-19, a linear regression analysis, correlation and factor analysis was carried out, relating the cumulative incidence rate of COVID-19 and the vulnerability indicator of the districts of the city of Madrid. RESULTS: The results showed important differences between the two episodes of the pandemic: on one hand, the first had more relationship with health factors, while in the second, a relationship appeared with the groups of greater social vulnerability, territorially located in the South-East of the City and related in this case to social factors rather than health. Thus we see that the TIA x 100,000 in the first episode in Chamberi and Usera -two extreme districts in vulnerability- were 896 and 843 cases respectively, while in the second they were 3,708 and 6,258 cases. CONCLUSIONS: The territorial differences in the city become evident with the arrival of a generalised, universal event such as COVID-19, where vulnerability increases for everyone, with greater repercussions in those territories where it already had an impact.

Shifts in learning assistants' self-determination due to COVID-19 disruptions in Calculus II course delivery.

BACKGROUND: The Learning Assistant (LA) model with its subsequent support and training has evidenced significant gains for undergraduate STEM learning and persistence, especially in high-stakes courses like Calculus. Yet, when a swift and unexpected transition occurs from face-to-face to online, remote learning of the LA environment, it is unknown how LAs are able to maintain their motivation (competence, autonomy, and relatedness), adapt to these new challenges, and sustain their student-centered efforts. This study used Self-Determination Theory (SDT) to model theoretical aspects of LAs' motivations (persistence and performance) both before and after changes were made in delivery of a Calculus II course at Texas Tech University due to COVID-19 interruptions. RESULTS: Analysis of weekly written reflections, a focus group session, and a post-course questionnaire of 13 Calculus II LAs throughout Spring semester of 2020 showed that LAs' reports of competence proportionally decreased when they transitioned online, which was followed by a moderate proportional increase in reports of autonomy (actions they took to adapt to distance instruction) and a dramatic proportional increase in reports of relatedness (to build structures for maintaining communication and building community with undergraduate students). CONCLUSIONS: Relatedness emerged as the most salient factor from SDT to maintain LA self-determination due to the COVID-19 facilitated interruption to course delivery in a high-stakes undergraduate STEM course. Given that online learning continues during the pandemic and is likely to continue after, this research provides an understanding to how LAs responded to this event and the mounting importance of relatedness when LAs are working with undergraduate STEM learners. Programmatic recommendations are given for enhancing LA preparation including selecting LAs for autonomy and relatedness factors (in addition to competence), modeling mentoring for remote learners, and coaching in best practices for online instruction.

A decision support system for fraud detection in public procurement

Over the past few years, investigators in Brazil have been uncovering numerous corruption and money laundering schemes at all levels of government and in the country's largest corporations. It is estimated that between 2% and 5% of the global GDP is lost annually because of such practices, not only directly impacting public services and private sector development but also strengthening organized crime. However, most law enforcement agencies do not have the capability to carry out systematic corruption risk assessment leveraging on the availability of data related to public procurement. The currently prevailing approach employed by Brazilian law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. As a result, a large number of companies involved in fraud remain undetected and unprosecuted. The decision support system (DSS) described in this work addresses these existing limitations by providing a tool for systematic analysis of public procurement. It allows the law enforcement agencies to establish priorities concerning the companies to be investigated. This DSS incorporates data mining algorithms for quantifying dozens of corruption risk patterns for all public contractors inside a specific jurisdiction, leading to improvements in the quality of public spending and to the identification of more cases of fraud. These algorithms combine operations research tools such as graph theory, clusterization, and regression analysis with advanced data science methods to allow the identification of the main risk patterns, such as collusion between bidders, conflicts of interest (e.g., a politician who owns a company contracted by the same government body where he or she was elected), and companies owned by a potentially straw person used for disguising its real owner (e.g., beneficiaries of cash conditional transfer programs). The DSS has already led to a detailed analysis of large public procurement datasets, which add up to more than 50 billion dollars. Moreover, the DSS provided strategic inputs to investigations conducted by federal and state agencies. © 2020 The Authors. International Transactions in Operational Research © 2020 International Federation of Operational Research Societies

Behavior of the gynecological emergency during the COVID-19 pandemic

Introduction: The COVID-19 pandemic has meant a decrease in gynecological visits to the emergency department. Objective: To characterize the attending in the gynecological emergency service, evaluating the amount of admissions, hospitalizations, need for surgical intervention, morbidity factors, among others, compared to the pre-pandemic situation. Method: A non-concurrent cohort study of hospitalized patients was carried out through a spontaneous attending to the gynecological emergency service between weeks 12 and 28 of 2019 and 2020 at the Hospital Clínico of the Pontificia Universidad Católica, in Santiago (Chile). Different morbidity factors were measured: admission diagnosis, surgical intervention, surgical complication, hospital stay, admission hemoglobin, and need for transfusion of blood units. Results: A total of 511 visitors to the emergency service were registered between weeks 12 and 28 of 2019, compared to 196 in 2020. In 2019 and 2020, 103 and 72 women were hospitalized respectively (odds ratio [OR]: 2.3). Admission for pregnancy loss decreased (24 vs. 12;p = 0.01), while admission for postmenopausal bleeding increased (9 vs. 22;p = 0.01). There was no change in the surgical interventions performed (86.4% vs. 84.7%;p = 0.7). The hospital stays increased (2.3 vs. 3.1;p < 0.0001) and the need for blood transfusion (2 vs. 7;p = 0.02;OR: 5.4;confidence interval: 1.09-26). Conclusions: The COVID-19 pandemic caused a decrease in spontaneous attending for gynecological pathology at the emergency service, causing an increase in the attend/admission ratio and greater morbidity in hospitalized patients characterized by a greater need for blood transfusion and an increase on the hospital stay. © 2021 Sociedad Chilena de Obstetricia y Ginecología.