Smart Travel Planning to the Algarve by Older Tourists before the Covid-19 Pandemic Crisis

Information and communication technologies, or ICT, have revolutionized societies' daily lives and the economy's development on a global scale. Senior tourism is considered a sector in solid expansion, and, as such, it matters to understand the importance that these tourists attribute to these technologies. The association between tourism and digitalization gave rise to the concept of "smart tourism". So, it is essential to have the necessary skills to be active in a world mediated by the internet. It is not just tourist destinations that have evolved in the present digital age;tourists, themselves, have also changed. However, this digitalization has not equitably reached the entire senior population. The main goal of this article is to analyze the internet use by older tourists to plan their trip to the Algarve before the COVID-19 pandemic crisis. It has the specific objective of identifying and exploring what type of service they sought the information. The results indicate that there are statistically significant differences between different groups of respondents regarding the use of the internet to plan a trip to the Algarve.

Impact of the pandemic on the demand for emergency services: A preliminary approach

Poor planning in the allocation of hospital resources can be devastating on a social level, causing excess mortality and comorbidities associated with its patients. Effective management where the demand for health care is controlled is essential, allowing a decrease in the budget and responding with the same quality and celerity. The fact that the study focuses on a single period, due to the SARS-COV-2 pandemic, means that the conclusions drawn in this study will serve as an example model for future situations. This study uses time series as methodology, an instrument widely used in studies of this scope, to predict the number of daily urgencies in a regional hospital. The research also studies the inclusion of explanatory variables already identified in the literature, namely average temperature, daily temperature range, average rainfall, day of the week, month, and holiday or non-holiday. © 2022 Author(s).

Smartphone addiction is associated with symptoms of anxiety, depression, stress, tension, confusion, and insomnia: A cross-sectional and comparative study with physically and non-physically active adults in self-isolation during the COVID-19 pandemic

Introduction. During the novel coronavirus disease (COVID-19) outbreak, social media exposure and the use of electronic devices have increased;still, these behaviors may cause adverse health effects. Objective. This study assessed sleep quality, insomnia, mood, and psychological aspects among physically (n = 46) and non-physically (n = 53) active individuals during self-isolation throughout the COVID-19 pandemic and ex-amined their association with smartphone addiction. Method. A cross-sectional study was conducted among adult Brazilian citizens in self-isolation for at least 60 days;ninety-nine volunteers from different Brazilian re-gions were enrolled in the online survey. The Depression, Anxiety and Stress Scale-Short Form, the Pittsburgh Sleep Quality Index, the Insomnia Severity Index, the Brunel Mood Scale, and the Smartphone Addiction Scale-Short Version were used to assess the study outcomes. Results. The results indicate moderate and large correlations of smartphone addiction with mood subscales, insomnia (r = .52), anxiety (r = .49), depres-sion (r = .49), and stress (r = .49) symptoms. Also, it was observed that physically active participants were less addicted to smartphones than the non-physically active during self-isolation (p < .01) and that the physically active ones had a better mood and lower anxiety (p = .02), depression (p < .01) and insomnia (p < .01) levels. Discussion and conclusion. These findings show the health implications of self-isolation and how essential it is to be physically active to avoid self-isolation & apos;s adverse psychological effects.

Predictors of mortality of covid-19 in chronic hemodialysis patients

BACKGROUND AND AIMS: Coronavirus disease 2019 (COVID-19) has affected the care of patients on chronic hemodialysis (HD). It has been reported that older adults and those with comorbidities, such as diabetes mellitus, hypertension, cardiovascular disease and chronic kidney disease are prone to develop severe disease and poorer outcomes. By virtue of their average old age, multiple comorbidities, immunosuppression and frequent contact with other patients in dialysis facilities, chronic HD patients are at particular risk for severe COVID-19 infection. The aim of this study was to compare clinical presentation, laboratory and radiologic data and outcomes between HD and non-HD COVID-19 patients and find possible risk factors for mortality on HD patients. METHOD: A single center retrospective cohort study including patients on HD hospitalized with a laboratory confirmed COVID-19 infection, from March 1st to December 31st of 2020 and matched them to non-dialysis patients (non-HD) (1:1). Data regarding patient baseline characteristics, symptoms, laboratory and radiologic results at presentation were collected, as well as their outcomes. Categorical variables are presented as frequencies and percentages, and continuous variables as means or medians for variables with skewed distributions. A paired Student's t-test was performed on parametric continuous values or Mann-Whitney for non-parametric continuous variables. Chi-squared test was performed for comparing categorical variables. Logistic regression was used to identify risk factors for mortality on HD patients. A p-value of less than 0,05 indicated statistical significance. RESULTS: A total of 34 patients HD patients were included, 70,6% male, mean age of 76,5 years, median time of dialysis of 3,0 years. Among them 85,3% were hypertensive, 47,1% diabetic, 47,1% had cardiovascular disease, 30,6% pulmonary chronic disease and 23,5% cancer. The most frequent symptoms were fever (67,6%), shortness of breath (61,8%) and cough (52,9%). At admission, 55,9% of patients needed oxygen supply, one required mechanic ventilation and was admitted to intensive care unit. Regarding laboratory data, the most common features were lymphopenia in 58,9% (median-795/uL), elevated LDH in 64,7% (median-255 U/L), raised C-reactive protein in 97,1% (median-6,3 mg/dlL, raised D-dimer in 95,8% (median 1,7 ng/mL), and all patients presented high ferritin (median 1658 ng/mL) and elevated Troponin T (median 130ng/mL). The majority presented with radiologic changes, particularly bilateral infiltrates in 29,4%. Concerning clinical outcomes, the median hospitalization time was 11 days and 13 patients (38,2%) developed bacterial superinfection. Mortality rate was 32,4%. When matched to 34 non-HD patients there was no statistical significant differences in sex, age and comorbidities. The HD group had a tendency to more ventilator support need (p=0,051), higher ferritin and troponin levels (p=<0,001 for both), whereas the non-HD group presented with greater levels of transaminases (p= 0,017). There was o significant difference in hospitalization time (median of 11 vs 7 days, p=0,222) neither in mortality (median of 32,4 vs 35,3%, p=0,798). When the logistic regression was performed, only bacterial superinfection was a predictor for mortality on hemodialysis patients (p=0,004). CONCLUSION: Our study compared outcomes for COVID-19 patients on chronic HD to non-dialysis patients and showed no difference in hospitalization time nor in death rate. In spite of these results, the mortality in patients on chronic HD is still not negligible, with up to 32% of in-hospital mortality. Bacterial superinfection is a predictive risk factor for mortality. Hence the importance of interventions to mitigate the burden of COVID-19 in these patients, by preventing its spread, particularly in hemodialysis centers.

Psychiatric disorders during acute hospital treatment of COVID-19 - a case series

IntroductionCoronavirus disease (COVID-19) has been associated with the development mental and behavioural symptoms and psychiatric disorders. This association is stronger in severe cases of the disease and in those needing inpatient treatment, particularly in intensive care units (ICU).ObjectivesTo determine the incidence of psychiatric disorders in a Portuguese hospital-based sample of patients with COVID-19. To describe relevant demographic and clinical data.MethodsWe reviewed all COVID-19 inpatients assessed by liaison psychiatry at our hospital between April and September 2020. Patients admitted due to a psychiatric disorder were excluded from the analysis. We reviewed medical records and retrieved relevant clinical data. ICD-10 was used to classify diagnoses.ResultsWe identified 36 cases with a mean age of 62.64 years-old (SD 19.23). The most common disorder was delirium, which occurred in 41.7% of our sample (15 patients), followed by adjustment disorder (22.2%, n=8), and depressive episode (16.7%, n=8). Most patients had no personal (61.1%, n=22) nor family (75%, n=27) history of a psychiatric disorder. Mean length of admission was 36.89 days (SD 28.91). Seventeen cases (47.22%) had at least one risk factor for severe COVID-19 disease and 14 (38.89%) were admitted at some point to the ICU.ConclusionsIn our sample, delirium was the main cause for mental or behavioural symptoms in COVID-19 patients. However, we observed a wide array of presentations in our center. A larger sample would allow to better characterize this often-overlooked symptoms and identify risk factors to psychiatric syndromes.DisclosureNo significant relationships.

COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling.

This study aimed to analyse the spatial-temporal distribution of COVID-19 mortality in Sergipe, Northeast, Brazil. It was an ecological study utilising spatiotemporal analysis techniques that included all deaths confirmed by COVID-19 in Sergipe, from 2 April to 14 June 2020. Mortality rates were calculated per 100 000 inhabitants and the temporal trends were analysed using a segmented log-linear model. For spatial analysis, the Kernel estimator was used and the crude mortality rates were smoothed by the empirical Bayesian method. The space-time prospective scan statistics applied the Poisson's probability distribution model. There were 391 COVID-19 registered deaths, with the majority among ⩾60 years old (62%) and males (53%). The most prevalent comorbidities were hypertension (40%), diabetes (31%) and cardiovascular disease (15%). An increasing mortality trend across the state was observed, with a higher increase in the countryside. An active spatiotemporal cluster of mortality comprising the metropolitan area and neighbouring cities was identified. The trend of COVID-19 mortality in Sergipe was increasing and the spatial distribution of deaths was heterogeneous with progression towards the countryside. Therefore, the use of spatial analysis techniques may contribute to surveillance and control of COVID-19 pandemic.

Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space-time modelling.

This study aimed to analyse the trend and spatial-temporal clusters of risk of transmission of COVID-19 in northeastern Brazil. We conducted an ecological study using spatial and temporal trend analysis. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May 2020. We used the segmented log-linear regression model to assess time trends, and the local empirical Bayesian estimator, the global and local Moran indexes for spatial analysis. The prospective space-time scan statistic was performed using the Poisson probability distribution model. There were 113 951 confirmed cases of COVID-19. The average incidence rate was 199.73 cases/100 000 inhabitants. We observed an increasing trend in the incidence rate in all states. Spatial autocorrelation was reported in metropolitan areas, and 178 municipalities were considered a priority, especially in the states of Ceará and Maranhão. We identified 11 spatiotemporal clusters of COVID-19 cases; the primary cluster included 70 municipalities from Ceará state. COVID-19 epidemic is increasing rapidly throughout the Northeast region of Brazil, with dispersion towards countryside. It was identified high risk clusters for COVID-19, especially in the coastal side.

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