Public Telemedicine Policy in Brazilian Unified Health System: An Impact Analysis.

There are several difficulties in evaluating interventions seeking to promote public health policies. In this article, we analyzed the promotion of the use of telemedicine during COVID-19 in Brazil. Using the random promotion method with instrumental variables, we showed that the policy of promoting telemedicine was adequate, with intense use of this type of care. Our results showed that telemedicine works if it is encouraged in the population. We contributed to the discussion of public health policies and their impact on the population's health in times of health crisis, such as during the COVID-19 pandemic.

The Influence of Behavioral Sciences on Adherence to Physical Activity and Weight Loss in Overweight and Obese Patients: A Systematic Review of Randomized Controlled Trials.

In recent years, weight gain and reduced physical activity in the general population have contributed to the development of obesity and other health problems; on the other hand, studies in behavioral sciences have been used to modify behaviors for a healthier life, so the objective of this study was to identify the evidence of interventions in behavioral sciences on adherence to physical activity and weight loss in obese patients. This systematic review study is based on a search of the electronic databases PubMed, Web of Science, Scopus, and Cochrane. Studies assessed the evidence from intervention studies that assessed the influence of intervention studies of behavioral sciences on public health. The articles were published between 2013 and 2023. The systematic search of the databases identified 2951 articles. The review analyzed 10 studies. Behavioral science interventions presented evidence through strategies such as multicomponent interventions, lottery and financial incentives, message framing, message framing with financial incentive and physical activity, and psychological satisfaction, demonstrating results in weight loss and maintenance and increased physical activity. This study presents scientific evidence through healthy behavior change methodologies, and future studies can explore these strategies in conjunction with public health technologies in the search for public-private partnerships to promote physical activity in adults.

The Intersection of Health Literacy and Public Health: A Machine Learning-Enhanced Bibliometric Investigation.

In recent decades, health literacy has garnered increasing attention alongside a variety of public health topics. This study aims to explore trends in this area through a bibliometric analysis. A Random Forest Model was utilized to identify keywords and other metadata that predict average citations in the field. To supplement this machine learning analysis, we have also implemented a bibliometric review of the corpus. Our findings reveal significant positive coefficients for the keywords "COVID-19" and "Male", underscoring the influence of the pandemic and potential gender-related factors in the literature. On the other hand, the keyword "Female" showed a negative coefficient, hinting at possible disparities that warrant further investigation. Additionally, evolving themes such as COVID-19, mental health, and social media were discovered. A significant change was observed in the main publishing journals, while the major contributing authors remained the same. The results hint at the influence of the COVID-19 pandemic and a significant association between gender-related keywords on citation likelihood, as well as changing publication strategies, despite the fact that the main researchers remain those who have been studying health literacy since its creation.

Analysis of the Effectiveness of Public Health Measures on COVID-19 Transmission.

In this study, we investigate the COVID-19 epidemics in Brazilian cities, using early-time approximations of the SIR model in networks and combining the VAR (vector autoregressive) model with machine learning techniques. Different from other works, the underlying network was constructed by inputting real-world data on local COVID-19 cases reported by Brazilian cities into a regularized VAR model. This model estimates directional COVID-19 transmission channels (connections or links between nodes) of each pair of cities (vertices or nodes) using spectral network analysis. Despite the simple epidemiological model, our predictions align well with the real COVID-19 dynamics across Brazilian municipalities, using data only up until May 2020. Given the rising number of infectious people in Brazil-a possible indicator of a second wave-these early-time approximations could be valuable in gauging the magnitude of the next contagion peak. We further examine the effect of public health policies, including social isolation and mask usage, by creating counterfactual scenarios to quantify the human impact of these public health measures in reducing peak COVID-19 cases. We discover that the effectiveness of social isolation and mask usage varies significantly across cities. We hope our study will support the development of future public health measures.

Analysis of connectivity between the world's banking markets: The COVID-19 global pandemic shock.

We contribute to the literature on financial networks by presenting empirical evidence that the global shock of the COVID-19 pandemic caused changes in the forms and intensity of banking sector connections between different countries. These changes include providing the highest level of connectivity observed in the timeline initiated in 2005. We used a comprehensive set of information containing data from 35 countries (developed and emerging economies) and showed the change in the classification of transmitting and receiving spillover during the COVID-19 crisis. Our results provide relevant insights into systemic integration between countries' banking markets, especially during difficult times. Our results are significant to Central Banks, banking sector investors, and governments seeking assistance from banks in the solutions for the resumption of the economy in the face of the COVID-19 shock.

Propension to customer churn in a financial institution: a machine learning approach.

This paper examines churn prediction of customers in the banking sector using a unique customer-level dataset from a large Brazilian bank. Our main contribution is in exploring this rich dataset, which contains prior client behavior traits that enable us to document new insights into the main determinants predicting future client churn. We conduct a horserace of many supervised machine learning algorithms under the same cross-validation and evaluation setup, enabling a fair comparison across algorithms. We find that the random forests technique outperforms decision trees, k-nearest neighbors, elastic net, logistic regression, and support vector machines models in several metrics. Our investigation reveals that customers with a stronger relationship with the institution, who have more products and services, who borrow more from the bank, are less likely to close their checking accounts. Using a back-of-the-envelope estimation, we find that our model has the potential to forecast potential losses of up to 10% of the operating result reported by the largest Brazilian banks in 2019, suggesting the model has a significant economic impact. Our results corroborate the importance of investing in cross-selling and upselling strategies focused on their current customers. These strategies can have positive side effects on customer retention.

Indirect and direct effects of the subprime crisis on the real sector: labor market migration.

The bursting of the US housing bubble in the second half of 2008 triggered an almost unprecedented systemic crisis in the world economy. The financial collapse quickly overflowed into the real economy and caused, among other effects, a sharp fall in the flow of world trade. Using export data from Brazilian municipalities, we show that the subprime crisis had a more significant effect on production and employment in exporting cities than municipalities more devoted to the domestic economy. We find that the manufacturing and construction sectors of exporting cities were the most affected during the crisis. However, exporting municipalities with a substantial share of services activities were more resilient to the external crisis. This difference is significant and sheds light on the debate on the effects of the crisis on Brazilian regions and cities. Using a unique business management dataset that contains firm-to-firm controls, we also find spillovers in the labor market from exporting to domestic-oriented cities through job reallocation. Our results suggest that workers migrate from exporting municipalities to other non-exporting municipalities within the same firm economic group.

Micro-level transmission of monetary policy shocks: The trading book channel.

We open the black box of the monetary policy transmission mechanism with a granular model that considers the balance-sheet composition and network relationships of each economic agent. Though there are several well-documented channels through which monetary policy operates, we focus on the overlooked trading book channel, which arises because of adjustments in the accounting value of trading book exposures on banks' balance sheets that have to be marked to market when interest rates change. Variations in banks' net worth due these adjustments are used as input to a network model that incorporates the financial and corporate sectors. The framework permits us to determine the effects of interest rate changes on every bank and firm in the economy and any second-round (contagion) effects in the short run. We apply the model to a comprehensive database of Brazilian banks and firms from 2015 to 2020. We find that interest rate shocks affect more strongly financial stability in periods of monetary policy tightening. We also find notable asymmetric effects of positive and negative interest rate shocks in the Brazilian economy, with positive interest rate shocks affecting more financial stability. Finally, our results also suggest a non-linear relationship between interest rate changes and financial stability, reinforcing the need to mitigate monetary policy shocks through interest rate smoothing and adequate communication and transparency to society.

Traffic campaigns and overconfidence: An experimental approach.

We use a controlled experiment to analyze the impact of watching different types of educational traffic campaign videos on overconfidence of undergraduate university students in Brazil. The videos have the same underlying traffic educational content but differ in the form of exhibition. We find that videos with shocking content (Australian school) are more effective in reducing drivers' overconfidence, followed by those with punitive content (American school). We do not find empirical evidence that videos with technical content (European school) change overconfidence. Since several works point to a strong association between overconfidence and road safety, our study can support the conduit of driving safety measures by identifying efficient ways of reducing drivers' overconfidence. Finally, this paper also introduces how to use machine learning techniques to mitigate the usual subjectivity in the design of the econometric specification that is commonly faced in many researches in experimental economics.

Data on forecasting energy prices using machine learning.

This article contains the data related to the research article "Long-term forecast of energy commodities price using machine learning" (Herrera et al., 2019). The datasets contain monthly prices of six main energy commodities covering a large period of nearly four decades. Four methods are applied, i.e. a hybridization of traditional econometric models, artificial neural networks, random forests, and the no-change method. Data is divided into 80-20% ratio for training and test respectively and RMSE, MAPE, and M-DM test used for performance evaluation. Other methods can be applied to the dataset and used as a benchmark.

Monetary Expansion and the Banking Lending Channel.

This paper examines the bank lending channel, which considers how monetary authority actions affect the variation of loans. We focus on the BRICS (Brazil, Russia, India, China and South Africa) totalizing 1254 banks from five countries in the period 2000-2012 (totalizing 13 years). The empirical results show that the effect of money supply growth on the growth of loans is non-linear and inverted U-shaped. In this context, our results show empirical evidence expansionary monetary policies do not increase the propensity of economic agents to systematically take greater risks on the market. After a certain level of money stock, increases in the money supply do not lead to increased negotiated credit.

An Evaluation of the Non-Neutrality of Money.

This paper evaluates the effect of a change in the quantity of money on relative prices in the U.S. economy based on quarterly time-series for the period of 1959 to 2013. We also estimate the implication of a change in relative prices on the rate of inflation and macroeconomic variables. The empirical results indicate that the change of money supply not only affects relative prices but also affects the inflation rate and real variables, such as investment, natural rate of unemployment and potential GDP, through the change in relative prices. The relevant finding of our study is that money is not neutral in a non-traditional sense because a change in the money supply disturbs relative prices and, consequently, the allocation of resources in the economy. This finding has serious implications that must be considered in the transmission mechanisms of monetary policy.