Search Results (14)

Background: The COVID-19 pandemic had significant impacts on healthcare systems, including the disruption of essential services such as childhood immunization. Containment measures, such as social distancing, contributed to reduced adherence to vaccination programs, increasing the risk of re-emerging vaccine-preventable diseases. We aim to assess the evolution of childhood vaccination coverage in Brazil from 2010 to 2024, identifying trends before, during, and after the COVID-19 pandemic. Methods: An interrupted time series (ITS) study was conducted using publicly available aggregated data on vaccination coverage for children under one year of age. Prais–Winsten regression models were applied to estimate trend changes and evaluate the impact of the pandemic on immunization levels. Results: The findings indicate a progressive decline in vaccination coverage between 2010 and 2019, which was intensified in 2020 by the pandemic. The BCG vaccine showed the greatest decline (−24.88%, p < 0.001), while pentavalent and hepatitis B vaccines decreased annually by −3.72% and −2.21%, respectively. From 2021 onwards, a gradual recovery in coverage was observed, with significant increases for BCG (+7.48% per year, p < 0.001), hepatitis B (+7.45%, p = 0.014), and MMR (+6.73%, p = 0.017) vaccines. Discussion: The results highlight a concerning decline in childhood immunization, exacerbated by the pandemic but showing recent signs of recovery. This scenario underscores structural challenges within the National Immunization Program, requiring coordinated efforts to reverse vaccination losses and ensure system resilience in the face of future crises. Full article

Global manufacturing economies have faced logistics performance challenges in recent decades. This study investigates the influence of industrialization, environmental technology, trade openness, foreign direct investment, and economic growth on the logistics performance index in the top 20 manufacturing economies from 2007 to 2023. This study used an advanced panel approach to obtain robust results, cross-section dependency, a unit root test and a panel cointegration test. The panel quantile regression (PQL) and panel quantile estimates based on income methods were employed to analyze long-run and short-run estimations. The empirical results show that industrialization accelerated across all the quantiles except at the 10th quantile, while environmental technology had a significantly positive impact on logistics performance across all quantiles (10th–90th). Moreover, our baseline model was further supported by the fact that we used Driscoll–Kraay and Prais–Winsten’s estimates and a panel causality test. Our findings reveal that in manufacturing countries, industrialization, environmental technology, and economic growth have a positive impact on logistics performance. This study proposes several recommendations to improve industrialization and environmental technology in manufacturing countries to promote logistics performance. At the same time, more resources should be allocated for industrialization as well as environmental technologies to promote logistics performance. Full article

Background/Objectives: Maternal mortality due to abortion in Brazil has shown a significant decline of 47.37% between 1996 and 2022. This study aims to analyze temporal trends in maternal mortality due to abortion across regions and sociodemographic groups, highlighting disparities and their implications for public health. Methods: Trends were assessed using Prais–Winsten regression models to estimate the annual percentage change (APC). Data were stratified by region and sociodemographic characteristics to identify vulnerable groups. Results: The findings reveal notable regional disparities, with some regions experiencing more pronounced declines than others. Vulnerable sociodemographic groups, including women with lower levels of education and younger age groups, exhibited persistently higher mortality rates. Conclusions: Despite a significant overall reduction in maternal mortality due to abortion, disparities remain among regions and sociodemographic groups. These findings underscore the need for targeted public health policies aimed at reducing inequalities and addressing the needs of the most affected populations. Full article

In the last decade, the size and complexity of datasets have expanded significantly, necessitating more sophisticated predictive methods. Despite this growth, limited research has been conducted on the effects of autocorrelation within widely used regression methods. This study addresses this gap by investigating how autocorrelation impacts the predictive accuracy and efficiency of six regression approaches: Artificial Neural Network (ANN), Ordinary Least Squares (OLS), Cochrane–Orcutt (CO), Prais–Winsten (PW), Maximum Likelihood Estimation (MLE), and Restricted Maximum Likelihood Estimation (RMLE). The study evaluates each method’s performance on three datasets characterized by autocorrelation, comparing their predictive accuracy and variability. The analysis is structured into three phases: the first phase examines predictive accuracy across methods using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE); the second phase evaluates the efficiency of parameter estimation based on standard errors across methods; and the final phase visually assesses the closeness of predicted values to actual values through scatter plots. The results indicate that the ANN consistently provides the most accurate predictions, particularly in large sample sizes with extensive training data. For GDP data, the ANN achieved an MSE of 1.05 × 10⁹, an MAE of 23,344.64, and an MAPE of 81.66%, demonstrating up to a 90% reduction in the MSE compared to OLS. These findings underscore the advantages of the ANN for predictive tasks involving autocorrelated data, highlighting its robustness and suitability for complex, large-scale datasets. This study provides practical guidance for selecting optimal prediction techniques in the presence of autocorrelation, recommending the ANN as the preferred method due to its superior performance. Full article

Global warming has become a big problem around the world, and it is because of what people do. As a possible answer, countries are looking for ways to keep their economies growing and invest in technologies that use clean energy. Therefore, the notion of carbon neutrality has emerged as a crucial policy strategy for nations to attain sustainable development. This study expands the existing discussions on carbon neutrality by investigating the influence of key factors, including green innovation, financial development, natural resources depletion, trade openness, institutional quality, growth, and urbanization on the progress made towards attaining a carbon neutral state in the BRICS nations. This study considers the Method of Moment Quantile-Regression (MM-QR) and Prais–Winsten correlated panel corrected standard errors (PCSEs) estimators to investigate the study objectives over the period of 1990–2021. Under the investigated outcomes, this study validated the significant role of urbanization and growth in carbon neutrality. On the other hand, this study finds the positive role of openness, green innovation, resource depletion, institutional quality, and financial development on environmental deterioration. However, under a systematic analysis, this study utilizes different proxies of the financial sector, for instance, financial complexity, financial efficiency, financial stability, and domestic credit by financial sector, and provides interesting outcomes. Based on these outcomes, this study also provides suggestions to attain desired levels of sustainability. Full article

Introduction: Neuropsychiatric diseases, particularly dementias, has become more prominent with a great impact on the quality of life of the elderly population. Objective: To verify the rate of increase in mortality due to Alzheimer’s disease in the Federal District, Brazil from 2010 to 2018. Method: An ecological study was conducted, with a time series, about the evolution of the mortality coefficient in the Federal District, Brazil carried out at the Federal District State Department of Health. Mortality rates were defined as the dependent variable and years evaluated as the independent variable—from 2010 to 2018. For temporal trend analysis, the Prais–Winsten linear regression model was used and the increment rate with the respective 95% confidence interval was estimated. Results: From 2010 to 2018, 1665 deaths which had Alzheimer’s disease as the underlying cause were recorded in the Mortality Information System. The results showed an overall mortality rate of 6.55 deaths per 100,000 inhabitants, with a higher predominance in females, non-Black people, and those aged 80 years or older. There was an increase in the annual trend of the overall mortality coefficient in both sexes. Conclusion: The findings demonstrated a significant increase in the temporal evolution of mortality due to Alzheimer’s disease in the Federal District, Brazil. It was recommended to conduct original studies to evaluate the factors that can cause the disease in order to collaborate in the process of formulating policies in the area of public health and improvements in clinical practice. Full article

We compared five regression approaches, namely, ordinary least squares, major axis, reduced major axis, robust, and Prais–Winsten to estimate δ¹⁸O–δ²H relationships in four water types (precipitation, surface water, groundwater collected in wells from lowlands, and groundwater from low-yield springs) from the northern Italian Apennines. Differences in terms of slopes and intercepts of the different regressions were quantified and investigated by means of univariate, bivariate, and multivariate statistical analyses. We found that magnitudes of such differences were significant for water types surface water and groundwater (both in the case of wells and springs), and were related to robustness of regressions (i.e., standard deviations of the estimates and sensitiveness to outliers). With reference to surface water, we found the young water fraction was significant in inducing changes of slopes and intercepts, leading us to suppose a certain role of kinetic fractionation processes as well (i.e., modification of former water isotopes from both snow cover in the upper part of the catchments and precipitation linked to pre-infiltrative evaporation and evapotranspiration processes). As final remarks, due to the usefulness of δ¹⁸O–δ²H relationships in hydrological and hydrogeological studies, we provide some recommendations that should be followed when assessing the abovementioned water types from the northern Italian Apennines. Full article

Amazonas suffered greatly during the COVID-19 pandemic. The mortality and fatality rates soared and scarcity of oxygen and healthcare supplies led the health system and funerary services to collapse. Thus, we analyzed the trends of incidence, mortality, and lethality indicators of COVID-19 and the dynamics of their main determinants in the state of Amazonas from March 2020 to June 2021. This is a time-series ecological study. We calculated the lethality, mortality, and incidence rates with official and public data from the Health Department. We used the Prais–Winsten regression and trends were classified as stationary, increasing, or decreasing. The effective reproduction number (Rt) was also estimated. Differences were considered significant when p < 0.05. We extracted 396,772 cases of and 13,420 deaths from COVID-19; 66% of deaths were in people aged over 60; 57% were men. Cardiovascular diseases were the most common comorbidity (28.84%), followed by diabetes (25.35%). Rural areas reported 53% of the total cases and 31% of the total deaths. The impact of COVID-19 in the Amazon is not limited to the direct effects of the pandemic itself; it may present characteristics of a syndemic due to the interaction of COVID-19 with pre-existing illnesses, endemic diseases, and social vulnerabilities. Full article

Since December 2019, the novel coronavirus (SARS-CoV-2) and its associated illness COVID-19 have rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, we used statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national levels in Mexico. In this paper, we propose two types of models. First, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model used to estimate the middle and the end of the outbreak. Model selection was performed using Vuong’s test. The proposed models showed overall fit similar to predictive models (e.g., time series and machine learning); however, the interpretation of parameters is simpler for decisionmakers, and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue. Full article

The carbon intensity of well-being (CIWB) (a ratio measuring the amount of CO₂ emitted per unit of life expectancy at birth) is an increasingly popular way to measure the ecological efficiency of nations. Although research demonstrates that economic development typically reduces this efficiency, little research has explored the extent to which social equality improves it. This study uses panel data for 70 nations between 1995 and 2013 to assess how various aspects of gender equality affect the ecological efficiency of nations. We estimate a series of Prais-Winsten regression models with panel-corrected standard errors (PCSE) to assess how increases in the percentage of women in parliament, expected years of education for women, and the percentage of women in the labor force independently affect CIWB. Our findings indicate that across all nations, increases in the percentage of women in parliament and expected years of schooling reduce CIWB; however, increases in the percentage of women in the labor force increase CIWB. Our results further show that the relationship between different dimensions of gender equality and CIWB differs between more developed and less developed nations. Finally, we find that increases in the number of women in parliament and women’s education attenuate the relationship between women’s labor force participation and CIWB. We discuss the variation in our results by reviewing relevant eco-gender literatures and feminist economics. Full article

The impact of economic growth on unemployment is commonly agreed and extensively studied. However, how age and gender shape this relationship is not as well explored, while there is an absence of research on whether education plays a role. We apply Okun’s law, aiming to estimate age-, gender- and educational attainment level-specific unemployment rate sensitivity to cyclical output fluctuations. Since the empirical literature provides evidence in favour of the non-linear impact of output change on the unemployment rate, supporting higher effects of recessions than that of expansions, we aim to enrich this analysis by estimating how the impact of positive/negative output change on the specific unemployment rate varies with the level of the total unemployment. The analysis is based on 28 European Union (EU) countries and covers the period of 1995–2019. The equations are estimated by least-squares dummy variables (LSDV), using Prais–Winsten standard errors. For the robustness check, we alternatively used Newey–West standard errors to address serial-correlations and heteroscedasticity, and the Arellano–Bond estimator for some specifications that assume dynamics in the panel. The results support previous findings of male- and youth-specific Okun’s coefficients and reveal that they significantly stand out just over the periods of negative output change. Additionally, we find that educational attainment level is an important factor explaining the heterogeneity of unemployment reaction to output change. Full article

Vocational rehabilitation (VR) aims at improving work ability to facilitate workers’ return to work. VR is provided in Brazil by the public social security system. The aim of the present study was to analyze trends in VR indicators for Brazil from 2007 to 2016. Based on open-access, secondary aggregate data, we calculated the cumulative incidence of VR indicators. We fitted Prais-Winsten generalized linear regression models to estimate trends and calculated annual percent variation with the corresponding 95% confidence interval (95% CI). The mean cumulative incidence of referrals to VR services was 37.16/1000 temporary disability benefits granted and exhibited a decreasing trend of −6.92% (95% CI: −8.38; −5.43). The mean cumulative incidence of admissions to VR services was 57.34/100 referrals and exhibited an increasing trend of 3.31% (95% CI: 1.13; 5.53). The mean cumulative incidence of rehabilitation was 57.43/100 admissions and remained stable along the analyzed period, −2.84 (95% CI: −5.87; 0.29). Our findings evidence a reduction in the number of workers referred for VR, an increase of admissions, and stability in the cumulative incidence of rehabilitated workers. Full article

This study examines the importance of natural resources, economic freedom, and sea-access in attracting foreign direct investment (FDI) inflows to the Commonwealth of Independent States (CIS), using panel data from 1998 to 2017. The Prais-Winsten regression with panel-corrected standard errors (PCSEs) is employed for all estimations. Feasible Generalized Least Squares (FGLS), Random Effects with Driscoll-Kraay standard errors (RE (D-K)), and Random Effects of Generalized Least Squares (RE (GLS)) estimators are used to test the sensitivity of PCSEs’ estimates to changes in the underlying empirical model, whereas Instrumental Variables with Two Stage Least Squares (IV (2SLS)), Limited Information Maximum Likelihood (LIML), and Baltagi’s Two-Stage Least-Squares Random-Effects (IV (EC2SLS)) estimators are used to address potential endogeneity concerns. The estimates confirm that natural resources, economic freedom, and sea-access are robust and decisive factors affecting FDI location decisions of foreign investors in CIS. More precisely, the results suggest that increased revealed comparative advantage in petroleum, higher economic freedom characterized by the increased government size and open markets, and territorial coastlines have a statistically significant and positive effect on FDI inflows to CIS transition economies. We also find that direct access to the Black Sea and the Caspian Sea provides a significant geographic competitive advantage to Azerbaijan, Kazakhstan, Georgia, Russia, Turkmenistan, and Ukraine in attracting FDI inflows over the other CIS member-states. Full article

The impact of economic fluctuations on the total unemployment rate is widely studied, however, with respect to age- and gender-specific unemployment, this relationship is not so well examined. We apply the gap version of Okun’s law, aiming to estimate youth unemployment rate sensitivity to output deviations from its potential level. Additionally, we aim to compare whether men or women have a higher equilibrium unemployment rate when output is at the potential level. Contrary to most studies on age- and gender-specific Okun’s coefficients, which assume that the effect of output on unemployment is homogenous, we allow a different effect to occur, depending on the output gap’s sign (positive/negative). The focus of the analysis is on 28 EU countries over the period of 2000–2018. The model is estimated by least squares dummy variable estimator (LSDV), using Prais–Winsten standard errors. We did not find evidence that higher equilibrium unemployment rates are more typical for men or for women. The estimates clearly show the equilibrium level of youth unemployment to be well above that of total unemployment, and this conclusion holds for both genders. We assess greater youth unemployment sensitivity to negative output shock, rather than to positive output shock, but when we take confidence intervals into consideration, this conclusion becomes less obvious. Full article