Spatiotemporal Pattern of COVID-19-Related Mortality during the First Year of the Pandemic in Brazil: A Population-based Study in a Region of High Social Vulnerability

Reduced HIV/AIDS diagnosis rates and increased AIDS mortality due to late diagnosis in Brazil during the COVID-19 pandemic

The COVID-19 pandemic has severely affected global health, leading to the suspension of numerous routine healthcare services and posing challenges in efforts to control other diseases, such as HIV/AIDS. This study aimed to assess the impact of the COVID-19 pandemic on HIV/AIDS diagnoses and mortality rates in Brazil during 2020 and 2021. The percentage change was calculated to determine whether there was an increase or decrease in HIV/AIDS diagnoses and mortality, considering the average numbers from the last 5 years. Additionally, a Joinpoint regression model and an interrupted time series analysis were applied to assess time trends before and after the onset of the pandemic. Lastly, choropleth maps were prepared. We observed a reduction of 22.4% (2020) and 9.8% (2021) in the diagnosis of HIV/AIDS in Brazil. Conversely, there was a significant increase in the percentage change of late diagnosis of AIDS deaths in 2020 (6.9%) and 2021 (13.9%), with some states showing an increase of over 87%. Decreasing time trends in the diagnosis of HIV/AIDS were identified before the pandemic in Brazil, especially in the Southeast and South regions, and then time trends stabilized after including the pandemic years. Along with the dissemination of COVID-19, there was a reduction in the diagnosis of HIV/AIDS and an increase in late diagnosis AIDS deaths, signaling a serious impact of the pandemic on HIV/AIDS control strategies in Brazil. Therefore, we highlight the need for continuous efforts to control both diseases, that is, maintaining regular health services even in crisis situations.

Socioeconomic inequalities in COVID-19 incidence during the first six waves in Barcelona

BACKGROUND

The emergence of SARS-CoV-2 affected urban areas. In Barcelona, six waves of COVID-19 hit the city between March 2020 and March 2022. Inequalities in the incidence of COVID-19 have been described. However, no studies have examined the daily trends of socioeconomic inequalities and how they changed during the different phases of the pandemic. The aim of this study is to analyse the dynamic socioeconomic inequalities in the incidence of COVID-19 during the six waves in Barcelona.

METHODS

We examined the proportion of daily cases observed in the census tracts in the lower income tercile compared with the proportion of daily cases observed in the sum of the lower and higher income terciles. Daily differences in these proportions were assessed as a function of the epidemic waves, sex, age group, daily incidence and daily change in the incidence. A logistic regression model with an autoregressive term was used for statistical analysis.

RESULTS

A time-dynamic effect was found for socioeconomic inequalities in the incidence of COVID-19. In fact, belonging to a lower-income area changed from being a risk factor (Waves 1, 2, 4 and 5) to being a protective factor in the sixth wave of the pandemic. Age also had a significant effect on incidence, which also changed over the different waves of the pandemic. Finally, the lower-income areas showed a comparatively lower incidence during the ascending phase of the epidemic waves.

CONCLUSION

Socioeconomic inequalities in COVID-19 changed by wave, age group and wave phase.

Association of socioeconomic indicators with COVID-19 mortality in Brazil: a population-based ecological study

The article presents an analysis of the spatial distribution of mortality from COVID-19 and its association with socioeconomic indicators in the north-eastern region of Brazil - an area particularly vulnerable with regard to these indicators. This populationbased ecology study was carried out at the municipal level in the years 2020 and 2021, with analyses performed by spatial autocorrelation, multiple linear regression and spatial autoregressive models. The results showed that mortality from COVID-19 in this part of Brazil was higher in the most populous cities with better socioeconomic indicators. Factors such as the onset of the COVID-19 pandemic in large cities, the agglomerations existing within them, the pressure to maintain economic activities and mistakes in the management of the pandemic by the Brazilian federal Government were part of the complex scenario related to the spread of COVID-19 in the country and this study was undertaken in an attempt to understand this situation. Analysing the different scenarios is essential to face the challenges posed by the pandemic to the world's health systems.

Impact of the COVID-19 pandemic on the actions of the Schistosomiasis Control Program in an endemic area in Northeastern Brazil

Schistosomiasis remains a serious public health concern in Brazil and the Schistosomiasis Control Program (PCE) was elaborated to assist in the control of the disease. Nevertheless, the irruption of the COVID-19 pandemic may have impacted the program. Herein, we assessed the impact of the pandemic on PCE actions in an endemic area in the region with the highest positivity rate for schistosomiasis in Brazil. We conducted an ecological, population-based study using data from the PCE of the state of Alagoas, between 2015 and 2021, to calculate the percentage of change. The temporal trend analysis was performed using the segmented log-linear regression model. To evaluate the spatial distribution of the data, choropleth maps were made showing the values of the% of change. Moran maps was elaborated to indicate the critical areas. Our analysis showed a decrease in the population surveyed in 2020 (-41.00%) and 2021 (-18.42%). Likewise, there was a reduction in the number of Kato-Katz tests performed (2020 = -43.45%; and in 2021 = -19.63%) and, consequently, a drop in the rate of positive tests (-37.98% in 2020 and -26.14% in 2021). Importantly, treatment of positive cases was lower than 80% (77.44% in 2020 and 77.38% in 2021). Additionally, spatial clusters with negative percentage values of up to -100% of the PCE indicators were identified mostly in the municipalities of the coastal areas that are historically most affected by schistosomiasis. Taken together, our analyzes corroborate that PCE actions in endemic municipalities of Alagoas were impacted by the COVID-19 pandemic.

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran's neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R2 of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO2, PM10, and O3); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.

COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities

COVID-19 has exacerbated endemic health inequalities resulting in a syndemic pandemic of higher mortality and morbidity rates among the most socially disadvantaged. We did a scoping review to identify and synthesise published evidence on geographical inequalities in COVID-19 mortality rates globally. We included peer-reviewed studies, from any country, written in English that showed any area-level (eg, neighbourhood, town, city, municipality, or region) inequalities in mortality by socioeconomic deprivation (ie, measured via indices of multiple deprivation: the percentage of people living in poverty or proxy factors including the Gini coefficient, employment rates, or housing tenure). 95 papers from five WHO global regions were included in the final synthesis. A large majority of the studies (n=86) found that COVID-19 mortality rates were higher in areas of socioeconomic disadvantage than in affluent areas. The subsequent discussion reflects on how the unequal nature of the pandemic has resulted from a syndemic of COVID-19 and endemic inequalities in chronic disease burden.

Application of Data Science for Cluster Analysis of COVID-19 Mortality According to Sociodemographic Factors at Municipal Level in Mexico

Mexico is among the five countries with the largest number of reported deaths from COVID-19 disease, and the mortality rates associated to infections are heterogeneous in the country due to structural factors concerning population. This study aims at the analysis of clusters related to mortality rate from COVID-19 at the municipal level in Mexico from the perspective of Data Science. In this sense, a new application is presented that uses a machine learning hybrid algorithm for generating clusters of municipalities with similar values of sociodemographic indicators and mortality rates. To provide a systematic framework, we applied an extension of the International Business Machines Corporation (IBM) methodology called Batch Foundation Methodology for Data Science (FMDS). For the study, 1,086,743 death certificates corresponding to the year 2020 were used, among other official data. As a result of the analysis, two key indicators related to mortality from COVID-19 at the municipal level were identified: one is population density and the other is percentage of population in poverty. Based on these indicators, 16 municipality clusters were determined. Among the main results of this research, it was found that clusters with high values of mortality rate had high values of population density and low poverty levels. In contrast, clusters with low density values and high poverty levels had low mortality rates. Finally, we think that the patterns found, expressed as municipality clusters with similar characteristics, can be useful for decision making by health authorities regarding disease prevention and control for reinforcing public health measures and optimizing resource distribution for reducing hospitalizations and mortality.