Epidemiology, Biodiversity, and Technological Trajectories in the Brazilian Amazon: From Malaria to COVID-19

Effect of long COVID-19 syndrome on health-related quality of life: a cross-sectional study

Purpose

This study aimed to assess the association of anxiety, headache, and insomnia on the QoL of patients with long COVID-19.

Methods

We conducted a cross-sectional survey between August 2020 and March 2023. A total of 200 participants were eligible, 53 were excluded and 147 patients with long COVID were included. QoL was evaluated across eight domains using the 36-Item Short Form Health Survey (SF-36). Standardized protocols including the Beck Anxiety Inventory (BAI) (n = 103), Pittsburgh Sleep Quality Index (PSQI) (n = 73), and Migraine Disability Assessment (MIDAS) (n = 67) were also used.

Results

Participants with sleep disorders had significantly lower Vitality (p < 0.001). Participants with anxiety disorders had significantly lower Vitality (p = 0.001), poorer Mental Health (p = 0.008), and more severe Bodily Pain (p = 0.008). Participants with headache had significantly lower Vitality (p = 0.032), poorer Mental Health (p = 0.036), and poorer Physical Functioning (p = 0.016). Participants with both headache and anxiety had significantly lower Vitality (p = 0.005) and Mental Health (p = 0.043) domain scores. Correlation analysis revealed that higher scores for anxiety, sleep disorder, and headache were independently correlated with poorer QoL across various domains. The presence of sleep disorder was associated with a fourfold increase in risk of experiencing diminished Vitality (odds ratio [OR]4.47; 95% CI 1.01-19.69; p = 0.048).

Conclusion

Participants with anxiety, sleep, and headache disorders tended to have a worse QoL. The Vitality and Mental Health domains were the most adversely affected in patients with long COVID. Sleep disorders were associated with a fourfold increase in the risk of poor Vitality.

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning

OBJECTIVE

Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the "riverine municipalities" and hypothesizes the presence of TB clusters in the area. We also aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors.

METHODS

Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran's I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities.

RESULTS

Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81.

CONCLUSION

Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities.

Influence of sociodemographic and obstetric factors on maternal mortality in Brazil from 2011 to 2021

BACKGROUND

Obstetric causes are classified as direct (complications of pregnancy, childbirth or the puerperium) or indirect (caused by pregnancy but not directly caused by it). This study aimed to analyze maternal mortality from obstetric causes in Brazil from 2011 to 2021.

METHODS

This was an ecological study on mortality and live births. The outcomes were the specific risk of mortality from direct and indirect cause adjustment and death during pregnancy and the puerperium. Binary and multiple linear logistic regressions were used to assess the influence of sociodemographic factors and maternal and child health indicators on maternal mortality and time of death (pregnancy and puerperium).

RESULTS

Regarding mortality during pregnancy and during the puerperium, increased (p = 0.003) and decreased (p = 0.004) mortality over the years, respectively; residing in the northern region was associated with lower (p < 0.05) and greater (p = 0.035) odds; and the Maternal Mortality Committee was the primary and least active source of investigation, respectively (p < 0.0001). The number of deaths from indirect causes increased with age (p < 0.001) and in the northern region (p = 0.011) and decreased in the white (< 0.05) and stable union (0.002) regions. Specifically, for mortality risk, the age group [women aged 15-19 years presented an increase in cesarean section (p < 0.001) was greater than that of women who had < 4 antenatal visits (p < 0.001)], education [women who completed high school (8 to 11 years) was greater when they had < 4 prenatal visits (p = 0.018)], and marital status [unmarried women had more than 4 antenatal visits (p < 0.001); cesarean birth (p = 0.010) and < 4 antenatal visits (p = 0.009) were predictors of marriage; and women in a stable union who had < 4 prenatal visits and live births to teenage mothers (p < 0.001) were predictors]. Women who had no education (p = 0.003), were divorced (p = 0.036), had cesarean deliveries (p < 0.012), or lived in the north or northeast (p < 0.008) had higher indirect specific mortality risk.

CONCLUSIONS

Sociodemographic factors and maternal and child health indicators were related to different patterns of obstetric mortality. Obstetric mortality varied by region, marital status, race, delivery, prenatal care, and cause of death.

Health from Brazilian Amazon food wastes: Bioactive compounds, antioxidants, antimicrobials, and potentials against cancer and oral diseases

Brazilian Amazon contains over 30,000 plant species and foods rich in bioactive compounds such as terpenes, phenolic acids, alkaloids, and flavonoids, of potential health benefits (antioxidant, antimicrobial, antiparasitic, anticancer, gastroprotection, prebiotic effects, among others). The existence of residues from non-edible parts of plants (leaves, roots, stems, branches, barks) or fruit wastes (peel, bagasse, seeds) in the agri-food industry and its supply chain is an important challenge in food loss and waste management. In this critical review several Amazon species, focusing on extracts/essential oils from nonedible parts or wastes, were analyzed in terms of phytochemicals, biological activity, and underlying mechanisms. We hope this review emphasizes the importance of Amazon's sustainability initiatives on population health due to the potential shown against cancer, infectious diseases, and prevention of oral diseases. It is urgent to think about the conversion of amazon food wastes and co-products into high-added-value raw materials to develop novel drugs, food packaging systems, or nutraceutical foods.

Edible Fruits from the Ecuadorian Amazon: Ethnobotany, Physicochemical Characteristics, and Bioactive Components

In the Ecuadorian Amazon region, there are various types of edible fruits that have distinct qualities and benefits. Understanding the uses, properties, and functions of these fruits is important for researching products that are only available in local markets. This review aims to gather and summarize the existing scientific literature on the ethnobotany, physicochemical composition, and bioactive compounds of these native fruits to highlight the potential of the region's underutilized biodiversity. A systematic review was carried out following the PRISMA methodology, utilizing databases such as Web of Science, Scopus, Pubmed, Redalyc, and SciELO up to August 2023. The research identified 55 edible fruits from the Ecuadorian Amazon and reported their ethnobotanical information. The most common uses were fresh fruit consumption, preparation of typical food, and medicine. Additionally, nine native edible fruits were described for their physicochemical characteristics and bioactive components: Aphandra natalia (Balslev and Henderson) Barfod; Eugenia stipitate McVaugh; Gustavia macarenensis Philipson; Mauritia flexuosa L.f; Myrciaria dubia (Kunth) McVaugh; Oenocarpus bataua Mart; Plukenetia volubilis L.; Pouteria caimito (Ruiz and Pav.) Radlk.; and Solanum quitoense Lam. The analyzed Amazonian fruits contained bioactive compounds such as total polyphenols, flavonoids, carotenoids, and anthocyanins. This information highlights their potential as functional foods and the need for further research on underutilized crops.

Deforestation impacts on dengue incidence in the Brazilian Amazon

The objective of the study is to perform the spatial analysis of the conditioning factors for the increase in the incidence rate of dengue cases in municipalities located in the Amazon biome, in the period from 2016 to 2021. Three statistical approaches were applied: Moran's index, ordinary least squares regression, and geographically weighted regression. The results revealed that the incidence rates of dengue cases cluster in two areas, both located in the south of the Amazon biome, which is associated with the Arc of Deforestation. The variable deforestation influences the increase in dengue incidence rates revealed by the OLS and GWR model. The adjusted R² of the GWR model was 0.70, that is, the model explains about 70% of the total case variation of dengue incidence rates in the Amazon biome. The results of the study evidence the need for public policies aimed at the prevention and combat of deforestation in the Amazon region.

Neglected tropical diseases risk correlates with poverty and early ecosystem destruction

BACKGROUND

Neglected tropical diseases affect the most vulnerable populations and cause chronic and debilitating disorders. Socioeconomic vulnerability is a well-known and important determinant of neglected tropical diseases. For example, poverty and sanitation could influence parasite transmission. Nevertheless, the quantitative impact of socioeconomic conditions on disease transmission risk remains poorly explored.

METHODS

This study investigated the role of socioeconomic variables in the predictive capacity of risk models of neglected tropical zoonoses using a decade of epidemiological data (2007-2018) from Brazil. Vector-borne diseases investigated in this study included dengue, malaria, Chagas disease, leishmaniasis, and Brazilian spotted fever, while directly-transmitted zoonotic diseases included schistosomiasis, leptospirosis, and hantaviruses. Environmental and socioeconomic predictors were combined with infectious disease data to build environmental and socioenvironmental sets of ecological niche models and their performances were compared.

RESULTS

Socioeconomic variables were found to be as important as environmental variables in influencing the estimated likelihood of disease transmission across large spatial scales. The combination of socioeconomic and environmental variables improved overall model accuracy (or predictive power) by 10% on average (P < 0.01), reaching a maximum of 18% in the case of dengue fever. Gross domestic product was the most important socioeconomic variable (37% relative variable importance, all individual models exhibited P < 0.00), showing a decreasing relationship with disease indicating poverty as a major factor for disease transmission. Loss of natural vegetation cover between 2008 and 2018 was the most important environmental variable (42% relative variable importance, P < 0.05) among environmental models, exhibiting a decreasing relationship with disease probability, showing that these diseases are especially prevalent in areas where natural ecosystem destruction is on its initial stages and lower when ecosystem destruction is on more advanced stages.

CONCLUSIONS

Destruction of natural ecosystems coupled with low income explain macro-scale neglected tropical and zoonotic disease probability in Brazil. Addition of socioeconomic variables improves transmission risk forecasts on tandem with environmental variables. Our results highlight that to efficiently address neglected tropical diseases, public health strategies must target both reduction of poverty and cessation of destruction of natural forests and savannas.

COVID-19 in the Eastern Brazilian Amazon: Incidence, Clinical Management, and Mortality by Social Determinants of Health, Symptomatology, and Comorbidities in the Xingu Health Region

This study aims to investigate the relationship between social determinants of health (SDH), incidence, and mortality to verify which sociodemographic factors, symptoms, and comorbidities predict clinical management; second, this study aims to conduct a survival analysis of individuals with COVID-19 in the Xingu Health Region. Consequently, this study adopted an ecological framework, employing secondary data of COVID-19-positive individuals from the Xingu Health Region, Pará State, Brazil. The data were obtained through the database of the State of Pará Public Health Secretary (SESPA) for the period from March 2020 to March 2021. The incidence and mortality were higher in Vitória do Xingu and Altamira. Municipalities with a higher percentage of citizens with health insurance and higher public health expenditure showed a higher incidence and mortality. A higher gross domestic product was associated with a higher incidence. Females were found to be associated with better clinical management. To live in Altamira was a risk factor for intensive care unit admission. The symptoms and comorbidities that predicted worse clinical management were dyspnea, fever, emesis, chills, diabetes, cardiac and renal diseases, obesity, and neurological diseases. There were higher incidence, mortality, and lower survival rates among the elderly. Thus, it can be concluded that SDH indicators, symptomatology, and comorbidities have implications for the incidence, mortality, and clinical management of COVID-19 in the Xingu Health Region of eastern Amazonia, Brazil.

Trajetorias: a dataset of environmental, epidemiological, and economic indicators for the Brazilian Amazon

The Trajetorias dataset is a harmonized set of environmental, epidemiological, and poverty indicators for all municipalities of the Brazilian Legal Amazon (BLA). This dataset is the result of a scientific synthesis research initiative conducted by scientists from several natural and social sciences fields, consolidating multidisciplinary indicators into a coherent dataset for integrated and interdisciplinary studies of the Brazilian Amazon. The dataset allows the investigation of the association between the Amazonian agrarian systems and their impacts on environmental and epidemiological changes, furthermore enhancing the possibilities for understanding, in a more integrated and consistent way, the scenarios that affect the Amazonian biome and its inhabitants.

Characterization of DNA Polymerase Genes in Amazonian Amerindian Populations

Due to their continuing geographic isolation, the Amerindian populations of the Brazilian Amazon present a different genetic profile when compared to other continental populations. Few studies have investigated genetic variants present in these populations, especially in the context of next-generation sequencing. Knowledge of the molecular profile of a population is one of the bases for inferences about human evolutionary history, in addition, it has the ability to assist in the validation of molecular biomarkers of susceptibility to complex and rare diseases, and in the improvement of specific precision medicine protocols applied to these populations and to populations with high Amerindian ancestry, such as Brazilians. DNA polymerases play essential roles in DNA replication, repair, recombination, or damage repair, and their influence on various clinical phenotypes has been demonstrated in the specialized literature. Thus, the aim of this study is to characterize the molecular profile of POLA1, POLE, POLG, POLQ, and REV3L genes in Amerindian populations from the Brazilian Amazon, comparing these findings with genomic data from five continental populations described in the gnomAD database, and with data from the Brazilian population described in ABraOM. We performed the whole exome sequencing (WES) of 63 Indigenous individuals. Our study described for the first time the allele frequency of 45 variants already described in the other continental populations, but never before described in the investigated Amerindian populations. Our results also describe eight unique variants of the investigated Amerindians populations, with predictions of moderate, modifier and high clinical impact. Our findings demonstrate the unique genetic profile of the Indigenous population of the Brazilian Amazon, reinforcing the need for further studies on these populations, and may contribute to the creation of public policies that optimize not only the quality of life of this population, but also of the Brazilian population.

Linking land-use and land-cover transitions to their ecological impact in the Amazon

Human activities pose a major threat to tropical forest biodiversity and ecosystem services. Although the impacts of deforestation are well studied, multiple land-use and land-cover transitions (LULCTs) occur in tropical landscapes, and we do not know how LULCTs differ in their rates or impacts on key ecosystem components. Here, we quantified the impacts of 18 LULCTs on three ecosystem components (biodiversity, carbon, and soil), based on 18 variables collected from 310 sites in the Brazilian Amazon. Across all LULCTs, biodiversity was the most affected ecosystem component, followed by carbon stocks, but the magnitude of change differed widely among LULCTs and individual variables. Forest clearance for pasture was the most prevalent and high-impact transition, but we also identified other LULCTs with high impact but lower prevalence (e.g., forest to agriculture). Our study demonstrates the importance of considering multiple ecosystem components and LULCTs to understand the consequences of human activities in tropical landscapes.