Transtorno mental comum na gravidez e sintomas depressivos pós-natal no estudo MINA-Brasil: ocorrência e fatores associados

OBJECTIVE To investigate the occurrence and factors associated with common mental disorders in pregnancy and depressive symptoms in postpartum, as well as the association between both in the Brazilian Western Amazon. METHODS This is a prospective cohort in the MINA-Brazil study with women who received primary health care in the town of Cruzeiro do Sul, Acre State. We performed two clinical evaluations during pregnancy (the first: 16–20 weeks; the second: 28 gestational weeks) and three postpartum evaluations (at 3, 6 and 12 months), in which demographic and socioeconomic, gestational, lifestyle and clinical data were collected. We used the Self-Reported Questionnaire (score ≥ 8) to screen the gestational common mental disorder and the Edinburgh Postnatal Depression Scale (score ≥ 10) to identify postpartum depressive symptoms. We used adjusted ordinal logistic regression to investigate the relationship between the covariates and the occurrence of common mental disorders in pregnancy and postpartum depressive symptomatology. RESULTS A total of 461 women completed the two clinical evaluations in pregnancy; of these, 247 completed the three postpartum evaluations. The occurrence of common mental disorder during pregnancy was 36.2% and 24.5% in the first and second evaluations, respectively, and the cumulative incidence was 9.2%. In addition, 50.3% maintained the disorder between evaluations. During postpartum, approximately 20% of the mothers presented depressive symptoms during the first year of their children’s lives. Parity (≥ 2) was associated with common mental disorders, while low maternal education was associated with postpartum depressive symptoms. Women with a common mental disorder in both evaluations during pregnancy were 5.6 times more likely (95%CI: 2.50–12.60) to develop postpartum depressive symptoms. CONCLUSION The occurrence of common mental disorder at any time assessed during pregnancy, but especially its persistence from the second trimester, was strongly associated with depressive symptoms after childbirth. These findings highlight the need for early screening and monitoring of the mental health of pregnant women at the start of prenatal care in order to reduce possible negative impacts on the health of the mother-child binomial caused by such events.OBJETIVO Investigar a ocorrência e os fatores associados com os transtornos mentais comuns na gestação e sintomas depressivos no pós-parto, bem como a associação entre ambos na Amazônia Ocidental Brasileira. MÉTODOS Coorte prospectiva no estudo MINA-Brasil com mulheres atendidas na atenção primária à saúde de Cruzeiro do Sul, Acre. Foram realizadas duas avaliações clínicas na gestação (primeira: 16–20 semanas; segunda: 28 semanas gestacionais) e três avaliações no pós-parto (aos 3, 6 e 12 meses), nas quais foram coletados dados demográficos e socioeconômicos, gestacionais, de estilo de vida e clínicos. Utilizou-se o Self-Reported Questionnaire (escore ≥ 8) para rastreamento do transtorno mental comum gestacional e a escala de depressão pós-natal de Edimburgo (escore ≥ 10) para identificação de sintomas depressivos pós-parto. Foi utilizada regressão logística ordinal ajustada para investigar a relação entre as covariáveis e a ocorrência de transtornos mentais comuns na gravidez e a sintomatologia depressiva no pós-parto. RESULTADOS Um total de 461 mulheres completaram as duas avaliações clínicas na gestação; dessas, 247 completaram as três avaliações pós-parto. A ocorrência de transtorno mental comum durante a gestação foi de 36,2% e 24,5% na primeira e segunda avaliações, respectivamente, e a incidência cumulativa foi de 9,2%. Ademais, 50,3% mantiveram o transtorno entre as avaliações. Durante o pós-parto, aproximadamente 20% das mães apresentaram sintomatologia depressiva ao longo do primeiro ano de vida de seus filhos. A paridade (≥ 2) foi associada ao transtorno mental comum, enquanto a baixa escolaridade materna associou-se com sintoma depressivo pós-parto. Mulheres com transtorno mental comum nas duas avaliações na gravidez apresentaram 5,6 vezes mais chance (IC95% 2,50–12,60) de desenvolverem sintoma depressivo pós-parto. CONCLUSÃO A ocorrência de transtorno mental comum em qualquer momento avaliado durante a gravidez, mas principalmente sua persistência a partir do segundo trimestre, foi fortemente associado ao sintoma depressivo posterior ao parto. Tais achados evidenciam a necessidade de rastreamento precoce e monitoramento da saúde mental de gestantes no início do pré-natal, a fim de reduzir possíveis impactos negativos para a saúde do binômio mãe-filho causados por tais eventos

Birds and bioenergy within the americas: A cross‐national, social–ecological study of ecosystem service tradeoffs

Although renewable energy holds great promise in mitigating climate change, there are socioeconomic and ecological tradeoffs related to each form of renewable energy. Forest‐related bioenergy is especially controversial, because tree plantations often replace land that could be used to grow food crops and can have negative impacts on biodiversity. In this study, we examined public perceptions and ecosystem service tradeoffs between the provisioning services associated with cover types associated with bioenergy crop (feedstock) production and forest habitat‐related supporting services for birds, which themselves provide cultural and regulating services. We combined a social survey‐based assessment of local values and perceptions with measures of bioenergy feedstock production impacts on bird habitat in four countries: Argentina, Brazil, Mexico, and the USA. Respondents in all countries rated birds as important or very important (83–99% of respondents) and showed lower enthusiasm for, but still supported, the expansion of bioenergy feedstocks (48–60% of respondents). Bioenergy feedstock cover types in Brazil and Argentina had the greatest negative impact on birds but had a positive impact on birds in the USA. In Brazil and Mexico, public perceptions aligned fairly well with the realities of the impacts of potential bioenergy feedstocks on bird communities. However, in Argentina and the USA, perceptions of bioenergy impacts on birds did not match well with the data. Understanding people’s values and perceptions can help inform better policy and management decisions regarding land use changes

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost