[Estimation of prevalence of chronic Chagas disease in Brazilian municipalitiesEstimación de la prevalencia de la enfermedad de Chagas crónica en los municipios brasileños]. / Estimativa de prevalência de doença de Chagas crônica nos municípios brasileiros.

Objective: The objective of this study is to estimate the prevalence of chronic Chagas disease (CCD) in Brazil: in the general population, in women, and in women of childbearing age. Methods: A meta-analysis of the literature was conducted to extract data on the prevalence of CCD in municipalities in Brazil in the 2010-2022 period: in the general population, in women, and in women of childbearing age. Municipal-level CCD indicators available in health information systems were selected. Statistical modeling of the data extracted from the meta-analysis (based on data obtained from information systems) was applied to linear, generalized linear, and additive models. Results: The five most appropriate models were selected from a total of 549 models tested to obtain a consensus model (adjusted R2 = 54%). The most important predictor was self-reported CCD in the primary health care information system. Zero prevalence was estimated in 1 792 (32%) of Brazil's 5 570 municipalities; in the remaining 3 778 municipalities, average prevalence of the disease was estimated at 3.25% (± 2.9%). The number of carriers of CCD was estimated for the Brazilian population (~3.7 million), for women (~2.1 million) and for women of childbearing age (~590 000). The disease reproduction rate was calculated at 1.0336. All estimates refer to the 2015-2016 period. Conclusions: The estimated prevalence of CCD, especially among women of childbearing age, highlights the challenge of vertical transmission in Brazilian municipalities. Mathematical projections suggest that these estimates should be included in the national program for the elimination of vertical transmission of Chagas disease.

Objetivo: El objetivo de este estudio fue estimar la prevalencia de la enfermedad de Chagas crónica en la población brasileña en general, en las mujeres y en las mujeres en edad fértil. Métodos: Se realizó un metanálisis de la bibliografía para extraer datos sobre la prevalencia de la enfermedad de Chagas crónica en la población brasileña en general, en las mujeres y en las mujeres en edad fértil, en los municipios de Brasil durante el período 2010-2022. Se seleccionaron los indicadores relacionados con esa enfermedad disponibles en los sistemas municipales de información de salud. La modelización estadística de los datos extraídos del metanálisis, en función de los obtenidos de los sistemas de información, se aplicó a modelos lineales, lineales generalizados y aditivos. Resultados: Se seleccionaron los cinco modelos más apropiados de un total de 549 modelos evaluados, para obtener un modelo de consenso (R2 ajustado = 54%). El factor predictor más importante fue el registro de la enfermedad de Chagas crónica autodeclarada en el sistema de información de atención primaria de salud. De los 5570 municipios brasileños, en 1792 (32%) la prevalencia estimada fue nula y en los 3778 restantes la prevalencia media fue del 3,25% (± 2,9%). El número estimado de pacientes con enfermedad de Chagas crónica en la población brasileña en general, en las mujeres y en las mujeres en edad fértil fue de ~3,7 millones, ~2,1 millones y ~590 000, respectivamente. La tasa calculada de reproducción de la enfermedad fue de 1,0336. Todas las estimaciones se refieren al período 2015-2016. Conclusiones: La prevalencia estimada de la enfermedad de Chagas crónica, especialmente en las mujeres en edad fértil, pone de manifiesto el desafío que representa la transmisión vertical en los municipios brasileños. Estas estimaciones están en línea con los patrones de las proyecciones matemáticas, y sugieren la necesidad de incorporarlas al Pacto Nacional para la Eliminación de la Transmisión Vertical de la Enfermedad de Chagas.

Estimativa de prevalência de doença de Chagas crônica nos municípios brasileiros / Estimation of prevalence of chronic Chagas disease in Brazilian municipalities / Estimación de la prevalencia de la enfermedad de Chagas crónica en los municipios brasileños

[RESUMO]. Objetivo. Este estudo teve como objetivo estimar a prevalência da doença de Chagas (DC) crônica (DCC) na população brasileira, em mulheres e em mulheres em idade fértil. Métodos. Foi realizada uma metanálise da literatura para extrair dados de prevalência de DCC na população brasileira, em mulheres e em mulheres em idade fértil, em municípios do Brasil, no período 2010–2022. Indi- cadores relacionados com a DCC disponíveis nos sistemas de informação em saúde foram selecionados em escala municipal. A modelagem estatística dos dados extraídos da metanálise em função daqueles obtidos dos sistemas de informação foi aplicada a modelos lineares, lineares generalizados e aditivos. Resultados. Foram selecionados os cinco modelos mais adequados de um total de 549 modelos testados para obtenção de um modelo de consenso (R2 ajustado = 54%). O preditor mais importante foi o cadastro autorreferido de DCC do sistema de informação da Atenção Primária à Saúde. Dos 5 570 munícipios brasi- leiros, a prevalência foi estimada como zero em 1 792 (32%); nos 3 778 municípios restantes, a prevalência média da doença foi estimada em 3,25% (± 2,9%). O número de portadores de DCC foi estimado na popu- lação brasileira (~3,7 milhões), mulheres (~2,1 milhões) e mulheres em idade fértil (~590 mil). A taxa de reprodução da doença foi calculada em 1,0336. Todas as estimativas se referem ao intervalo 2015–2016. Conclusões. As prevalências estimadas de DCC, especialmente entre mulheres em idade fértil, evidenciam o desafio da transmissão vertical em municípios brasileiros. Estas estimativas são comparadas aos padrões de projeções matemáticas, sugerindo sua incorporação ao Pacto Nacional para a Eliminação da Transmissão Vertical da DC.

[ABSTRACT]. Objective. The objective of this study is to estimate the prevalence of chronic Chagas disease (CCD) in Brazil: in the general population, in women, and in women of childbearing age. Methods. A meta-analysis of the literature was conducted to extract data on the prevalence of CCD in munici- palities in Brazil in the 2010–2022 period: in the general population, in women, and in women of childbearing age. Municipal-level CCD indicators available in health information systems were selected. Statistical mode- ling of the data extracted from the meta-analysis (based on data obtained from information systems) was applied to linear, generalized linear, and additive models. Results. The five most appropriate models were selected from a total of 549 models tested to obtain a con- sensus model (adjusted R2 = 54%). The most important predictor was self-reported CCD in the primary health care information system. Zero prevalence was estimated in 1 792 (32%) of Brazil’s 5 570 municipalities; in the remaining 3 778 municipalities, average prevalence of the disease was estimated at 3.25% (± 2.9%). The number of carriers of CCD was estimated for the Brazilian population (~3.7 million), for women (~2.1 million) and for women of childbearing age (~590 000). The disease reproduction rate was calculated at 1.0336. All estimates refer to the 2015–2016 period. Conclusions. The estimated prevalence of CCD, especially among women of childbearing age, highlights the challenge of vertical transmission in Brazilian municipalities. Mathematical projections suggest that these estimates should be included in the national program for the elimination of vertical transmission of Chagas disease.

[RESUMEN]. Objetivo. El objetivo de este estudio fue estimar la prevalencia de la enfermedad de Chagas crónica en la población brasileña en general, en las mujeres y en las mujeres en edad fértil. Métodos. Se realizó un metanálisis de la bibliografía para extraer datos sobre la prevalencia de la enfermedad de Chagas crónica en la población brasileña en general, en las mujeres y en las mujeres en edad fértil, en los municipios de Brasil durante el período 2010-2022. Se seleccionaron los indicadores relacionados con esa enfermedad disponibles en los sistemas municipales de información de salud. La modelización estadística de los datos extraídos del metanálisis, en función de los obtenidos de los sistemas de información, se aplicó a modelos lineales, lineales generalizados y aditivos. Resultados. Se seleccionaron los cinco modelos más apropiados de un total de 549 modelos evaluados, para obtener un modelo de consenso (R2 ajustado = 54%). El factor predictor más importante fue el registro de la enfermedad de Chagas crónica autodeclarada en el sistema de información de atención primaria de salud. De los 5570 municipios brasileños, en 1792 (32%) la prevalencia estimada fue nula y en los 3778 restantes la prevalencia media fue del 3,25% (± 2,9%). El número estimado de pacientes con enfermedad de Chagas crónica en la población brasileña en general, en las mujeres y en las mujeres en edad fértil fue de ~3,7 millo- nes, ~2,1 millones y ~590 000, respectivamente. La tasa calculada de reproducción de la enfermedad fue de 1,0336. Todas las estimaciones se refieren al período 2015-2016. Conclusiones. La prevalencia estimada de la enfermedad de Chagas crónica, especialmente en las mujeres en edad fértil, pone de manifiesto el desafío que representa la transmisión vertical en los municipios brasi- leños. Estas estimaciones están en línea con los patrones de las proyecciones matemáticas, y sugieren la necesidad de incorporarlas al Pacto Nacional para la Eliminación de la Transmisión Vertical de la Enfermedad de Chagas.

Cooperation for malaria control and elimination in the Guiana Shield.

The Guiana Shield, a small region of South America, is currently one of the main hotspots of malaria transmission on the continent. This Amazonian area is characterised by remarkable socioeconomic, cultural, health, and political heterogeneity and a high degree of regional and cross-border population mobility, which has contributed to the increase of malaria in the region in the past few years. In this context, regional cooperation to control malaria represents both a challenge and an indispensable initiative. This Viewpoint advocates for the creation of a regional cooperative mechanism for the elimination of malaria in the Guiana Shield. This strategy would help address operational and political obstacles to successful technical cooperation in the region and could contribute to reversing the regional upsurge in malaria incidence through creating a functional international control and elimination partnership.

The role of forest fragmentation in yellow fever virus dispersal.

The intense process of deforestation in tropical forests poses serious challenges for the survival of biodiversity, as well as for the human species itself. This scenario is supported by the increase in the incidence of epidemics of zoonotic origin observed over the last few decades. In the specific case of sylvatic yellow fever (YF), it has already been shown that an increase in the transmission risk of the causative agent (yellow fever virus - YFV) is associated with areas with a high degree of forest fragmentation, which can facilitate the spread of the virus. In this study we tested the hypothesis that areas with more fragmented landscapes and a higher edge density (ED) but a high degree of connectivity between forest patches favor YFV spread. To this end, we used YF epizootics in non-human primates (NHPs) in the state of São Paulo to build direct networks, and used a multi-selection approach to analyze which landscape features could facilitate YFV spread. Our results showed that municipalities with the potential to spread the virus exhibited a higher amount of forest edge. Additionally, the models with greater empirical support showed a strong association between forest edge density and the risk of occurrence of epizootic diseases, as well as the need for a minimum threshold of native vegetation cover to restrict their transmission. These findings corroborate our hypothesis that more fragmented landscapes with a higher degree of connectivity favor the spread of YFV, while landscapes with fewer connections tend to act as dead zones for the circulation of the virus.

Reaching the malaria elimination goal in Brazil: a spatial analysis and time-series study.

BACKGROUND: Since 2015, the Global Technical Strategy (GTS) for Malaria 2016-2030 has been adopted by the World Health Organization (WHO) as a comprehensive framework to accelerate progress for malaria elimination in endemic countries. This strategy sets the target of reducing global malaria incidence and mortality rates by 90% in 2030. Here it is sought to evaluate Brazil's achievements towards reaching the WHO GTS milestone in 2030. Considering the total number of new malaria cases in 2015, the main research question is: will Brazil reach the malaria elimination goal in 2030? METHODS: Analytical strategies were undertaken using the SIVEP-malaria official databases of the Brazilian Malaria Control Programme for the Brazilian Amazon region from 2009 to 2020. Spatial and time-series analyses were applied for identifying municipalities that support the highest numbers of malaria cases over the years. Forecast analysis was used for predicting the estimated number of new cases in Brazil in 2025-2050. RESULTS: Brazil has significantly reduced the number of new malaria cases in 2020 in comparison with 2015 in the states of Acre (- 56%), Amapá (- 75%), and Amazonas (- 21%); however, they increased in the states of Pará (156%), Rondônia (74%), and Roraima (362%). Forecast of the predicted number of new malaria cases in 2030 is 74,764 (95% CI: 41,116-141,160) in the Brazilian Amazon. CONCLUSIONS: It is likely that Brazil will reduce the number of new malaria cases in the Brazilian Amazon in 2030 in relation to that in 2015. Herein forecast shows a reduction by 46% (74,754 in 2030 forecast/137,982 in 2015), but this reduction is yet far from the proposed reduction under the WHO GTS 2030 milestone (90%). Stable and unbeatable transmission in the Juruá River Valley, Manaus, and Lábrea still support endemic malaria in the Brazilian Amazon. Today's cross-border malaria is impacting the state of Roraima unprecedently. If this situation is maintained, the malaria elimination goal (zero cases) may not be reached before 2050. An enhanced political commitment is vital to ensure optimal public health intervention designs in the post-2030 milestones for malaria elimination.

The influence of landscape structure on the dispersal pattern of yellow fever virus in the state of São Paulo.

Man-made changes to the landscape play a crucial role in altering the epidemiologic patterns of infectious diseases, mainly as a result of pathogen spillover. Sylvatic yellow fever is ideally suited to modeling of this phenomenon as the risk of transmission of the disease as well as its circulation and dispersal are associated with forest fragmentation. In this study we investigated the temporal dispersal pattern of yellow fever virus (YFV) by means of confirmed cases of epizootics in non-human primates in municipalities in the state of São Paulo where there was no recommendation for vaccination in 2017. We analyzed the resistance to dispersal associated with different classes of land use and the geographic distances between the different locations where epizootics were recorded. The model that best explained the temporal dispersal pattern of YFV in the study area indicated that this was influenced by the geographic distance between collection locations and by the permeability of the forest edges (150 m) at the interface with the following core areas: Water, Agricultural, Non-Forest Formation and Forestry. Water, Agricultural, Urban and Forest core areas and the interfaces between the latter two formed important barriers to circulation of the virus. These findings indicate that fragmentation of vegetation tends to decrease the time taken for pathogens to spread, while conservation of forest areas has the opposite effect.

Causal effects on low Apgar at 5-min and stillbirth in a malaria maternal-fetal health outcome investigation: a large perinatal surveillance study in the Brazilian Amazon.

BACKGROUND: Malaria elimination in Brazil poses several challenges, including the control of Plasmodium falciparum foci and the hidden burden of Plasmodium vivax in pregnancy. Maternal malaria and fetal health outcomes were investigated with a perinatal surveillance study in the municipality of Cruzeiro do Sul, Acre state, Brazilian Amazon. The research questions are: what are the causal effects of low birth weight on low Apgar at 5-min and of perinatal anaemia on stillbirth? METHODS: From November 2018 to October 2019, pregnant women of ≥ 22 weeks or puerperal mothers, who delivered at the referral maternity hospital (Juruá Women and Children's Hospital), were recruited to participate in a malaria surveillance study. Clinical information was obtained from a questionnaire and abstracted from medical reports. Haemoglobin level and presence of malarial parasites were tested by haematology counter and light microscopy, respectively. Low Apgar at 5-min and stillbirth were the outcomes analysed in function of clinical data and epidemiologic risk factors for maternal malaria infection using both a model of additive and independent effects and a causal model with control of confounders and use of mediation. RESULTS: In total, 202 (7.2%; N = 2807) women had malaria during pregnancy. Nearly half of malaria infections during pregnancy (n = 94) were P. falciparum. A total of 27 women (1.03%; N = 2632) had perinatal malaria (19 P. vivax and 8 P. falciparum). Perinatal anaemia was demonstrated in 1144 women (41.2%; N = 2779) and low birth weight occurred in 212 newborns (3.1%; N = 2807). A total of 75 newborns (2.7%; N = 2807) had low (< 7) Apgar scores at 5-min., and stillbirth occurred in 23 instances (30.7%; n = 75). Low birth weight resulted in 7.1 higher odds of low Apgar at 5-min (OR = 7.05, 95% CI 3.86-12.88, p < 0.001) modulated by living in rural conditions, malaria during pregnancy, perinatal malaria, and perinatal anaemia. Stillbirth was associated with perinatal anaemia (OR = 2.56, 95% CI 1.02-6.42, p = 0.0444) modulated by living in rural conditions, falciparum malaria during pregnancy, perinatal malaria, and perinatal fever. CONCLUSIONS: While Brazil continues its path towards malaria elimination, the population still faces major structural problems, including substandard living conditions. Here malaria infections on pregnant women were observed having indirect effects on fetal outcomes, contributing to low Apgar at 5-min and stillbirth. Finally, the utility of employing multiple statistical analysis methods to validate consistent trends is vital to ensure optimal public health intervention designs.

Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study. / Avaliação de modelos de predição para ocorrência de malária no estado do Amapá, 1997-2016: um estudo ecológico.

OBJECTIVE: To evaluate the predictive power of different malaria case time-series models in the state of Amapá, Brazil, for the period 1997-2016. METHODS: This is an ecological time series study with malaria cases recorded in the state of Amapá. Ten deterministic or stochastic statistical models were used for simulation and testing in 3, 6, and 12 month forecast horizons. RESULTS: The initial test showed that the series is stationary. Deterministic models performed better than stochastic models. The ARIMA model showed absolute errors of less than 2% on the logarithmic scale and relative errors 3.4-5.8 times less than the null model. It was possible to predict future malaria cases 6 and 12 months in advance. CONCLUSION: The ARIMA model is recommended for predicting future scenarios and for earlier planning in state health services in the Amazon Region.

A mathematical model for zoonotic transmission of malaria in the Atlantic Forest: Exploring the effects of variations in vector abundance and acrodendrophily.

Transmission foci of autochthonous malaria caused by Plasmodium vivax-like parasites have frequently been reported in the Atlantic Forest in Southeastern and Southern Brazil. Evidence suggests that malaria is a zoonosis in these areas as human infections by simian Plasmodium species have been detected, and the main vector of malaria in the Atlantic Forest, Anopheles (Kerteszia) cruzii, can blood feed on human and simian hosts. In view of the lack of models that seek to predict the dynamics of zoonotic transmission in this part of the Atlantic Forest, the present study proposes a new deterministic mathematical model that includes a transmission compartment for non-human primates and parameters that take into account vector displacement between the upper and lower forest strata. The effects of variations in the abundance and acrodendrophily of An. cruzii on the prevalence of infected humans in the study area and the basic reproduction number (R0) for malaria were analyzed. The model parameters are based on the literature and fitting of the empirical data. Simulations performed with the model indicate that (1) an increase in the abundance of the vector in relation to the total number of blood-seeking mosquitoes leads to an asymptotic increase in both the proportion of infected individuals at steady state and R0; (2) the proportion of infected humans at steady state is higher when displacement of the vector mosquito between the forest strata increases; and (3) in most scenarios, Plasmodium transmission cannot be sustained only between mosquitoes and humans, which implies that non-human primates play an important role in maintaining the transmission cycle. The proposed model contributes to a better understanding of the dynamics of malaria transmission in the Atlantic Forest.

A Cautionary Tale Regarding the Use of Causal Inference to Study How Environmental Change Influences Tropical Diseases.

There has been substantial interest on the effect of large-scale environmental change, such as deforestation, on human health. An important and relatively recent development has been the use of causal-inference approaches (e.g., instrumental variables [IVs]) to more properly analyze this type of observational data. Here, we discuss an important study that attempted to disentangle the effect of malaria on deforestation from the effect of deforestation on malaria using an IV approach. The authors found that deforestation increases malaria (e.g., they estimate that a 10% increase in deforestation leads to a 3.3% increase in malaria incidence) through ecological mechanisms, whereas malaria reduces deforestation through socioeconomic mechanisms. An important characteristic of causal-inference approaches is that they are critically dependent on the plausibility of the underlying assumptions and that, differently from standard statistical models, many of these assumptions are not testable. In particular, we show how important assumptions of the IV approach adopted in the study described earlier were not met and that, as a result, it is possible that the correct conclusion could have been the opposite of that reported by the authors (e.g., deforestation decreases, rather than increasing, malaria through ecological mechanisms). Causal-inference approaches may be critical to characterize the relationship between environmental change and disease risk, but conclusions based on these methods can be even more unreliable than those from traditional methods if careful attention is not given to the plausibility of the underlying assumptions.

Anthropogenic landscape decreases mosquito biodiversity and drives malaria vector proliferation in the Amazon rainforest.

Inter-relationships among mosquito vectors, Plasmodium parasites, human ecology, and biotic and abiotic factors, drive malaria risk. Specifically, rural landscapes shaped by human activities have a great potential to increase the abundance of malaria vectors, putting many vulnerable people at risk. Understanding at which point the abundance of vectors increases in the landscape can help to design policies and interventions for effective and sustainable control. Using a dataset of adult female mosquitoes collected at 79 sites in malaria endemic areas in the Brazilian Amazon, this study aimed to (1) verify the association among forest cover percentage (PLAND), forest edge density (ED), and variation in mosquito diversity; and to (2) test the hypothesis of an association between landscape structure (i.e., PLAND and ED) and Nyssorhynchus darlingi (Root) dominance. Mosquito collections were performed employing human landing catch (HLC) (peridomestic habitat) and Shannon trap combined with HLC (forest fringe habitat). Nyssorhynchus darlingi abundance was used as the response variable in a generalized linear mixed model, and the Shannon diversity index (H') of the Culicidae community, PLAND, and the distance house-water drainage were used as predictors. Three ED categories were also used as random effects. A path analysis was used to understand comparative strengths of direct and indirect relationships among Amazon vegetation classes, Culicidae community, and Ny. darlingi abundance. Our results demonstrate that Ny. darlingi is negatively affected by H´ and PLAND of peridomestic habitat, and that increasing these variables (one-unit value at ß0 = 768) leads to a decrease of 226 (P < 0.001) and 533 (P = 0.003) individuals, respectively. At the forest fringe, a similar result was found for H' (ß1 = -218; P < 0.001) and PLAND (ß1 = -337; P = 0.04). Anthropogenic changes in the Amazon vegetation classes decreased mosquito biodiversity, leading to increased Ny. darlingi abundance. Changes in landscape structure, specifically decreases in PLAND and increases in ED, led to Ny. darlingi becoming the dominant species, increasing malaria risk. Ecological mechanisms involving changes in landscape and mosquito species composition can help to understand changes in the epidemiology of malaria.

The COVID-19 crisis and Amazonia's indigenous people: Implications for conservation and global health.

Ecosystem health and zoonotic diseases are closely interwoven. Even as we grapple with the SARS-Coronavirus-2 pandemic, which may have its origins in wildlife, weakening environmental policies in the Brazilian Amazon are elevating the risk of additional zoonotic spillover events. We examine the links between deforestation and disease emergence in the Amazon, as illustrated by outbreaks of yellow fever virus, Venezuelan equine encephalitis virus, and Oropouche virus. It has been well established that in Brazil, indigenous territories exhibit lower rates of forest conversion and degradation than in areas designated for sustainable use. In this way, Amazonia's indigenous tribes promote public health while sustaining ecosystem services. However, indigenous land rights are under attack due to current policies enabling illegal land grabbing, mining and logging. Further adding to the existential struggle of indigenous tribes, malaria and SARS-Coronavirus-2 are wreaking havoc on these vulnerable populations. There is a critical need for protection of indigenous people's rights and health, as well as a sustained effort to support the study of mechanisms underlying anthropogenic land use change and zoonotic disease risk.

Complexity of malaria transmission dynamics in the Brazilian Atlantic Forest.

Plasmodium malariae and Plasmodium vivax are protozoan parasites that can cause malaria in humans. They are genetically indistinguishable from, respectively, Plasmodium brasilianum and Plasmodium simium, i.e. parasites infecting New World non-human primates in South America. In the tropical rainforests of the Brazilian Atlantic coast, it has long been hypothesized that P. brasilianum and P. simium in platyrrhine primates originated from P. malariae and P. vivax in humans. A recent hypothesis proposed the inclusion of Plasmodium falciparum into the transmission dynamics between humans and non-human primates in the Brazilian Atlantic tropical rainforest. Herein, we assess the occurrence of human malaria in simians and sylvatic anophelines using field-collected samples in the Capivari-Monos Environmental Protection Area from 2015 to 2017. We first tested simian blood and anopheline samples. Two simian (Aloutta) blood samples (18%, n = 11) showed Plasmodium cytb DNA sequences, one for P. vivax and another for P. malariae. From a total of 9,416 anopheline females, we found 17 pools positive for Plasmodium species with a 18S qPCR assay. Only three showed P. cytb DNA sequence, one for P. vivax and the others for rodent malaria species (similar to Plasmodium chabaudi and Plasmodium berghei). Based on these results, we tested 25 rodent liver samples for the presence of Plasmodium and obtained P. falciparum cytb DNA sequence in a rodent (Oligoryzomys sp.) liver. The findings of this study indicate complex malaria transmission dynamics composed by parallel spillover-spillback of human malaria parasites, i.e. P. malariae, P. vivax, and P. falciparum, in the Brazilian Atlantic forest.

Are ovarian reserve tests reliable in predicting ovarian response? Results from a prospective, cross-sectional, single-center analysis.

OBJETIVE: Several biomarkers of ovarian reserve have been proposed as possible predictors of the response to controlled ovarian stimulation (COS). We aimed to evaluate age, FSH, AMH, antral follicle count (AFC), and ovarian response prediction index (ORPI), as potential predictors of response to COS. METHODS: Cross-sectional study enrolling of 188 infertile women who underwent the first cycle of IVF/ICSI. AFC was evaluated; serum FSH and AMH levels were measured by ELISA. ORPI was calculated as AMH x AFC/patient´s age. RESULTS: As expected, hypo-responder group had less retrieved oocytes, MII, and embryos compared to the good responders. The hyper-response patients were younger, with lower FSH, increased AMH, AFC, and ORPI values. Regarding the assessment of the predictive capacity of ovarian reserve tests, none of them individually or combined showed a good predictive capacity for hypo-response. With respect to the hyper-responder group, individually AMH was the best predictor, while in the multivariable model, ORPI demonstrated the best predictive capacity. Furthermore, patients with serum AMH < 2.09 ng/mL (p25) had fewer AFC than patients with higher AMH values. CONCLUSIONS: Our findings suggest that none of the ovarian reserve tests showed a good predictive capacity for hypo-response, while the ORPI was the strongest predictor of hyper-response in normovulatory infertile women.

Multidrug therapy for leprosy can cure patients with lobomycosis in Acre State, Brazil: a proof of therapy study

Lobomycosis, also referred to as lacaziosis, is an endemic cutaneous and subcutaneous fungal disease that mainly affects Amazonian forest dwellers in Brazil. There is no disease control program in place in Brazil, and antifungal therapy failures are common, and the therapy is inaccessible to most patients. We performed a randomized, unblinded clinical trial testing the cure rate of multiple drug therapy (MDT) for leprosy with surgical excision, with or without itraconazole. A control arm consisted of patients who did not adhere to either therapeutic regimens but continued to be followed up. Multiple drug therapy consisted of monthly supervised doses of 600 mg rifampicin, 300 mg clofazimine, and 100 mg dapsone, in addition to daily doses of 50 mg clofazimine and 100 mg dapsone. The patients in the MDT plus itraconazole arm also received itraconazole 100 mg twice daily. We followed up 54 patients from the MDT group and 26 patients from the MDT plus itraconazole group for an average of 4 years and 9 months. The 23 controls were followed up for 6 months on average. The following endpoints were observed: 1) unchanged (no apparent improvement), 2) improved (reduction in lesion size and/or pruritus), and 3) cured (complete remission of the lesions, no viable fungi, and no relapse for 2 years after the end of the drug treatment). The results indicated a significantly greater likelihood of cure associated with the use of multidrug therapy for leprosy with or without itraconazole when compared with the control group. The addition of itraconazole to MDT was not associated with improved outcomes, suggesting that MDT alone is effective(AU).

Avaliação de modelos de predição para ocorrência de malária no estado do Amapá, 1997-2016: um estudo ecológico / Evaluación de modelos de predicción para la ocurrencia de aparición de la malaria en el estado del Amapá, Brasil, 1997-2016: un estudio ecológico / Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study

Objetivo: Avaliar a capacidade preditiva de diferentes modelos de série temporal de casos de malária no estado do Amapá, Brasil, no período 1997-2016. Métodos: Estudo ecológico de séries temporais com casos de malária registrados no Amapá. Foram utilizados dez modelos estatísticos determinísticos ou estocásticos para simulação e teste em horizontes de previsão de 3, 6 e 12 meses. Resultados: O teste inicial mostrou que a série é estacionária. Os modelos determinísticos apresentaram melhor desempenho do que os modelos estocásticos. O modelo ARIMA apresentou erros absolutos menores do que 2% na escala logarítmica e erros relativos 3,4-5,8 vezes menores em relação ao modelo nulo. A predição de casos futuros de malária nos horizontes de 6 e 12 meses de antecedência foi possível. Conclusão: Recomenda-se o uso de modelo ARIMA para a previsão de cenários futuros e para a antecipação do planejamento nos serviços de saúde dos estados da Região Amazônica.

Objetivo: Evaluar el poder predictivo de diferentes modelos de series de temporales de casos de malaria en el estado de Amapá, Brasil, en el periodo 1997-2016. Métodos: Se trata de un estudio ecológico de series de temporales con casos de malaria registrados en el estado de Amapá. Se utilizaron diez modelos estadísticos determinísticos o estocásticos para la simulación y la prueba en horizontes de predicción de 3, 6 y 12 meses. Resultados: La prueba inicial mostró que la serie es estacionaria. Los modelos determinísticos mostraron mejor desempeño que los modelos estocásticos. El modelo ARIMA mostró errores absolutos menores al 2% en la escala logarítmica y errores relativos 3,4-5,8 veces menores que el modelo nulo. La predicción de casos futuros en horizontes de 6 y 12 meses de antelación fue posible. Conclusión: Se recomienda utilizar el modelo ARIMA para predecir escenarios futuros y anticipar la planificación en los servicios de salud en los estados de la Región Amazónica.

Objective: To evaluate the predictive power of different malaria case time-series models in the state of Amapá, Brazil, for the period 1997-2016. Methods: This is an ecological time series study with malaria cases recorded in the state of Amapá. Ten deterministic or stochastic statistical models were used for simulation and testing in 3, 6, and 12 month forecast horizons. Results: The initial test showed that the series is stationary. Deterministic models performed better than stochastic models. The ARIMA model showed absolute errors of less than 2% on the logarithmic scale and relative errors 3.4-5.8 times less than the null model. It was possible to predict future malaria cases 6 and 12 months in advance. Conclusion: The ARIMA model is recommended for predicting future scenarios and for earlier planning in state health services in the Amazon Region.

Multidrug Therapy for Leprosy Can Cure Patients with Lobomycosis in Acre State, Brazil: A Proof of Therapy Study.

Lobomycosis, also referred to as lacaziosis, is an endemic cutaneous and subcutaneous fungal disease that mainly affects Amazonian forest dwellers in Brazil. There is no disease control program in place in Brazil, and antifungal therapy failures are common, and the therapy is inaccessible to most patients. We performed a randomized, unblinded clinical trial testing the cure rate of multiple drug therapy (MDT) for leprosy with surgical excision, with or without itraconazole. A control arm consisted of patients who did not adhere to either therapeutic regimens but continued to be followed up. Multiple drug therapy consisted of monthly supervised doses of 600 mg rifampicin, 300 mg clofazimine, and 100 mg dapsone, in addition to daily doses of 50 mg clofazimine and 100 mg dapsone. The patients in the MDT plus itraconazole arm also received itraconazole 100 mg twice daily. We followed up 54 patients from the MDT group and 26 patients from the MDT plus itraconazole group for an average of 4 years and 9 months. The 23 controls were followed up for 6 months on average. The following endpoints were observed: 1) unchanged (no apparent improvement), 2) improved (reduction in lesion size and/or pruritus), and 3) cured (complete remission of the lesions, no viable fungi, and no relapse for 2 years after the end of the drug treatment). The results indicated a significantly greater likelihood of cure associated with the use of multidrug therapy for leprosy with or without itraconazole when compared with the control group. The addition of itraconazole to MDT was not associated with improved outcomes, suggesting that MDT alone is effective.

Evaluation of the Models for Forecasting Dengue in Brazil from 2000 to 2017: An Ecological Time-Series Study.

We aimed to evaluate the accuracy of deterministic and stochastic statistical models by means of a protocol developed in a free programming environment for monthly time-series analysis of the incidence of confirmed dengue cases in the states and federal district of Brazil from January 2000 to December 2017. This was an ecological time-series study conducted to evaluate and validate the accuracy of 10 statistical models for predicting the new cases of dengue. Official data on the monthly cases of dengue from January 2000 to December 2016 were used to train the statistical models, while those for the period January-December 2017 were used to test the predictive capacity of the models by considering three forecasting horizons (12, 6, and 3 months). Deterministic models proved to be reliable for predicting dengue in a 12-month forecasting horizon, while stochastic models were reliable for predicting the disease in a 3-month forecasting horizon. We were able to reliably employ models for predicting dengue in the states and federal district of Brazil. Hence, we strongly recommend incorporating these models in state health services for predicting dengue and for decision-making with regard to the advanced planning of interventions before the emergence of epidemics.

Oral Trypanosoma cruzi Transmission Resulting in Advanced Chagasic Cardiomyopathy in an 11-Month-Old Male.

Our case report describes the youngest clinical acute Chagas disease case and their unusual disease presentation of cardiac failure. In parts of the Brazilian Amazon, cultural practices include weaning infants from breastmilk to solid foods with açaí consumption serving as an intermediary. This practice could place infants at an increased risk of oral Trypanosoma cruzi infection and severe Chagasic cardiac disease.

Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study / Evaluación de modelos de predicción para la aparición de la malaria en el estado del Amapá, Brasil, 1997-2016: un estudio ecológico / Avaliação de modelos de predição para ocorrência de malária no estado do Amapá, 1997-2016: um estudo ecológico

Objective. To evaluate predictive power of different time-series models of malaria cases in the state of Amapá, Brazil, in the period 1997-2016. Methods. This is an ecological study of time series with malaria cases registered in the state of Amapá. Ten 3 deterministic or stochastic statistical models were used for simulation and testing in 3, 6, and 12 month forecast horizons. Results. The initial test showed that the series is stationary. Deterministic models performed better than stochastic models. The ARIMA model showed absolute errors of less than 2% on the logarithmic scale and relative errors 3.4-5.8 times less than the null model. The prediction of future cases of malaria in the horizons of 6 and 12 months in advance was possible. Conclusion. It is recommended the use of the ARIMA model to predict future scenarios and to anticipate planning in state health services in the Amazon Region.

Objetivo. Avaliar a capacidade preditiva de diferentes modelos de série temporal de casos de malária no estado do Amapá, Brasil, no período 1997-2016. Métodos. Estudo ecológico de séries temporais com casos de malária registrados no Amapá. Foram utilizados dez modelos estatísticos determinísticos ou estocásticos para simulação e teste em horizontes de previsão de 3, 6 e 12 meses. Resultados. O teste inicial mostrou que a série é estacionária. Os modelos determinísticos apresentaram melhor desempenho do que os modelos estocásticos. O modelo ARIMA apresentou erros absolutos menores do que 2% na escala logarítmica e erros relativos 3,4-5,8 vezes menores em relação ao modelo nulo. A predição de casos futuros de malária nos horizontes de 6 e 12 meses de antecedência foi possível. Conclusão. Recomenda-se o uso de modelo ARIMA para a previsão de cenários futuros e para a antecipação do planejamento nos serviços de saúde dos estados da Região Amazônica.