Pooled lagged effect of runoff on leptospirosis cases in Colombia.

Leptospirosis is a global zoonotic disease caused by spirochete bacteria of the genus Leptospira. The disease exhibits a notable incidence in tropical and developing countries, and in Colombia, environmental, economic, social, and cultural conditions favor disease transmission, directly impacting both mortality and morbidity rates. Our objective was to establish the pooled lagged effect of runoff on leptospirosis cases in Colombia. For our study, we included the top 20 Colombian municipalities with the highest number of leptospirosis cases. Monthly cases of leptospirosis, confirmed by laboratory tests and spanning from 2007 to 2022, were obtained from the National Public Health Surveillance System. Additionally, we collected monthly runoff and atmospheric and oceanic data from remote sensors. Multidimensional poverty index values for each municipality were sourced from the Terridata repository. We employed causal inference and distributed lag nonlinear models to estimate the lagged effect of runoff on leptospirosis cases. Municipality-specific estimates were combined through meta-analysis to derive a single estimate for all municipalities under study. The pooled results for the 20 municipalities suggest a lagged effect for the 0 to 2, and 0-3 months of runoff on leptospirosis when the runoff is < 120 g/m2. No effect was identified for longer lagged periods (0-1, 0 to 4, 0 to 5, and 0-6 months) or higher runoff values. Incorporation of the multidimensional poverty index into the meta-analysis of runoff contributed to the models for the lagged periods of 0-3, and 0-4 months.

Policies that support working parents and gender health equity: needed research and methodological challenges.

In this issue of AJE, Platt et al (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX) shed new light on the potential for supportive employment benefits, including family leave, flexible work hours, and employer provided or subsidized child-care to mitigate depression risk among full-time working mothers. The authors use a longitudinal study design and rigorous methods to carefully consider potential sources of bias, and, more broadly, their article underscores the importance of employment benefits as a social determinant of mental health for working mothers. In this commentary, we discuss some of the policy context surrounding employer benefits that support parenting, particularly around paid versus unpaid family leave laws and ordinances. We consider the ways in which the policy context impacts larger structural inequities and the potential implications for internal and external validity.

Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.

BACKGROUND: The survival advantage of neoadjuvant systemic therapy (NST) for breast cancer patients remains controversial, especially when considering the heterogeneous characteristics of individual patients. OBJECTIVE: To discern the variability in responses to breast cancer treatment at the individual level and propose personalized treatment recommendations utilizing deep learning (DL). METHODS: Six models were developed to offer individualized treatment suggestions. Outcomes for patients whose actual treatments aligned with model recommendations were compared to those whose did not. The influence of certain baseline features of patients on NST selection was visualized and quantified by multivariate logistic regression and Poisson regression analyses. RESULTS: Our study included 94,487 female breast cancer patients. The Balanced Individual Treatment Effect for Survival data (BITES) model outperformed other models in performance, showing a statistically significant protective effect with inverse probability treatment weighting (IPTW)-adjusted baseline features [IPTW-adjusted hazard ratio: 0.51, 95% confidence interval (CI), 0.41-0.64; IPTW-adjusted risk difference: 21.46, 95% CI 18.90-24.01; IPTW-adjusted difference in restricted mean survival time: 21.51, 95% CI 19.37-23.80]. Adherence to BITES recommendations is associated with reduced breast cancer mortality and fewer adverse effects. BITES suggests that patients with TNM stage IIB, IIIB, triple-negative subtype, a higher number of positive axillary lymph nodes, and larger tumors are most likely to benefit from NST. CONCLUSIONS: Our results demonstrated the potential of BITES to aid in clinical treatment decisions and offer quantitative treatment insights. In our further research, these models should be validated in clinical settings and additional patient features as well as outcome measures should be studied in depth.

Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework.

The target trial framework has emerged as a powerful tool for addressing causal questions in clinical practice and in public health. In the healthcare sector, where decision-making is increasingly data-driven, transactional databases, such as electronic health records (EHR) and insurance claims, present an untapped potential for answering complex causal questions. This narrative review explores the potential of the integration of the target trial framework with real-world data to enhance healthcare decision-making processes. We outline essential elements of the target trial framework, and identify pertinent challenges in data quality, privacy concerns, and methodological limitations, proposing solutions to overcome these obstacles and optimize the framework's application.

Early Hyperoxemia and 2-year Outcomes in Infants with Hypoxic-ischemic Encephalopathy: A Secondary Analysis of the Infant Cooling Evaluation Trial.

OBJECTIVE: To determine the causal relationship between exposure to early hyperoxemia and death or major disability in infants with hypoxic-ischemic encephalopathy (HIE). STUDY DESIGN: We analyzed data from the Infant Cooling Evaluation (ICE) trial that enrolled newborns ≥35 weeks' gestation with moderate-severe HIE, randomly allocated to hypothermia or normothermia. The primary outcome was death or major sensorineural disability at 2 years. We included infants with arterial pO2 measured within 2 hours of birth. Using a directed acyclic graph, we established that markers of severity of perinatal hypoxia-ischemia and pCO2 were a minimally sufficient set of variables for adjustment in a regression model to estimate the causal relationship between arterial pO2 and death/disability. RESULTS: Among 221 infants, 116 (56%) had arterial pO2 and primary outcome data. The unadjusted analysis revealed a U-shaped relationship between arterial pO2 and death or major disability. Among hyperoxemic infants (pO2 100-500 mmHg) the proportion with death or major disability was 40/58 (0.69), while the proportion in normoxemic infants (pO2 40-99 mmHg) was 20/48 (0.42). In the adjusted model, hyperoxemia increased the risk of death or major disability (adjusted risk ratio 1.61, 95% CI 1.07-2.00, P = .03) in relation to normoxemia. CONCLUSION: Early hyperoxemia increased the risk of death or major disability among infants who had an early arterial pO2 in the ICE trial. Limitations include the possibility of residual confounding and other causal biases. Further work is warranted to confirm this relationship in the era of routine therapeutic hypothermia.

Native American ancestry and breast cancer risk in Colombian and Mexican women: ruling out potential confounding through ancestry-informative markers.

BACKGROUND: Latin American and Hispanic women are less likely to develop breast cancer (BC) than women of European descent. Observational studies have found an inverse relationship between the individual proportion of Native American ancestry and BC risk. Here, we use ancestry-informative markers to rule out potential confounding of this relationship, estimating the confounder-free effect of Native American ancestry on BC risk. METHODS AND STUDY POPULATION: We used the informativeness for assignment measure to select robust instrumental variables for the individual proportion of Native American ancestry. We then conducted separate Mendelian randomization (MR) analyses based on 1401 Colombian women, most of them from the central Andean regions of Cundinamarca and Huila, and 1366 Mexican women from Mexico City, Monterrey and Veracruz, supplemented by sensitivity and stratified analyses. RESULTS: The proportion of Colombian Native American ancestry showed a putatively causal protective effect on BC risk (inverse variance-weighted odds ratio [OR] = 0.974 per 1% increase in ancestry proportion, 95% confidence interval [CI] 0.970-0.978, p = 3.1 × 10-40). The corresponding OR for Mexican Native American ancestry was 0.988 (95% CI 0.987-0.990, p = 1.4 × 10-44). Stratified analyses revealed a stronger association between Native American ancestry and familial BC (Colombian women: OR = 0.958, 95% CI 0.952-0.964; Mexican women: OR = 0.973, 95% CI 0.969-0.978), and stronger protective effects on oestrogen receptor (ER)-positive BC than on ER-negative and triple-negative BC. CONCLUSIONS: The present results point to an unconfounded protective effect of Native American ancestry on BC risk in both Colombian and Mexican women which appears to be stronger for familial and ER-positive BC. These findings provide a rationale for personalised prevention programmes that take genetic ancestry into account, as well as for future admixture mapping studies.

Wildfire exposure and academic performance in Brazil: A causal inference approach for spatiotemporal data.

As the frequency and intensity of wildfires are projected to globally amplify due to climate change, there is a growing need to quantify the impact of exposure to wildfires in vulnerable populations such as adolescents. In our study, we applied rigorous causal inference methods to estimate the effect of wildfire exposure on the academic performance of high school students in Brazil between 2009 and 2015. Using longitudinal data from 8,183 high schools across 1,571 municipalities in Brazil, we estimated that the average performance in most academic subjects decreases under interventions that increase wildfire exposure, e.g., a decrease of 1.8 % (p = 0.01) in the natural sciences when increasing the wildfire density from 0.0035 wildfires/km2 (first quantile in the sample) to 0.0222 wildfires/km2 (third quartile). Furthermore, these effects considerably worsened over time. Our findings highlight the adverse impact of wildfires on educational outcomes.

Breastfeeding Protects from Overjet in Adolescence by Reducing Pacifier Use: A Birth Cohort Study.

Increased dental overjet in adolescence is a clinically relevant outcome associated with the complexity and high cost of treatment, indicating the need for prevention strategies. We investigated the long-term impact of breastfeeding and pacifier use on increased overjet (IOVJ) in permanent dentition. A prospective cohort nested in a randomized controlled trial was conducted from birth to 12 years of age (n = 214). Breastfeeding and pacifier use were recorded monthly until 12 months. Overjet was assessed at age 12 years. We employed a causal mediation analysis using parametric regression models assuming no interaction between breastfeeding and pacifier usage. We found a total protective effect of breastfeeding on IOVJ (OR 0.49; 95% CI 0.28-0.96), where 63.1% were mediated by pacifier use (OR 0.61; 95% CI 0.44-0.87). Breastfeeding directly decreased the odds of IOVJ by 20%; however, the confidence interval included the null estimate (OR 0.81; 95% CI 0.41-1.60). In conclusion, breastfeeding protects by half of the IOVJ in adolescence through reducing pacifier use. Oral and general health professionals should collaborate to support WHO breastfeeding guidelines during individual patient counseling. Guidelines for practice, policy or public information require messages that include a common risk approach to oral and general health.

Hyperscanning fNIRS data analysis using multiregression dynamic models: an illustration in a violin duo.

Introduction: Interpersonal neural synchronization (INS) demands a greater understanding of a brain's influence on others. Therefore, brain synchronization is an even more complex system than intrasubject brain connectivity and must be investigated. There is a need to develop novel methods for statistical inference in this context. Methods: In this study, motivated by the analysis of fNIRS hyperscanning data, which measure the activity of multiple brains simultaneously, we propose a two-step network estimation: Tabu search local method and global maximization in the selected subgroup [partial conditional directed acyclic graph (DAG) + multiregression dynamic model]. We illustrate this approach in a dataset of two individuals who are playing the violin together. Results: This study contributes new tools to the social neuroscience field, which may provide new perspectives about intersubject interactions. Our proposed approach estimates the best probabilistic network representation, in addition to providing access to the time-varying parameters, which may be helpful in understanding the brain-to-brain association of these two players. Discussion: The illustration of the violin duo highlights the time-evolving changes in the brain activation of an individual influencing the other one through a data-driven analysis. We confirmed that one player was leading the other given the ROI causal relation toward the other player.

Adverse childhood experiences and adult outcomes using a causal framework perspective: Challenges and opportunities.

BACKGROUND: Research on the effect of adverse childhood experiences (ACEs) on adult outcomes has typically relied on retrospective assessment of ACEs and cumulative scores. However, this approach raises methodological challenges that can limit the validity of findings. OBJECTIVE: The aims of this paper are 1) to present the value of directed acyclic graphs (DAGs) to identify and mitigate potential problems related to confounding and selection bias, and 2) to question the meaning of a cumulative ACE score. RESULTS: Adjusting for variables that post-date childhood could block mediated pathways that are part of the total causal effect while conditioning on adult variables, which often serve as proxies for childhood variables, can create collider stratification bias. Because exposure to ACEs can affect the likelihood of reaching adulthood or study entry, selection bias could be introduced via restricting selection on a variable affected by ACEs in the presence of unmeasured confounding. In addition to challenges regarding causal structure, using a cumulative score of ACEs assumes that each type of adversity will have the same effect on a given outcome, which is unlikely considering differing risk across adverse experiences. CONCLUSIONS: DAGs provide a transparent approach of the researchers' assumed causal relationships and can be used to overcome issues related to confounding and selection bias. Researchers should be explicit about their operationalization of ACEs and how it is to be interpreted in the context of the research question they are trying to answer.

"Does a Respiratory Virus Have an Ecological Niche, and If So, Can It Be Mapped?" Yes and Yes.

Although the utility of Ecological Niche Models (ENM) and Species Distribution Models (SDM) has been demonstrated in many ecological applications, their suitability for modelling epidemics or pandemics, such as SARS-Cov-2, has been questioned. In this paper, contrary to this viewpoint, we show that ENMs and SDMs can be created that can describe the evolution of pandemics, both in space and time. As an illustrative use case, we create models for predicting confirmed cases of COVID-19, viewed as our target "species", in Mexico through 2020 and 2021, showing that the models are predictive in both space and time. In order to achieve this, we extend a recently developed Bayesian framework for niche modelling, to include: (i) dynamic, non-equilibrium "species" distributions; (ii) a wider set of habitat variables, including behavioural, socio-economic and socio-demographic variables, as well as standard climatic variables; (iii) distinct models and associated niches for different species characteristics, showing how the niche, as deduced through presence-absence data, can differ from that deduced from abundance data. We show that the niche associated with those places with the highest abundance of cases has been highly conserved throughout the pandemic, while the inferred niche associated with presence of cases has been changing. Finally, we show how causal chains can be inferred and confounding identified by showing that behavioural and social factors are much more predictive than climate and that, further, the latter is confounded by the former.

A causal modelling framework for short-term effects of PM2.5 on hospitalisations: A nationwide time series study in Brazil.

Accurate estimates of the causal effect of air pollution on health outcomes, are critical when calculating attributable disease burdens. Brazil has a large population exposed to fast-growing emissions of air pollutants, however no national level studies have been conducted to examine the causal effect of PM2.5 exposure on health outcomes. This study proposes a novel approach, to accurately estimate the causal relationship between daily PM2.5 exposure and hospitalisations, across 1,814 Brazilian cities during 2000-2015. A variant of the difference-in-differences (DID) approach was applied under a counterfactual framework. Daily time series data were divided into panels. Seasonality and long-term trend were controlled using indicators for the panel. Variables which do not change within a short-period were controlled using a dummy variable for the day. Controls for variables which vary day by day, were included in the model. We found the proposed model exhibited competitive power performance in detecting causal associations between short-term PM2.5 exposure and hospitalisations in Brazil. A 10 µg/m3 increase in PM2.5 concentrations over four days (lag 0-3) was associated with a 1.06 % (95 % CI: 0.94 to 1.17) increase in all-cause hospitalisations and accounted for 1.26 % (95 % CI: 1.12-1.39) of total hospitalisations. Larger effects were found for children aged 0-4 years and the elderly aged 80+ years, suggesting policies should be developed to minimise the exposure of these age groups.

Formal models for the study of the relationship between fluctuating asymmetry and fitness in humans.

OBJECTIVES: To evaluate three of the main verbal models that have been proposed to explain the relationship between fluctuating asymmetry and fitness in humans: the "good genes," the "good development," and the "growth" hypotheses. MATERIALS AND METHODS: A formal model was generated for each verbal model following three steps. First, based on the literature, a theoretical causal model and the theoretical object of inquiry were outlined. Second, an empirical causal model and the targets of inference were defined using observational data of facial asymmetries and life-history traits related to fitness. Third, generalized linear models and causal inference were used as the estimation strategy. RESULTS: The results suggest that the theoretical and empirical assumptions of the "good genes" hypothesis should be reformulated. The results were compatible with most of the empirical assumptions of "the good development" hypothesis but suggest that further discussion of its theoretical assumptions is needed. The results were less informative about the "growth" hypothesis, both theoretically and empirically. There was a positive association between facial fluctuating asymmetry and the number of offspring that was not compatible with any of the empirical causal models evaluated. CONCLUSIONS: Although the three hypotheses focus on different aspects of the link between asymmetry and fitness, their overlap opens the possibility of a unified theory on the subject. The results of this study make explicit which assumptions need to be updated and discussed, facilitating the advancement of this area of research. Overall, this study elucidates the potential benefit of using formal models for theory revision and development.

Métodos para avaliação da efetividade de vacinas para COVID-19 com ênfase em abordagens quase-experimentais / Methods to evaluate COVID-19 vaccine effectiveness, with an emphasis on quasi-experimental approaches

Resumo A avalição da efetividade de vacinas é feita com dados do mundo real e é essencial para monitorar o desempenho dos programas de vacinação ao longo do tempo bem como frente a novas variantes. Até o momento, a avaliação da efetividade das vacinas para COVID-19 tem sido baseada em métodos clássicos como estudos de coorte e caso controle teste-negativo, que muitas vezes podem não permitir o adequado controle dos vieses intrínsecos da alocação das campanhas de vacinação. O objetivo dessa revisão foi discutir os desenhos de estudo disponíveis para avaliação de efetividade das vacinas, enfatizando os estudos quase-experimentais, que buscam mimetizar os estudos aleatorizados ao introduzir um componente exógeno para atribuição ao tratamento, bem como suas vantagens, limitações e aplicabilidade no contexto dos dados brasileiros. O emprego de métodos quase-experimentais, incluindo as séries temporais interrompidas, o método de diferença em diferenças, escore de propensão, variáveis instrumentais e regressão descontínua, são relevantes pela possibilidade de gerar estimativas mais acuradas da efetividade de vacinas para COVID-19 em cenários como o brasileiro, que se caracteriza pelo uso de várias vacinas, com respectivos número e intervalos entre doses, aplicadas em diferentes faixas etárias e em diferentes momentos da pandemia.

Abstract The evaluation of vaccine effectiveness is conducted with real-world data. They are essential to monitor the performance of vaccination programmes over time, and in the context of the emergence of new variants. Until now, the effectiveness of COVID-19 vaccines has been assessed based on classic methods, such as cohort and test-negative case-control studies, which may often not allow for adequate control of inherent biases in the assignment of vaccination campaigns. The aim of this review was to discuss the study designs available to evaluate vaccine effectiveness, highlighting quasi-experimental studies, which seek to mimic randomized trials, by introducing an exogenous component to allocate to treatment, in addition to the advantages, limitations, and applicability in the context of Brazilian data. The use of quasi-experimental approaches, such as interrupted time series, difference-in-differences, propensity scores, instrumental variables, and regression discontinuity design, are relevant due to the possibility of providing more accurate estimates of COVID-19 vaccine effectiveness. This is especially important in scenarios such as the Brazilian, which characterized by the use of various vaccines, with the respective numbers and intervals between doses, applied to different age groups, and introduced at different times during the pandemic.

All things equal? Heterogeneity in policy effectiveness against COVID-19 spread in chile.

Several variables and practices affect the evolution and geographic spread of COVID-19. Some of these variables pertain to policy measures such as social distancing, quarantines for specific areas, and testing availability. In this paper, I analyze the effect that lockdown and testing policies had on new contagions in Chile, especially focusing on potential heterogeneity given by population characteristics. Leveraging a natural experiment in the determination of early quarantines, I use an Augmented Synthetic Control Method to build counterfactuals for high and lower-income areas that experienced a lockdown during the first two months of the pandemic. I find substantial differences in the impact that quarantine policies had for different populations: While lockdowns were effective in containing and reducing new cases of COVID-19 in higher-income municipalities, I find no significant effect of this measure for lower-income areas. To further explain these results, I test for difference in mobility during quarantine for high and lower-income municipalities, as well as delays in test results and testing availability. These findings are consistent with previous results, showing that differences in the effectiveness of lockdowns could be partially attributed to heterogeneity in quarantine compliance in terms of mobility, as well as differential testing availability for higher and lower-income areas.

Identifying predictive biomarkers of CIMAvaxEGF success in non-small cell lung cancer patients.

BACKGROUND: Immunosenescence biomarkers and peripheral blood parameters are evaluated separately as possible predictive markers of immunotherapy. Here, we illustrate the use of a causal inference model to identify predictive biomarkers of CIMAvaxEGF success in the treatment of Non-Small Cell Lung Cancer Patients. METHODS: Data from a controlled clinical trial evaluating the effect of CIMAvax-EGF were analyzed retrospectively, following a causal inference approach. Pre-treatment potential predictive biomarkers included basal serum EGF concentration, peripheral blood parameters and immunosenescence biomarkers. The proportion of CD8 + CD28- T cells, CD4+ and CD8+ T cells, CD4/CD8 ratio and CD19+ B cells. The 33 patients with complete information were included. The predictive causal information (PCI) was calculated for all possible models. The model with a minimum number of predictors, but with high prediction accuracy (PCI > 0.7) was selected. Good, rare and poor responder patients were identified using the predictive probability of treatment success. RESULTS: The mean of PCI increased from 0.486, when only one predictor is considered, to 0.98 using the multivariate approach with all predictors. The model considering the proportion of CD4+ T cell, basal Epidermal Growth Factor (EGF) concentration, neutrophil to lymphocyte ratio, Monocytes, and Neutrophils as predictors were selected (PCI > 0.74). Patients predicted as good responders according to the pre-treatment biomarkers values treated with CIMAvax-EGF had a significant higher observed survival compared with the control group (p = 0.03). No difference was observed for bad responders. CONCLUSIONS: Peripheral blood parameters and immunosenescence biomarkers together with basal EGF concentration in serum resulted in good predictors of the CIMAvax-EGF success in advanced NSCLC. Future research should explore molecular and genetic profile as biomarkers for CIMAvax-EGF and it combination with immune-checkpoint inhibitors. The study illustrates the application of a new methodology, based on causal inference, to evaluate multivariate pre-treatment predictors. The multivariate approach allows realistic predictions of the clinical benefit of patients and should be introduced in daily clinical practice.

Estimating the causal effect of prenatal lead exposure on prepulse inhibition deficits in children and adolescents.

During pregnancy, maternal lead from earlier exposures mobilizes and crosses placental barriers, placing the developing fetus at risk for lead exposure and neurodevelopmental deficits. Some neuronal circuits known to be affected in neurodevelopment disorders can be probed with simple physiological behavioral paradigms. One such neural biomarker is Pre-Pulse Inhibition (PPI), an indicator of adequate sensorimotor gating processing. In clinical studies, deficits in PPI have been associated with neurodevelopmental disorders in human subjects. To our knowledge, no studies have examined the use of PPI as a biomarker of toxicant effects on the brain in epidemiological studies. We aimed to estimate the causal effect of prenatal lead exposure, assessed by maternal cortical bone lead concentrations, on PPI in 279 children from Mexico City. in vivo maternal cortical bone lead measurements were taken at four weeks postpartum at the mid-tibia shaft using a K-Shell X-ray fluorescence instrument. PPI recording occurred in an isolated clinical setting and eye blink responses were measured using electromyography. We assessed if the conditions for causal inference held in our study and used the results of our assessment to estimate the causal effect of prenatal lead exposure on PPI using an ordinary least squares regression model, a marginal structural model, and the parametric g-formula. Results were consistent across the three modeling approaches. For the parametric g-formula, a one standard deviation (10.0 µg/g) increase in prenatal lead significantly reduced PPI by approximately 19.0 % (95 % CI: 5.4 %, 34.3 %). This decrease is similar in magnitude to clinical studies on schizophrenia, which have observed PPI impairments in patients with schizophrenia as compared to controls. Our results are consistent with findings from other studies establishing an association between lead exposure and neurodevelopmental disorders in children and suggest that PPI may be useful as an objective biomarker of toxicant effects on the brain.

Exposure to community violence and Children's mental Health: A quasi-experimental examination.

RATIONALE: Community violence and mental health problems are global health concerns. Yet, assessing the causal links between community violent crime and mental health is challenging due to problems of selection bias. OBJECTIVE: This study examines the link between community violent crime and children's mental health problems, as well as the moderating role of parents' mental health. METHOD: The study employs a representative sample of 404 children (Mage=8.99, range=7-11) from Bogotá, Colombia, as well as longitudinal geocoded data on violent crimes from the national police. To account for problems of selection bias, the empirical strategy exploits naturalistic exogenous variation in the timing and location of an incident of violent crime relative to assessment of children's mental health problems, combined with matching techniques. RESULTS: Findings suggest an incident of violent crime in close proximity to children's homes is associated, on average, with increases in children's mental health problems by 0.28-0.38 SD; having parents with worse mental health exacerbates children's problems. Results from sensitivity checks and falsification tests further support the internal validity of the findings. CONCLUSIONS: Collectively, the results from the present study and those of previous research suggest that community violent crime has the potential to affect local residents negatively beyond direct victims, placing a heavy burden on individuals and society.

Como o desmatamento influencia a ocorrência de leishmaniose visceral? Uma abordagem contrafactual / How deforestation influence tha occurrence of Visceral Leishmaniasis? A counterfactual approach

A leishmaniose visceral (LV) é uma doença sistêmica de transmissão vetorial. No Brasil, ela é causada pelo protozoário Leishmania infantum e é transmitida por flebotomíneos do gênero Lutzomyia, tendo os cães como a principal fonte de infecção em áreas urbanas. Historicamente a LV era conhecida como uma doença endêmica rural, porém desde a década de 1980, ela atingiu proporções endêmicas e epidêmicas em grandes cidades brasileiras. Desde então vários fatores são considerados como norteadores da expansão da LV, como variáveis socioeconômicas, climáticas e ambientais. Mais especificamente sobre o efeito do desmatamento e perturbações antrópicas no ambiente, a maioria do que se encontra na literatura tem natureza qualitativa ou utiliza de abordagens reducionistas, sem considerar a complexidade da dinâmica de uma doença infecciosa de transmissão vetorial e de caráter zoonóticos. Na presente dissertação, investigamos o efeito do desmatamento na ocorrência de Lutzomyia longipalpis (Lu. longipalpis), leishmaniose visceral canina (LVC) e leishmaniose visceral humana (LVH), tomando como exemplo o estado de São Paulo. Para isso, utilizamos uma abordagem contrafactual para estimar os efeitos (geral, direto e indireto) do desmatamento na ocorrência do Lu. longipalpis/LVC/LVH. Isso foi feito em dois passos, primeiro estimamos os parâmetros por meio de um algoritmo de Metropolis-Hastings duplo e, por fim, estimamos os efeitos causais através de um amostrador de Gibbs, por meio do pacote autognet no R.Vimos que municípios desmatados apresentam 2.63, 2.07 e 3.18 maiores chances de apresentar o vetor, LVC e LVH, respectivamente quando comparados com os municípios que não apresentaram desmatamento. Foi observada também uma forte influência da presença do vetor, LVC e LVH dos municípios vizinhos na ocorrência dos mesmos em municípios previamente livres dos desfechos (6.67, 4.26 e 4.27). Já sob mudanças hipotéticas de prevalência do desmatamento de 50% para 0% no estado, são esperadas quedas na prevalência do vetor, LVC e LVH de 11%, 6.67% e 29.87% respectivamente. O desmatamento influi na ocorrência do vetor, doença em cães e humanos por duas principais vias, (i) alterando o funcionamento do ecossistema e estrutura da comunidade, permitindo a reprodução e colonização do vetor; e (ii) promovendo uma aproximação entre todos os componentes do ciclo da LV. De tal modo, para correto controle da LV e doenças infecciosas como um todo, é imprescindível um desenvolvimento ecologicamente correto com soluções viáveis para as compensações entre a agricultura, urbanização e conservação. urbanização e conservação.

Visceral leishmaniasis (VL) is a systemic vector-borne disease. In Brazil, it caused by the protozoan Leishmania infantum and is transmitted by sandflies of the genus Lutzomyia, with dogs as the principal source of infection in urban areas. Historically, VL was known as a rural endemic disease, since the 80's it has become endemic and epidemic in large Brazilian cities. Since then, many factors were hypothesised as driving VL expansion, as socioeconomic, climatic and environmental variables. More specifically, concerning deforestation and human-made actions in the environment, most studies tend to be qualitative in nature or use traditional reductionist approaches, ignoring the complexities that are inherent of vector-borne zoonotic infectious diseases. The present study aimed to investigate the effect of deforestation in the occurrence of Lutzomyia longipalpis, canine visceral leishmaniasis (CVL), and human visceral leishmaniasis (HVL), taking as a motivating example the São Paulo state (Brazil). To this end, we chose a counterfactual approach to estimate the effects (overall, direct and indirect) of deforestation in the occurrence of vector/CVL/HVL. We did it in two steps; first, we estimated the parameters through a double Metropolis-Hastings algorithm and, finally, we estimated the causal effects through a Gibbs sampler, using the autognet package in R. We observe that deforested cities show 2.63, 2.07, and 3.18 higher odds of vector/CVL/HVL occurrence, respectively, when compared to non-deforested municipalities. We also see a significant influence of vector, CVL, and HVL presence in the neighbours in its appearance in previous naive cities, 6.67, 4.26, 4,27 respectively. Lastly, under hypothetical changes in deforestation's prevalence from 50% to 0% in the whole state, is expected a decrease in its prevalence of the vector, LVC and LVH of 11%, 6.67% and 29.87% respectively. Deforestation in the occurrence of infectious diseases and, more specifically, VL importance, is two-folded: (i) changing's the ecosystem equilibrium and community structure, allowing its vector to reproduce and colonise; (ii) promoting a close contact through the VL cycle components. In such a way, for correct control of VL and infectious disease as a whole, it is essential an eco-friendly development with viable solutions for trade-offs between agriculture, urbanization and conservation.

Bayesian estimation of the average treatment effect on the treated using inverse weighting.

We develop a Bayesian approach to estimate the average treatment effect on the treated in the presence of confounding. The approach builds on developments proposed by Saarela et al in the context of marginal structural models, using importance sampling weights to adjust for confounding and estimate a causal effect. The Bayesian bootstrap is adopted to approximate posterior distributions of interest and avoid the issue of feedback that arises in Bayesian causal estimation relying on a joint likelihood. We present results from simulation studies to estimate the average treatment effect on the treated, evaluating the impact of sample size and the strength of confounding on estimation. We illustrate our approach using the classic Right Heart Catheterization data set and find a negative causal effect of the exposure on 30-day survival, in accordance with previous analyses of these data. We also apply our approach to the data set of the National Center for Health Statistics Birth Data and obtain a negative effect of maternal smoking during pregnancy on birth weight.