Could we find any signal of the stratosphere-ionosphere coupling in Antarctica?

An investigation searching for a possible coupling between the lower ionosphere and the middle atmosphere in Antarctica is here performed on the basis of stratospheric vertical temperature profiles and ionospheric absorption data observed at the Antarctic Italian Base of Terra Nova Bay (74.69S, 164.12E) during local summer time. The result obtained by applying a multi-regression analysis and a Superimposed Epoch Analysis (SEA) shows a statistically significant ionosphere-stratosphere interaction. In particular, by selecting stratospheric temperature maxima occurring at different heights as the referring epoch for the SEA approach, the ionospheric absorption is found to show a positive and/or negative trend (several days) around it. The tendency for an increasing/decreasing absorption is obtained for temperature maxima occurring below/above the stratospheric level of about 17-19 km, respectively

Characterisation of pulmonary function trajectories: results from a Brazilian cohort.

Background: Pulmonary function (PF) trajectories are determined by different exposures throughout the life course. The aim of this study was to investigate characteristics related to PF trajectories from 15 to 22 years in a Brazilian cohort. Methods: A birth cohort study (1993 Pelotas Birth Cohort) was conducted with spirometry at 15, 18 and 22 years. PF trajectories were built based on z-score of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and their ratio using a group-based trajectory model. Associations with exposures reported from perinatal to 22 years were described. Results: Three trajectories, low (LT), average (AT) and high (HT) were identified in 2917 individuals. Wealthiest individuals belonged to the HT of FEV1 (p=0.023). Lower maternal pregestational body mass index (BMI) (22.4±0.2; p<0.001 and 22.1±0.14; p<0.001) and lower birth weight (3164.8±25.4; p=0.029 and 3132.3±19.4; p=0.005) were related to the LT of FEV1 and FVC. Mother's smoking exposure during pregnancy (37.7%; p=0.002), active smoking at ages 18 and 22 years (20.1% and 25.8%; p<0.001) and family history of asthma (44.8%; p<0.001) were related to the LT of FEV1/FVC. Wheezing, asthma and hospitalisations due to respiratory diseases in childhood were related to the LT of both FEV1 and FEV1/FVC. Higher BMIs were related to the HT of FEV1 and FVC at all ages. Conclusions: PF trajectories were mainly related to income, pregestational BMI, birth weight, hospitalisation due to respiratory diseases in childhood, participant's BMI, report of wheezing, medical diagnosis and family history of asthma, gestational exposure to tobacco and current smoking status in adolescence and young adult age

Prevalence and Risk Factors of Gang Membership in a Brazilian Birth Cohort

Importance: There is no longitudinal evidence on risk factors for gang membership in low- and middle-income countries, despite organized crime groups posing major challenges, including high homicide rates in Latin America. Furthermore, adverse childhood experiences (ACEs) have been largely overlooked in gang-related research worldwide. Objectives: To examine the associations of ACEs up to 15 years of age with past-year gang membership at 18 years of age and to compare crime and criminal justice involvement between gang members and non-gang members. Design, setting, and participants: This cohort study assessed children from the 1993 Pelotas (Brazil) Birth Cohort-an ongoing population-based, prospective study. Assessments were undertaken perinatally (1993) and when the children were ages 11 (2004), 15 (2008), 18 (2011), and 22 (2015) years. All children born in 1993 were eligible (N = 5265), and 5249 (99.7%) were enrolled at birth. The study sample (N = 3794 [72.1%]) included those with complete data on ACEs. Data analyses were conducted from February to August 2024. Exposures: Twelve ACEs were assessed up to 15 years of age via child self-report and/or maternal report, including physical neglect, physical abuse, emotional abuse, sexual abuse, domestic violence, maternal mental illness, parental divorce, ever being separated from parents, parental death, poverty, discrimination, and neighborhood fear. These experiences were examined using a single adversity approach, cumulative risk, and latent classes. Main outcomes and measures: The main outcome was past-year gang membership at 18 years of age, assessed via self-report and analyzed using multivariate imputation. Results: Of 3794 participants, 1964 (51.8%) were female and 1830 (48.2%) were male, and 703 (18.5%) were Black, 2922 (77.0%) were White, and 169 (4.5%) were coded as "other" race or ethnicity (no additional details are available to further disaggregate the other category). On the basis of the imputed data, 1.6% (SE, 0.2 percentage points) of participants reported gang membership at 18 years of age. Physical abuse (odds ratio [OR], 2.76; 95% CI, 1.27-5.98), emotional abuse (OR, 2.76; 95% CI, 1.51-5.02), domestic violence (OR, 3.39; 95% CI, 1.77-6.48), parental divorce (OR, 2.04; 95% CI, 1.17-3.54), and separation from parents (OR, 3.13; 95% CI, 1.54-6.37) were associated with an increased risk of gang membership. A dose-response association was observed, with 4 or more ACEs increasing the risk (OR, 8.86; 95% CI, 2.24-35.08). In latent class analysis, the class with child maltreatment and household challenges was associated with a higher risk of gang membership than the low-adversities class (OR, 7.10; 95% CI, 2.37-21.28). There was no robust evidence that children exposed to household challenges and social risks were at increased risk of gang membership (OR, 2.28; 95% CI, 0.46-11.25). Conclusions and relevance: In this prospective cohort study, ACEs, particularly child maltreatment and family conflict, were associated with gang involvement when examined individually, cumulatively, and as clusters in a high-crime environment in Brazil. These findings underscore the value of integrating the ACE framework into gang-related research and the potential to reduce gang-related crime by reducing ACEs.This article is based on data from the 1993 Pelotas Birth Cohort Study conducted by the Postgraduate Program in Epidemiology at Universidade Federal de Pelotas with the collaboration of the Brazilian Public Health Association. From 2004 to 2013, the Wellcome Trust supported the 1993 Pelotas Birth Cohort Study. The European Union, the National Support Program for Centers of Excellence, the Brazilian National Research Council, and the Brazilian Ministry of Health supported previous phases of the study. The 22-year follow-up was supported by the Science and Technology Department/Brazilian Ministry of Health, with resources transferred through grant 400943/2013-1 from the Brazilian National Council for Scientific and Technological Development. Analyses were supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil Finance Code 001 and grant OPP1164115 from the Bill and Melinda Gates Foundation. This research was funded in whole, or in part, by grant 210735_A_18_Z from the Wellcome Trust. Dr Hammerton was supported by grant 209138/Z/17/Z from the Sir Henry Wellcome Postdoctoral Fellowship

Lifelong robbery victimisation and mental disorders at age 18 years: Brazilian population-based study

Purpose Urban violence is a major problem in Brazil and may contribute to mental disorders among victims. The aim of this study was to assess the association between robbery victimisation and mental health disorders in late adolescence. Methods At age 18 years, 4106 participants in the 1993 Pelotas Birth Cohort Study were assessed. A questionnaire about history of robbery victimisation was administered, the Self-Report Questionnaire was used to screen for common mental disorders, and the Mini International Neuropsychiatric Interview was used to assess major depressive disorder and generalised anxiety disorder. Cross-sectional prevalence ratios between lifetime robbery victimisation and mental disorders were estimated using Poisson regression with robust standard errors, adjusting for socioeconomic variables measured at birth and violence in the home and maltreatment measured at age 15. Results There was a dose–response relationship between frequency of lifetime robberies and risk of mental disorders. Adolescents who had been robbed three or more times had twice the risk (PR 2.04; 95% CI 1.64–2.56) for common mental disorders, over four times the risk for depression (PR 4.59; 95% CI 2.60–8.12), and twice the risk for anxiety (PR 1.93; 95% CI 1.06–3.50), compared with non-victims, adjusting for covariates. Experiencing frequent robberies had greater impact on common mental disorders than experiencing an armed robbery. Population attributable fractions with regard to robbery were 9% for common mental disorders, 13% for depression, and 8% for anxiety. Conclusions Robberies are associated with common mental disorders in late adolescence, independently of violence between family members. Reducing urban violence could significantly help in preventing common mental illnesses

Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care

A risk calculator to predict adult Attention-deficit/Hyperactivity disorder::Generation and external validation in three birth cohorts and one clinical sample.

AimFew personalised medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult attention-deficit/hyperactivity disorder (ADHD).MethodsUsing logistic regression models, we generated a risk tool in a representative population cohort (ALSPAC - UK, 5113 participants, followed from birth to age 17) using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's depression and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort (UK, 2040 participants, birth to age 18), the 1993 Pelotas Birth Cohort (Brazil, 3911 participants, birth to age 18) and the MTA clinical sample (USA, 476 children with ADHD and 241 controls followed for 16 years from a minimum of 8 and a maximum of 26 years old).ResultsThe overall prevalence of adult ADHD ranged from 8.1 to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an area under the curve (AUC) for predicting adult ADHD of 0.82 (95% confidence interval (CI) 0.79-0.83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was 0.75 (95% CI 0.71-0.78). In the Brazilian birth cohort test sample, the AUC was significantly lower -0.57 (95% CI 0.54-0.60). In the clinical trial test sample, the AUC was 0.76 (95% CI 0.73-0.80). The risk model did not predict adult anxiety or major depressive disorder. Machine Learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available online at https://ufrgs.br/prodah/adhd-calculator/.ConclusionsThe risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution

A risk calculator to predict adult Attention-deficit/Hyperactivity disorder::Generation and external validation in three birth cohorts and one clinical sample.

AIM: Few personalized medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult Attention-Deficit/Hyperactivity Disorder (ADHD).METHODS: Using logistic regression models, we generated a risk tool in a representative population cohort (ALSPAC – UK, 5113 participants, followed from birth to age 17) using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's Depression, and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting, and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort (UK, 2040 participants, birth to age 18), the 1993 Pelotas Birth Cohort (Brazil, 3911 participants, birth to age 18), and the MTA clinical sample (US, 476 children with ADHD and 241 controls followed for 16 years from a minimum of 8 and a maximum of 26 years old). RESULTS: The overall prevalence of adult ADHD ranged from 8.1% to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an Area Under the Curve (AUC) for predicting adult ADHD of .82 (95% confidence interval [CI], .79 to .83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was .75 (95% CI, .71 to .78). In the Brazilian birth cohort test sample, the AUC was significantly lower – 57 (95% CI, .54 to .60). In the clinical trial test sample, the AUC was .76 (95% CI, .73 to .80). The risk model did not predict adult Anxiety or Major Depressive Disorder. Machine learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available on-line at https://ufrgs.br/prodah/adhd-calculator/.CONCLUSIONS: The risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution. <br/

Decline in attention-deficit hyperactivity disorder traits over the life course in the general population: trajectories across five population birth cohorts spanning ages 3 to 45 years

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Low Maternal Capital Predicts Life History Trade-Offs in Daughters: Why Adverse Outcomes Cluster in Individuals

Background: Some individuals appear prone to multiple adverse outcomes, including poor health, school dropout, risky behavior and early reproduction. This clustering remains poorly understood. Drawing on evolutionary life history theory, we hypothesized that maternal investment in early life would predict the developmental trajectory and adult phenotype of female offspring. Specifically, we predicted that daughters receiving low investment would prioritize the life history functions of “reproduction” and “defense” over “growth” and “maintenance,” increasing the risk of several adverse outcomes. // Methods: We investigated 2,091 mother-daughter dyads from a birth cohort in Pelotas, Brazil. We combined data on maternal height, body mass index, income, and education into a composite index of “maternal capital.” Daughter outcomes included reproductive status at 18 years, growth, adult anthropometry, body composition, cardio-metabolic risk, educational attainment, work status, and risky behavior. We tested whether daughters' early reproduction (<18 years) and exposure to low maternal capital were associated with adverse outcomes, and whether this accounted for the clustering of adverse outcomes within individuals. // Results: Daughters reproducing early were shorter, more centrally adipose, had less education and demonstrated more risky behavior compared to those not reproducing. Low maternal capital was associated with greater likelihood of the daughter reproducing early, smoking and having committed violent crime. High maternal capital was positively associated with the daughter's birth weight and adult size, and the likelihood of being in school. Associations of maternal capital with cardio-metabolic risk were inconsistent. Daughters reproducing early comprised 14.8% of the population, but accounted for 18% of obesity; 20% of violent crime, low birth weight and short stature; 32% of current smoking; and 52% of school dropout. Exposure to low maternal capital contributed similarly to the clustering of adverse outcomes among daughters. Outcomes were worst among daughters characterized by both low maternal capital and early reproduction. // Conclusion: Consistent with life history theory, daughters exposed to low maternal capital demonstrate “future discounting” in behavior and physiology, prioritizing early reproduction over growth, education, and health. Trade-offs associated with low maternal capital and early reproduction contribute to clustering of adverse outcomes. Our approach provides new insight into inter-generational cycles of disadvantage