No abstract

To estimate the effects of consuming hot beverages, including mate (an infusion of the herb Ilex paraguayensis), tea, coffee and coffee with milk, and other food items on esophageal cancer risk, we analyzed data from 830 cases and 1,779 controls participating in a series of 5 hospital‐based case‐control studies of squamous‐cell carcinoma of the esophagus conducted in high‐risk areas of South America. After adjusting for the strong effects of tobacco and alcohol consumption, both heavy mate drinking (>1 l/day) and self‐reported very hot mate drinking were significantly associated with esophageal cancer risk in men and women. The magnitude and strength of the association for mate amount and, to a lesser extent, mate temperature were higher for women than men. The joint effects of mate amount and mate temperature were more than multiplicative, following a statistically significant synergistic interaction (p = 0.02) which was particularly evident among heavy drinkers (>1.50 l/day) of very hot mate (odds ratio = 4.14, 95% confidence interval: 2.24–7.67) compared to light drinkers (<0.50 l/day) of cold/warm/hot mate. Consumption of other very hot beverages, such as tea and coffee with milk but not coffee alone, was also significantly associated with an increased risk, in the 2‐ to 4‐fold range. Statistically significant protective associations were identified for high consumption of vegetables, fruits, cereals and tea. In contrast, frequent consumption of meat, animal fats and salt was associated with a moderately increased risk. This pooled analysis adds evidence for a carcinogenic effect of chronic thermal injury in the esophagus induced by the consumption of very hot drinks, including mate. Our study further confirms the protective effect of a dietary pattern characterized by daily consumption of fruits and vegetables and low consumption of meat and animal fats. Int. J. Cancer 88:658–664, 2000. © 2000 Wiley‐Liss, Inc.

Resumo: Este artigo tem o objetivo de discutir a construção da gravidez na adolescência enquanto um problema social. Visa também apresentar e analisar os dados da etapa qualitativa da pesquisa Gravidez naEste artigo é resultado das discussões coletivas dos dados qualitativos da pesquisa GRAVAD por todos os seus participantes. Por isto que, pouco usual na área da Antropologia, sua autoria é creditada a todos eles.

The synergism between nutrition status and hospital admissions due to diarrhea and pneumonia was studied in a population-based birth cohort of greater than 5000 children in southern Brazil. Children were identified soon after birth in 1982, and data on nutrition status (weight and length) and hospital admissions were collected in 1984 and in 1986. Diarrhea admissions were stronger predictors of malnutrition than were pneumonia admissions, but malnutrition was a more important risk factor for pneumonia than for diarrhea. All associations were stronger in the first 2 y of life, although the early effect of severe diarrhea and pneumonia on nutrition status could still be detected in the fourth year of life.

ObjectivesTo identify risk factors for antepartum stillbirth, including fetal growth restriction, among women with well‐dated pregnancies and access to antenatal care.DesignPopulation‐based, prospective, observational study.SettingEight international urban populations.PopulationPregnant women and their babies enrolled in the Newborn Cross‐Sectional Study of the INTERGROWTH‐21st Project.MethodsCox proportional hazard models were used to compare risks among antepartum stillborn and liveborn babies.Main outcome measuresAntepartum stillbirth was defined as any fetal death after 16 weeks’ gestation before the onset of labour.ResultsOf 60 121 babies, 553 were stillborn (9.2 per 1000 births), of which 445 were antepartum deaths (7.4 per 1000 births). After adjustment for site, risk factors were low socio‐economic status, hazard ratio (HR): 1.6 (95% CI, 1.2–2.1); single marital status, HR 2.0 (95% CI, 1.4–2.8); age ≥40 years, HR 2.2 (95% CI, 1.4–3.7); essential hypertension, HR 4.0 (95% CI, 2.7–5.9); HIV/AIDS, HR 4.3 (95% CI, 2.0–9.1); pre‐eclampsia, HR 1.6 (95% CI, 1.1–3.8); multiple pregnancy, HR 3.3 (95% CI, 2.0–5.6); and antepartum haemorrhage, HR 3.3 (95% CI, 2.5–4.5). Birth weight <3rd centile was associated with antepartum stillbirth [HR, 4.6 (95% CI, 3.4–6.2)]. The greatest risk was seen in babies not suspected to have been growth restricted antenatally, with an HR of 5.0 (95% CI, 3.6–7.0). The population‐attributable risk of antepartum death associated with small‐for‐gestational‐age neonates diagnosed at birth was 11%.ConclusionsAntepartum stillbirth is a complex syndrome associated with several risk factors. Although small babies are at higher risk, current growth restriction detection strategies only modestly reduced the rate of stillbirth.Tweetable abstractInternational stillbirth study finds individual risks poor predictors of death but combinations promising.

The aim of this study was to examine the effect of demographic, socioeconomic, environmental, maternal reproductive, dietary, and nutritional variables on diarrhea risk and prognosis using a hierarchical framework. A case-control study of children aged 0-23 months in Greater Metropolitan Porto Alegre was conducted during the peak season for diarrhea in 1987-1988. Three groups were investigated, with 192 children each. The first group included hospitalized children with an episode of acute diarrhea complicated by moderate to severe dehydration. The second group included children with acute mild diarrhea without signs of dehydration who were identified in the same neighborhood as hospitalized cases. The third group consisted of controls without diarrhea. Mothers were interviewed by trained interviewers using a standardized questionnaire. Data analysis included a hierarchical approach to control for confounding, using conditional logistic regression. Comparison of the three groups aimed to identify risk factors for diarrhea complicated by dehydration, prognostic factors for dehydration, and risk factors for mild diarrhea. Low birth weight, stunting, and lack or breastfeeding acted simultaneously as risk and prognostic factors for diarrhea.

Background Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population's general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18-36 days, even when the best ultrasound estimates are used. The aims of this study are to improve estimates of fetal gestational age and provide personalised predictions of future growth. Methods Using ultrasound-derived, fetal biometric data, we developed a machine learning approach to accurately estimate gestational age. The accuracy of the method is determined by reference to exactly known facts pertaining to each fetus-specifically, intervals between ultrasound visits-rather than the date of the mother's last menstrual period. The data stem from a sample of healthy, well-nourished participants in a large, multicentre, population-based study, the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st). The generalisability of the algorithm is shown with data from a different and more heterogeneous population (INTERBIO-21st Fetal Study). Findings In the context of two large datasets, we estimated gestational age between 20 and 30 weeks of gestation with 95% confidence to within 3 days, using measurements made in a 10-week window spanning the second and third trimesters. Fetal gestational age can thus be estimated in the 20-30 weeks gestational age window with a prediction interval 3-5 times better than with any previous algorithm. This will enable improved management of individual pregnancies. 6-week forecasts of the growth trajectory for a given fetus are accurate to within 7 days. This will help identify at-risk fetuses more accurately than currently possible. At population level, the higher accuracy is expected to improve fetal growth charts and population health assessments. Interpretation Machine learning can circumvent long-standing limitations in determining fetal gestational age and future growth trajectory, without recourse to often inaccurately known information, such as the date of the mother's last menstrual period. Using this algorithm in clinical practice could facilitate the management of individual pregnancies and improve population-level health. Upon publication of this study, the algorithm for gestational age estimates will be provided for research purposes free of charge via a web portal.

The influence of lactation centres on breastfeeding patterns, morbidity and nutritional status was assessed through a longitudinal study from birth up to the age of 6 months among 605 mothers and newborns in Guarujá, São Paulo, Brazil. Children recruited in the perinatal period who subsequently attended the lactation centres (54%) were exclusively breastfed significantly more at 4 months (43 versus 18%) and at 6 months of age (15 versus 6%), than non-attenders, even after adjusting for confounders. Also, attenders presented less diarrhoea in the last fortnight than non-attenders (10 versus 17%), and their weight for age was significantly better (mean z-scores of 0.26 and 0.02, respectively). Lactation centres are effective in promoting breastfeeding, and their use in areas with short breastfeeding duration should be considered. This is the first report of a significant impact of a breastfeeding promotion programme on children's morbidity and growth.