Agreement between self-reported prepregnancy weight and measured firsttrimester weight in Brazilian women

Background: Self-reported pre-pregnancy weight and weight measured in the first trimester are both used to estimate pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) but there is limited information on how they compare, especially in low- and middle-income countries, where access to a weight scale can be limited. Thus, the main goal of this study was to evaluate the agreement between self-reported pre-pregnancy weight and weight measured during the first trimester of pregnancy among Brazilian women so as to assess whether self-reported pre-pregnancy weight is reliable and can be used for calculation of BMI and GWG. Methods: Data from the Brazilian Maternal and Child Nutrition Consortium (BMCNC, n = 5563) and the National Food and Nutritional Surveillance System (SISVAN, n = 393,095) were used to evaluate the agreement between selfreported pre-pregnancy weight and weights measured in three overlapping intervals (30–94, 30–60 and 30–45 days of pregnancy) and their impact in BMI classification. We calculated intraclass correlation and Lin’s concordance coefficients, constructed Bland and Altman plots, and determined Kappa coefficient for the categories of BMI. Results: The mean of the differences between self-reported and measured weights was 0.90 for both datasets in all time intervals. Bland and Altman plots showed that the majority of the difference laid in the ±2 kg interval and that the differences did not vary according to measured first-trimester BMI. Kappa coefficient values were > 0.80 for both datasets at all intervals. Using self-reported prepregnancy or measured weight would change, in total, the classification of BMI in 15.9, 13.5, and 12.2% of women in the BMCNC and 12.1, 10.7, and 10.2% in the SISVAN, at 30–94, 30–60 and 30–45 days, respectively. Conclusion: In Brazil, self-reported pre-pregnancy weight can be used for calculation of BMI and GWG when an early measurement of weight during pregnancy is not available. These results are especially important in a country where the majority of woman do not initiate prenatal care early in pregnancy

Brazilian Maternal and Child Nutrition Consortium : establishment, data harmonization and basic characteristics

Pooled data analysis in the feld of maternal and child nutrition rarely incorporates data from low- and middle-income countries and existing studies lack a description of the methods used to harmonize the data and to assess heterogeneity. We describe the creation of the Brazilian Maternal and Child Nutrition Consortium dataset, from multiple pooled longitudinal studies, having gestational weight gain (GWG) as an example. Investigators of the eligible studies published from 1990 to 2018 were invited to participate. We conducted consistency analysis, identifed outliers, and assessed heterogeneity for GWG. Outliers identifcation considered the longitudinal nature of the data. Heterogeneity was performed adjusting multilevel models. We identifed 68 studies and invited 59 for this initiative. Data from 29 studies were received, 21 were retained for analysis, resulting in a fnal sample of 17,344 women with 72,616 weight measurements. Fewer than 1% of all weight measurements were fagged as outliers. Women with pre-pregnancy obesity had lower values for GWG throughout pregnancy. GWG, birth length and weight were similar across the studies and remarkably similar to a Brazilian nationwide study. Pooled data analyses can increase the potential of addressing important questions regarding maternal and child health, especially in countries where research investment is limited

Gestational weight gain charts : results from the Brazilian Maternal and Child Nutrition Consortium

Background: Monitoring gestational weight gain (GWG) is fundamental to ensure a successful pregnancy for the mother and the offspring. There are several international GWG charts, but just a few for low- and middle-income countries. Objectives: To construct GWG charts according to pre-pregnancy BMI for Brazilian women. Methods: This is an individual patient data analysis using the Brazilian Maternal and Child Nutrition Consortium data, comprising 21 cohort studies. External validation was performed using “Birth in Brazil,” a nationwide study. We selected adult women with singleton pregnancies who were free of infectious and chronic diseases, gestational diabetes, and hypertensive disorders; who delivered a live birth at term; and whose children were adequate for gestational age, and with a birth weight between 2500–4000 g. Maternal self-reported pre-pregnancy weight and weight measured between 10–40 weeks of gestation were used to calculate GWG. Generalized Additive Models for Location, Scale and Shape were fitted to create GWG charts according to gestational age, stratified by pre-pregnancy BMI. Results: The cohort included 7086 women with 29,323 weight gain measurements to construct the charts and 4711 women with 31,052 measurements in the external validation. The predicted medians for GWG at 40 weeks, according to pre-pregnancy BMI, were: underweight, 14.1 kg (IQR, 10.8–17.5 kg); normal weight, 13.8 kg (IQR, 10.7–17.2 kg); overweight, 12.1 kg (IQR, 8.5–15.7 kg); obesity, 8.9 kg (IQR, 4.8–13.2 kg). The 10th, 25th, 50th, 75th, and 90th percentiles were estimated. Results for internal and external validation showed that the percentages below the selected percentiles were close to those expected. Conclusions: The charts proposed provide a description of GWG patterns according to gestational age and pre-pregnancy BMI among healthy Brazilian women with good neonatal outcomes. The external validation indicates that this new tool can be used to monitor GWG in the primary health-care setting and to test potential recommended values

Analysis of the quality of prenatal data of pregnant women attended at Healthcare Services in the city of São Paulo between 2012 and 2020

ABSTRACT Objective: To analyze the quality of data collected during prenatal care recorded in the Integrated Health Care Management System (SIGA) of the Municipal Department of Health of São Paulo from 2012 to 2020. Methods: Descriptive study using SIGA data and the variables: maternal height (cm), weight (kg) measured throughout pregnancy, gestational age at prenatal consultation, systolic (SBP) and diastolic (DBP) blood pressure (in mmHg), and body mass index (BMI) at the beginning of pregnancy (up to 8 weeks). Quality analysis was carried out by calculating the indicators: percentage of incompleteness and zero values of all variables studied, percentage of implausible values for height, weight, BMI; preference for terminal digit of weight and height, and normality of distributions. Results: The database of pregnant women made available for analysis included 8,046,608 records and 1,174,115 women. The percentage of incompleteness and zero values was low (<1%) in all original variables of the system. There are more records at the end of pregnancy. For the four original variables of interest in the database (weight, height, SBP, DBP), there is a clear preference for the terminal digit. The variables of interest did not present an approximately normal distribution during the evaluated period. Conclusion: The quality analysis showed the need for improving the standardization of information collection and recording, the rounding of measurements and the need for encouraging pregnant women to start prenatal care as soon as possible, in such a way that it is important to invest in data quality, through educational resources for professionals who work in health care

Brazilian Maternal and Child Nutrition Consortium: establishment, data harmonization and basic characteristics.

Pooled data analysis in the field of maternal and child nutrition rarely incorporates data from low- and middle-income countries and existing studies lack a description of the methods used to harmonize the data and to assess heterogeneity. We describe the creation of the Brazilian Maternal and Child Nutrition Consortium dataset, from multiple pooled longitudinal studies, having gestational weight gain (GWG) as an example. Investigators of the eligible studies published from 1990 to 2018 were invited to participate. We conducted consistency analysis, identified outliers, and assessed heterogeneity for GWG. Outliers identification considered the longitudinal nature of the data. Heterogeneity was performed adjusting multilevel models. We identified 68 studies and invited 59 for this initiative. Data from 29 studies were received, 21 were retained for analysis, resulting in a final sample of 17,344 women with 72,616 weight measurements. Fewer than 1% of all weight measurements were flagged as outliers. Women with pre-pregnancy obesity had lower values for GWG throughout pregnancy. GWG, birth length and weight were similar across the studies and remarkably similar to a Brazilian nationwide study. Pooled data analyses can increase the potential of addressing important questions regarding maternal and child health, especially in countries where research investment is limited

Quality of child anthropometric data from SISVAN, Brazil, 2008-2017

OBJECTIVE: To evaluate the quality of anthropometric data of children recorded in the Food and Nutrition Surveillance System (SISVAN) from 2008 to 2017. METHOD: Descriptive study on the quality of anthropometric data of children under five years of age admitted in primary care services of the Unified Health System, from the individual databases of SISVAN. Data quality was annually assessed using the indicators: coverage, completeness, sex ratio, age distribution, weight and height digit preference, implausible z-score values, standard deviation, and normality of z-scores. RESULTS: In total, 73,745,023 records and 29,852,480 children were identified. Coverage increased from 17.7% in 2008 to 45.4% in 2017. Completeness of birth date, weight, and height corresponded to almost 100% in all years. The sex ratio was balanced and approximately similar to the expected ratio, ranging from 0.8 to 1. The age distribution revealed higher percentages of registrations from the ages of two to four years until mid-2015. A preference for terminal digits “zero” and “five” was identified among weight and height records. The percentages of implausible z-scores exceeded 1% for all anthropometric indices, with values decreasing from 2014 onwards. A high dispersion of z-scores, including standard deviations between 1.2 and 1.6, was identified mainly in the indices including height and in the records of children under two years of age and residents in the North, Northeast, and Midwest regions. The distribution of z-scores was symmetric for all indices and platykurtic for height/age and weight/age. CONCLUSIONS: The quality of SISVAN anthropometric data for children under five years of age has improved substantially between 2008 and 2017. Some indicators require attention, particularly for height measurements, whose quality was lower especially among groups more vulnerable to nutritional problems.OBJETIVOS: Avaliar a qualidade dos dados antropométricos de crianças registradas no Sistema de Vigilância Alimentar e Nutricional (Sisvan) no período 2008-2017. MÉTODOS: Estudo descritivo sobre a qualidade dos dados antropométricos de crianças menores de 5 anos atendidas nos serviços de atenção primária do Sistema Único de Saúde, a partir das bases de dados individuais do Sisvan. A qualidade dos dados foi avaliada anualmente por meio dos indicadores: cobertura, completude, razão entre sexos, distribuição da idade, preferência por dígitos de peso e estatura, valores de escore-z implausíveis, desvio-padrão e normalidade dos escores-z. RESULTADOS: N o t otal, 7 3.745.023 r egistros e 2 9.852.480 c rianças f oram i dentificados. A cobertura aumentou de 17,7% em 2008 para 45,4% em 2017. A completude da data de nascimento, peso e estatura correspondeu a quase 100% para todos os anos. A razão entre sexos foi equilibrada e aproximadamente similar a razão esperada, variando entre 0,8 e 1. A distribuição da idade revelou maiores percentuais de registros entre as idades de 2 a 4 anos até meados de 2015. Uma preferência pelos dígitos terminais “zero” e “cinco” foi identificada entre os registros de peso e estatura. As porcentagens de escores-z implausíveis excederam 1% para todos os índices antropométricos, com redução dos valores a partir de 2014. Uma alta dispersão dos escores-z, incluindo desvios-padrão entre 1,2 e 1,6, foi identificada principalmente nos índices incluindo estatura e nos registros de crianças menores de 2 anos e residentes das regiões Norte, Nordeste e Centro-Oeste. A distribuição dos escores-z foi simétrica para todos os índices e platicúrtica para estatura/idade e peso/idade. CONCLUSÕES: A qualidade dos dados antropométricos do Sisvan para crianças menores de 5 anos melhorou substancialmente entre 2008 e 2017. Alguns indicadores requerem atenção, sobretudo para medidas de estatura, cuja qualidade foi principalmente inferior entre os grupos mais vulneráveis a agravos nutricionais

Agreement between self-reported pre-pregnancy weight and measured first-trimester weight in Brazilian women

Background: Self-reported pre-pregnancy weight and weight measured in the first trimester are both used to estimate pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) but there is limited information on how they compare, especially in low- and middle-income countries, where access to a weight scale can be limited. Thus, the main goal of this study was to evaluate the agreement between self-reported pre-pregnancy weight and weight measured during the first trimester of pregnancy among Brazilian women so as to assess whether self-reported pre-pregnancy weight is reliable and can be used for calculation of BMI and GWG. Methods: Data from the Brazilian Maternal and Child Nutrition Consortium (BMCNC, n = 5563) and the National Food and Nutritional Surveillance System (SISVAN, n = 393,095) were used to evaluate the agreement between self-reported pre-pregnancy weight and weights measured in three overlapping intervals (30–94, 30–60 and 30–45 days of pregnancy) and their impact in BMI classification. We calculated intraclass correlation and Lin’s concordance coefficients, constructed Bland and Altman plots, and determined Kappa coefficient for the categories of BMI. Results: The mean of the differences between self-reported and measured weights was  0.90 for both datasets in all time intervals. Bland and Altman plots showed that the majority of the difference laid in the ±2 kg interval and that the differences did not vary according to measured first-trimester BMI. Kappa coefficient values were > 0.80 for both datasets at all intervals. Using self-reported pre-pregnancy or measured weight would change, in total, the classification of BMI in 15.9, 13.5, and 12.2% of women in the BMCNC and 12.1, 10.7, and 10.2% in the SISVAN, at 30–94, 30–60 and 30–45 days, respectively. Conclusion: In Brazil, self-reported pre-pregnancy weight can be used for calculation of BMI and GWG when an early measurement of weight during pregnancy is not available. These results are especially important in a country where the majority of woman do not initiate prenatal care early in pregnancy.Medicine, Faculty ofNon UBCObstetrics and Gynaecology, Department ofReviewedFacult

Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system

Abstract Background Several strategies for identifying biologically implausible values in longitudinal anthropometric data have recently been proposed, but the suitability of these strategies for large population datasets needs to be better understood. This study evaluated the impact of removing population outliers and the additional value of identifying and removing longitudinal outliers on the trajectories of length/height and weight and on the prevalence of child growth indicators in a large longitudinal dataset of child growth data. Methods Length/height and weight measurements of children aged 0 to 59 months from the Brazilian Food and Nutrition Surveillance System were analyzed. Population outliers were identified using z-scores from the World Health Organization (WHO) growth charts. After identifying and removing population outliers, residuals from linear mixed-effects models were used to flag longitudinal outliers. The following cutoffs for residuals were tested to flag those: -3/+3, -4/+4, -5/+5, -6/+6. The selected child growth indicators included length/height-for-age z-scores and weight-for-age z-scores, classified according to the WHO charts. Results The dataset included 50,154,738 records from 10,775,496 children. Boys and girls had 5.74% and 5.31% of length/height and 5.19% and 4.74% of weight values flagged as population outliers, respectively. After removing those, the percentage of longitudinal outliers varied from 0.02% (+6) to 1.47% (+3) for length/height and from 0.07 to 1.44% for weight in boys. In girls, the percentage of longitudinal outliers varied from 0.01 to 1.50% for length/height and from 0.08 to 1.45% for weight. The initial removal of population outliers played the most substantial role in the growth trajectories as it was the first step in the cleaning process, while the additional removal of longitudinal outliers had lower influence on those, regardless of the cutoff adopted. The prevalence of the selected indicators were also affected by both population and longitudinal (to a lesser extent) outliers. Conclusions Although both population and longitudinal outliers can detect biologically implausible values in child growth data, removing population outliers seemed more relevant in this large administrative dataset, especially in calculating summary statistics. However, both types of outliers need to be identified and removed for the proper evaluation of trajectories

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