Time trends and social inequalities in child malnutrition: nationwide estimates from Brazil's food and nutrition surveillance system, 2009-2017

Objective In Brazil, national estimates of childhood malnutrition have not been updated since 2006. The use of health information systems is an important complementary data source for analysing time trends on health and nutrition. This study aimed to examine temporal trends and socio-demographic inequalities in the prevalence of malnutrition in children attending primary health care services between 2009 and 2017. Design Time trends study based on data from Brazil's Food and Nutrition Surveillance System. Malnutrition prevalence (stunting, wasting, overweight and double burden) was annually estimated by socio-demographic variables. Prais-Winsten regression models were used to analyse time trends. Annual percent change (APC) and 95 % CI were calculated. Setting Primary health care services, Brazil. Participants Children under 5 years old. Results In total, 15,239,753 children were included. An increase in the prevalence of overweight (APC = 3·4 %; P = 0·015) and a decline in the prevalence of wasting (-6·2 %; P = 0·002) were observed. The prevalence of stunting (-3·2 %, P = 0·359) and double burden (-1·4 %, P = 0·630) had discrete and non-significant reductions. Despite the significant reduction in the prevalence of undernutrition among children in the most vulnerable subgroups (black, conditional cash transfer's recipients and residents of poorest and less developed areas), high prevalence of stunting and wasting persist alongside a disproportionate increase in the prevalence of overweight in these groups. Conclusions The observed pattern in stunting (high and persistent prevalence) and increase in overweight elucidate setbacks in advances already observed in previous periods and stresses the need for social and political strategies to address multiple forms of malnutrition

Time trends and social inequalities in infant and young child feeding practices: national estimates from Brazil’s Food and Nutrition Surveillance System, 2008–2019

Abstract Objective: To describe the time trends and socio-economic inequalities in infant and young child feeding practices in accordance with the Brazilian deprivation index (BDI). Design: This time-series study analysed the prevalence of multiple breast-feeding and complementary feeding indicators based on data from the Brazilian Food and Nutrition Surveillance System, 2008–2019. Prais–Winsten regression models were used to analyse time trends. Annual percent change (APC) and 95 % CI were calculated. Setting: Primary health care services, Brazil. Participants: Totally, 911 735 Brazilian children under 2 years old. Results: Breast-feeding and complementary feeding practices differed between the extreme BDI quintiles. Overall, the results were more favourable in the municipalities with less deprivation (Q1). Improvements in some complementary feeding indicators were observed over time and evidenced such disparities: minimum dietary diversity (Q1: Δ 47·8–52·2 %, APC + 1·44, P = 0·006), minimum acceptable diet (Q1: Δ 34·5–40·5 %, APC + 5·17, P = 0·004) and consumption of meat and/or eggs (Q1: Δ 59·7–80·3 %, APC + 6·26, P < 0·001; and Q5: Δ 65·7–70·7 %, APC + 2·20, P = 0·041). Stable trends in exclusive breast-feeding and decreasing trends in the consumption of sweetened drinks and ultra-processed foods were also observed regardless the level of the deprivation. Conclusions: Improvements in some complementary food indicators were observed over time. However, the improvements were not equally distributed among the BDI quintiles, with children from the municipalities with less deprivation benefiting the most

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

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

Additional file 1 of Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system

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