references.bib

@book{aho2023,
  title = {The {{AWK}} programming language},
  author = {Aho, Alfred and Kernighan, Brian and Weinberger, Peter},
  date = {2023},
  publisher = {Addison-Wesley Professional},
  location = {Boston, MA},
  url = {https://www.awk.dev},
  abstract = {Awk was developed in 1977 at Bell Labs, and it's still a remarkably useful tool for solving a wide variety of problems quickly and efficiently. In this update of the classic Awk book, the creators of the language show you what Awk can do and teach you how to use it effectively.Here's what programmers today are saying: "I love Awk." "Awk is amazing." "It is just so damn good." "Awk is just right." "Awk is awesome." "Awk has always been a language that I loved."It's easy: "Simple, fast and lightweight." "Absolutely efficient to learn because there isn't much to learn." "3-4 hours to learn the language from start to finish." "I can teach it to new engineers in less than 2 hours."It's productive: "Whenever I need to do a complex analysis of a semi-structured text file in less than a minute, Awk is my tool." "Learning Awk was the best bang for buck investment of time in my entire career." "Designed to chew through lines of text files with ease, with great defaults that minimize the amount of code you actually have to write to do anything."It's always available: "AWK runs everywhere." "A reliable Swiss Army knife that is always there when you need it." "Many systems lack Perl or Python, but include Awk."Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.},
  isbn = {978-0-13-826972-2},
  langid = {english},
  pagetotal = {240},
  keywords = {awk (programming language),computer science,exact sciences,programming languages}
}

@article{bagni2015,
  title = {Erro em antropometria aplicada à avaliação nutricional nos serviços de saúde: causas, consequências e métodos de mensuração},
  shorttitle = {Erro em antropometria aplicada à avaliação nutricional nos serviços de saúde},
  author = {Bagni, Ursula V. and Barros, Denise Cavalcante De},
  date = {2015},
  journaltitle = {Nutrire},
  shortjournal = {Nutrire},
  volume = {40},
  number = {2},
  pages = {226--236},
  issn = {2316-7874, 1519-8928},
  doi = {10.4322/2316-7874.18613},
  url = {http://www.sban.org.br/revista_acervo/47/acervo},
  urldate = {2025-04-30},
  abstract = {Objetivo: Discutir acerca dos erros em antropometria nos serviços de saúde, suas implicações no diagnóstico nutricional, principais causas, e métodos de mensuração. Métodos: Para subsidiar a discussão, consideraram-se os dados antropométricos de crianças em idade pré-escolar coletados por três profissionais de saúde e um supervisor (padrão-ouro) durante capacitação em antropometria. Procedeu-se também a revisão na literatura nacional e internacional acerca do tema. Resultados: Erros em antropometria podem ser causados pelos equipamentos utilizados, pela adequação do ambiente de medição, pelo grau de treinamento do avaliador (profissional de saúde) e por questões inerentes ao próprio indivíduo avaliado. Os métodos mais empregados para mensurar erros são a Padronização e o Erro Técnico de Medição, que se distinguem quanto ao rigor na avaliação, interpretação dos resultados e possibilidade de desdobramento nas análises da qualidade das mensurações. Tais distinções foram evidenciadas neste estudo, em que o grau de precisão e exatidão variou, para os três antropometristas, segundo o método adotado. A escolha do método dependerá do público-alvo envolvido e do objetivo pretendido. Conclusões: Avaliar erros de mensuração em antropometria é uma importante ferramenta para apontar a necessidade de capacitação e qualificação de profissionais em avaliação nutricional.},
  langid = {english},
  keywords = {anthropometry,data collection,health sciences,health services,nutrition,open data}
}

@article{bopp2008,
  title = {End-digits preference for self-reported height depends on language},
  author = {Bopp, Matthias and Faeh, David},
  date = {2008-12},
  journaltitle = {BMC Public Health},
  shortjournal = {BMC Public Health},
  volume = {8},
  number = {1},
  pages = {342},
  issn = {1471-2458},
  doi = {10.1186/1471-2458-8-342},
  url = {https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-8-342},
  urldate = {2025-04-30},
  langid = {english},
  keywords = {data validation,health sciences,open data}
}

@article{corsi2017,
  title = {Child anthropometry data quality from {{Demographic}} and {{Health Surveys}}, {{Multiple Indicator Cluster Surveys}}, and {{National Nutrition Surveys}} in the {{West Central Africa}} region: {{Are}} we comparing apples and oranges?},
  shorttitle = {Child anthropometry data quality from {{Demographic}} and {{Health Surveys}}, {{Multiple Indicator Cluster Surveys}}, and {{National Nutrition Surveys}} in the {{West Central Africa}} region},
  author = {Corsi, Daniel J. and Perkins, Jessica M. and Subramanian, S. V.},
  date = {2017-01},
  journaltitle = {Global Health Action},
  shortjournal = {Global Health Action},
  volume = {10},
  number = {1},
  pages = {1328185},
  issn = {1654-9716, 1654-9880},
  doi = {10.1080/16549716.2017.1328185},
  url = {https://www.tandfonline.com/doi/full/10.1080/16549716.2017.1328185},
  urldate = {2025-04-30},
  abstract = {Background: There has been limited work comparing survey characteristics and assessing the quality of child anthropometric data from population-based surveys. Objective: To investigate survey characteristics and indicators of quality of anthropometric data in children aged 0–59 months from 23 countries in the West Central Africa region. Methods: Using established methodologies and criteria to examine child age, sex, height, and weight, we conducted a comprehensive assessment and scoring of the quality of anthropometric data collected in 100 national surveys. Results: The Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) collected data from a greater number of younger children than older children while the opposite was found for the National Nutrition Surveys (NNS). Missing or implausible height/weight data proportions were 12\% and 8\% in MICS and DHS compared to 3\% in NNS. Average data quality scores were 14 in NNS, 33 in DHS, and 41 in MICS. Conclusions: Although our metric of data quality suggests that data from the NNS appear more consistent and robust, it is equally important to consider its disadvantages related to access and lack of broader socioeconomic information. In comparison, the DHS and MICS are publicly-accessable for research and provide socioeconomic context essential for assessing and addressing the burden of undernutrition within and between countries. The strengths and weaknesses of data from these three sources should be carefully considered when seeking to determine the burden of child undernutrition and its variation within countries.},
  langid = {english},
  keywords = {africa,anthropometry,data quality,health sciences,nutrition,open data}
}

@dataset{datasusb,
  title = {População residente – Estudo de estimativas populacionais por município, idade e sexo 2000-2024 – Brasil},
  author = {{Comitê de Gestão de Indicadores} and {Rede Interagencial de Informações para a Saúde} and {Coordenação-Geral de Informações e Análises Epidemiológicas} and {Secretaria de Vigilância em Saúde e Ambiente} and {Ministério da Saúde} and {Instituto Brasileiro de Geografia e Estatística}},
  publisher = {DATASUS - Tabnet},
  url = {http://tabnet.datasus.gov.br/cgi/deftohtm.exe?ibge/cnv/popsvs2024br.def},
  urldate = {2023-11-16},
  langid = {brazilian},
  keywords = {brazil,citizen science,datasus,exact sciences,nosource,open data,open science,population estimates,probability and statistics},
  note = {Resident population – Study of population estimates by municipality, age, and sex, 2000–2024 – Brazil}
}

@software{dirkschumacher,
  title = {{{anthro}}: {{Computation}} of the {{WHO}} child growth standards},
  shorttitle = {anthro},
  author = {{Dirk Schumacher}},
  doi = {10.32614/CRAN.package.anthro},
  url = {https://CRAN.R-project.org/package=anthro},
  abstract = {Provides WHO Child Growth Standards (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: {$<$}https://www.who.int/tools/child-growth-standards{$>$}.},
  keywords = {brazil,children,computer science,exact sciences,health sciences,nosource,nutrition,r (programming language),r packages}
}

@software{dirkschumachera,
  title = {{{anthroplus}}: computation of the {{WHO}} 2007 references for school-age children and adolescents (5 to 19 years)},
  shorttitle = {anthro},
  author = {{Dirk Schumacher}},
  doi = {10.32614/CRAN.package.anthroplus},
  url = {https://CRAN.R-project.org/package=anthroplus},
  abstract = {Provides WHO 2007 References for School-age Children and Adolescents (5 to 19 years) (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: {$<$}https://www.who.int/tools/growth-reference-data-for-5to19-years{$>$}.},
  keywords = {brazil,children,computer science,exact sciences,health sciences,nosource,nutrition,r (programming language),r packages}
}

@article{finaret2018,
  title = {Missingness of height data from the demographic and health surveys in {{Africa}} between 1991 and 2016 was not random but is unlikely to have major implications for biases in estimating stunting prevalence or the determinants of child height},
  author = {Finaret, Amelia B and Hutchinson, Matthew},
  date = {2018-05},
  journaltitle = {The Journal of Nutrition},
  shortjournal = {The Journal of Nutrition},
  volume = {148},
  number = {5},
  pages = {781--789},
  issn = {00223166},
  doi = {10.1093/jn/nxy037},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0022316622163182},
  urldate = {2025-04-30},
  abstract = {Background Obtaining accurate information on child height is essential for targeting interventions to reduce stunting. Thus, large-scale nutrition surveys must ensure that samples are representative of underlying populations of interest. Without accurate representation, resources for combating child stunting may be inefficiently allocated. Objective This study examined differences between children with (92.7\%) and without (7.3\%) complete and biologically plausible height data available from the Demographic and Health Surveys. Methods A total of 116 Demographic and Health Surveys conducted between 1991 and 2016 from 35 countries in sub-Saharan Africa were merged. Differences between children with and without biologically plausible height data were examined with the use of chi-square tests, t tests, and bivariate and multivariate logistic regression with survey cluster-level fixed effects. Results Of the whole sample, 97.9\% of children had complete height data and 92.7\% of children had complete and biologically plausible height data. There were sociodemographic and socioeconomic differences between those with and those without complete and biologically plausible height data. Children with usable height data were more likely to have a health card seen by the survey enumerator [mean height-for-age z score (HAZ): −1.32] than not (mean HAZ: −1.44) (P {$<$} 0.001), be older (mean HAZ: −1.63) than younger (mean HAZ: −1.11) (P {$<$} 0.001), have been ill in the previous 2 wk (mean HAZ: −1.43) than not ill (mean HAZ: −1.33) (P {$<$} 0.001), live in urban areas (mean HAZ: −1.13) than in rural areas (mean HAZ: −1.44) (P {$<$} 0.001), have literate mothers (mean HAZ: −1.16) than illiterate mothers (mean HAZ: −1.53) (P {$<$} 0.001), have mothers with more education (mean HAZ: −1.23) than not (mean HAZ: −1.54) (P {$<$} 0.001), and have more household wealth (mean HAZ: −0.82) than not (mean HAZ: −1.56) (P = 0.038). Conclusions Missing data from the DHS anthropometry questionnaires may affect research on child height, but overall effects are likely small. Given the trends in nutritional epidemiology toward the use of large-scale national surveys, understanding the ways in which biases arise as sample sizes increase is essential.},
  langid = {english},
  keywords = {africa,children,demographic data,health sciences,open data}
}

@article{lawman2015,
  title = {Comparing methods for identifying biologically implausible values in height, weight, and body mass index among youth},
  author = {Lawman, Hannah G. and Ogden, Cynthia L. and Hassink, Sandra and Mallya, Giridhar and Vander Veur, Stephanie and Foster, Gary D.},
  date = {2015-08-15},
  journaltitle = {American Journal of Epidemiology},
  shortjournal = {Am. J. Epidemiol.},
  volume = {182},
  number = {4},
  pages = {359--365},
  issn = {0002-9262, 1476-6256},
  doi = {10.1093/aje/kwv057},
  url = {https://academic.oup.com/aje/article-lookup/doi/10.1093/aje/kwv057},
  urldate = {2025-04-30},
  langid = {english},
  keywords = {anthropometry,children,data validation,health sciences}
}

@article{lyons-amos2017,
  title = {Trends in demographic and health survey data quality: {{An}} analysis of age heaping over time in 34 countries in sub saharan {{Africa}} between 1987 and 2015},
  shorttitle = {Trends in demographic and health survey data quality},
  author = {Lyons-Amos, Mark and Stones, Tara},
  date = {2017-12},
  journaltitle = {BMC Research Notes},
  shortjournal = {BMC Res Notes},
  volume = {10},
  number = {1},
  pages = {760},
  issn = {1756-0500},
  doi = {10.1186/s13104-017-3091-x},
  url = {https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-017-3091-x},
  urldate = {2025-04-30},
  langid = {english},
  keywords = {africa,data quality,data validation,health sciences,open data}
}

@article{mei2007,
  title = {Standard deviation of anthropometric {{Z-scores}} as a data quality assessment tool using the 2006 {{WHO}} growth standards: a cross country analysis},
  shorttitle = {Standard deviation of anthropometric {{Z-scores}} as a data quality assessment tool using the 2006 {{WHO}} growth standards},
  author = {Mei, Zuguo},
  date = {2007-06-01},
  journaltitle = {Bulletin of the World Health Organization},
  shortjournal = {Bull World Health Organ},
  volume = {85},
  number = {6},
  pages = {441--448},
  issn = {00429686},
  doi = {10.2471/BLT.06.034421},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2636355/pdf/06-034421.pdf},
  urldate = {2025-04-30},
  langid = {english},
  keywords = {anthropometry,health sciences,standards,world health organization}
}

@article{mourao2020,
  title = {Tendência temporal da cobertura do Sistema de Vigilância Alimentar e Nutricional entre crianças menores de 5 anos da região Norte do Brasil, 2008-2017*},
  author = {Mourão, Ester and Gallo, Caroline De Oliveira and Nascimento, Fabiana Alves Do and Jaime, Patrícia Constante},
  date = {2020-05},
  journaltitle = {Epidemiologia e Serviços de Saúde},
  shortjournal = {Epidemiologia e Serviços de Saúde},
  volume = {29},
  number = {2},
  issn = {1679-4974},
  doi = {10.5123/S1679-49742020000200026},
  url = {https://www.scielo.br/scielo.php?script=sci_arttext&pid=S2237-96222020000200314&lng=pt&nrm=iso&tlng=pt},
  urldate = {2025-04-30},
  langid = {brazilian},
  keywords = {brazil,health sciences,nutrition,open data,sisvan}
}

@article{nannan2019,
  title = {Estimating completeness of birth registration in {{South Africa}}, 1996 – 2011},
  author = {Nannan, Nadine and Dorrington, Robert and Bradshaw, Debbie},
  date = {2019-07-01},
  journaltitle = {Bulletin of the World Health Organization},
  shortjournal = {Bull. World Health Organ.},
  volume = {97},
  number = {7},
  pages = {468--476},
  issn = {0042-9686},
  doi = {10.2471/BLT.18.222620},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6593331/pdf/BLT.18.222620.pdf},
  urldate = {2025-04-30}
}

@article{nascimento2017,
  title = {Cobertura da avaliação do estado nutricional no Sistema de Vigilância Alimentar e Nutricional brasileiro: 2008 a 2013},
  shorttitle = {Cobertura da avaliação do estado nutricional no Sistema de Vigilância Alimentar e Nutricional brasileiro},
  author = {Nascimento, Fabiana Alves Do and Silva, Sara Araújo Da and Jaime, Patricia Constante},
  date = {2017-12-18},
  journaltitle = {Cadernos de Saúde Pública},
  shortjournal = {Cad. Saúde Pública},
  volume = {33},
  number = {12},
  issn = {1678-4464, 0102-311X},
  doi = {10.1590/0102-311x00161516},
  url = {http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2017001205010&lng=pt&tlng=pt},
  urldate = {2025-04-30},
  abstract = {Este estudo tenciona descrever e analisar a cobertura nacional da avaliação do estado nutricional da população usuária de serviços públicos de saúde registrada no Sistema de Vigilância Alimentar e Nutricional Web SISVAN), entre 2008 e 2013. Trata-se de um estudo ecológico com dados secundários de sistemas de informação. Os indicadores construídos foram: percentuais de cadastramento e utilização; cobertura total e de dados provenientes do Programa Bolsa Família, corrigidos pela população usuária do Sistema Único de Saúde SUS) e descritos segundo as Unidades da Federação UF), macrorregiões e/ou fases do curso da vida. A análise foi realizada por meio de estatística descritiva, regressão linear para a estimativa da variação temporal e correlação de Spearman entre a cobertura total e variáveis sociodemográficas e de saúde. Entre 2008 e 2013, mais de 99\% dos municípios possuíam indivíduos) cadastrados) e acompanhados) no sistema e as maiores frequências e variações totais de cobertura foram alcançadas no Nordeste e Norte. A cobertura nacional variou de 9,78\% a 14,92\%, com tendência estatisticamente significativa de aumento. O maior volume de informações advém de crianças e adolescentes. A participação do Programa Bolsa Família no SISVAN passou de 57,17\% para 85,78\% dos dados. O Índice de Desenvolvimento Humano Municipal e o Produto Interno Bruto per capita das UF mostraram-se inversamente correlacionados à cobertura, e as equipes de saúde da família, positivamente. Os resultados apontam para uma cobertura ainda baixa para um sistema que se pretende universal. Observa-se aumento significativo da cobertura no período e desvelam-se fatores relacionados a esse aumento.},
  langid = {brazilian},
  keywords = {data coverage,health sciences,nutrition,open data,sisvan}
}

@software{pereirab,
  title = {{{geobr}}: {{Download}} official spatial data sets of {{Brazil}}},
  shorttitle = {geobr},
  author = {Pereira, Rafael H. M. and Goncalves, Caio Nogueira},
  doi = {10.32614/CRAN.package.geobr},
  url = {https://ipeagit.github.io/geobr},
  abstract = {geobr is a computational package to download official spatial data sets of Brazil. The package includes a wide range of geospatial data in geopackage format (like shapefiles but better), available at various geographic scales and for various years with harmonized attributes, projection and topology.},
  keywords = {brazil,computer science,exact sciences,geocomputation,nosource,r (programming language),r packages,spatial statistics}
}

@article{perumal2020,
  title = {Anthropometric data quality assessment in multisurvey studies of child growth},
  author = {Perumal, Nandita and Namaste, Sorrel and Qamar, Huma and Aimone, Ashley and Bassani, Diego G and Roth, Daniel E},
  date = {2020-09},
  journaltitle = {The American Journal of Clinical Nutrition},
  shortjournal = {The American Journal of Clinical Nutrition},
  volume = {112},
  pages = {806S-815S},
  issn = {00029165},
  doi = {10.1093/ajcn/nqaa162},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S000291652200956X},
  urldate = {2025-04-30},
  issue = {Supplement 2},
  langid = {english},
  keywords = {anthropometry,children,health sciences,nutrition}
}

@software{rcoreteama,
  title = {R: {{A}} language and environment for statistical computing},
  author = {{R Core Team}},
  location = {Vienna, Austria},
  url = {https://www.R-project.org},
  organization = {R Foundation for Statistical Computing},
  keywords = {computer science,exact sciences,nosource,probability and statistics,programming languages,r (programming language)}
}

@article{silva2023a,
  title = {Qualidade dos dados antropométricos infantis do Sisvan, Brasil, 2008-2017},
  author = {Silva, Natanael de Jesus and family=Silva, given=Juliana Freitas de Mello, prefix=e, useprefix=false and Carrilho, Thaís Rangel Bousquet and Pinto, Elizabete de Jesus and family=Andrade, given=Rafaella da Costa Santin, prefix=de, useprefix=false and Silva, Sara Araújo and Pedroso, Jéssica and Spaniol, Ana Maria and Bortolini, Gisele Ane and Fagundes, Andhressa and Nilson, Eduardo Augusto Fernandes and Fiaccone, Rosemeire Leovigildo and Kac, Gilberto and Barreto, Maurício Lima and Ribeiro-Silva, Rita de Cássia},
  date = {2023-09-14},
  journaltitle = {Revista de Saúde Pública},
  volume = {57},
  number = {1},
  pages = {62--62},
  issn = {1518-8787},
  doi = {10.11606/s1518-8787.2023057004655},
  url = {https://www.revistas.usp.br/rsp/article/view/217586},
  urldate = {2025-04-30},
  abstract = {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.},
  issue = {1},
  langid = {brazilian},
  keywords = {anthropometry,children,data munging,data reliability,health information systems,health sciences,nutrition,open data,sisvan,sus}
}

@dataset{sisvana,
  title = {Microdados dos acompanhamentos de estado nutricional},
  author = {{Sistema de Vigilância Alimentar e Nutricional} and {Coordenação-Geral de Alimentação e Nutrição} and {Departamento de Promoção da Saúde} and {Coordenação Setorial de Tecnologia da Informação} and {Secretaria de Atenção Primária à Saúde} and {Ministério da Saúde}},
  publisher = {openDataSUS},
  url = {https://opendatasus.saude.gov.br/dataset/sisvan-estado-nutricional},
  urldate = {2023-11-16},
  abstract = {O Ministério da Saúde, por meio da Coordenação-Geral de Alimentação e Nutrição (CGAN) do Departamento de Promoção da Saúde (DEPROS) e da Coordenação Setorial de Tecnologia da Informação (COSTI) da Secretaria de Atenção Primária à Saúde (SAPS), realizam a gestão da base nacional do Sistema de Vigilância Alimentar e Nutricional (Sisvan). O Sisvan tem por objetivo consolidar os dados referentes às ações de Vigilância Alimentar e Nutricional (VAN) da Atenção Primária à Saúde (APS). A versão totalmente eletrônica do Sisvan apresenta dados de antropometria e de marcadores do consumo alimentar desde 2008. Deste modo, agrega os registros de estado nutricional provenientes do Sistema de Gestão do Programa Auxílio Brasil (atual programa de transferência condicionada de renda denominado até meados de 2021, como Programa Bolsa Família), bem como os registros de estado nutricional e de marcadores do consumo alimentar inseridos pelo e-SUS APS. E considerando que os dados do Sisvan são individualizados e organizados segundo CNS (Cartão Nacional de Saúde), os relatórios obtidos a partir do Sisvan permitem a avaliação de frequências relativas, logo, fornecem prevalências de estado nutricional e de marcadores do consumo alimentar da população atendida na APS. Conteúdo As bases de dados disponibilizadas nesta Plataforma referem-se aos dados individualizados e anonimizados de todas as pessoas atendidas quanto ao estado nutricional (antropometria) em serviços de APS do Brasil desde 2008. Neste sentido, poderão ser identificados um ou mais registros de antropometria para uma mesma pessoa. Esta medida permite a cessão de dados contidos nas bases nacionais dos sistemas de informação em saúde (que é o caso do Sisvan) e, portanto, cabe o atendimento à Lei de Acesso à Informação e Lei Geral de Proteção de Dados, por isso os dados serão disponibilizados com sigilo da informação pessoal, não sendo permitido o rastreio ou identificação de qualquer cidadão, mas poderá ser avaliada a prevalência de indicadores nutricionais. Limitações Conforme indicado no conteúdo, as bases são disponibilizadas com várias origens de dados, dessa forma, pode haver mais de um acompanhamento por indivíduo. Orientamos sempre em priorizar acompanhamentos gerados pelo SISVAN, AUXÍLIO BRASIL/BOLSA FAMÍLIA e por último E-SUS, nos casos de acompanhamentos com a mesma data. Os relatórios públicos do SISVAN consolidam sempre o último acompanhamento do indivíduo, dessa forma, realizar uma comparação entre a base disponibilizada e o consolidado do sistema, pode ocasionar uma diferença razoável, visto que existem dados sendo incorporados semanalmente.},
  langid = {brazilian},
  keywords = {brazil,citizen science,exact sciences,nosource,nutrition,nutritional status,open data,open science,probability and statistics,sisvan},
  note = {Microdata on nutritional status monitoring}
}

@software{usheya,
  title = {{{renv}}: {{Project}} environments},
  shorttitle = {renv},
  author = {Ushey, Kevin and Wickham, Hadley},
  url = {https://doi.org/10.32614/CRAN.package.renv},
  abstract = {A dependency management toolkit for R. Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.},
  keywords = {computer science,exact sciences,programming,r (programming language),r packages}
}

@software{vartanian2025b,
  title = {A reproducible pipeline for processing {{WorldClim}} 2.1 {{Historical Monthly Weather Data}} in {{Brazil}}},
  author = {Vartanian, Daniel and family=Carvalho, given=Aline Martins, prefix=de, useprefix=false},
  date = {2025-08-18},
  location = {São Paulo, Brazil},
  url = {https://sustentarea.github.io/brazil-historical-climate},
  abstract = {A reproducible pipeline for processing WorldClim 2.1 Historical Monthly Weather Data for Brazil using the R programming language.},
  organization = {Sustentarea Research and Extension Center at the University of São Paulo},
  keywords = {brazil,data science,exact sciences,historical data,open data,open science,pipelines,probability and statistics,r (programming language),spatial data,worldclim}
}

@book{who2006,
  title = {{{WHO}} child growth standards: {{Length}}/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: {{Methods}} and development},
  author = {{World Health Organization}},
  date = {2006},
  series = {{{WHO Child Growth Standards}}},
  publisher = {WHO Press},
  location = {Geneva, Switzerland},
  url = {https://www.who.int/tools/child-growth-standards/standards},
  abstract = {In 1993 the World Health Organization (WHO) undertook a comprehensive review of the uses and interpretation of anthropometric references. The review concluded that the NCHS/WHO growth reference, which had been recommended for international use since the late 1970s, did not adequately represent early childhood growth and that new growth curves were necessary. The World Health Assembly endorsed this recommendation in 1994. The WHO Multicentre Growth Reference Study (MGRS) was undertaken in response to that endorsement and implemented between 1997 and 2003 to generate new curves for assessing the growth and development of children the world over. The MGRS collected primary growth data and related information from 8440 healthy breastfed infants and young children from diverse ethnic backgrounds and cultural settings (Brazil, Ghana, India, Norway, Oman and USA). The growth standards developed based on these data and presented in this report provide a technically robust tool that represents the best description of physiological growth for children under five years of age. The standards depict normal early childhood growth under optimal environmental conditions and can be used to assess children everywhere, regardless of ethnicity, socioeconomic status and type of feeding.},
  isbn = {978-92-4-154693-5},
  langid = {english},
  pagetotal = {312},
  keywords = {children,health sciences,standards,world health organization}
}

@book{who2008,
  title = {Training course on child growth assessment},
  author = {{World Health Organization}},
  date = {2008-12-01},
  series = {{{WHO Child Growth Standards}}},
  publisher = {WHO Press},
  location = {Geneva, Switzerland},
  url = {https://www.who.int/publications/i/item/9789241595070},
  abstract = {In 1993 the World Health Organization (WHO) undertook a comprehensive review of the uses and interpretation of anthropometric references. The review concluded that the NCHS/WHO growth reference, which had been recommended for international use since the late 1970s, did not adequately represent early childhood growth and that new growth curves were necessary. The World Health Assembly endorsed this recommendation in 1994. The WHO Multicentre Growth Reference Study (MGRS) was undertaken in response to that endorsement and implemented between 1997 and 2003 to generate new curves for assessing the growth and development of children the world over. The MGRS collected primary growth data and related information from 8440 healthy breastfed infants and young children from diverse ethnic backgrounds and cultural settings (Brazil, Ghana, India, Norway, Oman and USA). The growth standards developed based on these data and presented in this report provide a technically robust tool that represents the best description of physiological growth for children under five years of age. The standards depict normal early childhood growth under optimal environmental conditions and can be used to assess children everywhere, regardless of ethnicity, socioeconomic status and type of feeding.},
  isbn = {978-92-4-159507-0},
  langid = {english},
  pagetotal = {312},
  keywords = {children,health sciences,standards,world health organization}
}

@online{wickham2023c,
  title = {The tidy tools manifesto},
  author = {Wickham, Hadley},
  date = {2023},
  url = {https://tidyverse.tidyverse.org/articles/manifesto.html},
  urldate = {2023-07-18},
  abstract = {tidyverse},
  langid = {english},
  organization = {Tidyverse},
  keywords = {data engineering,data science,engineering,guia de estilo,nosource,programming,r (programming language),software engineering}
}

@book{wickham2023e,
  title = {R for data science: {{Import}}, tidy, transform, visualize, and model data},
  shorttitle = {R for data science},
  author = {Wickham, Hadley and Çetinkaya-Rundel, Mine and Grolemund, Garrett},
  date = {2023},
  edition = {2},
  publisher = {O'Reilly Media},
  location = {Sebastopol, CA},
  url = {https://r4ds.hadley.nz},
  abstract = {Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverseâ??a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way.},
  isbn = {978-1-4920-9740-2},
  langid = {american},
  pagetotal = {576},
  keywords = {data engineering,data science,exact sciences,probability and statistics,programming,r (programming language)}
}

@book{wickhamb,
  title = {The tidyverse style guide},
  author = {Wickham, Hadley},
  url = {https://style.tidyverse.org},
  urldate = {2023-07-17},
  langid = {english},
  keywords = {computer science,exact sciences,guia de estilo,nosource,r (programming language),standards}
}

@book{wickhamc,
  title = {Tidy design principles},
  author = {Wickham, Hadley},
  url = {https://design.tidyverse.org},
  langid = {english},
  keywords = {computer science,exact sciences,guia de estilo,nosource,r (programming language),standards}
}