Artigos

Conteúdo (article):

Food intake markers in Sisvan: temporal trends in coverage and integration with e-SUS APS, Brazil 2015–2019

Joanna Manzano Strabeli Ricci – Public Health Nutrition Program, School of Public Health, University of São Paulo, São Paulo, SP, Brazil. E-mail: joanna.ricci@usp.br. ORCID: https://orcid.org/0000-0002-0340-8808.
Ana Lúcia Zovadelli Romito – Undergraduate Course in Nutrition, School of Public Health, University of São Paulo, São Paulo, SP, Brazil. E-mail: analuciazromito@usp.br. ORCID: https://orcid.org/0000-0002-0804-9410.
Sara Araújo da Silva – General Office for the Coordination of Food and Nutrition, Ministry of Health, Brasília, DF, Brazil. Research Group in Nutrition and Health, University of Brasília, Brasília, DF, Brazil. E-mail: sara.silva@saude.gov.br. ORCID: https://orcid.org/0000-0002-2605-378X.
Antonio Augusto Ferreira Carioca – Department of Nutrition, University of Fortaleza, Fortaleza, CE, Brazil. E-mail: carioca@unifor.br. ORCID: https://orcid.org/0000-0002-1194-562X.
Bárbara Hatzlhoffer Lourenço – Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, SP, Brazil. E-mail: barbaralourenco@usp.br. ORCID: https://orcid.org/0000-0002-2006-674X.

ABSTRACT
The aim of the present study was to estimate the population coverage of recording food intake markers in Brazil’s Food and Nutrition Surveillance System (Sisvan) and mean annual percent change (APC) in coverage according to the system used for data entry (e-SUS APS and Sisvan Web). We conducted an ecological time series study of the period 2015–2019. The data were stratified into region and age group. APC in coverage was calculated using Prais-Winsten regression and the correlation between APC and HDI, GDP per capita and primary healthcare coverage was assessed using Spearman’s correlation coefficient. Population coverage of recording food intake markers at national level was 0.92% in 2019. Mean APC in coverage throughout the period was 45.63%. The region and age group with the highest coverage rate were the Northeast (4.08%; APC=45.76%, p<0.01) and children aged 2-4 years (3.03%; APC=34.62%, p<0.01), respectively. There was an upward trend in data entry using e-SUS APS, to the detriment of Sisvan Web. There was a positive correlation between APC in coverage using e-SUS APS and HDI and GDP per capita in some age groups. Population coverage of recoding Sisvan food intake markers remains low across the country. The e-SUS APS has the potential to be an important strategy for expanding food and nutrition surveillance.
Key words: Food and Nutrition Surveillance; Health Information Systems; Time Series.

 
INTRODUCTION
Improving diet and nutrition depends on a range of factors, including the promotion of healthy eating practices and disease prevention and care1,2. Studies show that exposure to risk factors attributable to food intake, including a diet high in sugar-sweetened beverages and processed meats and low in low in fruits and vegetables, accounted for 13.5% of attributable deaths worldwide among females and 14.6% in males in 20193.
The World Health Organization recognizes that health surveillance systems are important tools for monitoring these factors, highlighting the importance of representativeness of data and frequency of data collection4. In Brazil, food and nutrition surveillance (FNS) actions are carried out by the primary care services of the country\'s public health system, the Sistema Único de Saúde (SUS) or Unified Health System2. Relevant data is recorded in the Food and Nutrition Surveillance System (Sisvan), providing a unique opportunity for ongoing monitoring. To identify essential dietary elements throughout different stages of the life course, the Sisvan proposes the use of food intake markers2, which are tools for screening dietary practices and intake of different food groups. These markers refer to selected items of food intake during the previous day and can be used by different types of health professionals2. While food markers do not reveal the detailed habitual daily intake of a food, they are suitable for use in primary care as a substitute for specialized methods for assessing nutrients5. The analysis of the data produced by these tools can help plan and monitor programs, actions and the work of health professionals with individuals, groups and catchment areas1.
Despite the importance of monitoring, evidence shows that the food intake component of the Sisvan is underused. Up to 2013, less than one-third of the country’s municipalities had at least one record of food intake in the system. In the same year 0.40% the Brazilian population who do not have private health insurance had records of food intake in the Sisvan, with coverage increasing 0.05% per year (95% CI: 0.01; 0.09) in the period 2008-20136.
In view of this situation, a revised version of food intake markers was proposed in 2015 in line with the recommendations of the second edition of the Dietary Guidelines for the Brazilian Population. To simplify data collection and encourage the recording of data, the forms are based on intake during the previous day and can be used by all primary care workers2. The Ministry of Health also published a technical guidance note on the integration of the SUS’s primary care health record system, e-SUS APS, and Sisvan’s online platform, Sisvan Web7. The integration of these two systems helps reduce the duplication of efforts by health professionals through single data entry in primary care services, helping to promote an increase in the population coverage of FNS actions8, which is essential to improve the representativeness of data9.
The aim of the present study was to estimate temporal trends in the population coverage of recording of Sisvan food intake markers at national level in the period 2015–2019. The estimates were stratified by age, region and system used for data entry to provide evidence on e-SUS APS’ contribution to FNS actions. Finally, we also explored potential correlations with primary care coverage and sociodemographic variables.

METHODS
Study design and data sources
We conducted an ecological time series study of population coverage of recording of Sisvan food intake markers at national level in the period 2015–2019. In the current format, food intake markers are recorded using one of three forms10: (i) exclusive breastfeeding for children aged under 6 months; (ii) introduction of complementary foods, identifying the risk of micronutrient deficiency and overweight in children aged between 6 months and 23 months and 29 days; and (iii) healthy and unhealthy behaviors and markers based on the consumption of food groups and exposure to ultra-processed foods by individuals aged over 2 years.
Launched in 2013, the aim of e-SUS APS is to promote greater integration between national primary care service health information systems11. Primary care professionals have two alternatives for inputting FNS data: Sisvan Web, which also receives data from the cash transfer initiative Programa Bolsa Família; and the Electronic Citizen Record and Simplified Data Collection software via e-SUS APS10. In 2016, the entry of the patient’s National Health Card number in Sisvan Web was made mandatory with the aim of integrating the systems. The records contained in the e-SUS APS up to 2015 were gradually incorporated into Sisvan Web. The incorporation of data ensures that all records, regardless of the system used, are contained in the reports produced by the Sisvan10.12. The integration of the two systems was consolidated in 20177.
Based on the revised Sisvan forms, the annual number of individuals with records of food intake markers between 2015–2019 was obtained using publicly available annual reports13 and information requests made to Ministry of Health’s General Office for the Coordination of Food and Nutrition. The data were extracted between October 2020 and May 2021.
For the sociodemographic variables and primary care coverage during the study period, we used secondary data from the Brazilian Institute of Geography and Statistics (IBGE)14.15, annual human development reports16, the Human Development Atlas17, and the Primary Care Information and Management System18. The study was conducted in accordance with the editorial policy of the Ministry of Health (Ministerial Order 884/2011)19 governing the transfer of data from the national databases of the information systems managed by the Department of Health Care. The study protocol was approved by the University of São Paulo’s School of Public Health’s ethics committee (reference 4.172.787).

Data analysis
We calculated annual utilization of Sisvan food intake marker forms in the period 2015–2019 by dividing the number of municipalities with at least one record of food intake markers in the system by the total number of municipalities in the country and multiplying by 1006. Population coverage of recording of food intake markers was calculated using the ratio between the number of individuals with at least one record in the system and the total resident population14 multiplied by 100. Annual coverage was calculated at national, regional, and state level and stratified into life stages: children aged under 2, children aged 2-4, children aged 5-9, adolescents (10-19 years), adults (20-59 years), and older persons (60 years and over). In addition to total coverage, in all groups coverage was stratified according to the system used for data entry (e-SUS APS or Sisvan Web).
Temporal trends in coverage were analyzed using Prais-Winsten regression, which allowed us to estimate the annual percent change (APC) using generalized linear regression, which considers first-order serial autocorrelation, as described by Antunes and Cardoso (2015)20. Serial autocorrelation is the dependence of a serial measure on its past values and indicates violation of the assumption of independence of the residuals, which is required for simple linear regression analysis20. These considerations are particularly interesting for the investigation of coverage of a health surveillance system whose structure at a point in time may be related to its past performance. In the Prais-Winsten regression, run using the prais command, the dependent variable was the log-transformed value for coverage (base 10) and the independent variable was the year of the record. The resulting β coefficient was used to calculate the APC in population coverage of recording food intake markers, based on the following formula:
APC=[-1+(10^β )] × 100
Where:
APC = annual percent change,
β = coefficient resulting from Prais-Winsten regression (log base 10).

A 5% significance level was adopted for the analysis, where significant p-values (<0.05) indicated that the time series displayed either an upward or downward trend in coverage (positive or negative APC value, respectively). Non-significant p-values (≥0.05) indicated that the time series was stationary.
The APC in population coverage of recording food intake markers is presented at national and regional level and by life stage, according to system used for data entry (total, e-SUS APS or Sisvan Web). Temporal trends in total coverage of recording food intake markers by state are presented in maps by life stage.
The analysis of the sociodemographic variables and primary care coverage was performed using Prais-Winsten regression, informing the APC in the human development index (HDI), gross domestic product (GDP) per capita, and primary care coverage during the study period. Primary care coverage was calculated by dividing the total number of people registered with primary care services by the total population and multiplying by 10021.
Dependence between APC in population coverage of recording food intake markers (total, e-SUS APS or Sisvan Web) and primary care coverage by life stage and sociodemographic characteristics was measured using Spearman’s correlation coefficient. Based on the information available at state level, the following variables were considered in the analysis: HDI (2016), GDP per capita (2015), primary care coverage (2015), and average annual variation in primary care coverage (2015–2019).
The statistical analyses were performed using Stata 11.2 (Stata Corp, College Station, TX, USA). The maps were created using Microsoft Excel 2016.

RESULTS
Table 1 shows sociodemographic characteristics, primary care coverage, utilization of food intake marker forms, and population coverage of recording food intake markers at national level in the period 2015–2019. All variables display an upward trend. In 2019, 62.24% of the municipalities in Brazil used Sisvan forms (APC: +20.05%; p=0.04). Total population coverage in Brazil rose from 0.20% in 2015 to 0.92% in 2019 (APC: +45.63%; p<0.01).
Tables 2 and 3 show population coverage of recording food intake markers between 2015–2019and APC for children, adolescents, adults, and older persons at national and regional level according to system used for data entry. At national level, in 2019, total population coverage of recording using both e-SUS APS and Sisvan Web was lowest in adults (0.62%) and highest in children aged 2-4 (3.03%). All life stages showed upward temporal trends in total population coverage of recording food intake markers at national level. The highest APC over the study period at national level was among children aged 5-9 (+63.58%; p<0.01). At regional level, upward temporal trends were observed across all locations and age groups, except children aged under 2 in the Mid-West and Southeast and children aged 2-4 in the Southeast, where the series was trend-stationary.
Coverage of recording using e-SUS APS showed an upward trend across all life stages. At national level, significant annual changes in recording of markers using e-SUS APS ranged from +75.01% in children aged under 2 to +98.10% among children aged 5-9. At regional level, total population coverage of recording food intake markers using e-SUS APS displayed an upward temporal trend across all strata except children aged under 2 in the Mid-West.
In contrast, coverage of recording using Sisvan Web showed stationary temporal trends at national level in various life stages, including children aged under 2, children aged 2-4 years, and adults. Downward trends in population coverage of recording using Sisvan Web was observed in the Mid-West (children aged 2-4, adolescents, adults, and older persons) and North (adolescents, adults, and older persons). Only the South showed an upward temporal trend in coverage of reporting using this system (among children aged 2-9 and adolescents).
Figure 1 shows temporal trends in total population coverage of recording food intake markers in the states. Most of the states showed upward or stationary trends over the study period. Exceptions included: the Federal District, in children aged under 2 (-43.28%; p=0.01), adults (-40.33%; p=0.01), and older persons (-46.17%; p=0.02); Roraima, among children aged under 2 (-29.52%; p=0.02) and adults (-26.31%; p=0.04); and Tocantins, in children aged under 2 (-15.59%; p=0.02) (data not shown).
Table 4 shows the correlation between APC in population coverage of recording food intake markers between 2015 and 2019 by life stages and HDI, GDP per capita, and primary care coverage. Although associations with APC in overall coverage were not found, it is interesting to note that APC in coverage of reporting using e-SUS APS among children and older persons was positively related to some factors. A significant positive moderate correlation was found between HDI and coverage of reporting using e-SUS APS among children aged under 2 (rho=0.41; p=0.03) and aged 2-4 (rho=0.41; p=0.03), and between GDP per capita and coverage of reporting using e-SUS APS in children under 2 (rho=0.48; p=0.01) and older persons (rho=0.42; p=0.03).

DISCUSSION
At national level, temporal trends in recording of Sisvan food intake markers were upward across all life stages over the study period. This finding is accompanied by a positive trend in the percentage of municipalities that use these forms. However, total population coverage is low, limiting the representativeness of the data. This analysis makes an original contribution to existing knowledge on this subject by stratifying the results by the system used to input FNS data and providing insights into the contribution of the e-SUS APS.
An analysis of nutrition surveillance in low-income countries showed that cross-sectional surveys, community-based sentinel monitoring, and data collection in schools were the primary sources of FNS data and suggested that data obtained from health systems are suitable secondary sources provided they are interpreted with contextual information22. In a recent systematic literature review, the main hosts of food and nutrition dashboards around the world were inter-governmental and non-governmental organizations, universities, and research institutions, especially in the United States23. Other studies suggest that the presence of government structures for coordination of FNS was decisive for the sustainable development of activities24, 25. The shift in health surveillance systems towards monitoring a broad range of risk factors for chronic diseases, including food intake markers, has given rise to a number of challenges, resulting in the need for the ongoing evaluation of system performance25.
This work contributes to initiatives assessing elements of coverage and, consequently, the representativeness of Sisvan records. Coverage is one of the most commonly addressed aspects of performance in evaluations of health information systems in Brazil26, with a focus on vital statistics in live births27 and mortality28 information systems.
In the first analysis of utilization and coverage of food intake markers at national level, Nascimento et al.6 reported that 32.5% of municipalities in Brazil used the system in 2013, with coverage of 0.40%. Using the latest version of the food intake markers, the present study showed that utilization and total population coverage were lower in 2015 (28.6% and 0.20%, respectively). Logistical problems during the adaptation of work processes to the new forms and the launch of Sisvan Web version 3.0 in 2017, providing a tool to merge records and reduce duplications29, may have affected the estimates. However, for comparison purposes, it is important to consider the denominator employed by each study. Nascimento et al.6 used the SUS patient population approach, excluding private health service users. In the present study, in line with recent analyses of coverage30, we opted to use total resident population, while at the same time minimizing potentially inconsistent values, given that primary care coverage exceeded 100% in 12 states when private health service users were subtracted.
Temporal trends in coverage of recording of Sisvan food intake markers were upward throughout the study period, with an APC of +45.63% and reaching 0.92% of the national population. Two considerations are important in this regard. First, APC is a proportional metric obtained using the Prais-Winsten procedure, which improves the management of time series data20 and provides insights into system performance. Notwithstanding methodological differences, using simple linear regression without serial autocorrelation, Nascimento et al.6 estimated that the crude variation in national coverage between 2008 and 2013 was 0.05%. Second, it is important to consider that, in absolute terms, total population coverage of recording food intake markers remains very low, compromising the achievement of the goals of FNS25 when it comes to diagnosing, monitoring, and predicting food intake and nutritional status using APS with greater population representativeness.
Given that there is substantial room for improving the coverage of the system, the analytical approaches explored in the present analysis shed light on how to create pathways to improving the evaluation of food intake markers using Sisvan. Against a backdrop of low coverage, rates among children aged up to 4 were higher than in all other life stages in 2019, supported by an upward temporal trend throughout the study period. In addition to the traditional link between the Sisvan Web and monitoring of conditioning factors for children’s health in the programs Milk is Health, Incentives for Combating Nutrient Deficiencies, and Bolsa Família, two studies have highlighted the role played by the National Strategy for Healthy Complementary Feeding in stimulating the collection of data on food intake markers31.32. More recently, the recording of food intake markers was made mandatory in the programs “Health at School” (Saúde na Escola - PSE) and “Grow up Healthy” (Crescer Saudável - PCS)2.33, which may partially explain the marked increases noted in children aged 5-9. Intersectoral actions developed in schools reach schoolchildren, who often fail to perform health check-ups with the recommended frequency2.
The Northeast had the highest absolute coverage in children aged 2-9 years and adults, while the South showed the highest relative increase across most age groups. In contrast, the Southeast displayed the lowest absolute coverage rates in children aged 5 years and over, which may be explained by the region’s high population density. The data from the Mid-West suggest the need to stimulate local performance, with the region having the lowest coverage rate in children aged under 5 and lowest APC across most life stages.
With regard to system used for data entry, the findings show overall stability or downward trends for recording using Sisvan Web and an upward trend for data entry using e-SUS APS during the study period. While it is possible to enter FNS data in e-SUS APS and generate reports showing FNS indicators via Sisvan Web21, it is important to note that e-SUS APS has not been adopted as a single FNS data entry system by all municipalities and that some Sisvan Web functions – such as the identification of priority groups and epidemiological surveillance strategies using customization tools – have yet to be included in e-SUS APS 29. The use of e-SUS APS has increased across the country in recent years. In 2019, 92.2% of municipalities displayed a level of implementation different to “not implemented”34, which is in line with recent international discussions on health data integration35. However, the findings do not allow us to infer that e-SUS APS utilization has contributed to substantial increases in coverage of recording of food intake markers in Brazil. The higher APC values observed for recording markers using e-SUS APS appear to be mainly due to migration from one system to the other. In addition, it is important to consider the potential effect of the use of electronic health records software developed by local health departments or purchased from third parties on study estimates34.
Studies show that factors such as overwork, difficulties incorporating data entry into primary care work routines, lack of training in data collection and entry, high rates of staff turnover, poor internet connection, and the length of the form used to enter the data into the system may explain the underuse of the system32, 36, 37. The wide range of other priority primary care actions, such as childcare and antenatal care, as well as follow-up of patients with chronic diseases, also tends to mean that FNS actions are given lower priority, despite the fact that diet is a fundamental factor for the care of individuals with these conditions3.12.
These factors should be considered in the light of the historic underfunding of the SUS, with Brazil being the only country with a universal health care system in which health expenditure is lower than in the private system38. The present study shows a positive correlation between APC in population coverage of recording food intake markers and HDI and GDP per capita in some life stages, suggesting that increased investment could lead to an expansion of coverage of FNS actions related to food intake. In the state of Minas Gerais for example, evidence shows that the prioritization of other areas of health to the detriment of Sisvan and the underestimation of the physical infrastructure and human resources needed to operationalize the system contributed to low coverage rates39.
This study has some limitations. The fact that we used secondary data from various sources means that the data is subject to collection, filling-in, and entry errors. Outdated census data may also have affected the denominators used in the calculations, which are dependent on the population estimates made by the IBGE14. Our analysis covers the period between 2015, the year in which the revised food intake markers were adopted, and 2019, the year before the COVID-19 pandemic. Although the use of a large number of points in time is beneficial for Prais-Winsten analysis20, the use of 5-year periods in the present analysis provided valuable insights into FNS actions in Brazil. In addition, this study is the first work to assess population coverage of recording of food intake markers at national and regional level across all life stages after the integration of the two recording systems used in primary care services. Our findings show that organizational improvements and the qualification of health care professionals for FNS are essential to achieving food intake monitoring goals in Brazil. It is also important to highlight that existing data should be interpreted with caution when proposing the formulation of programs and research evaluation, paying special attention to the context in which the information on food intake markers was obtained and recorded.
It can be concluded that the population coverage of recording food intake markers in Brazil increased over the study period, reaching 0.92% of the total population in 2019. Coverage was higher among children aged up to 4 years in all regions. There was an upward temporal trend in data entry using e-SUS APS over the study period, to the detriment of Sisvan Web. With low coverage and evidence of a shift in use from Sisvan Web to e-SUS APS, the latter may be considered an important strategy for designing future measures to strengthen FNS actions in the country.

Acknowledgements
We thank the Ministry of Health’s General Office for the Coordination of Food and Nutrition for their support in data extraction and for sharing their experiences to enrich this manuscript.

Funding
This work received funding from the National Council for Scientific and Technological Development (CNPq, grant 442963/2019-0), Call 26/2019 – Department of Science and Technology (Decit/SCTIE/MS) and General Office for the Coordination of Food and Nutrition (CGAN/DEPROS/SAPS/MS) of the Ministry of Health, and CNPq. AAFC received funding from CNPq, grant 442852/2019-3. JMSR received a scholarship from CNPq (Technological and Industrial Development, Level C). ALZR received a scholarship from the Unified Program of Scholarships for the Support and Training of Undergraduate Students at the University of São Paulo. Funding sources had no role in study design, data analysis and interpretation, or manuscript preparation.

Authorship
JMSR: design, analysis and interpretation of data, and writing of the manuscript. ALZR: analysis and interpretation of data. SAS: data interpretation and critical review of the manuscript. AAFC: design, data interpretation, and critical review of the manuscript. BHL: conception and design, analysis supervision, data interpretation, and critical review of the manuscript. All authors approved the final version and are responsible for the manuscript.

REFERENCES
Ministério da Saúde (BR). Secretaria de Atenção Básica à Saúde. Departamento de Atenção Básica. Política Nacional de Alimentação e Nutrição. 1 ed., 1 reimpr. – Brasília: Ministério da Saúde, 2013.
Ministério da Saúde (BR). Secretaria de Atenção à Saúde. Departamento de Atenção Básica. Marco de referência da vigilância alimentar e nutricional na atenção básica [internet]. Brasília: Ministério da Saúde, 2015 [acesso em 27 jan 2022]. 54 p. Disponível em: https://bvsms.saude.gov.br/bvs/publicacoes/marco_referencia_vigilancia_alimentar.pdf
GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020; 396(10258): 1223-1249.
World Health Organization. Food and nutrition surveillance systems: technical guide for the development of a food and nutrition surveillance system. WHO Regional Publications, Eastern Mediterranean Series; 33. Cairo: WHO Regional Office for the Eastern Mediterranean, 2013.
England CY, Andrews RC, Jago R, Thompson JL. A systematic review of brief dietary questionnaires suitable for clinical use in the prevention and management of obesity, cardiovascular disease and type 2 diabetes. Eur J Clin Nutr. 2015; 69(9): 977-1003.
Nascimento FA, Silva SA, Jaime PC. Cobertura da avaliação do consumo alimentar no Sistema de Vigilância Alimentar e Nutricional Brasileiro: 2008 a 2013. Rev Bras Epidemiol. 2019; 22: 1-15.
Brasil. Ministério da Saúde (BR). Nota Técnica N° 51-SEI/2017-CGAA/DAB/SAS/MS. Dispõe da Integração do Sistema de Vigilância Alimentar e Nutricional (SISVAN) à Estratégia e-SUS Atenção Básica (e-SUS AB). Departamento de Atenção Básica. Brasília: Ministério da Saúde, 2017.
Campos DS, Fonseca PC. A vigilância alimentar e nutricional em 20 anos de Política Nacional de Alimentação e Nutrição. Cad Saude Publica. 2021; 37 (Suppl 1): e00045821.
Venâncio SI, Levy RB, Saldiva SRDM, Stefanini MLR. Sistema de vigilância alimentar e nutricional no Estado de São Paulo, Brasil: experiência da implementação e avaliação do estado nutricional de crianças. Rev Bras Saúde Matern Infant. 2007; 7(2): 213-220.
Ministério da Saúde (BR). Secretaria de Atenção à Saúde. Departamento de Atenção Básica. Orientações para avaliação de marcadores de consumo alimentar na atenção básica [internet]. Brasília: Ministério da Saúde, 2015 [acesso em 21 fev 2022]. 35 p. Disponível em: https://bvsms.saude.gov.br/bvs/publicacoes/marcadores_consumo_alimentar_atencao_basica.pdf
Neto GCC, Andreazza R, Chioro A. Integração entre os sistemas nacionais de informação em saúde: o caso do e-SUS Atenção Básica. Rev Saúde Publica. 2021; 55: 93.
Ministério da Saúde (BR). Relatório de Gestão CGAN 2016. Coordenação Geral de Alimentação e Nutrição. Brasília: Ministério da Saúde, 2017.
Sistema de Vigilância Alimentar e Nutricional (Sisvan). Relatórios de Acesso Público [internet]. Brasília: Secretaria de Atenção Primária à Saúde [acesso em 11 nov 2021]. Disponível em: http://sisaps.saude.gov.br/sisvan/relatoriopublico/index
Instituto Brasileiro de Geografia e Estatística (IBGE). Tabela 6579 – População residente estimada [internet]. Brasília: Instituto Brasileiro de Geografia e Estatística [acesso em: 11 nov 2021]. Disponível em: https://sidra.ibge.gov.br/tabela/6579
Instituto Brasileiro de Geografia e Estatística (IBGE). Tabela 6784 – Produto Interno Bruto, Produto Interno Bruto per capita, População residente e Deflator [internet]. Brasília: Instituto Brasileiro de Geografia e Estatística [acesso em: 11 nov 2021]. Disponível em: https://sidra.ibge.gov.br/tabela/6784#/n1/all/v/9812/p/all/d/v9812%202/l/v,,t+p/resultado
Programa das Nações Unidas para o Desenvolvimento (PNUD). Relatório do Desenvolvimento Humano [internet]. Brasília: Organização das Nações Unidas [acesso em 11 nov 2021]. Disponível em: http://hdr.undp.org/en/global-reports.
Programa das Nações Unidas para o Desenvolvimento (PNUD). Atlas do desenvolvimento humano no Brasil [internet]. Brasília: Organização das Nações Unidas [acesso em 11 dez nov 2021]. Disponível em: http://www.atlasbrasil.org.br
Brasil. Informação e Gestão da Atenção Básica. Cobertura da Atenção Básica [internet]. Brasília: Secretaria de Atenção Primária à Saúde [acesso em 11 nov 2021]. Disponível em: https://egestorab.saude.gov.br/paginas/acessoPublico/relatorios/relHistoricoCoberturaAB.xhtml
Ministério da Saúde (BR). Portaria Nº 884, de 13 de dezembro de 2011. Estabelece o fluxo para solicitação de acesso de dados dos bancos nacionais dos Sistemas de Informação. Brasília: Diário Oficial da República Federativa do Brasil, 2011 (Seção 1). Disponível em: https://bvsms.saude.gov.br/bvs/saudelegis/sas/2011/prt0884_13_12_2011.html
Antunes JLF, Cardoso MRA. Uso da análise de séries temporais em estudos epidemiológicos. Epidemiol Serv Saúde. 2015; 24(3): 565-576.
Ministério da Saúde (BR). Nota Técnica – Relatório de Cobertura da Atenção Básica [internet]. Brasília: Secretaria de Atenção à Saúde [acesso em 28 fev 2022]. Disponível em: https://egestorab.saude.gov.br/paginas/acessoPublico/relatorios/nota_tecnica/nota_tecnica_relatorio_de_cobertura_AB.pdf
Tuffrey V, Hall A. Methods of nutrition surveillance in low-income countries. Emerg Themes Epidemiol. 2016; 13: 4.
Zhou B, Liang S, Monahan KM, Singh GM, Simpson RB, Reedy J, Zhang J, DeVane A, Cruz MS, Marshak A, Mozaffarian D, Wang D, Semenova I, Montoliu I, Prozorovscaia D, Naumova EN. Food and Nutrition Systems Dashboards: A Systematic Review. Adv Nutr. 2022; 13(3): 748-757.
Tuffrey, V. A perspective on the development and sustainability of nutrition surveillance in low-income countries. BMC Nutr. 2016; 2: 15.
Groseclose SL, Buckeridge DL. Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation. Annu Rev Public Health. 2017; 38: 57-79.
Lima CR, Schramm JM, Coeli CM, da Silva ME. Revisão das dimensões de qualidade dos dados e métodos aplicados na avaliação dos sistemas de informação em saúde. Cad Saude Publica. 2009; 25(10): 2095-2109.
Figueroa Pedraza D. Sistema de informações sobre nascidos vivos: uma análise da qualidade com base na literatura. Cad Saúde Colet, 2021; 29(1): 143-152.
Almeida WDS, Szwarcwald CL. Adequacy of mortality data and correction of reported deaths from the Proactive Search of Deaths. Cien Saude Colet. 2017; 22(10): 3193-3203.
Ministério da Saúde (BR). Manual operacional para uso do sistema de vigilância alimentar e nutricional - Sisvan – versão 3.0. Departamento de Atenção Básica [Internet]. Brasília: Ministério da Saúde, 2017 [acesso em 27 jan 2022]. 39 p. Disponível em: http://sisaps.saude.gov.br/sisvan/public/file/ManualDoSisvan.pdf
Silva RPC, Vergara CMAC, Sampaio HAC, Vasconcelos Filho JE, Strozberg F, Ferreira Neto JFR, Mafra MLP, Garcia Filho C, Carioca Filho AAF. Sistema de Vigilância Alimentar e Nutricional: tendência temporal da cobertura e estado nutricional de adultos registrados, 2008-2019. Epidemiol Serv Saude. 2022; 31(1): e2021605.
Ferreira CS, Cherchiglia ML, César CC. O Sistema de Vigilância Alimentar e Nutricional como instrumento de monitoramento da Estratégia Nacional para Alimentação Complementar Saudável. Rev Bras Saúde Matern Infant. 2013; 13(2): 167-177.
Rolim MD, Lima SML, Barros DC, Andrade CLT. Avaliação do Sisvan na gestão de ações de alimentação e nutrição em Minas Gerais, Brasil. Cienc Saude Colet. 2015; 20(8): 2359-2369.
Ministério da Saúde (BR). Instrutivo para o Programa Crescer Saudável - Alimente uma Infância Consciente [internet]. Secretária de Atenção Primária à Saúde [acesso em 8 fev 2022]. Brasília: Ministério da Saúde, 2019. Disponível em: http://189.28.128.100/dab/docs/portaldab/documentos/pse/instrutivo_crescer_saudavel_2019_2020.pdf
Cielo AC, Raiol T, Silva END, Barreto JOM. Implantação da Estratégia e-SUS Atenção Básica: uma análise fundamentada em dados oficiais. Rev Saude Publica. 2022; 56: 5.
Unim B, Mattei E, Carle F, Tolonen H, Bernal-Delgado E, Achterberg P, Zaletel M, Seeling S, Haneef R, Lorcy AC, Van Oyen H, Palmieri L. Health data collection methods and procedures across EU member states: findings from the InfAct Joint Action on health information. Arch Public Health. 2022; 80(1): 17. Erratum in: Arch Public Health. 2022; 80(1): 51.
Ministério da Saúde (BR). Avaliação de Marcadores de Consumo Alimentar na Atenção Básica: desafios e potencialidades. Coordenação-Geral de Alimentação e Nutrição. In: Encontro Nacional com a Rede de Alimentação e Nutrição do SUS, 2016.
Ferreira CS, Rodrigues LA, Bento IC, Villela MPC, Cherchiglia ML, César CC. Fatores associados à cobertura do Sisvan Web para crianças menores de 5 anos, nos municípios da Superintendência Regional de Saúde de Belo Horizonte, Brasil. Cienc Saude Colet. 2018; 23(9): 3031-3040.
Castro MC, Massuda A, Almeida G, Menezes-Filho NA, Andrade MV, de Souza Noronha KVM, Rocha R, Macinko J, Hone T, Tasca R, Giovanella L, Malik AM, Werneck H, Fachini LA, Atun R. Brazil’s unified health system: the 30 years and prospects for the future. Lancet. 2019; 394(10195): 345-356.
Vitorino SAV, Barreto CM, Corrêa MS, Bezerra OMPA, Passos MC, Bonomo E, Silva CAM. Avaliação da estrutura organizacional e do processo de implantação do Sisvan-Web no âmbito da Atenção Básica no Estado de Minas Gerais. Rev Saude Publica do SUS. 2013; 1: 117-118.