Abstract:

Few studies have investigated the simultaneous effect of individual and contextual factors on the occurrences of anemia. This study aims to evaluate the variability of children’s hemoglobin levels from municipalities in social vulnerability and its association with factors of individual and municipal nature. This is a cross-sectional, multi-center study, with children data (12-59 months) collected from 48 municipalities of the Southern region of Brazil, that were included in the Brazil Without Poverty Plan. Individuals’ data were collected using a structured questionnaire, and secondary and ecological data of children’s municipalities were collected via national surveys and institutional websites. The outcome was defined as the hemoglobin level obtained by HemoCue. A multilevel analysis was performed using Generalized Linear Models for Location Scale and Shape using R, with a 5% significance level. A total of 1,501 children were evaluated. The mean hemoglobin level was 12.8g/dL (95%CI: 12.7-12.8), with significant variability between municipalities. Lower values of hemoglobin were observed in children who lived in municipalities with a higher urbanization rate and a lower number of Community Health Agents, in relation to the reference categories. At the individual level, lower hemoglobin values were identified for children under 24 months, not enrolled at daycares, who were beneficiaries of the conditional cash transfer program and diagnosed with underweight. The results shed light on important factors at the municipal and the individual levels that were associated to the hemoglobin levels of children living in municipalities in social vulnerability.

Keywords:
Hemoglobin; Anemia; Child; Multilevel Analysis

Resumo:

Poucos estudos investigaram o efeito simultâneo dos fatores individuais e contextuais sobre a ocorrência da anemia. O estudo procura avaliar a variabilidade dos níveis de hemoglobina em crianças de municípios com vulnerabilidade social e a associação com fatores individuais e municipais. Foi realizado um estudo transversal com dados de crianças de idade pré-escolar (12-59 meses) de um estudo multicêntrico em 48 municípios do Sul do Brasil, incluídos no Plano Brasil Sem Miséria. Os dados dos indivíduos foram coletados com um questionário estruturado, e os dados secundários e ecológicos dos municípios das crianças foram obtidos através de inquéritos nacionais e websites institucionais. O desfecho foi definido como o nível de hemoglobina, obtido com o sistema HemoCue. Foi realizada análise multinível usando modelos lineares generalizados para posição, escala e forma, no R, com nível de 5% de significância . Foram avaliadas 1.501 crianças. O nível médio de hemoglobina foi 12,8g/dL (IC95%: 12,7-12,8), com variabilidade significativa entre os municípios. Níveis de hemoglobina mais baixos foram observados nas crianças em municípios com taxas de urbanização mais altas e menor número de agentes comunitários de saúde, comparado com as categorias de referência. Em nível individual, níveis de hemoglobina mais baixos foram identificados em crianças abaixo de 24 meses, não matriculadas em creches, beneficiárias do Programa Bolsa Família e diagnosticadas com baixo peso. Os resultados destacam fatores importantes nos níveis municipal e individual que estão associados com os níveis de hemoglobina em crianças de municípios com vulnerabilidade social.

Palavras-chave:
Hemoglobinas; Anemia; Criança; Análise Multinível

Resumen:

Pocos estudios han investigado el efecto simultáneo de los factores individuales y contextuales en la incidencia de anemia. El objetivo de este estudio fue evaluar la variabilidad de los niveles de hemoglobina en niños socialmente vulnerables en municipios del sur de Brasil y su asociación con factores en el nivel individual y municipal. Se trata de un estudio trasversal con datos de niños (12-59 meses), procedentes de un estudio multicéntrico, realizado en 48 municipios de la región sur de Brasil, incluidos en el Plan Brasil sin Pobreza. Se recogieron los datos de los participantes, usando un cuestionario estructurado, así como datos secundarios y ecológicos de los municipios de los niños, a través de encuestas nacionales y sitios web institucionales. El resultado se definió como el nivel de hemoglobina obtenido por HemoCue. Se realizó un análisis multinivel, usando modelos lineales generalizados para la escala de localización y forma usando R, con un nivel de un 5% de significancia. Un total de 1.501 niños fueron evaluados. La media de nivel de hemoglobina fue 12,8g/dL (95%CI: 12,7-12,8), con una significativa variabilidad entre municipios. Los valores más bajos de hemoglobina se observaron en niños que vivían en municipios con unas tasas más altas de urbanización, y un número de agentes de salud comunitario más bajo, en relación con las categorías de referencia. En el nivel individual, los valores de hemoglobina más bajos fueron identificados en niños con menos de 24 meses, no matriculados en guarderías, beneficiarios de ayudas económicas, enmarcadas en programas de ayuda económica, y diagnosticados como con bajo peso. Los resultados aclararon importantes factores en el nivel municipal e individual que estaban asociados a los niveles de hemoglobina de niños residentes en municipios, así como vulnerables socialmente.

Palabras-clave:
Hemoglobinas; Anemia; Niño; Análisis Multinivel

Introduction

Low blood levels of hemoglobin characterize anemia, a common deficiency in children under five years of age, with biological, social, and economic implications. According to estimates from the World Health Organization, 273 million children around the world presented low levels of hemoglobin in 2011, with variable distribution among the continents; with numbers up to 12.7 million children in Europe, 17.1 million in the Americas, and 96.7 million in Southeast Asia ¹1. World Healh Organization. The global prevalence of anaemia in 2011. Geneva: World Health Organization; 2015..

Despite the methodological differences observed in the studies, anemia is usually considered a moderate problem of public health in children under five years of age in Brazil ¹1. World Healh Organization. The global prevalence of anaemia in 2011. Geneva: World Health Organization; 2015.^,22. Ministério da Saúde. Pesquisa Nacional de Demografia e Saúde da Criança e da Mulher - PNDS 2006: dimensões do processo reprodutivo e da saúde da criança. Brasília: Ministério da Saúde; 2009. (Série G. Estatística e Informação em Saúde).. Based on hemoglobin levels, the occurrence of anemia is frequent in this age group, and it can be a severe condition in population groups living on areas of socio-economic vulnerability ³3. Ferreira HS, Assunção ML, Vasconcelos VS, Melo FP, Oliveira CG, Santos TO. Saúde de populações marginalizadas: desnutrição, anemia e enteroparasitoses em crianças de uma favela do "Movimento dos Sem Teto", Maceió, Alagoas. Rev Bras Saúde Matern Infant 2002; 2:177-85.^,44. De Castro TG, Campos FM, Priore SE, Coelho FMG, Campos MTFDS, Franceschini SDCC, et al. Saúde e nutrição de crianças de 0 a 60 meses de um assentamento de reforma agrária, Vale do Rio Doce, MG, Brasil. Rev Nutr 2004; 17:167-76.^,55. da Silva Vieira RC, da Silva Ferreira H. Prevalência de anemia em crianças brasileiras, segundo diferentes cenários epidemiológicos. Rev Nutr 2010; 23:433-44.^,66. Ferreira HS, Lamenha MLD, Xavier Júnior AFS, Cavalcante JC, Santos AM. Nutrition and health in children from former slave communities (quilombos) in the state of Alagoas, Brazil. Rev Panam Salud Pública 2011; 30:51-8.^,77. Ferreira AA, Santos RV, Souza JAM, Welch JR, Coimbra CEA. Anemia e níveis de hemoglobina em crianças indígenas Xavante, Brasil Central. Rev Bras Epidemiol 2017; 20:102-14.. Therefore, changes in life conditions and reduction of social inequalities have been prioritized in Brazil by policy makers, especially for infant’s first years of life ⁸8. World Healh Organization. Indicators for assessing infant and young child feeding practices. Geneva: World Health Organization; 2007.^,99. Barata RB. Epidemiologia e políticas públicas. Rev Bras Epidemiol 2013; 16:3-17.^,1010. van Deurzen I, van Oorschot W, van Ingen E. The link between inequality and population health in low and middle income countries: policy myth or social reality? PLoS One 2014; 9:e115109..

The implementation of social programs and policies in Brazil since the 1990s has contributed to the increase of the population’s income and their access to public goods and services ¹¹11. Victora CG, Barreto ML, Do Carmo Leal M, Monteiro CA, Schmidt MI, Paim J, et al. Health conditions and health-policy innovations in Brazil: the way forward. Lancet 2011; 377:2042-53.^,1212. Santos TG, Silveira JAC, Longo-Silva G, Ramires EKNM, Menezes RCE. Tendência e fatores associados à insegurança alimentar no Brasil: Pesquisa Nacional por Amostra de Domicílios 2004, 2009 e 2013. Cad Saúde Pública 2018; 34:e00066917.. However, evidence shows that the impact of these programs and policies on children’s health and nutrition was not homogeneous throughout the country, even within these regions ¹³13. Oliveira FCC, Cotta RMM, Ribeiro AQ, Sant'Ana LFR, Priore SE, Franceschini SCC. Estado nutricional e fatores determinantes do déficit estatural em crianças cadastradas no Programa Bolsa Família. Epidemiol Serv Saúde 2011; 20:7-18.. To reduce these inequalities, the Brasil Sem Miséria (Brazil Without Poverty) Plan was launched in 2011, not only to try to remove millions of Brazilians from poverty, but also to reduce the manifestations and aggravation of this condition ¹⁴14. Campello T, Mello J. O processo de formulação e os desafios do plano Brasil sem miséria: por um país rico e com oportunidades para todos. In: Campello T, Falcão T, Costa PV, organizadores. O Brasil sem miséria. Brasília: Ministério do Desenvolvimento Social; 2014. p. 33-66.. The government designed this Plan to help the less informed, most fragile and most isolated population, which is usually the group least assisted by public policies, and that often only has access to low-quality services ¹⁵15. Costa PV, Falcão T. Coordenação intersetorial das ações do Plano Brasil sem Miséria. In: Campello T, Falcão T, Costa PV, organizadores. O Brasil sem miséria. Brasília: Ministério do Desenvolvimento Social; 2014. p. 129-72.. Among the main strategies of this Plan, there was the Brasil Carinhoso (Brazil that Cares) action, which prioritized strategies related to early childhood, involving the prevention of iron deficiency in this group ¹⁶16. Costa PV, d'Aquino MR, Bachtold IV. O eixo de acesso a serviços e a ação Brasil carinhoso do plano Brasil Sem Miséria. In: Campello T, Falcão T, Costa PV, organizadores. O Brasil sem miséria. Brasília: Ministério do Desenvolvimento Social; 2014. p. 261-87..

Although iron deficiency due to dietary intake is recognized as the main cause of low hemoglobin levels ¹1. World Healh Organization. The global prevalence of anaemia in 2011. Geneva: World Health Organization; 2015.^,1717. World Healh Organization. Iron deficiency anemia. assessment, prevention, and control. A guide for programme managers. Geneva: World Health Organization; 2001., there is a growing interest in the influence of living conditions on the development of this deficiency in children ¹⁸18. World Healh Organization. Nutritional anaemias: tools for effective prevention and control. Geneva: World Health Organization; 2017.. Previous studies ¹⁹19. Oliveira CSM, Cardoso MA, Araújo TS, Muniz PT. Anemia em crianças de 6 a 59 meses e fatores associados no Município de Jordão, Estado do Acre, Brasil. Cad Saúde Pública 2011; 27:1008-20.^,2020. Leal LP, Batista Filho M, Lira PIC, Figueiroa JN, Osório MM. Temporal trends and anaemia-associated factors in 6- to 59-month-old children in Northeast Brazil. Public Health Nutr 2012; 15:1645-52.^,2121. Miglioli TC, Fonseca VM, Gomes Junior SC, da Silva KS, de Lira PIC, Batista Filho M. Factors associated with the nutritional status of children less than 5 years of age. Rev Saúde Pública 2015; 49:59.^,2222. André HP, Vieira SA, Franceschini SCC, Ribeiro AQ, Miranda Hermsdorff HH, Priore SE. Factors associated with the iron nutritional status of Brazilian children aged 4 to 7 years. Rev Nutr 2017; 30:345-55.^,2323. Balarajan Y, Ramakrishnan U, Özaltin E, Shankar AH, Subramanian S V. Anaemia in low-income and middle-income countries. Lancet 2011; 378:2123-35.^,2424. Goswmai S, Das KK. Socio-economic and demographic determinants of childhood anemia. J Pediatr (Rio J.) 2015; 91:471-7.^,2525. Choi H-J, Lee H-J, Jang HB, Park JY, Kang J-H, Park K-H, et al. Effects of maternal education on diet, anemia, and iron deficiency in Korean school-aged children. BMC Public Health 2011; 11:870. have emphasized the impact of poor living conditions on the prevalence of anemia and hemoglobin levels. Nevertheless, few have investigated the simultaneous effect of individual and community factors on the occurrence of anemia ²⁶26. Ngnie-Teta I, Kuate-Defo B, Receveur O. Multilevel modelling of sociodemographic predictors of various levels of anaemia among women in Mali. Public Health Nutr 2009; 12:1462-9.. Thus, this research intended to evaluate the variability of children’s hemoglobin levels from the Southern municipalities included in the Brazil Without Poverty Plan and their association with factors at the municipal and individual levels.

Methods

This was a cross-sectional multi-center study on the prevalence of hypovitaminosis A and anemia in children (12-59 months) residing in municipalities from the Southern region of Brazil, comprising the states of Paraná, Santa Catarina and Rio Grande do Sul. This region has one of the highest urbanization rates, level of education, and average monthly income in Brazil ²⁷27. Ministério do Desenvolvimento Social. Data social, 2016. http://www.aplicacoes.mds.gov.br/sagi-data/METRO/metro.php?_id=4 (acessado em 02/Nov/2016).
http://www.aplicacoes.mds.gov.br/sagi-da... . It also holds the smaller coefficients of infant mortality and illiteracy ²⁷27. Ministério do Desenvolvimento Social. Data social, 2016. http://www.aplicacoes.mds.gov.br/sagi-data/METRO/metro.php?_id=4 (acessado em 02/Nov/2016).
http://www.aplicacoes.mds.gov.br/sagi-da... . Despite these good indicators, in 2014, 2.4% of the population from this region lived in poverty with a monthly income per capita less than BRL 140.00 (approximately USD 42.04) and 0.7% in extreme poverty, earning less than BRL 70.00 (approximately USD 21.02) ²⁸28. Ministério do Desenvolvimentos Social e Agrário. Plano Brasil sem miséria no seu Estado. http://www.brasilsemmiseria.gov.br/estados (acessado em 25/Nov/2016).
http://www.brasilsemmiseria.gov.br/estad... .

As for the sample size, the estimation was done considering the set of municipalities that were exclusively members of the Brazil Without Poverty Plan in 2012 that were not covered by the National Vitamin A Supplementation Program in 2014. This set was adopted because the determination of hypovitaminosis A was also a goal of the multi-center study. In each of the three Southern states, the primary sampling unit (PSU) was considered to be the municipalities. The calculation of the sample size considered a 21.5% prevalence of anemia in children less than five years old in Southern Brazil, maximum error of 5%, 95% confidence interval (95%CI), infinite population, and sampling and design effect (deff) of 1.5. Furthermore, the design effect was reduced by the adoption of at least one-third of the eligible municipalities in each PSU. Therefore, it was defined by the selection of 16 municipalities, totaling 48 municipalities in the region. As a result, the sample size was estimated at 389 children per state, followed by the inclusion of 25% to cover possible losses, totaling 500 children, approximately 32 per municipality.

Children between 12 and 59 months old who were present in the basic health units (UBS) during the data collection days were selected to participate in the study. Exclusion criteria were children who performed blood transfusions and its derivatives; who were under immunosuppressive or cortico-therapeutic therapy; who had chronic illnesses; who were HIV-infected or had any severe infectious condition; who had congenital malformation and/or who were hospitalized due to diarrhea in less than a month.

Data collection was conducted by trained dieticians and took place from January to June 2015. It consisted of a face-to-face interview with the mother or legal guardian to apply a structured questionnaire in the UBS of each municipality. Then, blood samples from the children were collected and their weight and height, measured.

The questionnaire included individuals’ demographic, socioeconomic, and environmental information. The blood was collected by peripheral venipuncture and a drop of the collected blood was placed on a flat surface (glass slide). Then, the tip of a microcuvette touched the sample drop to capture the necessary amount of blood, followed by the immediate reading of the hemoglobin levels in a portable hemoglobinometer (HemoCue, HemoCue Limited, Sheffield, U.K.). The test was performed in duplicate for hemoglobin values less than 11g/dL ¹⁷17. World Healh Organization. Iron deficiency anemia. assessment, prevention, and control. A guide for programme managers. Geneva: World Health Organization; 2001., adopting the mean values. The weight and height data were collected in duplicate, following standardized procedures ²⁹29. Lohman TG, Roche AF, Martorel R. Anthropometric standardization reference manual. Illinois: Human Kinetics Books; 1988.. The indexes height-for-age (HAZ), weight-for-age (WAZ), and body mass index-for-age (BMI) were then classified according to the cut-off points of the z-score ³⁰30. World Health Organization. WHO child growth standards: training course on child growth assessment. Geneva: World Health Organization; 2008..

The children’s hemoglobin level (g/dL) was the dependent variable in this study. The selected independent variables were defined based on a theoretical model that considered reference studies in the area. These variables were classified in two levels: individual, which covers the children’s variables; and municipal, which contains variables related to the municipalities.

The children’s variables with significant association in a previous bivariate analysis (p < 0.05) were included in this study: (1) children’s age (< 24 months or ≥ 24 months); (2) maternal/legal guardian’s race/color (white or non-white: black, brown, yellow, indigenous or other); (3) monthly household income per capita based on minimum wage (MW) in 2015 (BRL 788.00/USD 236.63) (no income or income up to ¼ of the MW, income between ¼ and ½ MW, income between ½ and 1 MW or income higher than 1 MW); (4) number of residents in the house (up to 3, 4 to 5, 6 or more); (5) mother/legal guardian’s occupation (unemployed, employed with/without social security number or other (housewife, student or retired); (6) number of times the child went to the UBS in the year preceding the research (0 to 5 times, 6 to 12 times or more than 12 times); (7) enrollment at daycare center or kindergarten (yes or no); (8) Beneficiary of the Bolsa Família Program (Brazilian Income Transfer Program) (yes or no); (9) nutritional status according to the WAZ index (underweight - for both underweight and severely underweight children -, normal weight or overweight); (10) use of ferrous sulfate (never been taken, yes it was taken or yes still take it) and (11) beneficiary of the Paraná food fortification program called Leite das Crianças (Milk for Children) (yes, no or not applicable).

At the municipal level the variables were obtained or calculated using secondary and ecological data, available in national surveys and institutional websites, namely: (1) municipal Human Development Index in 2010 ³¹31. Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2010: aglomerados subnormais. https://censo2010.ibge.gov.br/resultados.html (acessado em 02/Nov/2016).
https://censo2010.ibge.gov.br/resultados... ; (2) Gini coefficient in 2010 (http://www.tabnet.datasus.gov.br/cgi/ibge/censo/cnv/ginibr.def.html); (3) monthly nominal income in 2015 (BRL. https://cidades.ibge.gov.br/); (4) extremely poor population in 2010 (%)³¹31. Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2010: aglomerados subnormais. https://censo2010.ibge.gov.br/resultados.html (acessado em 02/Nov/2016).
https://censo2010.ibge.gov.br/resultados... ; (5) urbanization rate in 2010 (%)³¹31. Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2010: aglomerados subnormais. https://censo2010.ibge.gov.br/resultados.html (acessado em 02/Nov/2016).
https://censo2010.ibge.gov.br/resultados... ; (6) adequate sanitation in 2010 (%)³¹31. Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2010: aglomerados subnormais. https://censo2010.ibge.gov.br/resultados.html (acessado em 02/Nov/2016).
https://censo2010.ibge.gov.br/resultados... ; (7) coverage of basic health care in 2014 (%. https://www.egestorab.saude.gov.br/paginas/acessopublico/relatorios/relHistoricoCoberturaAB); (8) Standardized number of community health agents (ACS) deployed in 2014, calculated as the number of agents considered when financial incentives were received from the Brazilian Ministry of Health in 2014 divided by the population of these localities in the same year (https://www.egestorab.saude.gov.br/paginas/acessopublico/relatorios/relHistoricoCoberturaAB); (9) attendance at daycare centers in 2010 (%) ³²32. Plano Nacional de Educação. Educação Infantil: dossie por localidades. http://www.observatoriodopne.org.br/metas-pne/1-educacao-infantil/dossie-localidades (acessado em 16/Ago/2017).
http://www.observatoriodopne.org.br/meta... ; and (10) infant mortality rate in 2014 ³³33. Instituto Brasileiro de Geografia e Estatísticas. Projeção da População do Brasil - 2013. Taxa de mortalidade infantil por mil nascidos vivos, Brasil - 2000 a 2015. http://www.brasilemsintese.ibge.gov.br/populacao/taxas-de-mortalidade-infantil.html (acessado em 04/Nov/2017).
http://www.brasilemsintese.ibge.gov.br/p... . All variables selected to compose the municipal level were categorized according to their quartiles, so that higher quartiles correspond to favorable life conditions or to better availability of public goods and services, except for the Gini coefficient and the percentage of the extremely poor population. Additionally, the infant mortality rate was not categorized by quartiles. It was considered low when less than 5/1,000 live births, moderate when between 6-15/1,000 and high when more than 16/1,000 live births.

The characteristics of the children and the municipalities were described by absolute and relative frequencies and by measures of central tendency and dispersion. The multilevel analysis was based on the methodology of Generalized Additive Models for Location Scale and Shape (GAMLSS) ³⁴34. Stasinopoulos MD, Rigby RA, Heller GZ, Voudouris VDBF. Flexible regression and smoothing: using GAMLSS. London: Chapman and Hall/CRC Press; 2017. (The R Series)., considering the non-normal distribution of hemoglobin levels.

Among the available models for continuous random variables, without the inclusion of independent variables, we verified the model with the best adjustment of hemoglobin levels according to the Akaike information criterion (AIC). For this criterion, the lower the value produced by a model, the greater its ability to explain the data adequately with a smaller quantity of parameters ³⁵35. Bozdogan H. Model selection and Akaike's Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika 1987; 52:345-70.. Based on this result, the probabilistic model chosen was the Johnson’s SU-distribution, defined by four parameters: mean (μ); standard deviation (σ); and two shape parameters (τ and ν) ³⁶36. Johnson N. Systems of frequency curves generated by methods of translation. Biometrika 1949; 36:149-76.. Since it is a four parameters model, Johnson’s SU-distribution allows modeling data with different levels of skewness and kurtosis. In the present study, the blood hemoglobin levels presented a fat-tailed distribution (high kurtosis), which was not properly fitted by the normal or other models based on fewer parameters. Although we have selected a four parameters model, the covariates were used only to model the location parameter, μ. Some additional attempts were performed to assess the inclusion of covariates for the remaining parameters, but we did not verify any substantial improvement in the fitted model. Finally, the identity link function was applied for the location parameter, such that the covariate effects on the mean hemoglobin levels must be interpreted additively.

Afterwards, we selected the variables for the adjusted model following two steps: (1) considering the variables at the municipal level; and (2) considering the variables at the individual level, including those selected at the first step. These variables were only included in the modeling of the mean distribution, an important parameter for measuring hemoglobin level of the individuals. As for the variability between the hemoglobin levels of children from different municipalities, a random variable with normal distribution of mean zero and variance was incorporated in the analyses.

The association between the municipal variables and the hemoglobin level was initially evaluated without adjusting the effect of the remaining municipal variables and verified by the likelihood-ratio test. The variables with p < 0.20 were selected for the sequence of the analysis, with adjustment to the effect of the remaining variables at this level. After this adjustment, the variables considered to be significant were those with p < 0.05. Then, the variables at the individual level were added one by one to the model adjusted by the municipal variables. Those that produced p < 0.20 in the test of likelihood-ratio were the ones which remained for the sequence of the analysis. Finally, we proceeded with a new selection in the model adjusted with all variables selected in both steps, based on the backward strategy. In this step, the variables with a smaller contribution to the adjustment, according AIC values, were removed from the adjusted model one by one. The identification of the final model occurred when the removal of any variable of the model would produce a higher AIC value. To calculate the mean hemoglobin level for each category of the variables that remained in the final model, the effect of the remaining variables was fixed to the mean.

Data were double-entered and validated on EpiData, version 3.2 (http://www.epidata.dk/) and evaluated statistically in the software R, version 3.4.2 (http://www.r-project.org) at the significance level of 5%. The package GAMLSS was applied in the choice of the probabilistic model and in the regression analysis.

The multi-center study was approved by the Ethics Committee in Research with Human Beings of the Federal University of Health Sciences of Porto Alegre (Protocol n. 722.702/2014) and all children’s mothers or legal guardians signed an informed consent form.

Results

A total of 1,567 children were evaluated in the multi-center study. The dosage of hemoglobin levels was possible in 1,501 of them, which resulted in a sample loss of 4.2%. The characteristics of the children can be found in Table 1 and the characteristics of the municipalities in Table 2.

The mean hemoglobin level was 12.8g/dL (95%CI: 12.7-12.8g/dL). Figure 1 shows the variability of the hemoglobin levels among the municipalities, without and with the adjustment for the variables of the final model proposed. In both cases, a significant variability was observed in hemoglobin levels between the municipalities (p < 0.001), with a variance reduction of σ² _m = 0.25 to σ² _m = 0.19 in the adjusted model. Furthermore, the municipalities 3 and 14 stood out as they had the most negative effects on the average hemoglobin levels, while the municipalities 19 and 40 stood out for having the most positive effects.

As for the municipal variables included in the first model, the Municipal Human Development Index, monthly nominal income, adequate sanitation, urbanization rate, number of ACS and attendance at daycare centers remained at p < 0.20. After adjusting for other variables of this same level, only the nominal monthly income, urbanization rate, number of ACS and the attendance at daycare centers had a significant effect (p < 0.05) on the hemoglobin level, and, thus, remained in the model to adjust the variables of the individual level.

Subsequently, variables at the individual level that remained in the model were: children’s age, maternal/legal guardian’s race/color, monthly household income per capita, number of residents in the house, maternal/legal guardian’s occupation, number of times the child went to the UBS in the year preceding the research, enrollment at daycare centers or kindergarten, beneficiary of the Brazilian Income Transfer Program, WAZ index and the use of ferrous sulfate. Finally, after adjustment of the model, the municipal factors that showed a significant effect (p < 0.05) on the mean hemoglobin level were urbanization rate and number of ACS; and the individual factors were children’s age, enrollment at daycare centers, participation as a beneficiary of the Brazilian Income Transfer Program and WAZ index.

Table 3 shows only the estimates of the variables that remained in the final model while their effect in the children’s mean hemoglobin level can be visualized in Figure 2. Children from municipalities with higher urbanization rates had lower mean hemoglobin levels when compared to children from municipalities with lower urbanization rates (quartile 1), that is 0.18 (p = 0.016) lower than children of quartile 2, and 0.16 lower than quartiles 3 (p = 0.024) and 4 (p = 0.033). Concerning the ACS, the positive effect of this variable was significant (p = 0.003) only in the children’s hemoglobin levels of quartile 2, who presented a mean level 0.22 higher than those of quartile 1. Furthermore, children older than 24 months had a mean hemoglobin level 0.37 (p < 0.001) higher than younger children. Regarding the access to social protection policies, children who were beneficiaries of the Brazilian Income Transfer Program had a mean hemoglobin level 0.23 (p < 0.001) lower than those without the benefit. In contrast, children enrolled at daycare centers had a mean level 0.21 (p = 0.001) higher than those not enrolled in such institutions. As for nutritional status, children with normal weight or overweight had a mean hemoglobin level 0.60 (p = 0.003) and 0.74 (p = 0.001) higher than children with underweight, respectively. The other parameters of the model were estimated by σ = 1.04 and ν = -0.48, revealing slight asymmetry of the data to the left, and by τ = 1.65

Discussion

The children’s mean hemoglobin level in this study (12.8g/dL; 95%CI: 12.7-12.8g/dL) was higher than the internationally adopted cut-off points for the diagnosis of anemia in children (6-60 months). It was also higher than the international estimates (11.1g/dL; 95%CI: 11.0-11.3g/dL) ¹1. World Healh Organization. The global prevalence of anaemia in 2011. Geneva: World Health Organization; 2015. and the national representative values (12.1g/dL ± 1.5g/dL) for children (6-59 months) ²2. Ministério da Saúde. Pesquisa Nacional de Demografia e Saúde da Criança e da Mulher - PNDS 2006: dimensões do processo reprodutivo e da saúde da criança. Brasília: Ministério da Saúde; 2009. (Série G. Estatística e Informação em Saúde).. In addition, this value was higher than evidenced by other studies with children from different Brazilian epidemiological scenarios, which varied between 11.1g/dL and 12.5g/dL ²²22. André HP, Vieira SA, Franceschini SCC, Ribeiro AQ, Miranda Hermsdorff HH, Priore SE. Factors associated with the iron nutritional status of Brazilian children aged 4 to 7 years. Rev Nutr 2017; 30:345-55.^,3737. Assunção MCF, Santos IS, Barros AJD, Gigante DP, Victora CG. Flour fortification with iron has no impact on anaemia in urban Brazilian children. Public Health Nutr 2012; 15:1796-801.^,3838. Oliveira TSC, Silva MC, Santos JN, Rocha DS, Alves CRL, Capanema FD, et al. Anemia entre pré-escolares - Um problema de saúde pública em Belo Horizonte, Brasil. Ciênc Saúde Colet 2014; 19:59-66.. However, these comparisons should be interpreted with caution, due to the different methodologies used by other studies, especially when measuring the hemoglobin levels.

Even though the Southern region of Brazil has one of the best indicators of social, economic and educational development in the country ²⁷27. Ministério do Desenvolvimento Social. Data social, 2016. http://www.aplicacoes.mds.gov.br/sagi-data/METRO/metro.php?_id=4 (acessado em 02/Nov/2016).
http://www.aplicacoes.mds.gov.br/sagi-da... , it is still marked by inequalities that influence the health and nutrition of the most vulnerable population. In fact, significant variability of hemoglobin levels among the municipalities was observed in this study. The combined effect of the explanatory variables contributed to the mean hemoglobin levels up to 0.55 lower and 0.48 higher than the estimated reference mean. This variability of hemoglobin levels was attributable to variables related to access to public goods and services available to the population at the municipal level, such as urbanization and the number of ACS. Likewise, variables of epidemiological importance at the individual level contributed to the variability of hemoglobin levels, such as the child’s age, enrollment at daycare centers or kindergarten, being a beneficiary in the Brazilian Income Transfer Program and the nutritional status based on the WAZ index.

Residing in municipalities with a higher urbanization rate had a negative influence on children’s hemoglobin levels. This could be explained by the fact that urbanized populations often depend directly on income to obtain food and meet their nutritional requirements; this population also usually makes poor food choices ²⁰20. Leal LP, Batista Filho M, Lira PIC, Figueiroa JN, Osório MM. Temporal trends and anaemia-associated factors in 6- to 59-month-old children in Northeast Brazil. Public Health Nutr 2012; 15:1645-52.^,3939. Floro M, Swain RB. Food security, gender and occupational choice among urban. World Development 2013; 42:89-99.. This scenario was evidenced by a study ⁴⁰40. Canuto R, Fanton M, Lira PIC. Social inequities in food consumption in Brazil: a critical review of the national surveys. Ciênc Saúde Colet 2019; 24:3193-212. that observed a direct relationship between income and the possibility of choosing food, especially among residents from the urban area. According to its results, having a higher income did not guarantee dietary quality, as a “contradictory” dietary pattern was observed. This pattern was composed of diversified and nutrient-rich food, but also industrialized and ready-to-eat meals, which could compromise the fulfillment of nutritional needs, such as iron intake. In addition, an urbanized population also require greater investments in basic sanitation and health ²⁹29. Lohman TG, Roche AF, Martorel R. Anthropometric standardization reference manual. Illinois: Human Kinetics Books; 1988.. As shown in the temporal analysis ¹²12. Santos TG, Silveira JAC, Longo-Silva G, Ramires EKNM, Menezes RCE. Tendência e fatores associados à insegurança alimentar no Brasil: Pesquisa Nacional por Amostra de Domicílios 2004, 2009 e 2013. Cad Saúde Pública 2018; 34:e00066917., the Brazilian population residing in urban areas is more exposed to factors that contribute to the intergenerational cycle of diminished opportunities, such as unemployment, social inequalities and inadequate access to basic services. These factors can expose these families to nutritional deficiencies, such as lower hemoglobin levels.

Children from municipalities with fewer ACS had lower mean hemoglobin levels when compared to children from municipalities with a higher number of these professionals, especially in the municipalities of quartile 2. The impact of the ACS on the basic health system is already well-known ⁴¹41. World Healh Organization. Optimizing health worker roles to improve access to key maternal and newborn health interventions through task shifting. Geneva: World Healyh Organization; 2012.^,4242. Tran NT, Portela A, De Bernis L, Beek K. Developing capacities of community health workers in sexual and reproductive, maternal, newborn, child, and adolescent health: a mapping and review of training resources. PLoS One 2014; 9:e94948.^,4343. Macinko JHM. Brazil's Family Health Strategy - delivering community-based primary care in a Universal Health System. N Engl J Med 2015; 372:2177-81.; but, to our knowledge, this is the first study which shows the possible effect of these workers on the hemoglobin level of children. Therefore, it is suggested that in some municipalities the greater number of ACS may have contributed to improve the health care quality in UBS, reflecting in higher hemoglobin levels. However, it is important to consider that the maximum number of ACS in each municipality is established by a law, i.e., 1 agent per 400 inhabitants ⁴⁴44. Brasil. Portaria nº 2.488, de 21 de outubro de 2011. Aprova a Política Nacional de Atenção Básica, estabelecendo a revisão de diretrizes e normas para a organização da Atenção Básica, para a Estratégia Saúde da Família (ESF) e o Programa de Agentes Comunitários de Saúde (PACS). Diário Oficial da União 2011; 24 out..

Age was also an important factor to explain the variability in the hemoglobin levels. Children under 24 months of age had the lowest concentrations of hemoglobin as observed in previous studies conducted in other Brazilian locations ⁴⁵45. Zuffo CRK, Osório MM, Taconeli CA, Schmidt ST, Corrêa da Silva BHAC. Prevalence and risk factors of anemia in children. J Pediatr (Rio J.) 2016; 92:353-60.^,4646. Oliveira MIC, Rigotti RR, Boccolini CS. Fatores associados à falta de diversidade alimentar no segundo semestre de vida. Cad Saúde Colet 2017; 25:65-72.. The age group less than 24 months is considered the most biologically vulnerable to nutritional and infectious diseases. These diseases occur mainly due to the imbalance between nutrient intake to meet nutritional requirements and the little-developed functional immunity ⁴⁴44. Brasil. Portaria nº 2.488, de 21 de outubro de 2011. Aprova a Política Nacional de Atenção Básica, estabelecendo a revisão de diretrizes e normas para a organização da Atenção Básica, para a Estratégia Saúde da Família (ESF) e o Programa de Agentes Comunitários de Saúde (PACS). Diário Oficial da União 2011; 24 out.^,4747. Oliveira APDN, Pascoal MN, Santos LC, Pereira SCL, Justino LEH, Petarli GB, et al. Prevalência de anemia e sua associação com aspectos sociodemográficos e antropométricos em crianças de Vitória, Espírito Santo, Brasil. Ciênc Saúde Colet 2013; 18:3273-80.^,4848. Melse-Boonstra A, Mwangi MN. What is causing anemia in young children and why is it so persistent? J Pediatr (Rio J.) 2016; 92:325-7.. Therefore, strategies aimed at controlling and preventing nutritional diversions and infections become essential even in places where access to quality food is adequate and health services are considered efficient ¹⁸18. World Healh Organization. Nutritional anaemias: tools for effective prevention and control. Geneva: World Health Organization; 2017.^,4848. Melse-Boonstra A, Mwangi MN. What is causing anemia in young children and why is it so persistent? J Pediatr (Rio J.) 2016; 92:325-7..

Although more than half of the children evaluated were not enrolled at daycare centers, higher mean hemoglobin levels were observed among those that were. According to the literature, this positive effect may be associated with a significant improvement in the consumption of healthy food (rich in nutrients for hematopoiesis) at daycare centers ⁴⁹49. Pereira AS, Peixoto NGA, Nogueira Neto JF, Lanzillotti HS, Soares EA. Estado nutricional de pré-escolares de creche pública: um estudo longitudinal. Cad Saúde Colet 2013; 21:140-7.^,5050. Lander RL, Bailey KB, Lander AG, Alsaleh AA, Costa-Ribeiro HC, Mattos AP, et al. Disadvantaged pre-schoolers attending daycare in Salvador, Northeast Brazil have a low prevalence of anaemia and micronutrient deficiencies. Public Health Nutr 2014; 17:1984-92.. On the other hand, a study conducted by Zuffo et al. ⁴⁵45. Zuffo CRK, Osório MM, Taconeli CA, Schmidt ST, Corrêa da Silva BHAC. Prevalence and risk factors of anemia in children. J Pediatr (Rio J.) 2016; 92:353-60. showed that these institutions have difficulty to meet the children’s iron nutritional requirements. Recognizing that early childhood education is the first step towards overcoming poverty ⁵¹51. Fernandes JHP. Acesso à educação e combate à desigualdade : o papel da educação no âmbito do Plano Brasil Sem Miséria. In: Campello T, Falcão T, Costa PV, organizadores. O Brasil sem miséria. Brasília: Ministério do Desenvolvimento Social; 2014. p. 543-61., our results reinforce the importance of providing high-quality meals by educational institutions.

Unlike studies carried out in different locations ⁵²52. Silva MA, Carvalho CA, Fonsêca PCA, Vieira SA, Ribeiro AQ, Priore SE, et al. Prevalência e fatores associados à anemia ferropriva e hipovitaminose A em crianças menores de um ano. Cad Saúde Colet 2015; 23:362-7., this study did not verify the positive effect of the Brazilian Income Transfer Program on the children’s hemoglobin levels. In fact, the establishment of a cause-and-effect relationship between the program and hemoglobin levels could not be explained by this cross-sectional study. A possible explanation for this scenario would be the timespan of participation in the Brazilian Income Transfer Program, sometimes too short to observe the effects on children’s health ⁵³53. Oliveira CCF, Cotta RMM, Rocha Sant'Ana LF, Priore SE, Francheschini SCC. Programa Bolsa Família e estado nutricional infantil: desafios estratégicos. Ciênc Saúde Colet 2011; 16:3307-16.. Furthermore, the accomplishment of the conditions of the program ⁵⁴54. Dalazen C. Deficiência de vitamina A e fatores associados em crianças de 12 a 59 meses de idade residentes em municípios do Plano Brasil Sem Miséria da Região Sul do Brasil [Tese de Doutorado]. Florianópolis: Universidade Federal de Santa Catarina; 2017. or the quality of the services provided ¹⁹19. Oliveira CSM, Cardoso MA, Araújo TS, Muniz PT. Anemia em crianças de 6 a 59 meses e fatores associados no Município de Jordão, Estado do Acre, Brasil. Cad Saúde Pública 2011; 27:1008-20.^,5353. Oliveira CCF, Cotta RMM, Rocha Sant'Ana LF, Priore SE, Francheschini SCC. Programa Bolsa Família e estado nutricional infantil: desafios estratégicos. Ciênc Saúde Colet 2011; 16:3307-16. might not have been sufficient for the prevention and early identification of nutritional deficiencies by biochemical indicators. In addition, families that participate in this program show lower income and greater vulnerability to food deprivation ⁵⁵55. Instituto Brasileiro de Análises Sociais e Econômicas. Repercussões do Programa Bolsa Família na segurança alimentar e nutricional das famílias beneficiadas. Rio de Janeiro: Instituto Brasileiro de Análises Sociais e Econômicas; 2008. and to nutritional deficiencies ⁵⁶56. Pedraza DF, Rocha ACD, Sousa CPC. Crescimento e deficiências de micronutrientes: perfil das crianças assistidas no núcleo de creches do governo da Paraíba, Brasil. Ciênc Saúde Colet 2013; 18:3379-90.. Hence, although the studies show an improvement in the anthropometric indicators of children that are beneficiaries of the program ¹³13. Oliveira FCC, Cotta RMM, Ribeiro AQ, Sant'Ana LFR, Priore SE, Franceschini SCC. Estado nutricional e fatores determinantes do déficit estatural em crianças cadastradas no Programa Bolsa Família. Epidemiol Serv Saúde 2011; 20:7-18.^,5757. Campello T, Neri MC. Programa Bolsa Família: uma década de inclusão e cidadania. Brasília: Instituto de Pesquisa Econômica e Aplicada; 2014., their families still live in food and nutritional insecurity scenarios. This could compromise the access to quality food for preventing nutritional deficiencies ⁵⁸58. Monteiro F, Schmidt ST, Costa IB, Almeida CCB, Matuda NS. Bolsa Família: insegurança alimentar e nutricional de crianças menores de cinco anos. Ciênc Saúde Colet 2014; 19:1347-57..

According to the WAZ index, a lower mean hemoglobin level was observed in children diagnosed as underweight. This result supports the premise that in the presence of a nutritional deficit scenario, hemoglobin levels are compromised as an adaptive response to food deprivation ⁵⁹59. Osório MM. Fatores determinantes da anemia em crianças. J Pediatr (Rio J.) 2002; 78:269-78.; however, the cross-sectional design of the study prevents any causal inference. Evidence also suggests that overweight children are subject to decreased hemoglobin levels due to their poor-quality diets ⁶⁰60. Batista Filho M, Souza AI, Miglioli TC, Santos MC. Anemia e obesidade: um paradoxo da transição nutricional brasileira. Cad Saúde Pública 2008; 24 Suppl 2:S247-57. and the increased blood volume, iron nutritional requirements ⁶¹61. Yanoff L, Menzie C, Denkinger B, Sebring N, McHugh T, Remaley A, et al. Inflammation and iron deficiency in the hypoferremia of obesity. International journal of obesity. Int J Obes 2007; 31:1412-9., pro-inflammatory cytokines and hepcidin ⁶²62. Tussing-Humphreys L, Pustacioglu C, Nemeth E, Braunschweig C. Rethinking iron regulation and assessment in iron deficiency, anemia of chronic disease, and obesity: introducing hepcidin. J Acad Nutr Diet 2012; 112:391-400.. The association between excess body weight and low hemoglobin levels, however, was not observed in this study.

The major strength of our study refers to the use of multilevel analysis to understand the simultaneous influence of the children and municipalities variables on hemoglobin levels. However, our study has limitations that should be considered in the interpretation of our findings and in future research. First, the municipal variables selected for this study constitute a proxy for their development, income distribution, access and quality of public services, and for the possible results of the actions developed under the Brazil Without Poverty Plan. Second, some of these variables represent the situation of the municipalities prior to the implementation of the Brazil Without Poverty Plan, considering the census intervals for the collection of information about the Brazilian population, and therefore might not reflect the scenario to which the children were exposed at the time of the multi-center study. Third, the presented data may not reflect the reality in other municipalities of the same region or other Brazilian regions, even though they were part of the same public policy. Fourth, the sample was not designed to be representative of each municipality individually, but rather of the set of the 16 municipalities in each state. Fifth, we did not investigate the effect of using other supplements on hemoglobin levels in this study. Last, although this study does not allow inference of causality, the findings are a starting point for prospective research that seeks to establish clear and precise relationships between the factors conditioning different levels of determination of nutritional deficiencies in children.

Through the Brazil Without Poverty Plan, 22 million Brazilians have overcome poverty from the income perspective ¹⁴14. Campello T, Mello J. O processo de formulação e os desafios do plano Brasil sem miséria: por um país rico e com oportunidades para todos. In: Campello T, Falcão T, Costa PV, organizadores. O Brasil sem miséria. Brasília: Ministério do Desenvolvimento Social; 2014. p. 33-66.. Considering this and the hemoglobin levels identified by this study, we suggest the possibility of a positive impact from the strategies aimed at the development of human potential in the municipalities included in this public policy, although a proper evaluation of the plan would be required to confirm this.

Conclusions

The variability of children’s hemoglobin levels was explained by individual and municipal predictors. These findings reinforce the importance of access to basic public goods and services and the development of policies aimed at reducing social inequalities. The permanence of individuals factors after adjustment for the municipal variables indicates the need for improvement, monitoring and continuity of public policies for the control of nutrient deficiencies in childhood.

Acknowledgments

The authors wish to thank the families of the children participating in the study, the Health Secretariats of the participating municipalities, and the State Health Secretariats of Rio Grande do Sul, Santa Catarina and Paraná. Moreover, they wish to thank the researchers who contributed to this study in some aspect: David Gonzalez-Chica, Márcia Regina Vítolo, and Silvia do Amaral Rigon. Inter-American Development Bank, Brazilian General Coordination of Food and Nutrition and Brazilian Graduate Studies Coordinating Board.

References

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