Prevalence and Determinants of Childhood Anemia in Urban and Rural Areas of West Java, Indonesia

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Therefore, this study aimed to determine the prevalence and determinants of anemia among children in urban and rural areas of West Java, Indonesia. Methods An observational analysis and cross-sectional study was conducted, with data was taken from secondary data of serosurvey of hand, foot, and mouth disease (HFMD) study of 560 healthy children aged 6–59 months in November 2022–January 2023 at the Garuda Primary Health Care in Bandung City as urban area and Padalarang Primary Health Care in West Bandung Region as rural area. The Chi-square test and logistic regression model were used to identify risk factors of anemia in urban and rural areas. Result The results showed anemia was not significantly higher in urban areas (25.6%) than in rural areas (21.3%) with a p-value 0.220. In urban areas, anemia was significantly associated with children aged 6–23 months (AOR = 4.38; 95% CI: 1.90–10.13) and 24–35 months (AOR = 2.93; 95% CI: 1.47–5.53), stunting children (AOR = 1.66; 95% CI: 1.04–2.67) and children with parents income below regional minimum wage (AOR = 1.74; 95% CI: 1.14–2.64). In rural areas, no variables had a significant relationship with anemia. Conclusion The current study showed that children in rural and urban areas can have anemia. Further research and evaluation are needed in the detection and monitoring of risk factors through a multisectoral approach. Anemia Children Rural Urban 6–59 months Figures Figure 1 Figure 2 Introduction Anemia is a major global public health problem affecting both developing and developed countries, with significant health and socioeconomic consequences. It disproportionately affects young children, menstruating adolescent girls, women of reproductive age, pregnant women, and postpartum women.¹,² The World Health Organization (WHO) defines anemia in children aged 6–59 months as a hemoglobin (Hb) concentration below 11.0 g/dL.¹ In this age group, anemia is most commonly caused by iron deficiency, although infections, parasitic infestations, genetic hemoglobin disorders, and deficiencies of other micronutrients may also contribute.⁷,²⁰ Children aged 6–59 months are particularly vulnerable to anemia due to rapid growth, expansion of blood volume, and increased iron requirements. During this developmental period, children transition from exclusive breastfeeding to complementary feeding, which may not adequately meet micronutrient needs, especially in low- and middle-income countries where dietary diversity is limited. Inadequate intake of iron-rich or iron-fortified foods, recurrent infections, and poor sanitation further increase the risk of anemia in this population.⁵,⁶ Globally, anemia remains highly prevalent among young children. WHO reports that the global prevalence of anemia in children aged 6–59 months was 39.8% (approximately 269 million children).¹,² Childhood anemia is a major nutritional problem in low- and middle-income countries, particularly in Africa and Asia, where nearly 45% of cases occur.¹–³ Anemia prevalence is generally higher in low- and middle-income countries due to socioeconomic disparities, nutritional deficiencies, and limited access to healthcare services. In Indonesia, anemia remains a significant public health concern. According to the Basic Health Research Survey (Riskesdas) in 2013, more than 50% of Indonesian children and adolescents were anemic, including 28% of children under five years and 26% of children aged 5–14 years.³,³⁰ WHO data in 2019 reported that the prevalence of anemia among Indonesian children aged 6–59 months was 38.4% (approximately 9.1 million children).²,²¹ These figures highlight the persistent burden of childhood anemia in Indonesia. The prevalence of anemia among children also varies between rural and urban areas. Several studies have shown higher anemia rates in rural populations compared to urban populations. For example, a study in China reported anemia prevalence of 13.3% in rural areas compared to 10.3% in urban areas.⁴,⁵ Similarly, research in Equatorial Guinea found a higher proportion of non-anemic children in urban areas compared to rural areas (17.3% vs. 10.3%).⁵ In North Sumatera, Indonesia, anemia prevalence was reported as 17.3% in rural areas and 12.7% in urban areas.³² These disparities may be influenced by differences in socioeconomic status, access to healthcare services, maternal education, dietary diversity, sanitation, and environmental exposures.⁵,⁶ Anemia in childhood has serious consequences. It increases susceptibility to infections and mortality, impairs cognitive development and academic performance, causes fatigue, and may negatively affect long-term growth and productivity.⁷,²⁰ Because anemia is a strong indicator of overall health and nutritional status, its reduction has been included as one of the six World Health Assembly Global Nutrition Targets within the Comprehensive Implementation Plan on Maternal, Infant and Young Child Nutrition.⁷,⁸,³¹ WHO has committed to developing a comprehensive multisectoral framework to prevent, diagnose, and manage anemia globally.⁷ According to recommendations from the Indonesian Pediatric Society, hemoglobin levels should be assessed starting at the age of two years and monitored annually until adolescence.²¹,²² If anemia is detected, the underlying cause should be investigated and managed appropriately, including iron supplementation when indicated, particularly in children aged 0–5 years and especially those aged 0–2 years.⁹,²⁸,²⁹ However, routine hemoglobin screening and systematic iron supplementation for children have not yet been fully integrated into national government programs in Indonesia.Given the substantial burden of childhood anemia and the potential rural–urban disparities, there is a need for comprehensive local data to inform policy and intervention strategies. Therefore, this study aims to estimate the prevalence of childhood anemia and examine associated risk factors in urban and rural areas of West Java, Indonesia. A better understanding of these determinants will support evidence-based policy formulation and targeted public health interventions to reduce childhood anemia. Materials and Methods This study was a secondary cross-sectional analysis of data obtained from a seroepidemiological survey of hand, foot, and mouth disease (HFMD) conducted between November 2022 and January 2023. The original serosurvey aimed to assess antibody responses to HFMD-related viruses using serum samples. The original HFMD serosurvey required venous blood collection for antibody testing. Hemoglobin concentration was measured from whole blood samples as part of routine laboratory assessment during sample processing. Given the availability of hemoglobin data and the inclusion of apparently healthy children from both urban and rural settings using structured recruitment procedures, the dataset provided a suitable opportunity for secondary analysis to estimate the prevalence of anemia and examine associated risk factors within the same population. The study included 562 apparently healthy children aged 6–59 months recruited from two primary healthcare centers: Garuda Primary Health Care in Bandung City (urban area) and Padalarang Primary Health Care in West Bandung Region (rural area), Indonesia. Parents or legal guardians received a full explanation of the study procedures and provided written informed consent prior to participation. Children were excluded if blood collection was unsuccessful (e.g., sample clotting) or if they had severe illness requiring immediate medical treatment at the time of recruitment. Sampling Method A total sampling approach was applied to all eligible participants enrolled in the original HFMD serosurvey. Recruitment at each site followed quota sampling based on age strata (6–11 months, 12–23 months, 24–47 months, and 48–59 months) to reflect the underlying age distribution of the population. Initially, 562 children were enrolled (274 from the urban site and 288 from the rural site). Two blood samples (one from each site) were excluded due to clotting during processing, resulting in 560 analyzable samples (Fig. 1 ). Blood Collection and Hemoglobin Measurement Venous blood samples were collected by trained healthcare personnel following standard aseptic procedures. Hemoglobin concentration was measured from whole blood samples using standard laboratory hematology methods. Serum separation was performed for the original HFMD antibody analysis; however, the anemia assessment in this study was based solely on hemoglobin concentration. Definition of Variables Anemia was defined according to World Health Organization (WHO) criteria as a hemoglobin (Hb) concentration < 11.0 g/dL in children aged 6–59 months. The severity of anemia was classified as mild (10.0–10.9 g/dL), moderate (7.0–9.9 g/dL), and severe (< 7.0 g/dL). Child characteristics included immunization and nutritional status. Immunization status was categorized as complete or incomplete based on the child’s vaccination record. Nutritional status was assessed using anthropometric measurements, including length/height and weight, and interpreted according to the WHO Child Growth Standards (2006). Height-for-age z-score (HAZ) < − 2 standard deviations (SD) was defined as stunting, weight-for-age z-score (WAZ) < − 2 SD as underweight, and weight-for-height z-score (WHZ) + 2 SD. Length was measured in children under 2 years using an infantometer, while standing height was measured in children aged 2 years or older using a calibrated stadiometer. All measurements were performed by trained personnel following standardized procedures. Parental characteristics included household income and education level. Parental income was categorized as above or below the regional minimum wage. Fathers’ and mothers’ education levels were classified as primary school or below, junior high school, senior high school, or college and above. Statistical Analysis Data were analyzed using SPSS version 26. Categorical variables were compared using the chi-square test. Statistical significance was set at p < 0.05. In addition, variables considered clinically or biologically relevant based on prior literature were retained in the model regardless of bivariate significance to control for potential confounding. Adjusted odds ratios (AORs) with 95% confidence intervals were calculated. Statistical significance was defined as p < 0.05. Ethics Approval Written informed consent was obtained from parents or legal guardians prior to enrollment. Ethical approval was granted by the Faculty of Medicine Universitas Padjadjaran Ethical Committee, Indonesia (No.: 100/UN6.KEP/EC/2022). The study was registered at ClinicalTrials.gov (NCT05637229) on 05 December 2022. Results The summary statistics for the selected variables were shown in Table 1 . These statistics showed that 25.6% in urban areas and 21.3% in rural areas were anemia. The variables that were statistically associated with anemia in the urban area were: the child’s age (p < 0.001), children with parents’ income below the regional minimum wage (p = 0.007), and stunting children (p = 0.029). In rural areas, no variables had a significant relationship with anemia. Table 1 Characteristic of anemia among children 6–59 months (n = 560) Characteristics Prevalance P value* RR (95% CI) Anemia (n = 131) Non-Anemia (n = 429) Location 0.220 Urban 70 (25.6%) 203 (74.4%) 1.21 (0.89–1.63) Rural 61 (21.3%) 226 (78.7%) 1.0 Age of Children *<0.001 6–23 months 62 (32.0%) 132 (68.0%) 1.70 (1.23–2.28) 24–59 months 69 (18.9%) 297 (81.1%) 1.0 Sex of child 0.934 Male 70 (23.3%) 231 (76.7%) 1.0 female 61 (23.6%) 198 (76.4%) 1.01 (0.75–1.37) Parent's income *0.007 Above regional minimum wage 44 (18.0%) 201 (82.0%) 1.0 Below regional minimum wage 87 (27.6%) 228 (72.4%) 1.54 (1.11–2.12) Father’s educational status 0.208 ≤ Primary School 6 (15.0%) 34 (85.0%) 0.90 (0.37–2.19) Junior High School 34 (26.2%) 96 (73.8%) 1.36 (0.76–2.42) Senior High School 78 (25.0%) 234 (75.0%) 1.50 (0.88–2.55) ≥ College 13 (16.7%) 65 (83.3%) 1.0 Mother’s educational status 0.389 ≤ Primary School 14 (26.9%) 38 (73.1%) 1.61 (0.84–3.11) Junior High School 34 (22.7%) 116 (77.3%) 1.30 (0.78–2.39) Senior High School 69 (25.2%) 205 (74.8%) 1.51 (0.90–2.54) ≥ College 14 (16.7%) 70 (83.3%) 1.0 Immunization Status 0.870 Complete Immunization 71 (23.1%) 236 (76.9%) 1.0 Incomplete Immunization 60 (23.7%) 193 (76.3%) 1.02 (0.76–1.38) HAZ *0.029 Stunting 36 (31.0%) 80 (69.0%) 1.45 (1.05–2.01) Normal 95 (21.4%) 349 (78.6%) 1.0 WAZ 0.645 Underweight 18 (21.4%) 66 (78.6%) 0.90 (0.58–1.40) Normal 113 (23.7%) 363 (76.3%) 1.0 WHZ 0.159 Wasting 12 (18.5%) 53 (81.5%) 0.74 (0.43–1.26) Overweight/Obesity 6 (14.0%) 37 (86.0%) 0.56 (0.26–1.19) Normal 113 (25.0%) 339 (75.0%) 1.0 *) Chi-square test. The distribution of hemoglobin levels (g/dL) is presented in Fig. 2 . A child was considered to be anemic when the estimated hemoglobin level was < 11.0 g/dL. Children were classified as severe anemia (< 7.0 g/dL), moderate anemia (7.0–9.9 g/dL), and mild anemia (10.0–10.9 g/dL). The severity of anemia is presented in Fig. 2 . The results from logistic regression analysis presented in Table 2 showed that the model for any level of anemia was statistically significant. The results showed that children aged 6–23 months (Adjusted Odds Ratio (AOR) = 2.17; 95% CI: 1.44–3.26; p < 0.001), and aged 24–59 months (AOR = 1; 95% CI: p < 0.001). Stunting children (AOR = 0.49; 95% CI: 0.26–0.92; p = 0.027) and children with parents income below regional minimum wage (AOR = 0.45; 95% CI: 0.24–0.83; p = 0.012) were more likely to be anemic. Table 2 Determinants of any level of anemia among children 6–59 months using multiple logistic regression Characteristics Adjusted Odds Ratio (95% CI) P Value Age of children (in months) 6–23 month 2.17 (1.44–3.26) < 0.001 24–59 month 1 Stunting Yes 1.71 (1.07–2.72) 0.025 No 1 Parent's income Above regional minimum wage 1 Below regional minimum wage 1.73 (1.14–2.63) 0.010 Discussion Detection of risk factors is important for planning and implementing programs to eradicate childhood anemia, especially in high-prevalence groups. 4 , 24 In this study, anemia prevalence was 25.6% in urban areas and 21.3% in rural areas. The slightly higher prevalence in urban areas is consistent with findings by Gang Gao et al. 12 but differs from studies conducted in Equatorial Guinea and Peru, which reported higher prevalence in rural settings. 5 , 6 A study in North Sumatera, Indonesia also found higher anemia prevalence in rural areas. 32 Several factors may explain the higher prevalence in urban settings. 35 , 36 Urban dietary patterns often include processed and convenience foods that may lack essential nutrients such as iron and vitamin B12. 13 , 23 Inadequate complementary feeding practices, limited dietary diversity, and lower intake of iron-rich or fortified foods may contribute to reduced hemoglobin levels among young children in urban areas. 35 , 36 In this study, several factors were significantly associated with anemia in urban areas. 5 , 35 Previous studies have similarly identified associations between anemia and maternal education, household conditions, sanitation, maternal age, and socioeconomic factors. 45 , 46 , 47 However, findings across settings remain inconsistent, with some studies reporting higher anemia burden in rural populations, particularly among low-wealth households. 35 , 43 , 44 These differences likely reflect contextual socioeconomic and environmental variations rather than a uniform urban–rural pattern. Age was a strong determinant of anemia. Children aged 6–23 months were 4.38 times more likely to be anemic, consistent with findings from Bangladesh and other countries. 14 , 41 , 42 , 47 , 71 , 72 This pattern is biologically plausible, as rapid growth and increased iron requirements during the first two years of life increase vulnerability to iron deficiency.37,38 The first 1000 days of life represent a critical window during which nutritional deficiencies and infections can significantly impact hematological status. 39 , 40 Iron deficiency accounts for nearly half of anemia cases globally, although other causes such as parasitic infections and nutritional deficiencies also contribute. 5 , 8 Socioeconomic status was significantly associated with anemia in this study. Children from families with income below the regional minimum wage were 1.74 times more likely to be anemic, consistent with findings from India and other regions. 15 , 32 , 47 , 48 Low family income influences dietary quality, access to healthcare, and overall child development. 49 , 50 , 51 , 52 , 53 , 54 Poor socioeconomic conditions may limit access to iron-rich foods and increase susceptibility to infections, both of which contribute to anemia risk. 49 , 50 Immunization status was not significantly associated with anemia in this study. 24 , 25 Although vaccination remains a critical public health intervention for reducing childhood morbidity and mortality, its direct relationship with anemia was not observed in our findings. 24 , 25 , 55 A significant association between stunting and anemia was observed in urban areas. 14 , 56 , 57 , 58 , 59 Children with stunting were 1.66 times more likely to be anemic. This aligns with studies from Bangladesh, China, and Haiti that reported a positive association between anemia and stunting. 14 , 16 , 17 Stunting reflects chronic undernutrition and is influenced by household, environmental, and socioeconomic conditions. 58 , 60 , 61 , 62 The coexistence of stunting and anemia may result from shared underlying factors such as poor dietary quality and infections. 63 , 64 , 65 Despite the substantial burden of anemia in young children, screening and iron supplementation programs have not been fully implemented in all primary healthcare settings in Indonesia. 9 , 21 Although the Indonesia Pediatric Society recommends iron supplementation for children, particularly those aged 0–2 years, 9 national programs remain primarily focused on adolescent girls and women of reproductive age. 19 , 26 Given the potential long-term consequences of iron deficiency in early childhood including impaired cognitive development, reduced productivity, and increased morbidity greater attention to childhood anemia prevention is warranted. 9 , 66 , 67 , 68 , 69 , 70 This study highlights the need for improved detection and monitoring of anemia risk factors when designing prevention strategies. Comprehensive interventions addressing nutrition, socioeconomic disparities, and early childhood health are necessary to reduce anemia prevalence and support national prevention efforts. The main limitation of this study is the absence of assessment of key determinants such as early feeding practices, complementary feeding patterns, dietary intake, infectious status, iron supplementation, and maternal gestational conditions. In addition, anemia was assessed solely using hemoglobin concentration, without additional hematological parameters, limiting further classification of anemia types. Conclusion In the current study showed that children both in rural areas and urban areas, can have anemia. Children aged 6–23 months, stunting children, and children with parents’ income below regional minimum wage were identified as factors associated with anemia in children. Further research and evaluation is needed in detection and monitoring of risk factors through a multisectoral approach. Therefore, it is important to know the etiological pattern of anemia in society so that we can take more effective actions and strategies at preventing anemia in children. Declarations Disclosure All other authors declare no competing interests Ethics approval and consent to participate Not applicable. This study is a systematic review and meta-analysis of previously published studies and does not involve direct participation of human subjects or access to identifiable individual participant data. Funding This research was funded by Sinovac Biotech Co., Ltd. This publication charge is funded by Unpad through the Indonesian Endowment Fund for Education (LPDP) on behalf of the Indonesian Ministry of Higher Education, Science and Technology and managed under theEQUITY Program (Contract No. 4303/ B3/DT.03.08/2025 and 3927/UN6. RKT/HK.07.00/2025. The funder had no role in the study design, data collection, data analysis or data interpretation. Author Contribution CRediT: RTG: Conceptualization, Data curation, Methodology, Project administration, Visualization, Writing - original draft, Writing - review & editing;. RA, NMS, FPF, MGDP, ADP, BZM, RKU, RPM, AB, DTN : Conceptualization, Formal analysis, Methodology, Visualization, Writing - review & editing; KS and EF: Conceptualization, Data curation, Supervision, Visualization, Writing - original draft, Writing - review & editing. Acknowledgement We thank the study participants for volunteering to participate in the study and for the data collectors for performing field work. Special thanks to Garuda Primary Health Care and Padalarang Primary Health Care for supporting us to carry out this study. The authors would like to thank all subjects who participated in this study, the Dean of the Faculty of Medicine Universitas Padjadjaran, the Head of Child Health Department Faculty of Medicine Universitas Padjadjaran/Hasan Sadikin Hospital, the staff of Clinical Research Unit of Child Health Department Faculty of Medicine Universitas Padjadjaran/Hasan Sadikin Hospital. We also thank Garuda Primary Health Care and Padalarang Primary Health Care for supporting us to carry out this study. Data Availability The data supporting the findings of this study are available from the corresponding author upon reasonable request. References World Health Organization GHODRHS. Prevalence of anemia among children (% of children ages 6–59 months). https://data.worldbank.org/indicator/SH.ANM.CHLD.ZS World Health Organization. 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Rev Paul Pediatr. 2020;38:e2019031. 10.1590/1984-0462/2020/38/2019031 . PMID: 32520297; PMCID: PMC7274531. Shimanda PP, Amukugo HJ, Norström F. Socioeconomic factors associated with anemia among children aged 6–59 months in Namibia. J Public Health Afr. 2020;11(1):1131. 10.4081/jphia.2020.1131 . PMID: 33209233; PMCID: PMC7649727. Lu D. Children's immunity at risk. New Sci. 2021;250(3332):8–9. doi: 10.1016/S0262-4079(21)00716-8. Epub 2021 Apr 30. PMID: 33967369; PMCID: PMC8087417. Bayoumi I, Parkin PC, Birken CS, Maguire JL, Borkhoff CM. for the TARGet Kids! Collaboration. Association of Family Income and Risk of Food Insecurity With Iron Status in Young Children. JAMA Netw Open. 2020;3(7):e208603. 10.1001/jamanetworkopen.2020.8603 . Hertzman C. Social geography of developmental health in the early years. Healthc Q 2010;14(Spec No 1):32–40. 10.12927/hcq.2010.21981 Rose-Jacobs R, Black MM, Casey PH, et al. Household food insecurity: associations with at-risk infant and toddler development. Pediatrics. 2008;121(1):65–72. 10.1542/peds.2006-3717 . Cabada-Yépez H, Blancas-Cabada S, Aparco JP. (2023). Association between complete vaccination and anemia in children under 5 years of age, in Peru, in the years 2019 to 2021. Win H, Shafique S, Mizan S, et al. Association between mother’s work status and child stunting in urban slums: a cross-sectional assessment of 346 child-mother dyads in Dhaka, Bangladesh (2020). Arch Public Health. 2022;80:192. https://doi.org/10.1186/s13690-022-00948-6 . Assaf S, Juan C. Stunting and Anemia in Children from Urban Poor Environments in 28 Low and Middle-income Countries: A Meta-analysis of Demographic and Health Survey Data. Nutrients. 2020;12(11):3539. 10.3390/nu12113539 . PMID: 33217992; PMCID: PMC7698615. Sserwanja Q, Kamara K, Mutisya LM, Musaba MW, Ziaei S. Rural and Urban Correlates of Stunting Among Under-Five Children in Sierra Leone: A 2019 Nationwide Cross-Sectional Survey. Nutr Metab Insights. 2021;14:11786388211047056. PMID: 34616156; PMCID: PMC8488416. Assaf S, Juan C. Stunting and Anemia in Children from Urban Poor Environments in 28 Low and Middle-income Countries: A Meta-analysis of Demographic and Health Survey Data. Nutrients. 2020;12(11):3539. 10.3390/nu12113539 . PMID: 33217992; PMCID: PMC7698615. Shibre G, Zegeye B, Haidar J. Extent of and trends in inequalities in child stunting in Sierra-Leone from 2005 to 2013: evidence from demographic and health surveys and multiple indicator cluster surveys. Int J Equity Health. 2020;19:88. Stewart CP, Iannotti L, Dewey KG, Michaelsen KF, Onyango AW. Contextualising complementary feeding in a broader framework for stunting prevention. Matern Child Nutr. 2013;9(suppl 2):27–45. Grantham-McGregor S, Cheung YB, Cueto S, Glewwe P, Richter L, Strupp B. Developmental potential in the first 5 years for children in developing countries. Lancet. 2007;369:60–70. Raiten DJ, Bremer AA. Exploring the Nutritional Ecology of Stunting: New Approaches to an Old Problem. Nutrients. 2020;12(2):371. 10.3390/nu12020371 . PMID: 32023835; PMCID: PMC7071191. Mutumba R, Mbabazi J, Pesu H, Greibe E, Olsen MF, Briend A, Mølgaard C, Ritz C, Mupere E, Filteau S, Friis H, Grenov B. Micronutrient Status and Other Correlates of Hemoglobin among Children with Stunting: A Cross-Sectional Study in Uganda. Nutrients. 2023;15(17):3785. 10.3390/nu15173785 . PMID: 37686816; PMCID: PMC10489905. Balarajan Y, Ramakrishnan U, Özaltin E, Shankar AH, Subramanian S. Anaemia in low-income and middle-income countries. Lancet. 2011;378:2123–35. 10.1016/S0140-6736(10)62304-5 . Moscheo C, Licciardello M, Samperi P, La Spina M, Di Cataldo A, Russo G. New Insights into Iron Deficiency Anemia in Children: A Practical Review. Metabolites. 2022;12(4):289. 10.3390/metabo12040289 . PMID: 35448476; PMCID: PMC9029079. Sundararajan S, Rabe H. Prevention of iron deficiency anemia in infants and toddlers. Pediatr Res. 2021;89:63–73. https://doi.org/10.1038/s41390-020-0907-5 . Abu-Ouf NM, Jan MM. The impact of maternal iron deficiency and iron deficiency anemia on child's health. Saudi Med J. 2015;36(2):146–9. 10.15537/smj.2015.2.10289 . PMID: 25719576; PMCID: PMC4375689. Radlowski EC, Johnson RW. Perinatal iron deficiency and neurocognitive development. Front Hum Neurosci. 2013;7:585. 10.3389/fnhum.2013.00585 . PMID: 24065908; PMCID: PMC3779843. East P, Doom JR, Blanco E, Burrows R, Lozoff B, Gahagan S. Iron deficiency in infancy and neurocognitive and educational outcomes in young adulthood. Dev Psychol. 2021;57(6):962–75. 10.1037/dev0001030 . PMID: 34424013; PMCID: PMC8386013. Soh P, Ferguson EL, McKenzie JE, Homs MYV, Gibson RS. Iron deficiency and risk factors for lower iron stores in 6–24 month-old New Zealanders. Eur J Clin Nutr. 2004;58(1):71–9. 10.1038/sj.ejcn.1601751 . Gessner BD. Geographic and racial patterns of anemia prevalence among low-income Alaskan children and pregnant or postpartum women limit potential etiologies. J Pediatr Gastroenterol Nutr. 2009;48(4):475–81. 10.1097/MPG.0b013e3181888fac . Additional Declarations No competing interests reported. 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selection.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8883167/v1/5aee2b4f1c178e38a72e1285.jpeg"},{"id":103178028,"identity":"94444b1e-b166-42f3-8bf2-d273ff97ba01","added_by":"auto","created_at":"2026-02-22 16:57:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of hemoglobin level among urban and rural\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8883167/v1/8f043d0364e8a4cb53c85a70.png"},{"id":103504928,"identity":"954a1909-b960-4d78-9dad-5780c8714092","added_by":"auto","created_at":"2026-02-26 13:22:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1213947,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8883167/v1/abc88a1b-7ea3-4e0e-8b51-9603c2a6059c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Determinants of Childhood Anemia in Urban and Rural Areas of West Java, Indonesia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnemia is a major global public health problem affecting both developing and developed countries, with significant health and socioeconomic consequences. It disproportionately affects young children, menstruating adolescent girls, women of reproductive age, pregnant women, and postpartum women.\u0026sup1;,\u0026sup2; The World Health Organization (WHO) defines anemia in children aged 6\u0026ndash;59 months as a hemoglobin (Hb) concentration below 11.0 g/dL.\u0026sup1; In this age group, anemia is most commonly caused by iron deficiency, although infections, parasitic infestations, genetic hemoglobin disorders, and deficiencies of other micronutrients may also contribute.⁷,\u0026sup2;⁰\u003c/p\u003e \u003cp\u003eChildren aged 6\u0026ndash;59 months are particularly vulnerable to anemia due to rapid growth, expansion of blood volume, and increased iron requirements. During this developmental period, children transition from exclusive breastfeeding to complementary feeding, which may not adequately meet micronutrient needs, especially in low- and middle-income countries where dietary diversity is limited. Inadequate intake of iron-rich or iron-fortified foods, recurrent infections, and poor sanitation further increase the risk of anemia in this population.⁵,⁶\u003c/p\u003e \u003cp\u003eGlobally, anemia remains highly prevalent among young children. WHO reports that the global prevalence of anemia in children aged 6\u0026ndash;59 months was 39.8% (approximately 269\u0026nbsp;million children).\u0026sup1;,\u0026sup2; Childhood anemia is a major nutritional problem in low- and middle-income countries, particularly in Africa and Asia, where nearly 45% of cases occur.\u0026sup1;\u0026ndash;\u0026sup3; Anemia prevalence is generally higher in low- and middle-income countries due to socioeconomic disparities, nutritional deficiencies, and limited access to healthcare services.\u003c/p\u003e \u003cp\u003eIn Indonesia, anemia remains a significant public health concern. According to the Basic Health Research Survey (Riskesdas) in 2013, more than 50% of Indonesian children and adolescents were anemic, including 28% of children under five years and 26% of children aged 5\u0026ndash;14 years.\u0026sup3;,\u0026sup3;⁰ WHO data in 2019 reported that the prevalence of anemia among Indonesian children aged 6\u0026ndash;59 months was 38.4% (approximately 9.1\u0026nbsp;million children).\u0026sup2;,\u0026sup2;\u0026sup1; These figures highlight the persistent burden of childhood anemia in Indonesia.\u003c/p\u003e \u003cp\u003eThe prevalence of anemia among children also varies between rural and urban areas. Several studies have shown higher anemia rates in rural populations compared to urban populations. For example, a study in China reported anemia prevalence of 13.3% in rural areas compared to 10.3% in urban areas.⁴,⁵ Similarly, research in Equatorial Guinea found a higher proportion of non-anemic children in urban areas compared to rural areas (17.3% vs. 10.3%).⁵ In North Sumatera, Indonesia, anemia prevalence was reported as 17.3% in rural areas and 12.7% in urban areas.\u0026sup3;\u0026sup2; These disparities may be influenced by differences in socioeconomic status, access to healthcare services, maternal education, dietary diversity, sanitation, and environmental exposures.⁵,⁶\u003c/p\u003e \u003cp\u003eAnemia in childhood has serious consequences. It increases susceptibility to infections and mortality, impairs cognitive development and academic performance, causes fatigue, and may negatively affect long-term growth and productivity.⁷,\u0026sup2;⁰ Because anemia is a strong indicator of overall health and nutritional status, its reduction has been included as one of the six World Health Assembly Global Nutrition Targets within the Comprehensive Implementation Plan on Maternal, Infant and Young Child Nutrition.⁷,⁸,\u0026sup3;\u0026sup1; WHO has committed to developing a comprehensive multisectoral framework to prevent, diagnose, and manage anemia globally.⁷\u003c/p\u003e \u003cp\u003eAccording to recommendations from the Indonesian Pediatric Society, hemoglobin levels should be assessed starting at the age of two years and monitored annually until adolescence.\u0026sup2;\u0026sup1;,\u0026sup2;\u0026sup2; If anemia is detected, the underlying cause should be investigated and managed appropriately, including iron supplementation when indicated, particularly in children aged 0\u0026ndash;5 years and especially those aged 0\u0026ndash;2 years.⁹,\u0026sup2;⁸,\u0026sup2;⁹ However, routine hemoglobin screening and systematic iron supplementation for children have not yet been fully integrated into national government programs in Indonesia.Given the substantial burden of childhood anemia and the potential rural\u0026ndash;urban disparities, there is a need for comprehensive local data to inform policy and intervention strategies. Therefore, this study aims to estimate the prevalence of childhood anemia and examine associated risk factors in urban and rural areas of West Java, Indonesia. A better understanding of these determinants will support evidence-based policy formulation and targeted public health interventions to reduce childhood anemia.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis study was a secondary cross-sectional analysis of data obtained from a seroepidemiological survey of hand, foot, and mouth disease (HFMD) conducted between November 2022 and January 2023. The original serosurvey aimed to assess antibody responses to HFMD-related viruses using serum samples. The original HFMD serosurvey required venous blood collection for antibody testing. Hemoglobin concentration was measured from whole blood samples as part of routine laboratory assessment during sample processing. Given the availability of hemoglobin data and the inclusion of apparently healthy children from both urban and rural settings using structured recruitment procedures, the dataset provided a suitable opportunity for secondary analysis to estimate the prevalence of anemia and examine associated risk factors within the same population.\u003c/p\u003e \u003cp\u003e The study included 562 apparently healthy children aged 6\u0026ndash;59 months recruited from two primary healthcare centers: Garuda Primary Health Care in Bandung City (urban area) and Padalarang Primary Health Care in West Bandung Region (rural area), Indonesia. Parents or legal guardians received a full explanation of the study procedures and provided written informed consent prior to participation. Children were excluded if blood collection was unsuccessful (e.g., sample clotting) or if they had severe illness requiring immediate medical treatment at the time of recruitment.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling Method\u003c/h2\u003e \u003cp\u003eA total sampling approach was applied to all eligible participants enrolled in the original HFMD serosurvey. Recruitment at each site followed quota sampling based on age strata (6\u0026ndash;11 months, 12\u0026ndash;23 months, 24\u0026ndash;47 months, and 48\u0026ndash;59 months) to reflect the underlying age distribution of the population. Initially, 562 children were enrolled (274 from the urban site and 288 from the rural site). Two blood samples (one from each site) were excluded due to clotting during processing, resulting in 560 analyzable samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBlood Collection and Hemoglobin Measurement\u003c/h3\u003e\n\u003cp\u003eVenous blood samples were collected by trained healthcare personnel following standard aseptic procedures. Hemoglobin concentration was measured from whole blood samples using standard laboratory hematology methods. Serum separation was performed for the original HFMD antibody analysis; however, the anemia assessment in this study was based solely on hemoglobin concentration.\u003c/p\u003e\n\u003ch3\u003eDefinition of Variables\u003c/h3\u003e\n\u003cp\u003eAnemia was defined according to World Health Organization (WHO) criteria as a hemoglobin (Hb) concentration\u0026thinsp;\u0026lt;\u0026thinsp;11.0 g/dL in children aged 6\u0026ndash;59 months. The severity of anemia was classified as mild (10.0\u0026ndash;10.9 g/dL), moderate (7.0\u0026ndash;9.9 g/dL), and severe (\u0026lt;\u0026thinsp;7.0 g/dL).\u003c/p\u003e \u003cp\u003eChild characteristics included immunization and nutritional status. Immunization status was categorized as complete or incomplete based on the child\u0026rsquo;s vaccination record. Nutritional status was assessed using anthropometric measurements, including length/height and weight, and interpreted according to the WHO Child Growth Standards (2006). Height-for-age z-score (HAZ) \u0026lt; \u0026minus;\u0026thinsp;2 standard deviations (SD) was defined as stunting, weight-for-age z-score (WAZ) \u0026lt; \u0026minus;\u0026thinsp;2 SD as underweight, and weight-for-height z-score (WHZ) \u0026lt; \u0026minus;\u0026thinsp;2 SD as wasting. Overweight or obesity was defined as WHZ\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;2 SD. Length was measured in children under 2 years using an infantometer, while standing height was measured in children aged 2 years or older using a calibrated stadiometer. All measurements were performed by trained personnel following standardized procedures.\u003c/p\u003e \u003cp\u003eParental characteristics included household income and education level. Parental income was categorized as above or below the regional minimum wage. Fathers\u0026rsquo; and mothers\u0026rsquo; education levels were classified as primary school or below, junior high school, senior high school, or college and above.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS version 26. Categorical variables were compared using the chi-square test. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In addition, variables considered clinically or biologically relevant based on prior literature were retained in the model regardless of bivariate significance to control for potential confounding. Adjusted odds ratios (AORs) with 95% confidence intervals were calculated. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics Approval\u003c/h3\u003e\n\u003cp\u003e Written informed consent was obtained from parents or legal guardians prior to enrollment. Ethical approval was granted by the Faculty of Medicine Universitas Padjadjaran Ethical Committee, Indonesia (No.: 100/UN6.KEP/EC/2022). The study was registered at ClinicalTrials.gov (NCT05637229) on 05 December 2022.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe summary statistics for the selected variables were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These statistics showed that 25.6% in urban areas and 21.3% in rural areas were anemia. The variables that were statistically associated with anemia in the urban area were: the child\u0026rsquo;s age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), children with parents\u0026rsquo; income below the regional minimum wage (p\u0026thinsp;=\u0026thinsp;0.007), and stunting children (p\u0026thinsp;=\u0026thinsp;0.029). In rural areas, no variables had a significant relationship with anemia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristic of anemia among children 6\u0026ndash;59 months (n\u0026thinsp;=\u0026thinsp;560)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePrevalance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnemia (n\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Anemia (n\u0026thinsp;=\u0026thinsp;429)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.21 (0.89\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e226 (78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of Children\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e*\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;23 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62 (32.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132 (68.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.70 (1.23\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;59 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e297 (81.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e231 (76.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198 (76.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.75\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParent's income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e*0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove regional minimum wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201 (82.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow regional minimum wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228 (72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54 (1.11\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFather\u0026rsquo;s educational status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; Primary School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (85.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90 (0.37\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.36 (0.76\u0026ndash;2.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e234 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.50 (0.88\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; College\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s educational status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; Primary School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (73.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.61 (0.84\u0026ndash;3.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116 (77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.30 (0.78\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e205 (74.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.51 (0.90\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; College\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImmunization Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete Immunization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236 (76.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncomplete Immunization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193 (76.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02 (0.76\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHAZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e*0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStunting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80 (69.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.45 (1.05\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e349 (78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWAZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90 (0.58\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363 (76.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (81.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74 (0.43\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight/Obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (86.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56 (0.26\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e339 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*) Chi-square test.\u003c/p\u003e \u003cp\u003eThe distribution of hemoglobin levels (g/dL) is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A child was considered to be anemic when the estimated hemoglobin level was \u0026lt;\u0026thinsp;11.0 g/dL. Children were classified as severe anemia (\u0026lt;\u0026thinsp;7.0 g/dL), moderate anemia (7.0\u0026ndash;9.9 g/dL), and mild anemia (10.0\u0026ndash;10.9 g/dL). The severity of anemia is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results from logistic regression analysis presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed that the model for any level of anemia was statistically significant. The results showed that children aged 6\u0026ndash;23 months (Adjusted Odds Ratio (AOR)\u0026thinsp;=\u0026thinsp;2.17; 95% CI: 1.44\u0026ndash;3.26; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and aged 24\u0026ndash;59 months (AOR\u0026thinsp;=\u0026thinsp;1; 95% CI: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Stunting children (AOR\u0026thinsp;=\u0026thinsp;0.49; 95% CI: 0.26\u0026ndash;0.92; p\u0026thinsp;=\u0026thinsp;0.027) and children with parents income below regional minimum wage (AOR\u0026thinsp;=\u0026thinsp;0.45; 95% CI: 0.24\u0026ndash;0.83; p\u0026thinsp;=\u0026thinsp;0.012) were more likely to be anemic.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeterminants of any level of anemia among children 6\u0026ndash;59 months using multiple logistic regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted Odds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge of children (in months)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;23 month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17 (1.44\u0026ndash;3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;59 month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStunting\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71 (1.07\u0026ndash;2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParent's income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove regional minimum wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow regional minimum wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73 (1.14\u0026ndash;2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDetection of risk factors is important for planning and implementing programs to eradicate childhood anemia, especially in high-prevalence groups.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e In this study, anemia prevalence was 25.6% in urban areas and 21.3% in rural areas. The slightly higher prevalence in urban areas is consistent with findings by Gang Gao et al.\u003csup\u003e12\u003c/sup\u003e but differs from studies conducted in Equatorial Guinea and Peru, which reported higher prevalence in rural settings.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e A study in North Sumatera, Indonesia also found higher anemia prevalence in rural areas.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSeveral factors may explain the higher prevalence in urban settings.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Urban dietary patterns often include processed and convenience foods that may lack essential nutrients such as iron and vitamin B12.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Inadequate complementary feeding practices, limited dietary diversity, and lower intake of iron-rich or fortified foods may contribute to reduced hemoglobin levels among young children in urban areas.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn this study, several factors were significantly associated with anemia in urban areas.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Previous studies have similarly identified associations between anemia and maternal education, household conditions, sanitation, maternal age, and socioeconomic factors.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e However, findings across settings remain inconsistent, with some studies reporting higher anemia burden in rural populations, particularly among low-wealth households.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e These differences likely reflect contextual socioeconomic and environmental variations rather than a uniform urban\u0026ndash;rural pattern.\u003c/p\u003e \u003cp\u003eAge was a strong determinant of anemia. Children aged 6\u0026ndash;23 months were 4.38 times more likely to be anemic, consistent with findings from Bangladesh and other countries.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e This pattern is biologically plausible, as rapid growth and increased iron requirements during the first two years of life increase vulnerability to iron deficiency.37,38 The first 1000 days of life represent a critical window during which nutritional deficiencies and infections can significantly impact hematological status.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Iron deficiency accounts for nearly half of anemia cases globally, although other causes such as parasitic infections and nutritional deficiencies also contribute.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSocioeconomic status was significantly associated with anemia in this study. Children from families with income below the regional minimum wage were 1.74 times more likely to be anemic, consistent with findings from India and other regions.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e Low family income influences dietary quality, access to healthcare, and overall child development.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e Poor socioeconomic conditions may limit access to iron-rich foods and increase susceptibility to infections, both of which contribute to anemia risk.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eImmunization status was not significantly associated with anemia in this study.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Although vaccination remains a critical public health intervention for reducing childhood morbidity and mortality, its direct relationship with anemia was not observed in our findings.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA significant association between stunting and anemia was observed in urban areas.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e Children with stunting were 1.66 times more likely to be anemic. This aligns with studies from Bangladesh, China, and Haiti that reported a positive association between anemia and stunting.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Stunting reflects chronic undernutrition and is influenced by household, environmental, and socioeconomic conditions.\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e The coexistence of stunting and anemia may result from shared underlying factors such as poor dietary quality and infections.\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite the substantial burden of anemia in young children, screening and iron supplementation programs have not been fully implemented in all primary healthcare settings in Indonesia.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Although the Indonesia Pediatric Society recommends iron supplementation for children, particularly those aged 0\u0026ndash;2 years,\u003csup\u003e9\u003c/sup\u003e national programs remain primarily focused on adolescent girls and women of reproductive age.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Given the potential long-term consequences of iron deficiency in early childhood including impaired cognitive development, reduced productivity, and increased morbidity greater attention to childhood anemia prevention is warranted.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study highlights the need for improved detection and monitoring of anemia risk factors when designing prevention strategies. Comprehensive interventions addressing nutrition, socioeconomic disparities, and early childhood health are necessary to reduce anemia prevalence and support national prevention efforts.\u003c/p\u003e \u003cp\u003eThe main limitation of this study is the absence of assessment of key determinants such as early feeding practices, complementary feeding patterns, dietary intake, infectious status, iron supplementation, and maternal gestational conditions. In addition, anemia was assessed solely using hemoglobin concentration, without additional hematological parameters, limiting further classification of anemia types.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the current study showed that children both in rural areas and urban areas, can have anemia. Children aged 6\u0026ndash;23 months, stunting children, and children with parents\u0026rsquo; income below regional minimum wage were identified as factors associated with anemia in children. Further research and evaluation is needed in detection and monitoring of risk factors through a multisectoral approach. Therefore, it is important to know the etiological pattern of anemia in society so that we can take more effective actions and strategies at preventing anemia in children.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDisclosure\u003c/h2\u003e \u003cp\u003eAll other authors declare no competing interests\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable. This study is a systematic review and meta-analysis of previously published studies and does not involve direct participation of human subjects or access to identifiable individual participant data.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by Sinovac Biotech Co., Ltd. This publication charge is funded by Unpad through the Indonesian Endowment Fund for Education (LPDP) on behalf of the Indonesian Ministry of Higher Education, Science and Technology and managed under theEQUITY Program (Contract No. 4303/ B3/DT.03.08/2025 and 3927/UN6. RKT/HK.07.00/2025. The funder had no role in the study design, data collection, data analysis or data interpretation.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCRediT: RTG: Conceptualization, Data curation, Methodology, Project administration, Visualization, Writing - original draft, Writing - review \u0026amp;amp; editing;. RA, NMS, FPF, MGDP, ADP, BZM, RKU, RPM, AB, DTN : Conceptualization, Formal analysis, Methodology, Visualization, Writing - review \u0026amp;amp; editing; KS and EF: Conceptualization, Data curation, Supervision, Visualization, Writing - original draft, Writing - review \u0026amp;amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e We thank the study participants for volunteering to participate in the study and for the data collectors for performing field work. Special thanks to Garuda Primary Health Care and Padalarang Primary Health Care for supporting us to carry out this study. The authors would like to thank all subjects who participated in this study, the Dean of the Faculty of Medicine Universitas Padjadjaran, the Head of Child Health Department Faculty of Medicine Universitas Padjadjaran/Hasan Sadikin Hospital, the staff of Clinical Research Unit of Child Health Department Faculty of Medicine Universitas Padjadjaran/Hasan Sadikin Hospital. We also thank Garuda Primary Health Care and Padalarang Primary Health Care for supporting us to carry out this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization GHODRHS. Prevalence of anemia among children (% of children ages 6\u0026ndash;59 months). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org/indicator/SH.ANM.CHLD.ZS\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org/indicator/SH.ANM.CHLD.ZS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Anemia in women and children. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/data/gho/data/themes/topics/anemia_in_women_and_children#:~:text=In%202019%2C%20\u003c/span\u003e\u003cspan address=\"https://www.who.int/data/gho/data/themes/topics/anemia_in_women_and_children#:~:text=In%202019%2C%20\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eglobal%20anemia%20prevalence%20was%2039.8%25%20(95%25,UI%2056.6%25%2C%2063.7%25).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIndonesian Ministry of Health. Basic Health Res 2013. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kemkes.go.id/resources/download/general/Hasil %20Riskesdas%202013\u003c/span\u003e\u003cspan address=\"https://www.kemkes.go.id/resources/download/general/Hasil %20Riskesdas%202013\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Xiao J, Liao M, et al. Anemia prevalence, severity and associated factors among children aged 6\u0026ndash;71 months in rural Hunan Province, China: a community-based cross-sectional study. 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J Pediatr Gastroenterol Nutr. 2009;48(4):475\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MPG.0b013e3181888fac\u003c/span\u003e\u003cspan address=\"10.1097/MPG.0b013e3181888fac\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anemia, Children, Rural, Urban, 6–59 months","lastPublishedDoi":"10.21203/rs.3.rs-8883167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8883167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAnemia in children has become a serious global public health problem, which may lead to delayed growth and possibly have long term effects on neurodevelopmental and behavioral outcomes. Therefore, this study aimed to determine the prevalence and determinants of anemia among children in urban and rural areas of West Java, Indonesia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e An observational analysis and cross-sectional study was conducted, with data was taken from secondary data of serosurvey of hand, foot, and mouth disease (HFMD) study of 560 healthy children aged 6\u0026ndash;59 months in November 2022\u0026ndash;January 2023 at the Garuda Primary Health Care in Bandung City as urban area and Padalarang Primary Health Care in West Bandung Region as rural area. The Chi-square test and logistic regression model were used to identify risk factors of anemia in urban and rural areas.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe results showed anemia was not significantly higher in urban areas (25.6%) than in rural areas (21.3%) with a p-value 0.220. In urban areas, anemia was significantly associated with children aged 6\u0026ndash;23 months (AOR\u0026thinsp;=\u0026thinsp;4.38; 95% CI: 1.90\u0026ndash;10.13) and 24\u0026ndash;35 months (AOR\u0026thinsp;=\u0026thinsp;2.93; 95% CI: 1.47\u0026ndash;5.53), stunting children (AOR\u0026thinsp;=\u0026thinsp;1.66; 95% CI: 1.04\u0026ndash;2.67) and children with parents income below regional minimum wage (AOR\u0026thinsp;=\u0026thinsp;1.74; 95% CI: 1.14\u0026ndash;2.64). In rural areas, no variables had a significant relationship with anemia.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe current study showed that children in rural and urban areas can have anemia. Further research and evaluation are needed in the detection and monitoring of risk factors through a multisectoral approach.\u003c/p\u003e","manuscriptTitle":"Prevalence and Determinants of Childhood Anemia in Urban and Rural Areas of West Java, Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 16:57:01","doi":"10.21203/rs.3.rs-8883167/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-17T07:56:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T04:19:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152172410140180113846348172117934604604","date":"2026-02-27T02:38:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29308295270919505330051486101978370135","date":"2026-02-25T06:10:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110511092106781688053561882026996800826","date":"2026-02-25T02:21:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T01:39:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52567301287053789242967754626581978105","date":"2026-02-18T23:59:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77327882627737623236970837786974254143","date":"2026-02-18T09:30:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-18T06:28:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-18T06:16:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-18T03:35:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T03:34:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2026-02-15T02:02:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f412643-99cd-435e-bfd2-1c67fab6927e","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T04:39:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 16:57:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8883167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8883167","identity":"rs-8883167","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}