Mapping anemia prevalence across Indonesia

Abstract

Background and Objectives

Anemia is a major health problem worldwide, with complex etiologies and significantly affecting the quality of life and health outcomes. In Indonesia, anemia is a public health concern with a complex interplay between genetic, environmental, and infectious disease factors. The prevalence tends to increase in Indonesia from 2007 to 2018. This study aims to explore factors contributing to anemia in Indonesia.

Methods and Study Design

We used archived data from various population studies collected between 1995 and 2023. A total of 5,486 subjects from 17 study populations in Indonesia were included in the analyses.

Results

The proportions of anemic women are higher than anemic men (p<0.001), and the anemia prevalence in Indonesia is diverse in various populations. More than 50% of this study subjects were microcytic hypochromic anemia with 35% indicative of iron deficiency and 13% of thalassemia based on Mentzer Index and RDW index cut-off. Hb analysis showed that HbA2 and HbF proportions above normal were significantly higher in the anemic group (p<0.001). We also found beta thalassemia proportions were higher in the anemic group (p<0.001) indicating genetic disorders are prevalent in Indonesia.

Conclusions

The anemia prevalence in Indonesia is high, and the etiology is very complex, with nutritional and non-nutritional factors. Therefore, anemia mitigation in the Indonesian population should consider nutritional and non-nutritional factors. Policy makers should consider intervention programs beyond nutrient-specific strategies such as genetic background of the individuals.

Introduction

Anemia is a major health problem worldwide, with complex etiologies and significantly affects the quality of life and health outcomes in affected populations, particularly in children and women.^1,2 The World Health Organization (WHO) reported that 40% of children under five, 30% of adult women, and 37% of pregnant women are suffering from anemia.³ The prolonged effect of anemia leads to increased morbidity and mortality rates, poor birth outcomes, impaired neurological development in children, and decreased productivity in adults.^4–6

In Indonesia, anemia is a public health concern and impacts various segments of the population. According to Indonesia's Basic Health Research reports from 2007 to 2018, and the National Health Survey Report in 2023, the prevalence of anemia has been notably high. From 2007 to 2018, the anemia prevalence showed an increase from 19.7% to 38.5% in children under five, from 19.7% to 27.2% in women (≥15 years), and from 13.1% to 20.3% in men (≥15 years). The latest report in 2023 showed the anemia prevalence was 23.8%, 18.0%, and 14.4% in children, adult women, and adult men, respectively.^7–10

Anemia is described by hemoglobin (Hb) concentration and/or red blood cell (RBC) count below normal values.¹¹ Hb concentration is the common method for determining anemia, and WHO has published guidelines on hemoglobin cut-offs to define anemia.¹² The imbalance between RBC production (erythropoiesis) and RBC loss that can occur due to hemolysis and/or acute blood loss will lead to anemia. Commonly, anemia is divided into two ca- tegories: nutritional and non-nutritional. The causes of nutritional anemia include nutrient deficiencies through inadequate diets or inadequate absorption of nutrients.¹³ Nutritional deficiencies are often seen as the main factor of anemia, although other factors also play a role, especially in regions with high infectious disease cases and genetic disorders.^14,15 Non-nutritional anemia can include excessive blood loss, genetic disorders like inherited red blood cell disorders, and anemia of inflammation (i.e., both are linked to infectious and noninfectious diseases).^16,17

Indonesia is a country with complex interplay between genetic, environmental, and infectious disease factors. This study aims to explore and map the etiology of anemia, not only because of nutritional factors but also non-nutritional factors such as genetic disorders. Currently, national programs to overcome anemia only focus on nutritional factors, such as iron-folic acid supplementation for pregnant women and adolescent girls (12-18 years), education, and campaigns for a healthy and balanced diet.¹⁸ This study may help develop more effective and comprehensive strategies to tackle anemia by expanding the focus with non-nutritional anemia as it is not a one-size-fits-all strategy for combating anemia in Indonesia.

Methods

Study design and population

We used archived data from various population studies in Indonesia collected between 1995 and 2023 (Supplementary Table 1) generated by the Eijkman Institute for Molecular Biology (EIMB) and the Mochtar Riady Institute for Nanotechnology (MRIN). Samples from EIMB included two studies: firstly, population study on thalassemia consisting of 11 populations with 3989 subjects that conducted genetic testing for thalassemia and Southeast Asian Ovalocytosis (SAO), and secondly, study on G6PD deficiency consisting of 1 population with 610 subjects that screened for G6PD deficiency, thalassemia, and SAO. These samples that were genotyped for thalassemia, SAO, and G6PD deficiency were clinically healthy individuals from population genetics studies from regions in Indonesia that were either very remote or malaria endemic. Subjects were selected for thalassemia genetic screening based on the MCV value, red blood cell morphology using blood smear examination (microcytic hypochromic, present of target cells), and HbA2 and HbF levels using hemoglobin analysis by HPLC method, according to Dacie and Lewis Practical Haematology.¹⁹ Subjects having low MCV value, microcytic hypochromic red blood cell morphology, the presence of target cells, and high (>3.5%) or low (<2.5%) levels of HbA2 were selected for alpha and/or beta thalassemia genetic testing. We are aware that subjects with normal MCV value, red blood cell morphology, and HbA2 levels, might still have thalassemia mutation(s), however, we decided to apply the selection criteria mentioned above for further thalassemia genetic screening. Subjects were selected for SAO genetic screening based on blood smear analyses. Those with SAO hallmarks (oval shaped erythrocytes, transbridged with two stomas) were further confirmed genotypically by PCR. These samples were phenotypically screened for G6PD deficiency using spectrophotometer-based assay and the deficient samples were then genotyped by PCR/RFLP or sanger sequencing. Samples from MRIN included 5 populations with a total of 981 subjects.

The total subjects for this study were 5,486 subjects from 17 populations. All studies performed complete blood count analyses including Hb, MCV, RDW, MCH, MCHC, RBC count, and hematocrit. For the blood count analyses, samples collected by EIMB were analyzed using the Sysmex pocH-100i Hematology Analyzer or Sysmex XN 1000 Hematology Analyzer, while those conducted at MRIN were analyzed at Prodia Laboratory using the Sysmex XN 2000 Hematology Analyzer. Hb variants (HbA2 and HbF) were detected using Bio-Rad VARIANT II Hemoglobin testing System. Subjects with incomplete information on either sex, age, or Hb concentration were excluded from the analysis. Anemia classification followed the WHO guidelines, categorized as anemic and non-anemic, based on age (Figure 1, Supplementary Table 2).¹² Ethical approvals for each of the study included in the analyses, were obtained from the Eijkman Institute Research Ethics Commission (EIREC No. 32, 2008; EIREC No. 52, 2012, EIREC No. 69, 2014, EIREC No. 90, 2015) and from the Mochtar Riady Institute for Nanotechnology Ethics Committee (No. 005/MRIN-EC/ECL/III/2022, No. 010/MRIN-EC/ECL/V/2023).

Figure 1.

Flowchart of subject assessment.12

Statistical analysis

Statistical analyses were performed using R version 4.2.1 (www.r-project.org) with RStudio version 2023.12.1+402 (www.rstudio.com). Descriptive statistics were employed to describe the baseline characteristics of anemic and non-anemic groups. The continuous variables were presented as medians (interquartile range, IQR) and analyzed with Wilcoxon-Mann Whitney U test. The categorical variables were presented as percentages (%) and analyzed with Pearson's chi-squared test. We used Wilcoxon-Mann Whitney U test to compare the hemoglobin analysis between anemic and non-anemic group. For the proportions of red blood indices, hemoglobin analysis, and genetic risk factors between anemic and non-anemic group, we used Pearson's chi-squared test. Data analyses and graphical presentations were performed using “ggplot2”, “ggpubr”, “dplyr”, “ggrepel”, “data.table” packages in R.

Results

Clinical characteristics of anemic and non-anemic subjects

We analyzed sex distribution, age, and various hematological parameters. In this study, there is a significant difference in anemia prevalence between women and men (p-value <0.001). The proportions of anemic women (15.9%) are higher than anemic men (9.0%). We found significant differences in the proportions of anemic and non-anemic groups among children (<15 years), women (≥15 years) and men (≥15 years). The highest proportions of anemic subjects are women (16.1%), followed by children (14.3%), and the lowest is men (8.3%). Further, we performed pairwise comparisons, and the significant results were observed between children vs. men and women vs. men. These significant results remained after adjustment with Bonferroni correction (Supplementary Table 3). All clinical hematological parameters showed statistically significant differences between anemic and non-anemic groups, highlighting the distinct hematological profiles associated with anemia (Table 1).

Table 1.

Clinical characteristics of anemic and non-anemic subjects

Variables	All subjects N = 5486	Anemic N = 731	Non-anemic N = 4755	p-value
Sex
	Women	3469 (63.2%)	550 (15.9%)	2919 (84.1%)	<0.001*
	Men	2017 (36.8%)	181 (9.0%)	1836 (91.0%)
Age (N = 5486)	20.0 (17.0-32.0)	22.0 (18.0-41.0)	19.0 (17.0-30.0)	<0.001*
Children (<15 years)	356 (6.49%)	51 (14.3%)	305 (85.7%)	<0.001*
Women (≥15 years)	3270 (59.6%)	525 (16.1%)	2745 (83.9%)
Men (≥15 years)	1860 (33.9%)	155 (8.3%)	1705 (91.7)
Hb (g/dL) (N = 5486)	13.7 (12.7-14.9)	11.5 (10.7-11.8)	14.0 (13.1-15.1)	<0.001*
MCV (fl) (N = 5482)	84.3 (80.8-87.7)	79.0 (71.4-84.0)	85.0 (81.8-88.0)	<0.001*
RDW (%) (N = 5480)	13.9 (13.2-14.8)	15.0 (13.6-16.9)	13.8 (13.1-14.6)	<0.001*
MCH (pg) (N = 5480)	28.4 (26.6-29.7)	25.5 (22.4-27.9)	28.6 (27.1-29.8)	<0.001*
MCHC (g/dL) (N = 5480)	33.4 (32.4-34.5)	32.3 (31.1-33.4)	33.6 (32.6-34.6)	<0.001*
RBC count (millions/mm3) (N = 5480)	4.9 (4.6-5.3)	4.4 (4.1-4.9)	5.0 (4.6-5.4)	<0.001*
Hematocrit (%) (N = 5480)	41.1 (38.2-44.4)	34.9 (32.9-36.9)	41.9 (39.3-44.9)	<0.001*

Hb: hemoglobin, MCV: mean corpuscular volume, RDW: red cell distribution width, MCH: mean corpuscular hemoglobin, MCHC: mean corpuscular hemoglobin concentration, RBC: red blood cell.

Normally distributed variables are summarized as mean (±standard deviation). Non-normally distributed variables are summarized as median (interquartile range).

The p-values were calculated using either Wilcoxon-Mann Whitney U test for continuous variables or Pearson's chi-squared test for categorical variables.

^*Significant p-values are p<0.050.

Prevalence of anemia over the years across population from 1995 to 2023 in Indonesia

The result showed that anemia prevalence in Indonesia is diverse among various populations (Figure 2, Supplementary Figure 1). This study showed that Dieng has the lowest anemia prevalence (1%), while Sentani has the highest (49%) (Supplementary Table 4). In further analysis, we divided the region into West and East based on the Wallace line. We found that the anemic proportions in the Eastern region between 2006-2016 and 2017-2023 were higher than in the Western region (Supplementary Table 5).

Figure 2.

Anemia Prevalence Between Year 1995 to 2023 of Field Sampling done by Eijkman Institute for Molecular Biology and Mochtar Riady Research Institute of Nanotechnology. †South Sulawesi Includes Makassar, Toraja, Mandar, Kajang Populations.

Red blood cell analysis

The anemic group has a higher proportion of microcytic red cells (52.9%) and hypochromic red cells (55.6%) compared to non-anemic group. Meanwhile, macrocytic and hyperchromic red cells are rare in both groups (Figure 3). We also found that the Eastern region has a higher proportion of microcytic hypochromic compared to the Western region in the anemic group (Supplementary Table 6).

Figure 3.

Red cell indices: (A) MCV; (B) MCH. MCV (N = 5482): Microcytic (<80 fl), Normocytic (80 − 100 fl), Macrocytic (>100 fl). MCH (N = 5480): Hypochromic (<26 pg), Normochromic (26 − 32 pg), Hyperchromic (>32 pg). The p-values were calculated using Pearson's chi-squared test. The significant p-values are p<0.050. MCV: mean corpuscular volume, MCH: mean corpuscular hemoglobin.

Hematological indices for differentiating iron deficiency anemia and thalassemia trait in the anemic group

We calculated the Mentzer index and RDW index according to the formula in Supplementary Table 7. The Mentzer Index has a cutoff of 13 or higher for iron deficiency anemia, while values below 13 indicates thalassemia, and the RDW index has a cutoff of 220 or higher for iron deficiency anemia, while values below 220 implies thalassemia. In this study, we found from the Mentzer index that 86.8% of subjects indicated iron deficiency anemia, whereas 13.2% suggested thalassemia. This result is also supported by RDW index that 85.5% of subjects might have iron deficiency anemia, and 14.5% might have thalassemia (Table 2). Further, we analyze the red blood cell type and hematological indices. We found the proportions of microcytic normocytic anemia were 35% indicating iron deficiency and 13% might have thalassemia in both indexes (Supplementary Table 8).

Table 2.

Hematological indices for diagnosis of iron deficiency anemia and thalassemia trait

Variables	Anemic, N = 730
Mentzer Index	17.7 (14.7-20.5)
	Iron deficiency ≥13	634 (86.8%)
	Thalassemia <13	96 (13.2%)
RDW Index	263 (236-294)
	Iron deficiency ≥220	624 (85.5%)
	Thalassemia <220	106 (14.5%)

Mentzer Index and RDW Index were calculated using formula described in Supplementary Table 2.

Hemoglobin analysis

The comparison of hemoglobin subtype between anemic and non-anemic individuals revealed that a higher percentage of the anemic group (22.4%) exceeded the HbA2 >3.5% cutoff compared to the non-anemic group (8.6%). Further, 16.3% of anemic subjects had HbF above the 1% cutoff. These findings indicated significant differences in hemoglobin subtypes between the anemic and non-anemic groups (Table 3).

Table 3.

Hemoglobin analysis

Variables	Anemic	Non-anemic	p-value
HbA2 (%) (N = 4467)	2.80 (2.5-3.4)	2.80 (2.6-3.1)	0.890
	Cut off			<0.001*
	≤3.5%	433 (77.6%)	3573 (91.4%)
	>3.5%	125 (22.4%)	336 (8.6%)
	Total N (%)	558 (100%)	3909 (100%)
HbF (%) (N = 4468)	0.30 (0.0-0.7)	0.3 (0.0-0.6)	<0.001*
	Cut off
	<1%	467 (83.7%)	3570 (91.3%)	<0.001*
	≥1%	91 (16.3%)	340 (8.70%)
	Total N (%)	558 (100%)	3910 (100%)

HbA2: hemoglobin subunit alpha 2, HbF: fetal hemoglobin.

The p-values were calculated using either Wilcoxon-Mann Whitney U test for continuous variables or Pearson's chi-squared test for categorical variables.

^*Significant p-values are p<0.050.

Genetic risk factors for anemia

This study compiled data from various past studies. Some subjects were tested for red blood cell disorders (i.e., thalassemia, SAO, and G6PD deficiency). We found beta thalassemia to be significantly higher in the anemic group (17%). However, we did not find significant differences between the anemic and non-anemic groups for SAO and G6PD deficiency (Table 4), which is consistent with findings that SAO and G6PD deficiency are asymptomatic unless exposed to certain condition or drugs.

Table 4.

Genetic risk factors and anemia prevalence

Genetic factors	Subject tested (N)	Anemic N (%)†	Non-anemic N (%)†	p-value
Thalassemia traits	4517			<0.001*
	Alpha thalassemia		40 (7.0)	244 (6.2)
	Alpha, Beta thalassemia		0 (0)	1 (0.03)
	Beta thalassemia		96 (17.0)	213 (5.4)
	Unknown/ No mutation		430 (76.0)	3493 (88.4)
	Total N (%)		566 (100)	3951 (100)
SAO traits	1364			0.502
	Positive		28 (12.7)	124 (10.8)
	Negative		193 (87.3)	1019 (89.2)
	Total N (%)		221 (100)	1143 (100)
G6PD deficiency traits	610			1
	Deficiency		8 (6.3)	33 (6.8)
	Normal		118 (93.7)	451 (93.2)
	Total N (%)		126 (100)	484 (100)

^†The percentage calculation: (N/N tested x 100).

The p-values were calculated using Pearson's chi-squared test.

^*Significant p-values are p<0.050.

Discussion

In our study, there is a higher proportion of anemia in women than in men. This gender discrepancy in anemia prevalence is more likely due to higher iron requirements in women during menstrual blood loss and pregnancy.^20,21 Our study is consistent with previous studies that have reported high anemia prevalence among women, particularly in the reproductive age.^22–24 In addition the anemia prevalence between children (<15 years) and adults (women and men) (≥15 years) is significantly different in this study, particularly between children vs. men and women vs. men. Some studies showed the persistence of a high prevalence of anemia in children under 5 years and adolescent, especially in low- and middle-income countries.^25–28 The high prevalence of anemia in children can be attributed to factors such as low socioeconomic status, nutritional deficiencies, poor sanitation, infections, hemoglobinopathies, and other chronic diseases.^28–31 This result showed that children and women are more vulnerable to anemia compared to men. To tackle anemia in these vulnerable groups, it requires targeted interventions. WHO and UNICEF recommend public health preventive strategies, such as nutrient supplementation, food fortification, educational programs, and the prevention and control of parasitic and protozoal infections.³²

This study showed anemia prevalence in Indonesia from 1995 to 2023 varied by locations and populations. The result might be due to the complex interplay between environmental, socio-economic, and genetic factors. Indonesia is a vast and ethnically diverse country with over 17,000 islands and 730 ethnic groups, which contribute to the diversity of the Indonesian population.^33–35 This diversity led to various cultural practices, dietary habits, health beliefs, and genetic disorders, which could affect health outcomes like anemia. Chaparro and Suchdev stated that the causes of anemia vary by population, location, disease burden, and other factors (i.e., nutritional deficiencies, infections, chronic diseases, and genetic disorders).¹³ This was in line with several studies in Indonesia that found the complex etiology of anemia, not only due to nutritional deficiency but also due to non-nutritional factors such as genetic factors, infection, and chronic diseases.¹⁴, ¹⁷, ³⁶ Notably, in the Eastern region of Indonesia, where malaria is still endemic and genetic factors like hemoglobinopathy and G6PD played a role as protective traits toward malaria infection, the region is still underdeveloped compared to its western counterpart.^37–39

The analysis of red blood cell indices between anemic and non-anemic subjects in our study revealed significant differences. The anemic group showed a higher proportion of microcytic and hypochromic red cells compared to the non-anemic group, especially in the Eastern region. Microcytic anemia is the type of anemia whose RBC is smaller than normal, while hypochromic anemia has less Hb and thus red blood cell looks paler.⁴⁰ These conditions are usually caused by iron deficiency, chronic diseases, thalassemia, and defects in the synthesis of the heme group (sideroblastic anemia).^41,42 In this study, more than 50% of the subjects have microcytic and hypochromic anemia, which most probably might be due to iron deficiency. A review in 2021 reported that 8 studies out of 13 studies showed that anemia in Indonesian adolescents was associated with iron deficiency.⁴³ Further, we used the Mentzer index and RDW index to differentiate between iron deficiency anemia and thalassemia trait in the anemic group. Some studies used these alternative indexes to differentiate iron deficiency anemia and thalassemia due to limited resources, and these indexes have good sensitivity and specificity results.^44–46 Both indexes showed similar result, most microcytic hypochromic anemia cases were iron deficiency anemia, however, there were also subjects with thalassemia traits, demonstrating the importance of distinguishing iron deficiency anemia and thalassemia trait as the common causes of anemia. Additionally, considering that the Eastern region has high rate of malaria infection, and the contribution of genetic factors, if iron supplementation does not improve anemia status, especially in microcytic hypochromic anemia, practitioners should consider thalassemia factors. Moreover, microcytic hypochromic anemia also could result from thalassemia coexist with iron deficiency.⁴⁷ Most of the normocytic and normochromic anemia occurs as a result of infections, chronic diseases, or non-nutritional anemia.⁴⁸ These non-nutritional anemia prevalence increases with infection conditions such as malaria, TBC, HIV, and helminthiasis, as well as with chronic diseases such as diabetes, cardiovascular diseases, metabolic syndrome, and chronic kidney diseases.^14,17

Other non-nutritional anemia contributing factors were genetic factors and thus, we further analyzed the hemoglobin subtypes. This study showed that the anemic group has a higher proportion of elevated HbA2 and HbF levels than the non-anemic group. Elevated HbA2 and HbF levels were found in beta thalassemia carriers.^49,50 These might be an indication of the presence of beta thalassemia carriers or other hemoglobinopathies among anemic subjects. Beta thalassemia is characterized by the reduction in or absence of beta globin chain synthesis, resulting in reduced Hb, decreased RBC production, leading to anemia.⁵¹ This showed that hemoglobin subtype analysis is relevant in diagnosing and understanding the etiology of anemia. Our study was from various past studies, some subjects were from thalassemia and G6PD studies and were tested for thalassemia, SAO, and G6PD.⁵² We found that beta thalassemia was significantly higher in the anemic group. We found no significant differences were observed between the anemic and non-anemic groups for SAO and G6PD deficiency. However, the genetic mutation on SAO and G6PD altering the stability of the RBC could contribute to hemolytic anemia when exposed to certain conditions.^53–55 Thalassemia, SAO, and G6PD prevalences were increased in certain geographic regions where malaria is endemic, as a result of positive selection.^56–58 Thus, the results in this study highlight the importance of addressing not only nutritional factors but also non-nutritional factors to mitigate anemia elimination in Indonesia. Although iron deficiency is the main cause of anemia incidence in Indonesia, it is not sufficient to only focus on nutritional intervention since the main national program is iron-folic supplementation to decrease the prevalence of anemia.³⁶ Policy makers should consider other etiological factors when designing a comprehensive intervention program to overcome anemia.

Region-specific disparities were evident in this study, especially for intervention programs. Sentani in Jayapura, Papua, had the highest anemia prevalence in this study. The poverty rate in Jayapura was 11.45% higher than national poverty rate in 2023 which was 9.36%⁵⁹ and there was a high incidence of malaria infection.⁶⁰ Anemia interventions in this region should address not only nutritional factor but non-nutritional factors such as malaria infection. The programs should include malaria prophylaxis and genetic screening since genetic factors like hemoglobinopathy and G6PD play a role as protective traits toward malaria infection. The second highest prevalence was in rural area in Palembang (Ogan Komering Ilir). According to Badan Pusat Statistik (Central Agency of Statistics) the poverty rate in this region reached 15.98 % in 2010 decreased to 13.15% in 2023 this number was higher than the national poverty rate 13.33% (2010) and 9.36% (2023).^59,61 This region had a low socio-economic status, thus the intervention might focus in nutritional aspects, such as iron supplementation and education.

For more than three decades, Indonesia has implemented an iron supplementation program. Since 2016, Indonesia's Ministry of Health updated its national program for anemia prevention and control in adolescent girls and women of reproductive age and release the new specifications for iron folic acid (IFA) tablet in accordance with the WHO recommendations.⁶² Food fortification has been recognized as a cost-effective strategy for micronutrient malnutrition; thus, Indonesia launched the Wheat Flour Food Fortification Program in 2002. This program mandated that wheat flour should be fortified with iron, zinc, folic acid, and vitamins B1 and B2.⁶³ Nevertheless, these intervention programs face challenges such as inadequate supply of IFA tablet, lack of follow-up, facilities and infrastructure were insufficient for IFA tablet distribution, and dietary preferences different across regions of Indonesia.³⁶ In 2017, the Ministry of Health introduced Regulation No. 15 on helminthiasis control.⁶⁴ Helminth infections can cause anemia especially for children. However, deworming programs are hindered by lack of compliance, and inconsistency of application of deworming programs.⁶⁴ Further, cultural practices complicated anemia management. Indonesia has several customs and beliefs that are circulating in society regarding food taboos. Food taboos during pregnancy often restrict the intake of iron-rich foods like meat, fish and green vegetable, leading to higher anemia risks.^65–69 These taboos exist to protect the health of mothers and their babies but may also increase the risk of deficiency of iron in pregnant women. Misinformation on modern drugs also contribute to the reluctance to take iron supplement, due to some beliefs that taking iron supplements will make the baby bigger and more difficult to deliver.⁶⁷ Addressing these customs and beliefs is crucial to effectively reduce anemia in Indonesia.

Compared to neighboring countries, the prevalence of anemia in Indonesia among women of reproductive age (31%) exceeds Thailand (24%) and Vietnam (21%) but almost closely with Malaysia (32%).⁷⁰ Malaysia has integrated health programs and public health campaigns to mitigate anemia elimination.⁷¹ Thailand implements several programs such as, iron supplementation and food fortification programs, dietary improvement and complementary public health measures.⁷² Vietnam develops strategy implemented in National Nutrition Strategy in 2022 to minimize inequities or barriers to access to care.⁷³ Meanwhile, Indonesia's efforts for anemia mitigation require strengthened supplementation supply, infrastructure, and multifactorial approaches to address its unique etiological landscape.

This study has several limitations, we did not perform laboratory tests for iron deficiency and only a limited samples were analyzed for specific genetic disorders (thalassemia, SAO and G6PD deficiency). Additionally, we did not perform statistical analysis to determine sample size, as this study used data from previous studies with pre-existing blood count analyses. The population used in this study may not fully represent the entire state of the vast Indonesia. Nevertheless, this study included 9 populations from the Western and 8 populations from the Eastern regions in Indonesia. These populations were representative of the big islands of Indonesian archipelago, such as Sumatra (4 populations), Borneo (2 populations), Java-Bali (3 populations), Sulawesi (2 populations), Maluku (1 population), Nusa Tenggara Islands (4 populations), and Papua (1 population). A larger sample size and diverse demographic characteristics would provide a more comprehensive understanding of the underlying causes of anemia and inform targeted intervention strategies. Future research should include more representative sample of the Indonesian population, comprehensive tests for iron deficiency and genetic disorders across all samples.

Conclusion

The anemia prevalence in Indonesia is quite high, and the etiology is very complex, with not only nutritional factors but also non-nutritional factors. Therefore, the mitigation of anemia in the Indonesian population should not solely depend on Hb concentration analysis but also requires conducting complete blood count screening, Hb analysis, iron status, or genetic screening especially in areas that are known to have high RBC disorder prevalence prioritizing in women and children. Interventions should address both nutritional and non-nutritional factors, including infections and genetic factors. Authorities must consider non-nutritional factors and should make programs beyond nutrient-specific strategies and consider the complex interplay of personal behavior, sociocultural factors, dietary and health patterns, local community, and genetic diversity.³⁶

Supplementary Materials

All supplementary tables and figures are available upon request from the editorial office.

Acknowledgements

The authors would like to express our gratitude to all the subjects who participated in the study.

We thank various Laboratories/Groups at the EIMB including the Genome Diversity and Diseases, Thalassemia, and Red Blood Cell Membrane and Enzyme Disorder for their support during sample collections and genetics screening. We thank the study participants, the field medical doctors, medical faculty students, clinical pathology laboratories, local assistants, and research assistants -for their support in this study.

Conflict of Interest and Funding Disclosure

The authors declare no conflict of interest.

This study was from various population studies in Indonesia, some studies were conducted at the Eijkman Institute for Molecular Biology and were funded by the Indonesian Government through the Indonesian Ministry of Research and Technology block grant. We also received support from Variant Bio.

Funding Statement

This study was from various population studies in Indonesia, some studies were conducted at the Eijkman Institute for Molecular Biology and were funded by the Indonesian Government through the Indonesian Ministry of Research and Technology block grant. We also received support from Variant Bio.