European Journal of Cardiovascular Medicine

Anemia is a significant global health concern, particularly affecting children in low- and middle-income countries. [1] Defined by the World Health Organization (WHO) as hemoglobin levels below 11 g/dL in children under five, [2] anemia poses serious health risks, including impaired growth, developmental delays, and reduced immunity. [3] Among its various forms, iron deficiency anemia (IDA) is the most prevalent, accounting for nearly half of all anemia cases globally. [4] WHO estimates that approximately 42% of children under five are anemic worldwide, with South Asia and Sub-Saharan Africa bearing the heaviest burden. [5][6] In India, the prevalence is alarmingly high, with 67% of children aged 6–59 months affected, as reported in the National Family Health Survey-5 (NFHS-5, 2019–2021). [7] Despite government interventions like the Anaemia Mukt Bharat initiative, the prevalence of anemia in India has shown only modest improvement.

IDA arises due to inadequate iron intake, absorption, or increased demand during rapid growth periods. It is characterized by microcytic hypochromic anemia, which can overlap with conditions such as beta-thalassemia trait, complicating diagnosis. [8] Conventional diagnostic methods, such as serum ferritin level estimation and hemoglobin electrophoresis, are reliable but expensive and not readily available in resource-constrained settings. [9] This necessitates the use of simpler, cost-effective tools for preliminary screening.

The Mentzer Index (MI), first introduced in 1973, has emerged as a promising diagnostic tool. It is calculated as the ratio of mean corpuscular volume (MCV) to red blood cell (RBC) count, with a value greater than 13 suggesting IDA and a value below 13 indicating beta-thalassemia trait. [10]

The study aims to assess the validity of the Mentzer Index as a diagnostic tool for identifying iron deficiency anemia (IDA) by comparing its results with serum ferritin levels, which are considered the gold standard for diagnosing IDA. Furthermore, the study seeks to evaluate the sensitivity and positive predictive value of the Mentzer Index, aiming to establish its reliability and effectiveness in accurately diagnosing IDA, particularly in resource-constrained settings where cost-effective and accessible diagnostic methods are crucial.

OBJECTIVES:

1. To assess the validity of Mentzer index for diagnosing iron deficiency anemia by comparing Mentzer index to serum ferritin level.
2. To evaluate the sensitivity and positive predictive value of Mentzer index in the diagnosis of iron deficiency anemia.

Study Design:  Cross-sectional Study.

Study area:  The study was carried out in the Department of Pediatrics, Maheshwara Medical College & Hospital, Chitkul Village, Sangareddy District.

Study Period: 1 year.

Study population: Children aged between 1 to 14 years, admitted to the pediatric ward of Maheshwara Medical College & Hospital, and presenting with anemia, constituted the study population.

Sample size: The study consisted of a total of 100 subjects.

Sampling Technique: Convenient sampling method.

Inclusion Criteria: 

1. Children aged 1–14 years diagnosed with anemia without any clinical signs of acute or chronic inflammatory or infectious diseases.
2. Children whose parents or guardians provided written informed consent.

Exclusion criteria:

1. Children who received a blood transfusion within the last three months.
2. Children on iron therapy within the previous month.
3. Children with a history of blood loss in the previous month.

Ethical consideration: Institutional Ethical committee permission was taken before the commencement of the study.

Study tools and Data collection procedure:

A structured, pretested proforma was used for data collection. Detailed clinical history was obtained from each participant, including information on symptoms, prior illnesses, history of blood transfusion, iron supplementation, worm infestation, pica, and seizures. A thorough physical examination was performed for all study subjects.

Sample Collection and Laboratory Analysis

Venous blood samples (5 ml) were collected aseptically from a peripheral vein and divided into plain and EDTA tubes. The samples were stored in a cool box with ice packs and shielded from light using black plastic sheets to preserve sample integrity during transport to the laboratory.

Standardized protocols and quality control procedures were followed during blood collection and laboratory testing. The time of sample collection was documented on each specimen.

The following laboratory investigations were conducted:

• Complete Blood Picture (CBP) using an automated 5-part analyzer (Celtic).
• Serum Iron and TIBC using the Iron Ferrozine test kit.
• Serum Ferritin and Transferrin Saturation using the CLIA (Chemiluminescent Immunoassay) technique.

Haemoglobin Electrophoresis by electrophoretic technique to exclude hemoglobinopathies.

• C-Reactive Protein (CRP) using slide latex agglutination method.
• Stool Examination for occult blood, ova, and cysts.
• Urine Routine Examination for infections or abnormalities.

Mentzer Index Calculation

The Mentzer index was calculated using the formula:

Mentzer Index = MCV (fL) / RBC Count (million/mm³).

A Mentzer index value of more than 13 was considered indicative of iron deficiency anemia, while values below 13 were suggestive of beta-thalassemia trait.

Data Analysis:

Collected data were entered and analyzed using appropriate statistical software. The Mentzer index was compared against serum ferritin values to assess its diagnostic performance. Sensitivity and positive predictive value (PPV) of the Mentzer index in diagnosing iron deficiency anemia were evaluated.

Table 1: Age Distribution

Among the total of 100 individuals, 6% are between 12-23 months, 18% fall within the 24-59 months range, and the majority, 66%, belong to the 5-11 years category. Additionally, 10% are aged 12-14 years.

Out of a total of 100 individuals, 58% are males and 42% are females. 

The majority, 55%, belong to the lower socioeconomic group, followed by 24% in the upper lower category. Additionally, 14% fall within the lower middle group, while the smallest proportion, 7%, belongs to the upper middle category.

Regarding school performance, 56% categorized as having good performance, 32% classified as moderate, and 12% identified as poor.

Table 2: Distribution of population based on Severity of Anaemia

The majority, 78%, have moderate anemia, while 12% experience mild anemia, and 10% suffer from severe anemia.

Table 3: Distribution of population based on Mentzer Index

The Mentzer Index among individuals, showing that 15% have a value below 13, while the majority, 85%, have a value above 13. Regarding the distribution of serum ferritin levels among individuals, indicating that 52% have levels below 15, while 48% have levels above 15.

Regarding the distribution of liver span measurements among individuals, showing that 44% have a liver span of 8-9 cm, 30% fall within the 6-7 cm range, 14% have a span of 4-5 cm, and 12% have a span of 10-11 cm. The majority of individuals fall within the 8-9 cm category, while the smallest proportion belongs to the 4-5 cm range.

Table 4: Mean and SD of various hematological parameters

The table 4 presents the hematological parameters' mean and standard deviation (SD) values. The mean Haemoglobin was 9.28 ± 0.94, mean red blood cell (RBC) count was 4.16 ± 0.85, while the mean corpuscular volume (MCV) was 64.40 ± 7.91. Mean corpuscular hemoglobin (MCH) has a mean of 20.41 ± 3.71, whereas mean corpuscular hemoglobin concentration (MCHC) is 29.36 ± 4.50. Platelet count averages 3.44 ± 1.36, and total white blood cell (TWBC) count is 7656.40 ± 2586.34. Lastly, the reticulocyte percentage is 1.17 ± 1.13, providing a detailed overview of the distribution and variability of these blood parameters.

Table 5: Mean and SD of various Iron Related Biochemical Parameters

The table 5 presents the mean and standard deviation (SD) values for various iron- related biochemical parameters. Serum iron has a mean of 80.57 ± 32.55, while total iron-binding capacity (TIBC) averages 395.03 ± 78.68. The percentage of iron saturation is 21.56 ± 9.96, and transferrin levels have a mean of 213.94 ± 39.90. Lastly, serum ferritin shows a mean of 55.30 ± 66.14, reflecting considerable variability. These values provide a comprehensive overview of iron metabolism in the population.

The mean and standard deviation (SD) values for hemoglobin electrophoresis results. Hemoglobin A (Hb A) has a mean of 95.24 ± 1.14, while hemoglobin A2 (Hb A2) averages 2.48 ± 0.43. Hemoglobin F (Hb F) has a mean of 1.38 ± 0.89, indicating minimal fetal hemoglobin presence in the population.

Table 6: Relationship between age groups and serum ferritin levels

The table 6 presents the relationship between age groups and serum ferritin levels, showing the distribution of individuals with serum ferritin <15 and >15 across different age categories. Among those aged 12-23 months, 4 have low ferritin, while 2 have high levels. In the 24-59 months group, 4 have low ferritin, while 14 have high levels. The 5-11 years category has the highest numbers, with 38 having low ferritin and 28 having high levels. In the 12-14 years group, 6 individuals have low ferritin, while 4 have high levels. The chi-square value is 7.99015, with a p-value of 0.04621, indicating a statistically significant association between age and serum ferritin levels. 

Table 7: Relationship between age groups and the Mentzer Index

The table 7 examines the relationship between age groups and the Mentzer Index, showing the distribution of individuals with values <13 and >13. Among those aged 12-23 months, 4 have a Mentzer Index <13, while 2 have >13. In the 24-59 months group, 2 individuals fall below 13, whereas 16 are above. The 5-11 years category has 5 individuals with <13 and 61 with >13, while in the 12-14 years group, 4 have <13 and 6 have >13. The chi-square value is 20.5307, with a p-value of 0.00013, indicating a highly significant association between age and the Mentzer Index.

Table 8: Relationship between Serum Ferritin levels and the Mentzer Index

The table 8 examines the relationship between the Mentzer Index and serum ferritin levels, showing that among individuals with a Mentzer Index >13, 48 have serum ferritin <15, while 37 have >15. Conversely, among those with a Mentzer Index <13, 4 have serum ferritin <15, while 11 have >15. The chi-square value is 4.5374, with a p- value of 0.03316, indicating a statistically significant association between the Mentzer Index and serum ferritin levels.

Table 9: Statistical measures based on Mentzer index

The accompanying statistical measures assess the performance of the Mentzer Index in predicting low serum ferritin. Sensitivity is 92.31% (95% CI: 81.46% to 97.86%), meaning the test is highly effective in detecting true positives. However, specificity is low at 22.92% (95% CI: 12.03% to 37.31%), indicating limited ability to identify true negatives. The positive predictive value (PPV) is 56.47% (95% CI: 52.18% to 60.67%),

while the negative predictive value (NPV) is 73.33% (95% CI: 48.41% to 88.96%), reflecting the likelihood that positive and negative results are correct. The overall accuracy of the test is 59.00% (95% CI: 48.71% to 68.74%), showing moderate reliability in classification.

              Area Under the Curve

Area       under         the curve

Std. Error

Asymptotic Sig.

Asymptotic 95% Confidence Interval

Lower Bound

Upper Bound

0.635

0.059

0.028

0.520

0.749

The provided ROC curve evaluates the diagnostic performance of a test, with sensitivity plotted against 1-specificity. The area under the curve (AUC) is 0.635, indicating moderate discriminatory ability. The standard error is 0.059, and the asymptotic significance (p-value) is 0.028, suggesting statistical significance. The 95% confidence interval ranges from 0.520 to 0.749, implying that the true AUC value is likely within this range. Since the AUC is above 0.5 but not close to 1, the test has moderate effectiveness in distinguishing between conditions but is not highly accurate.

Iron deficiency anemia (IDA) remains one of the most prevalent nutritional deficiencies among children worldwide, significantly affecting their growth, cognitive development, and overall health. The Mentzer Index (MI), a simple ratio derived from Mean Corpuscular Volume (MCV) and Red Blood Cell (RBC) count, has been widely studied as a potential screening tool for differentiating IDA from beta-thalassemia trait (βTT). However, the demographic and clinical characteristics of study populations can influence the diagnostic utility of MI.

The present study included 100 children, with a significant proportion (66%) falling within the 5-11 years age group, followed by 18% in the 24-59 months range, 10% aged 12-14 years, and 6% in the 12-23 months category.

This distribution aligns with findings from Amer J et al. (2022), [11] where the highest prevalence of IDA was observed in children aged 6-12 months and 1-5 years, reflecting early childhood vulnerability due to inadequate dietary iron intake. Similarly, Alkamali A et al. (2024) [12] studied infants in their first year of life visiting primary health centers in Dubai and reported that iron deficiency is significantly higher among younger children due to rapid growth rates and higher iron demands. However, our study included a broader age range, making it more comparable to AlQarni AM et al. (2024), [13] where the sample consisted of children aged up to 16 years, allowing for better age-based stratification of anemia severity and iron status.

The gender composition of study populations influences anemia prevalence due to biological and dietary factors. In the present study, 58% of participants were male and 42% female, indicating a slight male predominance. This trend is consistent with Awais M et al. (2022), [14] which reported 41.5% males and 58.5% females in their study on children aged 6-12 years.

Socioeconomic status (SES) has a profound impact on the prevalence and severity of IDA in children, as it determines dietary diversity, healthcare access, and nutritional supplementation. The present study found that 55% of participants belonged to the lower socioeconomic group, followed by 24% in the upper-lower, 14% in the lower- middle, and 7% in the upper-middle category. Similar findings were reported by Sherali A et al. (2023), [15] where the prevalence of IDA was highest in children from lower-income families due to limited access to iron- rich foods and healthcare facilities. Iqbal S et al. (2024) [16] also observed a significant correlation between low SES and severe anemia, reinforcing that economic constraints exacerbate nutritional deficiencies.

Malnutrition is a leading risk factor for IDA, as iron-rich foods are often inaccessible or insufficiently consumed in economically disadvantaged communities. In the present study, 43.24% of children aged 1-5 years had normal weight-for-age, while 37.84% exhibited mild malnutrition, 10.81% moderate malnutrition, and 8.11% severe malnutrition. Among children aged 6-14 years, 79.37% were underweight, indicating a high burden of malnutrition. These findings agree with Alam LS et al. (2014), [17] who found a strong association between low body weight and IDA among school-aged children. Similarly, Ghosh A et al. (2024) [18] emphasized that underweight children had significantly lower hemoglobin, serum ferritin, and MCV levels, supporting the role of chronic malnutrition in anemia development.

Overall, the comparison of these studies demonstrates significant variations in the distribution of anemia severity. The present study highlights a particularly high burden of moderate anemia (78%), whereas other studies, such as Amer J et al. (2022) [11] and Ghosh A et al. (2024),[18] report higher proportions of mild anemia. The disparities in severe anemia prevalence further emphasize the need for context-specific investigations to understand the underlying factors contributing to these differences. These variations may stem from differences in study populations, diagnostic criteria, or environmental and nutritional factors, highlighting the necessity for tailored public health interventions to address anemia effectively.

Table 10: Mentzer Index Performance in Iron Deficiency Anemia (IDA)

The Mentzer Index is a well-established hematological parameter used to differentiate between iron deficiency anemia (IDA) and beta-thalassemia trait (β-TT). It is calculated as the ratio of mean corpuscular volume (MCV) to red blood cell (RBC) count, with a commonly accepted threshold of 13. A Mentzer Index greater than 13 is suggestive of IDA, whereas a value below 13 is indicative of β-TT. Despite its widespread use, the diagnostic accuracy of the Mentzer Index varies across different populations and clinical settings, as evidenced by disparities in sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) among various studies.

The present study found that the Mentzer Index had a sensitivity of 92.31%, meaning that it successfully identified over 92% of IDA cases. However, the specificity was significantly lower at 26.92%, indicating that a large proportion of patients with β-TT might be misclassified as having IDA. This suggests that while the Mentzer Index is effective in detecting IDA, it may lack the ability to reliably exclude β-TT cases.

Furthermore, the PPV was 56.47%, meaning that when the Mentzer Index indicated IDA, it was correct only 56% of the time. The NPV was 73.33%, signifying that when the Mentzer Index ruled out IDA, it was correct in approximately 73% of cases. These findings highlight the limitations of the Mentzer Index as a standalone diagnostic tool, particularly in populations with a high prevalence of both IDA and β-TT.

In contrast, a study by Ahmed HS et al. (2018) [19] conducted in Egypt among children aged 2 to 4 years reported a significantly higher sensitivity of 95.24% and specificity of 93.10%, suggesting that the Mentzer Index was highly effective in distinguishing IDA from β-TT in their study population. The PPV was 90.9%, indicating that when the Mentzer Index diagnosed IDA, it was correct nearly 91% of the time. Additionally, the NPV was 96.4%, demonstrating a strong ability to rule out β-TT when the Mentzer Index suggested otherwise. This remarkable diagnostic accuracy suggests that the performance of the Mentzer Index improves in pediatric populations, possibly due to fewer confounding factors such as mixed anemia types. Furthermore, the larger sample size and controlled study environment may have contributed to the improved reliability of the test.

Similarly, the study by Vehapoglu A et al. (2014) [20] demonstrated a sensitivity of 82.3% and an exceptionally high specificity of 98.7%, indicating a high level of diagnostic accuracy. Their PPV was 98.2%, meaning that almost all cases identified as IDA were truly IDA, while the NPV was 86.3%, reflecting a strong ability to rule out β-TT. Compared to the present study, where specificity was only 26.92%, the significantly higher specificity in Vehapoglu A et al.'s (2014) [35] research suggests a clearer distinction between IDA and β-TT within their study population. This could be due to lower rates of coexisting anemia types, better patient selection criteria, or differences in underlying genetic variations affecting β-TT prevalence.

Another study by Düzenli Kar Y et al. (2018) [39] examined the Mentzer Index in the context of IDA and alpha-thalassemia trait (α-TT) rather than β-TT. The study found that the Mentzer Index had a sensitivity of 64.52% and specificity of 92%, meaning that while many IDA cases were correctly identified, the test was even more effective at distinguishing α-TT from IDA than β-TT from IDA. The PPV was 82.6%, suggesting that the Mentzer Index was fairly reliable in predicting IDA, and the NPV was 79.3%, showing a reasonable ability to rule out α-TT. This indicates that the Mentzer Index may be more effective in populations where α-TT is a concern rather than β-TT, as specificity values were significantly higher compared to studies focusing on β-TT.

The study by Sirdah M et al. (2008) [40] specifically evaluated the Mentzer Index’s performance in differentiating IDA from β-TT. Their results demonstrated a sensitivity of 82.86% and specificity of 83.55%, indicating that the Mentzer Index provided a well- balanced diagnostic performance. Although PPV and NPV were not explicitly reported, these sensitivity and specificity values suggest that the Mentzer Index was more reliable in distinguishing IDA from β-TT compared to the present study. These findings imply that in populations where β-TT is well-defined and less frequently coexists with IDA, the Mentzer Index may yield more accurate results.

Aydogan K et al. (2019) [22] reported one of the highest specificity values at 97.36%, suggesting that the Mentzer Index was extremely effective at distinguishing IDA from other conditions. However, their sensitivity was only 67.53%, indicating a lower ability to detect IDA cases. This suggests a trade-off between sensitivity and specificity, where increasing the ability to correctly rule out β-TT may come at the cost of missing some IDA cases.

In the present study, 52% of children had serum ferritin levels below 15 ng/mL, which is indicative of iron deficiency, whereas 48% had serum ferritin levels above 15 ng/mL. These findings align with those of Iqbal S et al. (2024), [24] who found that 53.7% of children with anemia had serum ferritin <15 ng/mL, reinforcing the high prevalence of iron deficiency in pediatric populations.  The present study found a statistically significant association between the Mentzer Index and serum ferritin levels (p = 0.03316), with children having MI >13 more likely to exhibit low serum ferritin levels. This supports the hypothesis that MI is a useful screening tool for IDA, although confirmatory biochemical tests are still required.

The findings from the present study reinforce the Mentzer Index (MI) as a valuable, cost-effective, and easily accessible screening tool for diagnosing Iron Deficiency Anemia (IDA) in children. However, due to its moderate specificity, MI should not be used as a standalone diagnostic marker. Instead, its application is best suited for initial differentiation between IDA and β-thalassemia trait (βTT), particularly in resource- limited settings where advanced diagnostic tests may not be readily available.

The study highlights the importance of confirmatory biochemical markers, such as serum ferritin, transferrin saturation, and total iron-binding capacity (TIBC), to accurately diagnose IDA and guide appropriate iron supplementation therapy. Given the observed correlation between MI and anemia severity, incorporating MI into routine pediatric screenings could help in the early detection and intervention of iron deficiency, preventing complications like impaired cognitive development and poor academic performance.

MI serves as a practical first-line screening tool, its diagnostic value is optimized when combined with hematological and biochemical assessments, ensuring better clinical outcomes in pediatric anemia management.

This research underscores the widespread incidence of undernutrition and anemia, especially among children from disadvantaged socioeconomic backgrounds. A large portion of the participants was underweight, with a notable number suffering from moderate anemia and low serum ferritin levels. While the Mentzer Index demonstrated strong sensitivity in identifying iron deficiency, its specificity was lacking, highlighting the requirement for supplementary diagnostic approaches. There was no notable correlation found between gender and parameters related to anemia. These results stress the critical need for enhanced nutritional assistance, anemia screening, and healthcare interventions to improve the well-being of children in vulnerable populations.

1.       Preethi V, Hemalatha V, Arlappa N, Thomas M, Jaleel A. Trends and predictors of severe and moderate anaemia among children aged 6–59 months in India: an analysis of three rounds of National Family Health Survey (NFHS) data. BMC Public Health. 2024 Oct 14;24(1):2824.

2.       Hamed E, Syed MA, Alemrayat BF, Tirmizi SHA, Alnuaimi AS. Haemoglobin cut-off values for the diagnosis of anaemia in preschool-age children. Am J Blood Res. 2021;11(3):248-54.

3.       Saloojee H, Pettifor JM. Iron deficiency and impaired child development. BMJ. 2001 Dec 15;323(7326):1377-8.

4.       Short MW, Domagalski JE. Iron deficiency anemia: evaluation and management. Am Fam Physician. 2013 Jan 15;87(2):98-104.

5.       World Health Organization. Anaemia [Internet]. www.who.int. World Health Organisation; 2024. Available from: https://www.who.int/health- topics/anaemia#tab=tab_1

6.       Liu Y, Ren W, Wang S, Xiang M, Zhang S, Zhang F. Global burden of anemia and cause among children under five years 1990-2019: findings from the global burden of disease study 2019. Front Nutr. 2024; 11:1474664.

7.       Government of India. INDIA REPORT [Internet]. 2022 Mar. Available from: https://dhsprogram.com/pubs/pdf/FR375/FR375.pdf

8.       Chaparro CM, Suchdev PS. Anemia epidemiology, pathophysiology, and etiology in low- and middle-income countries. Ann N Y Acad Sci. 2019 Aug;1450(1):15-31.

9.       Kotecha PV. Nutritional anemia in young children with focus on Asia and India. Indian J Community Med. 2011 Jan;36(1):8-16.

10.    Ntaios G, Chatzinikolaou A, Saouli Z, Girtovitis F, Tsapanidou M, Kaiafa G, et al. Discrimination indices as screening tests for β-thalassemic trait. Ann Hematol. 2007 Jul;86(7):487-91.

11.    Amer J. A Retrospective Study Using Mentzer Index for Prevalence of Iron Deficiency Anemia among Infants Visiting Maternal Centers at the Age of One Year. Anemia. 2022 Mar 27; 2022:1-5.

12.    Alkamali A, Alshafiei LS, AlJasmi M, Helali H, Alhmid I, AlOlama F, et al. Evaluating the Mentzer Index for Screening of Iron Deficiency Anemia and Beta Thalassemia Among Infants Visiting Primary Health Centers in Dubai, United Arab Emirates: A Retrospective Study. Cureus. 2024 Aug 6;16(8): e66286.

13.    AlQarni AM, Althumairi A, Alkaltham NK, AlJishi S, Hakami AM, Abdalla LMOA, et al. Diagnostic test performance of the Mentzer index in evaluating Saudi children with microcytosis. Front Med. 2024 Jul 29; 11:1361805. 

14.    Awais M, Ahmad A, Farid A, Khan H. Mentzer index as a screening tool for iron deficiency anemia in 6-12 years old children. Journal of Postgraduate Medical Institute.2022;36(4):235-38.

15.    Sherali A, Ahad A, Tikmani SS, Sohail S. Screening of Iron Deficiency Anemia in Children Using Mentzer Index in Pakistan: A Cross-Sectional Study. Global Pediatric Health. 2023 Jan;10(1):1–5.

16.    Iqbal S, Amin W, Arshad S, Ain N. Diagnostic Accuracy Of Mentzer Index As A Screening Tool For The Diagnosis Of Iron Deficiency Anemia By Taking Iron Profile As Gold Standard In Patients Presenting With Hypochromic Microcytic Anemia At Tertiary Care Hospital, Karachi. Biol Clin Sci Res J. 2024 Jan 20;2024(1):669.

17.    Alam LS, Purnamasari R, Bahar E, Rahadiyanto EK. Mentzer index as a screening tool for iron deficiency anemia in 6-12-year-old children. Paediatr Indones. 2014;54(5): 294-298.

18.    Ghosh A, Dasgupta D, Biswas S. Analysis of Mentzer Index in children presenting with microcytic hypochromic anemia: A cross-sectional study. Asian Journal of Pharmaceutical and Clinical Research. 2024;17(8):147-150.

19.    Ahmed HS, Hashem EA, Al-asheer OM, Mahmoud AA. The Diagnostic Performance of Red Cell Distribution Width and Mentzer Index for Discrimination between Iron Deficiency Anemia and Beta Thalassemia Trait. Med J Cairo Univ 2018;86(7): 3979-88.

20.    Vehapoglu A, Ozgurhan G, Demir AD, Uzuner S, Nursoy MA, Turkmen S, et al. Hematological Indices for Differential Diagnosis of Beta Thalassemia Trait and Iron Deficiency Anemia. Anemia. 2014; 2014:1-7.

21.    Düzenli Kar Y, Özdemir ZC, Emir B, Bör Ö. Erythrocyte Indices as Differential Diagnostic Biomarkers of Iron Deficiency Anemia and Thalassemia. Journal of Pediatric Hematology/Oncology. 2020 Apr;42(3):208-13.

22.    SIRDAH M, TARAZI I, AL NAJJAR E, AL HADDAD R. Evaluation of the diagnostic reliability of different RBC indices and formulas in the differentiation of the β- thalassaemia minor from iron deficiency in Palestinian population. Int J Lab Hematology. 2008 Aug;30(4):324-30.

23.    Aydogan G, Keskin S, Akici F, Salcioglu Z, Bayram C, Uysalol EP, et al. Causes of Hypochromic Microcytic Anemia in Children and Evaluation of Laboratory Parameters in the Differentiation. Journal of Pediatric Hematology/Oncology. 2019 May;41(4):e221-e223.

24.    Demir A, Yarali N, Fisgin T, Duru F, Kara A. Most reliable indices in differentiation between thalassemia trait and iron deficiency anemia. Pediatrics International. 2002 Dec;44(6):612-6.

25.  Iqbal S, Amin W, Arshad S, Ain N. Diagnostic Accuracy Of Mentzer Index As A Screening Tool For The Diagnosis Of Iron Deficiency Anemia By Taking Iron Profile As Gold Standard In Patients Presenting With Hypochromic Microcytic Anemia At Tertiary Care Hospital, Karachi. Biol Clin Sci Res J. 2024 Jan 20;2024(1):669