Anemia is a disorder characterized by a diminished red blood cell count or low hemoglobin levels in the blood, leading to a decreased capability for oxygen transport. Iron deficiency is a primary contributor to anemia, representing over fifty percent of the global anemic population; yet, anemia may also arise from deficiencies in other micronutrients, including riboflavin, vitamins A and B [1-3] and folate. Several chronic diseases, including tuberculosis, cancer, acquired immunodeficiency syndrome, and malaria, as well as inherited or acquired disorders like thalassemia, can also result in anemia [4–6]. Chronic illnesses and infections are the second primary cause of anemia following iron deficiency. Prior evidence indicates that the majority of individuals afflicted by viral, bacterial, or parasitic infections, malignancies, autoimmune disorders (such as rheumatoid arthritis and systemic lupus erythematosus), and gastrointestinal disturbances (including inflammatory bowel syndrome) develop anemia as a result of increased production of proinflammatory cytokines and free radicals, which impair erythroid progenitor cells.

Anemia is a widespread nutritional condition worldwide, impacting approximately one-third of the global population due to iron deficiency. The World Health Organization (WHO) estimates that almost two billion individuals, or 25% of the worldwide population, are anemic, with approximately half of this group suffering from iron deficiency anemia (IDA).[7] In 2016, the World Health Organization (WHO) estimated that anemia affected 40% of women of reproductive age and 42% of children under five years old globally.[8]

Iron deficiency in India impacts 50% of the population, resulting in adverse pregnancy outcomes and 20% of maternal fatalities. Children between 6 and 59 months exhibit the most significant rise in anemia prevalence. As per the National Family Health Survey-5 (NFHS-5) conducted from 2019 to 2021, 67.1% of Indians [9]

Approximately 50% of children in developing countries get anemia, primarily due to iron deficiency, which impacts 32.8% of adolescent girls and women aged 15 to 49.[10] Iron deficiency anemia (IDA) elevates the risk of miscarriage, preterm labor, placental abruption, and low birth weight in women of reproductive age. [11,12]

Anemia is a significant impediment to the health of women and children. Iron is a vital nutrient and a cofactor for numerous hemoproteins and non-heme iron proteins. It is a constituent of the hemoprotein hemoglobin in erythrocytes and of myoglobin, facilitating oxygen delivery throughout the body and storing it in muscles and other tissues. Iron plays a crucial role by supplying the haem iron center of cytochrome C oxidase. It is vital for its activity in the terminal phase of the electron transfer chain and facilitates ATP generation during mitochondrial cellular respiration [13]. Concerns exist that iron shortage may undermine this process, which is crucial for brain development [14]. Inadequate iron status resulting from insufficient dietary iron intake is typically considered the predominant cause of anemia, particularly in low- and middle-income countries (LMICs) [15]. Nonetheless, there are growing apprehensions that hemoglobin content may not be an adequate signal of iron insufficiency.

The current study aimed to assess the prevalence of anemia in school-aged children in Washim District, Maharashtra

Study area:

This study was performed in the pediatric department of Maa Ganga Memorial Bahati Hospital, Washim District, Maharashtra, following approval from the Institutional Ethical Committee. This hospital is among the largest pediatric hospitals in Maharashtra.

Study population:

This study involved 500 children aged 6-12 in WASHIM District.

Sample Size Calculation:

The sample size is calculated at 80% study power and an alpha error of 0.05, assuming a prevalence of anemia in 40% of school-going children aged 6-12. At a 5% absolute sampling error, a sample size of 384 was required. After multiplying with the design effect of 1.3, it was enhanced to 500 children as the final sample size.

Sampling procedure:

A comprehensive list of all primary schools in Washim District was compiled, and their principals or heads of schools were contacted directly to elucidate the nature and aim of the study. Subsequently, four schools that granted consent for the study were randomly chosen. All children received consent forms; only those whose parents or legal guardians granted consent were included in the study. The four elementary school titles are Lion's Vidyaniketan School, Zilla Parishad School, Azad Public School, and Mount Carmel School. All four colleges were randomly chosen to eliminate selection bias.

Sample collection:

A skilled specialist collected blood samples. After wiping the surface of the skin with iodine, the next step was to use spirit. After the spirit had been allowed to dry naturally, 2 milliliters of venous blood were measured using a needle with a gauge of 23.

Blood samples were collected in suitable anticoagulant tubes and sent to Shri Chaturbhuj Path Lab, Washim, in iceboxes for complete blood count (CBC) analysis on the same day utilizing a 5-part differential cell counter BC 5300 autoanalyzer.

Data collection:

Data was collected using a predesigned and semi-structured schedule pertaining to the history of pica, school performance, diet and various socio-demographic and laboratory parameters. The Collected blood sample was used to perform a CBC to identify and classify various anemias.

Anthropometrics measurement—the nutritional status of all the selected children was assessed by measuring body heights (cm) and weights (kg). Each subject's height was measured in a standing position using non-stretchable steel tape to the nearest 0.1 cm. A personal weighing machine was used to measure body weight to the nearest 0.5 kg. The individuals were kept under basal conditions with minimum clothing and without shoes.

Children's 50th percentile function on height and weight for age were considered normal. Children's -2SD from the median height for age were considered Stunted, and those' with- 2SD from the median weight for age were considered underweight. With reference to the WHO growth chart, the 50th percentile function was considered the median value.

Statistical Analysis

All data thus collected was entered into an Excel Sheet and was subjected to statistical analysis. Epi Info Software was used for statistical analysis. Quantitative data was summarized as mean and standard deviation, and qualitative data as percentage. The chi-square test was used to determine the association and significance of the difference between the two proportions (p<0.05 was considered significant).

The present study is a prevalence study carried out in schools in Washim District. Five hundred children between the ages of 6 and 12 were screened for anemia.

Among the total (n=500) studied primary school-going children of 6-12 years, 183(36.60%) were anemic compared to the normal group (n=317, 63.40%). On application of a statistical test, no significant difference (p=0.708) was observed with age

Table: 1 Distribution of study population according to anemic status and age

Chi-Square =    3.641 with 5 Degrees of Freedom; P = 0.708

In the present study, the prevalence of anemia was highest among the 7-year age group(n=92,39.66%), followed by the second highest in the 8-year age group(n=36,37.11%), while minimum prevalence was noted in the 10-year age group. (Table 1)

Among the total studied children(n=500),317(63.40%) were in the normal group as compared to the mild anemic group(n=146,29.20), moderate anemic group(n=36,7.20) and severe anemic group(n=1,0.20%).

It is evident from the table that girls showed a prevalence of anemia at 39.66%, while boys had a prevalence of 33.96%.0n application of the statistical test,ficant difference(p=0.220) was observed ved with in terms of (Table 2)

Table 2 Distribution of study population according to anemic status and sex

Chi-Square =    1.504 with 1 Degree of Freedom; P = 0.220

It is evident from the data that the prevalence of moderate to severe anemia was 9.05% in girls, while in boys and children, it was 5.97%.on the application of the statistical test, it was not significant(p=0.283)

Table 3 Distribution of the study population according to anemic status and weight

Chi-Square = 142.459 With 1 Degree Of Freedom; P <0.001

It is evident from the data that children having normal weight showed a prevalence of anemia of 15.79%, while children with underweight had a prevalence of 68.88%.0n application of statistical test there was highly significant difference(p<0.001) observed. (Table 3)

The above data shows that the prevalence of moderate to severe anemia was 14.29% in the underweight children group, while in the normal group, it was 2.96%. The application of statistical tests was highly significant.

Table 4 Distribution of the study population according to anemic status and height

Chi-Square =  234.369 With 1 Degree Of Freedom;   P  <0.001

It is evident from the table that children with normal height showed an anemia prevalence of 8.01%, while children with stunted height had a prevalence of 75.12%.0n application of statistical test there was highly significant difference(p<0.001) observed. (Table 4)

It is evident from the data that the prevalence of moderate to severe anemia was 15.02% in the stunted children group, while in the normal group, it was 1.74%.on the application of statistical tests, it was highly significant(0.001).

Table 5    Distribution of study population according to anemic status and history of Pica

Chi-Square = 101.333 with 1 Degree of Freedom; P <0.001

It is evident from the table that children having a history of pica showed a prevalence of anemia of 84.09%, while children with no history of pica had a prevalence of 26.46%.0n application of statistical test there was highly significant difference(p<0.001) observed. (Table 5 )

It is evident from the data that the prevalence of moderate to severe anemia was 36.37% in the history of the pica children group, while in the normal group, it was 1.21%. It was highly significant in the application of statistical tests (p<0.001).

Table 6 Distribution of study population according to anemic status and eosinophilia

Chi-Square =    0.332 with 1 Degree of Freedom; P = 0.564

The above table shows that children with Eosinophilia showed a prevalence of anemia, while children with No Eosinophilia had a prevalence of 35.34%.0n application of statistical test, no significant difference(p=0.564) was observed. (Table 6)

It is evident from the data that the prevalence of moderate to severe anemia was 8.29% in children having eosinophilia. In contrast, in children with no eosinophilia, it was 6.71%.No significant difference was observed. On the application of the statistics (p=0.721).

Table 7 Distribution of study population according to the severity of anemic status and school performance

Chi-Square = 170.089 with 10 Degrees Of Freedom;   P = <0.001

The above table shows that in moderate to severe anemic children, school performance from A1 to A3 grade was 19.44% compared to normal children at 88.02%. It was highly significant in the application of statistical tests (p<0.001). (Table 7)

Table 8 Distribution of the study population according to anemic status and platelet count

Chi-Square =    1.472 with 2 Degrees of Freedom; P = 0.479

The above table shows that 48.09% of anemic children had a high platelet count compared to 42.90% of normal children. However, when a statistical test was applied, no significant difference (p=0.479) was observed.

The data show that 55.56% of moderate to severe anemic children had a high platelet count, compared to 42.90% of normal children. However, no significant difference (p=0.160) was observed when a statistical test was applied.

To this day, anemia is one of the most prevalent and stubborn nutritional issues people face. It is related to a higher risk of morbidity and death, particularly in pregnant women and young children, and it has repercussions for human health as well as for social and economic development. [16-18].

Due to the significance of this illness globally, several nations implement measures to mitigate anemia, especially among the most vulnerable populations: pregnant women and young children. Data on anemia prevalence must be gathered to evaluate the effectiveness of these interventions, the sufficiency of the tactics employed, and the advancements achieved in combating anemia. Recent studies on anemia prevalence have focused solely on preschoolers, indicating a necessity for more research concerning school-aged children. The primary purpose of this study is to ascertain the prevalence of anemia among school-going children in Washim District, Maharashtra.

The study was carried out from February 2022 to August 2023. A total of 500 students from four schools aged 6-12 participated in the study. Only those children whose parents gave their consent were included in the study.

Prevalence of Anaemia

In the present study, the prevalence of anemia in primary school-going children of 6-12 years of age in Washim District, Maharastra, is 36.6%. Out of 500 studied primary school children, 183(36.6%) were anaemic. Statistically, there was no significant difference (p=0.708) observed with age. The mean with a Standard deviation of Hb was 11.58+-1.01 in the present study.

Mild grade of anemia (n=146, 29.2%) was more prevalent among the studied population as compared to moderate grade (n=36, 7.2%) and severe grade of anemia (n=1, 0.2%). Grading was done according to WHO. Statistically, a ficant difference (p=0.708) was observed between the severity of anemia and age.

Saluja et al. [2011] [19] reported a similar result: the prevalence of Anemia in Schoolchildren in the urban area of Meerut, India, was 194 out of a total of 515 students (37.7%).

Emel Gür et al. also reported the same result that the Prevalence of Anaemia in Schoolchildren in Istanbul was found to be 27.6 percent.[20]

UmaKiran et al. (2011) also reported the same result in the prevalence of anemia in School Children aged 5-12. Out of the 484 children, 30.4% (147) were found to be anemic, and 69.6% (337) were non-anemic.[21]

Age and Sex incidence

This study found that anemia was greatest among 7-year-old children (n=92, 39.66%), followed by the 8-year-old cohort (n=36, 37.11%), while the prevalence was lowest in the 10-year-old group. The incidence of anemia was greater in girls (n=92, 39.66%) than in boys (n=91, 33.96%). The average age of the study was 7.36 years, with a standard deviation of 1.156. Statistically, no significant correlation was seen between anemia and sex (p=0.220).

Neelu Saluja [2011] reported the same result: the prevalence of anemia was higher in Girls (45.2%) than in boys (30.6%) among schoolchildren in the urban area of Meerut, India [19].

Sahu, NC Sahani, L Patnaik et al. [2007] studied Childhood anemia in the tribal area of Mohana block in Orissa and reported that there is no significant difference in mean Hb level among boys and girls in the 5- 14 age group[22].

Association with diet- In the present study, the prevalence of anemia cases was significantly higher (54.21%) in vegetarians than in non-vegetarians (23.43%). This p-value was <0.001, so there was a statistically significant association between anemia and dietary habits. The dietary association is also substantial with the severity of anemia. The prevalence of moderate to severe anemia was 12.15% in children in the vegetarian diet group, while in children in the non-vegetarian diet group, it was 3.85%. On application of the statistical test, it was highly significant (p<0.001).

Verma et al^. also reported that compared to non-vegetarians (38%), more vegetarians (65.9%) were anemic.[23]

In association with weight and height

In the present study, children with normal weight showed a prevalence of anemia of 15.79%, while children underweight had a prevalence of 68.88%. This indicates a highly significant association between anemia and the weight of children (p<0.001).

Children having normal height showed a prevalence of anemia at 8.01%, while children with stunted height had a prevalence of 75.12%. This indicates a highly significant association between anemia and children's height (p<0.001). The mean weight of the study population was 24.406±5.885, while the mean height was 123.47±9.545. Both weight and height had a significant association with the severity of anemia. The prevalence of moderate to severe anemia was 14.29% in the underweight children group, while in the normal group, It was 2.96%, and the prevalence of moderate to severe anemia was 15.02% in the stunted children group while in the normal group, it was 1.74%.

Sabita Basu, Srikanta Basu, and colleagues [24] documented analogous findings about the prevalence of anemia among school-aged adolescents in Chandigarh. It was observed that 14.3% (84/590) of girls and 14.2% (76/530) of boys were undernourished (BMI <5th percentile). The prevalence of anemia in females with a weight above the 5th percentile was 21.9%, in contrast to 35.7% in those with a BMI below the 5th percentile (P <0.001). In boys, 6.7% of the well-nourished group were anemic, compared to 14.4% in the undernourished group (P <0.05).

Association with PICA

At present study showed that children having a history of pica showed a prevalence of anemia of 84.09%, while children with no history of pica had a prevalence of 26.46%. This indicates a highly significant association between anemia and pica history in children (p<0.001). Pica also had a significant association with the severity of anemia. Like in the present study, the prevalence of moderate to severe anemia was 36.37% in the history of the pica children group, while in the normal group, it was 1.21%.

Adrien Kettaneh et al. (2005) [25] indicated that nonfood items, namely clay and ice, were regularly consumed by 35 patients (44%) with iron-deficiency anemia and by 7 (9%) in the control group (P0.0001). The incidence of food cravings did not significantly vary between the groups. Pica was found to be related to iron deficiency, even after controlling for the geographic origin of the patients. This indicates that pica may be an ethnic or culturally associated characteristic. Conversely, food hunger was not linked to iron deficiency anemia and did not seem to be an ethnic or culturally specific occurrence. Craving was observed in both iron-deficient and non-iron-deficient subjects.

James et al. (2010) reported that among 230 no pregnant women, 118 patients (45.0%) reported pica; of these, 87.3% reported ice pica (pagophagia). The prevalence of iron deficiency, with or without anemia, did not differ significantly between patients with and without pica reports. Mean hemoglobin and mean corpuscular volume (MCV) were lower, and mean red blood cell distribution width (RDW) and platelet count were higher in patients with pica [26].

In association with Eosinophilia

Present study, the children having Eosinophilia showed a prevalence of anemia of 35.34%, while children with No Eosinophilia had a prevalence of anemia of 38.25%. This shows no significant association between anemia and eosinophilia (p=0.564)

In the present study, in a normal population (Hb=>11.5g/dl), 61.75% of children had eosinophilia, while 64.76% did not.

Chakma et al.54 [27] reported that 30.3% of the children had severe anemia (Hb < 7g/dl), and 50% of the children had intestinal parasites. The most common parasites were hookworm (16.3%) and A lumbricoids (18.5%). Though hookworm ova loads indicated mild to moderate infestation in most children, the continued presence of worms in marginally nourished children could contribute significantly to blood loss in the intestine with resultant anemia.

Triteeraprapab et al. reported that in the Mae Chan subdistrict, five individuals exhibited microfilaremia, 72% of those examined were infected with at least one type of intestinal parasite, and 50% were anemic, with a normal mean red cell volume (MCV). In the Mae Lamung subdistrict, 46% were parasitized, but none exhibited microfilaremia or anemia. Eosinophilia was observed in both populations at a rate of 77%. They observed that hookworm infection was substantially correlated with eosinophilia but not with anemia or microcytosis of red blood cells.  Triteeraprapab et al. noted that the association between parasites, anemia, and eosinophilia has been infrequently documented due to restricted healthcare access, particularly in rural regions, necessitating additional research.[28]

Guyatt HL, Brooker S et al^{. 22} reported that the prevalence of anemia was high (54%) among schoolchildren in Tanzania, particularly those with high intensities of hookworm[29].

In the present study, eosinophilia was present in both anemic and normal school-going children, although this was not statistically significant. According to previous studies, hookworm infection was mostly and significantly associated with eosinophilia but not anemia or microcytosis of red cells. So, further evaluation is needed in the eosinophilic children population(43.4%) either by stool or blood smear microscopic examination.

Association with Platelet Count

The present study showed that 34.31% of children with normal platelet count had anemia, 50.0% of children with low platelet count had anemia, and 39.29% of children with high platelet count had anemia. The mean platelet count of the study population was 2.995±0. 695. No association was observed between anemia and low or high platelet count (p=0.479).

According to Jonathan L. et al., IDA was substantially more prevalent in stroke case-patients (8 of 15 [53%]) compared to control participants (13 of 143 [9%]; OR: 12; 95% CI: 4–37). Additionally, stroke case-patients (7 of 15 [47%]) had thrombocytosis at higher rates than control subjects (21 of 133 [16%]; OR: 5; 95% CI: 2–14). The relationship between IDA and stroke was still significant when platelet count was controlled using logistic regression (adjusted OR: 10; 95% CI: 3–33). IDA and thrombocytosis did not interact in a statistically significant way. [30].

In association with educational performance, in the present study, school performance was considered in terms of grades in the last examination. Studies show that school performance from A1 to A3 grade in anemic children was 66.67% compared to normal children, which was 88.02%. This association was statistically significant (p<0.001). This association is also substantial with the severity of anemia. Studies show that in moderate to severe anemic children, school performance from A1 to A3 grade was 19.44% compared to normal children, 88.02%.

Pollitt et al. studied the assessment of the impact of iron treatment on the IQ and educational attainment of 1358 9-11-year-old children. A Thai language and a math test were administered to assess school attainment. A 50-mg/d ferrous sulfate tablet was given for 2 wk and a 100 mg/d tablet for 14 wk. An anthelminthic drug was given on the day of the blood test before treatment and three mo after the intervention started. There is evidence of a positive association between iron status and IQ and a language school achievement test [31].

Association of erythrocyte indices with anemia: In the present study, Microcytic RBC(reduced cell size of RBC)  was seen in 44.26% of anemic children compared to 13.25% of normal children. This association was statistically highly significant (p<0.001). The most common cause of microcytic anemia is iron deficiency anemia. In the present study, microcytic RBC was also seen in normal children (13.25%), showing an iron deficiency without anemia. In the present study, the mean value of MCV was 83.225+-8.95SD.

Sunil Gomber, Bhawna, Nishi Madan, and colleagues discovered that pure or mixed iron deficiency anemia was the most prevalent kind of anemia observed in 68.42 percent (65 of 95) of children, followed by pure or mixed B12 deficiency identified in 28.42 percent (27 of 95) of anemic children. Iron insufficiency was the predominant cause of the pure form, affecting 41.05 percent (39 of 95) of children.  [32].

The current study revealed that normocytic anemia (55.74%) was the highest prevalence among anemic children, followed by microcytic anemia (44.26%). John et al. (2000) investigated normocytic anemia in children. They demonstrated that iron insufficiency, typically marked by a normal MCV in its initial phases, is a prevalent cause of mild normocytic anemia in this population. Other prevalent childhood normocytic anemias arise from acute hemorrhage, sickle cell disease, erythrocyte membrane abnormalities, and present or recent infections. [33].

The present study showed that LOW MCHC was seen in 95.08% of anemic children compared to 80.44% of normal children. The mean value of MCHC was 30.725+-1.53SD. This association was statistically highly significant (p<0.001).

The present study showed a LOW hematocrit value in 76.50% of anemic children compared to 9.78% of normal children. The mean value of Hematocrit was 37.60+-2. 895SD.This association was statistically highly significant (p<0.001).  The present study showed high RDW-CV in 8.20% of anemic children compared to 0.32% of normal children. The mean value of RDW-CV was 13.09+-1.192SD in the study population. This association was statistically highly significant (p<0.001).

In our study, the prevalence of anemia in primary school-going children in the 6-12 year age group in Washim District was 36.60%, a moderate public health problem. Mild grade of anemia with Normocytic RBCs was more prevalent among studied children. Anemia was highly commonplace in the 7-year age group, followed by the 8-year age group, and girls were more affected than boys. However, an association of anemia with age and sex was statistically insignificant. Our study showed a significant association of anthropometric parameters like weight and height with anemia. Underweight and Stunted children show a prevalence of anemia. No significant association was noted between eosinophilia and platelet count with anemia.

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