Abstract
Background
Prevalence of anaemia is 67% in children between 6 and 59 months of age. There is a discrepancy in prevalence of anaemia in view of various cutoffs and capillary versus venous sampling. Prevalence studies at various levels are mandated for planning and implementation of policies to control anaemia.
Methods
This cross-sectional observational hospital-based multi-centric study enrolled apparently healthy eligible children aged between 6 and 59 months from Armed Forces Medical Services hospitals and estimated the prevalence of anaemia in “venous blood samples”. Demographic and anthropometric details and a questionnaire-based survey to study individual and community-related risk factors associated with anaemia were noted.
Results
A total of 10,814 children from 20 hospitals were eligible. A total of 2231 children (56.9% male) were analyzed. The prevalence of anaemia was 49.4% (95% confidence interval [CI]: 47.3–51.5) as defined by the World Health Organization (mild: 24.1%, moderate: 23.5%, and severe: 1.8%) and 19.6% (95%CI:18–21.3) as per the Comprehensive National Nutrition Survey. Majority of anaemia was microcytic (67.2%). Presumptive iron-deficiency anaemia was seen in 55.7%. In binary logistic regression analysis, age<24 months, inadequacy of nutrition and poor maternal education were the associated risk factors in all children with anaemia, while not giving exclusive breast feed till 6 months was an additional risk factor in children with moderate to severe anaemia.
Conclusions
We report a high prevalence (49.4%) of anaemia in children aged between 6 and 59 months, with predominantly lower-middle socioeconomic status. Individual factors such as age<24 months, inadequate nutrition, and community-related risk factors such as poor maternal education and not giving exclusive breast feed for 6 months were associated with anaemia in our cohort.
Keywords: Anaemia, Children, National family health survey, Comprehensive national nutrition survey, Anaemia mukt bharat
Introduction
Anaemia is a national public health challenge, especially in young children who are extremely vulnerable to anaemia. The National Family Health Survey (NFHS)-5 report shows a staggering 67% children aged from 6 to 59 months to be anaemic,1 which is higher than the global prevalence.2 In spite of public health programs that aim to ameliorate iron deficiency, there is in-fact an increase in prevalence of anaemia in the last 10 years in children less than five years of age. There is a discrepancy in prevalence of anaemia in view of various cut-offs and capillary versus venous sampling.3,4 The NFHS has accepted the lacunae in estimation of anaemia with capillary blood sampling and has dropped anaemia prevalence as one of the objectives from NFHS-6.5 It is suggested to use venous blood sampling as gold standard and estimate local prevalence of anaemia to encourage the implementation of modified national guidelines at the local level. Recently, national guidelines on prevention of anaemia in children recommended nation-wide monitoring of children for anaemia between 6 and 59 months of age.6
Prevalence studies at various levels are mandated for planning and implementation of policies to control anaemia. There was, therefore, a felt need for estimation of anaemia prevalence in children by utilizing venous blood sampling in our cohort.
Material and methods
This cross-sectional observational hospital-based multi-centre study was conducted over a three-month period (Feb 2023–Apr 2023). All Armed Forces Medical Services (AFMS) hospitals with paediatric services were requested to participate. Inclusion criteria were apparently healthy eligible children aged between 6 and 59 months, attending the Paediatric out-patient department (OPD) of these hospitals. Children with acute febrile illness, known case of anaemia on therapy, syndromic children, children with physical, cognitive, and mental disability, children with chronic illnesses, and children who had received a blood transfusion previously were excluded.
Sample size calculation
The population of children aged between 6 and 59 months is around 6,50,000, considering approximate strength of our clientele to be 25,00,000, with an existing fertility rate of 2.18% and percentage of population of children aged between 6 and 59 months to be 12%.7 With an established prevalence of anaemia as 67.1% in children aged between 6 and 59 months as per the NFHS-5 and 0.02 precision of estimation (revised precision based on recruitment rate), the calculated sample size using Epitools software (Ausvet 2023) was 2117, assuming 5% level of significance and 80% power.
Sampling and consent
Eligible children were enrolled after informed consent was received from parents. A single 2-ml “venous blood sample” was taken in an ethylenediamine tetraacetic acid vacutainer for complete blood-count estimation. Sample processing was done in pathology laboratories of various participating hospitals in situ by automated haematology analyzers (AHAs). Ethical clearance was obtained from Institutional Ethics Committee (IEC Registration 158/22). All guidelines as per the Declaration of Helsinki and good clinical practice guidelines were followed.
Data collection
In addition to demographic information, the collected data included (a) a questionnaire-based survey including nutritional history (initiation of breast feed in first hour/exclusive breast feed till 6 months/complementary feeding, nutritional adequacy), iron supplementation for child and education, profession and number of children for the mother, (b) anthropometry for the mother and child, and (c) nutritional adequacy by “24-h recall method” was recorded by a paediatrician. The prevalence of anaemia was estimated on the basis of World Health Organization (WHO) and Comprehensive National Nutrition Survey (CNNS) cut-offs. All children found to be anaemic were prescribed iron supplementation and were followed-up as per existing guidelines.6 No follow-up data were collected.
Definitions
Anaemia (WHO definition): Haemoglobin (Hb) < 11 g/dl; mild anaemia: Hb = 10–10.9 g/dl, moderate anaemia: Hb = 7–9.9 g/dl, and severe anaemia: Hb < 7 g/dl.
Microcytic anaemia: ≤2 years: Mean Corpuscular Volume (MCV) < 70 fL, >2 years: 70+ (age in years). 6
Macrocytic anaemia: MCV >84+ (0.6 × age in years). 6,8
Presumptive iron-deficiency anaemia (IDA): Triad of low MCV, low mean corpuscular Hb (<20 pg), and low mean corpuscular Hb concentration (<32 g/dl).6
Underweight: Weight for age < −2z (as per WHO).
Stunting: Height for age < −2z (as per WHO).
Adequate nutrition (as per Infant & Young Child Feeding guidelines). 9
Iron supplementation:
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•
For children aged >1 year: Oral iron at minimum 1 mg/kg/day for 100 days/year.
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•
For infants: Any oral iron supplementation for last 7 days as per NFHS definition.
Abnormal maternal body mass index (BMI): <18.5 kg/m2 or ≥25 kg/m2.
Statistical analysis
Data were collected on a monthly basis at the nodal centre, and data cleaning was performed to check for missed values, wrong/duplicate entries, and accuracy. Data were analyzed using SPSS version 22. The primary objective of the study was to estimate the prevalence of anaemia in children aged between 6 and 59 months. Prevalence along with severity and type of anaemia was calculated as proportion with 95% confidence interval (CI). Secondary objectives were to study risk factors affecting prevalence of anaemia and to compare anaemia prevalence on the basis of WHO and CNNS cut-offs.2,3A univariate analysis was done using Chi-square test to identify risk factors linked to anaemia. Variables with p < 0.05 in the univariate analysis were subjected to a binary logistic regression analysis in order to control confounders and identify the true effect of predictor variables as risk factors associated with anaemia. Newton method was used to find the value of β (logistic regression coefficient) that maximized log-likelihood function. An odds ratio with 95%CI was measured to delineate different variables related to anaemia. A p-value of <0.05 was considered significant.
Results
Twenty AFMS hospitals distributed evenly across the geographical areas in the country participated in the study. Four hospitals were located in the northern, six in the southern, three in the central, two in the eastern, and five in the western part of the country. A total of 10,814 children aged between 6 and 59 months who attended the Paediatric OPD during the study period were screened. Of these, 8583 were excluded for various reasons (Fig. 1). The remaining 2231 children (56.9% male) with similar number of children from each age group (Table 1) and geographic region across the country (north: 489, central: 571, south: 340, east: 432, and west: 399) aged between 6 and 59 months were analyzed. The prevalence of anaemia was 49.4% (95%CI: 47.3–51.5) as defined by WHO cut-offs [mild: 24.1% (95%CI: 22.3–25.9), moderate: 23.5% (95%CI: 21.7–25.2), and severe: 1.8% (95%CI:1.3–2.4)] (Table 1). The prevalence of anaemia was 19.6% (95%CI: 18–21.3) as per CNNS cut-offs (Table 1). In both cases, the prevalence decreased as the age-group increased from 6 to 59 months. Prevalence of anaemia was equally distributed in males (48.9%) and females (50.1%).
Fig. 1.
Flow chart of study participants. ∗Two centres did not contribute exclusion data. Data extrapolated by multiple imputation.
Table 1.
Prevalence of anaemia as per WHO and CNNS cut-offs.
| WHO cut-offs |
CNNS cut-offs |
|||||
|---|---|---|---|---|---|---|
| Age group (months) | No. of children (% of total) | Any anaemia (%) | Mild anaemia | Moderate anaemia | Severe anaemia | Any anaemia (%) |
| 6–11 | 354 (15.9) | 226 (63.8) | 82 | 131 | 13 | 90 (25.4) |
| 12–23 | 560 (25) | 327 (58.4) | 137 | 173 | 17 | 130 (23.2) |
| 24–35 | 421 (18.9) | 198 (47) | 108 | 86 | 4 | 80 (19) |
| 36–47 | 402 (18.1) | 168 (41.8) | 87 | 76 | 5 | 73 (18.2) |
| 48–59 | 494 (22.1) | 184 (37.2) | 124 | 58 | 2 | 65 (13.1) |
| Total (%) | 2231 | 1103 (49.4) | 538 (24.1) | 524 (23.5) | 41 (1.8) | 438 (19.6) |
WHO: World Health Organization; CNNS: Comprehensive National Nutrition Survey.
Majority of anaemia in the studied population was microcytic (67.2%), while macrocytic anaemia contributed 3.5% (Table 2). Presumptive IDA was seen in 614 children (88.7% of microcytic and 55.7% of total anaemia). Only 20.9% of our cohort was on prophylactic iron supplementation. Timely initiation of breast feed was recorded in 83%, complementary feed in 90.2%, exclusive breast feed for initial 6 months in 93.3%, adequacy of nutrition in 70.6%, prevalence of underweight in 12.6%, stunting in 15.7%, and normal maternal BMI in 62.8% in our cohort (Table 3).
Table 2.
Prevalence of anaemia according to type of anaemia in various age groups.
| Age group (months) | Total children | Any anaemia (%) | Microcytic anaemia | Normocytic anaemia | Macrocytic anaemia |
|---|---|---|---|---|---|
| 6–11 | 354 | 226 (63.8) | 140 | 83 | 3 |
| 12–23 | 560 | 327 (58.4) | 207 | 112 | 8 |
| 24–35 | 421 | 198 (47) | 128 | 64 | 6 |
| 36–47 | 402 | 168 (41.8) | 103 | 56 | 9 |
| 48–59 | 494 | 184 (37.2) | 114 | 57 | 13 |
| Total | 2231 | 1103 | 692 (62.7%)a | 372 (33.8%)a | 39 (3.5%)a |
Percentage of anaemic children.
Table 3.
Univariate and binary logistic regression analysis of risk factors associated with anaemia irrespective of severity.
| S no. | Variable | N = 2231 | All anaemia + (%) (n = 1103) |
Anaemia (%) (n = 1128) |
P value | Odds ratio |
95% CI |
P-value |
|---|---|---|---|---|---|---|---|---|
| Univariate analysis | Binary logistic regression | |||||||
| Individual factors | ||||||||
| 1 | Breast feed within 01 h | |||||||
| Yes | 1851 (83) | 906 (82.1) | 945 (83.8) | 0.31 | ||||
| No | 197 (17.9) | 183 (16.2) | ||||||
| 2 | Exclusive breast feed for 6 months | |||||||
| Yes | 2082 (93.3) | 966 (87.6) | 1016 (90.1) | 0.06 | ||||
| No | 137 (12.4) | 112 (9.9) | ||||||
| 3 | Complementary feed started between 6 and 8 months | |||||||
| Yes | 2013 (90.2) | 995 (90.2) | 1018 (90.2) | 1.0 | ||||
| No | 108 (9.8) | 110 (9.8) | ||||||
| 4 | Adequate nutrition | |||||||
| Yes | 1575 (70.6) | 736 (66.7) | 839 (74.4) | 0.0001 | Ref | |||
| No | 367 (33.3) | 289 (25.6) | 3.59 | 1.59–5.41 | 0.0003 | |||
| 5 | Iron supplementation | |||||||
| Yes | 467 (20.9) | 232 (21) | 235 (20.8) | 0.91 | ||||
| No | 871 (79) | 893 (79.2) | ||||||
| 6 | Cow's milk > 600 ml/day | |||||||
| Yes | 1080 (48.4) | 539 (48.9) | 541 (48) | 0.67 | ||||
| No | 564 (51.1) | 587 (52) | ||||||
| 7 | Underweight | |||||||
| Yes | 280 (12.6) | 164 (14.9) | 116 (10.3) | 0.001 | 0.1215 | −1.13-(-0.45) | 0.2551 | |
| No | 939 (85.1) | 1012 (89.7) | Ref | |||||
| 8 | Stunting | |||||||
| Yes | 351 (15.7) | 223 (20.2) | 128 (11.3) | 0.0001 | 0.2013 | −0.30-(-0.27) | 0.7595 | |
| No | 880 (79.8) | 1000 (88.7) | Ref | |||||
| 9 | Sex | |||||||
| Male | 1269 (56.9) | 621 (56.3) | 648 (57.4) | 0.6 | ||||
| Female | 482 (43.7) | 480 (42.6) | ||||||
| 10 | Age in months | |||||||
| <24 | 914 (41) | 553 (50.1) | 361 (32) | 0.0001 | 8.64 | 6.11–9.70 | 0.0000 | |
| 24–59 | 550 (49.9) | 767 (68) | Ref | |||||
| Maternal factors | ||||||||
| 11 | Mother BMI | |||||||
| Normal | 1402 (62.8) | 695 (63) | 707 (62.7) | 0.89 | ||||
| Abnormal | 408 (37) | 421 (37.3) | ||||||
| 12 | No of children | |||||||
| ≤2 | 2097 (94) | 1033 (93.7) | 1064 (94.3) | 0.53 | ||||
| >2 | 70 (6.3) | 64 (5.7) | ||||||
| 13 | Mother education | |||||||
| Undergraduate | 823 (36.9) | 454 (41.2) | 369 (32.7) | 0.0001 | 5.02 | 2.85–6.51 | 0.0000 | |
| Post-graduate | 649 (58.8) | 759 (67.3) | Ref | |||||
| 14 | Mother profession | |||||||
| Stay home mother | 2083 (93.4) | 1035 (93.8) | 1048 (92.9) | 0.39 | ||||
| Job outside home | 68 (6.2) | 80 (7.1) | ||||||
Ref: Reference variable; BMI: body mass index; CI: confidence interval.
In the univariate analysis, anaemia was found to be significantly associated with younger age (<24 months), anthropometry (underweight and stunting), inadequacy of nutrition, and poor maternal education (Table 3). When mild anaemia was excluded from analysis, in addition to aforementioned factors, feeding-related factors such as non-initiation of breast feed within 1 h of birth, non-exclusive breast feed till six months, and non-initiation of timely complementary feeds were also found to be significantly associated with anaemia (Table 4). In binary logistic regression analysis, children with age<24 months, inadequacy of nutrition and poor maternal education were associated risk factors in all children with anaemia, while non-exclusive breast feed till 6 months was an additional risk factor in children with moderate to severe anaemia (Table 3, Table 4).
Table 4.
Univariate and binary logistic regression analysis of risk factors associated with combined moderate and severe anaemia.
| S no. | Variable | Moderate and severe anaemia (%) (n = 565) |
Anaemia- (%) (n = 1128) |
P value | Odds ratio |
95% CI |
P-value |
|---|---|---|---|---|---|---|---|
| Univariate analysis | Binary logistic regression | ||||||
| Individual factors | |||||||
| 1 | Breast feed within 01 h | ||||||
| Yes | 445 (78.8) | 945 (83.8) | 0.012 | 1.70 | −0.33-4.8 | 0.08 | |
| No | 120 (21.2) | 183 (16.2) | Ref | ||||
| 2 | Exclusive breast feed for 6 months | ||||||
| Yes | 479 (84.8) | 1016 (90.1) | 0.0017 | Ref | |||
| No | 86 (15.2) | 112 (9.9) | 2.55 | 1.05–7.19 | 0.01 | ||
| 3 | Complementary feed started between 6 and 8 months | ||||||
| Yes | 490 (86.7) | 1018 (90.2) | 0.03 | 1.45 | 0.85–5.71 | 0.14 | |
| No | 75 (13.3) | 110 (9.8) | Ref | ||||
| 4 | Adequate nutrition | ||||||
| Yes | 360 (63.7) | 839 (74.4) | 0.0001 | Ref | |||
| No | 205 (36.3) | 289 (25.6) | 3.22 | 1.37–5.65 | 0.0012 | ||
| 5 | Iron supplementation | ||||||
| Yes | 109 (19.3) | 235 (20.8) | 0.48 | ||||
| No | 456 (80.7) | 893 (79.2) | |||||
| 6 | Cow's milk> 600 ml/day | ||||||
| Yes | 272 (48.1) | 541 (48) | 0.95 | ||||
| No | 293 (51.9) | 587 (52) | |||||
| 7 | Underweight | ||||||
| Yes | 101 (17.9) | 116 (10.3) | 0.0001 | −1.63 | −5.69-0.50 | 0.1 | |
| No | 464 (82.1) | 1012 (89.7) | Ref | ||||
| 8 | Stunting | ||||||
| Yes | 143 (25.3) | 128 (11.3) | 0.0001 | −0.86 | −3.77-1.45 | 0.38 | |
| No | 422 (74.7) | 1000 (88.7) | Ref | ||||
| 9 | Sex | ||||||
| Male | 336 (59.5) | 648 (57.4) | 0.43 | ||||
| Female | 229 (40.5) | 480 (42.6) | |||||
| 10 | Age in months | ||||||
| <24 | 334 (59.1) | 361 (32) | 0.0001 | 9.71 | 8.12–12.23 | 0.0000 | |
| 24–59 | 231 (40.9) | 767 (68) | Ref | ||||
| Maternal factors | |||||||
| 11 | Mother BMI | ||||||
| Normal | 355 (62.9) | 707 (62.7) | 0.95 | ||||
| Abnormal | 210 (37.1) | 421 (37.3) | |||||
| 12 | No of children | ||||||
| ≤2 | 528 (93.5) | 1064 (94.3) | 0.51 | ||||
| >2 | 37 (6.5) | 64 (5.7) | |||||
| 13 | Mother education | ||||||
| Undergraduate | 240 (42.5) | 369 (32.7) | 0.0001 | 3.81 | 1.97–6.15 | 0.0001 | |
| Postgraduate | 325 (57.5) | 759 (67.3) | Ref | ||||
| 14 | Mother profession | ||||||
| Stay home mother | 535 (94.7) | 1048 (92.9) | 0.17 | ||||
| Job outside home | 30 (5.3) | 80 (7.1) | |||||
BMI: body mass index; CI: confidence interval.
Discussion
NFHS-5 data have been criticized for higher prevalence of anaemia for two main reasons—use of capillary blood samples for Hb estimation and use of WHO cut-off for anaemia definition. As a result, the NFHS-6 has dropped anaemia estimation as an agenda point. The gold standard for Hb estimation is the cyanmethemoglobin method in venous blood sample. It is now replaced by the AHA, which has been validated against this gold standard. Studies have compared two methods of Hb estimation in venous versus capillary samples and found conflicting results. The biological basis of difference in the two sampling methods for estimation of anaemia is not well defined, and it has been suggested that venous blood sampling–based studies should be implemented for anaemia survey in India.4 A population-based study (the CNNS study) has prompted the re-examination of WHO anaemia threshold levels and has devised reference percentiles for anaemia cut-offs.3
The lesser prevalence of anaemia in our cohort than in the NFHS-5 may be explained by the better socioeconomic status of our study population and use of venous samples. However, the proportion of anaemia as per severity and age and sex predilection is in concurrence with NFHS-5 data. Our study also had comparable prevalence of anaemia when CNNS cut-offs were used. Any other such large-scale studies are lacking.
Majority of anaemia (67.2%) in our study was microcytic. We did not investigate further to subclassify the anaemia due to administrative reasons. These results are comparable to other studies from the country.6
Demographic, maternal health, nutrition and growth, family size, prophylactic measures, socioeconomic, sanitation, environmental, and recent illnesses are the factors contributing to anaemia in low-middle-income countries (LMICs).10, 11, 12 Identifying these risk factors highlights potential target for intervention. The population in our study is not native of a particular region and consists predominantly of the lower-middle socioeconomic class as per the modified Kuppuswamy scale, thereby nullifying the impact of socioeconomic and environmental factors. We excluded recent illnesses and studied rest of the aforementioned risk factors. These factors which were evaluated as part of the study were similar to the factors used in the NFHS survey and were derived from Bayesian geo-additive logistic regression models, which focus on known measurable factors as the cause of anaemia.13 Indicators of socioeconomic and nutrition factors in our cohort such as timely initiation of breast feed and complementary feed, exclusive breast feed, adequacy of nutrition, prevalence of underweight and stunting, and factors of maternal well-being are way better than the national average.1 Only one-fifth of children were on iron supplementation in our study. Iron supplementation was not independently associated with risk of anaemia (Table 3). This discrepancy could be explained by poor compliance of oral iron. Poor compliance due to side-effects of iron supplementation is a major constraint for efficient implementation of “Anaemia Mukt Bharat (AMB)”.14 An age of <24 months, inadequacy of nutrition, and poor maternal education were risk factors for anaemia in our cohort. Similar findings were seen in other large studies from LMICs.10, 11, 12 Non-initiation of timely complementary feeding and high intake of cow's milk were also not associated with anaemia in our cohort. Available literature suggests that these two factors are associated more commonly with anaemia in children with <24 months of age, whereas inadequate nutrition is major factor contributing to anaemia in subsequent age groups.15 However, we did not study risk factors for anaemia in various age groups separately.
As underscored in the CNNS study, the average difference between WHO and CNNS cut-off for anaemia across children with 6–59 months of age is around 1 g/dl.3 This roughly corresponds to anaemia sans mild anaemia, as per the WHO cut-off. Therefore, we independently studied risk factors affecting anaemia excluding mild anaemia. In addition to the aforementioned risk factors, not giving exclusive breast feed till 6 months was delineated as a risk factor for moderate/severe anaemia. Similar trends were noted in the NFHS-5 study.
We analyzed large-scale data from our cohort. The cohort was evenly distributed across the age-groups and geographical areas of the country. The venous blood sample was incorporated for the first time in large-scale anaemia prevalence in our cohort. Pediatricians supervised the filling of questionnaires and not grass-root-level health workers, thereby minimizing errors. Independent analysis of moderate/severe anaemia helped us in diluting the discrepancy in WHO and CNNS cut-offs. Estimation of prevalence and risk factors for anaemia may help in understanding the quantum of problem and associated demographics in our clientele. We recommend more such population-based large-scale follow-up studies to strengthen outcomes of our study.
Our study has few limitations. This was not a population-based study and has an inherent selection bias. The study population mainly constituted the lower-middle socioeconomic class.16 Therefore, the results cannot be generalized. As per the NFHS-5, the prevalence of anaemia differs drastically in different districts of a state. A statistical and econometric analysis of NFHS-5 data in 707 districts revealed small-area variations within districts when designing targeted intervention strategies to kerb anaemia. It advocates variable target strategies at a subdistrict level rather than a uniform nation-wide strategy such as AMB. It thereby encourages estimation of anaemia prevalence at the local level and identifies the unique problem of that area and implements tailor-made solution.17 Our hospital-based study on apparently healthy children is bound to have high exclusion rates as seen with other such studies.3 A population-based study was not feasible due to lack of resources and the need for a venous blood sample for anaemia estimation. The estimation of complete blood count (CBC) at different labs of participating hospitals utilizing different AHAs may result in a possibility of analytic error, though the AHAs were of similar make and are validated internally on a daily basis and externally on a quarterly basis as per standard guidelines.18 We did not subclassify anaemia, but we studied anaemia indices. We also did not extensively study maternal factors such as maternal anaemia since we presumed that the factors responsible for maternal anaemia are the same as those for anaemia in children.
To conclude, we report a high prevalence of anaemia (49.4% as per WHO and 19.6% as per CNNS cut-offs) in children aged between 6 and 59 months, with a predominantly lower-middle socioeconomic status. Individual factors such as age < 24 months, inadequate nutrition, and community-related risk factors such as poor maternal education and not giving exclusive breast feed for 6 months were associated with anaemia in our cohort.
Patients/ Guardians/ Participants consent
Parents informed consent was obtained.
Ethical clearance
Institute/hospital ethical clearance certificate was obtained.
Source of support
Nil.
Disclosure of competing interest
The authors have none to declare.
Acknowledgements
We acknowledge Rahul Sinha, Reema Solanki, Divya Tatachari, Anshuka Yadav, Arunava Ghosh, Swati Dash, Narender Sharma, R Sateesh, Randhir Ranjan, Ajay Beriwal, Ravindra Bhunia, Desari Varun Kumar, Udhayakumar R, Priyanka V Garg, Sandeep D Paimode, Niranjan Sahoo and Vaishali for their contribution in the study.
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