|Year : 2022 | Volume
| Issue : 2 | Page : 182-186
Prevalence of anemia and associated risk factors among the lactating and nonpregnant-nonlactating Tangkhul women
Chingmila Shimrah1, H Sorojini Devi2
1 Research Scholar, D.M College of Science, DMU, Imphal, Manipur, India
2 Associate Professor, P.G Department of Anthropology, D.M College of Science, DMU, Imphal, Manipur, India
|Date of Submission||03-Aug-2021|
|Date of Decision||15-Nov-2021|
|Date of Acceptance||29-Dec-2021|
|Date of Web Publication||12-Jul-2022|
P.G Department of Anthropology, D.M College of Science, DMU, Imphal, Manipur
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Anemia is a major public health issue throughout the world. Nutritional deficiencies in terms of iron, B12 (cobalamin) and B9 (folate) are the main causes of anemia in the absence of genetic abnormalities and chronic diseases in many countries. Lactating mothers are susceptible to anemia because of maternal iron depletion during lactation as well as blood loss during childbirth. Objective: The present study examines the prevalence of anemia among the lactating (cases) and nonpregnant-nonlactating (control) married Tangkhul women of Ukhrul district. Materials and Methods: A community-based cross-sectional study was conducted among 400 individuals (lactating: 150; nonpregnant-nonlactating women: 250) from 11 villages of the Ukhrul district in Manipur. A pretested schedule, which included height, weight, and socio-demographic parameters were used. Body mass index = weight (in kg)/height (in metre2) was computed of each subject and categorized. Hemoglobin concentration was measured using Sahli's method. Statistical methods namely Chi-square (χ2) test and binary logistic regression were applied. Results: The prevalence of anemia was higher in the lactating women (62.0%) than nonpregnant-nonlactating women (56.8%). Odds ratio (OR) of multivariate logistic regression analysis indicated that anemia was significantly associated in both cases and control with low literacy level (OR = 5.03 and 3.71), low income (OR = 2.51 and 3.56), cultivator (OR = 6.20 and 3.86), and multigravida >4 (OR = 5.25 and 2.67), respectively. Conclusion: Dietary practices play an important role in causing anemia. Low literacy level, low income, cultivator, and high gravidity were identified as the associated risk factors of anemia.
Keywords: Anemia, hemoglobin, lactating, nonpregnant-nonlactating, tangkhul
|How to cite this article:|
Shimrah C, Devi H S. Prevalence of anemia and associated risk factors among the lactating and nonpregnant-nonlactating Tangkhul women. Indian J Public Health 2022;66:182-6
|How to cite this URL:|
Shimrah C, Devi H S. Prevalence of anemia and associated risk factors among the lactating and nonpregnant-nonlactating Tangkhul women. Indian J Public Health [serial online] 2022 [cited 2022 Aug 13];66:182-6. Available from: https://www.ijph.in/text.asp?2022/66/2/182/350650
| Introduction|| |
Nutritional anemia was defined in 1968 by (World Health Organization [WHO]) in the technical report as “a condition in which the hemoglobin content of the blood is lower than the normal as a result of a deficiency of one or more essential nutrients, regardless of the cause of such deficiency.” According to the National Family Health Survey (NFHS)-(V), the prevalence of anemia (<12.0 g/dl) among the nonpregnant rural women was 30.5% and in the urban women was 28.8%. Lactation is the most energy demanding phase of human reproduction. Women in developing countries generally enter lactation with low bodily energy reserves, which makes them to be at the risk of adverse nutritional consequences. Furthermore, lactating mothers are highly susceptible to iron depletion if the energy and nutrient intake in their diets is inadequate. About half of the global maternal deaths due to anemia occur in south Asian countries. The prevalence of anemia also varies with population characteristics such as age, sex, socio-economic status, and bio-demographic factors such as pregnancy and lactation. Studies have reported that lower educational level is associated with low occupation, low socio-economy, and dietary iron deficiency, thereby affecting the nutritional status and increasing the risk of anemia. The tribal communities of Manipur have different dietary habits, which are associated with their habitats. They depend on the locally available foods such as cabbage, mustard, squash, potato, tapioca, pumpkin, broad bean, pea, ash gourd, bottle gourd, Colocasia, onion, garlic, and coriander. Therefore, during the longer phase of lean season, they face shortage of different kinds of foods except rice. The prevalence of anemia among the reproductive age group women of various tribal communities of Kom, Chothe, and Vaiphei of three districts of Manipur was 32.29%, 17.58%, and 38.95%, respectively. The objective of the present study is to examine the prevalence of anemia and associated socio-demographic risk factors among the lactating (cases) and nonpregnant-nonlactating (control) Tangkhul women.
| Materials and Methods|| |
The Tangkhuls are one of the recognized scheduled tribes of Manipur, mostly inhabiting the Ukhrul district. It lies about 84 km (52 miles) northeast of Imphal covering an area of 4544 sq km. The community-based cross-sectional study was conducted among 400 individuals (lactating: 150; nonpregnant-nonlactating women: 250), randomly collected from 11 villages of the Ukhrul district in Manipur. The ages of the women ranged from 18 to 45 years. The sample size was determined using the equation given by Lemeshow and Lwanga at 95% confidence interval (CI). A pretested schedule was employed and consisted of height, weight, and socio-demographic variables. Height and weight were measured and body mass index (BMI) was calculated by taking the ratio of weight to height squared (weight in kg and (height in meter)2 and classified following WHO. Hemoglobin concentration was measured using Sahli's hemoglobinometer. Anemia was classified as Hb <8 gm/dl, Hb 8–10 gm/dl, and Hb 11–11.9 gm/dl as severe, moderate, and mild. Hemoglobin level ≥12 gm/dl was considered normal. Household income was collected and converted into monthly per-capita income. Thereafter, per-capita income was classified as high-income group (HIG), middle-income group (MIG), and low-income group, according to Khongsdier.
The data were compiled and tabulated using Microsoft excel 2010 and analyzed using IBM SPSS Statistics for Windows, version 20 (IBM Corp., Armonk, N.Y., USA). Group comparisons were done by Chi-square test, and logistic regression was applied to measure the strength of association between anemia and potential risk factors. Variable with P < 0.05 was considered statistically significant.
| Results|| |
[Table 1] shows the prevalence of anemia among the cases and control women. It is revealed that anemia was found higher in the cases (62.0%) with 36%, 25.3%, and 0.7% for mild, moderate, and severe as compared to control (56.8%) whose anemia in the mild, moderate, and severe were 34.4%, 22%, and 0.4%, respectively.
|Table 1: Prevalence of anaemia among the Tangkhul lactating and control women|
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[Table 2] presents the distribution of women having anemia in different socio-demographic variables of age, literacy, income, occupation, family size, number of children, gravidity, and BMI. It is observed that anemia was higher in the age group of 36-45 years of cases (70.3%) and control (59.9%) as compared to other age groups. However, the differences were not significant in both the cases (χ2 = 1.26, P > 0.05) and control (χ2 = 0.83, P > 0.05). The prevalence of anemia was higher in the none to primary education level group in both lactating (76.2%) and nonpregnant-nonlactating (65.0%) as compared to women with higher education level. Intra comparisons among the various literacy levels of cases (χ2 = 7.82, P < 0.05) and control (χ2 = 9.65, P < 0.05) showed significant differences, statistically. Low-income women had higher anemia as compared to middle- and high-income women in both lactating (68.7%) and nonpregnant-nonlactating women (63.9%). The difference was significant in anemia among the various income groups of the control women (χ2 = 7.58, P < 0.05). However, no such significant variation was observed among the income groups of lactating mothers (P > 0.05). Anemia was also found higher among the cultivators of both cases (86.1%) and control (67.8%) as compared to homemaker and employed women, and the difference was significant (χ2 = 3.97, P < 0.05). Moreover, intra comparisons of various occupations of cases (χ2 = 11.79, P < 0.05) and control (χ2 = 9.33, P < 0.05) indicated significant differences. Family size having >5 members had higher anemia in both cases (69.7%) and control (61.55%). However, no significant variations were found in all comparisons (P > 0.05). Women, having a number of children >4, had higher anemia in the cases (88.8%) and in the control (60.1%) as compared to mothers having ≤4 children. Significant differences was found in between the cases and the control having >4 children in anemia (χ2 = 5.59, P < 0.05) and also among the cases, having ≤4 and >4 children (χ2 = 6.27, P < 0.05). As well as, anemia was found higher in the cases (87.5%) than the control (75.0%) with no significant differences in having gravidity >4 and ≤4 in the two comparing groups (χ2=1.48 and 0.56, P>0.05). However, intra gravidity comparison show significant differences in the cases (χ2 = 7.89, P < 0.05) and in the control (χ2 = 7.21, P < 0.05).Underweight women were more anemic in both lactating (66.7%) and nonlactating (71.7%) as compared with normal and overweight BMI women. Statistically, no significant variations were found in comparing women having different BMI categories of the cases and the control and among the same groups (P < 0.05).
|Table 2: Prevalence of anemia in different independent categorical variables|
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Odd ratio of binary logistic regression analysis for risk factor of anemia [Table 3] have revealed that none or primary education women have higher risk of having anemia in both cases (odds ratio [OR] = 5.03); (CI = 1.54–16.43, P < 0.05) and control (OR = 3.71); (CI = 1.47–9.37, P < 0.05). Low-income mother had also significant risk of anemia as compared to middle and high income women counterparts in both the groups of cases (OR = 2.51); (CI = 1.07-5.94, P < 0.05) and control (OR = 3.56); (CI = 1.52-8.33, P < 0.05). Similarly, women who are cultivators had a significant risk of anemia as compared to housewives or employed in both cases (OR = 6.20); (CI = 1.42–27.07, P < 0.05) and control (OR = 3.86); (CI = 1.58-9.40, P < 0.05). Among the control women, homemakers had a significant risk of having anemia than the employed (OR = 2.46); (CI = 1.13-5.35, P < 0.05). It was also further revealed that bigger family size >5 had a significant risk of having anemia compared to mothers having ≤5 family members, but differences were not significant in the inter and intra comparisons (P > 0.05). Mothers having number of children >4 were significantly associated with anemia as compared to women with ≤4 children in the cases (OR = 12.52); (CI = 1.62–96.93, P < 0.05), but differences were not significant among the control women (P > 0.05). Women having >4 pregnancies had a significant risk of anemia in the lactating (OR = 5.25); (CI = 1.49-18.51, P < 0.05) and nonlactating women (OR = 2.67); (CI = 1.28–5.58, P < 0.05).
|Table 3: Sociodemographic factors associated with anemia among the lactating and nonlactating women|
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| Discussion|| |
The prevalence of anemia was observed to be higher among lactating women (62.0%) as compared to nonpregnant- nonlactating women (56.8%). The prevalence among Tangkhul women, as observed in this study, were higher than the NFHS-5 measures for Manipur (rural: 30.5%), and also higher as compared with the data for tribal women of Kom (32.29%) in Churachandpur district, Chothe (17.58%) in Chandel district and Vaiphei (38.95%) in Kangpokpi district in the state. The prevalence of anemia was 66.0% among the pregnant and lactating women of Dehradun district, Uttarakhand. Patil and Bhovi also reported the prevalence of anemia (65.89%) among the lactating women of Vijayapur, district, Karnataka, India and 34.8% among the south Indian reproductive age group women. Older women (36–45 years) had higher anemia as compared to younger age groups. Such findings were also reported among the lactating women by other researchers. The possible reason for the increased risk of anemia in the higher age group may be related to reproductive history and maternal workload which may contribute to the high prevalence of anemia among those age groups. Lower literacy level, none-primary, which was significantly associated with anemia as compared to higher education level was also found by Siddique et al. and Malhotra et al.
Due to low educational level, awareness about food values was very low among the tribal women and many iron-rich foods such as amaranth, lotus stem, mint, wheat flour and green leafy vegetables were not included in their dietary planning. This might be due to the fact that the provision of education enables people to earn or increase their earnings. The prevalence of anemia was found to be higher among the low-income women as compared to middle and high-income women of both cases and control. This finding is supported by studies of Siddique et al. and Lakew et al. The absence of market in the village and low buying capacity was also other factor, thereby, compelled them to depend entirely on the locally available seasonal foods. Regular consumption of meats, milk, eggs, fish and fruits were also too low. During the lean season, scarcity of food items was very common in the present study of hilly villages. Their dietary habit indicated that the daily required amount of iron (haem), B12 (cobalamin), B9 (folate) were not in the line of (recommended dietary allowances), and as such, caused anemia among the women. Iron is a major component of hemoglobin, while B12 play as a co-factor enzyme in the chemical reaction of DNA synthesis. Equally, B9 also required for red blood cells growth and maturation.
Statistically significant differences have been observed among the women belonging to various occupations in both cases and controls. Women who were cultivators were more likely to be anemic than housewives and employed women; this was consistent with the observations of Bharti et al. It was also found that larger family size, ≥5, women had higher rate of anemia as compared to smaller family size, ≤4, women of both cases and control women. There was a significant difference among the women with number of children, ≥4 and number of children, <4 of lactating women and a higher prevalence of anemia was also found in the women with greater number of children as compared to lesser number of children of both cases and control women. Similar findings were also observed in the study of Zhao et al., More children not only give economic burden to the family, it also caused blood loss during childbirth and forced the mothers to anemia. Grand multigravida women had greater chance of being anemic than their counterparts. Mothers who had more than four pregnancies were more likely to be anemic. This finding is consistent to the study conducted by Agarwal et al. in India and southwestern Ethiopia. This may be due to repeated and frequent pregnancies and births, which may not provide a sufficient time period to replenish lost nutrient stores before childbirth. A higher prevalence of anemia was found among the underweight women as compared to normal and overweight women of both cases and control women. A similar result was observed by Malhotra et al.
The main causal factor of anemia would be due to dietary iron deficiency, vitamin B12 and B9 because their dietary habit was confined to locally available a limited number of food items. Due to low literacy level, many women did not know about the food values. For many people, food is to fulfill hungry and satisfaction. Iron, B12 and B9 rich foods were not included regularly in their dietary planning. The absence of market in the village and low buying capacity were also another factors, as a result, compelled them to depend entirely on the locally available foods. High rates of gravidity and lactation also caused anemia. Loss of red blood cells from blood during frequent childbirth and lactation also led them to anemia. Majority of cases and control women could not fulfill the daily dietary requirements due to cultivator and low-income, thereby causing anemia among the majority women of the present study.
| Conclusion|| |
Anemia is a major health problem among the lactating and nonpregnant-nonlactating Tangkhul women of Manipur. Tribal women largely depend on a limited number of locally available food items and found missing valuable nutrients for red blood cell formation, growth, and maturation and suffered from anemia. Low literacy level, low income, low-status occupation of cultivator, high gravidity and more children play as associated risk factors for causing anemia among the present study women. Efforts should be made to improve the level of education, economic status, family planning and awareness about food values.
There were no Ethical issues in this present study. The participants were informed about the objective of the study and consent was obtained from each subject.
The study was conducted after the ethical approval had been obtained from the institutional ethics committee.
The author would like to thank all the women who voluntarily participated in the present study.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
World Health Organisation. WHO Scientific Group on Nutritional Anaemias & World Health Organisation. Nutritional Anaemias: Report of a WHO Scientific Group [Meeting held in Geneva from 13 to 17 March 1967]; 1968. Available from: http://apps.who.int/iris/handle/10665/40707
. [Last accessed on 2021 Mar 21].
Brown KH, Akhtar NA, Robertson AD, Ahmed MG. Lactational capacity of marginally nourished mothers: Relationships between maternal nutritional status and quantity and proximate composition of milk. Pediatrics 1986;78:909-19.
Axemo P, Liljestrand J, Bergström S, Gebre-Medhin M. Aetiology of late fetal death in Maputo. Gynecol Obstet Invest 1995;39:103-9.
Ezzati M, Lopus AD, Dogers A, Vander HS, Murray C. Selected major risk factors and global and regional burden of diseases. Lancet 2002;360:1347-60.
Sharma A, Patnaik R, Garg S, Prema Ramachandran. Detection & management of anaemia in pregnancy in an urban primary health care institution. Indian J Med Res 2008;128:45-51.
] [Full text]
Tympa-Psirropoulou E, Vagenas C, Dafni O, Matala A, Skopouli F. Environmental risk factors for iron deficiency anemia in children 12-24 months old in the area of Thessalia in Greece. Hippokratia 2008;12:240-50.
Devi HS. Status of anaemia among the tribal women in Manipur. Arch Curr Res Int 2018;12:1-7.
Lwanga SK, Lemeshow S. Sample Size Determination in Health Studies: A Practical Manual. Geneva, Switzerland: World Health Organization; 1991.
WHO Expert Consultation Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. London: Lancet; 2004;363:157-63.
Khongsdier R. Body mass index and morbidity in adult males of the War Khasi in Northeast India. Eur J Clin Nutr 2002;56:484-9.
Singh AB, Kandpal SD, Chandra R, Srivastava VK, Negi KS. Anaemia amongst pregnant and lactating women in district Dehradun. Indian J Pev Soc Med. 2009;40:20.1.
Patil SD, Bhovi RA. Prevalence of anaemia in pregnant and lactating women in rural Vijayapur. Int J Community Med Public Health 2020;7:224-7.
Raghuram V, Anil M, Jayaram S. Prevalence of anaemia amongst women in the reproductive age group in a rural area in south India. Int J Biol Med Res 2012;3:1482-4.
Umeta M, Haidar J, Demissie T, Akalu G, Ayana G. “Iron deficiency anaemia among women of reproductive age in nine administrative regions of Ethiopia.” Ethiopian J H Development 2008;22:3.
Siddiqui MZ, Goli S, Reja T, Doshi R, Chakravorty S, Tiwari C, et al
. Prevalence of anaemia and its Determinants among pregnant, lactating, and nonpregnant nonlactating Women in India. SAGE Open. 2017;7:3.
Malhotra P, Kumari S, Kumar R, Varma S. Prevalence of anaemia in adult urban population of north India. J Assoc Physicians India 2004;52:18-20.
Tilak JB. Education and poverty. J Hum Dev 2002;3:171-207.
Lakew Y, Biadgilign S, Haile D. Anaemia prevalence and associated factors among lactating mothers in Ethiopia: Evidence from the 2005 and 2011 demographic and health surveys. BMJ Open 2015;5:e006001.
Bharati P, Som S, Chakraborty S, Bharati S, Pal M. Prevalence of anaemia and its determinants among nonpregnant and pregnant women in India. Asia Pacific J Public Health 2008;20:4.
Zhao A, Zhang Y, Li B, Wang P, Li J, Xue Y, et al.
Prevalence of anemia and its risk factors among lactating mothers in Myanmar. Am J Trop Med Hyg 2014;90:963-7.
Zhao A, Cao S, Gao HC, Xiao QY, Win NN, Zhand YM. Anaemia among lactating mothers in Kokang, Myanmar. Southeast Asian J Trop Med Public Health 2016;47:6.
Agarwal KN, Agarwal DK, Sharma A, Sharma K, Prasad K, Kalitta MC, et al.
Prevalence of anaemia in pregnant and lactating women in India. Indian J Med Res 2006;124:173-84.
] [Full text]
Alemayehu M. Factors associated with anaemia among lactating mothers in subsistence farming households from selected districts of Jimma Zone, South Western Ethiopia: A community based cross-sectional study. J Nutr Food Sci 2017;7:595.
Abdelrahman EG, Gasim GI, Musa IR, Elbashir LM, Adam I. Red blood cell distribution, width and iron deficiency anaemia among pregnant Sudanese women. Diagn Pathol 2012;7:169.
[Table 1], [Table 2], [Table 3]