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 Table of Contents  
Year : 2022  |  Volume : 2  |  Issue : 1  |  Page : 15-19

Nutritional anemia in a rural community in Tamil Nadu

1 Department of Clinical Research, SRM Institute of Gastroenterology, Hepatobiliary Sciences and Transplantation, SIMS Hospital, Chennai, Tamil Nadu, India
2 Department of Community Medicine, SRM Medical College Hospital and Research Centre, SRM University, Kancheepuram, Tamil Nadu, India
3 Department of Community Medicine, Chettinad Hospital and Research Institute, Kelambakkam, Tamil Nadu, India
4 Department of Medical Research, SRM Medical College Hospital and Research Centre, SRM University, Kancheepuram, Tamil Nadu, India

Date of Submission20-Aug-2021
Date of Acceptance12-Oct-2021
Date of Web Publication01-Jan-2022

Correspondence Address:
John P Mechenro
Institute of Gastroenterology, SRM Institutes for Medical Science, Vadapalani, Chennai - 600 026, Tamil Nadu
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ghep.ghep_37_21

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Background: The prevalence of anemia in India is moderately high, leading to a thrust toward iron fortification of commonly used dietary cereals. We undertook a study to determine the prevalence of anemia in a rural population in Tamil Nadu and to evaluate its association with social, cultural, and dietary practices. Methods: Four hundred and twenty-three adults living in 39 villages comprising the Kattankulathur block of Kancheepuram district in Tamil Nadu were recruited for this study. Data regarding social, cultural, and dietary practices were recorded and hemoglobin estimated using capillary blood samples. Anemia was classified according to the criteria specified by the World Health Organization. Results: Anemia was found in 91 of 244 (37.3%) female respondents and in 17 of 179 (9.5%) male respondents. In univariate analysis, anemia exhibited associations with marital status, level of education, occupation, and socioeconomic status. The prevalence of anemia was higher in diabetics, and in those who had no awareness of anemia. Anemia was less prevalent in those who frequently consumed milk, fish, beef, or dates. In multivariate analysis, when gender was eliminated, consumption of dates, socioeconomic class, frequent milk consumption, and alcohol consumption were independently associated with anemia. Conclusions: Socioeconomic class and dietary practices were the strongest determinants of anemia in a rural South Indian community and should inform interventions in the community.

Keywords: Anemia, diet, nutrition, socioeconomic status

How to cite this article:
Mechenro JP, Kumar BM, John K R, Balakrishnan D. Nutritional anemia in a rural community in Tamil Nadu. Gastroenterol Hepatol Endosc Pract 2022;2:15-9

How to cite this URL:
Mechenro JP, Kumar BM, John K R, Balakrishnan D. Nutritional anemia in a rural community in Tamil Nadu. Gastroenterol Hepatol Endosc Pract [serial online] 2022 [cited 2022 Aug 18];2:15-9. Available from: http://www.ghepjournal.com/text.asp?2022/2/1/15/334699

  Introduction Top

Anemia, specifically that caused by iron deficiency, is one of the five leading causes of years lived with disability (morbidity) among 195 countries sampled between 1990 and 2016.[1] A review from India earlier in this decade found that anemia prevalence in young children and in women of reproductive age was over 70% in many parts of India.[2] The National Family Health Survey-4 covering the period 2015–2016 found that anemia was common both in adult women (53% overall, 50.8% urban, and 54.2% rural) and adult men (22.7% overall, 18.4% urban, and 25.2% rural).[3] Anemia is considered a severe (World Health Organization [WHO] Grade 2) public health problem in the four southern Indian states of Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana.[4]

Iron-deficiency anemia results from a number of factors including inadequate dietary intake; defective iron absorption due to high dietary phytate, enteropathy, or hypochlorhydria; excessive physiological losses during menstruation, pregnancy, and lactation; and pathological gastrointestinal loss due to hookworm infestation or other causes.[5] Additional factors contributing to iron deficiency include poor iron reserves at birth, the timing of umbilical cord clamping, and the timing and type of complementary food introduction.[5]

Socioeconomic status (SES) is one of the most important social determinants of health and disease. Pooled data from several countries indicate that SES, but not sanitation or improved water supply, is significantly associated with anemia prevalence in a community.[6] Among adolescent girls in rural Maharashtra, anemia was prevalent in 87% and was associated with nutritional factors such as mid-upper arm circumference, consumption of fruit, and consumption of rice and with social factors such as incomplete schooling.[7]

In this study, we evaluated the prevalence of anemia in a rural population residing in the Kattankulathur block of Kancheepuram district in Tamil Nadu and attempted to identify socioeconomic and cultural risk factors that were associated with anemia in this population.

  Methods Top

Study area

The study was conducted in the Kattankulathur revenue block located in Kancheepuram district of Tamil Nadu. The block comprises 133 villages and habitations comprising a total population of 213,850.

Study population

Inclusion criteria for participants were age 18 years and above, either gender, and residence in the area for at least the past 1 year. Participants were excluded if they had any chronic illness other than uncomplicated diabetes or systemic hypertension.

Study period

The study was undertaken over a period of 1 year from June 2015 to July 2016.

Sample size calculation

We used a 30 × 7 cluster sampling technique described for health surveys in developing countries.[8],[9] Sample size was based on detecting a prevalence of anemia of 50% with a precision of 5%, assuming that two individuals would be sampled per household and with a design effect of 1, and calculated using the formula n = Z2 PQ/L2 (Z = constant [1.96], P = prevalence, Q = 1 − p, L = Precision). Based on this, we arrived at a sample size of 420, i.e., 14 consecutive households per cluster.

Sampling procedure

The population for individual habitations was computed with total population being 213,850 distributed in 133 villages/habitations. For the study to be representative of the block, 30 of the 133 habitations were selected. The total population of 213,850 was divided by 30 and the sampling interval attained was 7129. The first village/habitation was selected using a random number (from a table of random numbers) and it was 4007 which was the K. K Nagar habitation from Alapakkam panchayat. Subsequent clusters were selected by adding 7129 sequentially, until 30 villages/habitations were selected, the last cluster being Rathinamangalam habitation.

Data and sample collection

An interview schedule was created, and direct personal interview was conducted by JM for each consenting participant using a structured questionnaire. The Expanded Program on Immunization (EPI) recommendation for the selection of first household was followed, i.e., the center of the area corresponding to the cluster (habitation) was reached and a direction selected after spinning a bottle. The first house in the direction indicated by the bottle cap was selected as the first household and subsequent households were selected by following the EPI strategy of going to the household whose door was nearest to the chosen household until seven eligible households participated. A predesigned questionnaire was administered to each participant to collect data on sociodemographic profile (age, sex, religion, marital status, family size and type, education, income, occupation, and financial dependency) and cultural and lifestyle factors (food habits, fruit and vegetable consumption, date and raisin eating frequency, milk and milk product intake, and habits of smoking and drinking). Socioeconomic score was computed using a modified Kuppuswamy scoring system,[10] and socioeconomic class was attributed accordingly. Capillary blood samples were obtained from each individual, and hemoglobin level was measured in all participants on this sample using HemoCue HB 301.[11] The WHO classification was used to determine the presence and categorize the severity of anemia.[12]


The study protocol and consent forms were approved by the Institutional Ethical Committee of the SRM Medical College Hospital and Research Centre. Informed written consent was obtained from all participants.

Statistical analysis

Univariate analysis was done using Pearson's Chi-square test for associations with anemia. P <0.05 was considered statistically significant. Variables that showed significant (P < 0.05) association in univariate analysis were tested in a multivariable analysis, in which forward conditional binary logistic regression was done to test for associations of these variables with the presence or absence of anemia.

  Results Top


A total of 1432 households were screened, and 432 individuals consented to participate in the study. Eventually, 423 of those consenting provided both data and blood samples for the study. The demographic details of these participants are provided in [Table 1].
Table 1: Prevalence of anemia by demographics

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Association of anemia with sociodemographic variables

Among 423 subjects included in this study, 42% were male and 58% were female. Anemia was found in 91 of 244 (37.3%) female respondents and in 17 of 179 (9.5%) male respondents [Table 1]. Married people were more likely to be anemic than unmarried. Anemia was more common in those without formal education compared to those with any educational qualification. Anemia was more prevalent in unskilled workers and unemployed people than in white-collar workers. SES was inversely associated with the prevalence of anemia. The prevalence of anemia was higher in diabetics than in nondiabetics. Anemia was more prevalent in individuals who had no awareness of anemia compared to those who were aware of the condition.

Association of anemia with lifestyle variables

The prevalence of anemia was not significantly different between nonvegetarians (97 of 388, 25%) and vegetarians and lacto-ovo-vegetarians (11 of 35, 31.4%) [Table 2]. When evaluating consumption of individual categories of nonvegetarian diet, consumption of chicken, mutton, or liver was not significantly associated with anemia. Frequent consumption of fish was associated with lesser prevalence of anemia (59 of 275, 21.4%) compared to those who rarely or never ate fish (49 of 148, 33.1%). Frequent consumption of beef was associated with lesser prevalence of anemia (18 of 110, 16.3%) compared to those who rarely or never ate beef (90 of 313, 28.7%). Frequent consumption of milk was associated with lesser prevalence of anemia (23 of 128, 17.9%) compared to those who drank milk less than once a week (85 of 295, 28.8%). Anemia was less prevalent in those who regularly ate dates (55 of 264, 20.8%) compared to those who never ate dates (53 of 159, 33.3%). There was no significant association of anemia with raisin consumption, intake of fruits and vegetables, intake of butter and cheese, or intake of tea and coffee.
Table 2: Association of anemia with lifestyle variables

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Multivariate analysis

In multivariate analysis, three variables were independently associated with the presence or absence of anemia [Table 3]. These included gender which had the strongest influence on the presence of anemia, regular eating of dates which had a protective association, and lower SES class (Classes IV and V of Kuppuswamy) which was positively associated with anemia. Gender clearly influenced the presence of anemia, due to iron losses during menstruation and pregnancy in women. In order to detect other factors independently associated with anemia, gender as a variable was removed from a second analysis in which consumption of dates and lower SES class continued to significantly associate with anemia while drinking milk more than once a week and drinking alcohol exhibited protective associations with anemia.
Table 3: Multivariate analysis using forward conditional binary logistic regression

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  Discussion Top

The participants in the study were drawn from a rural population in Tamil Nadu, with the majority being lower middle class followed by those belonging to the lower classes. In the present study, anemia was less prevalent in both women (37.3%) and men (9.5%) compared to the national average (53% and 22.7%, respectively) found in NHFS-4.[3] The latter survey documented regional variations in anemia prevalence within India probably due to variations in socioeconomic, lifestyle, and cultural factors between different regions. In the present study, we have explored the association of such factors with anemia in this population residing in rural Tamil Nadu.

Focus group discussions in India have identified the high prevalence of anemia as being attributable to factors such as poverty, social neglect, lack of food, lack of personal hygiene, worm infestation, and poor decision-making power of women belonging to low-income groups.[13],[14] In the present study, univariate analysis identified a number of factors associated with anemia. As expected, men had much less anemia than women and gender turned out to be the strongest factor associated with anemia. Higher frequency of anemia in women can be attributed to losses during menstruation and pregnancy. The latter losses are probably responsible for the increased prevalence of anemia in married women compared to unmarried women. Lack of formal education, less privileged occupational status, and lower SES were all associated with anemia in univariate analysis. Interestingly, there also was an association of diabetes with anemia in the present study, but the cause for this association is not clear.

Lifestyle variables associated with anemia included diet, smoking, and alcohol use. Smoking and alcohol use are both likely to lead to polycythemia, and this explains their negative association with anemia. A study from Karnataka found that low dietary intake of multiple micronutrients, and higher intakes of nutrients that inhibit Fe absorption such as Ca and P, were associated with maternal anemia in India.[15] Interestingly, that same study also found that higher intake of meat, fish, and poultry was associated with higher prevalence of anemia, which is contrary to reasoning and to popular belief. A vegetarian diet may include more grains, nuts, and legumes that are rich in phytate which inhibits iron absorption from the intestine and might be associated with higher risk of anemia. However, in our study, we noted that vegetarians did not have a higher prevalence of anemia compared to those who were nonvegetarians. This is probably because most vegetarians in our society are not vegans but do drink milk and are lacto-vegetarians or lacto-ovo-vegetarians and also may be because they eat more green leafy vegetables and sprouted legumes. In addition, many rural nonvegetarians may consume meat only once a week or less for cultural or economic reasons. In our study, regular consumption of fish, beef, or milk was associated with lesser prevalence of anemia compared to individuals who did not regularly eat these. Frequent consumption of dates was likewise protectively associated with anemia.

In multivariate analysis, the only variables independently associated with the presence of anemia were gender, regular eating of dates, and lower SES class. Gender clearly influenced the presence of anemia, due to iron losses during menstruation and pregnancy in women. In order to detect other factors independently associated with anemia, gender as a variable was removed from a second analysis in which consumption of dates and lower SES class continued to significantly associate with anemia while drinking milk more than once a week and drinking alcohol exhibited protective associations with anemia.

Anemia continues to be fairly prevalent in women in this rural population in Tamil Nadu. The major associations with socioeconomic class and dietary practices indicate that public health approaches to mitigation of iron deficiency should target these factors when attempting to ameliorate anemia in this population.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

GBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: A systematic analysis for the global burden of disease study 2016. Lancet 2017;390:1211-59.  Back to cited text no. 1
Kotecha PV. Nutritional anemia in young children with focus on Asia and India. Indian J Community Med 2011;36:8-16.  Back to cited text no. 2
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Ministry of Health and Family Welfare. National Family Health Survey-4, 2015-2016. India Fact Sheet. Available from: http://rchiips.org/NFHS/pdf/NFHS4/India.pdf. [Last accessed on 2017 Dec 28].  Back to cited text no. 3
Challa S, Amirapu P. Surveillance of anaemia: Mapping and grading the high risk territories and populations. J Clin Diagn Res 2016;10:C01-6.  Back to cited text no. 4
Anand T, Rahi M, Sharma P, Ingle GK. Issues in prevention of iron deficiency anemia in India. Nutrition 2014;30:764-70.  Back to cited text no. 5
Wirth JP, Woodruff BA, Engle-Stone R, Namaste SM, Temple VJ, Petry N, et al. Predictors of anemia in women of reproductive age: Biomarkers reflecting inflammation and nutritional determinants of anemia (BRINDA) project. Am J Clin Nutr 2017;106:416S-27S.  Back to cited text no. 6
Ahankari AS, Myles PR, Fogarty AW, Dixit JV, Tata LJ. Prevalence of iron-deficiency anaemia and risk factors in 1010 adolescent girls from rural Maharashtra, India: A cross-sectional survey. Public Health 2017;142:159-66.  Back to cited text no. 7
Henderson RH, Sundaresan T. Cluster sampling to assess immunization coverage: A review of experience with a simplified sampling method. Bull World Health Organ 1982;60:253-60.  Back to cited text no. 8
Bennett S, Woods T, Liyanage WM, Smith DL. A simplified general method for cluster-sample surveys of health in developing countries. World Health Stat Q 1991;44:98-106.  Back to cited text no. 9
Kumar BP, Dudala SR, Rao JR. Kuppuswamy's socio-economic status scale – A revision of economic parameter for 2012. Int J Res Dev Health 2013;1:2-4.  Back to cited text no. 10
National Family Health Survey (NFHS)-4 India: Report of Pretest Conducted during December 2013. Available from: http://rchiips.org/NFHS/NFHS4/pdf/Final%20Pretest%20Report.pdf. [Last accessed on 2017 Dec 28].  Back to cited text no. 11
WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. In: Vitamin and Mineral Nutrition Information System. Geneva: World Health Organization; 2011. Available from: http://www.who.int/vmnis/indicators/haemoglobin.pdf. [Last accessed on 2017 Dec 28].  Back to cited text no. 12
Dasgupta A, Sarkar K, Chowdhury R, Ray A, Shahbabu B. Anemia and its determinants among women of reproductive age of a slum in Kolkata: A focus group discussion among health workers in a slum of Kolkata. J Family Med Prim Care 2016;5:276-80.  Back to cited text no. 13
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Diamond-Smith NG, Gupta M, Kaur M, Kumar R. Determinants of persistent anemia in poor, urban pregnant women of Chandigarh City, North India: A mixed method approach. Food Nutr Bull 2016;37:132-43.  Back to cited text no. 14
Samuel TM, Thomas T, Finkelstein J, Bosch R, Rajendran R, Virtanen SM, et al. Correlates of anaemia in pregnant urban South Indian women: A possible role of dietary intake of nutrients that inhibit iron absorption. Public Health Nutr 2013;16:316-24.  Back to cited text no. 15


  [Table 1], [Table 2], [Table 3]


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