Abstract
Background:
Malnutrition is a major problem, particularly among young women (aged 15–24 years) in rural India. Malnutrition not only affects their own health but may also affect the health of their future offspring. In India, there is a triple burden of malnutrition, that is, underweight, overweight, and anemia. However, only a few studies have assessed the triple burden in the community simultaneously. Therefore, we aimed to estimate the prevalence of malnutrition in a comprehensive manner and study the associated factors among young women in rural Haryana.
Methodology:
In this community-based study, 490 non-pregnant women aged 15–24 years were interviewed for socio-demographic details, menstrual and diet history, and anthropometry was performed. World Health Organization Asian adult body mass index cut-offs (for those aged ≥18 years) and extended International Obesity Task Force cut-offs (for those <18 years) were used to classify weight categories. Hemoglobin (Hb) concentration was estimated to identify anemia, defined as Hb concentration (<12 g/dL). The association between underweight, overweight, anemia, and selected independent variables was assessed by multivariate analysis.
Results:
The prevalence of underweight and overweight was 35.1% and 18.0%, respectively. The prevalence of anemia was 60.7%. The majority (98.2%) of the participants did not have adequate dietary diversity. Economic status and history of chronic disease or other infections had a significant association with being underweight. Women who were overweight had significantly lower odds of being anemic (odds ratio: 0.42; 95% confidence interval: 25–77%) (P = 0.01).
Conclusion:
The triple burden of malnutrition was high among young women residing in a rural area of Haryana, India.
Keywords: Dietary diversity, nutrition, rural women, triple burden of malnutrition, young women
INTRODUCTION
Malnutrition refers to deficiencies, excesses, or imbalances in a person’s intake of energy and/or nutrients.[1] Roughly every second adult in the world is either underweight or overweight, and every third person has anemia.[1,2] In India, at population level, both underweight and overweight or obese people coexist. Thus, India is experiencing a double burden of malnutrition. At the same time, the magnitude of micronutrient deficiencies, that is, deficiencies of various vitamins and minerals, is also rising, leading to a triple burden of malnutrition. Among the micronutrient deficiencies, anemia is of particular significance, as it may result in various short-term and long-term complications.
Underweight individuals have more chances of developing osteoporosis, infertility, and infections.[3,4,5] Overweight increases the risk for cardiovascular disease, type 2 diabetes, metabolic syndrome, joint and muscular disorders, and psychological problems.[6] Anemia reduces physical capacity, impairs cognitive function, and decreases immunity.[2,7]
While malnutrition is prevalent among all segments of the population, women are particularly vulnerable as they are physiologically and socially disadvantaged. In addition, they are also vulnerable to the intergenerational cycle of malnutrition. Pre-pregnancy underweight can cause intra-uterine growth retardation, preterm birth, low birth weight (LBW) in the fetus, as well as greater risk of hemorrhage and dystocia in the mother.[8] Obese and overweight women have a higher risk of preeclampsia and gestational diabetes.[9] Maternal anemia can increase the risk of pre-eclampsia, abruptio placentae, precipitate labor, and heart failure in the mother, as well as LBW, intra-uterine growth restriction, preterm birth, and intra-uterine death in the fetus.[8]
Improving the nutrition of young women improves both maternal and fetal outcomes, especially if the intervention is made at pre-conception rather than at the post-conception stage.[10] However, the nutritional programs in India are largely focused on interventions during pregnancy, with relatively less focus on young women, who are mostly in the pre-conceptional group. Under the Anemia Mukt Bharat program (2022–23), approximately 90% of the pregnant women had received iron folic acid (IFA) tablets, while only 49% of the adolescents had received IFA tablets.[11] This suggests a relatively limited focus of the nutritional program on women at the pre-conceptional stage.
As per the National Family Health Survey-5 (NFHS-5), the median age of marriage among Indian women was 18.6 years, and the age at first birth was 21 years. Nearly 27% of the rural women got married before the age of 18 years, and about 8% of women in the age group 15–19 years were pregnant.[12] Hence, it is important to focus on women aged 15–24 years to understand the scope of pre-conceptional nutrition. Since nearly 66% of the married women were in the age group of 15–19 years and 26% of women aged 20–24 years were still nulliparous, hence, there is an opportunity to assess and meet the nutrition gaps in this group.[12]
In India, only secondary analysis of nationally representative data from the NFHS is available regarding the burden of underweight, overweight, and anemia among young women.[13,14,15] However, many of the known determinants of malnutrition like education, wealth, marital status, and family size, can vary over time. Hence, there is a need to study malnutrition more frequently and holistically, with a context-specific perspective. With this background, we estimated the prevalence of underweight, overweight, and anemia and assessed the associated factors among women aged 15–24 years residing in a rural area of Haryana.
MATERIALS AND METHODS
Study site
This community-based cross-sectional study was conducted in the district of Faridabad, Haryana. Five villages with the largest population under the health and demographic surveillance site (HDSS) of a subdistrict hospital in district of the Faridabad were included. The HDSS served a population of nearly 100,000 individuals. The health-related information was maintained in a computerized database, that is, the Health Management and Information System (HMIS). The database was updated every month based on the information collected by the multi-purpose health workers during their monthly domiciliary visits and every year based on the annual census.
The study participants were young women in the age group of 15–24 years who were self-reportedly non-pregnant. Both married and unmarried women were included. We excluded the women who had a child aged less than six months.
The study was carried out between October 2021 and March 2023. The period of data collection was from May 2022 to July 2022.
Sample size
In a secondary data analysis of NFHS-4, Sethi et al.[15] reported the prevalence of underweight, overweight, and anemia as 29.8%, 17.2%, and 52.9%, respectively. The required sample size was calculated at 468 based on the prevalence of overweight (which had the lowest prevalence) and a relative precision of 20%. Provisioning an anticipated non-response rate of 5%, the final sample size was increased to 495.
Sampling technique
A list of all women aged 15–24 years was generated from the HMIS. The study participants were selected from this list by simple random sampling.
Data collection
The investigator made house-to-house visits to contact the study participants. Data were collected regarding sociodemographic, obstetric, and selected clinical characteristics such as history of menorrhagia, chronic illnesses, infections (such as diabetes mellitus, hypothyroidism, and tuberculosis within the previous year), receipt of IFA tablets, and dietary practices. The Minimum Dietary Diversity for Women (MDDW) questionnaire was administered to assess the dietary diversity.[16] A list of locally available foods was generated based on pre-testing. The 24-hour recall method was paired with a list-based recall to assess dietary practices. Weight and height were measured as per the standard guidelines.[17] Hemoglobin concentration was estimated by using an auto-analyzer (HORIBA ABX Micros ES).[18]
Operational definitions
Triple burden of malnutrition: Prevalence of underweight, overweight, and anemia among the study participants.
The triple burden refers to underweight, overweight, and deficiencies of micronutrients, that is, various minerals and vitamins; however, due to logistic reasons, we included only anemia in the present study.
Below poverty line (BPL): Participants belonging to the families who possessed a BPL card issued by the government.
Adequate dietary diversity: If a participant consumed five or more different food groups, dietary diversity was considered adequate.[16]
Non-vegetarian: The participants who consumed non-vegetarian food, including eggs, at least twice a week.
Occasional non-vegetarian: The participants who consumed non-vegetarian food, including eggs, at least once a month but not more frequently than once a week
Menorrhagia: Presence of either the increased duration of the cycle (>7 days) or the necessity to change the sanitary pads within 2 hours, at any time during most or all of the menstrual periods.[19]
Based on body mass index (BMI), the participants were categorized as severely underweight, underweight, normal weight, overweight, and obese. World Health Organization (WHO) Asian adult BMI cutoffs[20] were applied for participants aged 18 years or older. For participants younger than 18 years, age and gender-specific extended International Obesity Task Force (IOTF) cutoff points (for 2–18 years of age)[21] were applied to classify the BMI into different categories.
Participants aged 18 years or older were considered to be severely underweight if the BMI was <16 kg/m2, underweight (excluding severe underweight) if the BMI was 16.0–18.4 kg/m2, and normal weight if the BMI was 18.5–22.9 kg/m2. The BMI cut-off for overweight (excluding obesity) was 23.0–24.9 kg/m2, and for obesity, it was ≥25.0 kg/m2.[20] For participants younger than 18 years of age, the categorization was based on age-specific extended IOTF cut-off points.[21]
Anemia was considered when the hemoglobin concentration was <12.0 g/dL. Participants were classified as having mild, moderate, and severe anemia when hemoglobin was 11.0–11.9 g/dL, 8.0–10.9 g/dL, and <8.0 g/dL, respectively.[22]
Statistical analysis: Epicollect version 5 was used to collect the data, and STATA version 16 was used for analysis. The prevalence of underweight, overweight, and anemia was expressed in percentages and 95% confidence interval (CI). Odds ratio (OR) was calculated by logistic regression for the association between each outcome variable (underweight, overweight, and anemia) and independent variables. Variables with values <0.2 in bivariate analysis were included in multivariate analysis.
The study was conducted after obtaining approval from the Institute Ethics Committee. Participants were informed in detail in their local language (Hindi) about the study regarding the purpose of the study, a detailed procedure including the amount of blood taken for sampling, expected duration, cost of participation, benefits and risks to be expected for the study participants, the confidentiality of obtained information, and freedom to participate/withdraw from the study at any time without penalty or loss of previously entitled benefits. Written consent was obtained from all the adult participants. For participants younger than 18 years of age, written consent was obtained from legally authorized representatives (parents in the case of unmarried participants; husbands or fathers-/mothers-in-law in the case of married participants), along with assent from the participants. The results of the study were individually informed to all the participants. Appropriate dietary advice was provided based on the results. Participants with anemia were referred to a nearby primary health center for further management.
RESULTS
A total of 490 young women participated in the study. Among all participants, 252 participants (51.4%) were adolescents (15–19 years) and 238 participants (48.6%) were young adults (20–24 years). Table 1 depicts the distribution of participants by socio-demographic and other selected characteristics. A total of 81 (16.5%) participants belonged to BPL families. Ninety (18.4%) participants were married, and 62 (12.7%) participants had past history of pregnancy. History of menorrhagia was present in 20.1% of the study participants. Nine participants (1.8%) had adequate dietary diversity. Approximately three-fourth of the study participants had not received any IFA tablets in the past one month.
Table 1.
Distribution of participants by socio-demographic and other characteristics
| Variables | Number of participants (n=490) | Percentage (n %) |
|---|---|---|
| Years of education | ||
| ≤8 | 69 | 14.1 |
| >8 | 421 | 85.9 |
| Type of family | ||
| Nuclear | 291 | 59.4 |
| Extended | 199 | 40.6 |
| Economic status | ||
| APL | 409 | 83.5 |
| BPL | 81 | 16.5 |
| Marital status | ||
| Unmarried | 398 | 81.2 |
| Married and living together | 90 | 18.4 |
| Married but separated, divorced, or widowed | 2 | 0.4 |
| Occupation | ||
| Student | 359 | 73.3 |
| Homemaker | 71 | 14.5 |
| Unemployed | 37 | 7.5 |
| Others* | 23 | 4.7 |
| Dietary habits | ||
| Vegetarian | 386 | 78.9 |
| Occasional non-vegetarian | 97 | 19.8 |
| Non-vegetarian | 7 | 1.3 |
*Others in occupation include tailor (11), laborer (1), shopkeeper (3), company worker (2), and agriculture (2)
The prevalence of underweight, overweight, and anemia was 35.1%, 18.0%, and 60.7%, respectively. [Table 2] Among the study participants whose anthropometry and blood samples were assessed, 22.7% had both underweight and anemia, while 7.6% were both overweight and anemic. Nearly one-third (30.3%) of the participants with normal weight had anemia. Table 3 shows the distribution of BMI categories and anemia. The overlap between underweight, overweight, and anemia is shown in Figure 1.
Table 2.
Distribution of participants by weight category (n=490) and anemia (n=422)
| Variable | Number of study participants (n=490) | Percentage (95% CI) |
|---|---|---|
| Weight category (n=490) | ||
| Severely underweight | 45 | 9.2 (6.8–12.1) |
| Underweight (excluding severe underweight) | 127 | 25.9 (22.1–30.0) |
| Normal weight | 230 | 46.9 (42.4–51.5) |
| Overweight (excluding obesity) | 36 | 7.4 (5.2–10.0) |
| Obesity | 52 | 10.6 (8.0–13.7) |
| Anemia (n=422) | ||
| Present | 256 | 60.7 (55.8–65.4) |
| Absent | 166 | 39.3 (34.6–44.2) |
| Severity of Anemia | ||
| No anemia | 166 | 39.3 |
| Mild anemia (Hb 11.0–11.9 g/dl) | 115 | 27.3 |
| Moderate anemia (Hb 8.0–10.9 g/dl) | 122 | 28.9 |
| Severe anemia (Hb <8.0 g/dl) | 19 | 4.5 |
Hb=Hemoglobin
Table 3.
Distribution of participants by BMI and anemia (n=422)
| BMI (n=422) |
Anemia (n=422) |
||
|---|---|---|---|
| Category | n (%) | Present n (%) | Absent n (%) |
| Underweight | 148 (35.1) | 96 (22.7) | 52 (12.3) |
| Normal weight | 197 (46.7) | 128 (30.3) | 69 (16.4) |
| Overweight | 77 (18.2) | 32 (7.6) | 45 (10.7) |
| Total | 422 (100) | 256 (60.7) | 166 (39.3) |
Column-wise percentages are shown in the table. The denominator for the calculation of the percentage is 422
Figure 1.
Figure showing the prevalence of triple burden of malnutrition (underweight, overweight, and anemia) and their overlap among the participants (N = 422)
In bivariable analysis, homemakers had a significant association with underweight (OR: 0.40, 95% CI: 0.17–0.92, P = 0.03). However, there was no significant association after adjusting for other variables, that is, age, economic status, marital status, type of family, history of chronic disease, and diet diversity (adjusted odds ratio [aOR]: 1.23, 95% CI: 0.27–5.65, P = 0.79). Married women had lower odds of being underweight (OR: 0.48, 95% CI: 0.29–0.82, P = 0.01) in the bivariable analysis. However, there was no significant association after adjusting for other variables (aOR: 0.40, 95% CI 0.11–1.45, P = 0.16).
In multivariable logistic regression, those who belonged to the above poverty line (APL) category had lower odds of being underweight compared to those who belonged to BPL (P = 0.01). History of chronic disease and other infections was associated with underweight with aOR of 4.97 (95% CI: 1.45–17.0, P = 0.01). Those aged 20–24 years had significantly higher odds of being overweight after adjusting for all other variables (aOR: 1.91, 95% CI: 1.14–3.21). Those who were overweight had significantly lower odds of being anemic after adjusting for other variables (aOR: 0.42, 95% CI: 0.23–0.75) [Table 4].
Table 4.
Association of underweight, overweight, and anemia with socio-demographic and other variables
| Variables | Categories | Total (n) | Outcome present n (%) | Unadjusted |
Adjusted |
||
|---|---|---|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | ||||
| Outcome 1: Underweight | |||||||
| Age group (in years) | 15–19 | 252 | 103 (40.9) | Ref | Ref | ||
| 20–24 | 238 | 69 (29.0) | 0.59 (0.41–0.86) | 0.01 | 0.66 (0.43–1.00) | 0.05 | |
| Economic status | BPL | 81 | 41 (50.6) | Ref | Ref | ||
| APL | 409 | 131 (32.0) | 0.46 (0.28–0.75) | 0.01 | 0.48 (0.29–0.79) | 0.01 | |
| History of chronic disease and other infection | Absent | 477 | 163 (34.2) | Ref | Ref | ||
| Present | 13 | 9 (69.2) | 4.33 (1.31–14.28) | 0.01 | 4.97 (1.45–17.0) | 0.01 | |
| Outcome 2: Overweight | |||||||
| Age group (in years) | 15–19 | 252 | 33 (13.1) | Ref | Ref | ||
| 20–24 | 238 | 55 (23.1) | 1.99 (1.24–3.20) | 0.01 | 1.91 (1.14–3.21) | 0.02 | |
| No. of family members | ≤5 | 240 | 50 (20.8) | Ref | Ref | ||
| >5 | 250 | 38 (15.2) | 0.68 (0.43–1.08) | 0.11 | 0.67 (0.41–1.08) | 0.09 | |
| Marital status† | Unmarried | 398 | 66 (16.6) | Ref | Ref | ||
| Married | 92 | 22 (23.9) | 1.58 (0.91–2.73) | 0.10 | 1.19 (0.65–2.18) | 0.57 | |
| Outcome 3: Anemia | |||||||
| Type of family | Nuclear | 245 | 157 (64.1) | Ref | Ref | ||
| Extended | 177 | 99 (55.9) | 0.71 (0.48–1.06) | 0.09 | 0.71 (0.47–1.07) | 0.10 | |
| Adequate dietary diversity | Absent | 415 | 254 (61.2) | Ref | Ref | ||
| Present | 7 | 2 (28.6) | 0.25 (0.05-1.32) | 0.10 | 0.23 (0.04–1.28) | 0.09 | |
| BMI category | Normal | 197 | 128 (65.0) | Ref | Ref | ||
| Underweight | 148 | 96 (64.9) | 1.00 (0.64–1.55) | 0.98 | 1.01 (0.64–1.61) | 0.96 | |
| Overweight | 77 | 32 (41.6) | 0.38 (0.22–0.66) | 0.01 | 0.42 (0.23–0.75) | 0.01 | |
†Married category includes – those who are married and living together (90) or separated or divorced or widowed (2)
DISCUSSION
We found a high prevalence of underweight, overweight, and obesity among young women aged 15–24 years in rural Haryana. Few studies are available regarding triple burden of malnutrition in this age group. Sethi et al.[15] conducted a secondary analysis of NFHS-4 (2015–16) data among married, nulliparous women in the age group of 15–24 years, while Banerjee et al. and Gupta et al. included women of reproductive age (15–49 years) in their analysis of NFHS-4 data.[23,24] Kumar and Barik (2024) reported the double burden of malnutrition among women of reproductive age (15–24 years) from the NFHS-5 (2019–21) data.[25] The latter three studies also reported sub-group data on young women (15–24 years). Sethi et al.[15] and Gupta et al. used WHO Asian adult cutoffs for BMI classification, while the other two studies used WHO global adult cutoffs.[23,24,25]
In our study, the prevalence of underweight among rural women aged 15–24 years was 35.1%. This was higher than the reported observations by Kumar and Barik (31.5%), Sethi et al. (29.8%), and Banerjee et al. (27.2%).[15,24,25] The prevalence of overweight (BMI ≥23 kg/m2) among women aged 15–24 years was 18.0%, which was similar to the prevalence rates reported by Sethi et al. (17.1%) and Gupta et al. (14.4%).[15,23] The prevalence of obesity (BMI ≥25 kg/m2) was 10.6% in our study. This was higher compared to Banerjee et al. (7.3%) but similar to that of Kumar et al. (9.2%).[24,25] The above data might suggest that both underweight and obesity are increasing over time, however, such comparisons must be interpreted with caution, due to differences in study settings, study populations, as well as difference in BMI cutoffs used in various studies.
We used extended IOTF cutoff points (2–18 years) to categorize underweight, overweight, and obese participants younger than 18 years of age. The use of extended IOTF reduced the possibility of misclassification bias. The cut-off values of the WHO BMI-for-age chart for 5–19 years correspond to the WHO global adult BMI cutoffs, which may underestimate overweight and obesity.[26] The Indian Academy of Pediatrics (IAP) charts for adolescents correspond to Asian BMI cutoff points.[27] Extended IOTF cutoff points correspond to IAP BMI cutoffs for adolescents, making it a reliable tool in Indian settings.[26]
We found that participants who belonged to the APL had lower odds of being underweight (OR: 0.48). This inverse relationship between socio-economic status and underweight has been reported in other studies as well.[15,24] In our study, participants with a history of chronic disease and other infections had higher odds (aOR: 4.97) of being underweight. The majority of those who had chronic disease and were underweight (67%) had a history of tuberculosis in the past year. However, the 95% CI of the observed OR in our study was wide, possibly a reflection of the small number of participants who had a chronic disease. Participants aged 20–24 years had higher odds of being overweight. This positive relationship between age group and BMI category has also been reported previously.[15] The relationship between parity and BMI could not be studied due to the fact that very few participants had a past history of pregnancy.
The prevalence of anemia among participants aged 15–24 years was 60.7%, which was similar to the prevalence of anemia among women of reproductive age in rural Haryana (62.1%) (NFHS-5).[12] Education and socio-economic status were previously reported to be associated with anemia.[13,14] However, we did not observe similar findings in our study.
One of the important aspects missed in nutritional studies in India is assessing the complicated inter-relationship between anemia and being underweight or overweight. Existing evidence suggests the possibility of any of the three types of relationships between anemia and underweight/overweight. Underweight was reported to exist along with anemia in some studies.[13] Some studies reported that overweight was associated with anemia.[28] Others reported that being overweight may be protective against anemia.[13,29] The variation in the type of association reported by different studies can be due to differences in the type of diet consumed. Hence, it is necessary to study the association between anemia and being underweight or overweight while adjusting for the type of diet consumed.
We found that those who were overweight had lower odds (OR 0.42) of being anemic. Similar findings have been reported by some other studies as well.[13,29] One of the possible explanations could be the type of diet consumed by the study participants. The overall dietary diversity of the participants was inadequate (median MDDW <5). Dietary diversity is an indicator of the adequacy of micronutrient consumption like iron, folate, and vitamin B12.[16] The inadequate dietary diversity could indicate that the participants may not have been able to meet the recommended daily dietary intake of micronutrients. However, if a less diverse diet is consumed in large amounts, the micronutrient requirements can be met, albeit at the cost of consuming excess calories. This could be a possible explanation for why we observed that overweight provided protection against anemia. Our findings, therefore, suggest the existence of hidden hunger among study participants.
In our study, only 1.8% of the participants had adequate dietary diversity. This was low compared to the findings of other studies (30%) reported from India among women in the reproductive age group.[30,31] The low rate of adequate dietary diversity among participants in our study could be due to the fact that the proportion of participants who consumed non-vegetarian food was low (1.3%). Second, our study participants were young women (15–24 years), whereas other studies had included women of the entire reproductive age group, that is, 15–49 years. This difference in the age group of study participants included in various studies might have led to different dietary diversity scores. In addition, seasonality is known to affect dietary diversity. Therefore, studies conducted in different seasons could yield varying dietary diversity score.[26] We used 24-hour open dietary recall combined with systematic question-based recall and selected list-based probing for forgotten food items during open recall. This list was developed through experiences from pilot testing and informal interviews with the target population not included in the study. All these factors make our assessment more reliable by reducing misclassification bias.
Our study had a few limitations. The non-response rate for blood sampling was 13.8%. However, the socio-demographic characteristics of participants who consented to blood sampling and those who did not consent were statistically comparable (data not shown). This indicates the absence of potential selection bias. Another limitation was that we could not assess seasonality in dietary diversity.
The community-based study design, adequate sample size, and simple random sampling were some of the strengths of the study. Extended IOTF cut-off points were used to avoid misclassification of overweight and obesity among adolescents. Hemoglobin concentration was estimated by an auto-analyzer, which has high sensitivity and specificity.
Our study indicates the existence of triple burden of malnutrition among young women in rural areas and highlights the need to expand the scope of the existing nutritional programs, with a special focus on young women.[32] The possibility of hidden hunger illustrated by our findings also suggests the need for food diversification from production to consumption level, as envisioned in the National Nutritional Strategy.[33]
CONCLUSION
The magnitude of the triple burden of malnutrition was high among non-pregnant women aged 15–24 years residing in a rural area of Haryana.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
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