The outcome variable of the study, i.e. minimum dietary diversity is defined as the intake of food belonging to at least 5 or more food groups in the past 24 hours, as defined by FAO and FHI 360, 2016. For the computation of the standard measure of Minimum dietary diversity (MDD-W) for women, FAO defines 10 diverse types of food groups as: Grains, white roots and tubers, and plantains; Pulses (beans, peas and lentils); Nuts and seeds; Dairy; Meat, poultry and fish; Eggs; Dark green leafy vegetables; Other vitamin A-rich fruits and vegetables; Other vegetables and; Other fruits. Out of these 10, UDAYA asks information only for the first seven food groups in its data. For the last three food groups, questions are asked on the overall intake of “fruits” or “vegetables”, without giving emphasis to whether they were vitamin-A rich or non-rich fruits and vegetables. In order to adjust for this data limitation, a weightage of 1.5 is given to the instead of 1, to the two proxy food groups namely “Fruits” and “Vegetables”, as they may be vitamin-A rich as well as well as vitamin-A non-rich. This way, we get the total dietary diversity score to fall in the range of 0 to 10 as it generally should, where consumption of food from five or more food groups is defined as the proxy measure of minimum dietary diversity among male and female adolescents.
Independent variables consist of socio-demographic characteristics like age of the adolescent, sex, completed years of education, whether doing paid work in the past 1 year, caste, number of siblings, media exposure, mother’s completed years of education, presence of any grandparent in the household, food consumption behaviour of the household, wealth index of the household, place of residence and state. For making of media exposure index, firstly, eight binary indicators are created representing frequent (high) and infrequent (low) watching of television, reading of newspaper, listening of radio, watching movies, usage of internet; Owning of mobile and laptop; and using of social media in last three years. All of these are added together to make a score of range 0 to 8, which is then categorised in to three terciles as Low (0-3), Medium (4-5) and High (6-8). Here, frequent usage refers to “almost every day” and “at least once a week”, whereas infrequent usage refers to “at least once a month”, “rarely” and “not at all”.
The Food Consumption Score (FCS) is an index that was developed by the World Food Programme (WFP) in 1996 to represent household caloric availability. The FCS aggregates household-level data on the diversity (quality) and frequency (quantity) of food groups consumed over the previous seven days, which is then weighted according to the relative nutritional value of the consumed food groups. For instance, food groups containing nutritionally dense foods, such as animal products, are given greater weight than those containing less nutritionally dense foods, such as tubers. Broad food groups and associated FCS weights are: main staples—weighted at 2, pulses—weighted at 3, vegetables—weighted at 1, fruit—weighted at 1, meat and fish—weighted at 4, milk—weighted at 4, sugar—weighted at 0.5, and oil—weighted at 0.5. (Condiments can also be captured but are weighted at 0). Consumption frequencies are computed by adding the number of days a food item is eaten in a week’s time is added and is rounded off to a maximum limit of 7 per food group. An additive score combining the weighted consumption frequencies of each food group result in a total score ranging from 0 to 112.
UDAYA study in their wave 1, instead of following the standard measure of 7-days recall, uses a 30 days recall and collects information on 6 responses namely, whether a food item was eaten daily, once in a week, 2-3 times a week, once in two weeks, once in four weeks or never. For finding number of days per week a food item was consumed, these responses were substituted with 7, 1, 2.5, 0.5, 0.25 and 0 number of days respectively. To represent food consumption status of a household as poor, borderline or acceptable, cut offs defined by WFP are 0-21; 21.5-35 and >35. But these cut-offs are often criticised in literature and are termed as subjective because assigning cut-off points to a continuous quantitative measure is usually a matter of analytical judgment about the extent to which such categorical cut-offs are universally applicable [21]. Hence, instead of using these cut-offs, we have simply divided the score in to three terciles as Low (0-70), Medium (70-85), High (85-112).
Statistical Analysis
For analysis, cross-tabulation and chi-square test is used to test independence of various groups. Binary logistic regression is used to identify determinants of minimum dietary diversity (Yes=1, No=0) of the adolescents, the MDD was constructed using the wave 2 of UDAYA which was conducted in 2018-19. The explanatory variables was used for the wave 1 of UDAYA which was conducted in 2015-16, to majorly explain the role of food consumption score in wave 1 on the MDD in wave2. Adjusted odds ratio are computed for Uttar Pradesh and Bihar separately, as well as for combined sample.
Findings
Table 1 represents consumption of items from different food groups in the past 24 hours among younger and older adolescents by the sex of the respondents. It was found that almost 100% young and old adolescents were found consuming Grains, White root tubers and Plantains in their diet. It was found that 80% younger adolescents consume dairy food products, while 83% older adolescents consume dairy (milk) products. The difference among males and females in terms of their consumption of dairy products were found to be significant, where males consume more dairy products than females. Among the young and older adolescents, the intakes of fruits was almost similar (65%), while the differences was found among male and females adolescents, where female adolescents were found to have more fruits than males. The intake of nuts and seeds was also found more among the older adolescents and differences were seen in the males and females, where 28% young males were found to consume more nuts than young females. And among the old adolescents, there found a three percent point difference in the nuts intake among male and females, where 30% males were found consuming the nuts and seeds compare to 27% female adolescents. Also, the intake of non-vegetarian food was found more among the older adolescents than younger. The intake of Meat, Poultry and fish was among 15% young adolescents, while it was 16% among old ones. The gender differentials can be easily visible in the non-veg eating practices among adolescents. 14% young female compared to 17% males was found to consume Meat, Poultry and fish, while the differences with the age of the adolescents, 17% of older male adolescents and 12% female adolescents were found to have meat in their dietary pattern. Similar pattern was observed in the consumption of eggs where 10% compared to 18% male older adolescents consume eggs in the past 24hr, while there was less differences in the consumption of eggs at the younger ages.
Table 2 shows prevalence of minimum dietary diversity where Bihar portrays better dietary habits (MDD=61%) compared to Uttar Pradesh (MDD=57%). Sex differentials were more evident in UP compared to Bihar, where almost no gender inequality was observed. With age, and increasing education level of self as well as that of mother’s education showed positive effect on minimum dietary diversity of the adolescents. Those working in the last one year showed ate a less diverse diet compared to those not working. Adolescents belonging from SC/ST caste were less likely to have a diverse diet compared to OBC, General and other caste people. Adolescents from urban area and those belonging to households with better food consumption and wealth status had more chances to eat a diverse diet compared to their counterparts. Media exposure showed good association with minimum dietary diversity, as those with high media exposure had their minimum dietary diversity as high as 74%. Living arrangement in the household could also affect intake of a balanced diet. Lesser number of siblings and presence of grandparent in the household was more associated with intake of a diverse and nutritious diet by the adolescent. Food consumption score also significantly affected the MDD among adolescents in UP, and Bihar, where those with low food consumption in wave 1 was less likely to have MDD in wave 2, and 73% adolescents have higher MDD when they have high food consumption score in wave1.
Table 3 shows output of Binary logistic regression analysis. We find out that media exposure, wealth index and household food consumption status plays a major role in determining minimum dietary diversity of the adolescents. The caste of the household also holds a significant impact on the dietary pattern of adolescents. Those belonging from OBC caste was 1.3 times odds more likely to have minimum dietary diet (A.O.R.=1.3, 95% CI (1.1-1.5). Wealth of the household also determined the pattern of diet, where richer adolescents were 40% more receiving the minimum dietary diversity than poor (95% CI (1.0-1.7)). Those belonging to households with high food consumption are three times more likely to have a diverse diet compared to those belonging to households with low food consumption (A.O.R.=3.0, 95% CI (2.3-3.8) for U.P.; .O.R.=2.2, 95% CI (1.6-2.9) for Bihar; A.O.R.=2.6, 95% CI (2.1-3.1) overall). Adolescents highly exposed to media are twice as much likely to have a minimum dietary diversity compared to those with low media exposure (A.O.R.=2.1, 95% CI (1.7-2.7)). Better education level and high caste correspond to better dietary diversity in the state of U.P. Overall, adolescents from Bihar are 20% more likely to have a minimum dietary diversity compared to those from Uttar Pradesh.