Does socio-economic inequality exist in micro-nutrients supplementation among children aged 6–59 months in India? Evidence from National Family Health Survey 2005–06 and 2015–16

Evidence suggests that iron deficiency causes negative effects on cognitive development among children [4]. Moreover, decreased physical capacity and reduced immunity are associated with iron deficiency among children [5]. Reduced immunity causes children to be at higher susceptibility to infectious diseases [6]; additionally, iron deficiency causes retarded growth of pre-school and school-going children’s [6]. Children from lower socio-economic status (SES) had higher iron deficiency than children from higher SES [7]. Previous studies found a significant association between iron deficiency among children and breastfeeding status [8]. The educational status of parents and the wealth status of the family are strong correlates of iron deficiency among children [9]. Further birth order of children was carved out to be a strong predictor of iron supplementation among children [5]. Moreover, the study found that there was high spatial autocorrelation of anemia among children in India, i.e. anemia was highly correlated with spatial factors [10]. Additionally, iron deficiency (ID) mostly resulting in anemia which was very prevalent among children throughout the world, especially in developing countries [11]. The higher prevalence of anemia among children in developing countries can be attributed to low intake of iron supplements in the early years of life which is known for the fast development of children’s lifestyle, geographical factors, socio-economic status and children’s nutritional status [12].

It was concluded in previous studies that vitamin A deficiency was magnified by poverty status and higher prevalence of infectious diseases [13], and VAD is the underlying cause of measles, diarrhea and malaria globally [13]. Additionally, VAD causes night blindness problem, Bitot’s spots and other morbid conditions [14]. Further, VAD was positively associated with stunting and wasting among children [15]. Interestingly, it was too argued that VAD was highly correlated with the development of anemia because it plays an important role in red blood cell production and iron metabolism [14]. Vitamin A deficiency was strongly associated with maternal educational level [16], breastfeeding practices [16], being a male child [17], a child age 48–59 months [17] and children from poor socio-economic status [16]. Moreover, children from lower levels of social and economic development and higher birth orders had a 50% lesser likelihood to receive vitamin A supplements [16].

Awareness of important dietary intakes influences the nutritional status of infant and young child. As the World Health Organization (WHO) has already cited that, iron deficiency (ID) was the most prevalent nutritional deficiency [18, 19], whereas a different study reported that vitamin A deficiency (VAD) was a common form of micronutrient malnutrition that affecting 21% of pre-school age children worldwide [20]. Micronutrient deficiencies were mainly associated with inadequate dietary intake, increased losses from the body, and increased requirements [21]. A systematic review indicated that VAD leads to serious health problems among pre-school age children, which were mainly due to high prevalence of morbidity, poor consumption of fruits and vegetables, the monotonous cereal-legume diet, poor consumption of vitamin A diet and lower vitamin A supplementation [22].

Micronutrients are acknowledged as an important component in public health. Such micronutrients like zinc, iron, selenium, copper, vitamin A, E, C, D, B2, B6, B12 and folate are necessary for the human body, especially among children [23]. Although the implementation of many micronutrient supplementation programs, very few countries have achieved the targets and have undertaken comprehensive surveys on micronutrient deficiencies [24]. In last, there is a need for a comprehensive micronutrient implementation program with a focus on multiple micronutrients altogether.

There was existing literature that focuses on the determinants of micro-nutrients supplementation among children aged 6–59 months in India. Various literature found the important correlates of VAS and IS among children aged 6–59 months in India. However, there seems a scarcity of studies that aimed to focus on the change in socio-economic related inequality for VAS and IS over the period of time in India. Therefore the present study aims to determine the extent of socio-economic inequality which exist in VAS and IS among children aged 6–59 months in India and to estimate the change in the percent contribution of different socio-economic correlates for such inequality from 2005-06 to 2015–16. The study hypothesized that there was no change in socio-economic inequality in terms of VAS and IS among children aged 6–59 months in India from 2005 to 06 to 2015–16.

The study used data from two rounds of the National Family Health Survey (NFHS) as NFHS-3 and NFHS-4 conducted in 2005–06 and 2015–16, respectively. The nodal agency for conducting these surveys is the International Institute for Population Sciences, Mumbai. The NFHS-3 and NFHS-4 are national representatives; the cross-sectional survey used a systematic, two-stage, cluster sample of the household. The survey provides information on several new and emerging issues, including family planning, nutrition, education, adolescent reproductive health and morbidities. The women’s questionnaire collected all eligible women aged 15–49 years in both rounds of the survey. We included 46,890 samples of children for 2005–06 and 236,977 children for 2015–16 aged 6–23 months [25].

The dependent variable for this study was vitamin A and iron supplementation among children aged 6–59 months. The response was recorded by asking the question, ‘whether vitamin A dose was given in the last six months? The responses were recorded as either ‘yes’ or ‘no’. Likewise, the question on iron supplements was asked ‘in the last 7 days, was given iron pills or iron syrup? The response was recorded as 0 “yes” and 1 “no”. The children who did not receive the supplementations were considered to be deficient in vitamin A and iron.

The covariates included in the analysis are children’s age in months was coded as 6–23, 24–59. The sex of the child was coded male and female. Mother’s educational status was coded as not educated, primary, secondary and higher. Birth order was coded as 1, 2–3 and 4–6. Current breastfeeding status was coded as yes and no. The wealth index was coded poor, middle and rich. The variable of wealth status was created using the information given in the survey. Households were given scores based on the number and kinds of consumer goods they own, ranging from a television to a bicycle or car, and housing characteristics such as the source of drinking water, toilet facilities, and flooring materials. These scores are derived using principal component analysis. National wealth quintiles are compiled by assigning the household score to each usual (de jure) household member, ranking each person in the household population by their score, and then dividing the distribution into three equal categories [25]. Caste was coded as SC/ST, and non-SC/ST includes OBC and others. The Scheduled Caste include “untouchables”, a group of the population that is socially segregated and financially/economically by their low status as per Hindu caste hierarchy. The Scheduled Castes (SCs) and Scheduled Tribes (STs) are among the most disadvantaged socio-economic groups in India. The OBC are the group of people who were identified as “educationally, economically and socially backwards”. The OBC’s are considered low in the traditional caste hierarchy but are not considered untouchables. The “other” caste category are identified as having higher social status [26]. Religion was coded as Hindu, Muslim and others. Other religious category included Jain/Sikh/Jewish/Buddhist/others. Place of residence was coded as rural and urban, and the states of India were grouped as Empowered Action Group (EAG) states and non-EAG states [27]. The Indian states were classified into EAG and Non-EAG regions based on key development indicators such that the states belonging to the EAG region significantly lag behind the states in the Non-EAG region on the basis of key human development indicators. The EAG region includes the eight Indian states of Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha, Rajasthan, Uttaranchal, and Uttar Pradesh, whereas all the other remaining Indian states comprise the Non-EAG region.

Bivariate analysis was carried out to estimate the prevalence of children who do not receive vitamin A supplementation (VAS) and iron supplementation (IS). Two sample proportion test was used to find whether the difference for VAS and IS between two time period (2005–06 and 2015–16) was significant or not. Additionally, binary logistic regression analysis was used to establish the association between the dependent variable with the covariates [28]. To estimate socio-economic inequalities for VAS and IS, a concentration index, concentration curve and decomposition analysis was employed, which represents the degree of inequality.

The concentration curve is obtained by plotting the cumulative proportion of outcome variables (VAS and IS) on the y-axis against the increasing percentage of the population ranked by the socio-economic indicator (wealth index) on the x-axis. The curves show that whether the socio-economic status-related inequality in the outcome variable (on the x-axis) prevails or not. If the curve is above the line of equality (45-degree line), that means the index value is negative; hence it shows that the outcome variable is disproportionally concentrated among the poor and vice-versa. Income-related inequality in VAS and IS was measured by the concentration index (CI) and the concentration curve (CC), using the wealth score as the socio-economic indicator and binary outcome as VAS and IS. The concentration index is defined as twice the area between the concentration curve and the line of equality. The concentration index measures the inequality of one variable (say VAS and IS) over the distribution of another variable (wealth index). The index ranges from − 1 to + 1, where the index value of 0 (zero) shows no socio-economic inequality. However, the positive value of the index shows pro-rich inequality and vice-versa. Additional, on either scales higher the value, the higher the extent of socio-economic inequality. The study used Wagstaff decomposition analysis to decompose the concentration index. Wagstaff’s decomposition demonstrated that the concentration index could be decomposed into the contributions of each factor to the income-related inequalities [29]. For any linear regression model on a health outcome (y) (say VAS and IS), such as

The concentration index for y, C, can be written as follows,

Where μ is the mean of y, \( {\overline{x}}_k \) is the mean of x_k, C_k is the concentration index for x_k (defined analogously to C), and GC_ε is the generalized concentration index for the error term (ε). Eq. (2) shows that C is equal to a weighted sum of the concentration indices of the k regressor, where the weight for x_k is the elasticity of y with respect to x_k \( \left({\eta}_k={\beta}_k\frac{{\overline{x}}_k}{\mu}\right) \). The residual component captured by the last term reflects the socio-economic status-related inequality in health that is not explained by systematic variation in the regressor by income, which should approach zero for a well-specified model. Each contribution is the product of elasticity with the degree of economic inequality. Moreover, the percentage contribution is obtained by dividing each absolute contribution by total absolute contribution multiplied by 100 to obtain the estimates [30].