We analyzed a cross-sectional secondary data extracted from NDHS 2018. MEASURE DHS provided technical input in the process of data collection in DHS and supported by the National Population Commission (NPC) (35). Data on 2,936 children below the age of 6 months was extracted for analysis. In addition, data on 21,569 women interviewed for EIBF and SSC were analyzed. NDHS is a vital source of data on EBF, EIBF and SSC especially as it consists of a nationally representative sample of households. DHS data was collected through a stratified multistage cluster sampling technique. The procedure for stratification approach divides the population into groups by geographical region and commonly crossed by place of residence - urban-rural. A multi-level stratification approach is used to divide the population into first-level strata and to subdivide the first-level strata into second-level strata, and so on. A two-level stratification in DHS is region and urban/rural stratification. DHS data is available in the public domain and accessed at; http://dhsprogram.com/data/available-datasets.cfm.
Since 1984, Demographic and Health Surveys have been conducted in over 85 countries and repeated every five years. A major advantage is that the sampling design and data collection approach are similar across countries which making the results of different settings comparable. Though from onset, DHS was designed to expand on fertility, demographic and family planning data collected in the World Fertility Surveys and Contraceptive Prevalence Surveys, nonetheless, it has become the prominent source of population surveillance for the monitoring of population health indices particularly in resource-constrained settings. DHS elicits information from respondents in a wide range of health related areas including vaccination, child and maternal mortality, fertility, intimate partner violence, female genital mutilation, nutrition, lifestyle, infectious and non-infectious diseases, family planning, water and sanitation amongst others. DHS has great merits in collecting high quality data through proper interviewer training, national coverage, standardized data collection instrument and proper operational definition of concepts to enhance understanding among policy and decision makers. DHS data is useful in formulating epidemiological research to estimate prevalence, trends and inequalities. The details of DHS has been reported previously (36).
Selection and measurement of variables
- Early initiation of breastfeeding: This is a measure of children who were put to breast within 1 hour of delivery.
- Exclusive breastfeeding: This is a measure of infants less than 6 months of age who were fed exclusively with breastmilk. This indicator was based on the diets of infants younger than 6 months during the 24 hour before the survey.
- Skin-to-skin contact was measured dichotomously; “Was child put on mother's chest and bare skin after birth” yes vs. no
Socioeconomic characteristics was measured by women’s educational attainment, thus: no education, primary, secondary and higher. In addition, household wealth quintile was computed by DHS using principal components analysis (PCA) to assign the wealth indicator weights. In their computation, they assigned scores and standardized the wealth indicator variable using household assets including; wall, floor, roof and wall type; whether a household had improved vs. unimproved sanitation amenities and water source; whether a household had essential assets such as electricity, radio, television, cooking fuel, refrigerator, furniture amongst others. Further, the factor loadings and z-scores were calculated. For each household, they multiplied the indicator values by the factor loadings and summed to produce the household’s wealth index value. The standardized z-score was disentangled to classify the overall scores to wealth quintiles; poorest, poorer, middle, richer and richest (37). Household wealth quintiles and mothers’ educational attainment were used as measures of socioeconomic status similar to previous studies (38–40).
Residential status was classified as urban vs. rural;
Geographical region and States: North Central: Benue, Federal Capital Territory, Kogi, Kwara, Nasarawa, Niger, Plateau; North East: Adamawa, Bauchi, Borno, Gombe, Taraba, Yobe; North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto,Zamfara; South East: Abia, Anambra, Ebonyi, Enugu, Imo South South: Akwa-Ibom, Bayelsa, Cross River, Edo, Delta, Rivers; South West: Ekiti, Lagos , Ogun, Ondo, Osun, Oyo.
This study was based on an analysis of population-based datasets that exist in public domain and available online with all identiﬁer information removed. The authors were granted access to use the data by MEASURE DHS/ICF International. DHS Program is consistent with the standards for ensuring the protection of respondents’ privacy. ICF International ensures that the survey complies with the U.S. Department of Health and Human Services regulations for the respect of human subjects. The DHS project sought and obtained the required ethical approval from the National Health Research Ethics Committee (NHREC) in Nigeria before the surveys were conducted. No further approval was required for this study. More details about data and ethical standards are available at http://goo.gl/ny8T6X.
Stata survey (‘svy’) module was used to adjust for sampling weights, stratification and clustering in data analysis. Percentage and Chi-square test were used for summary statistics and bivariate analysis respectively. To determine socioeconomic inequalities in EBF, EIBF and SSC, we used concentration index and Lorenz curve. When the concentration index value is positive or Lorenz curve lies below the diagonal line (line of equality), it indicates that EBF, EIBF and SSC coverage is greater among high socioeconomic groups. Conversely, when concentration index value is negative or Lorenz curve is above the line of equality, it indicates that EBF, EIBF and SSC coverage is higher among low socioeconomic groups. Lorenz curves and concentration index were used to decipher socioeconomic inequalities in line with previous studies (41,42). Statistical signiﬁcance was determined at p < 0.05. Stata Version 14 (StataCorp., College Station, TX, USA) was used for data analysis.