Prevalence and social determinants of breastfeeding practices in urban slums and urban non-slum areas in India: a comparative analysis

Understanding how the prevalence and determinants of breastfeeding practices differ between urban slum areas, where living conditions are stringent, and urban non-slum areas, which have relatively more resources is key to context-specific interventions. We conducted a comparative analysis to investigate the prevalence of breastfeeding practices in the urban slum and urban non-slum areas of India. We also used the socio-ecological framework to assess the individual, community, and policy-level correlates of breastfeeding practices. Secondary analysis of data from the National Family and Health Survey (2015-2016) in India was conducted to estimate the prevalence of early breastfeeding initiation (EBFI) and exclusive breastfeeding (EBF) of children living in urban slum and urban non-slum areas, and the prevalence estimates were further stratified by the seven states where slums were sampled. Logistic regression analysis was used to examine the correlates of breastfeeding practices. EBFI was significantly higher in the urban slum areas (50.4%) compared to urban non-slum areas (37.4%). In contrast, EBF was lower in urban slums (50.1%) than in urban non-slum areas (55.8%). EBFI varied widely across states, with urban slum areas showing higher rates in Maharashtra and Tamil Nadu, but lower in Delhi and West Bengal compared to urban non-slum areas. EBF was generally higher in urban non-slum populations, though some slum areas (e.g., Madhya Pradesh and Telangana) reported notably high rates. In urban slums, community-level factors, such as being part of Scheduled Tribes and Other Backward Classes (other marginalized classes), were associated with higher EBFI. At the policy level, home-based child delivery was associated with EBF in urban non-slum areas. The study showed that breastfeeding practices need to be urgently addressed in both the urban slum and urban non-slum areas, and the wider health determinants, such as community and policy level factors should be considered in designing effective interventions.