Analyzing and improving national and local child protection data in Nepal: A mixed methods study using 2014 Multiple Indicator Cluster Survey (MICS) data and interviews with 18 organizations. (March 2020) - Bhatia, Amiya; Krieger, Nancy; Victora, Cesar; Tuladhar, Sumnima; Bhabha, Jacqueline; Beckfield, Jason
Abstract: Background: Globally, progress to improve data on child protection outcomes has been slower than efforts to improve data on child nutrition, vaccination and development outcomes in the under-five age group. The Sustainable Development Goals included several child protection targets further necessitating the need to track progress on child protection, but few studies have examined the varied data landscape for child protection within countries. Objective: This mixed-methods study aims to examine (1) the prevalence of child protection outcomes in Nepal, (2) the types of data the child protection sector uses, and (3) recommendations to improve the collection, analysis and use of child protection data. Participants and Setting: We used: (a) secondary data from the nationally-representative 2014 Nepal MICS which surveyed over 13, 000 households to measure the national prevalence of child labor, child marriage, and violent discipline, and (b) primary data from 18 qualitative key informant interviews with organizations in Nepal's child protection sector. Methods: We conducted descriptive quantitative analyses of the secondary data and thematic inductive and deductive qualitative analyses of transcripts of key informant interviews. Results: The burden of violent discipline (82%), child labor (37%), child marriage (12%), and their co-occurrence is high in Nepal. Respondents described using a range of data sources which included: large-scale surveys, case data from the police, court system, newspapers, community consultations, and child participation. Recommendations to improve data included developing a national child protection information system, ensuring the definitions of child protection outcomes were comparable across data sources, and improving the dissemination of data.