Selecting indicators to monitor nurturing care (NC) environments that support decision‐making and guide the implementation of integrated early childhood development (ECD) programmes has become a priority globally. Several population‐based approaches have been attempted to create a set of indicators or a composite index methodology to measure the NC environment using existing secondary data. However, they have not been systematized. Our scoping review aimed to analyse the population‐based approaches for monitoring the domains of the NC (e.g. good health, adequate nutrition, responsive caregiving, security and safety, and opportunities for early learning). ECD experts, peer‐reviewed, and grey literature were systematically searched with no year or language restrictions. Data extraction used a standard predefined protocol. Thirty‐two population‐based approaches were identified. Most approaches were composed of a set of indicators (53.1%, n = 17) versus composite indexes (46.9%, n = 15) and had the country as their unit of analysis (68.8%, n = 22). Twenty‐seven approaches were applied in middle‐income countries (84.4%) and thirteen in low‐income countries (40.6%). Four approaches were guided by the NC framework (12.5%), and 56.3% (n = 18) did not include any indicator representing responsive caregiving. NC indicators (n = 867) were sorted into 100 groups of indicators. Twenty of the 32 approaches had some kind of methodological validation (62.5%). We identified six methodological challenges to build a population‐based approach. Standardized methods for selecting and validating indicators, and coordinated efforts to share findings/data with stakeholders should be prioritized. Given the great variability in methods and indicators used to measure NC environments, valid approaches should be flexible to work well across different contexts.
The Brazilian Early Childhood Friendly Municipal Index (IMAPI) is a population‐based approach to monitor the nurturing care environment for early childhood development (ECD) using routine information system data. It is unknown whether IMAPI can be applied to document metropolitan urban territorial differences in nurturing care environments. We used Brasilia, Brazil's capital with a large metropolitan population of 2,881,854 inhabitants divided into 31 districts, as a case study to examine whether disaggregation of nurturing care data can inform a more equitable prioritization for ECD in metropolitan areas. IMAPI scores were estimated at the municipal level (IMAPI‐M, 31 indicators) and at the district level (IMAPI‐D, 29 indicators). We developed a quantitative prioritization process for indicators in each IMAPI analysis, and those selected were jointly mapped in the socioecological model for the role of indicators in relation to the enabling environment for nurturing care. Out of 28 common nurturing care indicators across IMAPI analysis, only four were prioritized in both analyses: one from the Adequate nutrition, two from the Opportunities for early learning, and one from the Responsive caregiving domains. These four indicators were mapped as enabling policies, supportive services, and caregivers’ capabilities (socioecological model) and Effort, Coverage, and Quality (indicator's role). In conclusion, the different levels of nurturing care data disaggregation in the IMAPI can better inform decision‐making than each one individually, especially in metropolitan areas where municipalities and districts within metropolitan areas have relative decision‐making autonomy.
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