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SummaryThe contribution of adolescents' childbearing to total fertility rates in many sub-Saharan African countries is higher than in other parts of the world. In this paper, data collected from 897 female adolescents aged 15–19 years are analysed to investigate patterns and determinants of entry into motherhood in two informal settlements in Nairobi, Kenya, using Kaplan–Meier estimates and Cox regression models. About 15% of these adolescents have had a child. The findings show that marriage, being out of school and having negative models in peer, family and school contexts are associated with early childbearing among females aged 15–17 years. For adolescents aged 18–19 years, school attendance considerably delays entry into motherhood while marriage hastens its timing. Furthermore, older adolescents with high levels of social controls (parental monitoring or perceived peer orientation to or approval of prosocial behaviours) and individual controls (high religiosity and positive orientation to schooling) are likely to delay childbearing. Programmes aiming to reduce risky sexual behaviours that could lead to childbearing among adolescents should be introduced very early, and before the onset of sexual activity. Also, the findings underscore the need to identify and address the risky factors and reinforce the protective ones in order to improve sexual and reproductive health outcomes of adolescent girls in Nairobi slum settlements.

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11 - Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups.

Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The first approach, classifying "slum households" in census and survey data, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human (visual) interpretation and machine classification of air or spaceborne imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of public services. We summarize common areas of understanding, and present a set of requirements and a framework to produce routine, accurate maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making across Low- and Middle-Income Countries (LMICs). We suggest that machine learning models be extended to incorporate social area-level covariates and regular contributions of up-to-date and context-relevant field-based classification of deprived urban areas.