Impacts of climate change adaptation strategies need to be evaluated using principled methods spanning sectors and longer time frames. We propose machine-learning approaches to study the long-term impacts of flood protection in Bangladesh. Available data include socio-economic survey and events data (death, migration, etc.) from 1983–2014. These multidecadal data, rare in their extent and quality, provide a basis for using machine-learning approaches even though the data were not collected or designed to assess the impact of the flood control investments. We test whether the embankment has affected the welfare of people over time, benefiting those living inside more than those living outside. Machine-learning approaches enable learning patterns in data to help discriminate between two groups: here households living inside vs. outside. They also help identify the most informative indicators of discrimination and provide robust metrics to evaluate the quality of the model. Overall, we find no significant difference between inside/outside populations based on welfare, migration, or mortality indicators. However, we note a significant difference in inward/outward movement with respect to the embankment. While certain data gaps and spatial heterogeneity in sampled populations suggest caution in any conclusive interpretation of the flood protection infrastructure, we do not see higher benefits accruing to those living with higher levels of protection. This has implications for Bangladesh’s planning for future and more extreme climate futures, including the national Delta Plan, and global investments in climate resilient infrastructure to create positive social impacts.

While the Sustainable Development Goals (SDGs) were being negotiated, global policymakers assumed that advances in data technology and statistical capabilities, what was dubbed the “data revolution”, would accelerate development outcomes by improving policy efficiency and accountability. The 2014 report to the United Nations Secretary General, “A World That Counts” framed the data-for-development agenda, and proposed four pathways to impact: measuring for accountability, generating disaggregated and real-time data supplies, improving policymaking, and implementing efficiency. The subsequent experience suggests that while many recommendations were implemented globally to advance the production of data and statistics, the impact on SDG outcomes has been inconsistent. Progress towards SDG targets has stalled despite advances in statistical systems capability, data production, and data analytics. The coherence of the SDG policy agenda has undoubtedly improved aspects of data collection and supply, with SDG frameworks standardizing greater indicator reporting. However, other events, including the response to COVID-19, have played catalytic roles in statistical system innovation. Overall, increased financing for statistical systems has not materialized, though planning and monitoring of these national systems may have longer-term impacts. This article reviews how assumptions about the data revolution have evolved and where new assumptions are necessary to advance the impact across the data value chain. These include focusing on measuring what matters most for decision-making needs across polycentric institutions, leveraging the SDGs for global data standardization and strategic financial mobilization, closing data gaps while enhancing policymaker analytic capabilities, and fostering collective intelligence to drive data innovation, credible information, and sustainable development outcomes.

This article proposes Bayesian adaptive trials (BATs) as both an efficient method to conduct trials and a unifying framework for the evaluation of social policy interventions, addressing the limitations inherent in traditional methods, such as randomized controlled trials. Recognizing the crucial need for evidence-based approaches in public policy, the proposed approach aims to lower barriers to the adoption of evidence-based methods and to align evaluation processes more closely with the dynamic nature of policy cycles. BATs, grounded in decision theory, offer a dynamic, “learning as we go” approach, enabling the integration of diverse information types and facilitating a continuous, iterative process of policy evaluation. BATs’ adaptive nature is particularly advantageous in policy settings, allowing for more timely and context-sensitive decisions. Moreover, BATs’ ability to value potential future information sources positions it as an optimal strategy for sequential data acquisition during policy implementation. While acknowledging the assumptions and models intrinsic to BATs, such as prior distributions and likelihood functions, this article argues that these are advantageous for decision-makers in social policy, effectively merging the best features of various methodologies.

Sustainable development agendas in Bangladesh are frequently framed in the media as being constrained by water insecurity and the decision-making challenges posed by multiple risks of both natural and human origins. This article suggests, however, that the reverse also occurs: The boundaries of risk narratives constructed by the media serve to mitigate public concerns, reinforce national development agendas, and legitimize structural changes of the dominant institutions. These relationships are explored through a set of risk discourse filters that merge content analysis approaches with the defining phenomena of risk society theory. Advancing an inventory of 3,211 drinking water specific articles published by the Ittefaq newspaper between 1980 and 2016, the analysis identifies three temporal clusters of discursive behaviors and content focus that contribute a historic perspective of how Bengali newspapers construct, define, and react to multiple water safety crises, specifically cholera and arsenic. The risk discourse filters suggest that the media’s problematization of drinking water remains aligned to nation-building processes and the political narratives of development successes defined by international targets. The results map the discursive distribution of concerns across geographies and risk positions, extending interpretations of how and where blame is allocated. The inventory reveals an identifiable temporal and content pattern of inclusion and exclusion of topics and draws links to the overall institutional dynamics of political and economic change. Key Words: arsenic, Bangladesh, media, risk, water security.

Australia is striving for ambitious early childhood reform, both nationally and within local communities. Despite the scale of change, we are not consistently using the best available information and data to guide critical decisions about children’s health, wellbeing and development. The problem lies not only in data gaps, but in how we use and apply the data that do exist. There is broad agreement that ensuring data play a central role in driving more equitable outcomes for our youngest Australians is critical to effective change. Despite vast amounts of data across health, education and social services, their impact remains limited. Fragmented systems, inconsistent definitions, disconnected technologies and non-evidence-informed indicators leave local early years partnerships, services and governments without the information needed to drive improvement at speed and scale. At the same time, critical gaps persist. Timely, actionable data to demonstrate what is driving outcomes, and feedback data to support continuous improvement, are often missing. This paper proposes an early years data logic – a structured approach that signposts what data are needed, for what purpose and how data can be used. Anchored in principles of equity, precision, access, evidence and utility, and supported by good data governance, the logic aims to provide a roadmap that ensures the right data are in the right hands at the right time for the right purpose. It makes clear why data are collected and how data can be used to drive change. By making purpose explicit, a data logic can guide decision makers at all levels. It clarifies how the system is functioning, highlights which children and families most need support and identifies the gaps that require targeted action to achieve change. It also positions data as a resource for learning, enabling decision makers and frontline practitioners to generate insights, test approaches and adapt through cycles of continuous improvement. There are current challenges, including over-reliance on population-level outcome (lag) measures, inconsistency in definitions and measures used to track progress, and limited workforce capability and inaccessible information systems. However, this paper outlines practical recommendations to mitigate these challenges. These include roles for government, funders, data stewards, local early years partnerships (such as place-based initiatives and integrated service hubs) and services in embedding the logic in practice. Examples from promising initiatives illustrate how better use of data can improve equitable access, service quality and participation. These positive examples sit alongside our practical recommendations to highlight how improvements are possible. Ultimately, this data logic offers a roadmap for shifting from systems that may be data rich but insight poor to a connected ecosystem capable of delivering on the promise of equity. By embedding a shared logic across early years systems, Australia can ensure that every child is visible, every service is accountable and every decision is informed – enabling change at speed and scale.

This paper provides the conceptual framework for a new review series that bring together the global literature on population approaches to nurturing relational health across the first three years of life. Early relational health is defined as ‘the everyday interactions that happen between children and their carers across the many settings in which they live and grow … that meet universal needs for food and shelter, for safety, and for a sense of belonging, worth, identity and place that collectively establish secure foundations for lifespan health and development'. The first four reviews describe (1) the relational ecology of early childhood, (2) associations with early brain development, (3) associations with relational outcomes beyond early childhood, and (4) evidence-based interventions for nurturing relational health within universal, population-based, settings (e.g., maternal-child health). The next four reviews tackle the deeper intergenerational question of what it means to raise children and young people in ways that also set developmental conditions for future roles as parents by describing (5) Australian Aboriginal ways of nurturing relationally healthy children and young people, (6) findings from intergenerational cohort studies, (7) population based interventions for nurturing relational health from childhood to parenthood (e.g., schools and communities), and (8) approaches to implementing evidence-based interventions in ways that ensure sustainability. An additional technical paper describes the development and testing of a new AI platform (LitQuest) that was purpose built to expedite title and abstract screening for the review series. Findings from the complete series are synthesised in a final paper that identifies critical gaps in the evidence base and provides recommendations for future research to advance population-level approaches to nurturing relational health from the earliest years of life.