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\"Discerning Experts assesses the assessments that many governments rely on to help guide environmental policy and action. Through their close look at environmental assessments involving acid rain, ozone depletion, and sea level rise, the authors explore how experts deliberate and decide on the scientific facts about problems like climate change. They also seek to understand how the scientists involved make the judgments they do, how the organization and management of assessment activities affects those judgments, and how expertise is identified and constructed.\"--cover

This Evidence and Gap Map (EGM) protocol aims to identify, map, and provide an overview of the existing evidence and gaps on gender‐responsive macro‐level policies and women's economic empowerment in sub‐Saharan Africa. Specifically, the EGM will: (1) identify which evidence clusters present opportunities for evidence synthesis and (2) identify the evidence gaps that require additional studies, research, and evaluations.

Policy Points This Perspective connects the dots between the polarization in US states’ policy contexts and the divergence in population health across states. Key interlocking forces that fueled this polarization are the political investments of wealthy individuals and organizations and the nationalization of US political parties. Key policy priorities for the next decade include ensuring all Americans have opportunities for economic security, deterring behaviors that kill or injure hundreds of thousands of Americans each year, and protecting voting rights and democratic functioning.

Policy Points The historic 2022 Supreme Court Dobbs v Jackson Women's Health Organization decision has created a new public policy landscape in the United States that will restrict access to legal and safe abortion for a significant proportion of the population. Policies restricting access to abortion bring with them significant threats and harms to health by delaying or denying essential evidence‐based medical care and increasing the risks for adverse maternal and infant outcomes, including death. Restrictive abortion policies will increase the number of children born into and living in poverty, increase the number of families experiencing serious financial instability and hardship, increase racial inequities in socioeconomic security, and put significant additional pressure on under‐resourced social welfare systems.

Policy Points Despite increased spending and consuming more health care services than any other country in the world, the United States global health rankings experience continued decline, including worsening performance in life expectancy and mortality owing to lack of investment in and strategies on the upstream determinants of health. These determinants of health are found in our access to adequate, affordable, and nutritious food options; safe housing, blue and green spaces; reliable and safe transportation; education and literacy; opportunities for economic stability; and sanitation, among other important factors and all share a common root driver in the political determinants of health. Health systems are increasingly investing in programs and exerting influence over policies to address these upstream determinants of health, including population health management, however these programs will continue to be hindered without addressing the political determinants through government, voting, and policy. Although these investments are laudable, it is important to understand what gives rise to the social determinants of health and—more importantly—why have they disproportionately and detrimentally affected historically marginalized communities and vulnerable populations for so long? Deeply entrenched and pervasive throughout society, the political determinants of health are the fundamental instigators of these unjust and inequitable outcomes.

Policy Points Cities have long driven innovation in public health in response to shifting trends in the burden of disease for populations. Today, the challenges facing municipal health departments include the persistent prevalence of chronic disease and deeply entrenched health inequities, as well as the evolving threats posed by climate change, political gridlock, and surging behavioral health needs. Surmounting these challenges will require generational investment in local public health infrastructure, drawn both from new governmental allocation and from innovative financing mechanisms that allow public health agencies to capture more of the value they create for society. Additional funding must be paired with the local development of public health data systems and the implementation of evidence‐based strategies, including community health workers and the co‐localization of clinical services and social resources as part of broader efforts to bridge the gap between public health and health care. Above all, advancing urban health demands transformational public policy to tackle inequality and reduce poverty, to address racism as a public health crisis, and to decarbonize infrastructure. One strategy to help achieve these ambitious goals is for cities to organize into coalitions that harness their collective power as a force to improve population health globally.

This paper analyses policy integration in the field of urban policies. Specifically, the policy framework on sustainable urban development promoted by various international organisations is analysed as an exemplar combining multi-sectoriality in its substantive dimension (policy goals in different policy subsystems) and integration in its procedural dimension (integration between policy actions across policy subsystems involved). It is assumed that urban policies often take the form of multi-level policy mixes, and that integration involves a process of collective action between different policy subsystems. Based on the literature on policy integration and actor-centred institutionalism frameworks, it is postulated that in the absence of clear indications about the integrated strategy and policy integration capacities in the policy frame, the collective action dilemmas that this strategy entails in local projects will prevail, reducing the possibility of policy integration. The implementation of the urban dimension of the European Union's cohesion policy in Spain between 1994 and 2013 is analysed a total of 82 urban projects, where the integrated strategy is a central element but understood as multi-sectorial objectives rather than a complementarity between policy subsystems. Empirical results show a high level of diversity of objectives across policy sectors and a very low level of integration; specifically, a curvilinear pattern in the relationship between these two aspects. The results highlight the need to include policy instruments and capacities in the policy frame to address the collective action dilemmas that policy integration implies, especially if the policy frame calls for a broad multi-sectorial agenda across different policy subsystems.

Background Reliable evaluations of state-level policies are essential for identifying effective policies and informing policymakers’ decisions. State-level policy evaluations commonly use a difference-in-differences (DID) study design; yet within this framework, statistical model specification varies notably across studies. More guidance is needed about which set of statistical models perform best when estimating how state-level policies affect outcomes. Methods Motivated by applied state-level opioid policy evaluations, we implemented an extensive simulation study to compare the statistical performance of multiple variations of the two-way fixed effect models traditionally used for DID under a range of simulation conditions. We also explored the performance of autoregressive (AR) and GEE models. We simulated policy effects on annual state-level opioid mortality rates and assessed statistical performance using various metrics, including directional bias, magnitude bias, and root mean squared error. We also reported Type I error rates and the rate of correctly rejecting the null hypothesis (e.g., power), given the prevalence of frequentist null hypothesis significance testing in the applied literature. Results Most linear models resulted in minimal bias. However, non-linear models and population-weighted versions of classic linear two-way fixed effect and linear GEE models yielded considerable bias (60 to 160%). Further, root mean square error was minimized by linear AR models when we examined crude mortality rates and by negative binomial models when we examined raw death counts. In the context of frequentist hypothesis testing, many models yielded high Type I error rates and very low rates of correctly rejecting the null hypothesis (< 10%), raising concerns of spurious conclusions about policy effectiveness in the opioid literature. When considering performance across models, the linear AR models were optimal in terms of directional bias, root mean squared error, Type I error, and correct rejection rates. Conclusions The findings highlight notable limitations of commonly used statistical models for DID designs, which are widely used in opioid policy studies and in state policy evaluations more broadly. In contrast, the optimal model we identified--the AR model--is rarely used in state policy evaluation. We urge applied researchers to move beyond the classic DID paradigm and adopt use of AR models.

Immunizing pregnant women is a promising strategy to reduce infectious disease-related morbidity and mortality in pregnant women and their infants. Important pre-requisites for the successful introduction of new vaccines for immunization in pregnancy include political commitment and adequate financial resources: trained, committed and sufficient numbers of healthcare workers to deliver the vaccines; close integration of immunization programs with antenatal care and Maternal and Child Health services; adequate access to antenatal care by pregnant women in the country (especially in low and middle-income countries (LMIC)); and a high proportion of births occurring in health facilities (to ensure maternal and neonatal follow-up can be done). The framework needed to advance a vaccine program from product licensure to successful country-level implementation includes establishing and organizing evidence for anticipated vaccine program impact, developing supportive policies, and translating policies into local action. International and national coordination efforts, proactive planning from conception to implementation of the programs (including country-level policy making, planning, and implementation, regulatory guidance, pharmacovigilance) and country-specific and cultural factors must be taken into account during the vaccines introduction.

The concept of parametric design is changing the way 3D modeling works, allowing precise manipulation of complex forms in the areas of architecture, digital fabrication, and product design. However, exploring and optimizing large coupled spaces of parameters remains a significant computational challenge. We present a new, Hierarchical Reinforcement Learning based Dynamic Optimization Strategy (HRL-DOS), which decomposes the parametrized design process into a series of multi-level subproblems. The high-level policy determines the global direction of the design while the low-level policy adapts individual parameters, responding to changes from multiple performance criteria (structural stability, geometric efficiency, and fabrication constraints). The hierarchical approach provides greater efficiency in learning and computational scaling in a complex design environment. Experimental tests on benchmark 3D modeling tasks revealed a 27% improvement in convergence and 18% improvements in quality of the model, relative to simple heuristic or gradient-based optimizations. In addition, HRL-DO permits adaptability in real-time, and the approach can potentially translate to various domains, including automated form-finding for architectural structures, generative design of products, or intelligent computer-aided design (CAD) systems. Through the use of HRL, we have developed a new and adaptive approach for the additional automation of parametric design tasks in the future.