Despite global commitments to achieving gender equality and improving health and well-being for all, quantitative data and methods to precisely estimate the effect of gender norms on health inequities are underdeveloped. Nonetheless, existing global, national, and sub-national data provide key opportunities for testing associations between gender norms and health. Using innovative approaches to analysing proxies for gender norms, we generated evidence that gender norms impact the health of women and men across life stages, health sectors, and world regions. Six case studies demonstrated that: 1) gender norms are complex and may intersect with other social factors to impact health over the life course; 2) early gender-normative influences by parents and peers may have multiple and differing health consequences for girls and boys; 3) non-conformity with, and transgression of, gender norms may be harmful to health, in particular when they trigger negative sanctions; and 4) the impact of gender norms on health can be context-specific, demanding care when designing effective gender-transformative health policies and programs. Limitations of survey-based data are described that resulted in missed opportunities for exploring certain populations and domains. Recommendations for optimising and advancing research on the health impacts of gender norms are made.

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3•5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.

Two streams of theory and practice on gender equity have begun to elide. The first is work conducted to change social norms, particularly using theory that emerged from studies in social psychology. The second is work done on gender norms, emerging historically from feminist scholars working to counter gender inequality. As these two streams of work intersect, conceptual clarity is needed to understand differences and similarities between these two traditions. Increased clarity will improve efforts to address harmful norms and practices. In this article, we review similarities and differences between social and gender norms, reviewing the history of the concepts and identifying key tension points of contrast. We identified six areas of comparison that might be helpful for practitioners working for the promotion of global health as they make sense of social and gender norms. We then offer a definition of gender norms for practitioners and researchers working at the intersection between these two theories. Our definition draws from the two different streams of thought of how norms influence people's actions, acknowledging the double nature of gender norms: beliefs nested in people's minds and embedded in institutions that profoundly affect health-related behaviours and shape differential access to health services.

The theoretical literature on social norms is multifaceted and at times contradictory. Looking at existing reviews, we aimed to offer a more complete understanding of its current status. By investigating the conceptual frameworks and organizing elements used to compare social-norms theories, we identified four theoretical spaces of inquiry that were common across the reviews: what social norms are, what relationship exists between social norms and behavior, how social norms evolve, and what categories of actors must be considered in the study of social norms. We highlight areas of consensus and debate in the reviews around these four themes and discuss points of agreement and disagreement that uncover trajectories for future empirical and theoretical investigation.

Social norms can greatly influence people's health-related choices and behaviours. In the last few years, scholars and practitioners working in low- and mid-income countries (LMIC) have increasingly been trying to harness the influence of social norms to improve people's health globally. However, the literature informing social norm interventions in LMIC lacks a framework to understand how norms interact with other factors that sustain harmful practices and behaviours. This gap has led to short-sighted interventions that target social norms exclusively without a wider awareness of how other institutional, material, individual and social factors affect the harmful practice. Emphasizing norms to the exclusion of other factors might ultimately discredit norms-based strategies, not because they are flawed but because they alone are not sufficient to shift behaviour. In this paper, we share a framework (already adopted by some practitioners) that locates norm-based strategies within the wider array of factors that must be considered when designing prevention programmes in LMIC.

BackgroundRecently, Global Health practitioners, scholars, and donors have expressed increased interest in “changing social norms” as a strategy to promote health and well-being in low and mid-income countries (LMIC). Despite this burgeoning interest, the ability of practitioners to use social norm theory to inform health interventions varies widely.Main bodyHere, we identify eight pitfalls that practitioners must avoid as they plan to integrate a social norms perspective in their interventions, as well as eight learnings. These learnings are: 1) Social norms and attitudes are different; 2) Social norms and attitudes can coincide; 3) Protective norms can offer important resources for achieving effective social improvement in people’s health-related practices; 4) Harmful practices are sustained by a matrix of factors that need to be understood in their interactions; 5) The prevalence of a norm is not necessarily a sign of its strength; 6) Social norms can exert both direct and indirect influence; 7) Publicising the prevalence of a harmful practice can make things worse; 8) People-led social norm change is both the right and the smart thing to do.ConclusionsAs the understanding of how norms evolve in LMIC advances, practitioners will develop greater understanding of what works to help people lead change in harmful norms within their contexts. Awareness of these pitfalls has helped several of them increase the effectiveness of their interventions addressing social norms in the field. We are confident that others will benefit from these reflections as well.

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Background Self-rated health is widely considered a good indicator of morbidity and mortality but its validity for health equity analysis and public health policies in Italy is often disregarded by policy-makers. This study had three objectives. O1: To explore response distribution across dimensions of age, chronic health conditions, functional limitations and SRH in Italy. O2: To explore associations between SRH and healthcare demand in Italy. O3: To explore the association between SRH and household income. Methods Cross-sectional data were obtained from the 2015 Health Interview Survey (HIS) conducted in Italy. Italian respondents ( n = 20,814) were included in logistic regression analyses. O1: associations of chronic health conditions (CHC), functional limitations (FL), and age with self-rated health (SRH) were tested. O2: associations of CHC, FL, and SRH with hospitalisation (H), medical specialist consultations (MSC), and medicine use (MU) were tested. O3: associations of SRH and CHC with household income (PEI) were tested. Results O1: CHC, FL, and age had an independent summative effect on respondents’ SRH. O2: SRH predicted H and MSC more than CHC; age and MU were more strongly correlated than SRH and MU. O3: SRH and PEI were significantly correlated, while we found no correlation between CHC and PEI. Conclusions Drawing from our results and the relevant literature, we suggest that policy-makers in Italy could use SRH measures to: 1) predict healthcare demand for effective allocation of resources; 2) assess subjective effectiveness of treatments; and 3) understand geosocial pockets of health inequity that require special attention.