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Diabetes is a leading contributor to cardiovascular disease and mortality; social determinants of health (SDOH) are associated with disparities in diabetes risk. Quantifying the cumulative impact of SDOH and identifying the SDOH most associated with diabetes prevalence at the neighborhood level can help policy-makers design and target local interventions to mitigate these disparities. Machine learning (ML) methods can provide novel insights and help inform public health intervention strategies in a place-based manner. In a cross-sectional study, we used gradient boosting ML models to estimate the cumulative contribution of a set of SDOH variables to diabetes prevalence (%) at the census tract level within New York City (NYC); Shapley Additive Explanations were used to assess the magnitude and shape of relationships between our SDOH variables and model-predicted NYC diabetes prevalence. SDOH measures included socioeconomic position, educational attainment, food access, air quality, neighbourhood environment, housing conditions and insurance coverage.

A functional and reliable Health Information System (HIS) is vital for data-based decision-making in public health. The paper describes the assessment of data processes and general HIS principles by adapting a global approach to three cities. The assessments supported the data strategy of the CARDIO4Cities initiative in each city aiming to improve urban population health by increasing the use of cardiovascular disease (CVD) data to inform decision-making.

Government-led, population-wide initiatives are crucial for advancing the management of hypertension − a leading cause of cardiovascular disease (CVD) morbidity and mortality. An urban population health initiative was conducted against this backdrop, focussing on hypertension in the primary health system in São Paulo, Brazil. Within the frame of the initiative and under the supervision and leadership of the municipal health authorities, a situational analysis was conducted on the needs in hypertension management, marking the first phase of a Design Thinking process. This article describes the situational analysis process and presents the identified elements to be strengthened considering hypertension diagnosis, treatment and control.

Hypertension is the leading risk factor for cardiovascular diseases (CVDs) and a major public health issue worldwide. In Brazil, it affects approximately 52.5% of the adult population. We describe the solutions package and the impact of a population health initiative in São Paulo city, following the CARDIO4Cities approach for the management of cardiovascular risk.

Assessing the contributions of an urban population health initiative to shift political priority towards cardiovascular health: three case studies from Brazil, Mongolia and Senegal

Effect of the KaziBantu school-based health intervention on NCDs

Uses of social determinants of health data to address CVD and Health Equity

Visualizing rural urban disparities in health outcomes

Economic distress and health equity

Design thinking is a sound and viable tool to use as part of an implementation strategy for engaging with health system stakeholders and successfully translating evidence-based practices and new innovations into routine practice, thereby addressing an important knowledge—practice gap and, more broadly, contributing to the strategic repertoire available to implementation science.

Artificial Intelligence in health care offers a way to transform health systems from being reactive to being proactive, predictive, and even preventive. The Novartis Foundation and PATH launched the tool in six countries in 2021 and 2022: Argentina, Brazil, Chile, Philippines, Uruguay, and Vietnam. This white paper shares an overview of the experience these countries had in conducting the self-assessment.