AI4HealthyCities

Only 10-20% of your health outcomes are determined by the healthcare you receive. Conversely, the conditions in which you are born, grow and work shape up to 80-90%¹. And with the arrival of new technologies, we can analyze and understand what truly drives our health.

AI4HealthyCities is an initiative by the Novartis Foundation in collaboration with Microsoft AI for Health and local partners, bringing together existent but disconnected sets of data within a city and using advanced analytics and AI to uncover cardiovascular risk factors among its population.

With an aim to provide decision makers with insights into the origins of disparities in cardiovascular health outcomes in their jurisdiction, AI4HealthyCities gathers data from health- and health-related sectors and identifies relevant and informative patterns within the data. This includes factors as diverse as housing, access to healthy food, income, physical exercise or green space, education, professional occupation, pollution, migration, and the influence of structural racism and agism. Findings can enable informed decision making and support the design of effective interventions by local stakeholders and governments to address critical health problems like cardiovascular disease (CVD). The data-driven insights may also require involvement of other governing bodies, which could go as far as the departments for urban planning, transport, or education.

Cities with ongoing AI4HealthyCity programs

Analyses in action

New York, where inhabitants of high-poverty neighborhoods are 2.4 times more likely to die prematurely from CVD than people living in wealthier parts of town², was the first city to launch AI4HealthyCities in September 2022. Here, the Novartis Foundation is partnering with NYU School of Global Public Health and Weill Cornell Medicine.

Since its launch, New York has brought together the data from multiple public sources and from different electronic health records (EHR) to understand the factors driving disparities in cardiovascular health outcomes at an individual zip code or census tract level within New York and across the US. Examples of these different data include (but are not limited to)

• neighborhood data (including income, unemployment rate, and education levels);
• data on mental health conditions (such as depression, stress, anxiety, and substance abuse);
• and social needs captured in different sector data or in some hospital EHR systems (e.g., to understand concerns around paying bills, housing conditions and interpersonal violence).

Insights may soon shed light on the factors contributing to poor cardiovascular outcomes among individuals residing in one zip code/census tract in New York as compared to those living in a neighboring zip code or tract just a short stroll away.

The need for complex, longitudinal data sets that allow for thorough analysis poses a particular challenge in lower income settings, where some of the highest burden of the disease exists. Therefore, the focus for AI4HealthyCities currently is on data-rich environments, with learnings and insights potentially to be applied to less data-rich environments.

The AI advanced analytics steps that are implemented within AI4HealthyCities include:

1. Identifying the determinants of health that characterize populations with poor cardiovascular outcomes
2. Quantifying the impact of each of these determinants on cardiovascular outcomes, and that of their combinations
3. Identifying which determinants are the main drivers of unequal cardiovascular outcomes (risk factors and events)
4. Defining how modifying these (combinations of) determinants could change cardiovascular outcomes

AI4HealthyCities Global Expert Council

The Expert Council will support unleashing the full potential of AI4HealthyCities by playing a crucial role in reviewing insights from different data types, evaluate potential differences, and guide additional analytics, in addition to providing us with advice on how best to translate insights into action. Ultimately, we are workings towards the creation and handover of decision-making tools for policymakers, health system managers and civil society.

The AI4HealthyCities Global Expert Council is co-chaired by Michelle Williams, Joan and Julius Jacobson Professor of Epidemiology and Public Health, Harvard T.H. Chan School of Public Health and Ann Aerts, Head of the Novartis Foundation.

What determines our risk of developing a disease?

There is a broad public consensus about specific behaviors and habits that can be detrimental to our health. Take cardiovascular disease (CVD), the world's leading cause of death and disability³ - one of the most important advances in CVD research within the last century has been the identification of risk factors such as smoking, unhealthy diets, and lack of physical activity to name only a few.

That said, there are many more factors that might tip the scales of your health: so called social determinants of health (SDoH) encompass economic (e.g., employment, financial situation), social (e.g., immigration status, acculturation), environmental (e.g., climate, air pollution, transportation), digital (e.g., access to internet or a computing device) and psychosocial factors (e.g., local language, literacy) that may influence our risk of developing a disease, our ability to receive healthcare, and our expected health outcomes⁴. These determinants, however, are not well-understood yet.

References:

[1] Hood, C. M., Gennuso, K. P., Swain, G., & Catlin, B. B. (2016). County health rankings. American Journal of Preventive Medicine, 50(2), 129–135. https://doi.org/10.1016/j.amepre.2015.08.024 

[2] Gresia V, Wright M, Li W, Jasek J, Sun Y, Di Lonardo S, Chamany S. Premature Heart Disease and Stroke Deaths in New York City. New York City Department of Health and Mental Hygiene: Epi Data Brief (95); November 2017.

[3] World Health Organization (2023). Cardiovascular diseases. Retrieved August 17, 2023, from https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1

[4] Powell-Wiley, T. M., Baumer, Y., Baah, F. O., Baez, A. S., Farmer, N., Mahlobo, C. T., Pita, M. A., Potharaju, K. A., Tamura, K., & Wallen, G. R. (2022). Social determinants of cardiovascular disease. Circulation Research, 130(5), 782–799. https://doi.org/10.1161/circresaha.121.319811