In August 1854, London was hit with a cholera epidemic that killed over 500 people. A doctor named John Snow teamed with cartographer Charles Cheffins to map where people were getting sick, and was able to trace the illness to a contaminated water pump. This was one of the earliest uses of using mapping to study public health.
Now, as then, where someone lives is a major factor in how healthy they are. But although finding environmental contaminants and tracking the spread of contagions are still important, we now know that social factors play a prominent role in determining their level of health. In the same way that exposure to asbestos might increase one’s risk for lung cancer, being in poverty can increase risk for type 2 diabetes. Access to health resources, income, education, and housing quality are just a few of these factors, known as social determinants of health.
For a public health policy analyst, this opens a tremendous number of possibilities. Health outcome data—like number of people with a specific disease—exists, but data available nation-wide is hard to come by, and rarely made available at useful local geographies. But a broad array of data on demographic and economic factors, which we know can directly influence health outcomes, is available at the neighborhood level (such as a ZIP code or census tract).
Looking for Proxy Indicators
For example, let’s say you wanted to study coronary heart disease among Latinos in the Pilsen neighborhood of Chicago. Data from the CDC tells us that in Cook County, which encompasses the core of the Chicago metro area, 4,864 people died of coronary heart disease in 2017. That’s an important starting point: that’s the second highest number of these deaths in any county in the country.
But if we’re studying a specific population group in a specific neighborhood, that’s not enough information to go on. But knowing that stress is a driver of heart disease, and that poverty is a driver of stress, we can look at poverty data from the Census’s American Community Survey (ACS), which can show us group-specific data at a very local level:
This data shows us that among the Latino population, there are pockets of poverty in the Pilsen area, but only in certain areas. This gives a public health policymaker clear guidance on where and how to improve public health in this area.
Health and Education
Studies have shown that higher levels of education lead to better health outcomes. The more education someone has, the more likely they are to have a steady job with sufficient income and health insurance, and they may be more likely to have resources necessary to make healthy decisions (such as following instructions from and communicating with health providers).
Again, looking at the Latino community in Pilsen, we can see a sharp contrast between the southern neighborhoods with lower education levels, and northern neighborhoods, closer to the University of Illinois at Chicago, with higher levels.
Plenty of Data
Once you start thinking about health in terms of its social determinants, the relevance of indicators seemingly unrelated to health becomes apparent. Quality and availability of housing can have direct impacts on stress levels. Availability of health insurance is clearly important to one’s ability to find care. Even looking at the type of job someone has could be important. Not to mention more basic indicators like age, sex, and race, which are among the most important determinants of health. This is why our Community Health Report relies on socioeconomic indicators, in addition to data on disease screening and prevalence.
Even more useful is the ability to look at various economic and social indicators broken down for specific populations. For example, for anyone focused on health among Latinos, women, or the elderly, it is possible to explore a range of variables such as homeownership, internet access, economic mobility, workforce trends, incarceration rates, and school enrollment for any population.
We’ve come a long way from mapping cholera patients. Thanks to our understanding of social determinants of health, data-driven approaches to health policy are more feasible than they’ve ever been.