This post is part of a larger series on inequality in Philadelphia. Check the introductory post for more information.

Inequality is about more than just money. If you live in a disadvantaged neighborhood, it is highly likely that your health is far worse than somebody who lives in an advantaged neighborhood.

The best indicator of this holistically is life expectancy at birth. If you were born in the most disadvantaged Philadelphia neighborhood today, you could expect to live 20 years less than if you were born just three miles away. 20 years. That is immense. For reference, a life-long smoker can expect to live ten years less than her nonsmoker counterpart.

Here is my source for Philadelphia’s life expectancy statistics. This comes from the Robert Wood Johnson Foundation, which has selected Philadelphia as one of its target cities for studying health. Oddly, the data from this private foundation is far, far more rigorous and useful than any data I have been able to find from government sources, like the census.

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Find more maps of major American cities here.

I couldn’t find a more detailed map, so I used this incredibly interesting life-expectancy-by-ZIP-code search to build my own map of Philadelphia. You have to dig a bit to find a listing of ZIP codes within a county.

Philly Life Expectancy by ZIP Code
I made this using my rudimentary Photoshop skills to combine this map with this data. I’m positive there’s an easier way. Please tell me better methods if you know of them.

Maybe an even better frame of reference is to compare this to other countries. Those born in the most disadvantaged Philadelphia neighborhoods have the same life expectancy of somebody born in Iraq, an ongoing zone of conflict. To grow up in these neighborhoods is the equivalent disadvantage and inequality of growing up in a developing country—only, this is right in your back yard, oftentimes less than a mile away.

ZIP codes with the lowest life expectancies have comparable life expectancies to countries like Iraq, India, the Philippines, Cambodia, Mongolia, or Nepal. ZIP codes with the highest life expectancies exceed even Switzerland, Singapore, and Japan’s, the highest in the world (excluding micro-countries).

Life expectancy is a great indicator for quality of life overall, but let’s dig a little deeper into more specific health inequalities. Here are a couple maps of key health indicators, again from the Robert Wood Johnson Foundation, in collaboration with the CDC. Check out the 500 cities project here.

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2015: Model-based estimates for obesity among adults aged 18 or over.
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2015: Model-based estimates for current smoking among adults aged 18 or over.
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2015: Model-based estimates for binge drinking among adults aged 18 or over.
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2014: Model-based estimates for visits to dentist or dental clinic among adults aged 18 or over.
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2015: Model-based estimates for visits to doctor for routine checkup within the past year among adults aged 18 or over.
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2015: Model-based estimates for current lack of health insurance among adults aged 18-64 years.
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2015: Model-based estimates for physical health not good for greater than 14 days among adults aged 18 and over.
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2015: Model-based estimates for mental health not good for greater than 14 days among adults aged 18 years or over.
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2015: Model-based estimates for diagnosed diabetes among adults aged 18 and over.
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2015: Model-based estimates for cancer (excluding skin cancer) among adults aged 18 and over.
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2015: Model-based estimates for current asthma among adults aged 18 and over.

Some of these are not surprising at all, like the fact that poor health on most indicators has a high correlation with low income (see my earlier post for income maps). For instance, occurrences of diabetes, obesity, and asthma are all much more likely in census tracts with very low incomes. Likewise, areas with low incomes are exceptionally unlikely to visit the dentist regularly, or to have health insurance.

I was shocked to find that some things just don’t match up, or are bizarrely difficult to explain. For instance, there does seem to be a correlation with income and visiting the doctor, but it doesn’t match up perfectly. Additionally, binge drinking seems to have an inverse relationship—the wealthier your neighborhood, the more likely you are to binge drink.

Here’s a quick writeup of the categories for the datasets we have available and their correlations to the poverty rate, as I see them. This is, of course, arbitrary categorizing.

High or very high correlation with poverty: Arthritis, asthma, high blood pressure, COPD,  diabetes, poor mental health, poor physical health, teeth loss, stroke, no health insurance, no dental visit, no cholesterol screening, no mammography, no pap smear, no colorectal cancer screening, no core preventative services for the elderly, smoking, physical inactivity, obesity, and lack of sleep.

No or little correlation with poverty: Cancer, high cholesterol, chronic kidney disease, coronary heart disease, no annual checkup, and taking high blood pressure medication.

Inverse correlation with poverty: Binge drinking.

Individual health indicators apparently have a much more complex story to them than just “the more wealth, the better the health.” However, that mantra does apply generally. And, it is abundantly clear that health divides are stark. I also want to mention that the whole of Philadelphia is already a very unhealthy county. This ranking puts Philadelphia as the least healthy county in Pennsylvania (yes, another initiative of the Robert Wood Johnson Foundation). So, the disparities we see are probably even more extreme when we broaden the scope to the greater Philadelphia area, much like with income disparities.

Overall, my point is that inequality is incredibly multi-faceted. It is not just about income or wealth, though these are all intricately tied together, along with race and gender. It’s important to realize this if we are to change it.