…and managed CRP’s Data
Source Neighborhood Data Access website. He also has
over ten years’ experience in child advocacy and policy
support with KidsOhio.org and Children’s Defense
Fund — Ohio. And prior to that, as a workshop instructor
and journal editor with the American Chemical Society.
David holds a master’s degree in public policy and management
from the Glenn School at the Ohio State University and
a bachelor’s degree in zoology from Ohio University. Now,
without further ado, Mr. Norris, you have the
floor, and we can begin. DAVID: Excellent. Thank
you, Maria, and good afternoon, everyone. Let me just get
my slides up here. Let’s see. Here we go. Alright. Well, this
afternoon, what I’d like to do is explore a little
bit with you the linkage between place and health, and specifically the
linkage between place and neighborhood health in
regards to infant mortality and some of the
things that you might encounter as you work as
peer educators with folks that you’ll be working with. Let me just say a little
bit about the Kirwan Institute for the Study of Race and
Ethnicity. As Maria said, we are located at the
Ohio State University in Columbus, Ohio. This
slide lays out just a few of the many subject
matter areas that we deal with. I’m not going to go into a
lot of details about what we do specifically, although
I do want to point out, on the left-hand side, that
graphic with the four circles and then the
larger gray circles. At the top of that,
you’ll see a phrase “structural racialization.” That phrase describes how
race and prejudice have been incorporated into the very
fabric of our society through our structures of government,
economics, through the neighborhoods that
we live in, and so on; the healthcare
systems that we all utilize. And that is primarily the area that we’re going to
be talking about today, when we talk about
the linkage between place and infant mortality. So here’s what I’d
like to do this afternoon. Just very briefly go
through infant mortality; the definition, just so
we’re all on the same page. I’m going to speak quite a
bit about some mapping work and some analysis
that we have done in Ohio around infant mortality.
But even though the work is specific to Ohio, the
concepts that we’ll discuss and discover together apply
in cities across the country. We’ll talk about specifically,
the linkages, or what we call the spatial correlates
between infant mortality and some of the social
determinants of health and neighborhood conditions
in three Ohio cities. And then we’ll talk about
how our neighborhoods came to be the places that they
are in terms of better or worse health outcomes
for their residents. Now, just briefly, and I
think you all know all of this, so let’s just go
through this really quickly. But infant mortality, in
terms of birth outcomes, is the absolute
worst birth outcome. It’s the death of an infant
within the first year of life. And you can see the
leading causes listed there. Some are things that
we may be able to do little about, such as birth
defects. Other things that preconception and that
prenatal interventions might be able to have some
influence over, like being born too small, being born too
early, and some of the maternal complications of pregnancy.
And then some of the causes of infant mortality
happen after the baby leaves the hospital and is living in
the home within the first year of the baby’s life. And we’ll
look at mostly those factors that happen after the baby
leaves the hospital today. Just real quickly, the infant
mortality rate, I’ll be using that phrase quite a bit today;
you’ll see it abbreviated as IMR. It’s the number of
infant deaths before age one per one thousand live
births. And you may have heard the phrase, the infant
mortality rate is the canary in the coal mine
for community health. If you see high rates of infant
mortality, that generally tends to signal that there
are other poor health outcomes happening in those
neighborhoods as well. So we’re actually
going to test that today. We’re going to look at,
not only infant mortality, but other health outcomes
that are linked to place. One of the reasons that we
are really concerned about infant mortality here in
Ohio is that our state doesn’t do very well in terms of
infant mortality. We’ll look at some statistics on that in
a second. But in fact, our entire nation doesn’t do well
in terms of infant mortality. When you look at the
infant mortality rate in the United States compared
with other advanced countries, you can see in that bar
chart to the right that, among those
countries displayed there, we are pretty close to the
bottom, if not the bottom. There are probably some
countries that are below us, but not among the
developed countries. And you’ll also notice
that the infant mortality rate tends to go up in that bar chart
or in that graph on the left, as we go from the prenatal
period; I’m sorry, the perinatal period to the postnatal period,
and then the postnatal period. So not only do we not
do well as a country, but there are also differences
in infant mortality that vary by the affluence of
the mother, essentially. And so you can see that here
in the United States, again, the infant mortality rate goes
up for both wealthy mothers and disadvantaged
mothers from one day to one year of birth. The gap widens quite
substantially between disadvantaged mothers and
mothers who have more resources. Now I want you to keep that gap
in mind as we look ahead here, because if you look
at where babies die in that neonatal period, one month after birth, versus
the post-neonatal period, you see that the location
of the infant’s death varies quite a bit
between those two periods. In the neonatal period,
overwhelmingly babies who die, die in the
hospital as inpatients. These are most
likely babies that have had congenital problems. They’re babies that have
struggled from the point of birth, and most likely,
these are largely babies that never leave the hospital. When you look at the
post-neonatal period, however, you see that the majority
of infants who die, either die in the home or in the emergency
room, probably because they were having trouble
at home and were brought to the emergency
room and never made it to inpatient status. And it’s
in that post-neonatal period when the socioeconomic status
issues [unintelligible] in how well a baby thrives. So I want you to keep that
in mind as we move forward and talk about place. Now, here’s Ohio, compared
to the rest of the states in the union, and you can
see in that graph on the left, Ohio is pretty far down in terms of overall infant
mortality for white babies. We rank probably, depending
on whose numbers you look at, between 42, 48 or so, for
overall infant mortality. The chart on the right shows
you the infant mortality rate for black babies, and you
can see that Ohio is really far down the list. Either at
the bottom, or again, depending on whose numbers you look
at, among the bottom five. Now I’d also like for you
to take a look at the x-axis across the bottom of both those
charts, because you’ll see that the difference in
rate between whites and blacks in terms of infant
mortality is pretty stark. In fact, if we
overlay the entire range of infant mortality rates across all 50 states for
white babies on the rates for black babies, you can see
that with the exception of Washington up there at the
top, the absolute lowest rates of infant mortality for
black babies are higher than the highest
rates for white babies. So there are some real
issues there that we have to unpack a little bit about why
it is that infant mortality is so much worse for black babies
than it is for white babies. Now we’re going to be
talking about Ohio quite a bit, so if you’ve never been to
Ohio, this slide will just help to orient you. We are firmly in
the Midwest, in the Rust Belt. We’re a Great Lakes state. Lake
Erie is to the north of us. And we’re going to talk
specifically about research that we’ve done in Columbus,
Cleveland, and Toledo. This is a map that we prepared
for the Ohio Equity Institute to help inform their work on infant
mortality across the state. And what you’re looking at is a
density map of infant mortality. This is where babies have
died in the greatest numbers, in this case. And you read this just
like you would a weather map. The hot spot areas are
the areas that are red. Those are the areas with
the highest concentration of infant death. And it’s not surprising,
I think, to most folks, that the highest
concentrations of infant death are gonna be in the
urban centers of the state, simply because that’s
where the most people live. But you can see, even
across a particular city, there are
variations across the city. And so it’s those
variations across the city that we want to talk
about for the most part today. This is just a brief map to
show you what you’re looking at when you look at a dot density
map. It literally is showing you where the dots are the most
dense. Each point on that map represents one infant death
from the period 2007 to 2011 in Franklin County. That’s
the county where Columbus is located. It’s where
The Ohio State University is. And there are 770 dots
on that map. Seven hundred and seventy infants who died
before their first birthday over that period
in Franklin County. Now we have top-notch
medical facilities here, and so one of the things that is
really a mystery and a question that we need to answer is why is
it in the midst of such wealth and such great healthcare
resources are we still seeing infant mortality as such
a dire problem in our state and across the nation? So when
we look across an urban area like Columbus, we
see, obviously, that there are
different neighborhoods, and so we want to
be able to tease out the characteristics of
those different neighborhoods to see if we can find patterns
that will help us address this problem of
infant mortality. Now one other
thing to point out about these maps is that
even though there are areas on the map that don’t have
any color overlay on them, that doesn’t mean there
weren’t any infant deaths in those areas. We’ve just
chosen to show areas that have more than one infant death per
square mile, because the point, here again, was to
find those areas that have the highest concentration
of infant deaths in the city of
Columbus, Franklin County. Once we identify those
areas using hot spot mapping, we then overlay the
census tracts for those areas because what we want to do is
understand the characteristics of those neighborhoods. We
know where babies are dying, now we need to take a closer
look at those neighborhoods to see if we can find patterns.
And to do that, we use largely census data, or data
that’s linked to census tracts, because that’s the geography
that a lot of people work with. Now, the names of
these specific neighborhoods aren’t important. They’re
important, obviously, to us here in Columbus, because
they identify areas that we are familiar with. But what’s
more important is just to notice that we’re describing different
areas of the city that have a similar problem. They all have
high rates or high incidences of infant mortality. In fact, when we look at
all of those areas together, what we see is that each one of
those areas, not surprisingly, has a higher rate
of infant mortality than the county as a whole. In fact, those areas taken all
together have only nine percent of the county’s population, only
about 12 percent of the county’s births, and yet when we look
at infant deaths, 22 percent of all infant deaths and 30 percent
of all non-white infant deaths occur in those hot spot areas. So if we can address infant
mortality in those areas, we’ll be taking a substantial
step toward solving or addressing infant mortality
for our county as a whole. Now a moment ago, we saw that
you could overlay census tracts on these hot spot areas, and in
essence, that’s what we’ve done with this map as well. Each dot on that map represents
one census tract, and in this case, the size of each
dot represents the incidence of STDs, a measure or
proxy for safe sex practices, in each one of
those census tracts. The larger the dot, the more
cases of chlamydia and gonorrhea per thousand population. And when you look
at a map like this, you start to see that there
are definitely some overlaps between what you might consider
not safe sex practices, using this as proxy,
and infant mortality. Now it’s not 100 percent
alignment, as you can see. There are some areas
that have high rates of STDs that don’t have high
infant mortality rates. And conversely, there
are areas that do have high infant mortality rates
that don’t necessarily have high STD rates. You can
see that on the west side of the city, for example;
I’ll circle it over here with my cursor. Not
terribly high STD rates, and yet that’s a hot
spot for infant mortality. Another factor we can
look at is teen births. There is some correlation
or some linkage, at least, that we can see in the
overlap of the incidence of teen births and the
infant mortality rates. Now again, not 100
percent, and we’re really just doing a visual correlation. We’re not doing any
mathematics or statistics in order to say that
this is a specific linkage. Nevertheless, when
you look at the overlap, you can see that for
most of those hot spot areas, we have an increased
number, an increased percentage of teen births in those areas. We can also look at some
of the social determinants of health; socioeconomic
factors. In this case, what we’re looking at is
the percentage of households receiving SNAP benefits,
that’s food assistance. And again, the size of the
dot represents the percentage of households
receiving SNAP benefits. Again, some rough
alignment. In particular, in the northwest
corner of the city, you see very low rates
of infant mortality and also very low rates
of SNAP benefit usage. Out here on the west
side, where we saw low rates of STDs, over this particular
hot spot, we see that there’s a fairly high
incidence of households receiving SNAP benefits. So again, we’re
starting to tease out here that even though these
neighborhoods experience the same problem, infant
mortality, they’re different neighborhoods in terms of
their socioeconomic status and other aspects
of their composition. And that’s going to
inform them what kind of strategies we might want to
use in addressing this problem in those neighborhoods. So let me just kind of
cut to the chase here. We see differences in
the racial composition of those neighborhoods.
We see differences in the percentage of
foreign-born persons in those neighborhoods,
differences in crime rates, and differences of
housing vacancies. When we take those
all together, the graph that you see there on
the left just represents six of those factors and you
can think of each vertical line there as being a
ranking from low to high. And so, for example, when
you look at, let’s just follow the red line. The red
line is for an area of town we call Franklinton. And it’s
got an infant mortality rate about halfway between
the highest and the lowest infant mortality rates
for those hot spot areas. Looking further to the right,
the percentage of pre-term births is about 75 percent,
and then when you look at the percentage of
non-white and foreign-born, very low percentages of both. Looking at these different
arrays of characteristics, let’s characterize these
neighborhoods so that we can make some conclusions
about the folks who live there and the types of interventions
that might work in those areas. And strategies
for doing outreach. And so, when you look
at Franklinton Hilltop, this is a low-income,
predominantly white community, a lot of urban
Appalachian influence, and there’s significant
neighborhood distress. You can see that
by the vacant housing rate and the violent crime rate. Kind of at the other
end of the spectrum, these three communities all again
track pretty closely together on those indicators
that we show on the chart, and these are
immigrant communities. A lot of Hispanic Latino
families, African-born blacks. We have a very large
Somali community in Columbus. And relatively less
neighborhood distress. And you can see very high
percentages of both non-white and foreign-born. And then finally,
these three neighborhoods that go through the north-south
axis of the central part of the city are predominantly
African-American communities; low-income with significant
neighborhood distress. These are South Linden,
Near East, and Near South. And it’s these neighborhoods
that the Columbus Infant Mortality Task Force chose
as their pilot neighborhoods to begin to work on interventions
to address infant mortality. And their choices were
based in part on the research that we did for them
through these mapping efforts. Now we just looked at infant
mortality, but if you look at the other end of the spectrum,
the end of the life course, look at life expectancy from
birth, you see that there are also similar patterns
with life expectancy. The central city portions have
the lowest life expectancies and the outlying suburbs have
very high life expectancies. The key here is the place seems
to be the common denominator. In fact, when you look at
the range there, the span of life expectancy is almost 20
years from zip code to zip code across Franklin County.
It’s a pretty stark difference, and we’ve seen similar
differences through our research and through the
research of others, in cities all across the country. So let’s talk about
the foundation here. Let’s talk about place as a
determinant of health outcomes. We’re gonna talk some
about our historical policies that shape neighborhoods,
and we’re gonna shift away from Columbus for a moment and talk a
little bit about another city, Toledo. That’s the one on the
map that was in the upper left corner of the state.
It’s a moderate-sized city. It’s a little bit smaller than
both Cleveland and Columbus, a little bit larger than a town.
I guess the population there is probably around
250,000 in the central city. So let’s talk about
place a little bit, and urban development. It’s important to keep in
mind that the way our cities are structured is not a
result of natural processes. The way our cities are
structured are the result of policies and practices that
human beings have implemented. And so we need to talk about
the role of racial and social exclusion, and frankly,
exploitation, as driving forces in shaping the
cities that we live in. Now we’re not going to
have time to talk about all of these different
drivers of racial segregation in the structuring of our
cities. I’ll just mention very quickly some of the
ones on the right there. For example, zoning
and land use practices. Zoning is just designating,
municipalities designating particular land uses and
requirements for buildings on different parcels of
land. And so for example, one way that a city could
have a discriminatory practice of zoning would be, if you think
about the outlying suburbs, saying that, requiring
that parcels in those areas have to have very large lots;
the homes have to have a minimum of three bedrooms. Things
like that that would price out lower income buyers
from that housing market. Urban renewal was a
practice in the 50s and the 60s that attempted to
provide affordable housing. We won’t go into any of
that, other than to say that I’ll give you some references
at the end that will kind of walk you through some of
the steps of urban renewal and how those policies kind
of both misjudged the social structure of the
country, and also actually were discriminatory
in their practices. On the right there, you
can see another program that significantly
affected our cities, the building of
the interstate system. This happens to be Interstate 71
as it was being built through Columbus. It follows the
north-south trajectory through the middle of the city, and
if you think back a few slides, it slices right through a good
portion of the African-American community in Columbus. And
I think if you, as you drive in your own city down an
interstate and look to your left and look to your right and
imagine what was in the place of that interstate before
the highway arrived, I think you’ll get a sense of just how
divisive and how destructive the interstates were to particular
neighborhoods in our cities. Remember that the folks
who created the interstates and created the policy
listened to their constituents and they also listened to
the folks with political power. And if you think about a typical
low-income neighborhood that doesn’t have a lot of
organization or a lot of resources, not a lot of
political clout there either. And so it’s not surprising
that those neighborhoods, in particular, were
devastated by the interstate. And then of course, there are
the kinds of explicit racial discrimination and intimidation
practices that I think most of us are most familiar
with, because they’re the interpersonal kinds of
things. For example, a realtor may show a black family fewer
properties than a white family. Or they may show black
and white families properties in different parts of the
city. Those are discriminatory practices and the further
segregation in our cities. But I want to go over to
the left there and talk about redlining and investment
practices, and we’re gonna spend a little bit of time on
this, because of all of these, these are the ones that are
probably the most universal practices and the ones that
probably had the most effect overall on shaping our cities to
be the way that they are today. Now before we talk about
redlining specifically, and I’ll define that
term in a second; I do want to also
point out that one practice that was used to prevent
the, as they say, at the infiltration of undesirables,
as they defined them. And undesirables at this point
were blacks, Asians, people of Eastern European
descent. In essence, anybody who wasn’t what, in
the language of the day, they called a natural-born
white, which is an odd phrase, but it was the way that
whites in those days referred to folks who were white
and were well-established. One of the structures that were
utilized were racial covenants inserted into deed
documents or incorporated into subdivision bylaws and
property owners associations. You can see an example of
one of those here on the right. “None of the said
lands interest therein or improvements thereon shall
be sold, resold, conveyed, leased, rented to, or
in any way used, occupied, or acquired by any
person of Negro blood or to any person of the
Semitic race, blood, or origin, which racial description shall
be themed to include Armenians, Jews, Hebrews,
Persians, or Syrians.” Now this language might
have actually been inserted into the deed document itself,
and when a person purchased that home, this was, at
the time it was written, a binding contract saying
that if they agreed to purchase the home, then they also were
agreeing not to sell the home to any of these folks
that were listed here. Those have long since been
determined to be unenforceable, but if you have an older home,
say built in the 20s or before, or maybe a little bit later, take a look at your
deed and see if it includes some of that language.
It might be a little bit surprised at what you
might find in your deed. Well, let’s talk about
suburban growth and race. Specifically, we’re
gonna talk a little bit about the
development of the suburbs. You may have heard the term
“white flight,” and so this is gonna describe how, basically,
whites left the central cities for the suburbs, leaving the
central city, the central core, without an adequate tax base.
And therefore, kind of set it on the course to
decline. But there were some other factors involved,
and we’ll go into those. If you look at the
prime suburb-shaping years, from 1930 to 1960, fewer than one percent of
all African Americans were able even to obtain a mortgage. They
were entirely closed out of the housing market. And if that phrase
“formation of white America” sounds a little strange to you;
again, I’ll have a resource listed at the end that helps to
explain exactly what that means. In essence, what it means is
that the concept of white and the concept of race overall
is essentially just that. It’s a social construct.
It’s not a genetic reality. And that social construct has
shaped policies and practices that have led to
discrimination and segregation. And just to put a fine point
on this, the kinds of things I described earlier about
realtors showing homes and those sorts of things, those
are interpersonal, one-on-one types of discrimination.
But I want to point out on the
right-hand of this slide some text from the
FHA underwriting manual. This is from 1947, but this text
remained in the manuals until the 60s. “If a neighborhood is to retain
stability, it’s necessary that properties shall continue to
be occupied by the same social and racial classes. A change
in social or racial occupancy generally contributes to
instability and a decline in values.” This reflects kind of an
overarching conceptual framework that said that neighborhoods
progress in a certain way. When neighborhoods are
first built, they’re pristine. They have nice facilities.
The homes are all new. And as the
infrastructure declines, and as some people move
out, and other people move in, the decline is hastened. And one of the things that it
was believed hastened decline was the influx of blacks
and immigrants and other folks that were deemed to
be, again, undesirable. Again, federal policy.
This is part of that structural racialization that I pointed out
on the slide at the beginning. This is prejudice. This is
discrimination that’s built into federal policy. Now, you might have heard
the term “redlining’ before. It’s a term that’s reached kind
of general use, but this is where the term originated. What
you’re looking at here is a map of the city of Toledo
from, I believe, 1938 or somewhere thereabouts. This map and maps for about 240
other US cities were produced in the 1930s by the
Homeowners Loan Corporation. This was a federal office that
was set up in the wake of the Great Depression. The purpose
was to insure refinanced loans for homeowners who were
struggling to keep their home. So the idea was, the Homeowners
Loan Corporation, it did issue some loans, but its main
purpose was to insure the loans on mortgages for folks who
needed to refinance in order to keep their homes. In order
to protect taxpayer dollars, the Homeowners Loan
Corporation had local realtors, real estate professionals, and
bankers assess the neighborhoods in their cities
for the perceived risk of insuring a loan. So when you look at the
map, the assessors ranked neighborhoods from A to D and
used color codes to represent those, green being the
highest value and red being the lowest value. In other
words, green being the lowest risk for insurance and red being
the highest risk for insurance. So if you wanted to write a
mortgage loan for one of those red areas, one of those redlined
areas, if the loan defaulted and you were the one
who issued the mortgage, federal government
essentially wouldn’t insure it. It’s a very large disincentive
to pour money into those redlined areas and to put any
kind of investment in place. Now the thing that was
different about the way that those assessors evaluated the
properties around their cities and the way we do
property appraisals today is that the appraisers of
those days didn’t just assess the neighborhoods on
the basis of the properties and the land that were
present in those neighborhoods. They also assessed the
neighborhoods on the basis of the folks who lived
in those neighborhoods. Because remember that general theory that said cities
decline in a certain way, and one of the ways that leads
to decline of neighborhoods is through the influx of folks
who are going to lower your property values. So this is a copy of
one of the documents. Each one of those areas that
you saw on the map; red, green, yellow, blue; also had assessor
notes associated with it. You can get these for
some cities from the National Archives, or you can do some
web searches and find these. This happens to be a grade
A area description from one of those greenlined
areas in the city of Toledo. And what’s really interesting
about these, if you look at the upper right, there’s
a space specifically for the percentage of Negro
population in that area. And in this case, there were
no blacks living in the area. The availability of mortgage
funds for home purchase and home building was ample.
And if you read the clarifying remarks, you see things like
“This is a somewhat older but very fine
high-type neighborhood; pride of
ownership, well-planned homes soundly constructed within
short distance to University of Toledo. Abutment to
cemetery and lower-grade areas should not jeopardize
our ability for several years.” So, you know, cemetery,
obviously a fixed use. And so you were
pretty much guaranteed that nobody was
going to move in next to you if you
lived in this area. Now let’s take a look at
a grade D area description. Again, it’s from Toledo.
Factory and common laborers, about 20 percent black.
Availability of mortgage funds for home purchase very limited;
for home building, very limited. And in the clarifying remarks,
“Now rapidly being run down through influx of colored
and low-income group of whites. Heavy relief load,
high vacancy ratio.” Another grade D
area description, “about 95 percent black,” and look at that; availability
of mortgage funds for home purchase and home building,
just flat-out no.There was just gonna be no mortgage
investment in this area at all. And the only difference
between this area and the preceding one was
the percentage of black. So we talk about the
life course of individuals, this chart here really
summarizes more the life course of a redline neighborhood.
Now the redlining maps, when they were created,
reflected the discrimination and the segregation that was
already present in our cities at that time. And it’s
questionable whether folks actually used the redlining
maps to make case-by-case mortgage decisions, but
what we do know is that because local
appraisers were used in the process of
creating the redline maps, that’s how those
folks viewed their city. And those are the ones
who made the decisions on whether or not to issue
mortgages, based on whether they could get them insured or not. So the redline maps, we know,
do very specifically reflect where there would be
greater or lesser investment by the folks who
created them. In other words, a redlined area wasn’t going to
get a whole lot of investment in terms of mortgage funding. So you have disinvestment.
Because there’s no investment, then it becomes kind of
a self-fulfilling prophecy that the housing
stock would decline there, and then in later days, we
also see predatory lending in those areas and
property value loss. Again, a self-fulfilling
prophecy because if you’re not investing in those
neighborhoods, you’re going to see some decline. Some results that
come out of that: foreclosures and
vacancy. You also have crime and safety and health
problems because the tax base is declining in those
areas. And then you also, if you are a homeowner in
those areas, your property values have gone down,
and so you see a loss of the equity that you’ve built up in
the home that you’ve purchased, if you’ve been
able to purchase one. Well, let’s see if that bears
out. Again, this is Toledo. The upper map there is
just to remind you that’s the redlined area. The
lower maps are from 2013. These are residential parcels;
in other words, each little space on that map represents one
residential or one home parcel. And you can see, largely,
in the areas that correspond to the areas that were redlined,
most of the residential properties were built before
1950, so there hasn’t been a whole lot of new construction
at all in those areas. Some neighborhoods
have seen absolutely none. And also the property values in
those areas are very depressed. Well, let’s look at some
health correlates as well. We said earlier that
place makes a difference in health outcomes. And so
if you look at the risk of lead poisoning in those
areas, and let me go back here for a second.
These two factors together, low property value and
homes built before 1950, or even homes in
yellow built before 1970, those two factors combined
to create a pretty high risk of lead poisoning, because
of lead-based paint dust in the homes. And in fact,
we’re doing some of that work in Toledo now with some of the
legal aid agencies up there. And not surprisingly, if
you look at the redline map, you think about the current
valuation of those properties and the age of those properties;
again, it’s not surprising that the disinvestment
in those properties has led to a high risk of lead
poisoning, as shown on the map on the right. We can
also overlay other things on the redline map; again,
remembering that the redline map correlates pretty highly
with lead poisoning risk. We also see that, in the
2000s, if you look at those neighborhoods that
were previously redlined, there’s a pretty high proportion
of those neighborhoods that were also targeted
with high-cost mortgage loans. We think of this as actually
being reverse redlining. In the redlining days, it was
a lack of information about the lending practices that got
folks into trouble when they actually tried to obtain
a mortgage for those areas. In the era of the high-risk,
high-cost mortgage loans, it was actually an
overabundance of information. People were given way more
information than they could absorb, and simply signed
on the basis of, essentially, trust, to get a
high-cost mortgage loan. And not surprisingly, a lot of
those loans went into default. The effect being that what
little equity was left in those areas that had been
previously disinvested, was more or less
sucked out of those areas, further depressing them. Well, let’s look
at health outcomes. Here we go. The infant
mortality hot spot map is shown overlain on
Toledo. It’s a little difficult to see, but if you look
in the upper right there you can see just the
lip of Lake Erie dipping in to the city there. So that’ll
orient you a little bit. These are the hot spots,
and overlaid on those hot spots are the outlines of the
census tracts that we used to analyze those neighborhoods.
And if we overlay those census tracts that correspond
with infant mortality, I think you can see that
here again, for the most part, except in the
northwest corner of the city, those previously-redlined
neighborhoods are also the areas where we see high
incidences of infant mortality. Again, disinvestment in
the central neighborhoods has led to conditions that
are rife for creating poor health outcomes. And it’s
not just those two cities that we’ve looked at here. Just
to kind of put a final point on this. This happens to be
Cleveland. This is the redline map from 1940s
Cleveland. Lake Erie is up here to the left, right along here. The Cuyahoga River
comes down through here, the river that caught
fire a few years ago. And when you look at
the redline map compared to current health outcomes,
in those previously redlined areas, you see infant mortality
rates five to six times higher than the
non-redlined areas. You have higher rates of
lead exposure, just as you have in Toledo. Higher exposure
to toxic waste release sites. You have the highest
vacant property rates. You have more than one
half of all residential loans subprime. And there’s a 15-year
difference in life expectancy between those previously
redlined areas and areas in the rest of the city.
And again, let me just toggle between these two,
just so you can see. Redline, infant mortality.
Again, for the redline, it’s both red and yellow
areas that were disinvested. The redlined areas
primarily, but the yellow areas were seen as being in
decline, and so there was very low tendency to
invest in those neighborhoods as well. The red and
yellow areas, and again, infant mortality
current outcomes. And again, if we look at
more contemporary things, the neighborhoods that
receive the most high-cost mortgage loans; again,
very high visual correlation between the areas that received
high-cost mortgage loans and those areas that
were previously redlined. Now I want to point
out that a neighborhood’s being redlined was not
necessarily a death sentence for that neighborhood.
We do see exceptions where some of the previously-redlined
neighborhoods have turned around and
actually become very nice areas of town. And the
question we need to ask when we see those
kinds of transformations is whether it was a straight
line progression between being a redlined area
and kind of a nicer area. In other words, the area picked
itself up by its bootstraps. Or did we see some other
things happening in those neighborhoods? Things
like gentrification, where the neighborhood declined to a
point that folks either left or were forced out, and
new construction and new neighborhoods took their
place, displacing the folks who lived in those
neighborhoods previously. A lot of history happened
between 1930 and current day; almost 80 years. But again,
the fact that these areas were redlined led to a
lot of disinvestment in those areas and more often
than not, those areas subsequently didn’t prosper. Well, why is this
history relevant today? This is kind of
bringing everything together. There is a direct
relationship between historical patterns of systemic
discrimination and today’s community-based
health challenges. Because of the way
that our cities developed and the practices and
policies that were in place, you saw what we described
earlier as white flight. Whites tended to leave
the central cities for the suburbs, because
they could afford to. And what you were
left with were folks who didn’t have the
means to move elsewhere being concentrated
in the central cities and segregated racially. Now if you happen to be somebody who’s living in
one of those areas, then you lack access
to adequate healthcare and other
necessary assets to thrive. For example,
healthy foods. Now again, that’s not to say
that these areas should be written off, certainly
not. It’s also not to say that these areas
don’t have resources. One of the things that
we do when we work with a neighborhood is not
only to help them identify the areas of need, but
also to identify the assets that are present in those
neighborhoods that can be marshalled to address
the problems and the needs of those neighborhoods.
And sometimes those assets aren’t physical. Sometimes
the assets that are strongest in a community are
the community’s cohesion, its social linkages. Or the
third places, as we call them, where folks meet to
discuss and organize and plan. So assets aren’t necessarily
physical but they are definitely important
in these