Use of Area-based Poverty as a
Demographic Variable for Routine
Surveillance Data Analysis
CT and NYC
CSTE Annual Conference
June 10, 2013
J Hadler
CT EIP, NYC DOHMH
Outline
Rationale
Public Health Disparities Geocoding Project (PHDGP)
• Recommended standard Area-based SES measure
Connecticut EIP
• Influenza hospitalizations, bacterial foodborne
pathogens, HPV
New York City
• Workgroup formation and recommendations
• All cause mortality, TB
Conclusions
Rationale 1
• Describing health disparities and monitoring progress
in reducing them has been a national priority (HP
2010 and 2020).
• Major variable used to describe health disparities has
been race-ethnicity.
• Use of race/ethnicity as a major means to describe
disparities has some severe limitations
– not always available
– >20 official race/ethnic groups
– difficult to interpret – disparities are only sometimes genetic
or cultural; mostly race-ethnic disparities reflect SES
differences
Rationale for use of area-based SES
(ABSES) measure for data analysis
• US has no recommended SES measure for
routine collection, analysis and display of
surveillance data – race-ethnicity is a very
unsatisfying surrogate.
• Geocoding accessibility and ease have made
it possible to use area-based SES measures
where have street address or ZIP code.
• PHDGP already laid groundwork
Public Health Disparities Geocoding
Project 1
• Harvard-based lead by Nancy Krieger, ~1998 - 2004
• Recognized potential in public health data for analysis
using ABSES
• Explored wide range of health outcomes using MA
and RI data from 1990 using different area sizes and
SES indices
• Found ABSES measures described disparities as big
or bigger than those by race/ethnicity and usually
described disparities within race/ethnic groups.
Public Health Disparities Geocoding
Project 2
• Recommended use of census tract level percentage
of residents living below federal poverty level for
routine data analysis.
– <5%, 5-9.9%, 10-19.9%, >20%
• “Painting a truer picture of US socioeconomic and
racial/ethnic health inequalities: The PHDGProject”.
Am J Public Health 2005; 95: 312-323.
• http://www.hsph.harvard.edu/thegeocodingproject/
Connecticut
Objectives
• Gain experience using census tract poverty
level to describe health disparities
• Began to analyze surveillance data routinely
as part of the EIP in ~2009.
–
–
–
–
Invasive pneumococcal disease*
Influenza hospitalizations (pediatric*, adult**)
Cervical cancer precursors (CIN 2,3; AIS)*
Foodborne bacterial pathogens (campylobacter**,
STEC, salmonella)
* published; ** submitted
Incidence of influenza-associated
hospitalizations by census tract poverty
level, Children 0-17 years, NH County, CT,
2003/04 -2009/10
Incidence per 100,000
person-years
80
70
60
50
40
30
20
10
0
<5%
5-9.9%
10-19.9%
20+%
Census tract poverty level
AJPH 2011;101:1785
Incidence ratio
Ratio of highest to lowest census tract-level
poverty incidence of influenza-associated
hospitalizations by year, Children 0-17 yrs, CT,
2003/04 – 2009/10
9
8
7
6
5
4
3
2
1
0
2003
2004
2005
2006
2007
2008
2009
H1N1
AJPH 2011;101:1785
Age-adjusted incidence of influenzaassociated hospitalizations of adults 18+ yrs
by selected ABSES measures, NH County, CT,
2005-2011
Incidence per 100,000
person-years
Highest SES
Less high
Lower
Lowest SES
100
80
60
40
20
0
Poverty
Crowding
No high
school
diploma
No English
in
household
Median
income
Incidence ratio
Ratio of highest to lowest census tract-level
poverty incidence of influenza-associated
hospitalizations by year, Adults 18+ yrs, CT,
2005-2011
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
2005
2006
2007
2008
2009
H1N1
2010
Age-adjusted incidence of influenzaassociated hospitalizations of adults 18+ yrs
by poverty level* and race/ethnicity, NH
County, CT, 2005-2011
Incidence per 100,000
person-years
<5%
5-9%
10-19%
20+%
100
80
60
40
20
0
White
non-Hispanic
Black
non-Hispanic
Hispanic
Incidence of Cervical Intraepithelial
Neoplasia Grade 2+
by census tract poverty level, Women 20-39
years, NH County, CT, 2008-2009
Incidence per 100,000
person-years
600
500
400
300
200
100
0
<5%
5-9.9%
10-19.9%
20+%
Census tract poverty level
AJPH 2012;103:156
Incidence of CIN2+ by census tract poverty
and age group, Women 20-39 years, NH
County, CT, 2008-2009
Incidence per 100,000
person-years
<5%
5-9%
10-19%
20+%
1000
800
600
400
200
0
20-24 yrs
AJPH 2012;103:156
25-29 yrs
30-39 yrs
Foodborne bacterial pathogen age-adjusted
incidence by census tract poverty level and
pathogen, CT, 1999-2011
Incidence per 100,000
person-years
<5%
5-9.9%
10-19.9%
20+%
20
15
10
5
0
Campylobacter
Salmonella
STEC
Foodborne bacterial pathogen risk in
children by census tract poverty level, CT,
1999-2011
Incidence per 100,000
person-years
<5%
5-9.9%
10-19.9%
20+%
50
40
30
20
10
0
Campy 0-9 yrs
Salmonella 0-4 years
Age Group
STEC 0-4 yrs
Implications of identified SES disparities
• Influenza – target efforts to improve vaccination rates
to neighborhoods with high rates of neighborhood
poverty
• HPV vaccination – needed for all, not just a subset of
the population. Very high rates of cervical cancer
precursors in neighborhoods with low poverty levels.
• Bacterial foodborne pathogens – Focus prevention
and prevention research efforts on high SES
populations.
– More research needed to understand risk factors in children –
why children in high poverty neighborhoods have higher risk
of campy/salmonella but not STEC.
New York City
Background 2010
• NYC has had long-standing emphasis on
describing and minimizing health disparities.
• Most programs used race/ethnicity; some
programs used SES measures: income,
neighborhood poverty
• No standardization of measures,
neighborhood size, cut-points
Background (cont)
• Has cross-cutting “Data Task Force” as forum
for discussion of data issues agency-wide
• Following presentation of PHDGP
recommendations for standard area-based
SES measure, workgroup set up to explore
NYC-specific issues and make
recommendations.
Challenges for a NYC standard
• Population distribution not the same as MA
and RI
• “Neighborhoods” used have been UHF areas,
not census tracts
• With higher cost of living than most of rest of
US, is federal poverty level the best level to
use?
Poverty Measure Workgroup 1
• Poverty measure workgroup formed to
explore these issues and develop
recommendations re: a standard
neighborhood SES measure.
• Composed of volunteers from Communicable
disease, Epi Services, HIV, Immunizations,
STD, TB, Vital Statistics
Poverty Measure Workgroup 2
• Agreed early on to the following:
– Important to have a standard measure that can be
used and compared to other public health
jurisdictions (cities, states)
– Accept the background work of the PHDGP and
use a neighborhood poverty measure
– May need different neighborhood poverty cut
points than those recommended based on work in
MA & RI
– Need to explore NYC data to determine best cut
points and neighborhood size to use.
Results
Percentage of population by census tract
Percentage of population
poverty level, NYC, 2000 & PHDGP 1990
NYC
PHDGP
46%
50
40
30
20
10
0
<5%
5-9%
10-19%
Percent below poverty in census tract
20+%
Percentage of population by % of residents
in census tract, zip code and UHF area who
Percentage of Population
live below poverty, NYC, 2000
Census
Zipcode
UHF area
50
40
30
20
10
0
<5%
5-9%
10-19%
20-29%
30-39%
Percent below poverty in census tract
40+%
Age-adjusted Mortality Rate
by % in census tract who live below poverty,
NYC, 2000
Death Rate per 1000
12
10
8
6
4
2
0
<5%
5-9%
10-19%
20-29%
30-39%
Percent below poverty in census tract
40+%
Age-adjusted Mortality Rate
by % in census tract who live below poverty
by race/ethnicity, NYC, 2000
Death Rate per 1000
<5%
5-9%
10-19%
20-29%
30-39%
40+%
14
12
10
8
6
4
2
0
White (non-H)
Black (non-H)
Hispanic
Percent below poverty in census tract
Asian
Age-adjusted Mortality Rate
by % in census tract who live below poverty,
NYC, 1990 and 2000
Death Rate per 1000
1990
2000
16
14
12
10
8
6
4
2
0
<5%
5-9%
10-19%
20-29%
30-39%
Percent below poverty in census tract
40*%
Age-adjusted TB Rate
by % of residents in census tract who live
Rate of TB per 100,000
below poverty, NYC, 2000
30
25
20
15
10
5
0
<5%
5-9%
10-19%
20-29%
30-39%
Percent below poverty in neighborhood
40+%
Age-adjusted TB Rate
by % in census tract who live below poverty
by race/ethnicity, NYC, 2000
Rate of TB per 100,000
<5%
5-9%
10-19%
20-29%
30-39%
40+%
90
80
70
60
50
40
30
20
10
0
White (non-H)
Black (non-H)
Hispanic
Percent below poverty in census tract
Asian
Age-adjusted TB rate by % of residents in
census tract who live below poverty, NYC,
2000 and 2008
Rate of TB per 100,000
2000
2008
30
25
20
15
10
5
0
<5%
5-9%
10-19%
20-29%
30-39%
Percent below poverty in census tract
40+%
Key Recommendations
1.
All routinely collected surveillance data with
geolocating info should be analyzed using
neighborhood poverty as a standard variable
2.
•
Standard Measure
% in neighborhood who live below federal poverty
level
• 6 categories for analysis:
<5%, 5-9%, 10-19%, 20-29%, 30-39%, 40+%
• 4 categories as needed for small numerators or
display:
<10%, 10-19%, 20-29%, 30+%
•
Use census tract when possible (rather than ZIP,
UHF)
Conclusions
1.
Analysis of data using census tract poverty (CTP)
is a meaningful way to describe disparities for
some diseases and provides new insights relevant
to control
–
–
–
–
2.
Find disparities within race/ethnic groups
Some diseases more common among those of higher
SES
Can be used regardless of whether have race/ethnicity
data
Targeting groups for intervention based on SES more
attractive than based solely on race/ethnicity
Use of CTP level is gaining traction
–
Increasing experience using it, CSTE involved
Where do we go from here?
Up to state and local health dep’t epidemiologists
and CSTE to bring SES measures to the data we
collect – to take the lead.
1.
–
–
–
We are the experts in analyzing and using the info we
collect.
Academia has shown the way – is best suited to studying
the mechanisms related to SES disparities.
CDC is interested, but is slower to move than state and
local jurisdictions – and doesn’t have address data.
continued ….
Where do we go from here? (cont)
2.
Take advantage of the PHDGP work:
–
Begin to routinely include ABSES measures in
surveillance data analyses, ideally, including the
recommended “standard”
–
Help CSTE move ABSES, esp. census tract
poverty level, into the national dialogue about
measuring and addressing health disparities.
Thanks!