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Use of Area-based Poverty as a Demographic Variable for Routine
Surveillance Data AnalysisCT and NYC
CSTE Annual Conference
June 10, 2013
J Hadler
CT EIP, NYC DOHMH
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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
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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
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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
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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.
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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/
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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
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Incidence of influenza-associated hospitalizations by census tract poverty
level, Children 0-17 years, NH County, CT, 2003/04 -2009/10
0
10
20
30
40
50
60
70
80
Inci
den
ce p
er 1
00,0
00
per
son-
yea
rs
Census tract poverty level
<5% 5-9.9% 10-19.9% 20+%
AJPH 2011;101:1785
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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
0
1
2
3
4
5
6
7
8
9
2003 2004 2005 2006 2007 2008 2009
Inci
den
ce r
atio
H1N1
AJPH 2011;101:1785
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Age-adjusted incidence of influenza-associated hospitalizations of adults 18+ yrs
by selected ABSES measures, NH County, CT, 2005-2011
0
20
40
60
80
100
Poverty Crowding No highschool
diploma
No Englishin
household
Medianincome
Highest SES Less high Lower Lowest SES
Inci
den
ce p
er 1
00,0
00
per
son-
yea
rs
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Ratio of highest to lowest census tract-level poverty incidence of influenza-associated
hospitalizations by year, Adults 18+ yrs, CT, 2005-2011
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
2005 2006 2007 2008 2009 2010
Inci
den
ce r
atio
H1N1
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Age-adjusted incidence of influenza-associated hospitalizations of adults 18+ yrs
by poverty level* and race/ethnicity, NH County, CT, 2005-2011
0
20
40
60
80
100
White Black Hispanic
<5% 5-9% 10-19% 20+%
Inci
den
ce p
er 1
00,0
00
per
son-
yea
rs
non-Hispanic non-Hispanic
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Incidence of Cervical Intraepithelial Neoplasia Grade 2+
by census tract poverty level, Women 20-39 years, NH County, CT, 2008-2009
0
100
200
300
400
500
600
Inci
den
ce p
er 1
00,0
00
per
son-
yea
rs
Census tract poverty level
<5% 5-9.9% 10-19.9% 20+%
AJPH 2012;103:156
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Incidence of CIN2+ by census tract poverty and age group, Women 20-39 years, NH
County, CT, 2008-2009
0
200
400
600
800
1000
20-24 yrs 25-29 yrs 30-39 yrs
<5% 5-9% 10-19% 20+%
Inci
den
ce p
er 1
00,0
00
per
son-
yea
rs
AJPH 2012;103:156
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Foodborne bacterial pathogen age-adjusted incidence by census tract poverty level and
pathogen, CT, 1999-2011
0
5
10
15
20
<5% 5-9.9% 10-19.9% 20+%
Inci
den
ce p
er 1
00,0
00
per
son-
yea
rs
Campylobacter Salmonella STEC
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Foodborne bacterial pathogen risk in children by census tract poverty level, CT,
1999-2011
0
10
20
30
40
50
Campy 0-9 yrs Salmonella 0-4 years STEC 0-4 yrs
<5% 5-9.9% 10-19.9% 20+%
Inci
den
ce p
er 1
00,0
00
per
son-
yea
rs
Age Group
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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.
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New York City
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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
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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.
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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?
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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
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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.
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Results
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Percentage of population by census tract
poverty level, NYC, 2000 & PHDGP 1990
0
10
20
30
40
50
<5% 5-9% 10-19% 20+%
NYC PHDGP
Pe
rce
nta
ge
of p
op
ula
tion
Percent below poverty in census tract
46%
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Percentage of population by % of residents in census tract, zip code and UHF area who
live below poverty, NYC, 2000
0
10
20
30
40
50
<5% 5-9% 10-19% 20-29% 30-39% 40+%
Census Zipcode UHF area
Pe
rce
nta
ge
of P
op
ula
tion
Percent below poverty in census tract
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Age-adjusted Mortality Rate by % in census tract who live below poverty,
NYC, 2000
0
2
4
6
8
10
12
<5% 5-9% 10-19% 20-29% 30-39% 40+%
De
ath
Ra
te p
er 1
000
Percent below poverty in census tract
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Age-adjusted Mortality Rate by % in census tract who live below poverty
by race/ethnicity, NYC, 2000
0
2
4
6
8
10
12
14
White (non-H) Black (non-H) Hispanic Asian
<5% 5-9% 10-19% 20-29% 30-39% 40+%
De
ath
Ra
te p
er 1
000
Percent below poverty in census tract
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Age-adjusted Mortality Rateby % in census tract who live below poverty,
NYC, 1990 and 2000
0
2
4
6
8
10
12
14
16
<5% 5-9% 10-19% 20-29% 30-39% 40*%
1990 2000
De
ath
Ra
te p
er 1
000
Percent below poverty in census tract
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Age-adjusted TB Rate by % of residents in census tract who live
below poverty, NYC, 2000
0
5
10
15
20
25
30
<5% 5-9% 10-19% 20-29% 30-39% 40+%
Ra
te o
f TB
pe
r 1
00,0
00
Percent below poverty in neighborhood
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Age-adjusted TB Rate by % in census tract who live below poverty
by race/ethnicity, NYC, 2000
0102030405060708090
White (non-H) Black (non-H) Hispanic Asian
<5% 5-9% 10-19% 20-29% 30-39% 40+%
Ra
te o
f TB
pe
r 1
00,0
00
Percent below poverty in census tract
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Age-adjusted TB rate by % of residents in census tract who live below poverty, NYC,
2000 and 2008
0
5
10
15
20
25
30
<5% 5-9% 10-19% 20-29% 30-39% 40+%
2000 2008
Ra
te o
f TB
pe
r 1
00,0
00
Percent below poverty in census tract
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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)
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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
– 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
2. Use of CTP level is gaining traction– Increasing experience using it, CSTE involved
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Where do we go from here?
1. Up to state and local health dep’t epidemiologists and CSTE to bring SES measures to the data we collect – to take the lead.
– 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 ….
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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.
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Thanks!