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

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

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

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

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

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

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

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

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

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

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

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

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%

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

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

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

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

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

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

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

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

– 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

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 ….

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!