Analysis of Public Health Data Using Census Tract-level Poverty

CSTE Epi Methods Webinar

March 6, 2014

J Hadler

Outline

RationalePublic Health Disparities Geocoding Project (PHDGP)• Recommended standard Area-based SES measure• Examples of recent analyses Principles of analysis and steps involvedSpecial considerations• choice of poverty cut-points, geocoding, selection of

source of census tract poverty, denominators, age-adjustment

Current use of this method by public health agenciesConclusions

Rationale 1

• Describing health disparities and monitoring progress in reducing them has been a national priority (HP 2010 and 2020).

• Disparities usually described in terms of differences in rates or rate-ratios by various demographic groups: age, sex, race-ethnicity, place, SES– Health inequities occur when disparities/differences between groups

occur because the group(s) with high rates are at a social or economic disadvantage.

• Haven’t been standard variables to describe disparities by place or by SES

Rationale 2

• Major variable used to describe health disparities/inequities by SES 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

– Not easy to intervene based on race alone– May be disparities within race-ethnic groups

Rationale 3

• 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: street addresses available

• Also recognized value of ABSES measures– “Place” (neighborhood) can have a profound influence on health. – ABSES is more than a surrogate for individual SES

• 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

• Found ABSES measures described disparities as big or bigger than those by race-ethnicity and usually described disparities within race/ethnic groups.

• 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 EIP

• Gain experience using census tract poverty level to describe health disparities and differences between groups.

• 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)– Varicella incidence and hospitalizations

* 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

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

IRV 2014:DOI:10.1111/irv.12231

Ratio of highest to lowest census tract-level poverty incidence of influenza-associated

hospitalizations by year, Adults 18+ yrs, CT, 2005-2011

1

1.5

2

2.5

3

3.5

4

4.5

2005 2006 2007 2008 2009 2010

Inci

den

ce r

atio

H1N1

IRV 2014:DOI:10.1111/irv.12231

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

IRV 2014:DOI:10.1111/irv.12231

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

Implications of identified SES disparities and differences

• Influenza – target efforts to improve vaccination rates to neighborhoods with high rates of neighborhood poverty

• Bacterial foodborne pathogens – Focus prevention and prevention research efforts on high SES populations.

CSTE Data Analysis Project

• In 2012-2013, 10 sites received $5000 funding each to geocode a public health dataset of their choice and analyze it using census tract poverty.

• Monthly conference calls to discuss & standardize methods.• Presentations at 2013 CSTE annual conference

• Sites: MA, AR, MS, NM, AZ, WA, Houston, Harris County, Multnomah County, Seattle-King County

• Projects included HIV, HCV, salmonella, low birth weight, mortality (stroke, cardiac, all cause), pneumonia hospitalizations

Steps in analysis using area-based poverty

Principles 1

• Choose cut-points to group census tract poverty levels (e.g., <5%, 5-<10%, etc.)

Numerators• Assign a census tract poverty level to each “case” • N for each poverty level = sum of all the cases living in census

tracts with the respective poverty level• For each poverty level’s “N”, should be able to stratify by age,

sex and race-ethnicity (as possible)

Denominators• Assign a census tract poverty level to each census tract• Get the denominator data for each census tract, overall and

stratified by age, sex and race-ethnicity • N for each poverty level = sum of population in all census tracts

with the respective poverty level

Principles 2

Calculate crude incidence/prevalence • For each poverty level, divide numerator by denominator and

determine rate per unit population (e.g., per 100,000)• Can do analysis by age-specific groups, by sex, by race-ethnicity

by simply sub-setting the numerator and denominator for each specific characteristic

Calculate age-adjusted incidence/prevalence• May need to age-adjust if outcome incidence is age-related (e.g.,

mortality), since different poverty levels may have a different age structure.

Choose cut-points for poverty level

• For comparability across jurisdictions, choose fixed cut-points rather than fractions (e.g., quartiles).

• PHDGP recommended 4 categories: <5%, 5-<10%, 10-<20% and >20% living below federal poverty level.

• NYC recently examined population structure: ~50% were in >20% group. – Decided to expand poorest group to three groups 20-<30%,

30-<40% and >40% – Does analyses by six groups, often condenses to 4 groups:

<10%, 10-<20%, 20-<30%, and >30%

• Bottom line: Need to know your population structure to choose groupings that will distribute your population meaningfully.

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%

Basic steps - numerators

• Geocode outcome (case) data• Link outcome data with 2010 census tract• Access ACS data (e.g., 2007-2011) to get each

census tract’s poverty level • Link each case to their census tract poverty level• Sum cases within poverty level

Issues• Geocoding: how to handle institutionalized populations, esp.

jails/prisons; what to do with rural areas and postal boxes where >1 census tract per ZIP; which census to geocode to (2000, 2010)?

• Which source of poverty data to use: census 2000? ACS 2005-2009? 2006-2010? 2007-2011?

Basic steps - denominators

• Determine how stratified you want your denominators to be: examine incidence/prevalence of outcome by age, by sex and by race-ethnicity

• Access 2010 Census to get the census tract-level population data with appropriate stratifications by age, sex and/or race-ethnicity

• Link census tract poverty level to each census tract• Sum populations of all census tracts in a given

poverty-level grouping– including subsets of denominators by age group, sex, race-

ethnicity,

Basic steps - denominators

Issue• What denominators to use: Census 2000? Census

2010? Other?– Number of census tracts may change over time– No reliable inter-censal population estimates at the

census tract level?

Basic steps – calculate incidence/prevalence

• For each poverty level, divide numerator by denominator and determine rate per unit population (e.g., per 100,000)

• Can do analysis by age-specific groups, by sex, by race-ethnicity by simply sub-setting the numerator and denominator for each specific characteristic

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

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

IRV 2014:DOI:10.1111/irv.12231

Basic steps – age-adjust

• May need to age-adjust if outcome incidence is age-related (e.g., mortality), since different poverty levels may have a different age structure.

Issues• What age groups to use to adjust?

– Variable: mortality – as small as 5 year age groups– Others may need less

• What reference population: local jurisdiction vs US standard million?

Crude mortality rate by % of residents 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,

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

References

1. PHDGP website– http://www.hsph.harvard.edu/thegeocodingproject/

2. CSTE health disparities workgroup website– http://www.cste.org/group/Disparities– Guidance: http://www.cste.org/?GuidanceforStates– Updated Pdf prepared by CT EIP for CSTE

3. NYC Epi Research Report on standard NYC area-based poverty measure

– http://www.nyc.gov/html/doh/downloads/pdf/epi/epiresearch-SES-measure.pdf

4. Growing number of publications

Current status of use of census tract poverty

1. Some jurisdictions using it regularly: e.g., WA, CT EIP, NYC

2. The 10 EIP sites nationally beginning to routinely geocode all data and conduct analyses, some using census tract SES

– Developed standard geocoding guidance for all 10 EIP sites– Overcome obstacles to sending geocoded data to CDC– Conducting all site analyses

3. CSTE Health Disparities Workgroup provides an ongoing forum to encourage use of area-based poverty analyses and to discuss current analyses

4. CDC leading EIP workgroup and participating in CSTE Health Disparities Workgroup

Conclusions 1. Analysis of data using census tract poverty (CTP) is

a meaningful way to describe disparities/inequities 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– Targeting groups for intervention based on SES more

attractive than based solely on race/ethnicity– Can be used regardless of whether have race/ethnicity data

2. With use of references listed, any master’s level epidemiologist should be able to do them

3. CTP analyses gaining traction nationally

Thanks!

Join the CSTE Health Disparities Workgroup to share analyses using census tract poverty

measures / get input on issues arising when use them … and more