Measuring SES/SEP Notes

1) Why SES?

a ‘down-n-dirty’ review

The strong relationship between SES and health has been documented for centuries, dating back to ancient Greece, Egypt, and China

A better understanding of the relationship between SES and disease may reveal important new points for intervention and epi screening

The socioeconomic structure in the US, and elsewhere, is rapidly changing (eg, outsourcing, career?, women, elderly.

Racial/ethnic disparities in health may be construed as signs of genetic differences or behavioral choices rather than powerful clues about how forms of racial discrimination and structural constraints, past and present, harm health

No consensus on a nominal definition of SES; it’s more than income and/or educational attainment , it’s a latent variable

A widely accepted SES instrument does not exist

Appears to be among the more difficult and controversial subjects in all of social research

Prominent scholars have debated the theory, operationalization, and usefulness of SES constructs for about 125 years

Basically, we got nothing…

Gap between “SES Measurement” & “SES and Health

Studies”

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SES Measurement

SES & Health Research

Chris Hamlin (2007)

“Social class as species”

2) Historical Review

Even more ‘down-n-dirty’ and American-focused

Americans are aware of social stratification and have fairly firm views about their social standing and that of others… think of Britney!

But social scientists have not made much progress in measuring SES

Progress retarded due to lack of conceptual clarity about social stratification… and it’s all about social hyper-dimensional stratification

Early empirical sociological research mostly were studies of single small communities

Status was assigned to households through an unarticulated theory of stratification mainly based on individuals’ reputation

Underlying this approach is the assumption that everyone in a small community knows its status hierarchy and can place most individuals in it

In 1947, NORC conducted a national sample survey asking respondents to rate on a 5 point scale "the social standing" of 90 occupations

The average social standing given to each occupation can be regarded as the societal consensus (circa 1947) concerning the status of each occupation

These social standing averages (also known as prestige scores) were the first measures of the national consensus on occupational status.

Problem was NORC prestige scores were known for only 90 out of the thousands of occupations.

OD Duncan wanted status scores for all Census occupations.

Regressed known NORC occupational scores on the median occupational educational and incomes

Predicted values were called Duncan’s SEI...

a continuous variable that could be calculated for almost all occupational titles recognized in the Census

Nam and Powers didn’t like “subjective” ratings in SEI and thought an “objective” approach was better

Education as dues, Occupation as reward

Occupational status score (OSS) was a simple function of educational attainment and income derived from a given occupation.

In 1974, Rossi produced a Household Prestige Score

Factorial survey: Husband’s occupation and education, along with wife’s occupation were randomly varied in vignettes

Regressed ratings on characteristics of vignette examples to infer the relative influence of the social characteristics of households

Predictive equation gives HHP scores to households based on the occupations, educational levels and ethnicities of spouses

Worked pretty well… but ignored!

But despite, SEI, OCC, HHP, we still have two main problems:

(1) Lack of consensus on a nominal definition

Empirical researchers must either adapt vague theories and develop idiosyncratic indicators or use whatever vaguely related data elements exist to construct ad hoc measures of SES

(2) Absence of sound measurement theory

Psychometrics has not been exploited in the development, testing, and validation of SES measures

Routinely done in latent constructs, such as depression

Early efforts of Lundberg (1940) and Gordon (1952), and the empirical efforts of Rossi (1951) have been overlooked

Oakes & Rossi’s effort

Oakes, JM & Rossi, PH. 2003. The measurement of SES in health research: current practice and steps toward a new approach. Social Science & Medicine, 56(4), 769-784.

• Define SES as differential access (realized and potential) to desired resources

• Use existing theory (Jim Coleman’s) which aims to understand and explain the functioning and organization of the social system

Two kinds of elements and two ways in which they are related: The elements are (1) actors and (2) resources, related through (3) interests and (4) control.

Components of the theory have been increasingly subjected to theoretical and empirical scrutiny, with a few pleasing results

Resources may take the form of

(1) Material and monetary goods

(2) Skills and capabilities

(3) The strengths of social relationships & resources of others

SES = f (Material Capital, Human Capital, Social Capital)

CAPSES Scale

SES

Subjects’ Self-rating of

their SES

Population Rating of

Subjects’ SES I

PopulationRating of

Subjects’ SES II

Scale Items

MaterialCapital

HumanCapital

SocialCapital

Indicator Variables

MC i

MCj

MC k

HC i

HC j

HC k

SC i

SC j

SC k

CAPSES Conceptual ModelCAPSES Conceptual Model

CAPSES Ratings of Subject SES

Visual Analogue Scale (VAS)

The rating scale ranges from lowest possible socioeconomic status on the left to highest possible socioeconomic status on the right. Place an “X” on each bar to indicate your rating of the subject’s status.

Subject Self-Rated SES

Self-reported scale (LADDER)

Self-reported social class (CLASS)

• Our own (pilot) survey work showed no contribution of social capital to “SES” beyond income and education.

• Psychometrics were “inconclusive”

But…

Forget SES, use Poverty!

Sit tight… more coming…

Forget SES, use Income!

Not very good…

Income changes year-to-yearRetired

20-somethingsTrust funds

Forget SES, use Wealth!

Good luck!

Forget SES, use Education!

Good, but…

Continuous or discrete,Cohort effects,“foreigners”,

Trade-workers

SES = Neighborhood?My new approach

“Facts”

• People are sorted in demarcated geographic areas, called neighborhoods; some good, some not so good

• Thus, persons from same area are more alike than persons from other areas… a clustering phenomena

• Early work showed SES was poor proxy for individual SES, but I think this is backwards!

Oakes, JM. 2004. "The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology." Soc Sci Med 58:1929-52. (with discussion)

Oakes, JM. 2006. “Invited Commentary: Advancing neighbourhood-effects research--selection, inferential support, and structural confounding." Int J Epidemiol 35:643-7.

Oakes, JM and PJ Johnson. 2006. "Propensity score matching methods for social epidemiology." Pp. 370-392 in Methods in Social Epidemiology, edited by JM Oakes and JS Kaufman. San Francisco: Jossey-Bass

Major talks – 2007 SER; 2006 JSM; 2005 ALR

3) Analysis with SES measures?

1 2b E by a eX

What is impact of measurement error in confounder?

a) Mismeasure SES and you’ve got trouble!

Compare: Boulder v. Mobile

Hmmm… an insurmountable comparison problem!

b) The trouble with Neighborhood SES

‘We can only evaluate sharply distinct treatments that could happen to anyone.’

Paul Rosenbaum (2002)

‘If the differences between groups is large, the average value applied to each group with adjustment may represent “no man’s land”, a place where no actual observations exist. Given this scenario, the interpretation of the estimate becomes speculative rather than soundly based. Heroic modeling assumptions are required.’

William Cochran (1957)

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Comparative inference is off-support of data and thus requires “heroic” modeling assumptions.

Source: Oakes, JM and PJ Johnson. 2006. "Propensity score matching methods for social epidemiology.“ Pp. 370-392 in Methods in Social Epidemiology, edited by Oakes and Kaufman. San Francisco: Jossey-Bass

See also – Johnson PJ. 2004. "The Effect of Neighborhood Poverty on American Indian Infant Death." PhD Dissertation, UMN Hearst MO. 2007. "The Effect of Racial Residential Segregation on Infant Death in the US.“ PhD Dissertation, UMN

What is the effect of neighborhood poverty on American Indian infant death in Minnesota?

<5% 5-19% 20-39% 40-100%

All-cause infant death 7.5 16.2 17.4 23.3Endogenous-cause death 3.8 7.8 10.1 12.0

Exogenous-cause death 3.8 8.4 7.3 13.3

Neighborhood Poverty

Compare AI IMR in poor vs. not-so-poor neighborhood

More technically, AI with a high probability of living in poverty rarely reside in low poverty neighborhoods, but some must if a meaningful

(ie, empirically based) counterfactual comparison is to be made.

The trouble is, there are few AI living in low poverty areas who are like (ie, exchangeable) to those AI living in poverty areas.

But neighborhood effects are “independent”, which means we must rule out (ie, adjust out) individual-level confounders.

0-.025-.075-.125-.175-.225-.275-.325-.375-.425-.475-.525-.575-.625-.675-.725-.775-.825-.875-.925-.975-

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Number of Infants

40-100% Poverty < 5% Poverty

Propensity of AI living in high-poverty Mpls neighborhoods

Conventional regression adjustment for individual characteristics does not reveal that there are few comparison subjects;

the model equates subjects thru linear interpolation/extrapolation.

Heroic modeling assumptions are required.

1Y

2Y

1 2.0b

Randomized Study (No Confounding!)

Unexposed

Exposed

PA, or any other measure!

Absent Randomization

1ig igy a T eb 2xb

Covariates serve to adjust groups for confounding…

a substitution problem called selection bias

1 BI Sˆ Ab

Unless selection-equation, X, is perfect, bias

Exposed / High PA

Unexposed / Low PA

SES SES

Regression adjustment?

BM

I

exposedy

*1b

SES is a confounder(mean SES is diff across exposures & related to BMI)

SES SES

Unexposed

Exposed

unexposedy

SES

*1b

The model yields adjusted Tx effect by using slope of SES within Tx arms and then

calculating effects at (grand) mean SES

1 adjY

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SES SES

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The adjusted Tx effect is based on pure extrapolation… Exposure groups have non-overlapping distributions of SES; worse, adjustment is fictitious if members of group 1 could not conceivable be members of group 2; Oakes calls this “structural confounding.”

1 adjb

See: Cochran WG. 1957. "Analysis of Covariance: Its Nature and Uses." Biometrics:261-281. -- . 1969. "The Use of Covariance in Observational Studies." JRSS C 18:270-75.

How much adjustment is too much?

expy

Note well:

Regression adjustment makes up or imputes data!

It says, if a poor man had same bank account as rich man, his health would be thus and such.

This may not be bad. The health of the poor man would be as

imputed/predicted…

GIVEN THE MODEL!!!!

So the question is:

IS YOUR MODEL CORRECT?

Well, is it, punk?

Vandenbroucke JP .1987. "Should we abandon statistical modeling altogether?” American Journal of Epidemiology 126:10-3.

Petitti DB & DA Freedman. 2005. “Invited commentary: How far can epidemiologists get with statistical adjustment?” American Journal of Epidemiology 162:1–4.

Hurvich CM & Chih-Ling Tsai. 1990. “The impact of model selection on inference in linear regression” The American Statistician 44:214-217.

Leamer EE. 1978. Specification Searches: Ad Hoc Inference with Nonexperimental Data Wiley

NB: WHI (HRT) and perhaps new diabetes trial results

4) Other issues

• Life-course approaches to SES

• Intergenerational mobility

• Differential returns to SES by race/gender

• Genes – susceptibility and SES

5) Recommendations

• We don’t know what SES is and there is no agreed upon measure of it; be careful with composites

• You have never “controlled for SES”

• You must appreciate what regression adjustment implies

• If stuck, use educational attainment (level completed)

• Be wary of income as an SES proxy by itself

• Caution with kids (less than 30 years), SAHMs, military?

“ … how can you possibly award prizes when everyone missed the target?” said Alice.

“Well” said the Queen, “Some missed by more than others, and we have a fine normal distribution of misses, so we can forget about the target.”

(Kennedy 1988, p.292)