Preferred Label : PhenX - neighborhood concentrated disadvantage protocol 211301:-:Pt: Patient:-:PhenX;
LOINC status : TRIAL;
LOINC long common name : PhenX - neighborhood concentrated disadvantage protocol 211301;
LOINC short name : Neighborhood disadvantage proto;
LOINC description : The protocol is based on extracting data from the U.S. Census Bureau on a set of variables
related to the concept of concentrated disadvantage (Sampson, Raudenbush, & Earls,
1997). All the relevant variables are available from the long form of the 1990 and
2000 decennial Censuses. Once the data are extracted, an index score of concentrated
disadvantage can be calculated at the neighborhood level of interest; this is usually
based on census tract or census block-group data. Assuming that information on current
address (see PhenX Demographics domain, Current Address measure) and any previous
address(es) (see PhenX Environmental Exposures domain, Residential History measure)
has been collected for a study respondent, then via geocoding it is possible to link
the address of a study participant to his or her local neighborhood (a geographic
area), typically by a Census-defined area, such as a census block-group or census
tract, or by Zone Improvement Plan (ZIP) code area. The original paper by Sampson
et al. (1997) was based on the use of variables from the 1990 decennial Census and
applied to a neighborhood definition based on aggregates of Census tracts, called
neighborhood clusters. The Social Environments Working Group recommends that researchers
follow Sampson et al (1997) and conduct a factor analysis (e.g., a principal components
analysis using varimax rotation methods or alpha-scoring factor analysis).The extracted
variables are typically very highly correlated undermining any investigation of unique
effects. Sampson et al (1997, p. 920) find that consistent with urban theory these
six poverty-related variables are highly associated and load on the same factor (note:
their work was based on 1990 Census data for Chicago). Other studies in other settings
confirm that these six variables (poverty, percentage of single-parent families, percentage
of family members on welfare and unemployed, and a measure of racial segregation)
load on a single factor with individual factor loadings typically exceeding 0.8. The
Social Environments Working Group recommends that investigators record and report
the factor loading scores for each variable used in the factor analysis. These would
vary across studies but knowing how they vary (i.e., what other studies found) would
allow for comparison between studies. The calculation of concentrated disadvantage
based on factor analysis generates a measure that is sample dependent (i.e., study
specific). However, it is important to note that this is a well established, robust
and highly cited measure across the social sciences and public health. The social
science literature has long argued that neighborhood disadvantage is not a single-item
construct captured by, for example, a measure of poverty (e.g., percent of individuals
below the poverty level) or measures such as the Index of Concentration at the Extremes
(Massey, 2001).;
Origin ID : 63036-8;
UMLS CUI : C3172005;
Has component
- Has method
- Has system
- Has time aspect
- Panel of LOINC codes
- Part of panel
- Semantic type(s)
