1. Deep Demographics: Understanding Variation in Donor Registration Rates
Michael Reibel
Cal Poly Pomona
October 13, 2016

2. My Background in Donor Research
United Network for Organ Sharing. Co-Investigator for Geography and Demographics, Deceased Donor Potential Study
California Transplant Donor Network (Now Donor Network West - Northern California regional organ bank) PI. Market research study on donor population patterns.
OneLegacy (Southern California regional organ bank) PI. Market research study on donor population patterns.
Reibel, M., Olmo, C. Andrada, S. and Koertzen, J Deep Demographics: Understanding Local Variation in Donor Registration Rates. Progress in Transplantation Vol. 26(2)

3. The Problem
Not enough donor organs – especially kidneys – to go around
Alternatives to transplantation include either death (very frequently), or terrible quality of life (e.g. dialysis)

4. The Solutions Long term: Artificial organs Shorter term:
Live donation (kidneys, partial liver)
Maximize deceased donors

5. Pathways of Deceased Donation
Donor Registries (Donate Life California)
Legally binding
Can be done online
Next of Kin consent

6. Obstacles to Deceased Donation
Cultural (especially religious) beliefs about the integrity of the corpse – often involve resurrection, the afterlife
Fear and concern regarding withholding of medical care at end of life (notably among African Americans)
Failure to understand the value of donation (notably among immigrants and the less educated)
Children of elderly parents skeeved out by the notion of dissection for organ recovery

7. Intervention
Micro targeting of individuals and communities based on multiple demographic indicators associated with depressed donation rates
Supported by research on these complex associations of factors with the willingness to donate

8. Data
Data for registrants on the organ donor registry were provided by Donor Network West. There were 3,650,028 registrants reported in the California. These were aggregated to the zip code level as local counts.
The census data used to model social environments for the DNW service area were 2012 five-year sample American Community Survey data at the zip code level of aggregation.
The local counts of registered donors were divided by their respective zip code populations to yield local registration rates (the dependent variable).

9. Methods
OLS models regressing local registration rates on a set of social environment factors
Specified social environment factors in models two ways:
As separate variables selected based on local population patterns
As complex social ecosystems identified through a data-driven classification (k-means cluster analysis)

10. Why Cluster Analysis? Immigrants have lower donor registration rates
The less educated have lower donor registration rates
BUT which have lower rates: well educated immigrants or the less educated native born?
These sorts of non-linear and asymmetrical interactions are the reason neighborhood sociodemographic profiling techniques of the sort used here are the industry standard in market research.

11. Model 1: Separate Variables
(Constant) **
Total Population
0.271
Median Household Income
0.008 *
>= Bachelor’s Degree
42.216
%Non-Hispanic Black
-3.461
%Born in China (incl. HK & Taiwan)
%Born in Vietnam
%Born in Mexico
%Born in Central America
-69.63
%Born in Philipines
%Born in SW Asia
Adjusted R2, (F test of model significance)
.846

12. Discussion, Model 1
Income and education effects are powerful and significant
African American effect is not significant when income and education are controlled for
All foreign born nationalities associated with lower registration but only Mexico and SE Asia significant

13. The Social Environment Clusters Variable Cluster Centers Expressed as Z scores (standard deviations)
Cluster
Zip Code Area Variable 1 2 3 4 5 6 7
Land Area
-0.47
0.09
-0.19
-0.10
5.00
-0.36
-0.45
Dependency Ratio (Old)
-0.23
-0.01
6.85
-0.38
0.18
-0.05
-0.39
Dependency Ratio (Child)
-0.27
0.04
-1.12
0.63
-0.31
0.24
Non-Hispanic Black %
0.69
-0.32
1.47
-0.33
0.26
Non-Hispanic Asian %
3.89
-0.40
-0.42
0.41
-0.44
0.03
3.32
Hispanic %
0.39
-0.35
-0.58
2.07
-0.26
2.03
Median Household Income
0.79
-0.55
-0.18
-0.20
0.93
0.43
Some High School %
0.29
-0.65
0.44
-0.03
-0.62
0.40
Bachelors or Higher %
0.91
-0.49
1.11
Came to U.S or later %
2.82
1.36
-0.52
-0.07
2.66
Foreign Born %
2.89
-0.48
1.37
-0.53
3.13
Born in China %
4.22
-0.28
0.05
0.90
Born in Vietnam %
-0.25
0.16
-0.24
-0.09
7.75
Central America %
0.35
-0.51
2.04
-0.37
-0.29
1.93
Born in Mexico %
0.08
-0.50
2.02
-0.30
1.97
Born in Philippines %
2.59
-0.34
0.60
-0.06
2.61

14. Social Environment Clusters: Summary & Description
# of zips in Cluster
Description
Location Pattern 1 27
Urban, diverse, moderate to high socio-economic status, high concentration of Asian immigrants
San Francisco City (less affluent) and some inner suburbs 2
430
Native white, lower socioeconomic status
Outer non-Bay Area suburbs, small town, rural 3 9
Elderly, native white, moderate socioeconomic status
Rural 4 94
Diverse (Black and Hispanic), many families with children, moderate concentration of immigrants, lower socioeconomic status
Agricultural areas (San Joaquin and Salinas Valleys), African American concentrations in the Bay Area 5 15
Rural, native white, low to moderate socioeconomic status
Rural (remote, low density) 6
250
Moderately diverse, high socioeconomic status
Wealthy San Francisco neighborhoods and outer Bay Area suburbs 7 10
Urban, younger, diverse (Asian and Hispanic), moderate socioeconomic status, very high foreign born
Milpitas and East San Jose (border of East Bay/South Bay)

15. 1
San Francisco City (less affluent) and some inner suburbs 2
Outer non-Bay Area suburbs, small town, rural 3
Rural 4
Agricultural areas (San Joaquin and Salinas Valleys), African American concentrations in the Bay Area 5
Rural (remote, low density) 6
Wealthy San Francisco neighborhoods and outer Bay Area suburbs 7
Milpitas and East San Jose (border of East Bay/South Bay)

16. Effects of Cluster Membership on Predicted Registration Count Relative to Omitted Cluster 2
(Constant)
&
Total Population
.302 **
Cluster1
Cluster3
Cluster4
 **
Cluster5
Cluster6
Cluster7
Adj. R2, (F test of model significance)
.844
(**)

17. Discussion, Neighborhood Profile Model
Except at the highest levels of SES, race, ethnicity and immigration drive variation in registration.
With the same exception, the interactions between these factors appear mostly linear
The outlier is Cluster 6: The wealthiest cluster of zip code areas, but one that is relatively diverse in terms of ethnicity and immigration.
Cluster 6 has by far the largest positive effect on registration of any of the clusters.
We can conclude that beyond some unidentified threshold of SES, identity apparently no longer matters and registration is uniformly high.
This finding shows the power of the cluster analysis approach for discovering joint effects that differ from straight linear interactions.