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

My Background in Donor Research United Network for Organ Sharing. Co-Investigator for Geography and Demographics, Deceased Donor Potential Study. 2011-2013. California Transplant Donor Network (Now Donor Network West - Northern California regional organ bank). 2014. PI. Market research study on donor population patterns. OneLegacy (Southern California regional organ bank). 2012-2013. PI. Market research study on donor population patterns. Reibel, M., Olmo, C. Andrada, S. and Koertzen, J. 2016. Deep Demographics: Understanding Local Variation in Donor Registration Rates. Progress in Transplantation Vol. 26(2) 191-198.

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)

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

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

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

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

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).

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)

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.

Model 1: Separate Variables (Constant) -869.847 ** 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) -12.907 %Born in Vietnam -189.174 %Born in Mexico -30.211 %Born in Central America -69.63 %Born in Philipines -123.669 %Born in SW Asia -40.172 Adjusted R2, (F test of model significance) .846

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

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. 2000 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

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)

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)

Effects of Cluster Membership on Predicted Registration Count Relative to Omitted Cluster 2 (Constant) -204.945 & Total Population .302 ** Cluster1 -1786.476 Cluster3 417.236   Cluster4 -3123.535  ** Cluster5 -119.357 Cluster6 1327.898 Cluster7 -5597.660 Adj. R2, (F test of model significance) .844 (**)

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.