fundamental-causes-ances

Transcript

1. Fundamental Causes Race, Class, and 
 COVID-19 Health 
 Disparities in 
 St. Louis, MO Christopher 
 Prener, Ph.D. Assistant Professor 
 of Sociology Saint Louis University
2. AGENDA 1. Preface 2. Situating Public Science 3. Tracking a Pandemic 4. Fundamental Causes & COVID-19 5. Where We Go From Here 1. PREFACE
3. ▸ Medical and urban sociologist 
 with an interest in spatial and computational methods ▸ Af fi liations: • SLU’s Advanced HEAlth Data Research Institute • SLU’s Institute for Healing Justice and Equity • Northeastern University’s Institute for Health Equity and Social Justice Research 1. PREFACE “HI, I’M CHRIS”
4. 1. PREFACE INTELLECTUAL TRAJECTORY
5. 2 SITUATING PUBLIC 
 SCIENCE
6. 2. SITUATING PUBLIC SCIENCE PUBLIC SOCIOLOGY MICHAEL BURAWOY Indiana University
7. 2. SITUATING PUBLIC SCIENCE PUBLIC SOCIOLOGY MICHAEL BURAWOY “presenting fi ndings in an 
 accessible manner” Indiana University
8. 2. SITUATING PUBLIC SCIENCE PUBLIC SCIENCE “presenting fi ndings in an 
 accessible manner”
9. 2. SITUATING PUBLIC SCIENCE PUBLIC SCIENCE “presenting fi ndings in an 
 accessible manner” engaging in descriptive 
 research that moves public 
 discourse forward
10. 3 TRACKING A PANDEMIC
11. Everybody's got a dashboard these days… Unsplash
12. DIFFERENT TOOLS Dashboards are being powered by a number of different commercial tools, including ESRI, Microsoft, Tableau, and in-house solutions.
13. 3. TRACKING A PANDEMIC DATA ARE OFTEN POORLY VISUALIZED Basic rules, like using per capita rates, are often ignored.
14. DASHBOARDS ≠ OPEN DATA Few dashboards provide easy access to underlying data, though it is there if you know where to look. There is also little to no standardization.
15. Similar fabric panels with jagged edges 
 that we need to stitch together. Data Seams. Unsplash
16. PULLING THE FABRIC TOGETHER New York Times COVID-19 Database (via GitHub) State of Missouri and Illinois (via scrapers + manual download) Missouri COVID Tracking Data Sets St. Louis Pandemic Task Force (manual entry) CMS Nursing Home Data & 
 HHS Hospitalization Data (via API) Census Bureau (via API) County Public Health Zip Code Data (via scrapers+API calls)
17. Sewing these seams requires an array 
 of computational tools for scraping and 
 standardizing various jurisdictions’ data, 
 plus a communications strategy. Unsplash
18. 3. TRACKING A PANDEMIC PULLING THE FABRIC TOGETHER
19. THE TWEET THAT LAUNCHED… MANY MORE TWEETS
22. 4 FUNDAMENTAL CAUSES & COVID-19
23. 4. FUNDAMENTAL CAUSES & COVID-19 FUNDAMENTAL CAUSE THEORY 
 segregation (Williams & Collins 2001 and Sewell 2016) 
 structural racism (Gee & Ford 2011) 
 systematic racism (Phelan & Link 2015) 
 racial capitalism (Pirtle 2020)
24. A LABORATORY FOR RACISM INDIGENOUS EXPULSION THE MISSOURI COMPROMISE DRED SCOTT BLEEDING KANSAS Clockwise from Upper-left: Author’s Work; Smithsonian Institution; Wikipedia; Wikipedia
25. A LABORATORY FOR RACISM DEED COVENANTS EXCLUSIONARY ZONING “SLUM” CLEARANCE REDLINING Clockwise from Upper-left: Erenow; Google; Wikipedia; Missouri Bar Association
26. REDLINING A - “Best” B - “Still Desirable” C - “De fi nitely Declining” D - “Hazardous”
27. REDLINING A - “Best” B - “Still Desirable” C - “De fi nitely Declining” D - “Hazardous” “In St. Louis, the white middle class suburb of Ladue was colored green because…it had ’not a single foreigner or negro.’” (Rothstein 2017) Rothstein, Richard. 2017. The Color of Law. New York, NY: W.W. Norton & Co.
28. REDLINING A - “Best” B - “Still Desirable” D - “Hazardous” C - “De fi nitely Declining” “‘in fi ltration of a lower grade population’” (Hillier 2005:217) Hillier, Amy. 2005. “Residential Security Maps and Neighborhood Appraisals.” Social Science History 29(2):207-233.
29. REDLINING A - “Best” B - “Still Desirable” C - “De fi nitely Declining” D - “Hazardous” Hillier, Amy. 2005. “Residential Security Maps and Neighborhood Appraisals.” Social Science History 29(2):207-233. “‘detrimental in fl uences in a pronounced degree,’ and ‘undesirable population or an in fi ltration of it’” (Hillier 2005:217)
30. REDLINING A - “Best” B - “Still Desirable” C - “De fi nitely Declining” D - “Hazardous” Rothstein, Richard. 2017. The Color of Law. New York, NY: W.W. Norton & Co. “Lincoln Terrace was colored red because ‘it had little or no value today…due to the colored element now controlling the district’” (Rothstein 2017)
33. Relationship 
 with Redlining: 
 r = 0.750 (p < 0.001)
34. 4. FUNDAMENTAL CAUSES & COVID-19 MEASURING SEGREGATION The Index of Concentration at the Extremes (ICE) provides a sub-county measure of segregation that produces scores per feature from -1 (total segregation of the marginalized group) to 1 (total segregation of the privileged group). Formula: 
 ICEi = (Ai - Pi )/Ti Where: 
 Ai = Privileged [white] 
 Pi = Marginalized [Black] 
 Ti = Total Population Massey, Douglas. 2001. “The prodigal paradigm returns: ecology comes back to sociology.” Pp. 41-48 in Does It Take a Village? Community Effects on Children, Adolescents, and Families, edited by A. Booth and A. Crouter. Mahwah, NJ: Lawrence Erlbaum Associates. Krieger, Nancy, et al. 2017. "Measures of local segregation for monitoring health inequities by local health departments." American Journal of Public Health 107(6): 903-906.
36. Relationship 
 with Redlining: 
 r = -0.525 (p < 0.001)
37. FOUR ‘PECULIAR’ INSTITUTIONS Instituion Form of Labor Core of Economy Dominant Social Type Slavey (1619-1865) Unfree fi xed labor Plantation Slave Jim Crow (South,1865-1965) Free fi xed labor Agrarian and extractive Sharecropper Ghetto (North, 1915-1968) Free mobile 
 labor Industrial manufacturing Menial worker Hyperghetto and Prison (1968-) Fixed surplus labor Postindustrial services Welfare recipient and criminal Loïc Wacquant (2002) argues that there are four successive institutions - particular to the U.S. - that link slavery with contemporary racial inequality. COVID-19 
 (2020-present) Wacquant, Loïc. 2002. “From Slavery to Mass Incarceration.” New Left Review 13:41-60.
38. These layers form the structure 
 on which health disparities are built. Unsplash
41. March-June: 
 r = -0.736 (p < 0.001) July-September: 
 r = 0.047 (p = 0.718) October-December: 
 r = 0.642 (p < 0.001)
42. March-June: 
 r = 0.644 (p < 0.001) July-September: 
 r = -0.063 (p = 0.628) October-December: 
 r = -0.633 (p < 0.001)
48. 5 WHERE WE GO FROM HERE
50. 5. WHERE WE GO FROM HERE PARTING THOUGHTS ▸ Research, data, and communication have not been the most pressing concerns for local public health agencies in MO. ▸ Public science and public sociology can help cut through the most pressing issues we face right now - COVID, racism, poverty. ▸ COVID-19 patterns that appear durable as cross-sections have important period effects that we need to interrogate. ▸ We need to focus on how power relations in fl uence COVID risk. ▸ Iterating on analyses is not something we always get to do, but it is tremendously gratifying.
51. Slides available via SpeakerDeck 
 Follow on the web: 
 speakerdeck.com/chrisprener/ fundamental-causes-ances Raw data, code available via GitHub 
 github.com/slu-openGIS/ MO_HEALTH_Covid_Tracking chris.prener@slu.edu chris-prener.github.io 
 LEARN MORE THANKS FOR COMING! @chrisprener Visualization code available via GitHub 
 github.com/slu-openGIS/ covid_daily_viz slu-opengis.github.io/ covid_daily_viz/ chrisprener.substack.com