1. Will Public Health’s Response to Bioterrorism be Fair? AcademyHealth 2004 Annual Research Meeting David P. Eisenman, M.D., M.S.H.S. Assistant Professor, UCLA School of Medicine Associate Natural Scientist, RAND

2. Trust & Terrorism Preparedness/Response Trust may influence success of onerous and controversial prevention and control measures Isolation/Quarantine Travel restrictions Targeted or widespread distribution of medications or vaccines Dark Winter exercise demonstrated importance of perceived fairness in obtaining public cooperation

3. Do Minorities Have Reason to Expect Biased Public Health Response? Historically, bias influences services during public health emergencies –1900 plague outbreak –1918 flu pandemic –2001 anthrax attacks postal workers reported bias and unequal attention due to race/class

4. Research Questions What is the association of race/ethnicity with anticipating an unfair public health response to bioterrorism? Within racial/ethnic groups, what are the correlates of anticipating an unfair public health response to bioterrorism?

5. Data Source - 1 Los Angeles County Health Survey –periodic, RDD telephone survey of the non- institutionalized, adult population in Los Angeles County –Two stage sampling (household, adult) –Oct, 2002 to Feb, 2003 –6 languages (English, Spanish, Mandarin, Cantonese, Korean, and Vietnamese)

6. Data Source - 2 Los Angeles County Health Survey –15,262 households contacted and 8,167 interviews completed (cooperation rate = 58%) –All respondents answered 120 core questions. A random sub-sample of 1,041 participants answered 12 additional items regarding terrorism. –Weighted to adult population of LA County based on 2000 U.S. Census

7. Study variables - 1 Main independent variable –self-reported race/ethnicity non-Latino White (White), non-Latino African- American (AA), Latino, and Asian/Pacific Islander (API). Dependent variable –anticipated fairness “If there is a bioterrorist attack in LA, do you think the County’s public health system will respond fairly to your health needs regardless of race, ethnicity, income or other personal characteristics?” Yes, no, don’t know, and refused “Yes” = anticipated fairness

8. Study variables - 2 Covariates –Age –Gender –Education –Income (2002 FPL) –Interview language (English, Spanish, any Asian) –Health insurance (yes/no) –Perceived neighborhood safety (“how safe from crime to you consider your neighborhood to be?”)

9. Analysis 1.Proportion reporting fairness by race/ethnicity and covariates 2.Multivariate logistic regression models:  (Fairness) =  (race/ethnicity) +  (age) +  (gender) +  (education) +  (income) +  (language) +  (insurance) +  (neighborhood safety) Performed in overall sample and stratified within each race/ethnicity subgroup

10. Total (N=1018) AA (N=104) API (N=98) Latino (N=404) White (N=412) Age (years)*18-292522253414 30-443239293826 45-592419311730≥602021161231 Language* English73100674699Spanish2200541 Asian503300 Sample Characteristics (weighted %) * p­<.001 for chi-square test of group differences

11. Sample Characteristics (weighted %) Total (N=1018) AA (N=104) API (N=98) Latino (N=404) White (N=412) ≤High School Education* 4743287128 Some College 2429231632 College Degree 2928481240 Income/poverty* <100% 232113398 100-199% 2523243117 200-299% 1918291323 ≥300% 3439331752 Health Insurance* Yes7682815991 No2419 419 * p­<.001 for chi-square test of group differences

12. Sample Characteristics (weighted %) * p­<.001 for chi-square test of group differences Perceived neighborhood safety* Safe8081897089 Unsafe2019113111 Total (N=1018) AA (N=104) API (N=98) Latino (N=404) White (N=412)

13. Proportions Anticipating Fair Response (weighted %) Unadjusted Proportion (%)p value Total 73 Race/ethnicity White 77<0.005AA 63 Latino 73 Asian/PI 68 - Variable

14. Proportions Anticipating Fair Response (weighted %) Unadjusted Proportion (%) p value Interview Language English<0.005 Spanish Neighborhood Safety Safe0.03 74 75Asian43 75 Unsafe 65 Variable

15. 0.5** 0.7 1.1 0.5*0.70.9Race/ethnicity (White) AA Latino Asian Age (>60) 18-29 30-44 45-59 In Multivariate Analysis, Four Variables were Associated with Anticipating Fair Response (AOR ) Neighborhood Safety (Safe) 0.5** Unsafe Interview Language (English) Spanish Asian 1.7 0.3* Multivariate logistic regression model also included gender, education, annual family income, health insurance; *p<0.05 **p<0.005

16. Differences Emerged within Race/ethnicities African Americans –Age 18-29 years (AOR = 0.1; p<0.05) Asian/Pacific Islanders –Asian language (AOR = 0.1; p<0.05) –Unsafe neighborhood (AOR = 0.01; p<0.005) Latinos –Spanish language (AOR = 2.2; p<0.05)

17. Summary African Americans, Asians speaking Asian language, younger age groups, and persons reporting unsafe neighborhood less likely to anticipate fair response Responses differed within race/ethnicities by language of interview, perceived unsafe neighborhood and age

18. Limitations Sampling frame excludes homes without telephones 58% cooperation rate –compares favorably with national & California response rates in federal surveys Single item, hypothetical outcome –Face validity –May not capture full construct –Different meanings across racial/ethnic groups

19. Implications If certain groups expect a biased response, public health will face greater challenge in risk communication and promoting interventions and policies Social issues must be considered in planning of public health response Future research needed to understand issues of trust within Asian/Pacific Islanders in LA

20. Team and Support RAND Claude Setodji, Scot Hickey, Bradley Stein Los Angeles County Department of Health Services Cheryl Wold, Anna Long, Ben Lee, Gladys Bonilla Funding RAND Independent Research and Development Fund First 5 LA California Department of Health Services DHHS Public Health Response and Bioterrorism Preparedness grant.