1. Thinking Critically About Ethnicity Categories: Lessons from HIV Surveillance With Gay, Bisexual and other Men who have Sex with Men in New Zealand Nathan Lachowsky PhD candidate (epidemiology) University of Guelph, Canada nlachows@uoguelph.ca

2. Outline The HIV Pandemic – HIV in Aotearoa New Zealand Research Questions: why and how of ethnicity Method – Behavioural surveillance – “MSM” terminology – Ethnicity classification Findings Discussion & Next Steps

3. The HIV Pandemic HIV inequities persist between ethnic groups – The HIV pandemic has differentially affected many ethnoracialized groups Inequities in health are the result of social processes, which need to be addressed Race/ethnicity data is commonly collected – But methods rarely discussed in biomedical research (Lee 2009) – Differences between race & ethnicity

4. New Zealand’s HIV Epidemic AIDS Epidemiology Group Newsletter, March 2013 (http://dnmeds.otago.ac.nz/departments/psm/research/aids/pdf/71_AIDS-NZ_March2013.pdf)http://dnmeds.otago.ac.nz/departments/psm/research/aids/pdf/71_AIDS-NZ_March2013.pdf

5. Treaty of Waitangi

6. Research Questions How is ethnicity classified within research? – By public health/epidemiology versus other disciplines What are the implications of these methodological decisions? – Evaluated by whom, for whom?

7. Method The study included 8,041 MSM from the 2006, 2008, and 2011 rounds of New Zealand’s sociobehavioural HIV surveillance: – Gay Auckland Periodic Sex Survey (GAPSS) and – Gay men’s Online Sex Survey (GOSS) Anonymous and self-completed questionnaires Reproducible convenience sample Second-generation surveillance: “gold standard” – World Health Organization recommended

8. “MSM”: Behaviour vs. Identity? “Demeans us all?” (Prestage 2011) “MSM” GayBisexual Takatāpui (Māori) Fa’afafine (Pacific) StraightQueer

9. Method Offline recruitment – 1 week in February – Community fair day, bars, and sex-on-site venues Online recruitment – 2-4 weeks following – Internet dating sites (44.3-64.0% of sample) Statistically significant associations (p<0.05) explored between ethnicity and select HIV- related behavioural outcomes

10. Aotearoa New Zealand Census Statistics New Zealand, 2013 Census “Individual Form”

11. Ethnicity Classification Processes Prioritisation Assign everyone to a single ethnic group i.e. Māori, Pacific, Asian, European, other More commonly used method in public health Single-Combined Assign everyone to a single ethnic group Use individual major groups and multiple/mixed e.g. Māori-European and Māori-Pacific Total Response Assign a person to every group they identified Create a non / “or else” group in contrast More commonly used in social science

12. Sample Size Results How do we capture and categorize ethnicity in our research? Pooled sample size of MSM from GAPSS & GOSS (2006-2011) by three different methods: – Prioritisation – Single-combined – Total response Prioritisation Single- Combined Total Response European6155 Māori801420801 Pacific248139304 Asian643601694 Other207180207 M ā ori-European313 Pacific-European78

13. Demographic Results PrioritisationSingle- Combined Total Response AgeNo differences * All * All Recruitment venue * All * All * Pacific & Asian Identity * All * All * All Education * All * All * Asian

14. HIV-related Outcome Results HPrioritisationSingle-CombinedTotal Response HIV Testing (ever) * All * Pacific & Asian * All HIV Testing (last 12 months) No differences * All No differences Sexual health testing (last 12 months) No differences * Asian * All Condom use with casual partner (last 6 months) * All * Māori No differences

15. Discussion Different classification approaches: – Affect group size and demographics – Reveal and mask associations No global solution to gathering or analyzing ethnicity data – Need for sensitivity towards ethnicity at a local level (Williams & Husk 2012) – Need to consider social processes, not just identity categories in research and surveillance

16. Discussion Variety of motivations underpin preferences for certain classification approaches: – Impact on sample size (and thus statistical power) – Statistical tests that are available to use Which “macro” categories/groupings? – Who is included within categories? (Cole 2009) – e.g. Samoan, Tongan, Niuean Pacific people Which comparison group (if any)? – e.g. European-only as the referent group

17. Conclusions Identification of inequities can activate public health responses, but also (re)produce stigma – First Nation’s OCAP principles Further consideration of theoretical frameworks with regards to power and social hierarchy – (e.g. intersectionality; Anthias 2012) Recommendations: – Greater intention and explicit explanations regarding methods by researchers – Meaningful involvement and leadership of community leaders, organizations and informants

18. Acknowledgements Participants, NZAF and AIDS Epidemiology Group Terryann Clarke & Rhys Jones, Auckland Univeristy Lanuola Asiago, Massey University Ivan Yeo Matt Soeberg