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

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

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

New Zealand’s HIV Epidemic AIDS Epidemiology Group Newsletter, March 2013 (

Treaty of Waitangi

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?

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

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

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 ( % of sample) Statistically significant associations (p<0.05) explored between ethnicity and select HIV- related behavioural outcomes

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

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

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

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

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

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

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

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

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