Evidence map›Paper›PMID 32958004›Full record

ArticleBMC public health2020

Ethnicity classification systems for public health surveys: experiences from HIV behavioural surveillance among men who have sex with men.

Nathan J Lachowsky, Peter J W Saxton, Nigel P Dickson, Anthony J Hughes, Rhys G Jones, Terryann C Clark, Elsie Ho, Alastair J S Summerlee, Cate E Dewey

Abstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Participatory Approaches to Addressing Missing COVID-19 Race and Ethnicity Data.International journal of environmental research and public health · 2021
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Nathan J LachowskySchool of Public Health and Social Policy, Faculty of Human and Social Development, University of Victoria, Victoria, BC, V8Q 2Y2, Canada. nlachowsky@uvic.ca.ORCID http://orcid.org/0000-0002-6336-8780
Peter J W SaxtonSchool of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, 1023, New Zealand.
Nigel P DicksonDepartment of Preventive and Social Medicine, Dunedin School of Medicine, University of Otago, Dunedin, 9054, New Zealand.
Anthony J HughesNew Zealand AIDS Foundation, Auckland, 1011, New Zealand.
Rhys G JonesTe Kupenga Hauora Māori (TKHM), Faculty of Medical and Health Sciences, University of Auckland, Auckland, 1072, New Zealand.
Terryann C ClarkSchool of Nursing, Faculty of Medical and Health Sciences, University of Auckland, Auckland, 1142, New Zealand.
Elsie HoSocial and Community Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, 1072, New Zealand.
Alastair J S SummerleeDepartment of Biomedical Sciences, University of Guelph, Guelph, ON, N1G 2W1, Canada.
Cate E DeweyDepartment of Population Medicine, University of Guelph, Guelph, ON, N1G 2W1, Canada.

Funding

CIHR Vanier CGSMichael Smith Foundation for Health Research 16863
6 · The paper itself

Abstract

backgroundRace and ethnicity classification systems have considerable implications for public health, including the potential to reveal or mask inequities. Given increasing "super-diversity" and multiple racial/ethnic identities in many global settings, especially among younger generations, different ethnicity classification systems can underrepresent population heterogeneity and can misallocate and render invisible Indigenous people and ethnic minorities. We investigated three ethnicity classification methods and their relationship to sample size, socio-demographics and sexual health indicators.

methodsWe examined data from New Zealand's HIV behavioural surveillance programme for men who have sex with men (MSM) in 2006, 2008, 2011, and 2014. Participation was voluntary, anonymous and self-completed; recruitment was via community venues and online. Ethnicity allowed for multiple responses; we investigated three methods of dealing with these: Prioritisation, Single/Combination, and Total Response. Major ethnic groups included Asian, European, indigenous Māori, and Pacific. For each classification method, statistically significant associations with ethnicity for demographic and eight sexual health indicators were assessed using multivariable logistic regression.

resultsOverall, 10,525 MSM provided ethnicity data. Classification methods produced different sample sizes, and there were ethnic disparities for every sexual health indicator. In multivariable analysis, when compared with European MSM, ethnic differences were inconsistent across classification systems for two of the eight sexual health outcomes: Māori MSM were less likely to report regular partner condomless anal intercourse using Prioritisation or Total Response but not Single/Combination, and Pacific MSM were more likely to report an STI diagnosis when using Total Response but not Prioritisation or Single/Combination.

conclusionsDifferent classification approaches alter sample sizes and identification of health inequities. Future research should strive for equal explanatory power of Indigenous and ethnic minority groups and examine additional measures such as socially-assigned ethnicity and experiences of discrimination and racism. These findings have broad implications for surveillance and research that is used to inform public health responses.

Indexed as

HIV InfectionsSexual and Gender MinoritiesEthnicityHomosexuality, MaleHumansMaleMinority GroupsPublic HealthSexual BehaviorSexual PartnersEthnicity classificationHealth equityMen who have sex with men (MSM)New ZealandPublic healthRaceRacismSexual healthSurveillanceSurveys

Identifiers

PMID32958004
PMCPMC7507687

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.