race research paper

• Study protocol
• Open access
• Published: 28 March 2019

The impact of racism on the future health of adults: protocol for a prospective cohort study

• James Stanley   ORCID: orcid.org/0000-0002-8572-1047 1 ,
• Ricci Harris 2 ,
• Donna Cormack 2 ,
• Andrew Waa 2 &
• Richard Edwards 1  

BMC Public Health volume  19 , Article number:  346 ( 2019 ) Cite this article

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Racial discrimination is recognised as a key social determinant of health and driver of racial/ethnic health inequities. Studies have shown that people exposed to racism have poorer health outcomes (particularly for mental health), alongside both reduced access to health care and poorer patient experiences. Most of these studies have used cross-sectional designs: this prospective cohort study (drawing on critical approaches to health research) should provide substantially stronger causal evidence regarding the impact of racism on subsequent health and health care outcomes.

Participants are adults aged 15+ sampled from 2016/17 New Zealand Health Survey (NZHS) participants, sampled based on exposure to racism (ever exposed or never exposed, using five NZHS questions) and stratified by ethnic group (Māori, Pacific, Asian, European and Other). Target sample size is 1680 participants (half exposed, half unexposed) with follow-up survey timed for 12–24 months after baseline NZHS interview. All exposed participants are invited to participate, with unexposed participants selected using propensity score matching (propensity scores for exposure to racism, based on several major confounders). Respondents receive an initial invitation letter with choice of paper or web-based questionnaire. Those invitees not responding following reminders are contacted for computer-assisted telephone interview (CATI).

A brief questionnaire was developed covering current health status (mental and physical health measures) and recent health-service utilisation (unmet need and experiences with healthcare measures). Analysis will compare outcomes between those exposed and unexposed to racism, using regression models and inverse probability of treatment weights (IPTW) to account for the propensity score sampling process.

This study will add robust evidence on the causal links between experience of racism and subsequent health. The use of the NZHS as a baseline for a prospective study allows for the use of propensity score methods during the sampling phase as a novel approach to recruiting participants from the NZHS. This method allows for management of confounding at the sampling stage, while also reducing the need and cost of following up with all NZHS participants.

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Differential access to the social determinants of health both creates and maintains unjust and avoidable health inequities [ 1 ]. In New Zealand, these inequities are largely patterned by ethnicity, particularly for Māori (the indigenous peoples) and Pacific peoples, and intertwined with ethnic distributions of socioeconomic status [ 2 , 3 ]. In models of health, racism is recognised as a key social determinant that underpins systemic ethnic health and social inequities, as is evident in New Zealand and elsewhere [ 4 , 5 ].

Racism can be understood as an organised system based on the categorisation and ranking of racial/ethnic groups into social hierarchies whereby ethnic groups are assigned differential value and have differential access to power, opportunities and resources, resulting in disadvantage for some groups and advantage for others [ 4 , 6 ]. Historical power relationships underpin systems of racism [ 7 ], which in New Zealand relates specifically to our colonial history and ongoing colonial processes [ 8 ].

Racism can be expressed at structural and individual levels, with several taxonomies describing different levels of racism. Institutionalised racism, for example, has been defined as, “the structures, policies, practices, and norms resulting in differential access to the goods, services, and opportunities of society by race[/ethnicity]” (p. 10) [ 6 ]. In contrast, personally-mediated racism has been defined as, “prejudice and discrimination, where prejudice is differential assumptions about the abilities, motives, and intents of others by ‘race[/ethnicity],’ and discrimination is differential actions towards others by ‘race[/ethnicity]’” (p. 10) [ 6 ].

The multifarious expressions of racism can affect health via several recognised direct and indirect pathways. Indirect pathways include differential access to societal resources and health determinants by race/ethnicity, as evidenced by long-standing ethnic inequities in income, education, employment and living standards in New Zealand, with subsequent impacts on living environments and exposure to risk and protective factors [ 4 , 6 , 9 , 10 ]. At the individual level, experience of racism can affect health directly through physical violence and stress pathways, with negative psychological and physiological impacts leading to subsequent mental and physical health consequences. In addition, racism influences healthcare via institutions and individual health providers, leading to ethnic inequities in access to and quality of care. For example, ethnic disparities in socioeconomic status can indirectly result in differential access to care, while health provider ethnic bias can influence the quality and outcomes of healthcare interactions [ 11 ].

There has been considerable recent growth in research supporting a direct link between experience of racism and health. A recent systematic review and meta-analysis summarised the evidence for direct links between self-reported personally-mediated racism and negative physical and mental health outcomes [ 12 ], with the strongest effect sizes demonstrated for mental health. Related work has also shown that experience of racial discrimination is associated with other adverse health outcomes and preclinical indicators of disease and health risk across various ethnic groups and countries, including in New Zealand [ 9 , 13 , 14 , 15 ]. Experience of racism has also been linked to a range of negative health care-related measures [ 16 ].

However, most studies have used cross-sectional designs: very few of the articles in a recent systematic review [ 12 ] used prospective or longitudinal designs ( n  = 30, 9% of total, including multiple articles from some studies), limiting our ability to draw strong causal conclusions as the direction of causality cannot be determined when racism exposure and health outcomes are measured at the same time. Additionally, cross-sectional studies may give biased estimates of the magnitude of association between experience of racism and health: for example, bias may occur if experience of ill health (outcome) increases reporting or perception of racism (exposure) [ 12 ]. This is suggested by meta-analyses where effect sizes for the association between racism and mental health were larger for cross-sectional compared to longitudinal studies [ 12 ]. Longitudinal research on the effects of racism has been particularly limited with respect to physical health outcomes and measures of healthcare access and quality [ 12 , 16 ]. Finally, existing prospective studies have largely been restricted to quite specific groups (e.g. adolescents, females, particular ethnic groups), with a limited number of studies undertaken at a national population level and few with sufficient data to explore the impact of racism on the health of Indigenous populations [ 12 ].

In New Zealand, reported experience of racism is substantially higher among Māori, Asian and Pacific ethnic groupings compared to European [ 3 , 17 ]. In our own research, we have examined cross-sectional links between reported experience of racism and various measures of adult health in New Zealand using data from the New Zealand Health Survey (NZHS), an annual national survey by the Ministry of Health including ~ 13,000 adults per annum [ 2 , 18 , 19 ]. In these studies [ 17 , 20 , 21 , 22 ] we have shown that both individual experience of racism (e.g. personal attacks or unfair treatment) and markers of structural racism (deprivation, other socioeconomic indicators) are independently associated with poor health (mental health, physical health, cardiovascular disease), health risks (smoking, hazardous alcohol consumption) and healthcare experience and use (screening, unmet need and negative patient experiences). Other New Zealand researchers have reported similar findings including studies among older Māori [ 23 ], adolescents [ 24 ], and for maternal and child health outcomes [ 25 ]. However, evidence from New Zealand prospective studies is still limited. The NZ Attitudes and Values study showed that, among Māori, experience of racism was negatively linked to subsequent wellbeing [ 26 ], and the Growing Up in New Zealand study reported that maternal experience of racism (measured antenatally) was linked to a higher risk of postnatal depression among Māori, Pacific and Asian women [ 27 ].

While empirical evidence of the links between racism and health is growing in New Zealand, it remains limited in several areas. There is consistent evidence from cross-sectional studies for the hypothesis that racism is associated with poorer health and health care. This study seeks to build on existing research to provide more robust causal evidence using a prospective design that helps to rule out reverse causality, in order to inform policy and healthcare interventions.

Theoretical and conceptual approaches

Addressing racism as a health determinant is intrinsically linked to addressing ethnic health inequities. In New Zealand, Māori health is of special relevance given Māori rights under the Treaty of Waitangi [ 28 ] and the United Nations Declaration on the Rights of Indigenous People [ 29 ], and in recognition of the inequities for Māori across most major health indicators [ 28 ]. We recognise the direct significance of this project to Māori and understand racism in its broader sense as underpinning our colonial history with ongoing contemporary manifestations and effects [ 8 ]. As such, our work is informed by critical approaches to health research that are explicitly concerned with understanding inequity and transforming systems and structures to achieve the goal of health equity. This includes decolonising and transformative research principles [ 30 ] that influence our approach to the research question, data collection, analysis and interpretation of data, and translation of research findings. The team includes senior Māori researchers as well as advisors with experience in Māori health research and policy.

Aims and research questions

The overall aim is to examine the relationship between reported experience of racism and a range of subsequent health measures. The specific objectives are:

To determine whether experience of racism leads to poorer mental health and/or physical health.

To determine the impact of racism on subsequent use and experience of health services.

Study design

The proposed study uses a prospective cohort study design. Respondents from the 2016/17 New Zealand Health Survey [ 2 , 18 , 19 ] (NZHS) provide the source of the follow-up cohort sample and the NZHS provides baseline data. The follow-up survey will be conducted between one and two years after respondents completed the NZHS. Using the NZHS data as our sampling frame provides access to exposure status (experience of racism), along with data on a substantial number of covariates (including age, gender, and socioeconomic variables) allowing us to select an appropriate study cohort for answering our research questions. Participant follow-up will be conducted by a multi-modality survey (mail, web and telephone modalities).

This study explores the impact of racism on health in the general NZ adult population (which is the target population of the NZHS that forms the baseline of the study).

Participants

Participants were selected from adult NZHS 2016/17 interviewees ( n  = 13,573, aged 15+ at NZHS interview) who consented to re-contact for future research within a 2 year re-contact window (92% of adult respondents). The NZHS is a complex-sample design survey with an 80% response rate for adults [ 18 ] and oversampling of Māori, Pacific, and Asian populations (who experience higher levels of racism), which facilitates studying the impact of racism on subsequent health status. Participants who had consented to re-contact ( n  = 12,530) also needed to have contact details recorded and sufficient data on exposures/confounders to be included in the sampling frame ( n  = 11,775, 93.9% of consenting adults). All invited participants will be aged at least 16 at the time of follow-up, as at least one year will have passed since participation in the NZHS (where all participants were aged at least 15).

Exposure to racism was determined from the five previously validated NZHS items [ 31 ] asked of all adult respondents (see Table  1 ) about personal experience of racism across five domains (verbal and physical attack; unfair treatment in health, housing, or work). Response options for each question cover recent exposure (within the past 12 months), more historical exposure (> 12 months ago), or no exposure to racism.

Identification of exposed and unexposed individuals

Individuals were classified as exposed to racism if they answered “yes” to any question in Table  1 , in either timeframe (recent or historical: referred to as “ever” exposure). This allows for analysis restricted to the nested subset of individuals reporting recent exposure to racism (past 12 months) and those only reporting more historical exposure (> 12 months ago). The unexposed group comprised all individuals answering “No” to all five domains of experience of racism. We selected all exposed individuals for follow-up, along with a matched sample of unexposed individuals. Individuals missing exposure data were explicitly excluded.

Matching of exposed and unexposed individuals

To address potential confounding, we used propensity score matching methods in our sampling stage to remove the impact of major confounders (as measured in the NZHS) of the causal association between experience of racism and health outcomes. Propensity score methods are increasingly used in observational epidemiology as a robust method for dealing with confounding in the analysis stage [ 32 , 33 , 34 , 35 , 36 ] and have more recently been considered as a useful approach for secondary sampling of participants from existing cohorts for subsequent follow up [ 37 ].

All exposed NZHS respondents will be invited into the follow-up survey. To find matched unexposed individuals, potential participants were stratified based on self-reported ethnicity (Māori, Pacific, Asian, European and Other; using prioritised ethnicity for individuals identifying with more than one grouping) [ 38 ] and then further matched for potential sociodemographic and socioeconomic confounders using propensity score methods [ 39 , 40 ]. Stratification by ethnicity reflects the differential prevalence of racism by ethnic group, and furthermore allows ethnically-stratified estimates of the impact of racism [ 22 ].

Propensity scores were modelled using logistic regression for “ever” exposure to racism based on major confounder variables of the association between racism and poor health (Table  2 ), with modelling stratified by ethnic group. Selection of appropriate confounders was based on past work using cross-sectional analysis of the 2011/12 NZHS (e.g. [ 21 , 22 ]) and the wider literature that informed the conceptual model for the project. Some additional variables were considered for inclusion in the matching process but were removed prior to finalisation (details in Table  2 ).

Within each ethnic group stratum, exposed individuals were matched with unexposed individuals (1:1 matching) based on propensity scores to make these two groups approximately exchangeable (confounders balanced between exposure groups). The matching process [ 41 ] used nearest neighbour matching as implemented in MatchIt [ 42 ] in R 3.4 (R Institute, Vienna, Austria). As the propensity score modelling is blind to participants’ future outcome status, the final propensity score models were refined using just the baseline NZHS data to achieve maximal balance of confounders between exposure groups, without risking bias to the subsequent primary causal analyses [ 39 ]. Balance between groups was then checked on all matching variables prior to finalisation of the sampling lists.

Questionnaire development

Development of the follow-up questionnaire was informed by a literature review and a conceptual model (Figs.  1 and 2 ) of the potential pathways from racism to health outcomes (Fig.  1 ) and health service utilisation (Fig.  2 ) [ 4 , 10 , 16 , 43 , 44 ]. The literature review focussed on longitudinal studies of racism and health among adolescents and adults that included health or health service outcomes. The literature review covered longitudinal studies post-dating the 2015 systematic review by Paradies et al. [ 12 ], using similar search terms for papers between 2013 and 2017 indexed in Medline and PubMed databases, alongside additional studies from systematic reviews [ 12 , 16 ].

Potential pathways between racism and health outcomes. Direct pathway: Main arrow represents the direct biopsychosocial and trauma pathways between experience of racial discrimination (Time 1) and negative health outcomes (Time 2) Indirect pathways: Racial discrimination (Time 1) can impact negatively on health outcomes (Time 2) via healthcare pathways (e.g. less engagement, unmet need). Racial discrimination (Time 1) can impact negatively on physical health outcomes (Time 2) via mental health pathways

Potential pathways between racism and healthcare utilisation outcomes. Main pathway: Main arrow represents the pathway between experience of racial discrimination (Time 1) and negative healthcare measures (Time 2), via negative perceptions and expectations of healthcare (providers, organisations, systems) and future engagement. Secondary pathway: Racial discrimination (T1) can impact negatively on healthcare (Time 2) via negative impacts on health increasing healthcare need

We used several criteria for considering and prioritising variables for the questionnaire. The conceptual model also informed prioritisation of variables for the questionnaire. For outcome measures, these included: alignment with study aims and objectives; existing evidence of a relationship between racism and outcome; New Zealand evidence of ethnic inequities in outcome; previous cross-sectional relationships between racism and outcome in New Zealand data; availability of baseline measures (for health outcomes); plausibility of health effects manifesting within a 1–2 year follow-up period; and data quality (e.g. validated measures, low missing data, questions suitable for multimodal administration). Mediators and confounders were considered for variables not available in the baseline NZHS survey, as was recent experience of racism (following the NZHS interview) to provide additional measurement of exposure to recent racism. A final consideration for prioritising items for inclusion was keeping the length of the questionnaire short in order to maximise response rates (while being able to fully address the study aims). The questionnaire was extensively discussed by the research team and reviewed by the study advisors prior to finalisation.

Table  3 summarises the outcome measures by topic domain and original source (with references). The final questionnaire content can be found in the Additional file  1 , and includes: health outcome measures of mental and physical health (using SF12-v2 and K10 scales); health service measures (unmet need, satisfaction with usual medical centre, experiences with general practitioners); experience of racism in the last 12 months (adapted from items in the NZHS); and variables required to restrict data (e.g. having a usual medical centre, type of centre, having a General Practitioner [GP] visit in the last 12 months) or potential confounder and mediator variables not available at baseline (e.g. number of GP visits).

Recruitment and data collection

Recruitment is currently underway. The sampling phase provided a list of potential participants for invitation, and recruitment for the follow-up survey uses the contact details from the NZHS interview (physical address, mobile/landline telephone, and email address if available). Recruitment will take place over three tranches to (1) manage fieldwork capacity and (2) allow tracking of response rates and adaptation of contact strategies if recruitment is sub-optimal.

To maximise response rates, we chose to use a multi-modal survey [ 45 ]. Participants are invited to respond by a paper questionnaire included with the initial invitation letter (questionnaire returned by pre-paid post), by self-completed online questionnaire, or by computer-assisted telephone interview (CATI, on mobile or landline.) A pen is included in the study invitation to improve initial engagement with the paper-based survey [ 46 ]. Participants completing the survey are offered a NZ$20 gift card to recognise their participation. The contact information contains instructions for opting out of the study.

Those participants not responding online or by post receive a reminder postcard mailed out two weeks after the initial letter, containing a link to the web survey and a note that the participant will be contacted by telephone in two weeks’ time.

Two weeks after the reminder postcard (four weeks post-invitation) remaining non-respondents are contacted using CATI processes. For those with mobile phone numbers or email addresses, a text (SMS) or email reminder is sent two days before the telephone contact phase. Once contact is made by telephone, the interviewer asks the participant to complete the survey over the telephone at that time or organises a subsequent appointment (interview duration approximately 15 min). Interviewers make up to seven telephone contact attempts for each participant, using all recorded telephone numbers. Respondents who decline to complete the full interview at telephone follow-up are asked to consider answering two priority questions (self-rated health and any unmet need for healthcare in the last 12 months: questions 1 and 8 in Table  3 and Additional file 1 ).

Past surveys conducted in NZ have frequently noted lower response rates and hence under-representation of Māori [ 47 , 48 ]. Drawing on Kaupapa Māori research principles, we are explicitly aiming for equitable response rates of Māori to ensure maximum power for ethnically stratified analysis. This involves providing culturally appropriate invitations and interviewers for participants, and actively monitoring response rates by ethnicity during data collection to allow longer and more frequent follow-up of Māori, Pacific and Asian participants if required [ 48 , 49 ]. The use of a multi-modal survey is also expected to minimise recruitment problems inherent to any single modality (e.g. lower phone ownership or internet access in some ethnic groups).

We have contracted an external research company to co-ordinate recruitment and data collection fieldwork under our supervision (covering all contact processes described here), which follows recruitment and data management protocols set by our research team.

Statistical analysis

Propensity score methods for the sampling stage are described above: this section focuses on causal analyses for health outcomes in the achieved sample. The sampling frame selects participants based on “ever” experience of racism, which is our exposure definition.

All analyses will account for both the complex survey sampling frame (weights, strata and clusters from the NZHS) and the secondary sampling phase (selection based on propensity scores). Complex survey data will be handled using software to account for these designs (e.g. survey package [ 50 ] in R); propensity scores will be handled in the main analysis by using inverse probability of treatment weights (IPTW) combined with the sampling weights [ 51 ].

Linear regression methods will be used to compare change in continuous outcome measures (e.g. K10 score) by estimating mean score at follow-up, adjusted for baseline. Analysis of dichotomous categorical outcomes (e.g. self-rated health) will use logistic regression methods, again adjusted for baseline (for health outcomes). We will conduct analyses stratified by ethnic group to explore whether the impact of racism differs by ethnic group. Models will adjust for confounders included in creating the propensity scores (doubly-robust estimation) to address residual confounding not fully covered by the propensity score approach [ 52 ]. Analysis for other outcomes will use similar methods.

As we hypothesise that some outcomes (e.g. self-reported mental distress) will be more strongly influenced by recent experience of racism, we will also examine our main outcomes restricted to those only reporting historical (more than 12 months ago) or recent (last 12 months) racism at baseline. These historical and recent experience groups (and corresponding unexposed individuals) form nested sub-groups of the total cohort, and so analysis will follow the same framework outlined above. Experience of racism in the last 12 months (measured at follow-up) will be examined in cross-sectional analyses and in combination with baseline measures of racism to create a measure to examine the cumulative impact of racism on outcomes.

Sensitivity analyses

While the sampling invitation lists are based on matched samples, we have no control about specific individuals choosing to participate in the follow-up survey, and so the original matching is unlikely to be maintained in the achieved sample. We will conduct sensitivity analyses using re-matched data (based on propensity scores for those participating in follow-up) to allow for re-calibration of exposed and unexposed groups in the achieved sample.

To consider potential for bias due to non-response in our follow-up sample, we will compare NZHS 2016/17 cross-sectional data for responders and non-responders on baseline sociodemographic, socioeconomic, and baseline health variables.

Sample size

Based on NZHS 2011/12 responses, we anticipated a total pool of 2100 potential participants with “ever” experience of racism, with approximately 1100 expected to be Māori/Pacific/Asian ethnicity, and 10,000 with no report of racism (at least 2 unexposed per exposed individual in each ethnic group).

For the main analyses (based on “ever” experience of racism) we assumed a conservative follow-up rate of 40%, giving a final sample size of at least 840 exposed individuals. This response rate includes re-contact and agreement to participate, based on past experience recruiting NZHS participants for other studies and the relative length of the current survey questionnaire.

Initial projections (based on NZHS2011/12 data) indicated sufficient numbers of unexposed individuals for 1:1 matching based on ethnicity and propensity scores. This gives a feasible total sample size of n  = 1680, providing substantial power for the K10 mental health outcome (standard deviation = 6.5: > 95% power to detect difference in change of 2 units of K10 between groups.) For the second main health outcome (change in self-rated health), this sample size will have > 85% power for a difference between 8% of those exposed to racism having worse self-reported health at follow-up (relative to baseline) compared to 5% of unexposed individuals.

For analyses of effects stratified by ethnicity, we expect > 95% power for Māori participants ( n  = 280 each exposed and unexposed) for the K10 outcome (assumptions as above); change in self-rated health will have 80% power for a difference between 12% of exposed individuals having worse self-reported health at follow-up (relative to baseline) compared to 5% of unexposed individuals. Stratified estimates for Pacific and Asian groups will have poorer precision, but should still provide valid comparisons.

Ethical approval and consent to participate

The study involves recruiting participants who have already completed the NZHS interview (including questions on racial discrimination) The NZHS as conducted by the Ministry of Health has its own ethical approval (MEC/10/10/103) and participants are only invited onto the present study if they explicitly consented (at the time of completing the NZHS) to re-contact for future health research. The current study was reviewed and approved by the University of Otago’s Human Ethics (Health) Committee prior to commencement of fieldwork (reference: H17/094). Participants provided informed consent to participate at the time of completing the follow-up survey depending on response modality: implicitly through completion and return of the paper survey which stated “By completing this survey, you indicate that you understand the research and are willing to participate” (see Additional file 1 : a separate written consent document was not required by the ethics committee); in the online survey by responding “yes” to a similarly worded question that they understood the study and agreed to take part (recorded as part of data collection, and participation could not continue unless ticked), or by verbal consent in a similar initial question in the telephone interview (since written consent could not be collected in this setting). These consent methods were approved by the reviewing Ethics committee [ 53 ]. Ethical approval for the study included using the same consent processes for those participants aged 16 to 18 as for older participants.

This study will contribute robust evidence to the limited national and international literature from prospective studies on the causal links between experience of racism and subsequent health. The use of the NZHS as the baseline for the prospective study capitalises on the inclusion of racism questions in that survey to provide a unique and important opportunity to build on and substantially strengthen the current evidence base for the impact of racism on health using data spanning the entire New Zealand adult population. In addition, our use of propensity scores in the sampling phase is a novel approach to prospective recruitment of participants from the NZHS. This approach should manage confounding while reducing the need (and cost) of following up all NZHS participants, without compromising the internal validity of the results. The novel methods developed for using the NZHS as the base for a prospective cohort study will have wider application to other health priority areas. One general limitation of this approach is that baseline data (for both propensity score development and baseline health measures) is limited to the data captured in the existing larger survey. We anticipate that this study will assist in prioritising racism as a health determinant and inform the development of anti-racism interventions in health service delivery and policy making.

Current stage of research

Funding for this project began October 1st 2017. The first set of respondent invitations was mailed out on July 12th 2018; fieldwork for the final tranche of invitations was underway at the time of submission and is expected to be completed by 31 December 2018. Analysis and reporting will take place in mid-to-late 2019.

Abbreviations

Computer Assisted Telephone Interview

General Practitioner

General Social Survey

Index of Multiple Deprivation

Inverse Probability of Treatment Weights

• New Zealand

New Zealand Deprivation Index

New Zealand Health Survey

12/36-Item Short Form Survey

short message service

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Acknowledgements

We would like to acknowledge the assistance of the Ministry of Health’s New Zealand Health Survey Team for facilitating access to the NZHS data and respondent lists, and for help with constructing the questionnaire (including providing the Helpline contact template).

We would also like to acknowledge the expertise and input of our project advisory team: Natalie Talamaivao (Senior Advisor, Māori Health Research, Ministry of Health), Associate Professor Bridget Robson (Director, Eru Pōmare Māori Health Research Centre, University of Otago, Wellington), and Dr. Sarah-Jane Paine (Senior Research Fellow, University of Auckland and University of Otago, Wellington). Thanks also to Ms. Ruruhira Rameka (Eru Pōmare Māori Health Research Centre, University of Otago, Wellington) for providing administrative support. Research New Zealand was contracted to undertake the data collection and other fieldwork for the follow-up survey.

This project was funded by the Health Research Council of New Zealand (HRC 17–066). The funding body approved the study but has no further role in the study design or outputs from the study.

Availability of data and materials

Data from the follow-up study is not available to other researchers as participants did not provide their consent for data sharing. The NZHS 2016/17 data used as the baseline for the study described in this protocol is available to approved researchers subject to the New Zealand Ministry of Health’s Survey Microdata Access agreement https://www.health.govt.nz/nz-health-statistics/national-collections-and-surveys/surveys/access-survey-microdata .

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JS and RH initiated the project and are co-principal investigators of the study, and jointly led writing of the grant application and this protocol paper. JS designed the sampling plan, led the development of the contact protocol, led the development of the statistical analysis plan, contributed to revising the questionnaire, and is guarantor of the paper. RH designed the questionnaire, contributed to development of the sampling and contact protocol, and co-led the statistical analysis plan. DC led the conceptual plan with support from RH. AW and RE contributed to the contact protocol. DC, AW and RE all contributed to writing the grant application, revising the questionnaire and sampling plans, and revising the draft protocol paper. All authors read and approved the final version of the manuscript.

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The follow-up study protocol and questionnaire were approved by the University of Otago’s Human Ethics (Health) Committee prior to commencement of fieldwork (reference: H17/094). The NZHS has its own ethical approval as granted to the New Zealand Ministry of Health (NZ Multi-Region Ethics Committee, MEC/10/10/103), and consent for re-contact was gained from participants at the time of their NZHS interview. Participants provided informed consented to participate at the time of completing the follow-up survey: implicitly through completion and return of the paper survey which stated “By completing this survey, you indicate that you understand the research and are willing to participate”; in the online survey by responding “yes” to a similarly worded question that they understood the study and agreed to take part, or by verbal consent in a similar initial question in the telephone interview.

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JS, RH, DC, AW, and RE report funding from the Health Research Council of New Zealand to complete this work. JS and RH report personal fees from the Health Research Council of New Zealand for service as external members on committees (neither are employees of the HRC), outside the scope of the current work.

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Stanley, J., Harris, R., Cormack, D. et al. The impact of racism on the future health of adults: protocol for a prospective cohort study. BMC Public Health 19 , 346 (2019). https://doi.org/10.1186/s12889-019-6664-x

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Article Contents

Introduction, a call for race in the marketplace scholarship, demystifying race in the marketplace, what is rim consumer research, guidance for conducting race-relevant research, evolving an understanding of race in consumer research, author notes.

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Race in Consumer Research: Past, Present, and Future

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Sonya A Grier, David Crockett, Guillaume D Johnson, Kevin D Thomas, Tonya Williams Bradford, Race in Consumer Research: Past, Present, and Future, Journal of Consumer Research , Volume 51, Issue 1, June 2024, Pages 56–65, https://doi.org/10.1093/jcr/ucad050

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Race has been a market force in society for centuries. Still, the question of what constitutes focused and sustainable consumer research engagement with race remains opaque. We propose a guide for scholars and scholarship that extends the current canon of race in consumer research toward understanding race, racism, and related racial dynamics as foundational to global markets and central to consumer research efforts. We discuss the nature, relevance, and meaning of race for consumer research and offer a thematic framework that critically categorizes and synthesizes extant consumer research on race along the following dimensions: (1) racial structuring of consumption and consumer markets, (2) consumer navigation of racialized markets, and (3) consumer resistance and advocacy movements. We build on our discussion to guide future research that foregrounds racial dynamics in consumer research and offers impactful theoretical and practical contributions.

The scholarship on race in the marketplace has a long history. Dating back to late 19th-century Western social science, it emerged in no small part to oppose the vulgar race science of earlier epochs. For instance, significant portions of Du Bois’ (1899) pioneering Philadelphia Negro investigate the link between market-based practices and racial segregation in the turn of the century U.S. Despite this lengthy history, many have noted that the Journal of Consumer Research ( JCR ) has published relatively few studies on the topic over its first 50 years ( Arsel, Crockett, and Scott 2022 ; Burton 2009 ; Davis 2018 ; Pittman Claytor 2019 ; Williams 1995 ). On this occasion of JCR ’s semi-centennial, we renew calls to revivify the study of race, racialization, and racism in consumer research and to situate it globally.

Race is a political, rather than zoological, categorization system that assigns physical and sociocultural traits to people and arranges them hierarchically based on those identifiers. Although racial categorization occurs around the world, it shows considerable variation across time and place. Consider that polling data from Pew Research suggest that people worldwide believe their country is becoming more racially and ethnically diverse ( Poushter, Fetterolf, and Tamir 2019 ). Yet, even as people perceive shifting demographics, their experience in national and local contexts differs fundamentally on many dimensions. Scholarship reflects how pernicious power dynamics that often take the form of anti-blackness, antisemitism, Islamophobia, anti-Asian racism, and White supremacist ideology permeate race relations. Racialization is the work of assigning ethno-racial meanings to categories and drawing boundaries around them to incorporate some and expel others ( Fanon 1961 ; Omi and Winant 2015/1986 ; Thomas, Cross, and Harrison 2018 ). Finally, racism orders and systematizes the distribution of material and symbolic resources to ethno-racial groups. It legitimizes and promotes the withholding of such resources in cultural discourse, polity, civic life, and the political economy to those positioned at or toward the bottom of the hierarchy ( Emirbayer and Desmond 2021 ). These dynamic social forces undergird and energize various social, political, and economic projects that intersect with consumer behaviors and markets ( Grier, Thomas, and Johnson 2019 ). Some of these projects foster genuinely inclusive consumer journeys. Others foster racially biased ones. And many, if not most, journeys are environmentally noxious, built on fossil fuel and exploited labor from the Global South. Thus, it behooves consumer researchers to consider race and its impact more fully on markets and in the journeys of the consumers that empower them. As an organizing feature of social life, race is central to the discipline of consumer research.

We, therefore, call for a renewed scholarly focus on the role of market institutions and consumer culture in reproducing racial group boundaries, (re-)articulating racialization logics, and challenging or exacerbating racism. This is in addition to traditional examinations of race’s influence on marketplace experiences and approaches, racialized messaging, and offerings.

We outline an inclusive vision for engaging race in consumer research by identifying important areas that dimensionalize prior and future research. However, we begin by offering some details about the authorial team that are relevant to that vision. In a spirit of reflexivity-as-praxis rather than confession, we note that each author identifies as Black, as middle-class, as North American or European, as cisgender, and as part of the Race in the Marketplace (RIM) research network, which is a multiracial and global network of scholars that examines race’s role in markets and the market’s role in race. Our purpose in this article is to introduce a broadened conceptualization of race in consumer research under the RIM moniker. To that end, we briefly discuss the nature, relevance, and meaning of race for consumer research and offer a thematic framework that critically categorizes and synthesizes prior consumer research on race along the following dimensions of meaning: (1) the racial structuring of consumption and consumer markets; (2) how consumers navigate racialized markets; and (3) the consumer resistance and advocacy movements. These dimensions are partially overlapping and variant across time and place, level of analysis (micro, meso, and macro), and defining practice. Our discussion of these dimensions generates a guide for conducting impactful consumer research that fully integrates race.

What we label “RIM scholarship” is a characterization of past and ongoing cross-disciplinary research organized around our identified themes. This labeling intends to underscore the framework’s defining dimensions of meaning. It is not our contention that all consumer research on race corresponds to RIM scholarship. Finally, although the framework is appropriate for exploring racialized phenomena outside of consumer research, that is not our present focus.

Prior to elaborating on the features of the framework, we first address a set of prevailing myths that have contributed to the historical marginalization of consumer research on race. Myths are functionally stories with morals. Myths are powerful when their morals resonate, both animating action and justifying it after the fact. Some myths perpetuate harm, especially once embedded in sociocultural systems and institutions. Once there, they can endure despite their logical flaws and factual inaccuracies. Moreover, “debunking” them, or drawing attention to those shortcomings, rarely dilutes their staying power (and ironically can facilitate resonance). Disempowering harmful myths requires direct confrontation, but for the purpose of demystifying rather than debunking them. We confront three prevalent myths about consumer research on race to first demystify them and to help advance competing myths that are more coherent, more resonant, and more perceptive.

Theoretical Insufficiency

The most enduring (and pernicious) myth about consumer research on race is that the race construct is insufficient for theory development. The argument is that race is a categorical variable, useful as a demographic or market segment identifier but not otherwise beneficial for developing theory. Theory development is hallowed ground for scholars, and obviously at JCR . But this harmful myth poisons that ground by encouraging adherents to adopt an essentialized, check-the-box notion of race that reduces it to a one-dimensional caricature rather than the unstable but legible product of various intersecting social and historical processes ( Omi and Winant 2015/1986 ). It is not surprising then that myth adherents might struggle to see the construct’s theoretical utility. Unfortunately, an impoverished understanding of race is too often misattributed to the construct itself rather than to a narrow conceptualization in the discipline, even relative to other academic disciplines. It is even more unfortunate that when this myth animates action, it poisons the ground right where theory takes root. It does so in doctoral programs, in the form of well-intentioned advice to interested students to avoid or re-frame race-related topics of inquiry. It does so when early-career scholars internalize the myth in ways that shape their scholarship. And it does so in myriad ways throughout the publication process after manuscripts are submitted. This kind of harm contributes to the marginalization of scholarship on race in consumer research, which has negative consequences for theoretical development.

Like most 20th-century social science, RIM scholarship is premised on the notion that race is socially constructed, with no immutable essence, biological or otherwise. This fundamental ontological instability is an obvious problem for static accounts of race. Yet, RIM-based inquiry treats such instability as a matter to be theorized rather than problematized. In the current era of neoliberal globalization, and in the preceding historical eras, markets and consumption have evolved in ways that situate race as a central axis of social power but with ethno-racial group boundaries and meanings that are locally contested and unstable ( Crockett 2022 ). Race is of course one of numerous axes of power that have evolved across different historical eras. We see no benefit—only loss—in pitting them against one another, as any are suitable grounds for developing theory. We posit that each warrants sustained, critical inquiry on its own terms to generate important theoretical insights independently and at their intersections.

Me-Search Is Not Research

A related harmful myth contends that marketing and consumer research on race is self-focused “me-search,” whose insights do not generalize beyond a focal racial group. This myth likewise poisons the ground for developing theory in at least two ways. First, the me-search myth presumes that race-focused inquiry constitutes politics, which moves the discipline away from an ideal of objective, dispassionate scholarship. Racially minoritized scholars who do race-focused research are effectively framed as incapable of embodying this ideal and/or find their research delegitimized when it actively attempts to unsettle this ideal. A related way it poisons the ground is by situating Western notions of middle-class whiteness as a status quo that generalizes unproblematically to other people and settings. Those others must then explain and validate their position vis a vis the status quo ( Williams 1995 ). This effectively stigmatizes research that centers the agency and experiences of people of color.

Enacting the me-search myth presupposes the wisdom of avoiding a focus on race. That renders it invisible, especially in spaces where the focal racial group is marked as White. But in the marketplace—a quintessentially social space—race is operating even if it is rarely theorized. Apart from RIM scholarship, it is uncommon for consumer researchers to report the ethno-racial composition of samples, a necessary condition for understanding even the simplest categorical effects of race. Few systematic efforts are underway to change this status quo ( Turner and Uduehi 2021 ). RIM scholars then find themselves in a catch-22—conduct research that is perceived as self-serving (and thus devalued in the academic marketplace) or limit their investigations to conceptual frameworks and methods that greatly limit the explication of meaningful insights. We posit that the more fruitful ground for theory development is the one rich with explorations of race as a global social force with local particularities rather than the one that leaves it untheorized. The RIM research network exemplifies the impressive potential of discovery-oriented scholarly exploration that centers race to operate across paradigmatic and methodological divides around the globe ( Johnson et al. 2019 ).

Race Is an “American” Problem

A third harmful myth is that RIM scholarship provides a race-only analysis that centers U.S. racial categories (especially Black and White) and politics that are not analogous to other national contexts. This criticism may reflect the U.S. origins of consumer research on race rather than its actual scope of practice. RIM scholars have written compellingly about race as a global phenomenon shaped under complex local conditions in Asia, Africa, Latin America, Europe, and the Middle East. Nevertheless, they have avoided an “exceptionalism trap” that would fix race, racialization, and racism to any specific national boundaries or deny their operation therein. They have challenged the discursive and material power of various national myths about racial “universalism” (e.g., France), “colorblindness” (e.g., U.S.), and “racial democracy” (e.g., Brazil) that would poison the ground by rendering persistent racialized inequities invisible ( Johnson et al. 2017 ; Rocha et al. 2020 ).

RIM scholarship challenges these myths where they appear in consumer research, in part, by moving away from a tidy-but-false dichotomy of “race” as phenotype and lineage and “ethnicity” as sociocultural. Although race and ethnicity are analytically distinct and draw from different intellectual traditions, in practice, they can prevail on the same social, historical, and political content. Ultimately, to avoid enacting this myth and poisoning the ground for theory development, RIM researchers must account for the relevant sociocultural, historical, and political features of a specific context that actors mobilize into a race-making project. Next, we expound on the RIM thematic framework and what it offers to a broad array of scholarly, managerial, and public policy stakeholders.

We offer a concise thematic overview that critically synthesizes prior consumer research on race along three broad dimensions: (1) the racial structuring of consumption and consumer markets, (2) consumer navigation of racialized markets, and (3) consumer resistance and advocacy movements. These three dimensions are not mutually exclusive, as scholarship can and does encompass more than one, and potentially all three dimensions.

Racial Structuring of Consumption and Consumer Markets

RIM scholarship on this dimension explains how, why, and where racialization and racial inequity take place in markets. Commonly but not exclusively operating at the macro-level of conceptualization, these studies aim to destabilize dominant conceptualizations of markets. Meaning, they reimagine markets as sites that are constituted by racism rather than sites where racialization and racial inequities merely take place sometimes. The key implication of this reimagining is that it reconceptualizes marketplace racialization and racial inequity as at least as likely to be pervasive, conspicuous, or routine as to be episodic, inconspicuous, or aberrational. The research on this dimension draws on multidisciplinary theoretics such as racial formation theory ( Omi and Winant 2015/1986 ), racial capitalism ( Robinson 2005/1983 ), intersectionality ( Crenshaw 2011/1991 ), critical race theory ( Bell 1995 ), and whiteness theory ( Roediger 1991 ) to demonstrate how racism is pervasive and routine in markets and directs their functioning. For instance, Crockett (2022) and Jamerson (2019) each draw on racial formation theory to explore the ways market systems reify racial inequities in contrast to Burton (2009) and Rosa-Salas (2019) , who incorporate whiteness theory to demonstrate the ways scholarly and practice-oriented research have historically constructed the “consumer” and the “mass (general) market” as White. Although studies on this dimension are predominantly conceptual, some utilize approaches like empirical modeling (e.g., Jaeger and Sleegers 2023 ) and mystery shopping field experiments ( Scott et al. 2023 ) to examine racial dynamics in the marketplace. Research on this dimension also explores racialization and inequity in specific market domains, including advertising ( Crockett 2008 ), alcohol and food ( Barnhill et al. 2022 ; Gaytán 2014 ), finance ( Friedline and Chen 2021 ), gentrification ( Grier and Perry 2018 ), and online markets ( Rhue 2019 ). For instance, Dhillon-Jamerson (2019) focuses on matrimonial ads in India to demonstrate how colorism intersects with social class and caste to impact the lives of women during the process of matchmaking.

Navigating Racialized Markets

RIM scholarship on this dimension assesses the effects of racialized markets on consumers and the myriad ways they attempt to construct lifestyles while living within such constraints. Researchers ask: how does the racial structuring of consumption and consumer markets impact consumer choices; how do consumers make meaning from such a structuring; and how is that meaning supported or contested by other market actors? Scholars typically broach these questions by employing micro-level methodological approaches such as one-on-one in-depth interviews ( Crockett 2017 ) and quasi-experiments ( Brumbaugh, 2002 ). It is common practice in this research to pair micro-level methodologies with macro-level conceptualizations when analyzing data. RIM scholarship that addresses navigating racialized markets represents the largest of the three dimensions discussed here and operates across a broad array of consumptive and geographic contexts. These include explorations of marketplace experiences among people in specific racialized groups, such as Black, Latinx, Asian, and Indigenous populations in the U.S., as well as racially minoritized people worldwide ( Bogatsu 2002 ; Crockett, 2017 ; Rocha et al. 2020 ; Veresiu and Giesler 2018 ). For instance, Alkayyali (2019) examines the individual coping strategies implemented by “veiled” Muslim women living in France in response to their racialized marketplace experiences. In contrast, only a few studies examine the experiences of consumers racialized as White expressly on that basis ( Johnson et al. 2017 ; Luedicke 2015 ; Peñaloza and Barnhart 2011 ). Collectively, research on navigating racialized markets explores and demonstrates the ways in which a variety of fluid coping strategies are deployed by consumers as they navigate an ever-evolving marketplace.

Consumer Resistance and Advocacy

RIM scholarship on this dimension centers on consumers’ collective actions to advance their race-related political agenda. Often using meso-level conceptual frameworks and/or historical approaches, this research considers consumer collectives and markets as sites of political expression and resistance. The core question driving this scholarship is about how consumers engage in cooperation and conflict to challenge or support the racialization of markets. Studies examine diverse consumer movements involving protests, boycotts, buycotts, and/or the establishment of self-organization. For instance, research on boycotts investigates consumer movements that oppose products and services connected to slavery ( Page 2017 ), segregation ( Brown 2017 ), and (neo)colonialism ( Parnell-Berry and Michel 2020 ). It also examines racist collective projects like consumer boycotts against Jewish populations in pre-Nazi Germany ( Stolle and Huissoud 2019 ) and far-right extremist organizations mobilizing White consumer movements ( Miller-Idriss 2018 ). Researchers also explore “buycotts” and self-organized consumer groups and segments ( Branchik and Davis 2009 ). Drawing on notions such as sovereignity, solidarity, and agency, these studies investigate self-organizing in domains as diverse as recreation ( Harrison 2013 ), access to food ( Reese 2018 ), and personal finance ( Krige 2014 ). Exploring “financialization from below” in an all-male savings club in Soweto (South Africa), Krige (2014) shows how participants viewed self-organizing as a means to move away from apartheid’s racial capitalism and embrace the political and economic promises of the “New” South Africa. Overall, research on consumer resistance and advocacy demonstrates how consumer collectives emerge, develop, and collapse as they challenge or sustain the marketplace’s racialized allocation of resources.

Table 1 summarizes each RIM consumer research dimension, its distinguishing characteristics, and opportunities for research.

RIM Consumer Research Dimensions.

Leveraging the thematic organization of prior research, we now provide broad guidance on how consumer researchers could engage race and racial dynamics in a manner that yields important theoretical and practical contributions.

Crafting a Solid Foundation

Whether a new investigator or a seasoned researcher, the basic aspects of research merit self (and research team) reflexivity. Across a multitude of design choices throughout the research process (e.g., questions, methods, sample), each warrants consideration of race. Researchers can reflect on how their backgrounds, beliefs, and motivations challenge efforts at neutrality and filter approaches to conducting research. Additionally, taking an intersectional approach to contemplate the interconnected nature of different forms of oppression in the research endeavor will better capture institutionalized modes of gendered, racialized, and economic oppression at the core of any consumer research project ( Poole et al. 2021 ). Deliberate and systematic attention to race and intersecting power dynamics in the conceptualization, design, and implementation of research studies is more likely to generate theoretical knowledge with real practical impact.

Adopting Theories, Frameworks, and Constructs

Consumer researchers adopt theories that influence how they conceive of race and related constructs, as well as shape study design and ascertained knowledge. Seemingly “universal” theories, frameworks, and constructs carry ontological assumptions that structure or constrain ideas and understanding. Many are empirically calibrated on homogeneous, primarily White, middle-class samples and have not been tested with other populations. The late Williams (1995 , 240) lamented the discipline’s reliance on theories and approaches developed with populations that are “vastly different from today.” The use of race-focused theoretical approaches that incorporate history and sociopolitical concerns can help broaden rather than limit disciplinary knowledge.

The dynamic nature of racialization reinforces the need for attention to how category boundaries are defined. Scholars should define what “race” means in their study—identify how it is embedded in the consumer ideologies and/or practices under study, and where relevant, influenced by sociopolitical forces. This involves reflecting on constructs explicitly about race (e.g., racial identity and racial socialization). But it may also involve reconceptualizing presumably race-neutral constructs (e.g., self-efficacy and deservingness) to include racially influenced perspectives that may be unaccounted for yet still operating. Intentional use of both types of constructs can enhance research protocols.

Echoing recent calls in management studies ( Phillips et al. 2022 ), RIM researchers should explore constructs that mark advantage (e.g., privilege, trust) as well as disadvantage (e.g., prejudice, stigma, stereotyping). Indeed, framing inequity solely as disadvantage shapes beliefs about inequity and its causes and impacts ( Phillips et al. 2022 ; Thomas 2017 ). For example, a focus on disadvantaging constructs (e.g., reducing prejudice) in retail discrimination may diminish racial inequity without eradicating it in part because advantaging mechanisms (e.g., helping) that fuel discrimination have not been addressed. Scholars can strategically and creatively use common constructs (e.g., trust and satisfaction) to support theoretical understandings of race-related phenomena. We suggest that consumer researchers shift their orientation from stigma-centric to one focused on privilege and related power dynamics to fully grasp the persistence of racial inequality in markets and envision possible alternatives.

Innovative Methods

An enhanced engagement with race in consumer research could benefit from developing and using multiple, innovative methods. Research methods utilizing artistic processes such as photography, video, poetry, drawings, or a creative combination of thereof ( Harrison 2019 ; Sobande et al. , 2021 ; Wilson 2020 ) can creatively reflect the theoretical articulation of sociopolitical forces that influence consumer markets. While a full accounting of these approaches is beyond the scope of this commentary, a key point is that innovative methods that identify specific ways to connect the individual to systems of power and the environment will best support future research efforts in this area.

Global pandemics, economic turmoil, military conflict, and climate crisis, each of which intersects with consumption, imperil human survival on this planet. These ongoing threats simultaneously shape contemporary consumer markets and disproportionately impact those at the bottom of the global racial hierarchy who do not have equal access to harm-mitigating resources. Consumption of mass-produced products, especially those reliant on fossil fuels, encompasses issues of environmental justice and social sustainability, and all consumer research, including RIM research, must be situated in this macro-social context.

The thematic framework presented above provides a springboard to examine a vast array of groups, dynamics, and innovative consumer research topics around the world, anchored in various ways to race. Scholars may center directly on race as a topic, examine race-related domains, or infuse their current investigations with a better and deeper understanding of the role race may be playing. For those with an interest but less certainty about where to focus, we add to recent scholarship on race and racism that highlights future consumer research paths ( Grier, Johnson, and Scott 2022 ; Thomas, Johnson, and Grier 2023 ; Wooten and Rank-Christman 2022 ).

Category Construction and Racialization

Given the dynamic nature and instability of racial categories in our globalized marketplaces, attention to how category boundaries are defined by the self and others, and how this relates to consumption remains an important issue. For example, additional focus on consumers labeled “mixed race” and how they navigate markets based on affinity-based (e.g., how they identify) and ascribed identities (e.g., how they “look”) could offer rich insight into category construction and boundaries ( Harrison, Thomas, and Cross 2015 ). Similarly, the U.S. pan-ethnic category of “Asian American,” which includes individuals from many different national origins, highlights the complications of omnibus racial categories, particularly where disaggregation may yield very different insights. Consumer researchers should explore people’s strategic use of a diverse array of identity-related categories as a marketplace resource, such as when they identify as Asian, Asian American, or Filipino.

Furthermore, consumer research lacks many studies that focus on consumption and whiteness—explicitly at least. Given that consumers described as White are the dominant economic, social, and numeric group in many countries, examination of the relationship between whiteness and consumption in diverse geographies can further enrich our understanding of category construction and racialization. For example, White self-racialization has a long history related to economic and social dynamics that heighten perceptions of threat or replacement ( Roediger 1991 ). Understanding when and how White consumers leverage whiteness both individually and collectively to distinguish themselves from other groups in the service of consumption could yield important insights. In addition, scholars could expand upon research that examines the relationship between colorism ( Mitchell 2020 ), skin tone discrimination (which is related to whiteness in some contexts and not at all in others), and racialization processes across diverse groups, geographies, and market domains. Finally, echoing the field’s recent emphasis on socio-spatial marketplace dynamics, research could be enriched by investigations of how and under what conditions consumer spaces contribute to racialization (of the self and others) and how these constructions impact consumer perceptions and experiences.

Health, Genetics, and Consumer Well-Being

Although health has been studied in a multitude of ways in consumer research, racial dynamics have received relatively minimal attention. Racial health disparities provide an opportunity to question systemic processes related to consumer well-being beyond the individual consumer behavior approach. For example, Trujillo-Torres and DeBerry-Spence (2023) explore the link between race, high-risk consumption, and high-risk environments among persons afflicted with sickle cell disease, many of whom do not adopt potentially life-saving innovative therapies. Studies can interrogate the relationship between race, consumption, and important dimensions of health across diverse market domains to both enrich our understanding of consumer well-being and support marketplace equity. The role of genetics marketing in identity construction, racialization, and consumer health is an important future research path as recent marketing efforts in the DNA and fertility industries ( Mimoun, Trujillo-Torres, and Sobande 2022 ) have reinforced biological notions of race which potentially reifies race and rationalizes discrimination based on “inherent” genetic differences.

Market-Based (Anti-)Racist Efforts

RIM researchers should continue to examine the narratives and counternarratives racially minoritized consumers develop to disrupt, reinforce, or enliven marketplace racial hierarchies. This includes understanding what (and how) consumers sacrifice psychologically and materially to navigate racialized markets through individual coping strategies and innovative collective actions. Mady et al. (2023) , in a rarely utilized comparative cross-national study of India, Egypt, and Ghana, examine the extent to which women embrace or challenge perceptions of whiteness as a beauty standard. The dynamics associated with whiteness (or any racially majoritized identity) in consumer movements and other consumer collectives that support, or challenge racialized markets remain underexamined. For example, research on #AboriginalLivesMatter highlights tensions that arise when integrating allies into race-focused social justice activism ( Dejmanee et al. 2022 ).

Broadly, RIM researchers should be critically evaluating the avowed dedication to racial equity in markets made by organizations worldwide in the aftermath of the 2020 global racial justice protests. Governments, universities, and businesses are claiming to review practices through an anti-racist lens to combat structural racism. This activity has reconfigured anti-racism as a conceptual tool usable for supporting marketplace equity and generated related research. Inevitably, some efforts will reflect authentic attempts to change structural conditions that foster inequity while others will reflect so-called “woke washing.” Indeed, at the time of writing, just 3 years after the reckoning, there has been a retrenchment of many organizational commitments to racial equity ( Robinson 2023 ). Examining how consumers give meaning to evolving market practice and race-related brand activism across diverse geographies can deepen our understanding of market-based and consumption-oriented responses to injustice and enhance the practical impact of consumer research on marketplace equity.

Technology and Democracy

The onslaught of big data, artificial intelligence, and machine learning has reinvigorated traditional concerns related to consumer privacy and raised new social justice concerns related to race ( Poole et al. 2021 ). 1 As digital technologies increasingly regulate ideas of racial difference, they have transformed them into information (and other valuable commodities) and weaponized them to deepen racial resentment and conflict for political and economic gain worldwide ( Jamerson 2019 ). Facebook’s algorithms intensified the spread of hateful anti-Rohingya content in Myanmar before the 2017 genocide. A public relations firm orchestrated a massive social media campaign to stoke racial tensions in South Africa using fake Twitter accounts and websites. RIM-oriented big data analysis could examine the network of persuasions employed by these misinformation strategies, and experimental analysis could assess the conditions under which consumers are aware of or persuaded by racialized misinformation to support the design of marketing interventions.

In closing, as we have implied throughout and reinforced here, consumption occurs within a global racialized market system that affects everyday routines of practice, meaning-making, and social relations. Its racialized features (e.g., ideologies, norms, and practices), which are embedded in societal structures, institutions, and related policies across time and space, have direct implications for consumption that too often go undertheorized. RIM research exists to investigate racialized consumers’ experiences to mark their variety, because this is worth knowing, and to make plain the ways that power shapes those experiences.

Sonya A. Grier ( [email protected] ) is a professor of marketing at the Kogod School of Business, American University, 4400 Massachusetts Ave NW, Washington, DC 20016-8001, USA.

David Crockett ( [email protected] ) is a professor of marketing at the College of Business, University of Illinois Chicago, 601 S. Morgan Street Chicago, IL 60607, USA.

Guillaume D. Johnson ( [email protected] ) is a research scholar at the Centre National de la Recherche Scientifique (CNRS), Université Paris-Dauphine, Pl. du Maréchal de Lattre de Tassigny, 75016 Paris, France.

Kevin D. Thomas ( [email protected] ) is a research associate at the University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA.

Tonya Williams Bradford ( [email protected] ) is an associate professor of marketing at the Paul Merage School of Business, University of California, Irvine, 4293 Pereira Drive, SB2-331, Irvine, CA 92697, USA.

The authors express gratitude to colleagues and friends of the RIM Research Network (and beyond) who have trailblazed the study of race and consumer research.

This article was invited for the special anniversary issue by editors Bernd Schmitt, June Cotte, Markus Giesler, Andrew Stephen, and Stacy Wood but went through the journal's full peer review process.

See also the Technology Race and Prejudice Lab (T.R.A.P. LAB). https://www.jointhetrap.com/ .

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• v.40(5 Pt 2); 2005 Oct

Conceptualizing and Categorizing Race and Ethnicity in Health Services Research

Veterans Affairs (VA) patient populations are becoming increasingly diverse in race and ethnicity. The purpose of this paper is to (1) document the importance of using consistent standards of conceptualizing and categorizing race and ethnicity in health services research, (2) provide an overview of different methods currently used to assess race and ethnicity in health services research, and (3) suggest assessment methods that could be incorporated into health services research to ensure accurate assessment of disease prevalence and incidence, as well as accounts of appropriate health services use, in patients with different racial and ethnic backgrounds.

A critical review of published literature was used.

Principal Findings

Race is a complex, multidimensional construct. For some individuals, institutionalized racism and internalized racism are intertwined in the effects of race on health outcomes and health services use. Ethnicity is most commonly used as a social–political construct and includes shared origin, shared language, and shared cultural traditions. Acculturation appears to affect the strength of the relationships among ethnicity, health outcomes, and health services use.

Conclusions

Improved and consistent methods of data collection need to be developed for use by VA researchers across the country. VA research sites with patients representing specific population groups could use a core set of demographic items in addition to expanded modules designed to assess the ethnic diversity within these population groups. Improved and consistent methods of data collection could result in the collection of higher-quality data, which could lead to the identification of race- and ethnic-specific health services needs. These investigations could in turn lead to the development of interventions designed to reduce or eliminate these disparities.

The appropriate assessments of race and ethnicity are crucial to health services research. Data based on racial and ethnic classifications are used by many federal and state agencies, public and private organizations, and the private business sector to allocate resources to needed areas, develop interventions designed to improve health outcomes, and to develop marketing plans ( Sillitoe and White 1992 ; Sondik, Lucas, Madans, and Smith 2000 ; Wallman, Evinger, and Schecter 2000 ). In addition, most health services research studies include race or ethnicity as a descriptive variable, and many include race or ethnicity as a covariate, primary or secondary outcome. In order for instrument-based categorizations of race and ethnicity to be most meaningful, they should (1) produce consistent data over time, (2) allow comparability across populations and surveys, and (3) use terms that are widely understood by the groups completing the instruments ( Sawyer 1998 ). This paper reviews the evolution of the definitions of “race” and “ethnicity,” describes how these social constructs are currently assessed in health services research, and provides suggestions for improving measurement of these constructs.

Race as a Social Construct

The validity of race as an indicator of distinct, genetically different population groups has been widely questioned ( Chaturedi and McKeigue 1994 ; McKenney and Bennett 1994 ; Senior and Bhopal 1994 ; Williams, Lavizzo-Mourey, and Warren 1994 ; Beutler et al. 1996 ). Greater genetic variation exists within racial groups than between them ( Senior and Bhopal 1994 ; Williams, Lavizzo-Mourey, and Warren 1994 ). In fact, the variation in gene frequency is approximately 85 percent within racial groups, and only 15 percent between racial groups ( Freeman 1998 ).

For these reasons, many researchers have shifted paradigms, defining race as a social construct based on phenotypic genetic expression rather than as a biological construct ( Sheldon and Parker 1992 ; LaVeist 1994 ; Senior and Bhopal 1994 ; Williams, Lavizzo-Mourey, and Warren 1994 ; Freeman 1998 ; Jones 2001 ). As Beutler et al. (1996) argue, while race as a biological construct is illusory, its function as a social–psychological and social–political construct is very real. In fact, the U.S. Bureau of the Census uses race as a social–political construct rather than a biological one.

Within the framework of race as a social–political construct, race is used to understand the health consequences of variations in factors such as health care quality and utilization, adequate housing, education, and nutrition. Race, in this sense, is a multidimensional construct and a predictor of exposure to external health risks posed by environmental, social, and behavioral factors ( Fullilove 1994 ; Hahn and Stroup 1994 ; LaVeist 1994 ; Freeman 1998 ). Freeman (1998) argues that biologic expressions of race result in social interactions, which in turn produce racial and ethnic disparities in morbidity and mortality (i.e., discrimination). Indeed, Kent et al. (2001) contend that the best way to understand race is to view it as a social construct that is influenced by social and political factors. From this perspective, much of the racial differentiation in cardiovascular disease can be explained by social–environmental factors influencing susceptibility and access to medical care ( Manson and Ridker 1990 ). For example, members of racial minority groups may have dietary and lifestyle habits and socioeconomic status that might lead to predispositions for cardiovascular disease and less access to preventive and emergency medical care and advice.

This evidence suggests a new taxonomy for racial identification is needed, focusing on socio-environmental effects of phenotypic characteristics of patients, rather than on the characteristics themselves. Proponents of this view might suggest that, instead of classifying a patient's “race,” we should classify “racism” inherent in the patient's immediate and broader environments. As Jones (2000) argues, while race as a variable only roughly approximates socioeconomic status, culture, and genetic history, it is precisely related to social classification, which significantly impacts daily life experiences. This argument suggests that race is a social construct that includes the effects of racism on an individual. Jones (2000) further asserts that differences in health outcomes associated with race are a direct result of the effects of racism. According to Jones (2001) , racism encompasses elements of institutionalized racism and internalized racism. Each of these elements is discussed in the following sections.

Institutionalized racism is defined as having differential levels of access, based on race, to societal goods, services, and opportunities ( Jones 2000 ). Williams (1999) argues that institutionalized racism negatively affects access to educational attainment, employment opportunities, and attainment of higher levels of socioeconomic status. We propose two types of institutionalized racism as most important in the context of health services research: educational racism and access racism. A person living in an environment characterized by institutionalized educational racism would have little exposure to health education materials, instruction, or preventive medical advice. This shortcoming is attributable to institutional factors: racism resulting in illiteracy, and racism causing a dearth of racial minority members being able to acquire medical education, get licensed to practice medicine, and return to their communities to practice. In the Veterans Affairs (VA) health services setting, educational racism manifests itself as, for example, veterans who are fully qualified to receive services but primarily because of lack of health education and information, fail to utilize the services available to them.

Someone living in an environment characterized by institutionalized access racism would have difficulty obtaining urgent medical assistance (e.g., medical visits without an appointment, emergency care) in a timely manner. In this case, racism causes delays by way of overload: too many patients in a low-socioeconomic status (SES) geographic area for the meager urgent care/emergency care equipment available from the institution(s) and government(s) for that area.

Residential segregation is an example of access racism. In fact, Williams (1999) argues that residential segregation is the single most important type of racism that negatively affects health, primarily because it affects access to adequate educational systems and medical care. Ernst (1999) has documented access racism, showing that biologic expressions of race result in discriminatory social interactions that produce racial disparities in morbidity and mortality. Experiencing institutionalized access racism has been linked with experiencing poorer health status, and this association appears to be strongest with mental health outcomes ( Williams, Neighbors, and Jackson 2003 ). Another example of access racism in health services emerges from stories of “economic profiling” of minority patients, such as the following: several years ago, the African American wife of a preeminent medical scientist in Houston (also African American) visited a particular health clinic for the first time and, before being given the opportunity to identify her health insurance, was “profiled” and brusquely routed to a financial office and forced to fill out forms—to see whether she could qualify for Medicaid!

In contrast to these two types of institutionalized racism, Jones 2000 Jones 2001 defines internalized racism as feelings of resignation, helplessness, and hopelessness. These feelings may manifest themselves in risky health behaviors. One example of internalized racism is cultural racism. A person living in an environment characterized by cultural racism could, for example, possess a lifestyle steeped in unhealthy habits, most of which are the result of longstanding institutional racism (e.g., the higher proportion of fast-food restaurants located in minority neighborhoods, the lack of easy accessibility to fitness centers and playgrounds, and the lack of safe neighborhoods in which to walk or jog for exercise) and impoverishment. Individuals exposed to cultural racism may decide that a healthier lifestyle, even if desirable, is simply too difficult to achieve in their (unhealthy) home environment. This lack of health self-efficacy may often be accompanied by broader doubt about achieving a brighter future for themselves in general. In a VA health services context, the effects of cultural racism on patients may, for example, be seen in veterans who visit their VA facility for an illness, then fail to adhere to their provider's treatment recommendations, or simply give up further treatment altogether, not because of lack of access or lack of knowledge of the illness and what treatment can do to cure it, but rather because they cannot envision themselves being able to succeed in changing their health habits in their home environment.

We contend, therefore, that it would be a useful pursuit for health services researchers to endeavor to develop measures—even crude ones—of the prevalence of the above-described forms of racism in patients' lives, with a goal for the near future of having this information collected reliably from patients along with the conventional categorizations of race. The real and potential impacts of these forms of racism could then be estimated for each patient and, optimally, interventions to minimize these impacts could be taken by health care providers. This type of strategy is already being used with some degree of success in the context of mitigating health illiteracy in racial minority patient populations ( Rudd, Goldberg, and Dietz 1999 ).

Ethnicity as a Social Construct

Many researchers have recently argued that the terms “race” and “ethnicity” should not be used synonymously ( Sheldon and Parker 1992 ; Hahn and Stroup 1994 ; McKenney and Bennett 1994 ; Senior and Bhopal 1994 ; Warren et al. 1994 ). Writings on this issue have suggested that one's race and/or ethnicity should be treated as an affiliation rather than a genetic predisposition, and individuals should be extended the respect of being allowed to specify the affiliation(s) of their choosing, in a way that suits them ( Bhopal, Rankin, and Bennett 2000 ).

Race, as it is used in health-related research, consists of personal identity and group identity facets as well as the more familiar biological indicators. Ethnicity, in contrast, is most commonly used as an entirely social–political construct, referring to the sharing of a common culture, including shared origin, shared psychological characteristics and attitudes, shared language, religion, and cultural traditions ( Sheldon and Parker 1992 ; Chaturedi and McKeigue 1994 ; LaVeist 1994 ; Senior and Bhopal 1994 ; Beutler et al. 1996 ; Freeman 1998 ). Thus, ethnicity refers to cultural identification, which is fluid and may change over time. For example, Sillitoe and White (1992) report that while in the early 1980s individuals in Britain responded with no comment to an item assessing their ethnicity as West Indian, in more recent years, Britons of this background have successfully lobbied for the term “black British” to be used instead. The reason for this change is that many “West Indians” now currently residing in Britain are Britain-born.

The concept of ethnicity has evolved to its current conceptualization as being a construct separate from a person's race, although in many cases the two co-occur. This growing recognition has led to the realization that each of the racial groups of Asian American, African or black American, American Indian or Native American, and Caucasian or white American includes a series of ethnic groups. For example, persons of Hispanic ethnicity include white, black, and Asian races (phenotypes), while persons of sub-Saharan African ethnicity(ies) are almost exclusively of black race (phenotype) and persons of Pacific Island ethnicity are almost exclusively of Asian race (phenotype). This raises a critical point: boundaries of ethnicity are not precise, and may be fluid across geopolitical boundaries. This is true not only at the international level, but at the intranational level as well. As a result, the term “ethnicity” is not always understood by study participants, even across adjacent local communities, and likely requires further interpretation in a relevant local context. Ethnicity may have an indirect effect on health outcomes by influencing health beliefs, the manner in which symptoms are expressed, physical functioning, entry into health service delivery systems, and medical treatment processes ( Atkinson, Casas, and Abreu 1992 ; Marin, Gamba, and Marin 1992 ; Williams and Jackson 2000 ). In fact, some researchers suggest that ethnicity (i.e., cultural identification) be assessed as part of the clinical encounter, in order to make sense of patients' responses to treatment.

The strength of relationship between ethnicity and health outcomes appears to be influenced by “acculturation,” that is, the extent to which members of an ethnic group have adopted the beliefs and practices of another ethnic group. Acculturation occurs when individuals from one ethnic group come into contact with individuals from another ethnic group ( Redfield, Linton, and Herskovits 1936 ). Acculturation may be assessed in a variety of ways, such as by examining individuals' language and food preferences, social activities, or political identification ( Padilla 1980 ). In some cases, higher levels of acculturation are correlated with the adaptation of negative health behaviors and subsequent poorer health outcomes ( Hubert, Snider, and Winkleby 2005 ; Rosenberg, Raggio, and Chiasson 2005 ; Vaeth and Willett 2005 ), while in other cases lower levels of acculturation are correlated with poorer health outcomes ( Rahman et al. 2005 ; Zsembik and Fennel 2005 ). These associations appear to be related to the specific ethnic groups examined (i.e., Mexican Americans versus Latinos from Caribbean islands) ( Zsembik and Fennell 2005 ) and to the outcomes being measured (i.e., dietary practices versus health services use and self-perceptions of health) ( Lara et al. 2005 ).

Measuring Race and Ethnicity as Variables

Next, we present a brief overview of the U.S. Government's official policy for categorizing race and ethnicity. The key role of such a policy cannot be overemphasized; the 2003 Institute of Medicine Report, Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care ( Institute of Medicine 2003 , pp. 31–35), highlights the importance of accurately measuring race and ethnicity as variables in health services research. Therefore, having a unified, federal policy in place for guiding the collection of race and ethnicity data is critical.

Revised Directive Number 15

The revised Directive Number 15 of the Office of Management and Budget (OMB) presents rules for classifying individuals into categories of race and ethnicity. For race, a minimum of five separate categories is mandated. Individuals self-identify their racial status by selecting one or more of the five categories to indicate their parentage. These categories are: (1) American Indian or Alaska Native (defined as a person having origins in any of the original peoples of North and South America [including Central America], and who maintains tribal affiliation or community attachment); (2) Asian (defined as a person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent, including people from Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam); (3) black or African American (defined as a person having origins in any of the black racial groups of Africa); (4) Native Hawaiian or Other Pacific Islander (defined as a person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands); and (5) white (defined as a person having origins in any of the original peoples of Europe, the Middle East, or North Africa) ( Bennett 2000 ). The policy also provides for two alternatives for identifying Hispanic ethnicity.

Of particular importance for health services researchers is the fact that, under revised Directive Number 15, respondents are to be permitted to mark more than one racial identification category. This provision enables analyses that compare survey responses between individuals who choose a single race and those who choose multiple races (e.g., African American race only versus African American race plus another race).

Next, we briefly describe three different approaches for measuring race and ethnicity as variables. While we do not advocate any one of these approaches, we think it is appropriate to highlight them. These approaches are particularly important in VA health services research, because we know that issues related to utilization of health services and treatment efficacy are closely tied to racial and ethnic health disparities.

Census 2000

The U.S. Census Bureau now obtains information on race through respondent self-identification. While this process more closely aligns data collection policy with the principle of respect for choice of affiliation, self-identification itself creates a perennial classification problem, in that peoples' self-concept of their race and/or ethnicity may change over time, leading to unpredictable classification variability within geographic areas. Techniques for minimizing the impact of this variability on Census data have been developed (e.g., averaging, oversampling certain areas), but their use is politically controversial. The 2000 Census allowed multiple responses to the “race” item. Interestingly, data from Census 2000 show that multiple responses had only a slight impact on the relative distributions of the main racial populations of the U.S. ( Hirschman, Alba, and Farley 2000 ; Kent et al. 2001 ). However, this outcome may change if the proportion of individuals who self-identify as being of multiple racial background increases. Current Census Bureau policy assigns newborns the race and ethnicity of the birth mother, although data on the father's race and ethnicity are also collected. The Social Security Administration, meanwhile, combines race and ethnicity into a single data item. The category of “other race” has been replaced with three new categories: “Asian, Asian American, or Pacific Islander,”“Hispanic,” and “Northern American Indian or Alaskan Native.”

Importantly, on the 2000 Census, individuals self-identified as being Hispanic or non-Hispanic. This item preceded the racial identification item in an effort to reduce confusion on the part of respondents ( Sawyer 1998 ). Table 1 shows the wording and structure of the Hispanic ethnic identification item, which includes Mexicans, Mexican Americans, Chicanos, Puerto Ricans, and Cubans as Hispanics, and allows the respondent to write in another Hispanic identification. The only versions of Census 2000 that did not include this categorization were the forms used in American Samoa, the Commonwealth of the Northern Mariana Island, Guam, individual forms used in the Pacific Island and the U.S. Virgin Islands, and military forms used in the Pacific Islands.

Racial and Ethnic Identification Items from Census 2000

Sillitoe Survey

Another approach to measuring race and ethnicity as variables was introduced in a survey conducted by Sillitoe and White (1992) . These investigators ascertained whether including a survey item pertaining to South Asian religions would influence the responses of British South Asians to the main ethnicity item. Two census forms were used, one of which contained the religion item while the other did not. It was discovered that the addition of the religion item did not change the responses to the ethnicity item. The wording of the ethnicity item is shown in Table 2 .

Racial, Ethnic, and Religious Identification Items Used in the Sillitoe and White (1992) Study

Stephenson Multigroup Acculturation Scale (SMAS)

Stephenson (2000) presented an approach to measuring ethnicity as a variable by assessing the degree of acculturation among individuals with different levels of immigrant status. Acculturation may be measured when an outcome is anticipated to be related to the degree of ethnic identification. For example, if a nutrition study were conducted using a sample that included first-, second-, and third-generation Japanese Americans, it might be suspected that diet could be related to generational status. That is, individuals born in Japan who later emigrated to the U.S. (first-generation Japanese) might have different dietary practices than individuals of Japanese heritage who were born in the U.S. to U.S.-born parents (third-generation Japanese). Therefore, the effects of acculturation on dietary practices might be assessed in such a study.

The SMAS ( Stephenson 2000 ) was developed and validated to assess the degree of ethnic identification (extent of acculturation) among individuals from five ethnic groups. Acculturation was defined as the degree of immersion in dominant and ethnic societies ( Stephenson 2000 ). Immersion was defined as language, interaction, food, media, and level of acceptance by the dominant culture. The SMAS was not designed to measure cultural change among acculturating individuals. This instrument was developed using an ethnically diverse research team including community professionals and consultants, and was tested among African Americans ( n =6), individuals of African descent ( n =18), Asian Americans ( n =6), caucasian (European) Americans ( n =14), and Hispanic Americans ( n =10) ( Stephenson 2000 ).

The psychometric properties of the SMAS were assessed using exploratory factor analysis. The 32-item SMAS was found to include two factors. These are ethnic society immersion (ESI, Factor 1) and dominant society immersion (DSI, Factor 2). The ESI factor includes 17 items and the DSI factor includes 15 items. Coefficient α 's were 0.86 for the entire scale, 0.97 for Factor 1, and 0.90 for Factor 2. The range of item-total correlations was 0.51–0.87 on Factor 1, and 0.57–0.83 on Factor 2. In terms of validity testing of Factors 1 and 2, it was discovered that both ESI and DSI were significantly correlated with ethnic group affiliation ( r =−0.39, p <.001 for ESI and r =0.46, p <.001 for DSI) ( Stephenson 2000 ). In addition, Factors 1 and 2 of the SMAS have been found to correlate significantly with two other widely used instruments designed to measure ethnic identification/acculturation, the Acculturation Rating Scale for Mexican Americans-II (ARMSMA-II) ( Cuellar, Arnold, and Moldonado 1995 ) and the Bidimensional Acculturation Scale for Hispanics (BAS) ( Marin and Gamba 1996 ).

Our intent in providing the instruments described above is to draw attention to the variety of approaches available for measuring race and ethnicity as variables. These approaches may be particularly applicable for use in health services research conducted with VA patient populations, which are becoming increasingly more racially and ethnically diverse.

We have presented definitions of the terms “race,”“ethnicity,” and “acculturation,” and provided an overview of different approaches to assessing these constructs. Specific examples include Census 2000, an international study, and the SMAS. A discussion of race as a social construct based on phenotypic genetic expression was presented. In addition, two types of racism resulting from this phenotypic expression were described. These were institutionalized racism (which includes access racism and educational racism) and internalized racism (which includes cultural racism).

We expect that this commentary communicates the need for researchers to rigorously explore reasons for the associations among racial and ethnic group status, socioeconomic status, and health outcomes. Understanding these associations is critically important for health services delivery. As Sheldon and Parker (1992) note, the inconsistencies in the measurement of race and ethnicity across many research studies would not be tolerated in the measurement of other study variables. The importance of this work for health services research is highlighted by the fact that without some standard means of measurement, the validity of comparisons of health-related data based on these constructs is questionable.

Suggestions for Health Services Researchers

One of the papers included in this special issue ( Morgan et al. 2005 ) highlighted the racial and ethnic diversity of users of VA health services. As noted in another paper included in this special issue, members of different racial and ethnic groups may respond to data collection instruments differently ( Ramírez, Ford, Stewart, and Teresi 2005 ). For example, as Lee et al. (2002) postulate, members of some racial and ethnic groups may leave more responses unanswered on Likert scales, and may be more likely to give responses outside of the range provided on Likert scales, compared with members of other racial and ethnic groups. Scale scores based on Likert responses may show less evidence of reliability and construct validity in some racial and ethnic groups.

Thus, a need exists to develop ethnically competent instruments that function equivalently in different ethnic groups. However, in order to ascertain the function of instruments in different ethnic groups, it is first imperative to develop consistent measures of race and ethnicity that can be used in health services research and medical care interventions. Other aspects of health services research related to the valid assessment of race and ethnicity include assessments of racial- and ethnic-associated use of specific types of health services and procedures, disease incidence and prevalence, differences in health outcomes, and mortality rates.

Our recommendations for health services researchers build upon a call for consistency by Ford et al. (2002) , who suggested that all investigators use a core set of basic demographic items while also using expanded, population-specific modules. This would allow data pertaining to members of these population groups to be described in greater detail, while retaining a core set of items that could be used in a consistent manner in many different studies. More than one expanded module could be used per site, in addition to the core set of items. For example, research sites in Southern California could use an expanded module for Asian Americans, which could contain categories such as Korean, Chinese, Japanese, Vietnamese, etc. Research sites in New York could use an expanded module for blacks, which could include categories such as Barbadian, Haitian, Jamaican, Nigerian, Panamanian, Senegalese, Trinidadian, etc. ( Ford et al. 2002 ). Individual researchers using existing data could combine these data into core demographic categories based on the Census 2000 classification scheme. This would make possible a comparison of researchers' collected data with Census-based population estimates. Improved and consistent methods of data collection could result in higher-quality data being collected, which could lead to the identification of race- and ethnicity-specific health services needs. Jones (2001) suggests that investigators explicate the reasons for any racial and ethnic health disparities they find in their data. These investigations have the potential to lead to the development of interventions designed to reduce or eliminate these disparities.

In addition, individual researchers could need the following suggestions related to the use of race and ethnicity data in health services research. First, race- and ethnicity-related differences in quality of care, access to care, treatment efficacy, and other outcomes related to health services research could be vigorously investigated and explicated ( Jones 2001 ; Institute of Medicine 2003 ; Betancourt and Maina 2004 ). Second, the effects of socioeconomic status on race- and ethnicity-related outcomes could be examined, with the purpose of disentangling these effects ( Williams 1999 ; Jones 2001 ). Third, the effects of racism on study outcomes could be examined for members of different racial and ethnic groups. Fourth, investigators could provide a rationale for the collection of race and ethnicity data (i.e., collection of this information is related to a documented disparity) ( Jones 2001 ). Fifth, the manner in which race and ethnicity data were collected could be reported (i.e., self-reported, observer rating, allowance for selection of multiple categories, etc.) ( Jones 2001 ). Sixth, new data demonstrate the influence of geographic differences on quality of health care received ( Baicker, Chandra, and Skinner 2005 ). Therefore, the interactive effects of geographic location, race, and ethnicity also need to be examined in future research.

It is imperative that health services researchers find ways to measure race and ethnicity more accurately. This is of great relevance to VA research, because the VA patient population is rapidly becoming more racially and ethnically diverse. In order to enhance the quality of VA health services research, we must now identify the best ways to measure race and ethnicity in a valid and reliable manner. In this way, the effects of race and ethnicity on assessments of quality of care, access to care, and treatment efficacy can be conducted with greater precision by VA health services researchers.

Acknowledgments

The research reported here was supported by the Measurement Excellence and Training Resource Information Center (METRIC) of the U.S. Department of Veterans Affairs, Health Services Research and Development Service. Dr. Kelly is supported by a career development award granted by the U.S. Department of Veterans Affairs, Health Services Research and Development Service. The views expressed in this manuscripts are those of the authors and do not necessarily reflect the views of the U.S. Department of Veterans Affairs.

This research was also supported by Department of Defense Grant No. DAMD 17-96-1-6246; National Institutes of Health R24 EXPORT Center Grant No. RFA-MD-04-002; and National Institutes of Health Resource Center for Minority Aging Research (RCMAR) Grant No. 1 P30 AG 21677. The authors thank Mr. Ken Kato, Ms. Ellen Matthiesen, and Ms. Shannon Hancock for their assistance with the manuscript preparation.

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Results are from logistic regression models controlling for age, Hispanic or Latina/x ethnicity, marital status, parity, tobacco use, prenatal visit utilization, stillbirth, and placental abruption. Other race includes Alaska Native, American Indian, Chinese, Filipino, Guam/Chamorro Hawaiian, Indian, Japanese, Korean, Other Asian/Pacific Islander, Samoan, and Vietnamese. In the sample, 4100 patients had a history of substance use, and 33 760 had no history of substance use; 4636 had a urine toxicology test, and 2199 had any positive test result at labor and delivery. Error bars indicate 95% CIs.

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Jarlenski M , Shroff J , Terplan M , Roberts SCM , Brown-Podgorski B , Krans EE. Association of Race With Urine Toxicology Testing Among Pregnant Patients During Labor and Delivery. JAMA Health Forum. 2023;4(4):e230441. doi:10.1001/jamahealthforum.2023.0441

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Association of Race With Urine Toxicology Testing Among Pregnant Patients During Labor and Delivery

• 1 Department of Health Policy and Management, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania
• 2 Friends Research Institute, Baltimore, Maryland
• 3 Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco
• 4 Department of Obstetrics, Gynecology & Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
• 5 Magee-Womens Research Institute, Pittsburgh, Pennsylvania

An estimated 16% of pregnant persons in the US use alcohol (10%) or an illicit substance (6%, including cannabis). 1 Urine toxicology testing (UTT) is often performed at the time of labor and delivery for pregnant patients to evaluate substance use. 2 , 3 We sought to elucidate associations between race and receipt of UTT and a positive test result among pregnant patients admitted to the hospital for delivery.

This cohort study followed the STROBE reporting guideline. Data were extracted from electronic medical records (EMRs) of patients with a live or stillbirth delivery between March 2018 and June 2021 in a large health care system in Pennsylvania. The study was approved by the University of Pittsburgh institutional review board. Informed consent was waived because the research constituted minimal risk. All patients presenting for delivery were verbally screened for substance use using questions adapted from the National Institute on Drug Abuse Quick Screen. 4 Policy specified UTT would be performed for those with a positive screen result, history of substance use in the year prior to delivery, few prenatal visits, or abruption or stillbirth without a clear medical explanation.

We studied 2 binary outcomes: the receipt of UTT (point of care presumptive testing) and a positive test result at delivery. The primary variable of interest, patient race, was conceptualized as a social construct that could manifest in biased or discriminatory delivery of health care. Self-reported race was categorized as Black, White, and other (Alaska Native, American Indian, Chinese, Filipino, Guam/Chamorro Hawaiian, Indian, Japanese, Korean, Other Asian/Pacific Islander, Samoan, and Vietnamese). Substance use history was defined as having a diagnosis of an alcohol, cannabis, opioid, or stimulant use or disorder during pregnancy in the EMR within 1 year prior through delivery. A positive UTT result was defined as at least 1 positive result of a test component, including amphetamines, barbiturates, benzodiazepines, buprenorphine, cocaine, cannabis, methadone, opiates, or phencyclidine. We used multivariable logistic regression models including race and substance use history, adjusting for age, Hispanic or Latina/x ethnicity, marital status, parity, tobacco use, prenatal visit utilization, stillbirth, and placental abruption. We derived mean predicted probabilities of outcomes by race and substance use history. 5 Analyses were conducted using Stata, version 17.

Among 37 860 patients (100% female; mean [SD] age, 29.8 [5.5] years), 16% Black, 76% were White, and 8% were other race ( Table ). Overall, 11% had a history of substance use; opioid use was more common among White patients (40% of all substance use), whereas cannabis use was most common among Black patients (86% of all substance use). The mean predicted probability of having a UTT at delivery was highest among Black patients compared with White patients and other racial groups regardless of history of substance use ( Figure ). For Black patients without a history of substance use, the mean predicted probability of receiving a UTT at delivery was 6.9% (95% CI, 6.4%-7.4%) vs 4.7% (95% CI, 4.4%-4.9%) among White patients. Among Black patients with a history of substance use, the mean predicted probability of receiving a UTT at delivery was 76.4% (95% CI, 74.8%-78.0%) vs 68.7% (95% CI, 67.3%-70.1%) among White patients. In contrast, among those with a history of substance use, the mean predicted probability of having a positive test result was 66.7% (95% CI, 64.8%-68.7%) among White patients and 58.3% (95% CI, 55.5%-61.1%) among Black patients.

In this cohort study, Black patients, regardless of history of substance use, had a greater probability of receiving a UTT at delivery compared with White patients and other racial groups. However, Black patients did not have a higher probability of a positive test result than other racial groups. Limitations of the study include a lack of a sufficient sample size to investigate other racial and ethnic minoritized groups, such as Alaska Native and American Indian patients, and that data were from a single geographical area and may not generalize nationally. To address racial biases, health care systems should examine drug testing practices and adhere to evidence-based practices.

Accepted for Publication: February 4, 2023.

Published: April 14, 2023. doi:10.1001/jamahealthforum.2023.0441

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Jarlenski M et al. JAMA Health Forum .

Corresponding Author: Marian Jarlenski, PhD, MPH, University of Pittsburgh School of Public Health, 130 DeSoto St, A619, Pittsburgh, PA 15261 ( [email protected] ).

Author Contributions: Dr Jarlenski and Mr Shroff had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Jarlenski, Terplan, Krans.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Jarlenski, Krans.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Shroff, Terplan, Brown-Podgorski, Krans.

Obtained funding: Jarlenski, Krans.

Administrative, technical, or material support: Krans.

Supervision: Jarlenski, Krans.

Conflict of Interest Disclosures: Dr Roberts reported receiving grants from the Foundation for Opioid Response Efforts and the University of California, San Francisco CSF Bixby Center for Global Reproductive Health and National Center of Excellence in Women's Health outside the submitted work. Dr Krans reported receiving grants from the National Institutes of Health, Merck, and Gilead outside the submitted work. No other disclosures were reported.

Funding/Support: This work was supported by grant R01DA049759 from the National Institute on Drug Abuse (Dr Jarlenski and Krans).

Role of the Funder/Sponsor: The National Institute on Drug Abuse had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement .

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70 years ago, school integration was a dream many believed could actually happen. it hasn’t.

WASHINGTON (AP) — Seventy years ago this week, the U.S. Supreme Court ruled separating children in schools by race was unconstitutional. On paper, that decision — the fabled Brown v. Board of Education, taught in most every American classroom — still stands.

But for decades, American schools have been re-segregating. The country is more diverse than it ever has been, with students more exposed to classmates from different backgrounds. Still, around 4 out of 10 Black and Hispanic students attend schools where almost every one of their classmates is another student of color.

The intense segregation by race is linked to socioeconomic conditions: Schools where students of color compose more than 90% of the student body are five times more likely to be located in low-income areas. That in turn has resounding academic consequences: Students who attend high-poverty schools, regardless of their family’s finances, have worse educational outcomes.

Efforts to slow or reverse the increasing separation of American schools have stalled. Court cases slowly have chipped away at the dream outlined in the case of Brown v. Board, leaving fewer and fewer tools in the hands of districts to integrate schools by the early 2000s.

The arc of the moral universe, in this case, does not seem to be bending toward justice.

“School integration exists as little more than an idea in America right now, a little more than a memory,” said Derek Black, a law professor at the University of Southern California. “It’s actually an idea that a pretty good majority of Americans think is a good idea. But that’s all.”

MORE THAN JUST

DIVERSE SCHOOLS

The dream of Brown was never as simple as diversity. It was about equality, and the opportunity that came with it.

From the beginning, funding and integration have been inseparable.

“Whiter schools and districts have more resources, and that is wrong,” said Ary Amerikaner, a former Obama administration official and the founder of Brown’s Promise. “But it is a reality. And that undermines opportunity for students of color, and it undermines our future democracy.”

We remember Brown v. Board as the end of segregated schools in the United States. But stating values does not, alone, change reality. Though the case was decided in 1954, it was followed by more than a decade of delay and avoidance before school districts began to meaningfully allow Black students to enter white schools.

It took further court rulings, monitoring and enforcement to bring a short-lived era of integration to hundreds of school districts. For the students who took part in those desegregation programs, their life trajectory changed — the more years spent in integrated schools, the better Black children fared on measures like educational attainment, graduation rates, health, and earning potential, with no adverse effects on white children.

For a brief period, it seemed the country recognized the deeper remedies required. “All things being equal, with no history of discrimination, it might well be desirable to assign pupils to schools nearest their homes,” Chief Justice Warren Burger wrote in Swann v. Mecklenburg, a 1971 decision that upheld the use of busing to integrate schools in North Carolina. “But all things are not equal in a system that has been deliberately constructed and maintained to enforce racial segregation.”

But not long after, another series of court decisions would unwind those outcomes. Fifty years ago, in Milliken v. Bradley, the court struck down a plan for integrating Detroit public schools across school district lines. The ruling undermined desegregation efforts in the north and Midwest, where small districts allowed white families to escape integration.

Other decisions followed. In Freeman v. Pitts, the court ruled resegregation from private choice and demographic shifts could not be monitored by the court. More than 200 districts were released from court-monitored desegregation plans. By 2007, when the court ruled in Parents Involved v. Seattle Public Schools, even voluntary integration plans could no longer consider assigning students on the basis of race.

“If you have the tools taken away from you … by the Supreme Court, then you really don’t have a whole lot of tools,” said Stephan Blanford, a former Seattle Public Schools board member.

ONE DISTRICT AS A MICROCOSM

The arc of history is clear in the city where the landmark Swann busing case originated.

At its peak, Charlotte-Mecklenburg Schools was considered such a success at integrating classrooms and closing the gap between Black and white students that educators around the country came to tour the district. Today, more than 20 years after a court ruling overturned busing students on the basis of race, CMS is the most segregated district in North Carolina.

While there are no laws that keep kids siloed by race and income, in so many schools that is the reality.

Charlotte’s sprawling, complex busing plan brought Black and white students into the same schools — and by extension, made white children’s resources available to Black students for the first time. The district’s integration program ended when white families sued after their children did not get their top choice of school placement in a lottery that considered race.

Instead, the district created a school assignment process that said diversity “will be based on the family’s decisions.” It left the families of Mecklenburg County, some of whom have always had better choices than others, on their own. In the first year of the district’s choice program, Black families were more likely to try to use the choice plan to pick an alternative school. They were also more likely to get none of the magnet schools they wanted.

In the decades that followed, the district re-segregated. Years of busing had unwound the segregated makeup of the schools, but the underlying disparities and residential segregation had been left untouched.

Charlotte is a place where the divide between affluence and poverty, and the clear racial lines that mirror it, are so stark that people who live there refer to the city in two parts — the well-off “wedge” and the poorer “crescent.” How could anything other than an explicit consideration of those conditions ever hope to ameliorate them?

Solutions to segregated schools exist in this context, often relying on individual families to make choices that are limited by their circumstances. Magnet schools and inter-district transfers — two common policies that may create great individual opportunities — are limited and will always leave some students behind.

Wherever you look, families are divided in how they view integration. For white and affluent families, it can exist as a noble idea, one filled with self-reflection.

But for families of color or poor families — those with less of a safety net — the point of integration often is to place their children somewhere better.

Efforts to integrate schools can take two paths, Stefan Lallinger, executive director of Next100, a public policy think tank, says.

They either fight around the margins, creating slightly less segregated spaces, or they address the problem head on, which in many parts of the country would mean tackling boundaries deliberately drawn to separate rich from poor.

HOW TO MOVE FORWARD IN A SYSTEM THAT RESISTS?

Amerikaner and Saba Bireda founded Brown’s Promise on the idea of bridging the divide between funding and integration, leveraging state courts to obtain the tools the Supreme Court has taken away from districts.

Their strategy has some precedence. In Connecticut, a 1989 lawsuit in state court resulted in the creation of an inter-district transfer program, which allows students in Hartford to transfer into suburban schools and magnet programs, breaking up concentrations of poverty and racially isolated schools.

“This country had to be moved to integration,” Bireda said. “And unfortunately, 70 years later, we feel like we still need litigation. We need the push of the courts.”

More recent lawsuits have taken place in New Jersey and in Minnesota. In 2015, Alex Cruz-Guzman became a plaintiff in a lawsuit challenging segregation in Minneapolis and St. Paul public schools. Cruz-Guzman immigrated to the United States from Mexico as a teenager. As a parent, he noticed his children’s schools consisted almost entirely of other Latino students. When he tried to place them in more integrated schools, the family faced long waitlists.

The case wound its way through court for nearly a decade, almost reaching a settlement in the legislature before that bill failed to pass.

Cruz-Guzman recalls people asking why he would join a case that likely would not resolve in time to benefit his own children, who struggled with learning English for a time in predominantly Latino schools. To him, the arc of the case is about the kids whose lives could change in the future.

“It’s not only my kids. My grandkids will benefit from it,” he says. “People for generations will benefit.”

How far those legal cases can reach remains to be seen. Actual solutions are imperfect. But integration is something this country has tried before, and while it lasted, by many measures, it worked.

Anniversaries are moments to stop and contemplate. Seventy years after Brown, the work towards achieving its vision remains unfinished. Where there are no perfect, easy answers, what other choice is there besides trying imperfect pathways that bring about an increasingly diverse country somewhere closer to the promise of Brown?

“What’s the alternative?” Bireda said. “We are headed towards a country that is going to be majority people of color. … We can be a strong multiracial democracy, but we cannot be that if we continue to allow most children in the United States not to go to school with children who are from different backgrounds.”

The Associated Press’ education coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP’s standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org.

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