Ethnicity as a demographic variable
In this first group of publications, ethnicity is used as a demographic category with no definition, justification, or specific reason for its use being given other than the presence of previous demographic or epidemiological data. Ethnicity is not conceptualised at all, and we see it operationalised solely as a demographic descriptor and a statistical variable. In these publications, an association or correlation between ethnicity (in the form of a specific ethnic category) and the investigated health subject is statistically analysed, and the statistical results are reported. As a demographic variable, ethnicity is often included as a matter of routine, along with other independent variables. It is frequently included as a statistical dummy variable in regression analyses (in the form of a ‘yes-or-no’ variable: for example, non-Western ethnicity: yes or no). An example is the research by Van Der Cammen-Van Zijp et al. (2010). These authors set out to investigate whether differences exist in children’s endurance capacity with regard to exercise that can be correlated to various variables, including ethnic origin, socioeconomic status, age, body mass index, sports participation, school transportation patterns, and smoking habits. No specific premise or rationale is given for the inclusion of ethnicity, other than the authors’ note that their sample is “representative” of the Dutch population. In relation to the categorisation of ethnicity, the authors state that “[W]e classified ethnic origin using the definition of Statistics Netherlands [CBS] into “Dutch”, “Western background” or “non-Western” background” (Van Der Cammen-Van Zijp et al, 2010, p. 132). Subsequently, these ethnic categories were included in the linear regression and found not to be related, and no further discussion of ethnicity was presented in this example.
In addition to the inclusion of ethnicity as a matter of routine, it can be observed that ethnicity is also included when it is a known variable relevant to the health topic being investigated. In their case control study on the pathophysiology of upper gastrointestinal symptoms, Bröker et al. (2009) cite previous research to state that there is a known relationship between dyspeptic complaints and ethnicity in general. The statement is made that “as dyspeptic complaints are known to be influenced by ethnic origin, in this case control study control patients were also matched on ethnicity” (Bröker et al, 2009, p. 2). Subsequently, ethnic groups were defined “according to the official categories of the Central Bureau of Statistics as native Dutch, Western immigrant (that is, from Europe (excluding Turkey), North America, Oceania, Israel, Japan, or Indonesia) and non-Western immigrant (Turkey, Asia (excluding Japan and Indonesia), Central and South America, Africa)” (Bröker et al, 2009, p. 2). The authors do not discuss or define ethnicity further, nor do they mention a possible causal association. This study analysed psychosocial co-morbidity, that is, it compared rates of psychological and social problems in patients with and without upper gastrointestinal (GI) symptoms. The authors mention ethnicity in the introduction and methods sections only, but they draw no ethnically specific conclusions at all.
Not much diversity was observed in how ethnicity was enacted as a demographic variable. The papers were similar in that they categorised ethnicity according to the official country-of-origin categories (Western or non-Western); these variables were included in the statistical analyses and, finally, the results were summarised as descriptions of prevalence and as statistical associations. One might state that this version of ethnicity is empty, given that it is devoid of any conceptual inscription; ethnicity is enacted solely in terms of standardised Dutch categories and is operationalised only in terms of demographic descriptors and statistical variables. Nevertheless, the use of ethnicity here as a risk indicator or known relevant variable is not without consequences – namely, that such epidemiological data can provide the premises for further ethnicity-specific research and policies.
Ethnicity as biological race
In our analysis of this second group of publications, we attend to how ethnicity is enacted as biological race in a surprisingly large number of publications. This is startling in that, unlike the country-of-origin and (non-)Western categories, racial categories are not included in the official Dutch categorisation system. Although researchers in this group define ethnicity as meaning country of origin or do not define it at all, ethnicity is also enacted as race through specific practices (Helberg-Proctor et al, 2016). These practices include: the establishment of the premises underlying a specific research question; the transformation of the country-of-origin categories into racial categories (‘re-categorising and renaming’); the comparison of these newly formed racial groups to other racialised groups (‘likening and comparing’); and lastly, the attribution of the differences found between ethnic/racial groups to biological causes (‘biologising difference’).
Referring to previous research
From our analysis, we conclude that, in order to understand how ethnicity is enacted as biological race, it is first necessary to examine the process by which researchers establish the premises for their ethnicity-specific research. Generally, in order to establish the premises for their ethnicity-specific study researchers refer to previous research and, in doing so, they often imply sameness between ethnic and racial groups in the cited research and the ethnic groups included in the project at hand (Helberg-Proctor et al, 2016). An example of this can be found in the work of Wolma et al. (2009) on the relationship between ethnicity and large- and small-vessel disease within a hospital-based population. The authors build upon the hypothesis that large- and small-vessel disease is related to ethnicity, and refer specifically to previous research stating that White patients often present with large-vessel strokes and Black patients with small-vessel strokes. The activity of referring to previous research, in which racial categories were used, becomes the point at which race and specific racial categories enter into Dutch research projects in the ‘Introduction’ section of the research article, in which the premise for including ethnicity is established or a specific research question is formulated. For example, these authors state:
For the analyses of the present study, we first categorised ethnic groups into (i) White patients and (ii) non-White patients, according to previous studies on UK and US populations. In the second analysis, we distinguished (i) White patients (i.e. Caucasian), (ii) Black patients (i.e. Africans and Creoles), (iii) Asian patients (i.e. Indonesians, Hindustanis, Chinese, Pakistanis and Indies), and a category with (iv) the other ethnicities. (Wolma et al, p. 523)
Although ethnicity is not specifically defined in the article above, the previous res2009earch referred to, which utilises specific racial categories, invokes the use of similar categories (White and non-White, of which the latter is also subsequently subdivided into Asian, Black, and other ethnicities), resulting in the specific enactment of ethnicity as race.
(Re)categorising and (re)naming: Likening and comparing
Our analysis indicates that ethnicity, when enacted as race, is generally categorised according to the official country-of-origin system first, and then is re-categorised and renamed into the various racial categories, such as Black, White, non-White, Caucasian, non-Caucasian, and Asian. We identified two ways that this can be done – either the authors explain why and how they have categorised the original country-of-origin categories into the new racial categories, or they do this without providing any explanation for the new categories. For example, in their research on gene polymorphisms associated with high blood pressure in an ethnicity- and gender-specific manner, Hahntow et al. (2009) conducted research using an existing database (SUNSET), in which ethnicity was determined on the basis of self-reporting (Helberg-Proctor et al, 2016). When the details in the methods section were examined, we learned that, indeed, in the original database, participants were grouped according to self-reported country-of-origin category (Dutch and Surinamese). However, these two groups were then re-categorised into three new groups: Black Suriname, South Asian Surinamese, and White Dutch. And finally, these authors refer to the categories Black, White, and South Asian, of which the former are then likened to African-Americans and the latter to White Americans. Furthermore, related to the re-categorised and renamed of the country-of-origin categories into the various racial categories. In this article, “Black” is used synonymously with “Creole”, which is specifically defined as “a mix of African, European and other ethnic groups”; “White” ethnicity in this instance entails being born in the Netherlands to parents who were also born in the Netherlands (Hahntow et al, 2009, p. 81); and lastly, the “Asian” ethnicity refers to self-identified “Hindustanis” born in Suriname (Hahntow et al, 2009, p. 81). Interestingly, the authors note that “subjects who could not unequivocally be assigned to one of the three ethnic groups” were excluded from the study (Hahntow et al, 2009, p. 81). This exclusion criterion indicates that these categories are perceived to be able to be unequivocally assigned to research subjects. In this example of the transformation of the original country-of-origin categories into racial categories some ethnicities are specifically defined, such as “Black/Creole” as “a mix of African, European and other ethnic groups”, while other ethnicities referred to in the article, such as “White”, are not defined beyond the country of birth (Hahntow et al, 2009, p. 81). Nevertheless, in this example, specific inclusion criteria are given for all the ethnic categories that are used. In other instances, researchers re-categorise the country-of-origin groups, but do not provide information as to how this occurs nor do they define the new categories.
For instance, in their article, Van Leeuwen et al. (2010) set out to better identify pregnant women at increased risk for gestational diabetes mellitus (GDM) so that a clinical prediction model could be developed. On the basis of literature from the American Diabetes Association, the researchers identified the following risk factors for GDM that were to be included in the model: maternal age over 25 years; a body mass index (BMI) above 30 kg/m2; a previous macrosomic baby (> 4500 kg); previous GDM; a first-degree relative with diabetes; and an “ethnic origin with a high prevalence of diabetes “(Van Leeuwen et al, 2010, p. 96). The term “ethnic origin” was used and ethnicity was reported to have been based on “self-reporting” during the intake of participants. The authors state that they intended to evaluate the associations between various country-of-origin categories and the occurrence of GDM. However, as the number of women in the various country-of-origin categories was too small for statistical analysis, the researchers differentiated instead only between Caucasians and non-Caucasians. No definition or conceptualisation of the categories Caucasian or non-Caucasian was provided, nor was any information given on how the eight country-of-origin ethnic categories had been divided into the categories Caucasian and non-Caucasian (Van Leeuwen et al, 2010).
These two examples illustrate that a second research practice involved in the enactment of ethnicity as race in the production of academic articles, is the re-categorisation and renaming of ethnic groups (in regard to previous research) and the comparison to other racialised groups.
Drawing conclusions and biologising difference
A third research practice relevant to the enactment of ethnicity as race involves the making of ethnicity-specific conclusions in research articles. For example, in the “Results” section of the article by Van Leeuwen et al. discussed above, the authors conclude that ethnicity is one of the independent predictors of gestational diabetes mellitus (GDM) in this cohort of women; in the “Discussion” section, the authors note that, specifically because of how ethnicity was included in their GDM risk prediction model (as Caucasians and non-Caucasians), their prediction model is applicable to identifying women at risk for GDM in the “general” population of pregnant woman:
Although the risk scoring system used by Caliskan et al. might have slightly higher accuracy measures, the scoring system was developed using data from women of Turkish origin only, thereby ignoring the influence of ethnicity on the development of GDM, and decreasing the applicability of the scoring system to other populations. In the present study, we accounted for the influence of ethnicity, making our prediction model more applicable to the general population of pregnant women. (Van Leeuwen et al, 2010, p.73)
Another example of racialised ethnicity-specific conclusions being made can be found in the work of Kuijper et al. (2010). In this research on the frequency distribution of polymorphisms in the follicle-stimulating hormone (FSHR) receptor gene, the authors seek to “determine the frequency distribution of FSHR polymorphisms at position 680 of exon 10 within a large group of women with fertility problems from different ethnic backgrounds” (Kuijper et al, 2010, p. 588), and state that they have distinguished five ethnic groups in their study population, namely Caucasian, Asian, Hindustani, Creole, and Mediterranean (Kuijper et al, 2010, p. 589). These specific ethnic categories are not defined; no inclusion or exclusion criteria for these categories are provided; and no information is given on how the Dutch country-of-origin categories relate to these five categories. Nonetheless, the authors conclude, among other results:
The results indicate unmistakably that a significantly lower number of Asian women have a Ser680Ser FSHR variant and a significantly higher number have an Asn680Asn variant, compared with Caucasians and Mediterraneans. (Kuijper et al, 2010, p. 591)
The women included in this study visited the Reproductive Medicine Unit of the Obstetrics and Gynaecology department at the VU University Medical Center in Amsterdam. This aspect of the research situation is relevant because the Nederlandse Vereniging voor Obstetrie en Gynaecologie (the Dutch Organisation for Obstetrics and Gynaecology) is part of the Netherlands Perinatal Registry (PRN), which collects medical data on all perinatal care in the Netherlands. The forms used for the PRN registry require a patient’s ethnicity to be categorised as Dutch, Other European, Asian, Hindustani, Creole, Mediterranean, and Others, with the “Mediterranean” category described as including women who are “Turkish” and “Moroccan” (Stichting Perinatale Registratie Nederland, 2013, p. 23). Thus, although in the final publication by Kuijper et al, the ethnic categories used are not defined or otherwise described (we can only assume that they refer to the PRN-form categories), “Caucasian”, “Mediterranean”, and “Asian”) are, nevertheless, enacted as racialised and geneticised ethnic categories with “unmistakable” genetic differences (Kuijper et al, 2010, p. 591).
A comparison between the article by Hahntow et al. (2009) discussed above and another article using the same SUNSET database provides specific insights into the multiplicity of ethnicity and distinct ethnicities. The manner in which these different enactments occur are, in fact, highly specific to one particular research project or publication. Using the same SUNSET database as Hahntow et al. (2009), De Munter et al. (2010) studied the relationship of total physical activity, along with its intensity and duration, with HDL and triglycerides. To this end, the original self-reported country-of-origin categories (Dutch and Surinamese) from the SUNSET database were transformed into the ethnicities of Dutch, Hindustani-Surinamese, and African-Surinamese, thus differing from the ethnic categories Black, White, and South Asian in Hahntow et al. This makes clear that the ethnicity assigned to the study population is not stable across different studies using the same database at the same research institution and during the same time period (in this case the University of Amsterdam Academic Medical Center).
This multiplicity can also be observed within one research project, where the same researchers categorised and operationalised data from the same cohort study differently for various publications. These inconsistencies draw attention to the importance of taking into consideration the unique research situation on the enactment of ethnicity. For example, the cohort study participants from the Van Leeuwen et al. (2010) article discussed above are, in a different publication, recategorised from the original country-of-origin categories into the racial categories “Caucasian”, “Black”, “Asian”, and “Other” (instead of “Caucasian” and “non-Caucasian”, as in the first article discussed above) (Van Leeuwen et al, 2009, 2010). Thus, the same data are categorised into different racialised groups in different publications by the same authors. From a praxiographic perspective and attending to the dynamics of the research situation, these variations can be understood in terms of the first two practices discussed above, namely the establishment of the premises for the research and the recategorising and renaming of the country-of-origin categories. In the first article by Van Leeuwen et al. (2010), the authors refer to a risk model developed by the American Diabetes Association (2009), in which an increased risk of developing this disease is attributed to “members of an ethnic/racial group with a high prevalence of diabetes (e.g. Hispanic American, Native American, Asian American, African American, and Pacific Islander)” (American Diabetes Association, 2009: S66), a phrase which is transformed by the authors into “non-Caucasian”, after they found that the individual country-of-origin ethnic groups were too small for any meaningful statistical analysis to take place. In the second article based on the same cohort study, the authors refer specifically to a different model developed by Naylor et al, (1997) in which the clinical risk groups are described as “Black”, “Asian”, and “Other”, with “White” being used as a reference group (Naylor et al, 1997. in Van Leeuwen et al, 2009). Referring to this particular model, Van Leeuwen et al. recategorise and rename the country-of-origin categories into “Caucasian”, “Black”, “Asian”, and “Other”, again without providing any information as to how the individual country-of-origin groupings were re-categorised (2009). Thus, different categorisations and the renaming of the data and participants from the same cohort study lead to the enactments of different racialised ethnic categories, depending on the dynamics of the research situation (here, needing a statistically significant sample size, and the specific categories used in previous research), even within the context of one research project by the same authors.
Ethnicity as unique mosaic of related factors
In the last group of publications we analysed, ethnicity is enacted as a combination of various related characteristics and themes. In this group of publications, authors define ethnicity as either meaning country of origin or do not define it at all, and adhere to the official country-of-origin and Western and non-Western categories. These articles usually start with a known ethnicity-related health risk, and then seek to further investigate the relationship between ethnicity and risk and prevalence. In doing so, many of these articles tease out how various risks and disease-determinants are related to and might differ by ethnic group. Again, we see ethnicity in relation to health not simply as something that is found as a result of the research leading up to these articles, but rather as something that is enacted by specific practices applied during a specific research situation, the result of which is the production of a research article. Two particular research dynamics are central to the enactment of ethnicity as a unique mosaic of related factors: The first involves the stage at which the various facets of ethnicity are introduced and discussed in order to formulate a specific research question or hypothesis; the second includes the discussion of ethnicity-specific research results. Below, three illustrative examples are discussed.
In their research on diabetes risk factors among ethnic minorities, Ujcic-Voortman et al. (2009) state that they have defined ethnicity as self-reported country of birth. To introduce their research and the specific research question, the authors state that the prevalence of diabetes in Western societies is rapidly rising, and that “migrants” to Western societies in particular are at increased risk of diabetes. Here, the authors cite research on “African and Asian migrants” in the United States and the United Kingdom, groups that are stated to have a far higher prevalence of diabetes than the “indigenous population” (Ujcic Voortman et al, 2009, p. 510). When examining the articles that are cited as research on “African and Asian migrants” by Ujcic-Voortman et al, we observed that the articles refer to Caribbeans, West Africans, South Asian Hindus, and Muslims as “ethnic groups” in South London (Cappuccio et al, 1997), and “African descendants” and “Hispanics” are referred to as “minorities” in the United States (LaRosa and Brown, 2005). Furthermore, in the article, the terms “ethnic minorities” and “migrants” are used interchangeably. The authors state that Turkish and Moroccan “migrants” are among the largest “ethnic minority” groups in Europe, and that diabetes may be more prevalent among this group; however, the recent information about these groups is scarce, and the sample sizes in previous research were relatively small (Ujcic-Voortman et al, 2009, p. 511). Thus, these authors have set out to study the prevalence of diabetes among a “relatively large number of Turkish, Moroccan, and Dutch individuals” aged 55 and living in Amsterdam, the Netherlands (Ujcic-Voortman et al, 2009, p. 511). In addition, the authors wanted to investigate differing determinants of diabetes in ethnic groups, and included differences in demographic and lifestyle factors in their analysis. In reporting their findings, the authors observe that the rates of diabetes in Turkish and Moroccan migrants were, respectively, two and three times higher than those in the indigenous Dutch population, and that diabetes occurred at younger ages among Turks, and especially among Moroccans. In presenting and discussing these results, they state that, after accounting for other known diabetes risk factors, such as low socioeconomic status and obesity, diabetes remained more frequent among Moroccan individuals, while the prevalence among Turkish individuals was higher, but not significantly different from that of the indigenous Dutch (reference) population (Ujcic-Voortman et al, 2009, p. 514). Furthermore, compared with the Dutch reference group, the typical age of onset was found to be one decade younger for Turks and two decades younger for Moroccans (Ujcic-Voortman et al, 2009, p. 514). Having established that the demographic and lifestyle variables included in the analyses did not explain all the differences between rates of diabetes among Moroccan migrants and the “indigenous” Dutch population, the authors suggested the following:
… [that] the fact that these demographic and lifestyle factors could not explain all of the ethnic differences in diabetes prevalence, indicates that other features such as genetic susceptibility, other endogenous factors or environmental factors may be responsible for the higher frequency of diabetes among Moroccan and Turkish migrants. (Ujcic-Voortman et al, 2009, p. 514)
In regard to other known endogenous factors, the authors state that variations in blood pressure and cholesterol levels were found between the ethnic groups, and that these might explain the differing prevalence of diabetes. Furthermore, seeking to understand the possible effects of migration on their findings of a relatively high prevalence of diabetes among Turks and Moroccans, the authors compare their results to data about Turks and Moroccans living in Turkey and Morocco. To this end, they find that, although the study results are comparable with regard to prevalence, the Turkish and Moroccans with diabetes living in Amsterdam were younger than those living in Turkey and Morocco, and thus diabetes prevalence among “migrants” is assessed as being “relatively high”. It is hypothesised that migration, which involves “adaptation to a Western lifestyle”, might play are role here. In addition, it is stated that the Turkish and Moroccan migrants in Amsterdam originated from rural areas and come from low socioeconomic backgrounds, a factor that increases their risk for diabetes (Ujcic-Voortman et al, 2009, p. 514).
Thus, in this article, ethnicity is enacted as a complex combination of various related characteristics and themes. By isolating various possible determinants related to ethnicity – including demographic and lifestyle factors and migration – a complex analysis has taken place and the discussion leads to the conclusion that the relevance of ethnicity to the prevalence of diabetes is related to migration to a Western society, possible shared genetic susceptibility, demographic and lifestyle factors, and other endogenous factors. Thus, while ethnicity is defined as self-reported “country of birth”, being “Turkish” or “Moroccan” in this article is enacted as referring to a “migrant”, that is, someone probably originating from a rural area in Turkey or Morocco and living in an urban area in Amsterdam. In this research project, “Turkish” and “Moroccan” ethnicity are thus enacted as a complicated mosaic of related factors.
The next example discusses another instance of how researchers combine different features of ethnicity into a particular complex mosaic, which in turn works to enact specific versions of ethnicity. Van Vliet et al. (2009) set out to determine the prevalence of metabolic syndrome (MetS) among overweight and obese children in three major ethnic groups in the Netherlands, namely among individuals from the Dutch, Turkish, and Moroccan communities. The authors assert that the premise underlying this research is the global increase in obesity and its accompanying health risks. In terms of ethnicity, the authors stated that, besides data on overweight, little is known about cardio-metabolic risk factors in children among ethnic minorities in the Netherlands. The authors asserted that, compared to the inhabitants of their country of origin, immigrant groups often have an increased risk of developing cardiovascular disease, writing that:
For example, data on Japanese immigrants living in the United States indicate that a westernised lifestyle aggravates the risk factors for atherosclerosis and its progression. (Van Vliet et al, 2009, 8:2)
Oral glucose-tolerance tests, anthropometric parameters, and blood samples were used to determine metabolic syndrome and insulin resistance in relation to each ethnic group. In summarising their findings, the authors state, among other conclusions, that Turkish children show a significantly higher prevalence of cardio-metabolic risk factors relative to their peers of Moroccan descent. The authors discuss these results as follows:
Some of the differences found may be attributed to environmental factors, such as socio-economic status, lifestyle and diet. However, regarding differences in diet among ethnic groups, its influence may be questioned, due to the fact that both Moroccan and Turkish children have a diet which is more in line with the Dutch guidelines for a healthy diet than the conventional diet as consumed by Dutch native children. The latter does not support the finding that Turkish children have the most unfavourable cardiometabolic risk profile overall, but may account, at least in part, for the relatively low LDL-cholesterol levels in Turkish and Moroccan children. Adaptation of a Western lifestyle after migration is considered by some as major cause of overweight and development of other cardiometabolic risk factors, since the prevalence of these factors is often lower in the country of origin. At least this applies for Turkish adults, but surprisingly, in a cohort of two thousand Moroccan adults living in Morocco, the prevalence of dyslipidaemia and hypertension was respectively three and four fold with respect to Moroccans living in the Netherlands. (Van Vliet et al, 2009, 8:2)
Here again, a unique and complex mosaic of features that have been brought together in the introduction to the research and in the in-depth discussion of the results can be seen. Although ethnicity is not defined (it is only asserted to be based on the country of birth of the child’s parents), it is enacted as a complex and dynamic effect of related facets, such as socioeconomic status, lifestyle, and diet. Interestingly however, this very complexity allows for the eventual naturalisation of ethnicity as a genetically significant category. This happens because, by isolating known and hypothesised determinants through comparison and reference to previous research (such as socioeconomic status, lifestyle and diet, and migration), these authors argue that “as shown previously, environmental factors can only partly explain differences found among racial groups, so it is likely that genetic profile plays a key role in expression of obesity related co-morbidities” (Van Vliet et al, 2009, 8:2). This conclusion is stated to be based on these authors’ own findings that insulin resistance was differently associated with cardiometabolic risk factor per ethnic group in their study, along with similar findings from previous research on susceptibility genes for MetS. Interestingly, however, when taking a closer look at the previous research cited here to draw this conclusion, we see that the research was conducted in the United States, which used the categories “non-Hispanic white”, “Hispanic”, “African-American”, and “Japanese-American” to identify susceptibility genes for the MetS (Edwards et al, 2008). Therefore, the previous study that is cited by the authors to establish that genetic differences exist between the Turkish, Moroccan and Dutch groups, actually uses a different racial and ethnic taxonomy, according to which the Turkish and Moroccan groups would very likely be seen as “White” or “Caucasian”,Footnote 1 and thus would, with regard to genetic profile and susceptibility genes for the MetS, be viewed as the same as the Dutch reference group. This messiness of comparing new research findings to previous research from elsewhere leads to the simultaneous enactment of ethnicity as specifically genetically significant, while it is never defined explicitly beyond the definition of country of birth.
The last example shows how ethnicity is enacted as specific versions of (non-)Westernism. In the publication discussed below, which examines the relationship between gender, overweight, and energy-balance-related behaviours among adolescents, Van Der Horst et al. (2009) refer to previous studies in order to put forward the hypothesis that girls, adolescents from non-Western ethnic backgrounds, and adolescents attending vocational schools have a higher risk of overweight and obesity and of unfavourable energy-balance-related behaviours (EBRB, such as dietary, physical activity and screen-viewing behaviours) compared to boys, adolescents from Western ethnic backgrounds, and adolescents participating in higher-level education (Van Der Horst et al, 2009, p. 374).
Because these authors defined ethnicity “according to the definition used by Statistics Netherlands”, they asserted that “[a]dolescents were considered to be from a Western ethnic background if both parents had been born in an European country, North America, Oceania, Indonesia or Japan” and that “[a]dolescents were considered to be from a non-Western ethnic background if one or both parents had been born in a non-Western country” (Van Der Horst et al, 2009, p. 374). Although the authors begin by defining ethnicity “according to the definition used by Statistics Netherlands”, it can be seen that “non-Western” and “Western” are, in fact, enacted in a multiplicity of ways, even within this one publication. One version involves the categorisation of persons from North Americans as “Western”, and then another version, through the comparison and discussion of the research results, entails the categorisation of African-American and Hispanic females in North America as “non-Western”:
Examining gender by “ethnicity interaction effect” and subsequent stratified analyses showed that girls from non-Western ethnic backgrounds were more likely to be overweight or obese compared with boys from non-Western ethnic backgrounds and that they were more likely to engage in risk behaviours. These gender differences were not found for adolescents from Western ethnic backgrounds. This indicates that girls from non-Western ethnic backgrounds in particular are an important target group. The same pattern of higher overweight prevalence among non-Western female groups has also been found in the United States. Ethnicity overweight differences were greater among females, showing a higher overweight prevalence among African-American girls compared with boys. […] These similarities in patterns are interesting, because the non-Western ethnic groups in the US are different from those in the Netherlands. Whereas in the US ethnic minority groups are African-American and Hispanic, in the Netherlands the most important ethnic minority groups are Turkish, Moroccan, Surinamese and Cape Verdean. (Van Der Horst et al, 2009, p. 377)
What “Western” or “non-Western” is is thus not simply a matter of reality, fact, perspective or definition – rather it is a matter of how these objects are enacted in different research practices (such as categorisation and comparison). The availability and specifics of previous research against which new results are compared and understood shape the research situation, and subsequently the specific enactment of these unique mosaics of ethnicity.