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Finding and Reviewing Research Evidence in the Literature

  Understand the steps involved in doing a literature review   Identify bibliographic aids for retrieving nursing research reports and locate references for a research topic   Understand the process of screening, abstracting, critiquing, and organizing research evidence   Evaluate the style, content, and organization of a literature review   Define new terms in the chapter Key Terms    Bibliographic database    CINAHL    Google Scholar    Keyword    Literature review    MEDLINE    MeSH    Primary source    PubMed    Secondary source A literature review is a written summary of the state of evidence on a research problem. It is useful for consumers of nursing research to acquire skills for reading, critiquing, and preparing written evidence summaries. BASIC ISSUES RELATING TO LITERATURE REVIEWS Before discussing the activities involved in undertaking a research-based literature review, we briefly discuss some general issues. The first concerns the purposes of doing a literature review. Purposes of Research Literature Reviews The primary purpose of literature reviews is to summarize evidence on a topic—to sum up what is known and what is not known. Literature reviews are sometimes stand-alone reports intended to communicate the state of evidence to others, but reviews are also used to lay the foundation for new studies and to help researchers interpret their findings. In qualitative research, opinions about literature reviews vary. Grounded theory researchers typically begin to collect data before examining the literature. As a theory takes shape, researchers turn to the literature, seeking to relate prior findings to the theory. Phenomenologists and ethnographers often undertake a literature search at the outset of a study. Regardless of when they perform the review, researchers usually include a brief summary of relevant literature in their introductions. The literature review summarizes current evidence on a topic and illuminates the significance of the new study. Literature reviews are often intertwined with the problem statement as part of the argument for the study. Types of Information to Seek for a Research Review Findings from prior studies are the “data” for a research review. If you are preparing a literature review, you should rely mostly on primary sources , which are descriptions of studies written by the researchers who conducted them. Secondary source research documents are descriptions of studies prepared by someone else. Literature reviews are secondary sources. Recent reviews are a good place to start because they offer overviews and valuable bibliographies. If you are doing your own literature review, however, secondary sources should not be considered substitutes for primary sources because secondary sources are not adequately detailed and may not be completely objective. TIP For an evidence-based practice (EBP) project, a recent, high-quality systematic review may be sufficient to provide the needed information about the evidence base, although it is usually a good idea to search for studies published after the review. We provide more explicit guidance on searching for evidence for an EBP query in the chapter supplement on website. A literature search may yield nonresearch references, including opinion articles, case reports, and clinical anecdotes. Such materials may broaden understanding of a problem or demonstrate a need for research. These writings, however, may have limited utility in research reviews because they do not address the central question: What is the current state of evidence on this research problem? Major Steps and Strategies in Doing a Literature Review Conducting a literature review is a little bit like doing a study: A reviewer starts with a question and then must gather, analyze, and interpret the information. Figure 7.1 depicts the literature review process and shows that there are potential feedback loops, with opportunities to go back to earlier steps in search of more information. Reviews should be unbiased, thorough, and up-to-date. Also, high-quality reviews are systematic. Decision rules for including a study should be explicit because a good review should be reproducible. This means that another diligent reviewer would be able to apply the same decision rules and come to similar conclusions about the state of evidence on the topic. TIP Locating all relevant information on a research question is like being a detective. The literature retrieval tools we discuss in this chapter are helpful, but there inevitably needs to be some digging for, and sifting of, the clues to evidence on a topic. Be prepared for sleuthing! Doing a literature review is in some ways similar to undertaking a qualitative study. It is useful to have a flexible approach to “data collection” and to think creatively about opportunities for new sources of information. LOCATING RELEVANT LITERATURE FOR A RESEARCH REVIEW An early step in a literature review is devising a strategy to locate relevant studies. The ability to locate evidence on a topic is an important skill that requires adaptability—rapid technological changes mean that new methods of searching the literature are introduced continuously. We urge you to consult with librarians or faculty at your institution for updated suggestions. Developing a Search Strategy Having good search skills is important. A particular productive approach is to search for evidence in bibliographic databases, which we discuss next. Reviewers also use the ancestry approach (“footnote chasing”), in which citations from relevant studies are used to track down earlier research on which the studies are based (the “ancestors”). A third strategy, the descendancy approach , involves finding a pivotal early study and searching forward to find more recent studies (“descendants”) that cited the key study. TIP You may be tempted to begin a literature search through an Internet search engine, such as Yahoo, Google, or Bing. Such a search is likely to yield a lot of “hits” on your topic but is unlikely to give you full bibliographic information on research literature on your topic. Decisions must also be made about limiting the search. For example, reviewers may constrain their search to reports written in one language. You may also want to limit your search to studies conducted within a certain time frame (e.g., within the past 10 years). Searching Bibliographic Databases Bibliographic databases are accessed by computer. Most databases can be accessed through user-friendly software with menu-driven systems and on-screen support so that minimal instruction is needed to retrieve articles. Your university or hospital library probably has subscriptions to these services. Getting Started With an Electronic Search Before searching a bibliographic database electronically, you should become familiar with the features of the software you are using to access it. The software has options for restricting or expanding your search, for combining two searches, for saving your search, and so on. Most programs have tutorials, and most also have Help buttons. An early task in an electronic search is identifying keywords to launch the search (although an author search for prominent researchers in a field is also possible). A keyword is a word or phrase that captures key concepts in your question. For quantitative studies, the keywords are usually the independent or dependent variables (i.e., at a minimum, the “I” and “O” of the PICO components) and perhaps the population. For qualitative studies, the keywords are the central phenomenon and the population. If you use the question templates for asking clinical questions in Table 2.1 , the words you enter in the blanks are likely to be good keywords. TIP If you want to identify all research reports on a topic, you need to be flexible and to think broadly about keywords. For example, if you are interested in anorexia, you might look up anorexia , eating disorders , and weight loss and perhaps appetite , eating behavior , food habits , bulimia , and body weight changes . There are various search approaches for a bibliographic search. All citations in a database have to be coded so they can be retrieved, and databases and programs use their own system of categorizing entries. The indexing systems have specific subject headings (subject codes). You can undertake a subject search by entering a subject heading into the search field. You do not have to worry about knowing the subject codes because most software has mapping capabilities. Mapping is a feature that allows you to search for topics using your own keywords rather than the exact subject heading used in the database. The software translates (“maps”) your keywords into the most plausible subject heading and then retrieves citation records that have been coded with that subject heading. When you enter a keyword into the search field, the program likely will launch both a subject search and a textword search. A textword search looks for your keyword in the text fields of the records, i.e., in the title and the abstract. Thus, if you searched for lung cancer in the MEDLINE database (which we describe in a subsequent section), the search would retrieve citations coded for the subject code of lung neoplasms (the MEDLINE subject heading used to code entries) and also any entries in which the phrase lung cancer appeared, even if it had not been coded for the lung neoplasm subject heading. Some features of an electronic search are similar across databases. One feature is that you usually can use Boolean operators to expand or delimit a search. Three widely used Boolean operators are AND, OR, and NOT (in all caps). The operator AND delimits a search. If we searched for pain AND children , the software would retrieve only records that have both terms. The operator OR expands the search: pain OR children could be used in a search to retrieve records with either term. Finally, NOT narrows a search: pain NOT children would retrieve all records with pain that did not include the term children . Wildcard and truncation symbols are other useful tools. A truncation symbol (often an asterisk, *) expands a search term to include all forms of a root. For example, a search for child* would instruct the computer to search for any word that begins with “child” such as children, childhood, or childrearing. In some databases, wildcard symbols (often ? or *) inserted in the middle of a search term permits a search for alternative spellings. For example, a search for behavio?r would retrieve records with either behavior or behaviour. For each database, it is important to learn what these special symbols are and how they work. Note that the use of special symbols, while useful, may turn off a software’s mapping feature. One way to force a textword search is to use quotation marks around a phrase, which yields citations in which the exact phrase appears in text fields. In other words, lung cancer and “lung cancer” might yield different results. A thorough search strategy might entail doing a search with and without wildcard characters and with and without quotation marks. Two especially useful electronic databases for nurses are CINAHL ( C umulative I ndex to N ursing and A llied H ealth L iterature) and MEDLINE ( Med ical Literature On- Line ), which we discuss in the next sections. We also briefly discuss Google Scholar. Other useful bibliographic databases for nurses include the Cochrane Database of Systematic Reviews, Web of Knowledge, Scopus, and EMBASE (the Excerpta Medica database). The Web of Knowledge database is useful for a descendancy search strategy because of its strong citation indexes. TIP If your goal is to conduct a systematic review, you will need to establish an explicit formal plan about your search strategy and keywords, as discussed in Chapter 18 . The CINAHL Database CINAHL is an important electronic database for nurses. It covers references to hundreds of nursing and allied health journals as well as to books and dissertations. CINAHL contains about 3 million records. CINAHL provides information for locating references (i.e., the author, title, journal, year of publication, volume, and page numbers) and abstracts for most citations. Links to actual articles are often provided. We illustrate features of CINAHL but note that some features may be different at your institution and changes are introduced periodically. A “basic search” in CINAHL involves entering keywords in the search field (more options for expanding and limiting the search are available in the “Advanced Search” mode). You can restrict your search to records with certain features (e.g., only ones with abstracts), to specific publication dates (e.g., only those after 2010), to those published in English, or to those coded as being in a certain subset (e.g., nursing). The basic search screen also allows you to expand the search by clicking the option “Apply related words.” To illustrate with a concrete example, suppose we were interested in research on the effect of music on agitation in people with dementia. We entered the following terms in the search field and placed only one limit on the search—only records with abstracts: By clicking the Search button, we got dozens of “hits” (citations). Note that we used two Boolean operators. The use of “AND” ensured that retrieved records had to include all three keywords, and the use of “OR” allowed either dementia or Alzheimer to be the third keyword. Also, we used a truncation symbol * in the second keyword. This instructed the computer to search for any word that begins with “agitat” such as agitated or agitation. By clicking the Search button, all of the identified references would be displayed on the monitor, and we could view and print full information for ones that seemed promising. An example of an abridged CINAHL record entry for a report identified through this search is presented in Figure 7.2 . The title of the article and author information is displayed, followed by source information. The source indicates the following:   Name of the journal ( Geriatric Nursing )   Year and month of publication (Jan/Feb 2016)   Volume (37)   Issue (1)   Page numbers (25–29)

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Ancestry Studies in Forensic Anthropology: Back on the Frontier of Racism

Ann h. ross.

1 Human Identification & Forensic Analysis Laboratory, Department of Biological Sciences, North Carolina State University, Raleigh, NC 276995, USA

Shanna E. Williams

2 Department of Biomedical Sciences, UofSC School of Medicine Greenville, Greenville, SC 29605, USA; ude.cs.demellivneerg@2993LLIW

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Simple Summary

Within the practice of forensic anthropology ancestry is oftentimes used as a proxy for social race. This concept and its implications were explored via a content analysis (2009–2019) of the Journal of Forensic Sciences. Our findings revealed antiquated views of race based on the trifecta of continental populations (Asia, Europe, and Africa) continue to be pervasive in the field despite scientific invalidation of the concept of race decades earlier. Moreover, our employment of modern geometric morphometric and spatial analysis methods on craniofacial coordinate anatomical landmarks from several Latin American samples produced results in which the groups were not patterned by ancestry trifecta. Based on our findings we propose replacing the assumption of continental ancestry with a population structure approach that combines microevolutionary and cultural factors with historical events in the examination of population affinity.

One of the parameters forensic anthropologists have traditionally estimated is ancestry, which is used in the United States as a proxy for social race. Its use is controversial because the biological race concept was debunked by scientists decades ago. However, many forensic anthropologists contend, in part, that because social race categories used by law enforcement can be predicted by cranial variation, ancestry remains a necessary parameter for estimation. Here, we use content analysis of the Journal of Forensic Sciences for the period 2009–2019 to demonstrate the use of various nomenclature and resultant confusion in ancestry estimation studies, and as a mechanism to discuss how forensic anthropologists have eschewed a human variation approach to studying human morphological differences in favor of a simplistic and debunked typological one. Further, we employ modern geometric morphometric and spatial analysis methods on craniofacial coordinate anatomical landmarks from several Latin American samples to test the validity of applying the antiquated tri-continental approach to ancestry (i.e., African, Asian, European). Our results indicate groups are not patterned by the ancestry trifecta. These findings illustrate the benefit and necessity of embracing studies that employ population structure models to better understand human variation and the historical factors that have influenced it.

1. Introduction

Forensic anthropology is a sub-discipline of biological anthropology, the science of studying what it means to be human via our biology. Forensic anthropologists are experts in human skeletal anatomy, growth, and development; expertise that we use in medicolegal death investigations for the recovery and analysis of human skeletal remains. A significant part of our analysis is the creation of the biological profile, an evaluation of four criteria that may assist with identification: age-at-death, sex (for adult skeletons), stature, and ancestry [ 1 ]. The estimation of ancestry is one of the most difficult (and controversial) parameters of the biological profile. It is often conflated with social race and ethnicity by medical examiners, law enforcement, forensic practitioners, and government agencies. Further, some practitioners have questioned the validity of estimating this parameter and if the estimation could even hinder identification because of racial bias on the part of investigative agencies [ 2 , 3 , 4 ]. Part of the reason its use is so controversial is that the biological race concept, namely, that the human species can be divided into biological races, was debunked decades ago [ 5 ]. In the 1990s there was discord within biological anthropology stimulated by a paper by Lieberman and colleagues [ 6 ], presented earlier in 1987 at the American Association of Physical Anthropologists annual meeting that reported 50% of the biological anthropologists polled believed in the race concept. Forensic anthropologists argued that it was a pragmatic decision to include “race” in their forensic case reports as “race” was used by law enforcement and medicolegal death investigators working the missing and unidentified cases [ 7 ]. Thus, in 1992 a name change from “race” to “ancestry” was proposed as a less loaded term [ 7 ]. This has been rationalized by the notion that we can connect craniofacial morphology (i.e., size and shape variants of skull bone features) to social race categories (e.g., United States Census categories) [ 8 , 9 ]. However, some biological anthropologists questioned the ethics of even estimating this parameter fearing that its continued use would endorse racist views and be complicit in the social injustices faced by underrepresented groups [ 2 , 10 , 11 , 12 ].

In a search for the term “ancestry” in the titles of the Journal Forensic Sciences (JFS) between the years of 2009-2019, 20 articles used “ancestry” and in 2010 and 2011, two articles still used “race.” The term ancestry appeared 24 times in the keywords between 2009-2019, with four papers using samples identified as black , white , and Hispanic . Five papers used samples identified as black and white , which included a paper on South African blacks and whites . There were 12 papers with various iterations of “ Hispanic ” (i.e., South West Hispanics ); as well as papers that defined their samples as Prehistoric Native Americans ; those that use a few country names (e.g., Japanese, Guatemala, Germany, Thailand, etc.); and a paper on Native American , Japanese , and Thai samples. This literature review clearly illustrates the lack of purpose, consensus, and consistent usage of the nomenclature; suggesting that the transition from race to ancestry was primarily a linguistic change (see [ 13 ] that covers the problems with nomenclature). The many iterations of “Hispanic” are a result of the 2008 migrant death symposium at the American Academy of Forensic Sciences annual meeting dealing with the difficulty of identifying unidentified border-crossers (UBCs) in the United States. Interestingly, the term Hispanic is still commonly used even though it has no biological meaning [ 14 ], and going as far back as 1992, pioneering forensic anthropologist Alice Brues understood that “Hispanics” from South Florida, the Southwest, and Texas should not be grouped under one umbrella because they represented different population migrations to the US [ 14 , 15 ].

The results of this literature review also illustrate the return to antiquated and over-simplistic views of race based on the trifecta of continental populations from Asia, Europe, and Africa used by typologists of the early 20th century, have regained popularity [ 16 ]. In part, this is because the reference databases we rely on to compare cranial measurements of an unknown person were constructed using such categories. However, this facile presumption ignores underlying microevolutionary mechanisms such as drift, migrations, and mutation that are responsible for human variation and diversity. Studies of global populations reveal that human craniofacial morphology fits a neutral evolutionary model because contiguous populations more frequently exchange genes and/or share common ancestry [ 17 ].

Therefore, rather than studying population affinity via an assumption of continental ancestry, we instead advocate for a population structure approach. The benefit of such an approach is that it allows us to understand how microevolutionary factors such as genetic drift act in concert with cultural factors (i.e., marriage patterns) and historical events (i.e., epidemics, colonization) to influence human variation. A population structure approach is empirically driven, meaning that it is based on firm observations without phylogenetic assumptions and by operational approaches that are hypothesis-driven by meaningful questions [ 18 ]. When comparing populations one can select various types of characters for investigation such as morphology, physiology, behavior, and/or ecology. However, common mistakes made in the selection of a character for estimating similarity is a failure to identify the biological factors that the characters represent (i.e., their heredity) and assumptions that they are all equally informative in providing evidence of group (i.e., phenetic) similarity [ 18 ]. One example of the former is with the use of the skull trait variant post-bregmatic depression [ 3 , 4 ]. As noted, a major consideration in the application of a population structure approach is to account for historical events such as population influxes and settlements, religious secularization, language differences, temporality, and spatial patterning that would be impacted by microevolutionary forces [ 19 ].

Myopically, forensic anthropology abandoned the study of human biological variation based on a strong foundation of examining human variation through a population structure lens grounded in microevolution, and instead re-embraced a typological approach that looks a lot like “race” of the early years [ 20 , 21 ]. Therefore, it is clear that a broad synthesis to better understand the underlying patterns of modern human variation that would disclose the underlying population structure of the group(s) under study is needed. Such information would also be of use to biological anthropology more broadly. Here, we use craniofacial coordinate anatomical landmarks from Latin American samples while employing modern geometric morphometric and spatial analysis methods to test the validity of applying the antiquated tri-continental approach to ancestry. These samples were chosen given the stated problems with the comprehensive, non-critical use of the “ Hispanic ” label for anyone from Latin America or Spain, and in an attempt to partition out how different historical socio-political events within Latin America have influenced biological variation. Further, we discuss how situating such approaches within a microevolutionary framework can enrich our understanding of how major historical events influence human variation and population structure.

2. Materials and Methods

2.1. samples.

The sample totals 397 modern adult individuals and includes individuals from Latin America (Chile, Colombia, Cuba, Guatemala, Panama, Puerto Rico, and Peru); and comparative skeletal samples from Spain and enslaved Africans from Cuba were included to explore the effects of colonialism and the Transatlantic Slave Trade on the population structure of the region. Males and females were analyzed separately when this information was available (see Table 1 ). Some samples were small due to poor preservation in tropical environments. To incorporate all of the observed biological information and to increase sample sizes males and females were pooled as it has been found that sex variation is negligible within each population included in population [ 22 ]. Latitude and longitude were recorded based on present-day political boundaries. The sample composition is presented in Table 1 .

Sample composition and provenience.

While we acknowledge the value data collected from such samples continue to contribute to discussions of human variation, it should also be noted that the history and ethics of human skeletal collections, in general, is often dubious. Such body harvesting all too often occurred under the umbrella of scientific racism, without the permission of the deceased or next of kin, and disproportionately targeted marginalized populations.

Sixteen type 1 and 2 standard anatomical craniofacial landmarks (for a total number of landmarks 16 × 3 dimensions = 48) that should reflect the among-group variation were utilized in the analyses ( Table 2 and Figure 1 ). The landmarks selected were those that are of particular interest in forensic anthropology and that would allow for broader shape coverage. To mitigate the effect of small sample sizes, a PCA was used as a dimension-reducing technique and limiting the number of variables [ 23 , 24 ].

Anatomical landmark location and associated landmark number from Table 1 .

Anatomical landmarks and associated numbers.

2.2. Landmark Precision and Reliability

Only type 1 and type 2 landmarks were included as they have been found to be reliably reproducible [ 25 ]. The landmarks included are those that were found to meet the less than 5 percent error threshold for digitizing and intra-observer error [ 25 ]. The coordinate data were collected using a Microscribe G2X digitizer with a reported average error rate of 239 mm [ 26 ]. These samples are part of the reference database for the classification software 3D-ID [ 27 ] and prior to inclusion in the software, data underwent extensive error checks via mapping (i.e., visualization) of all individuals using the Generalized Procrustes analysis or GPA function in Morpheus et al. [ 28 ].

2.3. Geometric Morphometrics

Before statistical analyses can be performed, coordinate data must first undergo a GPA transformation using the software MorphoJ , which is freely available for downloading and developed by Klingenberg [ 29 ]. GPA brings all specimens into a common coordinate system, after it translates, rotates, and scales each individual. The advantage of this method is that morphological shape and size can be examined separately, with shape defined as all of the geometric information that remains after the effects of location, scale, and rotational effects are removed [ 30 , 31 ]. Centroid size is defined as a measure of geometric scale that is mathematically independent of shape [ 31 ]. To reduce the dimensionality, a principal component analysis (PCA) of the covariance matrix was performed on the GPA-transformed coordinate data and these principal components were utilized for ensuing statistical analyses [ 31 ]. A canonical variates analysis (CVA) was performed to examine the most amount of the variation with the least dimensions possible of the a priori groups [ 29 ]. A generalized distance measure (or Mahalanobis distance) was used to examine group similarity [ 29 ]. A discriminant function analysis (DFA) was performed to visualize morphological variation between the consensus configurations of each group. The phenetic (e.g., morphological) among-group variation was examined using ANOVA for centroid size. Among-group variation for shape was analyzed using MANOVA of the principal components scores derived from MorphoJ. The ANOVA and MANOVA procedures were performed in JMP ® Pro 14.1 [ 32 ].

2.4. Hierarchical Clustering

Average linkage hierarchical (or agglomerative) clustering was conducted using the generalized distance matrix to examine group similarity [ 33 , 34 ]. The process begins with each population sample in a single cluster, then in each successive iteration, it merges the closest pair of clusters until all the data is in one cluster. The cluster analysis was performed in JMP ® Pro 14.1 [ 32 ].

2.5. Spatial Analysis

Moran’s I , a product-moment coefficient, was used to measure the spatial autocorrelation of shape (PC1 as only one variable can be utilized) and centroid size, which is a measure of genetic similarity between individuals with reference to geographic separation (latitude/longitude). Spatial correlograms were computed to evaluate the spatial autocorrelation coefficients for all pairs of localities at specified geographic distance classes [ 35 ], and were performed using the freeware software GeoDa v1.14.0 [ 36 ].

3.1. Geometric Morphometrics

Forty-one PC scores were generated from the covariance matrix, which were used as new variables in the subsequent statistical analyses. The ANOVA shows that size is significantly different among the groups (Centroid size: (F (11, 385) = 22.35, p ≤ 0.0001). The MANOVA (of 41 principal component scores derived by MorphoJ) also detected significant shape variation (Shape: Wilks’ Lambda 0.0058, df = 451, 3706.6, F = 5.12, p ≤ 0.0001). The anatomical landmarks used here are in the same location on each skull; this property enables evaluation and observation of any distinctions in overall cranial shape and size between groups. Morphological variation is illustrated via wireframe graphs that depict the magnitude and direction of shape change between two mean configurations with the direction of change depicted from light (light blue) to dark (blue). The starting shape is that of one sample mean configuration that is deformed into a target shape (second sample) mean configuration to visualize the differences. The groups illustrated were selected according to the clusters produced by the hierarchical cluster analysis. The similarity between the Chilean male mean configuration (light blue) and the Spanish male mean configuration (blue) is visualized showing little to no variation in the placement of the anatomical landmarks ( Figure 2 ).

Wireframe (superior view) depicting the Chilean male mean configuration (starting shape, light blue) deformed into the Spanish male mean configuration (target shape, blue). The numbers correspond to the landmarks in Table 2 .

To illustrate the importance of a population approach, Panama and Colombia, Panama and enslaved Africans, and Panama and Spanish consensus configurations were compared based on known historical events (i.e., conquest, colonialism, and slavery). The morphological differences between the Colombians and the Panamanians show that the Colombians (light blue) have shorter and narrower crania than Panamanians (blue), depicted by the more posteriorly and inferiorly placed anatomical landmarks bregma and lambda and more superiorly placed anatomical landmarks asterion and zygomaxillare ( Figure 3 ). It also shows that Colombians have a longer upper facial height with the anatomical landmark nasion positioned more superiorly and a more inferiorly placed anatomical landmark zygomaxillare. Enslaved Africans (light blue) have longer and narrower cranial vaults with anatomical landmark lambda more posteriorly placed and asterion more anteriorly placed compared to Panamanians (blue). The wireframe depicting the starting shape of Panamanians (light blue) shows that they have shorter cranial vaults and a shorter and more projecting upper face as evidenced by the more anteriorly placed anatomical landmarks subspinale, bregma, and lambda, and more inferiorly positioned anatomical landmarks bregma and zygomaxillare than the Spaniards’ target shape (blue), see Figure 3 .

Wireframes depicting the ( a ) Panama (light blue) into Spanish males (blue); ( b ) Colombian males (light blue) into Panama (blue); ( c ) Enslaved Africans (light blue) into Panama (blue). Numbers correspond to landmarks in Table 2 .

3.2. Hierarchical Clustering

The dendrogram produced from the hierarchical cluster analysis using the generalized distance matrix shows two distinct clusters: (1) Chile/Spain and (2) Panama, Cuba, Guatemala, and Colombia which branch off the Chile/Spain cluster. The enslaved African sample clusters with Peru, and Puerto Rico is the most dissimilar. This is further illustrated by the constellation plot ( Figure 4 ), which arranges the samples as endpoints. The length of a line between cluster joints represents the distance between them. The plot shows that the most distinct group is the sample from Puerto Rico, which is three times the distance from the Colombian samples and closest to Peru and enslaved African samples. Chileans and Spaniards are closer to each other than to the rest of the groups.

Constellation plot ( a ) and dendrogram ( b ) results from hierarchical cluster analysis showing group relationships.

3.3. Spatial Analysis

The spatial autocorrelation for shape (using PC1 accounting for 21 percent of the total variance) and size show that the groups are spatially patterned and heterogeneous indicated by the positive and significant Z-scores ( Table 3 ). While the correlograms show the autocorrelations decreasing with increased distance, the pattern is generally non-monotonic, meaning the pattern is not clinal as would be expected under an isolation-by-distance model such as kinship [ 35 ], for both shape and centroid size. Autocorrelations are expected to be positive at closer distances and negative at greater distances ( Figure 5 ). The correlograms do not support a clinal pattern.

Correlograms for shape ( a ) and size ( b ) depicting the spatial autocorrelation. Moran’s I by distance in kilometers.

Moran’s I results for shape using the first principal component and size using centroid size with reference to geographic location.

4. Discussion

Even though forensic anthropology as a discipline has moved away from using the term “race” to that of “ancestry”, the early critics of race estimation in forensics questioned whether the underlying approach to ancestry would change. Thirty years have passed since this initial criticism and as evidenced by the research published during this time period, ancestry studies have not advanced past the typological (see for example [ 37 ]). It is also clear that current research is not fundamentally grounded in an evolutionary framework to understand what has shaped modern human craniofacial [ 3 , 4 ]. The studies surveyed as part of our content analysis show an over-simplistic, typological, tri-continental approach that underscores the need for a paradigm shift to a population structure approach, which incorporates the study of population affinity to understanding modern human biological variation. This paradigm shift can be applied through meaningful hypotheses and avoiding thoughtless comparisons of one sample to another without purpose (e.g., Thai to European Americans, etc.) and by utilizing non-racialized and appropriate reference samples in forensic classification software. For example, implementing nomenclature changes and sample selection in existing commonly used forensic software such as Fordisc [ 38 ], which uses inconsistent terms such as “White, Black, Hispanic, Guatemala, and Japanese”, which reflect continental-level, biologically meaningless, and/or country labels; and AncesTrees [ 39 ], which uses prehistoric samples that are not applicable for forensic use with antiquated six race categories based in typology, would be a good path forward.

In a recent regional population structure study of pre-contact New World craniofacial variation, Ross and Ubelaker [ 40 ] demonstrated that craniofacial variation was a complex interplay between the environment and microevolutionary forces and not the result of a single mechanism. They demonstrated that generally, these pre-Contact populations were spatially patterned, consistent with an isolation-by-distance model. However, they also found a weak association between shape-related variation and altitude, and climate. In the present study, a similar population structure approach was applied to modern Latin American samples to test whether the antiquated trifecta approach to ancestry was valid. Our results demonstrate that Puerto Rico is the most different from the others; Spain and Chile are the most similar to each other compared to the other samples; Panama, Cuba, Guatemala, and Colombia link to the Spain and Chile cluster; and Peru and enslaved Africans form a separate cluster.

The Spanish conquistadors brought enslaved Africans with them beginning as early as 1501 to the Caribbean coast of Panama to colonize the New World [ 41 ]. Before the arrival of the Spaniards, there were an estimated 25,000 Amerindians in Panama; by 1522 their population estimates were 13,000 [ 41 ]. As a result of the decimation of these Indigenous populations resulting from epidemics and warfare, the Spaniards forced migrations of neighboring Indigenous populations from Panama and Nicaragua; and during Pizarro’s expedition to Peru in 1527, 10,000 Amerindians were forcibly displaced to Peru [ 41 ]. The association of the Spanish and Chilean samples can be therefore explained through the complex history of conquest and colonialism.

The city of Santiago, Chile was founded in 1542 by Spanish conquistador Pedro de Valdivia. However, the Spanish conquest of Chile was delayed by a long war with Auracanian Indians [ 42 ]. During the colonial period, entire Indian populations were decimated by disease and forced labor [ 42 ]. From the time of European arrival, slavery of abducted Africans was present, primarily on the Caribbean coast of South America (e.g., Venezuela and Colombia) and in Ecuador and Peru, as well as [ 42 ]. Recent work focused on La Isabela, the settlement established after Christopher Columbus’ second voyage to what is now the Dominican Republic, suggests that at least one person of African origin was present [ 43 ]. The influence of the Transatlantic Slave Trade was detected here by the hierarchical cluster analysis linking Peru and the enslaved African samples. The constellation plot further elucidates the relationship among the groups and illustrates that while the sample from Puerto Rico is the most dissimilar, it is closest to the Peru-enslaved African cluster, followed by Colombia, Guatemala, Cuba, and Panama—all depicting early contact with the Spanish conquistadors that brought enslaved Africans. The spatial analysis was used to assess if there was a spatial pattern based on geographic location. While Moran’s I was significant and positive for both shape and size, the correlograms show that they are not clinal. The morphological variation for pre-contact populations suggests heterogeneity from the initial population diffusion into the New World prior to European contact [ 40 ]. While there is a morphological spatial pattern of modern Latin Americans they do not show a monotonic decrease with distance, but rather indicate repeated population migrations and expansions such as European colonization, the Transatlantic Slave Trade, and forced migrations of Indigenous groups [ 44 ]. The argument that there are no races, only clines (or a neutral evolutionary model because neighboring populations more frequently exchange genes and/or share a common ancestry) is not supported here. This finding illustrates a more complex mechanism of modern craniofacial variation and underscores the need for applying a population structure and evolutionary lens to the practice of forensic anthropology.

We use Panama with its complicated history, which has been coveted since the Spanish conquest for its geographic feature as a land bridge of the American continents between the Atlantic and Pacific Oceans, to illustrate the complex nature of assessing population affinity in forensic practice. During the Spanish colonial period, jurisdiction for the Panama territory passed from the Viceroyalty of Lima to Bogotá in the 18th century; it finally gained independence from Spain in 1821 but was part of the Republic of Colombia until 1903 [ 41 ]. Importantly, before Panama’s split from Colombia, in 1847, a United States merchant set out to build a railroad across the Isthmus that would combine land and sea and open up the Pacific [ 45 , 46 ]. During its construction, a workforce was brought from across the globe (e.g., Austria, China, Colombia, England, France, Germany, India, Ireland, and Jamaica) with thousands dying of malaria, yellow fever, and hardships from the tropical environment [ 47 ]. Another important milestone after the failed attempt by the French in the late 1800s was the enormous federally funded undertaking by the United States from 1904–1914 to build an interoceanic canal, a massive earthwork project the likes of which had never been attempted [ 40 , 47 ].

These trans-isthmus ventures brought thousands of migrant workers (~60% from the West Indies) to Panama. The racial contrast of the workers to the engineers and project leaders is crucial to understanding the societal organization and marginalization in the Panama Canal Zone [ 40 ]. The colonial caste system transformed into the rigid racial categories imposed by the United States in the Panama Canal Zone, which segregated the workforce both physically and geographically. The Panama Canal Zone was a socialist experiment divided by the white elite minority and the West Indian majority. European Americans showed open disdain for the Panamanians which combined with a culture of flagrant inequality inherited from Spain [ 40 , 47 ]. This segregation, an apartheid not witnessed in any other 20th-century Latin American country [ 40 ], was still unmistakable as late as 1986 when the first author graduated from secondary school in the former Zone. Given the complexity of Panama’s history, our results are therefore not surprising when viewed against this backdrop. An analysis that rather solely focused on rigid ancestral categories would not have been able to pinpoint Panamanians’ dissimilarity to neighboring countries, in particular to Colombia with their shared history under colonial rule. In modern forensic anthropology, all of these heterogeneous groups would have been erroneously designated under the label “Hispanic.”

The results of the present study demonstrate that there is substantial diversity in Latin American populations, typically organized into the biologically meaningless grouping of “Hispanic” in contemporary forensic practice. Furthermore, this study obviates the rejection of the tricontinental approach to ancestry estimation and underscores the need for applying a population structure approach with an evolutionary lens to not only understand factors that have influenced craniofacial morphology but test hypotheses about population movements and the impact of major historical events such as conquest and slavery.

5. Conclusions

In 2000, Smay and Armelagos [ 2 ] stated that “it was interesting that the word race was being replaced by the less provocative term ancestry”, while also indicating they doubted that the logic behind race would change and that the analysis of races using exclusive categories based on folk taxonomy would continue simply under a different name—they were right. Ancestry has become a synonym for race. Given our current global political climate, continuing to type individuals in this way lends credence to existing power structures and socioeconomic inequalities. A mere word change is like putting lipstick on a pig, an ineffective attempt at beautifying and obfuscating something whose unsightly features are still evident. We need a fundamental, structural, and thoughtful shift in our paradigm beginning with hypotheses driven by meaningful questions and careful selection of informative characters for investigation. We need a return—or rather, beginning—to investigating real human biological variation.

Acknowledgments

We thank Kate Spradley for providing the Guatemalan sample, and John Fredy Ramirez for access to the Antioquia Modern Skeletal Reference Collection in Medellín, Colombia. We thank Elizabeth DiGangi and Jonathan Bethard for making the Colombian data available and for comments on the manuscript. We also thank Chris Kligenberg for MorphoJ guidance.

Author Contributions

A.H.R., S.E.W. conceptualized the paper, collected the data, and contributed to writing and editing the manuscript. A.H.R. performed the statistical analyses. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

How to undertake a literature search: a step-by-step guide

Affiliation.

• 1 Literature Search Specialist, Library and Archive Service, Royal College of Nursing, London.
• PMID: 32279549
• DOI: 10.12968/bjon.2020.29.7.431

Undertaking a literature search can be a daunting prospect. Breaking the exercise down into smaller steps will make the process more manageable. This article suggests 10 steps that will help readers complete this task, from identifying key concepts to choosing databases for the search and saving the results and search strategy. It discusses each of the steps in a little more detail, with examples and suggestions on where to get help. This structured approach will help readers obtain a more focused set of results and, ultimately, save time and effort.

Keywords: Databases; Literature review; Literature search; Reference management software; Research questions; Search strategy.

• Databases, Bibliographic*
• Information Storage and Retrieval / methods*
• Nursing Research
• Review Literature as Topic*

• Help & FAQ

Ancestry: How researchers use it and what they mean by it

Research output : Contribution to journal › Article › peer-review

Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term “ancestry” is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered. Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles. Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many—including geneticists—ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups. Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a “biological” construct, and it does not represent a “safe haven” for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity.

• genetic ancestry
• population descriptors
• population labeling

ASJC Scopus subject areas

• Genetics(clinical)
• Molecular Medicine

This output contributes to the following UN Sustainable Development Goals (SDGs)

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• 10.3389/fgene.2023.1044555

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• Link to publication in Scopus
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• Researcher Medicine and Dentistry 100%
• Genetics Biochemistry, Genetics and Molecular Biology 100%
• Methapyrilene Pharmacology, Toxicology and Pharmaceutical Science 100%
• genetics INIS 100%
• Genetic Research Medicine and Dentistry 50%
• Genetic Variation Medicine and Dentistry 50%
• Comprehension Medicine and Dentistry 50%
• Lead Pharmacology, Toxicology and Pharmaceutical Science 50%

T1 - Ancestry

T2 - How researchers use it and what they mean by it

AU - Dauda, Bege

AU - Molina, Santiago J.

AU - Allen, Danielle S.

AU - Fuentes, Agustin

AU - Ghosh, Nayanika

AU - Mauro, Madelyn

AU - Neale, Benjamin M.

AU - Panofsky, Aaron

AU - Sohail, Mashaal

AU - Zhang, Sarah R.

AU - Lewis, Anna C.F.

N1 - Publisher Copyright: Copyright © 2023 Dauda, Molina, Allen, Fuentes, Ghosh, Mauro, Neale, Panofsky, Sohail, Zhang and Lewis.

PY - 2023/1/23

Y1 - 2023/1/23

N2 - Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term “ancestry” is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered. Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles. Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many—including geneticists—ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups. Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a “biological” construct, and it does not represent a “safe haven” for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity.

AB - Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term “ancestry” is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered. Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles. Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many—including geneticists—ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups. Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a “biological” construct, and it does not represent a “safe haven” for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity.

KW - ancestry

KW - ethnicity

KW - genetic ancestry

KW - population descriptors

KW - population labeling

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UR - http://www.scopus.com/inward/citedby.url?scp=85147456817&partnerID=8YFLogxK

U2 - 10.3389/fgene.2023.1044555

DO - 10.3389/fgene.2023.1044555

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SN - 1664-8021

JO - Frontiers in Genetics

JF - Frontiers in Genetics

M1 - 1044555

ORIGINAL RESEARCH article

Ancestry: how researchers use it and what they mean by it.

• 1 Center for Global Genomics and Health Equity, University of Pennsylvania, Philadelphia, PA, United States
• 2 Department of Sociology, Northwestern University, Evanston, IL, United States
• 3 Edmond & Lily Safra Center for Ethics, Harvard University, Cambridge, MA, United States
• 4 Department of Anthropology, Princeton University, Princeton, NJ, United States
• 5 Department of the History of Science, Harvard University, Cambridge, MA, United States
• 6 Broad Institute of Harvard and MIT, Cambridge, MA, United States
• 7 Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
• 8 Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
• 9 Institute for Society & Genetics, University of California, Los Angeles, Los Angeles, CA, United States
• 10 Department of Public Policy, University of California, Los Angeles, Los Angeles, CA, United States
• 11 Department of Sociology, University of California, Los Angeles, Los Angeles, CA, United States
• 12 Centro de Ciencias Genomicas (CCG), Universidad Nacional Autonoma de Mexico (UNAM), Cuernavaca, Morelos, Mexico
• 13 University of California, Berkeley, Berkeley, CA, United States
• 14 Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States

Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term “ancestry” is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered.

Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles.

Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many—including geneticists—ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups.

Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a “biological” construct, and it does not represent a “safe haven” for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity.

Introduction

The use of population descriptors is currently under the spotlight, both in genetics research specifically and across biomedical research more broadly ( National Academies 2021 ; Vyas, Eisenstein, and Jones 2021 ; Khan et al., 2022 ). Social scientists have studied how race and ethnicity are used, but have paid much less attention to ancestry. Understanding how researchers conceptualize and use ancestry matters—both to genetics and to biomedicine more broadly—for several reasons. First, it is a key concept drawn upon in decisions about who we study and why ( Popejoy and Fullerton 2016 ; Bentley, Callier, and Rotimi 2017 ). Second, because it plays a key role in making sure methodologies yield robust and replicable results ( Martin et al., 2019 ; Peterson et al., 2019 ). Third, because further reliance on genetic ancestry is part of the proposed solution to the use of race in biomedicine ( Borrell et al., 2021 ; Oni-Orisan et al., 2021 ). Fourth, decisions made in research directly impact translational work, and ultimately medical practice ( Popejoy et al., 2018 ). And finally, understanding this concept is important because it is a key frame offered for understanding biological differences between groups of humans—including those that could be driving race-based health disparities ( Batai, Hooker, and Kittles 2021 ). This is an ethically fraught topic with a long and unpleasant history ( Reardon 2005 ; Roberts 2011 ; Bliss 2020a ). The stakes are hence high to ensure that concepts originating in genetics do not result in repetition of past atrocities stemming from the categorization of humans into a small number of biological types ( Mathieson and Scally 2020 ; Lewis et al., 2022 ).

A better understanding of how researchers use ancestry can also help provide raw material for normative recommendations about how the concept should be used. The National Academies of Science, Engineering and Medicine (NASEM) are currently convening a taskforce on the appropriate usage of race, ethnicity and ancestry in genetics research ( National Academies 2021 ). The appropriate usage of race, ethnicity, and ancestry is not a new topic: a scoping review found over 100 articles offering relevant normative guidance published since 2000 ( Mauro et al., 2022 ). A consistent theme in these recommendations has been the need for transparency by researchers, including why they are using population categories, and how any population categories used are defined ( Mauro et al., 2022 ). The use of race and ethnicity have been the main focus of these normative debates to date, with ancestry receiving relatively little scrutiny. For example, guidance from the American Medical Association has solely focused on race and ethnicity ( Flanagin et al., 2021 ). It has been explicitly suggested that ancestry is the least controversial of the population descriptors ( Lee, Mountain, and Koenig 2001 ), and this assumption seems to drive much of the move away from race and ethnicity categories. It is also seen as the most objective classifier. Pointing out how race and ethnicity categories are broad, imprecise, and ambiguous, Borrell et al. write, “In contrast, ancestry is a fixed characteristic of the genome” ( Borrell et al., 2021 ). Recent content analysis of research articles published in the American Journal of Human Genetics has shown that the term “ancestry” is increasingly used in genetics research ( Byeon et al., 2021 ). As Wagner et al. have argued, the increasing focus on ancestry as a way to “frame human difference” should be a motivating factor to bring more attention to its use ( Wagner et al., 2017 ).

There have been empirical insights into researchers’ use of ancestry. Articles using ancestry (compared to race or ethnicity) were the least likely to provide a rationale for its use ( Ali-Khan et al., 2011 ). Interviews with health researchers have demonstrated the degree of confusion amongst researchers about the interrelationships between race and ethnicity and genetic differences between populations ( Baer et al., 2013 ). Drawing on content analysis of articles published in Nature Genetics and interviews, Panofsky and Bliss explore geneticists’ use of population labels, demonstrating the increasing use of continental labels, which they argue are fundamentally ambiguous because they “blur racial and geographic understandings of population difference” ( Panofsky and Bliss 2017 ). In an ethnography of geneticists’ use of principal components analysis (PCA) to capture genetic ancestry, Fujimura and Rajagopalan argue that while there are opportunities to update how the field thinks about human biological difference, race and ethnicity nonetheless enter into the concept of ancestry ( Fujimura and Rajagopalan 2011 ). Focusing on biomedical articles using the terms “black”, “African” and “African American” Duello et al. find that most studies do not give a rationale for their focus on these populations, and conclude “we infer the authors of these studies believe African ancestry denotes a biological ‘race’ of people of common descent who share DNA unique from the rest of mankind” ( Duello et al., 2021 ).

Researchers across multiple fields employ the concept of ancestry; it is not straightforwardly a concept that is “owned” by genetics. The concept of ancestry is often employed in everyday conversation, carrying sociocultural implications outside of its usage in genetics and health research. This use across contexts gives many opportunities for miscommunication about what ancestry is and is not, and what we can learn about or from it. Genetics has a special role in our understanding of ancestry, but, as mentioned above, this concept then diffuses out to translational research, to the practice of medicine, and to popular conceptions about the human family tree.

In this study, we employ a mixed methodology—a systematic literature analysis and semi-structured interviews—to offer a comprehensive examination of how ancestry is used by researchers. Because diverse domains use the concept of ancestry, and because they are of mutual relevance to each other, we include research from multiple disciplines. We seek to answer five questions about researchers’ use of ancestry. First, what types of research use ancestry? Second, when and why does ancestry enter the research process, i.e., what are the use cases for the concept? Third, what does ancestry mean to researchers, i.e., what definitions do researchers offer for the concept? Fourth, how is ancestry operationalized, i.e., how is this abstract concept made into a measurable observation? And finally, what types of population labels are used for ancestry categories? Answers to the first two questions help indicate just how important the concept of ancestry is in structuring both research questions and methodologies; answers to the remaining questions shed light on whether ancestry as currently conceptualized and operationalized can bear this heavy weight.

Materials and methods

Study design.

This study employed two different methodologies to understand the use of ancestry by researchers: a systematic literature analysis and semi-structured interviews. The systematic literature analysis was performed on an original dataset composed of a sample of research articles in the population sciences. We designed this corpus of original research articles to capture as much of the diversity of the ways that ancestry is currently used by researchers as possible, in terms of divergent research questions and methodologies across disciplines. Given the motivation of our work to inform the use of ancestry and genetic ancestry across the biomedical sciences, we constrained the articles in our corpus to have at least some tangential relevance to human health. The study also aimed at diversity in terms of publication journals such that not only articles published in high impact factor journals were selected for the study, as this might represent a selection bias towards particular types of studies (e.g., Large N). We also identified a subset of these articles as engaging with the concept of ancestry particularly closely, and invited the first and last authors of these articles to participate in a semi-structured interview. This multi-method approach was designed to allow us to develop a robust understanding of researchers’ use of ancestry, with the systematic literature analysis revealing patterns of usage, and the interviews allowing us to understand why we observed these patterns. This strategy yielded 205 articles and 44 interviewees.

Article inclusion strategy

All searches were based on Web Of Science (WOS) and conducted in February 2021. We restricted all searches to articles that contained “ancestry” in the title, abstract, or keywords, and then deployed two search strategies. First, we restricted articles to those concerning certain phenotypes, published from 2019 on. This focus on phenotypes ensured that we obtained diversity along other dimensions that we cared about, specifically research methodology. We chose a range of different types of conditions, all of which (like most health conditions) present known health disparities: COVID-19, prostate cancer, chronic kidney disease, and schizophrenia. These articles were filtered to just retain original research articles where the phenotype of interest was a central explanandum of the article. Second, additional searches were conducted in order to obtain a sufficient number of anthropology, social science, and public health articles. The start year was adjusted to ensure an adequate sample size; this meant starting in 2010 for sociology (extending back to 2010 was necessary to achieve sufficient articles to analyze), 2015 for anthropology, and 2020 for public health. For the anthropology and sociology searches, we also required that the term “health” appear in the title, abstract, or keywords. The results from all three searches were then filtered to retain articles that had either a connection to “health” broadly defined, or to human evolution, or to the characterization of human populations. The number of articles from each of the searches before and after filtering are given in Supplementary Table S1 . The search strings and details of filtering are given in the Supplementary Material . The complete list of included articles is included as a Supplementary Material .

The search results were exported, and the PDFs of the articles and their Supplementary Information were downloaded. We used the abstract, journal, and affiliations of first and last authors to assign a primary subfield and field to each article (fields were anthropology, biology, medicine, public health, and sociology, see Supplementary Material for methodology details). The number of articles per field is given in Table 1 , and per subfield in Supplementary Table S2 . We also assigned a Country/Region to an article based on the Country/Region of the first author’s primary affiliation, see Table 1 .

TABLE 1 . Field and Country/Region of first author’s primary affiliation of the articles in our corpus. The countries within the “Others” category are Australia, Canada, New Zealand, Jamaica and Suriname. The US was not aggregated into a region because of the country’s dominance in the discourse around population descriptors in research, likely due to the historic antecedents on the use of race in the country.

Interviewee recruitment and interview guide

Based on reading the articles, we identified 97 that engaged with the concept of ancestry most closely. This was typically because they either used ancestry to frame or motivate their research question, because ancestry was evoked centrally in their methodology, or the term “ancestry” frequently occurred in the text. In order to ensure we heard from researchers at multiple career stages, we invited the first and last authors of these articles to participate. In a small handful of cases, interviewees recommended we contact a middle author or other close collaborator to interview. This yielded 190 names, 166 of which we found emails for. Of these 166, we interviewed 44 (27% response rate). The majority (29) were based in the United States, with 7 based in Europe, 6 in Central and South America, one in Canada, and one in India. The interviewees were assigned a subfield based on their training as inferred through their professional biographies, publication record, and in conversation during the interview. We achieved disciplinary diversity within our interviewees: 7 from anthropology, 14 from biology, 11 from medicine, 6 from public health, and 6 from sociology.

In the interviews, we utilized the article by virtue of which the participant was recruited to probe in greater depth how they use and think about ancestry. The semi-structured interview guide covered five areas: their background and the focus of their work; the justifications for their choices related to ancestry, and how they understood the limitations of these; conceptual questions, including “What does ancestry mean to you?’’; publishing and the mechanisms of funding; and their views on the status of the field.

Interviews were 1 hour long, and conducted by two interviewers, one with a biology background (AL) and one sociologist (SM). The interviews were conducted on a video conferencing platform, recorded, and auto-transcribed. These transcripts were then updated based on the recordings. Identifying information was removed. The interview study was deemed exempt by the Harvard University-Area Committee on the Use of Human Subjects (protocol ID IRB21-0496).

Data analysis

The documents—the full text of the 205 articles and the 44 anonymized transcripts—were first coded. This involves identifying, highlighting, and annotating the appropriate sections of text corresponding to a set of codes. Around 3,000 sections of text from the articles were tagged with 27 codes, and about 2,300 to 34 codes from the interview transcripts. The list of codes covered are given in Supplementary Tables S3, S4 , and further details of the development of these lists of codes and details of the coding process are given in the Supplementary Material .

On a code-by-code basis we then analyzed the sections of coded text for emergent themes. In addition to this qualitative analysis, we established two features of the articles which enable quantitative presentation of results. First, for those articles that operationalize ancestry, we coded the data type(s) they use to do so. Second, we categorized the type of population labels used in the articles. We used the types of population labels as previously analyzed in Panofsky and Bliss ( Panofsky and Bliss 2017 ), with some minor adaptations: see Supplementary Table S5 for these types, with examples. We stress that our corpus of articles is not representative of any clearly defined set of literature, and hence that our results do not generalize to all population sciences. Instead this curated dataset is enriched to identify salient variables that shape researchers’ conceptions of ancestry and to describe a wide variety of uses of the concept in scientific work.

What types of research use ancestry?

The 205 articles in our corpus had diverse research aims, which we group into five categories, giving examples.

The most commonly represented category was articles aiming to understand traits and outcomes. This includes identifying genetic variation linked to traits (often but not always via Genome Wide Association Studies (GWAS) (e.g., ( Legge et al., 2019 ; Lin et al., 2019 ; Du et al., 2020 ))), identifying causal influences on a trait using Mendelian Randomization [e.g., ( Jordan et al., 2019 ; Howe et al., 2020 )], identifying the interplay between genetic and environmental factors [e.g., (X. Chen et al., 2019 ; Ding et al., 2020 )], understanding the impact of structural determinants of health [e.g., ( Thayer et al., 2017 ; Whaley 2020 )], understanding the molecular mechanisms that contribute to a trait/health outcome [e.g., ( Emami et al., 2019 ; Gohlke et al., 2019 )], and controlling for genetics in understanding social traits ( Boardman et al., 2010 ).

A second set of articles, and the second most represented category, aimed at understanding between-group differences in traits/outcomes. Some of these articles compare traits/outcomes between those of different population categories [e.g., ( Weitz, Garruto, and Chin 2016 ; Wong et al., 2019 )] or those with different percentages of a particular ancestry category [e.g., ( Grizzle et al., 2019 ; Fritz et al., 2020 )]. Some articles compare trait-associated genetic variation between ancestry groups [e.g., ( Koga et al., 2020 ; Liu et al., 2020 )]. Some of these articles explicitly couch their efforts in terms of understanding health disparities [e.g., ( Boulter et al., 2015 ; Marden et al., 2016 )].

A third set of articles focuses on understanding genetic structure. These articles aimed to describe and infer population history, to understand evolutionary processes [e.g., ( Macholdt et al., 2015 )], and gain insight into how present-day genetic diversity is shaped [e.g., ( Leishangthem et al., 2020 ; Zhao et al., 2020 )].

A fourth set of articles aimed to understand social identities. This includes understanding how ancestry and genetic ancestry relate to categorical frameworks used in the present such as race [e.g., ( Liebler 2016 ; Paredes 2017 )], and understanding what factors influence these social identities ( Hunley et al., 2017 ). It also involves trying to gain insight into the life histories and lived experiences of those who lived in the past, particularly enslaved individuals [e.g., ( Wasterlain, Costa, and Ferreira 2018 ; Fleskes et al., 2021 )].

A final set of articles evoking ancestry were aimed at directly improving the provision of healthcare. This includes: improving patient/participant engagement for example by understanding the views of those of diverse ancestries ( Menzies et al., 2020 ; Saad et al., 2020 ); developing clinical tools for example by consideration of incorporation of genetic ancestry as a variable [e.g., ( Haas Pizarro et al., 2020 ; Canter et al., 2019 )]; studying the impact of genetic testing, for example by reporting the diagnosis rate by ancestry group ( Groopman, 2019 ); and enabling quality control, for example by comparing genetically inferred ancestry to self-reported data for cell lines ( Hooker et al., 2019 ).

Some articles conducted research that spanned these categories. For example, many of the articles aimed at understanding traits (e.g., a GWAS) additionally include a between-group comparison (e.g., comparing the frequency of an identified variant across ancestry groups). We observed that many articles do not clearly lay out their aims, with the relationship between research question and research motivation somewhat diffuse.

How does ancestry enter the research process?

Ancestry can enter the research process at multiple stages, representing different use cases for the concept.

As seen in the previous section, ancestry can be used to frame the research question when the focus is on the relevance of ancestry to a trait or outcome. The motivation for this is the assumption that ancestry reflects distinctive patterns of genetic difference, and that using it as a key variable will help identify whether genetic factors could be contributing to differences in incidence and prevalence rates of disease, or differences in traits, between groups [e.g., ( Yuan et al., 2020 )]. This can be couched explicitly in terms of understanding whether genetics plays a role in health disparities [e.g., ( Apprey et al., 2019 )].

Ancestry is also used to state the research question when it is seen as defining the population of interest. Two justifications for choice of population of interest were particularly common in our data. First, the lack of pre-existing research in that population, either noting the under-representation of those of certain ancestries in research generally, or the absence of a particular type of study in a particular population. To further strengthen the justification for using the named ancestral group in the study, some articles point to the consequences of not doing this work, for example to “ exacerbate existing health disparities” ( Harlemon et al., 2020 ). The second common justification given was the high prevalence of a phenotypic trait in that ancestry group/population.

Ancestry can also enter at the analysis stage. A notable example is to account for confounding in a GWAS, either in identifying suitable controls in a case-control study, or to account for population structure using principal components. While most use “ancestry” language explicitly to describe this process, some do not [e.g., ( Ohi et al., 2020 )], or circumscribe this use more carefully as “heterogeneity that is correlated with ancestry” ( Franceschini and Morris 2020 ). Another example where ancestry is treated as something to be “controlled for” is in controlling for the influence of genetics when understanding a trait ( Boardman et al., 2010 ), to help better understand the influence of other variables on that trait. Another example is in admixture mapping, which explicitly models aspects of genetic structure, using a process often referred to as local ancestry inference, in order to decrease bias in the estimates of the effect size of genotype-phenotype correlations [e.g., ( Du et al., 2020 )].

Finally, ancestry is evoked at the reporting stage of the research process, both in the statement of results and of the conclusions that follow from them [e.g., ( Yoshikawa et al., 2020 ; Darst et al., 2020 )]. This is true even when ancestry does not explicitly enter the research aim, for example in statements of how the results may or may not generalize to different populations ( Tonon et al., 2019 ; Brhane et al., 2020 ).

What does ancestry mean to researchers?

We’ve seen that ancestry is used to frame research questions and as a core part of research methodologies. What do researchers mean by it? Many researchers—all of whom were selected to interview because their work closely engaged with the concept of ancestry—struggled to answer the question “What does ancestry mean to you?’’. While the majority did offer definitions, often after pauses, some were not able to: “Yeah it is hard, I do not know in fact.” We observed that interviewees who were the most engaged in the concept were the most uncertain about it, and/or who gave the most expansive, multipart definitions. An example of the former is an interviewee who remarked that defining ancestry was “like trying to catch smoke”. An example of the latter was an interviewee who emphasized that the more one thinks about the concept, the more expansive the conceptualization becomes: “it is not a simple (question), to answer, because there are of course different ways of thinking about it… each project, to some extent, helps you to rethink what (ancestry) means”. Consistent with ancestry being a concept from everyday life, many interviewees drew from their own personal stories in the answers that they gave.

We identified two dimensions along which answers differed. The first is what ancestry is a property of: DNA; the individual; their family/kin; a population. The second is the criteria by which ancestry is shared: geographical origin; genealogical connections; culture; biology. Examples are given in Table 2 . The qualitative methodology we employed is not suitable to quantify the answers we received, but we note that the answers we received were well distributed along both of these dimensions. Of note, geneticists did not all just give definitions in terms of genetic information. The “culture” category encompasses shared ethnicity, but also more specifically, shared narrative. For example, one interviewee described ancestry as “intergenerational transmission of who I am, what my family is, what we do, who we are.”

TABLE 2 . Categorizing the definitions of ancestry offered by researchers whose work closely engages the concept by a) what ancestry is a property of, and b) the criteria by which individuals share ancestry.

Many individuals gave multipart answers spanning different cells in the matrix represented by Table 2 . For example, “there’s two aspects to it, one sort of the social kind of ancestry that people know about, that they’re talking about, and the other one is then what genetics actually shows.” Or, “Mostly genetic heritage, I mean you know literal inheritance… and something more like culture or religion or ethnicity or identity or family or community or group”.

Some interviewees were keen to emphasize the distinction of ancestry from race and/or ethnicity. For example, “race is a self identified construct and ancestry is biology”, or “it is very far from concepts like, race or ethnicity or something like that.” But others directly related the concepts, for example “African American ancestry - that racial group that, you know, would essentially have common genotypic and phenotypic characteristics.” In some cases there was a direct conflation which was masked by language, “Previously I used ethnicity. But then my mentor told me that nowadays people use ancestry.’’, or as a term to be used “instead of race, because we cannot use, like ‘mixed race’ because… the connotation is not good.”

How is ancestry operationalized?

Before it is used, ancestry first has to be operationalized, i.e. a process has to be defined to ascribe an ancestry to individuals.

Out of the 205 articles examined, 56 (27%) did not operationalize ancestry in their methodology or analysis. Many articles use the word “ancestry” haphazardly and interchange the term with other population descriptors. For example, an article states “More than half of our patients are from African ancestry” ( Arleo et al., 2021 ), but uses race groups throughout. In these cases, the authors seem to be grasping for a term that is suitably inclusive to cover a range of type of variation, or to use a word viewed as unobjectionable (in comparison to race or ethnicity), or to use a term that sounds more objective. Some use the term specifically to draw attention to the lack of data outside of European ancestry populations ( Guan et al., 2020 ). The term “ancestry” appears as a label for ethnic groups in two main cases: “indigenous ancestry” ( Marziali et al., 2021 ) and “mixed ancestry” ( Hill et al., 2020 ). Finally, some articles only contained mention of “ancestry” in the keywords (and not in the main body of the text), using the “ancestry group” MESH terms ( Dina 2022 ).

Of the remaining 149 articles that did operationalize ancestry, for 39 (26%) of these we could not determine what type of information was used in this operationalization (e.g., self report, geographic, genetic). For example, when articles simply refer to “men of African ancestry” ( Lam et al., 2019 ; Walavalkar et al., 2020 ). In some of these cases, it seems that the choice of population descriptor was not carefully made. For example, one article describes its sample as racially diverse, but demonstrates this using European ancestry , African ancestry and other categories ( Gur et al., 2019 ). In another paper, the authors refer to European , African and Mixed ancestries in some places, but in others use Mixed ancestry , Black African , and Caucasian ( Passchier et al., 2020 ). Some researchers use ancestry and ethnicity synonymously ( Franceschini and Morris 2020 ).

Of the 110 articles where it was possible to tell what type of data was used to operationalize ancestry, 72 (65%) used genetic information to do so, 53 (49%) used non-genetic data, and 15 (14%) used both genetic and non-genetic data, see Table 3 . Of the 38 articles that used exclusively non-genetic data to operationalize ancestry, 14 were genetics papers. The non-genetic data types used to operationalize ancestry were: self-reported information, geographical information, language spoken, surnames, dental morphology, and sometimes the intersection of more than one of these sources. Eleven articles (10%) used an intersection of genetic and non-genetic data types. Eight articles (8%) operationalized ancestry in more than one way in the same paper.

TABLE 3 . Types of data used to operationalize ancestry in articles

For those articles for which it was possible to determine the type of information used to operationalize ancestry, this information was often hard to find. For example, in Harrison et al., the main text referred to their Supplementary Material , which in turn pointed to a preprint. In the Supplementary Material of the preprint the details of how ancestry was operationalized are given ( Harrison et al., 2020 ).

Most of the articles that used self-identified information to operationalize ancestry are consistent with participants being asked to report their race and/or ethnicity rather than their ancestry. Suggestive evidence of this includes interchangeable use of ethnicity and ancestry, reference to “racial ancestry” ( Gendy et al., 2019 ; Kaur et al., 2019 ), and demonstration of “racial diversity” using ancestry categories ( Dupont et al., 2020 ). A notable source of data where individuals are actually asked to self-report their ancestry is the American Community Survey (ACS), administered by the US Census Bureau. It currently asks, “What is your ancestry or ethnic origin? (For example: Italian, Jamaican, African American, Cambodian, Cape Verdean, Norwegian, Dominican, French Canadian, Haitian, Korean, Lebanese, Polish, Nigerian, and so on.)". The ACS was used by six articles in our corpus: four from public health and two from sociology.

There was also diversity in how geographic data was used to operationalize ancestry: birth country of parents ( Hamilton et al., 2018 ; Yamasato et al., 2020 ); birth country of paternal grandfather ( Groeger et al., 2017 ); all of mother, father, and four grandparents born in a particular geographical area ( Martínez-Magaña et al., 2019 ); or, no clarity beyond the mention of a country ( Chen et al., 2019 ).

Ancestry was also operationalized using surnames, including: inferred place of origin of the surname of the individual ( Bakhtiari 2020 ); whether an individual’s two surnames (one inherited from each parent) in Mexico were deemed Mayan ( Azcorra et al., 2016 ); the fraction of the surnames of each of an individual’s two parents deemed Andean in Peru ( Pomeroy et al., 2015 ).

The articles in our corpus that used genetic data to operationalize ancestry used a variety of methodologies to do so. Some used analysis of Ancestry Informative Markers (AIMs), which are genetic variants specifically picked because they have very large frequency differences in different groups, where the groups of interest are chosen during design of the AIMs. The majority of articles used standard genotype chips and one of a small handful of methodologies. One common method is Principal Component Analysis (PCA), a dimension reduction technique that defines a space in which the first dimension (PC) captures the most variance in the inputted data, the second the next most, etc. Another common method is a version of the STRUCTURE/ADMIXTURE algorithms, which assign each individual a percentage of ancestry in different variation distribution clusters referred to as populations. These algorithms can be run in an unsupervised fashion, where the user provides only the number of populations (referred to as the “k” parameter). It can also be run in a supervised fashion, where the user defines the populations by providing reference genetic data for each. Of course, the labels for these populations are previously defined using some other form of data, typically geographic or self-identified. Methodologies other than these two were sometimes used, notably Multidimensional Scaling e.g., ( Maciukiewicz et al., 2019 ).

As previously mentioned, many articles use PCA in their analysis; most of these refer to an individual’s location in Principal Component space as their ancestry, though exceptions include referring to this as ethnicity ( Yoshikawa et al., 2020 ). In interviews, amongst those closest to population genetics, the relationship between PCs, population structure, and genetic ancestry was couched in diverse ways: ancestry as the “interpretation” of population structure (viewed as PCs) or as “arising out of” population structure; ancestry as “a lot more than” population structure, (interpreted as PCs); PCs as “indicators” of ancestry. Some articles view use of PCs as a quality control step to “confirm” participants’ self-reported ancestry ( Maciukiewicz et al., 2019 ) or ethnicity ( Yoshikawa et al., 2020 ).

When PCs were used to define a population, this was typically done through drawing ellipses (“bubbles”) in PC space. Our interviewees reported that this was mostly either an “eyeball” process, or simply following a rule of thumb because that is what another paper had done. When PCs were used to control for confounding, the number used to do so varied widely, and, when asked, interviewees explained they were either choosing a number based on what a previous paper had used, or followed a rule of thumb (such as the “elbow rule”, whereby visual inspection for a dropoff in the amount of variance explained with additional PCs is used to define a cutoff ( Cattell 1966 )). Few were able to further justify why this was appropriate. While most of our interviewees used PCs somewhat blindly, others drew attention to issues with the use of PCs, including that they depend entirely on the data inputted, that they are hard to interpret and it is not clear what they are actually picking up on, and that they can pick up on non “real” population structure, due to relatedness or QC issues. One interviewee pointed out that many databases are making PCs for their data available, increasingly enabling the “off the shelf” use of this way to operationalize ancestry.

With the use of statistical software packages such as STRUCTURE/ADMIXTURE, which characterize ancestry of an individual in terms of their percentage similarity with different populations, many interviewees showed a greater awareness of the ways it could be arbitrary, reflecting the fact that choices must be made to run it (either by providing reference data of the populations of interest, or by providing the k parameter). For example, one interviewee described the extent to which interesting observations could be made about their data as k varied (e.g., 3 vs. 5 populations using the same data). Some researchers, again those closest to population genetics, offered additional cautions about the use of this methodology to operationalize ancestry, including that it is very easy to over interpret the results, that newer admixture can be confused with identity by descent, and that there never have been “pure” populations. This last observation refers to the fact that the underlying population theoretic model motivating the design of STRUCTURE/ADMIXTURE models an individual as deriving proportions of ancestry from well-defined ancestral populations, but any such populations are themselves mixtures of other populations.

Examples of the “Genetic Intersect Self Report” operationalizations are found in articles that used the United Kingdom Biobank “White British” data ( Harrison et al., 2020 ) ( Kolin et al., 2020 ), which uses a “bubble” in genetic principal component space combined with a filter on an ethnicity category, and others with self-reported information that was further analyzed using multi-dimensional scaling (MDS) in PLINK ( Avinum et al., 2020 ).

We observed inconsistent use of terminology to describe ancestry operationalized using genetic data. Articles were very mixed about whether they referred to this as “genetic ancestry”, with about equal numbers systematically using “genetic ancestry” or using just “ancestry”, and with many others using a mixture of these two. Other terms in use include “population ancestry”, “genomic ancestry”, “ancestral population structure’’, and “DNA ancestry”. Additionally, some articles distinguished global from local ancestry, but meant different things by this distinction. For some, “global ancestry” means the percentage of ancestral populations inferred by programs like ADMIXTURE (typically, but not always, continental ancestral populations) whereas “local ancestry” involves assigning population labels to sections of chromosome (others used “chromosomal ancestry” for this ( Chen et al., 2020 )). For other researchers, “global ancestry” means continental ancestry, and “local ancestry” indicates finer-resolution categories, e.g., country-level ancestry.

We observed a similar lack of clarity around how the term “population” is used by researchers. For some, a population is a model from population genetics implying random mating. For others, drawing from statistics, data from a sample should be chosen such that it is representative of a population. But for most, usage was more diffuse, implying simply a group of people with something (anything) in common, or as one interviewee put it, simply “large N” (where N is the sample size).

We investigated how the data type used to operationalize ancestry varies across fields of study: anthropology, biology, medicine, public health, and sociology, see Table 4 . All fields of study used both genetic and other data types to operationalize ancestry. Genetic data is the most common data type used in biology, (22 operationalizations, 45% of those in biology), medicine (22, 40%), and public health (12, 39%). Of the 157 operationalizations, 119 appeared in articles that used genetic data, and it was not the case that they exclusively used genetic data to do so. Only 61 (51%) used exclusively genetic information, 11 (9%) used genetic and non-genetic information, 18 (15%) used exclusively non-genetic information (both geographic and self report), and 29 (24%) did not specify which type of data was used. The rates of not specifying what data were used to operationalize ancestry were particularly high in biology (15, 31%) and medicine (19, 35%).

TABLE 4 . Types of data used to operationalize ancestry by articles in different fields. Note that the 8 articles that operationalized ancestry in more than one way appear more than once in this table.

Throughout our analysis of the operationalization of ancestry we observed many sources of conflation between ancestry and race and ethnicity. As mentioned, it seems likely that many of the participants who were listed as self-reporting their ancestry actually self-reported their race or ethnicity. This is likely also true for the articles where it was not specified what type of data they used to operationalize ancestry. Other articles referred to their ancestry categories inferred from genetic data as ethnicities ( Bani-Fatemi et al., 2019 ). One article refers to “race assigning” using either self report or tertile of genetically inferred West African ancestry ( Gohlke et al., 2019 ).

What types of population labels are used for ancestry categories?

Just as there is diversity in what type of data is used to operationalize ancestry, there is also diversity in the types of population labels used to describe the resulting categories (see Table 5 ). Continental ancestry is the most used population label (68 articles, 43%). This is followed by the labels representing mixed types (not just continent, continental region, race) (24, 15%). The labels in the “Other” category were mostly White British ancestry , a label from the United Kingdom Biobank.

TABLE 5 . Types of population labels employed by articles that use different types of data to operationalize ancestry. Because continental and continental region categories are those geographical categories most likely to be conflated with racial categories, we separate out those articles that used a mixed set of labels into those that represented a mixture between these types of label (continent, continental region, and race) from other types of mixtures.

A combined analysis of population labels and type of data used to operationalize ancestry (see Table 5 ) reveals that no matter what type of data is used (and when it was not clear what type of data was used), continental ancestry categories are the most common. This usage was highest (56%) when no indication is given of what type of data is used to operationalize ancestry. For all data types, most other types of labels besides continental are also used.

Among the articles which use ancestry categories, most types of population labels are used by articles from all the fields of study (see Supplementary Table S6 ). Continental ancestry is the most used population label in biology (23 articles, 47%) medicine (28, 51%), and public health (15, 48%). In sociology, ethnicity was the most popular label type (3, 38%). Anthropology was marked by a spread of different label types.

An analysis of how population labels are used across the authors’ country/region of institutional affiliation (see Supplementary Table S7 ) indicates that for those based everywhere save Asia, Continental ancestry labels are the most commonly used. In Asia, researchers most often used Mixed labels—a combination of continent and a country is typical (e.g., European and Japanese).

We observed several instances of grand generalizations from samples from populations with ethnic or country labels to continental groups, for example from Japanese ancestry to Asian ancestry ( Brhane et al., 2020 ), and from French ancestry to European ancestry ( Tonon et al., 2019 ).

In this discussion we first summarize our main findings, and then reflect on the normative consequences of our findings for the type of research we included in this empirical work. We start by noting that our results are limited by the types of articles, and subsequently, researchers, that we included. There are some types of research questions, namely those that do not have even a tangential relationship to human health, that we do not cover.

Our results indicate that ancestry is a concept that is drawn upon in multiple key ways across a broad range of types of research. This is particularly true when it comes to understanding traits and outcomes, from blood pressure to income. For researchers seeking to identify genetic variation linked to these traits, ancestry is evoked as a central part of the methodology, as something to be controlled for. For researchers interested in understanding social outcomes who are not interested in identifying genetic variants, ancestry is again something to be controlled for. For other researchers, ancestry shapes their research question; they hope that studying how a trait varies with ancestry can enable the identification of genetic contributions to between-group differences, including health disparities. Having established that researchers evoke ancestry in key ways, understanding what the concept means to them becomes central.

We demonstrate a huge diversity of understandings of ancestry amongst researchers. Several observations stand out. First, the concept of ancestry encompasses much more than genetics. Many of our interviewees, including geneticists, gave definitions that were broader than what could be inferred from genetic data, stressing for example the narrative aspect of ancestry. The example of “indigenous ancestry” also illustrates that ancestry is more than genetics; several indigenous groups have explicitly rejected genetics as relevant to questions of ancestry, in favor of cultural affiliation ( TallBear 2013 ). Attempting to secure the term “ancestry” to refer to genetic variation—as done in e.g., ( Mersha and Tilahun, 2015 )—is not a viable strategy. Second, there is an absence of agreement on what is core to the concept of ancestry, for example, whether it has anything to do with geography. Third, many researchers, who were selected to be part of our sample precisely because their work engaged closely with the concept of ancestry, struggled to define it. Fourth, while some researchers stressed that ancestry was fundamentally different from the social constructs of race and ethnicity, we observed both in articles and in interviews frequent slippage between ancestry, race and/or ethnicity. Our results, which highlight the ways in which ancestry is ambiguous, can be read in the light of the broader literature on ambiguity in scientific concepts (reviewed in ( Panofsky and Bliss 2017 )). Some of this literature highlights positive roles that ambiguity can play, while the majority of the literature highlights negative functions of ambiguity, and the advantages that flow from standardization, see for example ( Timmermans and Epstein 2010 ).

We also report a huge diversity of operationalizations of the concept of ancestry. The process of operationalization involves taking a definition of an abstract concept and making it measurable. Given the diversity of definitions of ancestry, it is perhaps not surprising there are so many operationalizations. The fundamental ambiguity of the concept, as discussed above, is also evidenced by our result that a quarter of all papers that operationalize ancestry fail to state anything about how this was done, for example whether inclusion criteria were based on self-reported information, geography, or genetics. This was particularly the case in Biology and Medicine, with Anthropology and Sociology articles more frequently stating what type of data was used to operationalize ancestry, perhaps reflecting greater sensitivity in these fields to the ways in which these categories reflect decisions made by researchers. The failure to specify what type of data is used to operationalize ancestry allows for the introduction of further ambiguity between genetics and social identities, particularly given that researchers often use “ancestry” language (rather than “genetic ancestry”) when genetic data has been used to operationalize the concept, and particularly when ancestry is operationalized more than once in the same article, sometimes using genetic data and sometimes not.

Our results also highlight that practices associated with using genetic data to operationalize ancestry rest on unclear conventions, and that there is a lack of clarity concerning the relationships between the different key concepts. This is particularly true for the use of Principal Components. PCs are sometimes referred to as ancestry, genetic ancestry, population structure, or some more qualified term, suggesting that they “capture” or “correlate” with one of the above. Researchers often justified their choices about the use of PCs by reference to prior papers, without being able to give their own justifications of why those choices were appropriate. This may be concerning, given the growing usage of PCs not only within statistical genetics but by those seeking “off the shelf” solutions to “control for genetics” ( Boardman et al., 2010 ). It should also be concerning given that results can depend critically on choices made ( Elhaik 2022 ).

The predominance of continental ancestry labels, particularly when genetic data is used and when it is not specified what type of data is used, reinforces concerns that the turn to genetic ancestry may just essentialize the quasi-racial groupings represented by these categories ( Bliss 2020b ; Lewis et al., 2022 ). On the other hand, the diversity of population labels in use helps demonstrate that researchers have a wide range of choices when it comes both to their operationalization of ancestry, and to their framing of their results.

In repeated guidelines for the use of population descriptors, transparency of what is meant by the concepts and how they are operationalized is presented as a minimum bar ( Mauro et al., 2022 ). Our results show that, in the case of ancestry specifically, current research is very far from meeting this bar. Our results also help indicate why the goal of transparency may be hard to achieve: there is a huge diversity of ideas underlying the concept, which leads to a deep-running ambiguity about where the concept sits in relation to biology on the one hand and social identities on the other. The existing empirical work discussed in the background demonstrates the close links between racial ways of thinking and the way genetic ancestry is being conceptualized. By focusing our work not just on genetic ancestry, but on ancestry more broadly, we demonstrate additional ways in which this conflation happens.

The conflation between genetic and social ways of conceptualizing human difference—aided by the highly ambiguous term “ancestry”—is problematic because the concepts are importantly different ( Cerdeña, Grubbs, and Non 2022 ). As Ian Hacking has pointed out, whereas there is a sense in which all concepts are socially constructed, some concepts are “interactive kinds,” in which the entities being classified know they are being classified, and act and are treated differently based on the ways that they are classified ( Hacking 2001 ). People learn to treat people categorized by a structural system differently, depending on their category. This is true of race, which is a construct invented by white Europeans to secure their racial privilege ( Omi and Howardt 2015 ). Genetic ancestry refers to how DNA is passed down through the human family tree ( Mathieson and Scally 2020 ; Lewis et al., 2022 ). This human family tree has a complex structure; it is after all shaped by everything that shapes who has children with whom, which includes amongst other things geography and cultural practices. There are of course correlations between race and ethnicity and patterns of genetic variation, including at the continental level. But genetic variation is continuous, not categorical and not best represented by continental categories ( Lewis et al., 2022 ) . And whereas racial labels are a result of sociopolitical processes, it is researchers who choose to impose categories on genetic data, and then to attach labels to those categories. As Bonham et al. write, “It is critical to avoid creating fictitious, discrete genomic groups while recognizing that self-identified race and ethnicity are highly associated with genetic ancestry at the continental and population level” ( Bonham, Green, and Pérez-Stable 2018 ).

The ambiguity that use of the term “ancestry” provides is almost certainly helpful to researchers in some ways ( Panofsky and Bliss 2017 ). But it also confuses our attempts to gain genuine understanding of the dynamics and processes in the creation of health outcomes. The motivation for much genetic research is to contribute to a causal understanding of why health-related traits are distributed the way they are. Genetic ancestry cannot be a causal factor in such analysis, it can only act as a proxy for underlying genetic variants that are playing a causal role. Racism can play a causal role, through many mechanisms that are being elucidated (see e.g., ( Krieger 2021 )). Inadequate reflection of the relation between these systems for capturing human difference can lead to incorrect conclusions. For example, attempts to explain differences in health outcomes based on differences in genetic ancestry are confounded by racism ( Boulter et al., 2015 ). To make progress in accurately understanding the distribution of health outcomes, the different roles of genetic variation and social and environmental factors must be carefully considered.

While we agree with the dozens of other commentators on the importance of researchers transparently describing exactly who was studied, how they were classified, and why, we advocate for the following additional considerations.

(1) Use of the term “ancestry” by itself should be avoided. Rather, this term should always be qualified, for example as “genealogical ancestry” or “genetic ancestry”.

(2) Use of the term “population” should be avoided. It has no agreed upon meaning and only serves to make something sound more scientific than it in fact is. This is particularly true in genetics research when the term is apt to be confused with the term in population genetics theory for a group of individuals who are mating at random. Use of the term “group” is to be preferred, because it correctly draws attention to the question “by virtue of what are these individuals being grouped together” and because it evokes less scientific authority.

(3) Operationalizations of genetic ancestry that reflect the continuous nature of genetic variation, such as PCA, should be encouraged wherever possible ( Duello et al., 2021 ; Lewis et al., 2022 ).

(4) If genetic ancestry must be operationalized using categories, multiple sets of categories should be used, to reflect the fact that one can carve up the human family tree in multiple ways ( Lewis et al., 2022 ).

(5) Genetically inferred continental ancestry categories should only be used if necessary ( Panofsky and Bliss 2017 ; Lewis et al., 2022 ).

(6) Researchers need to make themselves familiar with the key limitations of the tools they use.

The hope of some is that ancestry represents the biological, objective counterpart to the social constructs of race and ethnicity, and that a turn to ancestry categories could help us get away from the bad science and damaging history of previous classification systems and practices. Our investigation of how ancestry is actually used indicates it is a far cry from the objective and straightforward concept hoped for. Nor is it a uniquely “biological construct”. Indeed, the concept is fundamentally ambiguous, and not more conceptually clear than race or ethnicity in practice. By just moving to a new term, there is a danger that research in this area fails to address the known issues in the uses of race and ethnicity as population descriptors. Part of the problem is precisely that some scientists are searching for a more objective term, thus treating this set of issues as purely semantic when in fact the problems run deeper: we need more careful attention to the different roles of genetic variants (which are not uniformly distributed) on the one hand and the myriad other contributors to health outcomes on the other. The move to “ancestry” just confuses the issue. Given the central role ancestry plays in genetics research and beyond, these deep-seated issues with how ancestry is conceptualized and operationalized should raise concern, and highlight the importance of strategies that will advance conceptual clarity.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by Harvard University-Area Committee on the Use of Human Subjects. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions

BD: coded articles for systematic literature analysis; SM: conducted interviews and coded interviews; NG: transcribed interviews; SZ: transcribed interviews and coded interviews; AL: conducted interviews, coded interviews, coded articles, wrote original draft. All authors: Conceptualized project, and reviewed and edited manuscript.

NIMH administrative supplement 5000747-5500001474 to 3R37MH107649-06S1. AP acknowledges support from the Carnegie Foundation.

Conflict of interest

AL owns stock in Fabric Genomics; BN is a member of the scientific advisory board at Deep Genomics and RBNC Therapeutics, Member of the scientific advisory committee at Milken and a consultant for Camp4 Therapeutics and Merck.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1044555/full#supplementary-material

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Keywords: population descriptors, ancestry, genetic ancestry, race, ethnicity, population labeling

Citation: Dauda B, Molina SJ, Allen DS, Fuentes A, Ghosh N, Mauro M, Neale BM, Panofsky A, Sohail M, Zhang SR and Lewis ACF (2023) Ancestry: How researchers use it and what they mean by it. Front. Genet. 14:1044555. doi: 10.3389/fgene.2023.1044555

Received: 14 September 2022; Accepted: 10 January 2023; Published: 23 January 2023.

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Establishing a comprehensive search strategy for Indigenous health literature reviews

• Louise Harding 1 ,
• Caterina J. Marra 1 &
• Judy Illes   ORCID: orcid.org/0000-0002-4791-8084 1  

Systematic Reviews volume  10 , Article number:  115 ( 2021 ) Cite this article

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Appropriate search strategies are essential to ensure the integrity and reproducibility of systematic and scoping reviews, as researchers seek to capture as many relevant resources as possible. In the case of Indigenous health reviews, researchers are met with the special challenge of creating a search strategy that can encompass this large, diverse population group with no universally agreed upon identification criteria.

With an aim to promote improved review methodologies that uphold standards of justice, autonomy, and equity for Indigenous peoples and other heterogeneous populations, we describe critical gaps and approaches to close them. We report organizational and transparency issues around how Indigenous populations are indexed in several major databases, and draw on examples of published reviews and protocols to demonstrate the challenges inherent to creating a comprehensive search strategy.

Conclusions

The conduct and communication of results from health literature research on global Indigenous populations are compromised by challenges of methodology that are rooted in the complexities inherent to defining Indigenous peoples. These challenges must be urgently addressed to improve this important field of inquiry moving forward.

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The integrity and reproducibility of systematic literature reviews can only be achieved if appropriate strategies exist to capture relevant resources and communicate results. This is an especially important challenge for researchers whose interests lie in health as it pertains to Indigenous communities and other populations for which there is a highly heterogeneous collection of literature. In this area of research, investigators may ask a broad array of pressing questions that, on the one hand, may be undifferentiated in some ways from studies that do not specifically have an Indigenous focus, such as incidence of disease in a community or region, health priorities, and perceptions about the risks and benefits of novel treatments. On the other hand, investigators may seek highly specific information from Indigenous communities as they pertain to perspectives, individual and community consent, data ownership and control, ways of knowing, and systemic inequities and disparities. The unique human rights challenges facing Indigenous peoples today, including the right to health and the use of their traditional territories, languages and cultures, make upholding standards of justice, autonomy and equity in systematic as well as scoping review research involving Indigenous peoples an imperative [ 1 ].

Recently, we encountered critical gaps in a scoping review of the peer-reviewed literature about global Indigenous perspectives on brain health and wellness. The first significant one centers around the absence of a search strategy that effectively enables the comprehensive capture of relevant discourse surrounding Indigenous populations. No accepted universal definition of Indigenous exists to encompass the approximately 5000 distinct indigenous communities worldwide, comprising a population about 370 million people across 90 countries today nor do all communities view it as desirable, per se [ 2 , 3 ]. Without such a definition—however broad or specific—researchers must generate their own working terms, rely on those created from an array of existing sources to formulate a search strategy, or some combination of creative approaches. The net effects of this gap are inadequate and inappropriate search strings for which efforts are not only inefficient, but fundamentally compromise the reproducibility of information captured and the authenticity of data interpretation.

Here, we provide examples and noteworthy attempts of how the task has been approached in the past, and point out accompanying shortfalls that pertain to a lack of transparency, inclusiveness, and acknowledgement of communities about which such research is conducted. We recognize that these are only select examples that illustrate our points. We make no value judgments and respect the intentions of the authors. We conclude with recommendations to improve the overall quality and scope of global Indigenous literature reviews.

Indigenous database approach

Literature reviews depend on a selection of a representative set of databases from which researchers gather their resources. Examples of these are PubMed, Medline, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Native Health Database, and Informit Indigenous Collections. In recent years, several research databases have been created that gather exclusively Indigenous content. The best available option is the large Informit Indigenous Collection, which is updated daily in consultation with Indigenous groups from both mainstream and obscure journals, and other sources [ 4 ]. However, we challenge whether their claim to provide content “of relevance to professionals and researchers involved in indigenous issues locally and globally” is accurate given that they only draw resources from limited regions of the world, namely, Australia, Indonesia, Malaysia, New Zealand, North America, and the Pacific. The collection is also limited with content that begins in 1977. An even more limited database by contrast—the Native Health database—archives work solely on North American Indigenous peoples [ 5 ]. While it does contain historical resources, it is functionally more limited than Informit Indigenous Collections, as it does not support the capability for complex searches or the export of articles.

Given such limitations, researchers must look to complement their literature searches with larger, non-specific databases. This generates a circular capture problem in that it necessarily involves the formulation of a string of search terms that encompass Indigenous populations globally; however, due to the ongoing challenges of creating a globally comprehensive search string, search returns frequently fail to support the intended purpose of the research.

MeSH approach

The Medical Subject Headings (MeSH) indexing system created by the National Library of Medicine (NLM) for journal articles and books in the life sciences is used widely across major databases such as PubMed and Medline [ 6 ]. Other databases, such as the Cumulative Index to Nursing and Allied Health Literature (CINAHL), use an adapted MeSH set [ 7 ]. While they can be very useful for narrowing the results of a large literature search, when our team attempted to select MeSH terms for the search strategy, we encountered issues around consistency, validity, transparency, and organization.

In 2020, the National Library of Medicine (NLM) added the MeSH term “Indigenous Peoples” under their Ethnic Group category, defining its scope as: “Descendants who self-identify as members of a group who inhabited a country or region at the time when people of different cultures or ethnic origins arrived. They often maintain their distinct language, culture, and beliefs.” Meanwhile, the corresponding Indigenous peoples MeSH term in the Cumulative Index to Nursing and Allied Health Literature (CINAHL) is the “Native population of a country, region, or area.” Having two MeSH terms with the same name in different databases presents a challenge for researchers who may assume that the definition is consistent. However, settling on any single definition can be problematic in and of itself, as it may lack aspects critical in defining indigeneity for some groups, such as “acceptance by community” as per the Métis people of Canada [ 3 ]. Additionally, while the CINAHL MeSH term has multiple subheadings for a range of global Indigenous groups, the NLM system only includes a subheading for Alaska Natives without providing any explanation for why this specific group was singled out.

Neither database specifies how it systematically applies these very general definitions to the practical task of indexing articles. Transparency is not only important for the critical evaluation and use of subject headings but also in upholding accountability to the Indigenous experts who are presumably consulted in the creation of such definitions. An example where this lack of transparency causes a problem is that Roma peoples are indexed as a distinct ethnic group in the NLM MeSH system, but whether they are also classified as indigenous peoples, as some Roma people self-identify as both, is not indicated.

The NLM also has additional MeSH terms that encompass Indigenous groups under their Continental Population Groups heading, the result of which is a convoluted organizational scheme that can skew search results. The four ancestry groups are American Native Continental Ancestry Group, Oceanic Ancestry Group, African Continental Ancestry Group, and Asian Continental Ancestry Group. While the scope notes, entry terms, and related headings for the first two continental groups in this list only refer to Indigenous populations (e.g., Alaska Natives, Native Hawaiians), none of the information provided about the MeSH for African and Asian Ancestry Groups explicitly alludes to the concept of indigeneity. As such, many researchers will just include the American and Oceanic Ancestry Group MeSH in their search strategies, which may contribute to an overrepresentation of discourse about these Indigenous groups in global literature reviews.

Key search terms approach

Keywords for Indigenous literature searches will typically, if not naturally, start with umbrella terms such as Indigenous, Aboriginal and Native. Using only these general search terms can be acceptable with adequate acknowledgement of their limitations, but many researchers pursue further breadth by adding a range of more specific terms. However, from what we have observed in the literature, this well-intentioned pursuit often results in the underrepresentation of Asian, European, and African Indigenous groups.

For example the authors of a systematic review about the factors that influence Indigenous peoples’ cancer treatment decision-making used two umbrella terms and three specific keywords referring to groups in the USA, Australia and New Zealand ([ 8 ] see Tranberg et al. 2016 in Additional file  1 ). As a result, the five articles that met their final inclusion criteria were about Australian Indigenous groups. We note the authors’ acknowledgement of their error in assuming that the search terms they chose could encompass all Indigenous groups globally. Similarly, in a systematic review comparing the incidence of suicide among Indigenous peoples with other populations the authors used a search list with 45 Indigenous communities for their review without justifying why those few communities were chosen to represent all Indigenous peoples ([ 9 ] see Pollock et al. 2018 in Additional file  1 ). While the authors acknowledge the possibility of bias given challenges around defining Indigenous peoples and their limited search terms they still assert that their search encompasses global Indigenous populations comprehensively.

Some authors have attempted to create their own complete lists of worldwide Indigenous communities with various successes and failings. For example, the systematic review protocol of Bishop-Williams et al. (2017) for studying the associations between weather parameters and acute respiratory infection outcomes in Indigenous and non-Indigenous peoples included an extensive list of community names compiled from two major international sources [ 10 ]. Theoretically, the scope of their list is global as it includes many international Indigenous groups. However, the search string is neither transparent nor complete; it includes small communities such as the Squamish and Haida Nations of the Pacific Northwest, but excludes other local nations such as Tsleil-Waututh and Cowichan (see Additional file  1 ). The authors acknowledge the challenges of their undertaking by stating that “[i]t is difficult to develop a search strategy that is robust enough to represent all nuances of the ter[m] Indigenous,” yet carry forth with the assumption that their search had an adequately global scope. The danger of such a dismissal is that the biases inherent in the original methods can be perpetuated if adopted uncritically by subsequent researchers. As a case in point, the authors of a 2020 scoping review examining how “global Indigenous mental health is impacted by meteorological, seasonal, and climatic changes” replicated the search strategy that was created by Bishop-Williams et al. (2017) with only very minor adjustments, and did not acknowledge any of the potential limitations or biases ([ 11 ], see Middleton et al., 2020 in Additional file  1 ). Such oversight can effectively silence the voices of certain indigenous communities and sustain a cycle of biased, incomplete, and inaccurate discourse, even while researchers endeavor to be productive advocates of justice for Indigenous peoples.

Ways forward

Toward the goal of improving the rigor of methodology for global Indigenous literature reviews, we suggest four changes in current practice: establish one or more databases of literature about global Indigenous populations, improve transparency about classification strategies, create a living list of Indigenous communities, and promote critical thinking and reflection on the part of researchers to ensure the appropriateness and reproducibility of their search methods (Table  1 ). As all of these tasks will necessarily involve continued, deep collaboration with Indigenous Knowledge Holders, we also recommend that the work of these experts be identified and acknowledged.

The establishment of a novel Indigenous database or improvement of an existing one such as Informit Indigenous Collections would functionally eliminate the need for researchers to create arduous and complex search strings to encompass global Indigenous populations. Such a database would allow researchers to seamlessly locate the relevant discourse regarding Indigenous peoples globally. As the many possible definitions of iIndigenous will need to be taken into account and applied transparently to indexing, multiple databases may be required to collectively serve this purpose.

Other databases and the organizations that index resources must also be transparent about how they are operationalizing their definitions of Indigenous peoples. For example, public access to the inclusion and exclusion criteria used by the National Library of Medicine for their MeSH terms would enable researchers to critically structure their search strategies.

Whether as part of this undertaking toward transparency or as a separate endeavor, the creation and maintenance of a comprehensive list of global Indigenous communities is needed. This will be resource-intensive to create, but its upkeep can be managed with a commitment to the task that is reflected in clearly defined operational procedures and a sustainable funding source. Such a list must skillfully incorporate a number of different possible definitions of Indigenous peoples, which will involve consulting with Indigenous communities about how they wish to be identified. Researchers may then use this list to create universally accepted working search strategies. Researchers may reference the date they accessed the search strategy in their methodology, and disclose any amendments they have made to answer their specific research questions. Such a list would also have an important impact on other types of work with Indigenous communities, such as serving advocacy and humanitarian purposes, and be a model for others.

Throughout the implementation of these three recommendations, consultation with Indigenous Knowledge Holders and other experts will be essential. Accordingly, we further recommend that the rationale for selecting these community contributors be noted explicitly, and their important work acknowledged.

Finally, until better methodologies for searching for global Indigenous groups are available, it is incumbent on researchers to critically consider and report which Indigenous groups and perspectives are most likely to be encompassed by the search strategies they formulate, and to acknowledge how this will impact their study results. Researchers who engage in this level of reflection at the time of developing their search protocol will likely find that using inclusive search terms and a broad range of databases will enhance the quality of their search. While out of scope for detailed discussion in this article, the inclusion of gray literature can also add breadth and an additional analytic layer. It is no longer acceptable for researchers to use biased or otherwise limited search strategies and then claim to capture global perspectives. In line with the concept of ethical reproducibility for the transparent reporting of research ethics methods used by biomedical researchers [ 12 ], literature reviewers also have an obligation to apply and report ethical considerations. Further, this interim effort will require support by international research bodies, major libraries, and academic publishers (e.g. Cochrane, National Library of Medicine, the American Association for the Advancement of Sciences) to set clear guidelines for search strategies including Indigenous populations.

In our own endeavor to create a literature review search strategy that encompasses global Indigenous populations, we were not able to find a precedent study with methodology that satisfied the criteria of comprehensiveness, rigor, and transparency. This raised important concerns for us about the quality and representativeness of much global Indigenous literature review research, and disorganized or uncoordinated approaches, and one-off methodologies. The process of improvement has already slowly begun with the creation of Indigenous databases and improved subject headings, and we call for greater urgency and attention to the ethical imperative of moving away from the status quo Footnote 1 . Ultimately, until options are available to address these challenges, authors must take extra care to acknowledge the limitations of their search strategies in order to avoid perpetuating oppressive notions of who is Indigenous and who is not. Indeed, it is essential that the methodologies used in such research do not inadvertently perpetuate the very same oppressive paradigms they aim to remediate.

Availability of data and materials

Not applicable.

Since writing the first version of this manuscript, the National Library of Medicine introduced its 2021 Medical Subject Headings (MeSH) that includes improved organization and categorization of North American Indigenous populations under the existing heading, Indians, North American.

Abbreviations

Medical Subject Headings

National Library of Medicine

Cumulative Index to Nursing and Allied Health Literature

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Acknowledgements

Neuroethics Canada is located on the traditional, ancestral, and unceded territory of the xʷməθkʷəỳəm (Musqueam people).

This work was generously supported by the Canadian Institutes of Health Research (CIHR) grant #171583;03027 IC-127354 and the North Growth Foundation. JI is Canada Research Chair in Neuroethics. The funding bodies had no role in the design of the study and collection, analysis, or interpretation of data or in writing the manuscript.

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LH and CM were responsible for the conception and design of the work, acquisition, analysis and interpretation of the data, and writing the manuscript. JI provided supervision and mentoring, and made substantial contributions to the interpretation of data and writing the manuscript. All authors read and approved the final manuscript.

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Judy Illes, CM, PhD is Director of Neuroethics Canada and Canada Research Chair in Neuroethics. Louise Harding is a researcher at Neuroethics Canada and a Master's student in UBC's School of Population and Public Health with a background in neuroscience and Indigenous health. Caterina Marra is the Cultural Diversity Research Assistant with the Canadian Brain Research Strategy, and a member of the Musqueam Nation.

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Additional file 1: table a1..

Search terms used in four global Indigenous health literature reviews.

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Examining the role of community resilience and social capital on mental health in public health emergency and disaster response: a scoping review

• C. E. Hall 1 , 2 ,
• H. Wehling 1 ,
• J. Stansfield 3 ,
• J. South 3 ,
• S. K. Brooks 2 ,
• N. Greenberg 2 , 4 ,
• R. Amlôt 1 &
• D. Weston 1  

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The ability of the public to remain psychologically resilient in the face of public health emergencies and disasters (such as the COVID-19 pandemic) is a key factor in the effectiveness of a national response to such events. Community resilience and social capital are often perceived as beneficial and ensuring that a community is socially and psychologically resilient may aid emergency response and recovery. This review presents a synthesis of literature which answers the following research questions: How are community resilience and social capital quantified in research?; What is the impact of community resilience on mental wellbeing?; What is the impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, What types of interventions enhance community resilience and social capital?

A scoping review procedure was followed. Searches were run across Medline, PsycInfo, and EMBASE, with search terms covering both community resilience and social capital, public health emergencies, and mental health. 26 papers met the inclusion criteria.

The majority of retained papers originated in the USA, used a survey methodology to collect data, and involved a natural disaster. There was no common method for measuring community resilience or social capital. The association between community resilience and social capital with mental health was regarded as positive in most cases. However, we found that community resilience, and social capital, were initially negatively impacted by public health emergencies and enhanced by social group activities.

Several key recommendations are proposed based on the outcomes from the review, which include: the need for a standardised and validated approach to measuring both community resilience and social capital; that there should be enhanced effort to improve preparedness to public health emergencies in communities by gauging current levels of community resilience and social capital; that community resilience and social capital should be bolstered if areas are at risk of disasters or public health emergencies; the need to ensure that suitable short-term support is provided to communities with high resilience in the immediate aftermath of a public health emergency or disaster; the importance of conducting robust evaluation of community resilience initiatives deployed during the COVID-19 pandemic.

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For the general population, public health emergencies and disasters (e.g., natural disasters; infectious disease outbreaks; Chemical, Biological, Radiological or Nuclear incidents) can give rise to a plethora of negative outcomes relating to both health (e.g. increased mental health problems [ 1 , 2 , 3 , 4 ]) and the economy (e.g., increased unemployment and decreased levels of tourism [ 4 , 5 , 6 ]). COVID-19 is a current, and ongoing, example of a public health emergency which has affected over 421 million individuals worldwide [ 7 ]. The long term implications of COVID-19 are not yet known, but there are likely to be repercussions for physical health, mental health, and other non-health related outcomes for a substantial time to come [ 8 , 9 ]. As a result, it is critical to establish methods which may inform approaches to alleviate the longer-term negative consequences that are likely to emerge in the aftermath of both COVID-19 and any future public health emergency.

The definition of resilience often differs within the literature, but ultimately resilience is considered a dynamic process of adaptation. It is related to processes and capabilities at the individual, community and system level that result in good health and social outcomes, in spite of negative events, serious threats and hazards [ 10 ]. Furthermore, Ziglio [ 10 ] refers to four key types of resilience capacity: adaptive, the ability to withstand and adjust to unfavourable conditions and shocks; absorptive, the ability to withstand but also to recover and manage using available assets and skills; anticipatory, the ability to predict and minimize vulnerability; and transformative, transformative change so that systems better cope with new conditions.

There is no one settled definition of community resilience (CR). However, it generally relates to the ability of a community to withstand, adapt and permit growth in adverse circumstances due to social structures, networks and interdependencies within the community [ 11 ]. Social capital (SC) is considered a major determinant of CR [ 12 , 13 ], and reflects strength of a social network, community reciprocity, and trust in people and institutions [ 14 ]. These aspects of community are usually conceptualised primarily as protective factors that enable communities to cope and adapt collectively to threats. SC is often broken down into further categories [ 15 ], for example: cognitive SC (i.e. perceptions of community relations, such as trust, mutual help and attachment) and structural SC (i.e. what actually happens within the community, such as participation, socialising) [ 16 ]; or, bonding SC (i.e. connections among individuals who are emotionally close, and result in bonds to a particular group [ 17 ]) and bridging SC (i.e. acquaintances or individuals loosely connected that span different social groups [ 18 ]). Generally, CR is perceived to be primarily beneficial for multiple reasons (e.g. increased social support [ 18 , 19 ], protection of mental health [ 20 , 21 ]), and strengthening community resilience is a stated health goal of the World Health Organisation [ 22 ] when aiming to alleviate health inequalities and protect wellbeing. This is also reflected by organisations such as Public Health England (now split into the UK Health Security Agency and the Office for Health Improvement and Disparities) [ 23 ] and more recently, CR has been targeted through the endorsement of Community Champions (who are volunteers trained to support and to help improve health and wellbeing. Community Champions also reflect their local communities in terms of population demographics for example age, ethnicity and gender) as part of the COVID-19 response in the UK (e.g. [ 24 , 25 ]).

Despite the vested interest in bolstering communities, the research base establishing: how to understand and measure CR and SC; the effect of CR and SC, both during and following a public health emergency (such as the COVID-19 pandemic); and which types of CR or SC are the most effective to engage, is relatively small. Given the importance of ensuring resilience against, and swift recovery from, public health emergencies, it is critically important to establish and understand the evidence base for these approaches. As a result, the current review sought to answer the following research questions: (1) How are CR and SC quantified in research?; (2) What is the impact of community resilience on mental wellbeing?; (3) What is the impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, (4) What types of interventions enhance community resilience and social capital?

By collating research in order to answer these research questions, the authors have been able to propose several key recommendations that could be used to both enhance and evaluate CR and SC effectively to facilitate the long-term recovery from COVID-19, and also to inform the use of CR and SC in any future public health disasters and emergencies.

A scoping review methodology was followed due to the ease of summarising literature on a given topic for policy makers and practitioners [ 26 ], and is detailed in the following sections.

Identification of relevant studies

An initial search strategy was developed by authors CH and DW and included terms which related to: CR and SC, given the absence of a consistent definition of CR, and the link between CR and SC, the review focuses on both CR and SC to identify as much relevant literature as possible (adapted for purpose from Annex 1: [ 27 ], as well as through consultation with review commissioners); public health emergencies and disasters [ 28 , 29 , 30 , 31 ], and psychological wellbeing and recovery (derived a priori from literature). To ensure a focus on both public health and psychological research, the final search was carried across Medline, PsycInfo, and EMBASE using OVID. The final search took place on the 18th of May 2020, the search strategy used for all three databases can be found in Supplementary file 1 .

Selection criteria

The inclusion and exclusion criteria were developed alongside the search strategy. Initially the criteria were relatively inclusive and were subject to iterative development to reflect the authors’ familiarisation with the literature. For example, the decision was taken to exclude research which focused exclusively on social support and did not mention communities as an initial title/abstract search suggested that the majority of this literature did not meet the requirements of our research question.

The full and final inclusion and exclusion criteria used can be found in Supplementary file 2 . In summary, authors decided to focus on the general population (i.e., non-specialist, e.g. non-healthcare worker or government official) to allow the review to remain community focused. The research must also have assessed the impact of CR and/or SC on mental health and wellbeing, resilience, and recovery during and following public health emergencies and infectious disease outbreaks which affect communities (to ensure the research is relevant to the review aims), have conducted primary research, and have a full text available or provided by the first author when contacted.

Charting the data

All papers were first title and abstract screened by CH or DW. Papers then were full text reviewed by CH to ensure each paper met the required eligibility criteria, if unsure about a paper it was also full text reviewed by DW. All papers that were retained post full-text review were subjected to a standardised data extraction procedure. A table was made for the purpose of extracting the following data: title, authors, origin, year of publication, study design, aim, disaster type, sample size and characteristics, variables examined, results, restrictions/limitations, and recommendations. Supplementary file 3 details the charting the data process.

Analytical method

Data was synthesised using a Framework approach [ 32 ], a common method for analysing qualitative research. This method was chosen as it was originally used for large-scale social policy research [ 33 ] as it seeks to identify: what works, for whom, in what conditions, and why [ 34 ]. This approach is also useful for identifying commonalities and differences in qualitative data and potential relationships between different parts of the data [ 33 ]. An a priori framework was established by CH and DW. Extracted data was synthesised in relation to each research question, and the process was iterative to ensure maximum saturation using the available data.

Study selection

The final search strategy yielded 3584 records. Following the removal of duplicates, 2191 records remained and were included in title and abstract screening. A PRISMA flow diagram is presented in Fig.  1 .

PRISMA flow diagram

At the title and abstract screening stage, the process became more iterative as the inclusion criteria were developed and refined. For the first iteration of screening, CH or DW sorted all records into ‘include,’ ‘exclude,’ and ‘unsure’. All ‘unsure’ papers were re-assessed by CH, and a random selection of ~ 20% of these were also assessed by DW. Where there was disagreement between authors the records were retained, and full text screened. The remaining papers were reviewed by CH, and all records were categorised into ‘include’ and ‘exclude’. Following full-text screening, 26 papers were retained for use in the review.

Study characteristics

This section of the review addresses study characteristics of those which met the inclusion criteria, which comprises: date of publication, country of origin, study design, study location, disaster, and variables examined.

Date of publication

Publication dates across the 26 papers spanned from 2008 to 2020 (see Fig.  2 ). The number of papers published was relatively low and consistent across this timescale (i.e. 1–2 per year, except 2010 and 2013 when none were published) up until 2017 where the number of papers peaked at 5. From 2017 to 2020 there were 15 papers published in total. The amount of papers published in recent years suggests a shift in research and interest towards CR and SC in a disaster/ public health emergency context.

Graph to show retained papers date of publication

Country of origin

The locations of the first authors’ institutes at the time of publication were extracted to provide a geographical spread of the retained papers. The majority originated from the USA [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ], followed by China [ 42 , 43 , 44 , 45 , 46 ], Japan [ 47 , 48 , 49 , 50 ], Australia [ 51 , 52 , 53 ], The Netherlands [ 54 , 55 ], New Zealand [ 56 ], Peru [ 57 ], Iran [ 58 ], Austria [ 59 ], and Croatia [ 60 ].

There were multiple methodological approaches carried out across retained papers. The most common formats included surveys or questionnaires [ 36 , 37 , 38 , 42 , 46 , 47 , 48 , 49 , 50 , 53 , 54 , 55 , 57 , 59 ], followed by interviews [ 39 , 40 , 43 , 51 , 52 , 60 ]. Four papers used both surveys and interviews [ 35 , 41 , 45 , 58 ], and two papers conducted data analysis (one using open access data from a Social Survey [ 44 ] and one using a Primary Health Organisations Register [ 56 ]).

Study location

The majority of the studies were carried out in Japan [ 36 , 42 , 44 , 47 , 48 , 49 , 50 ], followed by the USA [ 35 , 37 , 38 , 39 , 40 , 41 ], China [ 43 , 45 , 46 , 53 ], Australia [ 51 , 52 ], and the UK [ 54 , 55 ]. The remaining studies were carried out in Croatia [ 60 ], Peru [ 57 ], Austria [ 59 ], New Zealand [ 56 ] and Iran [ 58 ].

Multiple different types of disaster were researched across the retained papers. Earthquakes were the most common type of disaster examined [ 45 , 47 , 49 , 50 , 53 , 56 , 57 , 58 ], followed by research which assessed the impact of two disastrous events which had happened in the same area (e.g. Hurricane Katrina and the Deepwater Horizon oil spill in Mississippi, and the Great East Japan earthquake and Tsunami; [ 36 , 37 , 38 , 42 , 44 , 48 ]). Other disaster types included: flooding [ 51 , 54 , 55 , 59 , 60 ], hurricanes [ 35 , 39 , 41 ], infectious disease outbreaks [ 43 , 46 ], oil spillage [ 40 ], and drought [ 52 ].

Variables of interest examined

Across the 26 retained papers: eight referred to examining the impact of SC [ 35 , 37 , 39 , 41 , 46 , 49 , 55 , 60 ]; eight examined the impact of cognitive and structural SC as separate entities [ 40 , 42 , 45 , 48 , 50 , 54 , 57 , 59 ]; one examined bridging and bonding SC as separate entities [ 58 ]; two examined the impact of CR [ 38 , 56 ]; and two employed a qualitative methodology but drew findings in relation to bonding and bridging SC, and SC generally [ 51 , 52 ]. Additionally, five papers examined the impact of the following variables: ‘community social cohesion’ [ 36 ], ‘neighbourhood connectedness’ [ 44 ], ‘social support at the community level’ [ 47 ], ‘community connectedness’ [ 43 ] and ‘sense of community’ [ 53 ]. Table  1 provides additional details on this.

How is CR and SC measured or quantified in research?

The measures used to examine CR and SC are presented Table  1 . It is apparent that there is no uniformity in how SC or CR is measured across the research. Multiple measures are used throughout the retained studies, and nearly all are unique. Additionally, SC was examined at multiple different levels (e.g. cognitive and structural, bonding and bridging), and in multiple different forms (e.g. community connectedness, community cohesion).

What is the association between CR and SC on mental wellbeing?

To best compare research, the following section reports on CR, and facets of SC separately. Please see Supplementary file 4  for additional information on retained papers methods of measuring mental wellbeing.

• Community resilience

CR relates to the ability of a community to withstand, adapt and permit growth in adverse circumstances due to social structures, networks and interdependencies within the community [ 11 ].

The impact of CR on mental wellbeing was consistently positive. For example, research indicated that there was a positive association between CR and number of common mental health (i.e. anxiety and mood) treatments post-disaster [ 56 ]. Similarly, other research suggests that CR is positively related to psychological resilience, which is inversely related to depressive symptoms) [ 37 ]. The same research also concluded that CR is protective of psychological resilience and is therefore protective of depressive symptoms [ 37 ].

• Social capital

SC reflects the strength of a social network, community reciprocity, and trust in people and institutions [ 14 ]. These aspects of community are usually conceptualised primarily as protective factors that enable communities to cope and adapt collectively to threats.

There were inconsistencies across research which examined the impact of abstract SC (i.e. not refined into bonding/bridging or structural/cognitive) on mental wellbeing. However, for the majority of cases, research deems SC to be beneficial. For example, research has concluded that, SC is protective against post-traumatic stress disorder [ 55 ], anxiety [ 46 ], psychological distress [ 50 ], and stress [ 46 ]. Additionally, SC has been found to facilitate post-traumatic growth [ 38 ], and also to be useful to be drawn upon in times of stress [ 52 ], both of which could be protective of mental health. Similarly, research has also found that emotional recovery following a disaster is more difficult for those who report to have low levels of SC [ 51 ].

Conversely, however, research has also concluded that when other situational factors (e.g. personal resources) were controlled for, a positive relationship between community resources and life satisfaction was no longer significant [ 60 ]. Furthermore, some research has concluded that a high level of SC can result in a community facing greater stress immediately post disaster. Indeed, one retained paper found that high levels of SC correlate with higher levels of post-traumatic stress immediately following a disaster [ 39 ]. However, in the later stages following a disaster, this relationship can reverse, with SC subsequently providing an aid to recovery [ 41 ]. By way of explanation, some researchers have suggested that communities with stronger SC carry the greatest load in terms of helping others (i.e. family, friends and neighbours) as well as themselves immediately following the disaster, but then as time passes the communities recover at a faster rate as they are able to rely on their social networks for support [ 41 ].

Cognitive and structural social capital

Cognitive SC refers to perceptions of community relations, such as trust, mutual help and attachment, and structural SC refers to what actually happens within the community, such as participation, socialising [ 16 ].

Cognitive SC has been found to be protective [ 49 ] against PTSD [ 54 , 57 ], depression [ 40 , 54 ]) mild mood disorder; [ 48 ]), anxiety [ 48 , 54 ] and increase self-efficacy [ 59 ].

For structural SC, research is again inconsistent. On the one hand, structural SC has been found to: increase perceived self-efficacy, be protective of depression [ 40 ], buffer the impact of housing damage on cognitive decline [ 42 ] and provide support during disasters and over the recovery period [ 59 ]. However, on the other hand, it has been found to have no association with PTSD [ 54 , 57 ] or depression, and is also associated with a higher prevalence of anxiety [ 54 ]. Similarly, it is also suggested by additional research that structural SC can harm women’s mental health, either due to the pressure of expectations to help and support others or feelings of isolation [ 49 ].

Bonding and bridging social capital

Bonding SC refers to connections among individuals who are emotionally close, and result in bonds to a particular group [ 17 ], and bridging SC refers to acquaintances or individuals loosely connected that span different social groups [ 18 ].

One research study concluded that both bonding and bridging SC were protective against post-traumatic stress disorder symptoms [ 58 ]. Bridging capital was deemed to be around twice as effective in buffering against post-traumatic stress disorder than bonding SC [ 58 ].

Other community variables

Community social cohesion was significantly associated with a lower risk of post-traumatic stress disorder symptom development [ 35 ], and this was apparent even whilst controlling for depressive symptoms at baseline and disaster impact variables (e.g. loss of family member or housing damage) [ 36 ]. Similarly, sense of community, community connectedness, social support at the community level and neighbourhood connectedness all provided protective benefits for a range of mental health, wellbeing and recovery variables, including: depression [ 53 ], subjective wellbeing (in older adults only) [ 43 ], psychological distress [ 47 ], happiness [ 44 ] and life satisfaction [ 53 ].

Research has also concluded that community level social support is protective against mild mood and anxiety disorder, but only for individuals who have had no previous disaster experience [ 48 ]. Additionally, a study which separated SC into social cohesion and social participation concluded that at a community level, social cohesion is protective against depression [ 49 ] whereas social participation at community level is associated with an increased risk of depression amongst women [ 49 ].

What is the impact of Infectious disease outbreaks / disasters and emergencies on community resilience?

From a cross-sectional perspective, research has indicated that disasters and emergencies can have a negative effect on certain types of SC. Specifically, cognitive SC has been found to be impacted by disaster impact, whereas structural SC has gone unaffected [ 45 ]. Disaster impact has also been shown to have a negative effect on community relationships more generally [ 52 ].

Additionally, of the eight studies which collected data at multiple time points [ 35 , 36 , 41 , 42 , 47 , 49 , 56 , 60 ], three reported the effect of a disaster on the level of SC within a community [ 40 , 42 , 49 ]. All three of these studies concluded that disasters may have a negative impact on the levels of SC within a community. The first study found that the Deepwater Horizon oil spill had a negative effect on SC and social support, and this in turn explained an overall increase in the levels of depression within the community [ 40 ]. A possible explanation for the negative effect lays in ‘corrosive communities’, known for increased social conflict and reduced social support, that are sometimes created following oil spills [ 40 ]. It is proposed that corrosive communities often emerge due to a loss of natural resources that bring social groups together (e.g., for recreational activities), as well as social disparity (e.g., due to unequal distribution of economic impact) becoming apparent in the community following disaster [ 40 ]. The second study found that SC (in the form of social cohesion, informal socialising and social participation) decreased after the 2011 earthquake and tsunami in Japan; it was suggested that this change correlated with incidence of cognitive decline [ 42 ]. However, the third study reported more mixed effects based on physical circumstances of the communities’ natural environment: Following an earthquake, those who lived in mountainous areas with an initial high level of pre-community SC saw a decrease in SC post disaster [ 49 ]. However, communities in flat areas (which were home to younger residents and had a higher population density) saw an increase in SC [ 49 ]. It was proposed that this difference could be due to the need for those who lived in mountainous areas to seek prolonged refuge due to subsequent landslides [ 49 ].

What types of intervention enhance CR and SC and protect survivors?

There were mixed effects across the 26 retained papers when examining the effect of CR and SC on mental wellbeing. However, there is evidence that an increase in SC [ 56 , 57 ], with a focus on cognitive SC [ 57 ], namely by: building social networks [ 45 , 51 , 53 ], enhancing feelings of social cohesion [ 35 , 36 ] and promoting a sense of community [ 53 ], can result in an increase in CR and potentially protect survivors’ wellbeing and mental health following a disaster. An increase in SC may also aid in decreasing the need for individual psychological interventions in the aftermath of a disaster [ 55 ]. As a result, recommendations and suggested methods to bolster CR and SC from the retained papers have been extracted and separated into general methods, preparedness and policy level implementation.

General methods

Suggested methods to build SC included organising recreational activity-based groups [ 44 ] to broaden [ 51 , 53 ] and preserve current social networks [ 42 ], introducing initiatives to increase social cohesion and trust [ 51 ], and volunteering to increase the number of social ties between residents [ 59 ]. Research also notes that it is important to take a ‘no one left behind approach’ when organising recreational and social community events, as failure to do so could induce feelings of isolation for some members of the community [ 49 ]. Furthermore, gender differences should also be considered as research indicates that males and females may react differently to community level SC (as evidence suggests males are instead more impacted by individual level SC; in comparison to women who have larger and more diverse social networks [ 49 ]). Therefore, interventions which aim to raise community level social participation, with the aim of expanding social connections and gaining support, may be beneficial [ 42 , 47 ].

Preparedness

In order to prepare for disasters, it may be beneficial to introduce community-targeted methods or interventions to increase levels of SC and CR as these may aid in ameliorating the consequences of a public health emergency or disaster [ 57 ]. To indicate which communities have low levels of SC, one study suggests implementing a 3-item scale of social cohesion to map areas and target interventions [ 42 ].

It is important to consider that communities with a high level of SC may have a lower level of risk perception, due to the established connections and supportive network they have with those around them [ 61 ]. However, for the purpose of preparedness, this is not ideal as perception of risk is a key factor when seeking to encourage behavioural adherence. This could be overcome by introducing communication strategies which emphasise the necessity of social support, but also highlights the need for additional measures to reduce residual risk [ 59 ]. Furthermore, support in the form of financial assistance to foster current community initiatives may prove beneficial to rural areas, for example through the use of an asset-based community development framework [ 52 ].

Policy level

At a policy level, the included papers suggest a range of ways that CR and SC could be bolstered and used. These include: providing financial support for community initiatives and collective coping strategies, (e.g. using asset-based community development [ 52 ]); ensuring policies for long-term recovery focus on community sustainable development (e.g. community festival and community centre activities) [ 44 ]; and development of a network amongst cooperative corporations formed for reconstruction and to organise self-help recovery sessions among residents of adjacent areas [ 58 ].

This scoping review sought to synthesise literature concerning the role of SC and CR during public health emergencies and disasters. Specifically, in this review we have examined: the methods used to measure CR and SC; the impact of CR and SC on mental wellbeing during disasters and emergencies; the impact of disasters and emergencies on CR and SC; and the types of interventions which can be used to enhance CR. To do this, data was extracted from 26 peer-reviewed journal articles. From this synthesis, several key themes have been identified, which can be used to develop guidelines and recommendations for deploying CR and SC in a public health emergency or disaster context. These key themes and resulting recommendations are summarised below.

Firstly, this review established that there is no consistent or standardised approach to measuring CR or SC within the general population. This finding is consistent with a review conducted by the World Health Organization which concludes that despite there being a number of frameworks that contain indicators across different determinants of health, there is a lack of consensus on priority areas for measurement and no widely accepted indicator [ 27 ]. As a result, there are many measures of CR and SC apparent within the literature (e.g., [ 62 , 63 ]), an example of a developed and validated measure is provided by Sherrieb, Norris and Galea [ 64 ]. Similarly, the definitions of CR and SC differ widely between researchers, which created a barrier to comparing and summarising information. Therefore, future research could seek to compare various interpretations of CR and to identify any overlapping concepts. However, a previous systemic review conducted by Patel et al. (2017) concludes that there are nine core elements of CR (local knowledge, community networks and relationships, communication, health, governance and leadership, resources, economic investment, preparedness, and mental outlook), with 19 further sub-elements therein [ 30 ]. Therefore, as CR is a multi-dimensional construct, the implications from the findings are that multiple aspects of social infrastructure may need to be considered.

Secondly, our synthesis of research concerning the role of CR and SC for ensuring mental health and wellbeing during, or following, a public health emergency or disaster revealed mixed effects. Much of the research indicates either a generally protective effect on mental health and wellbeing, or no effect; however, the literature demonstrates some potential for a high level of CR/SC to backfire and result in a negative effect for populations during, or following, a public health emergency or disaster. Considered together, our synthesis indicates that cognitive SC is the only facet of SC which was perceived as universally protective across all retained papers. This is consistent with a systematic review which also concludes that: (a) community level cognitive SC is associated with a lower risk of common mental disorders, while; (b) community level structural SC had inconsistent effects [ 65 ].

Further examination of additional data extracted from studies which found that CR/SC had a negative effect on mental health and wellbeing revealed no commonalities that might explain these effects (Please see Supplementary file 5 for additional information)

One potential explanation may come from a retained paper which found that high levels of SC result in an increase in stress level immediately post disaster [ 41 ]. This was suggested to be due to individuals having greater burdens due to wishing to help and support their wide networks as well as themselves. However, as time passes the levels of SC allow the community to come together and recover at a faster rate [ 41 ]. As this was the only retained paper which produced this finding, it would be beneficial for future research to examine boundary conditions for the positive effects of CR/SC; that is, to explore circumstances under which CR/SC may be more likely to put communities at greater risk. This further research should also include additional longitudinal research to validate the conclusions drawn by [ 41 ] as resilience is a dynamic process of adaption.

Thirdly, disasters and emergencies were generally found to have a negative effect on levels of SC. One retained paper found a mixed effect of SC in relation to an earthquake, however this paper separated participants by area in which they lived (i.e., mountainous vs. flat), which explains this inconsistent effect [ 49 ]. Dangerous areas (i.e. mountainous) saw a decrease in community SC in comparison to safer areas following the earthquake (an effect the authors attributed to the need to seek prolonged refuge), whereas participants from the safer areas (which are home to younger residents with a higher population density) saw an increase in SC [ 49 ]. This is consistent with the idea that being able to participate socially is a key element of SC [ 12 ]. Overall, however, this was the only retained paper which produced a variable finding in relation to the effect of disaster on levels of CR/SC.

Finally, research identified through our synthesis promotes the idea of bolstering SC (particularly cognitive SC) and cohesion in communities likely to be affected by disaster to improve levels of CR. This finding provides further understanding of the relationship between CR and SC; an association that has been reported in various articles seeking to provide conceptual frameworks (e.g., [ 66 , 67 ]) as well as indicator/measurement frameworks [ 27 ]. Therefore, this could be done by creating and promoting initiatives which foster SC and create bonds within the community. Papers included in the current review suggest that recreational-based activity groups and volunteering are potential methods for fostering SC and creating community bonds [ 44 , 51 , 59 ]. Similarly, further research demonstrates that feelings of social cohesion are enhanced by general social activities (e.g. fairs and parades [ 18 ]). Also, actively encouraging activities, programs and interventions which enhance connectedness and SC have been reported to be desirable to increase CR [ 68 ]. This suggestion is supported by a recent scoping review of literature [ 67 ] examined community champion approaches for the COVID-19 pandemic response and recovery and established that creating and promoting SC focused initiatives within the community during pandemic response is highly beneficial [ 67 ]. In terms of preparedness, research states that it may be beneficial for levels of SC and CR in communities at risk to be assessed, to allow targeted interventions where the population may be at most risk following an incident [ 42 , 44 ]. Additionally, from a more critical perspective, we acknowledge that ‘resilience’ can often be perceived as a focus on individual capacity to adapt to adversity rather than changing or mitigating the causes of adverse conditions [ 69 , 70 ]. Therefore, CR requires an integrated system approach across individual, community and structural levels [ 17 ]. Also, it is important that community members are engaged in defining and agreeing how community resilience is measured [ 27 ] rather than it being imposed by system leads or decision-makers.

In the aftermath of the pandemic, is it expected that there will be long-term repercussions both from an economic [ 8 ] and a mental health perspective [ 71 ]. Furthermore, the findings from this review suggest that although those in areas with high levels of SC may be negatively affected in the acute stage, as time passes, they have potential to rebound at a faster rate than those with lower levels of SC. Ongoing evaluation of the effectiveness of current initiatives as the COVID-19 pandemic progresses into a recovery phase will be invaluable for supplementing the evidence base identified through this review.

• Recommendations

As a result of this review, a number of recommendations are suggested for policy and practice during public health emergencies and recovery.

Future research should seek to establish a standardised and validated approach to measuring and defining CR and SC within communities. There are ongoing efforts in this area, for example [ 72 ]. Additionally, community members should be involved in the process of defining how CR is measured.

There should be an enhanced effort to improve preparedness for public health emergencies and disasters in local communities by gauging current levels of SC and CR within communities using a standardised measure. This approach could support specific targeting of populations with low levels of CR/SC in case of a disaster or public health emergency, whilst also allowing for consideration of support for those with high levels of CR (as these populations can be heavily impacted initially following a disaster). By distinguishing levels of SC and CR, tailored community-centred approaches could be implemented, such as those listed in a guide released by PHE in 2015 [ 73 ].

CR and SC (specifically cognitive SC) should be bolstered if communities are at risk of experiencing a disaster or public health emergency. This can be achieved by using interventions which aim to increase a sense of community and create new social ties (e.g., recreational group activities, volunteering). Additionally, when aiming to achieve this, it is important to be mindful of the risk of increased levels of CR/SC to backfire, as well as seeking to advocate an integrated system approach across individual, community and structural levels.

It is necessary to be aware that although communities with high existing levels of resilience / SC may experience short-term negative consequences following a disaster, over time these communities might be able to recover at a faster rate. It is therefore important to ensure that suitable short-term support is provided to these communities in the immediate aftermath of a public health emergency or disaster.

Robust evaluation of the community resilience initiatives deployed during the COVID-19 pandemic response is essential to inform the evidence base concerning the effectiveness of CR/ SC. These evaluations should continue through the response phase and into the recovery phase to help develop our understanding of the long-term consequences of such interventions.

Limitations

Despite this review being the first in this specific topic area, there are limitations that must be considered. Firstly, it is necessary to note that communities are generally highly diverse and the term ‘community’ in academic literature is a subject of much debate (see: [ 74 ]), therefore this must be considered when comparing and collating research involving communities. Additionally, the measures of CR and SC differ substantially across research, including across the 26 retained papers used in the current review. This makes the act of comparing and collating research findings very difficult. This issue is highlighted as a key outcome from this review, and suggestions for how to overcome this in future research are provided. Additionally, we acknowledge that there will be a relationship between CR & SC even where studies measure only at individual or community level. A review [ 75 ] on articulating a hypothesis of the link to health inequalities suggests that wider structural determinants of health need to be accounted for. Secondly, despite the final search strategy encompassing terms for both CR and SC, only one retained paper directly measured CR; thus, making the research findings more relevant to SC. Future research could seek to focus on CR to allow for a comparison of findings. Thirdly, the review was conducted early in the COVID-19 pandemic and so does not include more recent publications focusing on resilience specifically in the context of COVID-19. Regardless of this fact, the synthesis of, and recommendations drawn from, the reviewed studies are agnostic to time and specific incident and contain critical elements necessary to address as the pandemic moves from response to recovery. Further research should review the effectiveness of specific interventions during the COVID-19 pandemic for collation in a subsequent update to this current paper. Fourthly, the current review synthesises findings from countries with individualistic and collectivistic cultures, which may account for some variation in the findings. Lastly, despite choosing a scoping review method for ease of synthesising a wide literature base for use by public health emergency researchers in a relatively tight timeframe, there are disadvantages of a scoping review approach to consider: (1) quality appraisal of retained studies was not carried out; (2) due to the broad nature of a scoping review, more refined and targeted reviews of literature (e.g., systematic reviews) may be able to provide more detailed research outcomes. Therefore, future research should seek to use alternative methods (e.g., empirical research, systematic reviews of literature) to add to the evidence base on CR and SC impact and use in public health practice.

This review sought to establish: (1) How CR and SC are quantified in research?; (2) The impact of community resilience on mental wellbeing?; (3) The impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, (4) What types of interventions enhance community resilience and social capital?. The chosen search strategy yielded 26 relevant papers from which we were able extract information relating to the aims of this review.

Results from the review revealed that CR and SC are not measured consistently across research. The impact of CR / SC on mental health and wellbeing during emergencies and disasters is mixed (with some potential for backlash), however the literature does identify cognitive SC as particularly protective. Although only a small number of papers compared CR or SC before and after a disaster, the findings were relatively consistent: SC or CR is negatively impacted by a disaster. Methods suggested to bolster SC in communities were centred around social activities, such as recreational group activities and volunteering. Recommendations for both research and practice (with a particular focus on the ongoing COVID-19 pandemic) are also presented.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Social Capital

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This study was supported by the National Institute for Health Research Research Unit (NIHR HPRU) in Emergency Preparedness and Response, a partnership between Public Health England, King’s College London and the University of East Anglia. The views expressed are those of the author(s) and not necessarily those of the NIHR, Public Health England, the UK Health Security Agency or the Department of Health and Social Care [Grant number: NIHR20008900]. Part of this work has been funded by the Office for Health Improvement and Disparities, Department of Health and Social Care, as part of a Collaborative Agreement with Leeds Beckett University.

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Osteochondrolipoma of the foot treated by surgical excision: a case report and literature review

• Fawzi Aljassir 1 ,
• Musab Alageel 1 ,
• Malak N. AlShebel 2 ,
• Abdulaziz M. Alsudairi 1 ,
• Ahmed Hashim 3 &
• Ibrahim Alshaygy 1 , 4  

BMC Musculoskeletal Disorders volume  25 , Article number:  275 ( 2024 ) Cite this article

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Osteochondromas, classified as a new benign subtype of lipomas and characterised by chondroid and osseous differentiation, are rare lesions that have been infrequently reported in previous literature. The maxillofacial region was reported as the most frequent localization, with infrequent occurrence in the lower limb. This paper represents the first documented case report of osteochondrolipoma in the foot.

Case presentation

A 51-year-old male patient presented with a chief complaint of right foot pain at the plantar aspect, accompanied by the observation of swelling between the first and the second metatarsal shafts. His complaint of pain and swelling started 10 and 4 years prior, respectively. Since their onset, both symptoms have progressed in nature. Imaging revealved a large mass exhibiting a nonhomogenous composition of fibrous tissue and bony structures. Surgical intervention through total excision was indicated.

Osteochodrolipoma is a benign lesion that can affect the foot leading to decreased functionality of the foot due to the pain and swelling. Surgical excision is the recommended approach for this lesion, providing both symptomatic relief and confirmation of the diagnosis through histopathological examination.

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Introduction

Lipomas have been reported to be the most prevalent benign soft tissue neoplasms, which are further classified into superficial and deep, depending on their location [ 1 ]. Differentiation into different mesenchymal elements, including fibrous tissues, blood vessels, or muscle, has been documented. However, differentiation into bone or cartilage has a low predilection and is often associated with parosteal localization [ 2 ]. Osteochondromas, classified as a new benign subtype of lipomas and characterized by chondroid and osseous differentiation, are rare lesions that have been infrequently reported in previous literature. The first case, observed by Soeder et al., presented with a 70-year-old male patient who underwent MRI of the left thigh, revealing the presence of nonhomogenous fibrinous tissue and bony structures. Furthermore, this isolated lesion was found to be independent of the neurovascular bundle and not attached to the bone. Histopathological examination demonstrated an encapsulated lesion with a smooth surface composed of vascularized fibrous capsule. Within the capsule, yellowish adipose tissue was observed, along with a notable presence of cartilaginous and bony components. Microscopic evaluation revealed a significant number of osteocytes, accompanied by a small rim of osteoblasts indicative of woven bone formation. Adjacent to the woven bone, cartilaginous tissue was also identified [ 3 ]. Other regions that have been reported in the literature include the forearm, ischial region, mandible, axilla, scapular region, popliteal fossa, and chest wall. Reports of this condition in the lower half of the body is relatively uncommon. All the reported cases treated this lesion with surgical excision, confirmed their diagnosis with histopathology, and reported no recurrence. Additionally, the patients were able to regain full functionality of the affected organ or limb [ 4 , 5 , 6 , 7 , 8 ].

This study provides a unique case of osteochondrolipoma, specifically located in the foot. This localization further adds to the rarity of this condition, as it has not been previously observed in such anatomical region. To the best of our knowledge, this represents the first documented case report of osteochondrolipoma in the foot. By highlighting this novel occurrence, our research expands on the current understanding of osteochondrolipoma and its diverse anatomical presentations.

A 51-year-old male patient was presented to the orthopedic clinic at King Saud University Medical City on July 11, 2023. He presented with a chief complaint of right foot pain at the plantar aspect, accompanied by the observation of swelling between the first and the second metatarsal shafts. His complaint of pain and swelling started 10 and 4 years prior, respectively. Since their onset, both symptoms have progressed in nature. Prior to his presentation, the patient had been surgically and medically free, and able to walk and undergo regular daily activities with no limitations. Throughout the years, he had been taking non-steroidal anti-inflammatory drugs (NSAIDs), including meloxicam, for pain relief. A year prior to his presentation, the pain started impeding daily activities, and he found no further relief with the use of analgesia. The patient denied prior instances of swelling in other areas in his body. Additionally, he denied any history of trauma, or constitutional symptoms such as fever, fatigue, night sweats, weight loss or loss of appetite. Furthermore, the patient denied any history of smoking.

Physical examination of his right foot revealed obvious swelling between the 1st and 2nd metatarsal shafts, extending to plantar aspect of the foot and the medial aspect of the first metatarsal shaft (Fig. 1 ). The swelling was 6*3 Cm in size, displaying an irregular shape accompanied by erythema. It was nonmobile, firm in its consistency, tender, and hot.

A . B . C .Clinical examination of the right foot

The patient demonstrated normal plantar flexion and dorsiflexion of the ankle joint with no pain, as well as pain-free and normal eversion and inversion of the subtalar joint. However, due to pain in the first and second phalanges of the first and second rays, there was limited range of motion in that area. He was neurovascularly intact and exhibited an antalgic gait while mobilizing. Plain radiographs demonstrated soft tissue swelling with a medial surrounding of bone density (Fig. 2 ).

Preoperative radiographs of the right foot showing a 4*3 calcified lesion between the first and third metatarsal shafts with no bony involvement. A Anteroposterior (AP) view B Oblique view. C Lateral view

Further investigation was deemed necessary, prompting the indication of magnetic resonance imaging (MRI). MRI revealed a large mass between the first and third metatarsal shafts, extending to the plantar aspect of the foot. The mass exhibited a nonhomogenous composition of fibrous tissue and bony structures, and surgical intervention was indicated (Fig. 3 ).

Preoperative MRI of the right foot showing a nonhomogeneous mass. A Axial T2-weighted. B Coronal T1-weighted. C Coronal T2-weighted D Sagittal T1-weighted. E Sagittal T2-weighted

The operation was performed under general anaesthesia, with the patient in the supine position. A 350 mmHg torniquet was applied, and a medial incision was made (Fig. 4 ). Following dissection of the fascia, the mass was observed to be large, composed of fibrous and chondral tissues, and reaching between the first and third metatarsal bones (Fig. 5 ). Total excision was performed, and triple washout with hydrogen peroxide, iodine, and saline was performed. The mass was sent to histopathology for confirmation of the diagnosis. The wound was sutured in layers with vicryl 1 and vicryl 0 the vicryl 2.0, the skin was closed with monocryl 4.0., and pressure dressing was applied. The patient was in stable condition and had intact vascularity. The excisional biopsy measured 5.5 × 3.5 × 3.0 cm. The lesion was observed to contain mature fatty tissue with areas of fibrous and cartilaginous tissue and was negative for malignancy. The intraoperative radiograph is shown post-excision (Fig. 6 ).

Right foot of the patient with medial incision markings

A . B . Intraoperative view following fascial dissection showing a large mass composed of fibrous and chondral tissues

Intraoperative radiograph of the right foot

Postoperatively, the patient had intact neurovascularity and experienced no complications. He was discharged home in stable condition with appropriate analgesia and a course of prophylactic antibiotic. He was advised for toe touch ambulation and further follow-up in the clinic, and had been on an ankle brace for two weeks. After the two weeks, the wound demonstrated appropriate healing, and sutures were removed. There had been no signs of infection or wound dehiscence. The patient commenced gradual range of motion two weeks postoperatively and had reached full range within one week. He returned to his normal activities four weeks following the excision. Radiographic imaging on the 8-month follow-up are shown (Fig. 7 )

Postoperative radiographs of the right foot A . AP view B . Oblique view. C Lateral view

Osteochondrolipoma is a benign lesion considered as a histological variant of lipoma. Few cases have been reported in the literature on this lesion in the mandible, chest wall, hand, scapula and ischium, indicating a limited number of occurrences [ 4 , 5 , 6 , 7 ]. According to Kitazawa et al., the maxillofacial region was reported as the most frequent location, with infrequent occurrence in the lower limb. The average patient age was also observed to be 57.4 years, with no clear gender predominance [ 7 ]. To date, we believe this represents the 18th case of osteochondrolipoma reported in the literature. According to our review of the literature with the keywords “osteochondrolipoma” and “ossifying chondrolipoma”, this is the first case reported to affect the foot.

Although it remains unclear, multiple theories have been suggested for the pathogenesis of osteochondrolipomas. One theory suggests that the different components independently arise from multipotent mesenchymal cells, while another suggests it indicates a metaplastic process in a previously existing chondrolipoma or lipoma, and some suggest repetitive trauma to cause secondary ossification [ 7 , 8 , 9 ]. Furthermore, the diagnosis of osteochondrolipomas depends on plain radiographs, computerized tomography (CT), MRI, and histopathology. MRI is regarded as the optimal imaging modality in this condition. To avoid misdiagnosis, a histopathological evaluation of the whole tumor, instead of an incisional biopsy, is the preferred method for diagnosis.

A variety of observations regarding its presentation have been made in the literature. Some case reports described it as firm, mobile, and nonadherent to muscle or bone, while others found it firmly attached. Furthermore, symptom presentation may also differ. In certain instances, and as opposed to scapular lesions, patients with osteochondrolipomas of the hand may present with complaints of pain and numbness [ 1 , 6 , 8 ]. The patient in our case experienced progressively worsening pain associated with swelling, eventually leading to a significant impact on their daily activities. Myositis ossificans, calcified or ossified tumours, hemangiomas, calcified bursae, and well-differentiated liposarcomas are all differentials of this condition [ 10 ].

In contrast to this condition, and on MRI, benign soft tissue tumors show uniform high intensity on T1, with low signal intensity appreciated on T2 weighted images [ 11 ]. On the other hand, myxoid liposarcomas show a well-defined and multilobular mass within the subcutaneous tissue, with T1-weighted images demonstrating low signal intensity with foci of linear high signal intensity, and heterogeneously high signal intensity on T2-weighted images [ 12 ].

In this rare location of osteochondrolipoma, we excised the lesion through the medial approach. We tried to avoid the plantar approach to avoid violating the plantar fascia or transecting nerve endings that may lead chronic pain with weight bearing. Chronic incisional pain was seen in 7.1% of patients who were treated with the plantar approach, and 5.1% showed a hypertrophied scar. Delayed wound healing was also noticed, and another study reported a 27% incidence of chronic incisional pain with planter approach [ 13 ].

In this study, we present the first osteochondrolipoma that presented in the foot. Our investigations included x-rays, MRI’s and our treatment modality of choice was through with surgical excision by using a medial approach to avoid complications of the plantar approach. The patient showed no chronic incisional pain, hypertrophic scar, or cyst formation.

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Abbreviations

Computerized tomography

Magnetic resonance imaging

Anteroposterior

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Fawzi Aljassir, Musab Alageel, Abdulaziz M. Alsudairi & Ibrahim Alshaygy

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The manuscript version of this contribution has been read and approved by all authors, and each author has committed to being personally responsible for the work. Conceptualization, idea, and research design: FA; Manuscript writing: FA, MN, MA, AA, AH, IA; Project management: FA, MA; Manuscript review: FA, MA.

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Aljassir, F., Alageel, M., AlShebel, M.N. et al. Osteochondrolipoma of the foot treated by surgical excision: a case report and literature review. BMC Musculoskelet Disord 25 , 275 (2024). https://doi.org/10.1186/s12891-024-07308-1

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DOI : https://doi.org/10.1186/s12891-024-07308-1

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• Osteochondrolipoma
• Total excision

BMC Musculoskeletal Disorders

ISSN: 1471-2474

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