Research on the health of lesbian, gay, bisexual, and transgender (LGBT) populations can provide important information to address existing health inequalities. Finding existing research in LGBT health can prove challenging due to the plethora of terminology used. We sought to describe existing search strategies and to identify more comprehensive LGBT search terminology. We iteratively created a search string to identify systematic reviews and meta-analyses about LGBT health and implemented it in Embase, PubMed/MEDLINE, and PsycINFO databases on May 28–29, 2015. We hand-searched the journal LGBT Health. Inclusion criteria were: systematic reviews and meta-analyses that addressed LGBT health, used systematic searching, and used independent coders for inclusion. The published search terminology in each record and search strings provided by authors on request were cross-referenced with our original search to identify additional terminology. Our search process identified 19 systematic reviews meeting inclusion criteria. The number of search terms used to identify LGBT-related records ranged from 1 to 31. From the included studies, we identified 46 new search terms related to LGBT health. We removed five search terms as inappropriate and added five search terms used in the field. The resulting search string included 82 terms. There is room to improve the quality of searching and reporting in LGBT health systematic reviews. Future work should attempt to enhance the positive predictive value of LGBT health searches. Our findings can assist LGBT health reviewers in capturing the diversity of LGBT terminology when searching.
Citation: Lee JGL, Ylioja T, Lackey M (2016) Identifying Lesbian, Gay, Bisexual, and Transgender Search Terminology: A Systematic Review of Health Systematic Reviews. PLoS ONE 11(5): e0156210. https://doi.org/10.1371/journal.pone.0156210
Editor: Nguyen Tien Huy, Institute of Tropical Medicine (NEKKEN), Nagasaki University, JAPAN
Received: December 1, 2015; Accepted: May 4, 2016; Published: May 24, 2016
Copyright: © 2016 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information file.
Funding: Research reported in this publication was supported by the National Cancer Institute (http://www.cancer.gov) of the U.S. National Institutes of Health under Award Number F31CA186434 to JGLL. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The historical invisibility of lesbian, gay, bisexual, and transgender (LGBT) lives is something of a pattern in LGBT health research, driven by invisibility in public health surveillance systems . Nonetheless, a growing number of high-quality data sources have documented health inequalities in chronic disease, infectious disease, mental health, and violent victimization . A growing research agenda seeks to examine the origins of these inequalities and evaluate interventions to address them . As the body of LGBT health research grows, evidence synthesis through reproducible systematic review and meta-analysis methodologies becomes increasingly important .
Systematic reviews provide a rigorous approach to identifying existing literature thereby limiting bias through the selection of studies . Additionally, systematic reviews and meta-analyses can show trends across multiple smaller studies that are individually difficult to interpret given their small size . Searches of the grey literature (i.e., unpublished in academic journals) can help counteract the effect of publication bias . Systematic reviews and meta-analyses can inform evidence-based interventions and identify practice-based evidence from community organizations . Systematic reviews are particularly important when study results are spread across multiple disciplines and academic as well as non-academic journals.
To achieve these important goals, however, systematic reviews and meta-analyses must be conducted in a high-quality manner . In the initial stages of identifying the existing literature through a systematic search process, bias can be introduced by failing to identify relevant studies. Implementing high-quality searches and reporting them according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines remains a challenge for systematic reviews in general [7, 8].
Although the importance of systematic review and meta-analysis methodologies and reporting are of general concern to health researchers, the incredible diversity of terminology used to describe and define LGBT communities by researchers, advocates, and community members provides an additional challenge to systematically reviewing LGBT health literature [9, 10]. To assist LGBT health researchers in the literature search process, we sought to examine the keyword searches used, report on the searches, and propose additional terminology for use in LGBT health searches. We operationalized this in two aims: (1) to describe characteristics of search strategies used in LGBT health systematic reviews and (2) to identify a comprehensive set of LGBT search terms that can be used to increase the sensitivity of LGBT health systematic review searches.
Using PubMed/MEDLINE, we developed keywords and MeSH terms in two domains (systematic reviews and homosexuality). We based our starting search keywords on resources from the University of Texas Libraries  for systematic review terminology and our previous work reviewing the literature on tobacco interventions for LGBT populations . After iteratively testing and improving our search strings, we then translated our search into the controlled vocabulary of other databases. We excluded certain unrelated terms because their abbreviations are used in LGBT health, for example: “markov state model” and “men who have sex with men” are both abbreviated MSM. A matrix of controlled vocabulary and individual database search strings are reported in S1 File. We implemented our search on May 28–29, 2015, in three health databases: Embase, PsycINFO, and PubMed/MEDLINE. We hand-searched the Web site of the journal LGBT Health on September 11, 2015. We set no date, geographic, or language limits in our search or in our inclusion process. Our final PubMed/MEDLINE search string was:
((bisexual[tiab] OR bisexuality[MeSH Terms] OR bisexuality[tiab] OR bisexuals[tiab] OR gay[tiab] OR gays[tiab] OR GLB[tiab] OR GLBT[tiab] OR homosexual[tiab] OR homosexualities[tiab] OR homosexuality[MeSH Terms] OR homosexuality[tiab] OR homosexuals[tiab] OR intersex[tiab] OR lesbian[tiab] OR lesbianism[tiab] OR lesbians[tiab] OR LGB[tiab] OR LGBT[tiab] OR "men who have sex with men"[tiab] OR msm[tiab] OR queer[tiab] OR "sexual minorities"[tiab] OR "sexual minority"[tiab] OR "sexual orientation"[tiab] OR transgender[tiab] OR transgendered[tiab] OR transgenders[tiab] OR transsexual[tiab] OR transsexualism[MeSH Terms] OR transsexualism[tiab] OR transsexuality[tiab] OR transsexuals[tiab] OR "women loving women"[tiab] OR "women who have sex with women"[tiab] OR WSW[tiab]) NOT (gay[au] OR "laparoscopic gastric bypass"[tiab] OR "markov state model" OR "multiple source method"[tiab]))
(systematic*[tiab] AND (bibliographic*[tiab] OR literature[tiab] OR review[tiab] OR reviewed[tiab] OR reviews[tiab])) OR (comprehensive*[tiab] AND (bibliographic*[tiab] OR literature[tiab])) OR "integrative literature review"[tiab] OR "integrative research review"[tiab] OR "integrative review"[tiab] OR “research synthesis”[tiab] OR “research integration”[tiab] OR meta-analys*[tiab] OR meta-analyz*[tiab] OR meta-analyt*[tiab] OR metaanalys*[tiab] OR metaanalyz*[tiab] OR metaanalyt*[tiab] OR “meta-analysis as topic”[MeSH:noexp] OR Meta-Analysis[ptyp] OR ((review[tiab] AND (rationale[tiab] OR evidence[tiab])) AND review[pt])
We set our criteria for inclusion as being a systematic review related to LGBT health. We defined systematic review as (a) using a set of keywords in (b) two or more databases with (c) independent coders assessing all identified records for inclusion or exclusion. Guidelines from Agency for Healthcare Research and Quality (AHRQ) (recommendation 7.6.6) , Cochrane (recommendation 7.2.4) , and the U.S. Institute of Medicine (IOM; recommendation 3.3.3)  all recommend dual independent coding for inclusion to reduce error and increase confidence in the findings.
In defining LGBT health, we sought to include studies that addressed domains such as injury prevention, chronic disease, mental health, violence, and sexual health and well-being. We a priori excluded: (a) HIV/AIDS-specific studies (because they often focus exclusively on same-sex behavior and we wished to focus this search on a broader definition of LGBT health), (b) studies about same-sex contact and resulting risk for HIV and sexually transmitted infections, (c) studies about the impact of LGBT parents on children (because the children may not be LGBT), (d) studies about treatment of homosexuality or gender dysphoria (including hormone therapy), and (e) studies about the origins of homosexuality.
After de-duplication, two authors independently screened the title and abstract of 1,226 records for potential inclusion or exclusion, removing studies clearly not related to the research question. Two authors then independently screened each of the 134 full text records identified for possible inclusion. At each stage, differences in coding were reconciled through discussion and consensus of at least two authors. We did not calculate reliability because we viewed the goal of independent coders being one of enhancing sensitivity to eligible records rather than one of establishing uniformity. We used Covidence (covidence.org) to manage the screening and coding process. Fig 1 shows the inclusion process.
Two authors independently abstracted the following information from the included records: (a) if the review reported a search string in keeping with the PRISMA guideline #8 (“Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated”) , (b) the keywords used to define the LGBT population of interest, (c) the databases searched, (d) any hand-searched journals, (e) inclusion of grey literature, (f) if the authors reviewed reference lists of included studies, (g) involvement of a librarian (because inclusion of a librarian has been shown to improve search quality ), (h) if the study assessed publication bias, (i) whether the study included meta-analysis, and (j) the area of LGBT health covered. We discussed any discrepancies in extraction coding and obtained consensus among authors, then exported the data into an evidence table.
In the interest of assembling maximum data on search terms used for LGBT health, we e-mailed the corresponding author to request the full search string if it was not reported in the manuscript. We then cross-referenced the abstracted search strategies with our own search strategy to create the most comprehensive search string for LGBT health systematic reviews.
We identified 19 studies meeting our inclusion criteria. These studies examined aging [18, 19], alcohol use , breast cancer , cardiovascular outcomes for transgender users of sex steroids , health information–seeking behaviors , intimate partner violence [24–26], mental health , stressful childhood experiences , substance abuse [29, 30], suicide , tobacco use [12, 32, 33], and victimization/abuse .
Review terminology and databases are reported in Table 1. In accordance with PRISMA reporting guidelines for searches, 13 presented a final search string from a specific database, including any limits. The number of LGBT-related keywords ranged from 1 [24, 31] to 31 . One study reported conflicting information about what databases were searched . For the remaining 18 studies, the number of databases ranged from 2 [24, 30, 34], the minimum required for study inclusion, to 15 . The most commonly used databases in the 19 identified studies were PubMed/MEDLINE (17 studies), PsycINFO (15 studies), CINAHL (8 studies), Web of Science/Knowledge (7 studies), and Embase (6 studies). Nine studies searched the grey literature, 17 reported searching reference lists, and 6 reported involvement of a librarian as an author in the methods or in the acknowledgements. Six of the 19 included studies assessed publication bias. Nine studies conducted a meta-analysis. Table 2 reports the search characteristics.
Our initial PubMed/MEDLINE search contained 36 search terms. Cross-referencing these with the identified search terms, we identified an additional 46 LGBT-related terms. We excluded three of these—“cross dress,” “drag king(s),” and “drag queen(s)”—used in a review on transgender aging  because these terms do not necessarily indicate a sexual orientation or gender identity . We excluded two additional terms, the abbreviations “SSA” and “SSAY” (for same-sex attracted [youth]), because these picked up thousands of un-related articles, leaving 41 new terms. We added five additional terms that were not used in any search. These are terms we have seen used in LGBT health research: “same gender loving” , “same sex couple” , “same sex couples” , “sexual and gender minority” , and its plural version, “sexual and gender minorities.” This full list of 82 terms is presented below with bolded terms coming from the identified reviews and italicized terms added based on their use in the field.
(bicurious[tiab] OR bisexual[tiab] OR bisexuality[MeSH Terms] OR bisexuality[tiab] OR bisexuals[tiab] OR “cross sex”[tiab] OR crossgender[tiab] OR F2M[tiab] OR “female-to-male”[tiab] OR gay[tiab] OR gays[tiab] OR “gender change”[tiab] OR “gender dysphoria”[tiab] OR “gender identity”[tiab] OR “gender queer”[tiab] OR “gender reassign”[tiab] OR “gender transform”[tiab] OR “gender transition”[tiab] OR genderqueer[tiab] OR GLB[tiab] OR GLBQ[tiab] OR GLBs[tiab] OR GLBT[tiab] OR GLBTQ [tiab] OR heteroflexible [tiab] OR homosexual[tiab] OR homosexualities[tiab] OR homosexuality[MeSH Terms] OR homosexuality[tiab] OR homosexuals[tiab] OR intersex[tiab] OR lesbian[tiab] OR lesbianism[tiab] OR lesbians[tiab] OR lesbigay[tiab] OR LGB[tiab] OR LGBQ[tiab] OR LGBS[tiab] OR LGBT[tiab] OR M2F[tiab] OR “male-to-female”[tiab] OR “men who have sex with men”[tiab] OR msm[tiab] OR queer[tiab] OR “same gender loving”[tiab] OR “same sex attracted”[tiab] OR “same sex couple”[tiab] OR “same sex couples”[tiab] OR “same sex relations”[tiab] OR “sex change”[tiab] OR “sex reassign”[tiab] OR “sex reversal”[tiab] OR “sex transform”[tiab] OR “sex transition”[tiab] OR “sexual and gender minorities”[tiab] OR “sexual and gender minority”[tiab] OR “sexual identity”[tiab] OR “sexual minorities”[tiab] OR “sexual minority”[tiab] OR “sexual orientation”[tiab] OR “sexual preference”[tiab] OR “trans female”[tiab] OR “trans male”[tiab] OR “trans man”[tiab] OR “trans men”[tiab] OR “trans people”[tiab] OR “trans person”[tiab] OR “trans woman”[tiab] OR “trans-sexuality”[tiab] OR transexual[tiab] OR transgender[tiab] OR transgendered[tiab] OR transgenders[tiab] OR transsexual[tiab] OR transsexualism[MeSH Terms] OR transsexualism[tiab] OR transsexuality[tiab] OR transsexuals[tiab] OR transvestite[tiab] OR “women loving women”[tiab] OR “women who have sex with women”[tiab] OR WSW[tiab] NOT ("laparoscopic gastric bypass"[tiab] OR gay[au] OR "markov state model" OR "multiple source method"[tiab]))
The body of evidence identified from the LGBT search domain in PubMed/MEDLINE on November 4, 2015, is 40,759 and 53,451 for our original and the expanded search, respectively.
There is room for improvement in the implementation and reporting of literature searches in LGBT health systematic reviews and meta-analyses. Strong evidence synthesis is essential to address a multitude of health concerns for LGBT populations. Authors have an ethical obligation to the field to reduce bias from study identification to ensure limited available resources are used effectively.
A strong evidence base for documenting, understanding, and intervening on LGBT health inequalities requires high-quality systematic reviews and meta-analyses. Comprehensive guidelines are available from AHRQ , Cochrane , and IOM . Based on this assessment of the state of LGBT health systematic reviews, we recommend that authors of systematic reviews in LGBT health use and report (and peer reviewers hold to account): (a) including a librarian or information specialist as collaborator to improve the search quality , (b) using more than one academic database, (c) using the controlled vocabulary of databases, (d) conducting searches of the reference lists of included studies, (e) reporting a complete specific search string so that the review can be updated as new literature emerges, (f) using dual coders for inclusion to improve data quality, and (g) using dual coders for abstraction or, at minimum, a reviewer to confirm and validate evidence tables . The work presented in this paper contributes to the development of better searches given the complex terminology used in LGBT health , but each of these recommendations on its own would contribute to stronger evidence synthesis in the field of LGBT health.
There are important limitations to this study. First, we used a somewhat restrictive definition of systematic review requiring dual, independent coding of titles and abstracts. Although AHRQ , Cochrane , and IOM  recommend dual independent coding for inclusion, many systematic reviews—some with strong search strategies—were ineligible due to not reporting the number of coders or having a single author decide which papers to include. Second, we did not empirically test our comprehensive search against other strings used by each of the studies identified in our search, thus we cannot be certain to what extent our search would improve the identification of relevant studies. We viewed this as being an unfair comparison because the identification of studies is a multi-step process that is unique to the aims of a given study. Third, searches must balance sensitivity and specificity; our work represents a preliminary effort to address search coverage by increasing sensitivity to LGBT health-related articles. Further work is needed to ensure a balance between sensitive searches and more specific searches. Fourth, changes in terminology to define and describe LGBT populations are likely already happening ; although our work provides a thorough list of keywords for searching, future reviewers should consider the ever-shifting landscape of LGBT terminology. Fifth, we conducted our original search in three academic databases; searching a larger number of databases could have resulted in inclusion of additional reviews. Sixth, we did not assess the role of publication bias in our identification of search terminology; results could be influenced by unpublished reviews that may have poorly designed search strings.
The lives of LGBT individuals have historically been invisible in health data  and in popular culture . With growing research to address health inequalities, it is imperative that rigorous methods to identify and synthesize existing research be employed. With diverse and shifting terminology being used, researchers should carefully consider the terminology used to identify as much of the relevant literature as possible. Efforts to combat health inequalities are only as strong as the evidence available to know what inequalities exist, how they come into being, and how to intervene against them.
Our thanks to Megan E. DeMarco for research assistance on this project; Thomas Wimark, PhD, for help with German language papers; Derrick D. Matthews, PhD, MPH, for thoughtful feedback on the manuscript as well as his love of reviewing reviews of reviews; and the three anonymous reviewers for thoughtful comments that improved the manuscript.
Conceived and designed the experiments: JGLL TY ML. Performed the experiments: JGLL TY ML. Analyzed the data: JGLL TY ML. Wrote the paper: JGLL TY ML.
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