Jain, A. K., Topchy, A., Law, M. H., & Buhmann, J. M. (2004, August). Landscape of clustering algorithms. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. (Vol. 1, pp. 260-263). IEEE.

The advent of machine learning and deep learning approaches in the past decade has not only advanced existing approaches and introduced a host of new methods such as deep clustering methods. This article provides the historical timeline for some of these developments. Some of these methods are also increasingly being used for health and epidemiological data, for example deep embedded clustering (DEC) used with critical care data in this paper by de Kok et al. (2024) , I’ve provided some examples below. A good review of recent clustering methods in this space by Ezugwu et al. (2022).

Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., & Akinyelu, A. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, 104743.

Collins, L. M., & Lanza, S. T. (2009). Latent Class and Latent Transition Analysis (1st ed.). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470567333

“Latent Class and Latent Transition Analysis is a comprehensive guide for identifying unobserved subgroups within a population using categorical data. Designed for researchers and students in social, behavioral, and health sciences, the book covers latent class and latent transition analysis techniques, which are used to infer hidden patterns within groups based on survey responses or other observed variables. It begins with foundational concepts and progresses to advanced topics like longitudinal latent class models, parameter restrictions, and multi-group analysis. Each method is presented with both theoretical background and practical applications, including examples of real-world data analysis. Chapters conclude with key takeaways, and the book offers online resources with data sets and specialized software tools (Proc LCA and Proc LTA in SAS) to help readers apply and experiment with the methods discussed. This resource is ideal for advanced coursework in categorical data analysis or for researchers using latent variable modeling.“

Hagenaars, J. A., & McCutcheon, A. L. (2002). Applied Latent Class Analysis. Cambridge University Press. https://www.cambridge.org/core/books/applied-latent-class-analysis/30C364913C52083262DD7CE5A2E05685

“Applied Latent Class Analysis is a hands-on guide for researchers seeking practical applications of latent class modeling to uncover hidden subgroups within data. This book is structured to support applied work, moving beyond theoretical explanations to focus on implementing latent class analysis (LCA) for real-world research challenges. The chapters cover essential methods, such as clustering and measurement models, and extend to sophisticated applications, including causal analysis, dynamic models, and handling missing data. Each section combines theoretical insights with concrete examples and step-by-step instructions on conducting analyses in SAS, providing readers with a toolkit for navigating complex datasets. The book is particularly valuable for social and behavioral researchers who need guidance on translating latent class techniques into empirical insights, with each chapter including practical case studies and applications that demonstrate the versatility of LCA in various research contexts. With empirical examples and tips on best practices, Applied Latent Class Analysis is an essential resource for those aiming to enhance their methodological skill set and apply LCA effectively in their own work. The book is also a tribute to Clifford C. Clogg, whose work laid foundational principles in the field.”

A great literature base or reference library is vital to help with writing and developing ideas; however, getting started in a new area or developing interdisciplinary / transdisciplinary work can be challenging. A literature base is the first step in achieving a comprehensive review, systematic review, or meta-analysis. Literature reviews are essential for constructing an introduction section of a manuscript, discussion section, or covering information within a book chapter. Sometimes finding the various sources and filtering out articles to synthesize can be a daunting task. Now there are several tools to build a solid literature base. The first step is to get started with a comprehensive literature review or even a systematic literature review and meta-analysis.

There are many bibliographic research tools available out there. Many use simple co-citation analysis to identify a network of relevant articles, while others use more powerful algorithms to generate and visualize their networks. Below, is a selection of some of the more unique tools, representing a slice of tools available. These can be used to build a base reference library. They are user-friendly and many have re-occurring updates. You can play around with these features to figure out which tool works best for you.

*This list is in alphabetical order, not by ranking. All videos provided by websites cited.

CoCites: www.cocites.com
CoCites uses a browser-based extension for Chrome and Firefox which adds an information box for papers in PubMed. It provides information about the numbers of times a paper is cited and its co-citated articles. The tool uses a co-citation similarity network to rank relevant papers, currently limited to 100 most recently published. This has been found to be effective in evaluation studies of the tool.

Connected Papers: www.connectedpapers.com
Connected Papers is a literature visualization tool, with a search engine connected to multiple databases including PubMed, Semantic Scholar and arXiv. Other features include:
i. A graph that details the lead author, year, and strength of the connection based on co-citation and bibliographic coupling to the original paper
ii. Recommends relevant articles, identifying articles that are brand new and articles that do not cite each other but may be related as it does not rely on a co-citation mechanism
iii. Once the graph developed, the articles in the graph can be downloaded, and the prior papers (likely to be most critical in the field) and derivative works (likely to be reviews) tab can also be downloaded
The demonstration below (image provided by Connected Papers) shows the website’s recent collaboration with arXiv, where every paper in arXiv.org links to a Connected Papers Graph.

Inciteful: inciteful.xyz
Inciteful.xyz is an academic article network finder with a fast, user friendly interface. It features various ways to engage with the “seed papers” users’ input. The tool’s powerful seeding mechanism allows for further refining the network of papers that are most relevant in multiple rounds. It also allows for filtering of the network by keywords, by distance and by year. As well, for adding additional papers to the network manually. The tool provides a range of metrics useful in analyzing the publication landscape including:
i. Similar papers
ii. Most important papers in the network as identified by pagerank
iii. Recent papers by the top 100 authors
iv. Most important recent paper with a ranking
v. Top authors in the area for the papers identified
vi. Institutions most published in the network
vii. Top journals for the research area

JSTOR Labs Text Analyzer: jstor.org/analyze
JSTOR Text Analyzer is a program that extracts and analyzes text in an article. It can help build better key terms for a systematic review and papers related to the example article analyzed. Following submission, the tool analyzes terms that are explicit or implied in the text and highlights relevant topics. It then generates a list of recommended topics that can be filtered by year, type, and accessibility.

Litmaps: www.litmaps.co
Litmaps website develops maps based on key terms and articles, which can be selected on the site or uploaded from a citation manager. There are multiple ways to create a map based on the articles selected or key terms, which shows the selected articles’ relatedness. The maps tab features allow users to develop their maps. The explore tab allows users to find articles based on the original map. The systematic tab is a new feature coming soon to develop a systematic search process.

Open Knowledge Maps: www.openknowledgemaps.org
Open Knowledge Maps promotes research discoverability by showing an overview of topics and relevant concepts using the 100 relevant papers. Retrieving literature with high meta-data quality, the maps cluster papers based on key terms used in the field.

Vosviewer: www.vosviewer.com
VOSViewer is a text mining and article network software that must be downloaded. The software allows users to build various maps based on authors, journals, universities, conferences, and key terms. These maps also have functions for customized clustering, how the maps can be visualized, and an analysis function. The tool has been extensively reviewed, validated, and is highly cited in literature. It features:
i. Rich range of data sourcesfrom Web of Science and Pubmed to OpenCitations and WikiData
ii. Mapping tool and visualization network with labeling
iii. Clustering of networks based on co-authorship, co-citations, and bibliographic coupling
iv.

Researchrabbit: www.researchrabbit.ai
ResearchRabbit is a newly-developed literature discovery tool which provides a personalized experience for users. Currently early access is available by request. The features include:
i. Finding earlier, latest, and similar works based on key article(s) provided to build a collection that can be saved if you need to come back to it
ii. A literature map based on the data provided
iii. A list of references from selected papers can also be added to the collection
iv. Ability to export the collection to a reference manager
v. Email updates based on your collections about articles that may be of interest

Epidemiologists and other population health researchers have made considerable developments in bridging intersectionality theory with epidemiological methods over the past decade. The following list attempts to reflect the dynamic and non-linear nature of this endeavor and is organized as follows. The first set of articles are conceptual, reviewing the why and how of incorporating an intersectional lens into epidemiological research. The second set of articles are methodological, presenting new or novel applications of existing analytic approaches to studying the distribution and determinants of health and disease. When possible, I mention published commentaries and responses to these articles to highlight the ongoing challenges involved in “quantifying” intersectionality and to emphasize the fact that no single method is inherently intersectional. Finally, because intersectionality theory is first and foremost a critical theory with the goal of enacting transformative social change, the third set of articles are focused on examining the process of epidemiological knowledge production itself as a means of reinforcing or challenging systems of power, as well as how epidemiological knowledge can be used to promote intersectional health equity. Of course, this list is nowhere near exhaustive, and is influenced by both extant interpretations of intersectionality theory in the field of epidemiology and my own social location, academic training, and worldview… read more »

Praxis Articles

Shim JK. Understanding the routinised inclusion of race, socioeconomic status and sex in epidemiology: The utility of concepts from technoscience studies. Sociol Heal Illn. 2002;24(2):129 – 150. 

In this sociological article, Shim reviews the use and conceptualization of social identities within epidemiological research and argues that the conflation of social identity with individual-level biology and/or behavior in mainstream epidemiology (re)produces hegemonic (and problematic) notions regarding differences between social groups. She offers a critique of the multifactorial model of disease causation, positing that the model ignores both the intersectional nature of social identities and the social and historical contexts in which disease occurs, and raises important questions about the process of epidemiological knowledge production (e.g., “To what extent are epidemiologists deliberately conscious of and concerned about the meanings that specific measures of race, class and sex/gender embody?” (p. 143)). Overall, Shim’s article is an important read as it encourages epidemiologists to view our work as both a scientific and social product. 

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Wemrell M, Merlo J, Mulinari S, Hornborg AC. Contemporary epidemiology: A review of critical discussions within the discipline and a call for further dialogue with social theory. Sociol Compass. 2016;10(2):153–171. 

Wemrell and colleagues offer a critical review of historical debates within epidemiology, with a focus on critiquing the hegemony of “risk factor epidemiology”. In particular, risk factor epidemiology is argued to erase within-group heterogeneity, ignore social and historical contexts, and prioritize scientific objectivity and neutrality over public health-promoting social change. They conclude by arguing for a greater incorporation of social theory (including intersectionality theory) into epidemiological research, particularly with respect to studying the social determinants of health and health disparities, in order to produce epidemiological knowledge that better promotes health equity. 

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Breilh J. Latin American critical (‘Social’) epidemiology: New settings for an old dream. Int J Epidemiol. 2008;37:745–750. 

Breilh’s article provides an important critique of the “scientific discrimination” in mainstream epidemiological and public health research, whereby the scientific contributions from Latin America are under-appreciated and under-acknowledged relative to contributions from Western countries. As a means of challenging this form of structural academic bias, he outlines the objectives and major contributions of “Latin American critical epidemiology”, illustrating how the field has “constructed a sound institutional and academic platform from which to exercise a democratic projection of science and mold an alternative public health movement” (p. 749). Breilh’s article is an essential read for epidemiologists looking to engage in intersectional scholarship as it encourages us to ask who is “at the table” of epidemiological knowledge production (and why), and what the implications are with respect to producing knowledge to promote intersectional health equity.

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Ng E, Muntaner C. A critical approach to macrosocial determinants of population health: Engaging scientific realism and incorporating social conflict. Curr Epidemiol Reports. 2014;1:27–37. 

Ng and Muntaner argue for the advancement of macrosocial epidemiology, defined as the study of macro-level factors, processes, and institutions (e.g., globalization, macroeconomics) on the population patterning and determinants of health and disease. Although they do not explicitly discuss intersectionality theory, macrosocial epidemiology’s emphasis on structural power and social justice is directly in-line with the theory’s core tenets and critical bent, making this read helpful for thinking about how intersectionality and social epidemiological theories can inform and build upon one another. 

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Inhorn MC, Whittle KL. Feminism meets the “new” epidemiologies: toward an appraisal of antifeminist biases in epidemiological research on women’s health. Soc Sci Med. 2001;53(5):553–67. 

Similar to Ng and Muntaner’s argument for macrosocial epidemiology, Inhorn and Whittle put forth their vision of a “feminist epidemiology”, characterized by a strong engagement with critical social theory (including intersectionality theory), an examination of the researcher’s positionality, a focus on structural power, and a grass-roots, participatory approach. 

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Cho S, Crenshaw KW, McCall L. Toward a field of intersectionality studies: Theory, applications, and praxis. Signs (Chic). 2013;38(4):785–810. 

Last but certainly not least, this article from Cho and colleagues provides a comprehensive overview of extant intersectional scholarship in an effort to define and distinguish a field of “intersectionality studies”. Although not specific to epidemiology, the authors “Template for a Collaborative Intersectionality” can provide epidemiologists with important guidelines for incorporating intersectionality into their work and foster a more critical, interdisciplinary, and social justice-oriented epidemiology.

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Epidemiologists and other population health researchers have made considerable developments in bridging intersectionality theory with epidemiological methods over the past decade. The following list attempts to reflect the dynamic and non-linear nature of this endeavor and is organized as follows. The first set of articles are conceptual, reviewing the why and how of incorporating an intersectional lens into epidemiological research. The second set of articles are methodological, presenting new or novel applications of existing analytic approaches to studying the distribution and determinants of health and disease. When possible, I mention published commentaries and responses to these articles to highlight the ongoing challenges involved in “quantifying” intersectionality and to emphasize the fact that no single method is inherently intersectional… read more »

Methodological Articles

Veenstra G. Race, gender, class, and sexual orientation: Intersecting axes of inequality and self-rated health in Canada. Int J Equity Health. 2011;10(3):1–11. 

Veenstra’s article is one of the first to explicitly present a quantitative analytic approach for incorporating intersectionality theory into epidemiological research. He uses statistical interaction (i.e., two- and three-way cross-product terms between social identity variables in multivariable models) to assess whether multiply marginalized groups are more likely to report poor self-rated health relative to singly- or non-marginalized groups, drawing on intersectionality core tenets of directionality, simultaneity, multiplicativity, and multiple jeopardy. Although the use of statistical interaction to assess intersectionality is increasingly critiqued by social epidemiologists, this article and those informed by it helped motivate the development of the methods introduced in the following articles. 

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Jackson JW, Williams DR, VanderWeele TJ. Disparities at the intersection of marginalized groups. Soc Psychiatry Psychiatr Epidemiol. 2016;51(10):1349–1359. 

Building off the aforementioned critiques of using statistical interaction to assess intersectionality, this article from Jackson, Williams, and VanderWeele argues in favor of evaluating additive-scale interaction (i.e., a situation in which the combined effect of two factors differ from the sum of each factor’s individual effect) as an alternative quantitative analytic approach. They present a novel additive-scale interaction method that is specific to intersectionality, the joint disparity and its decomposition, and illustrate how it and related measures can be used to quantify excess (i.e., intersectional) risk of disease among groups at the nexus of two marginalized social identities. Technical appendices provide equations for applying the methods to continuous and binary outcomes. 

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Commentaries:

Schwartz S. Commentary: on the application of potential outcomes-based methods to questions in social psychiatry and psychiatric epidemiology. Soc Psychiatry Psychiatr Epidemiol. 2017;52(2):139–42. 

Jackson JW. Explaining intersectionality through description, counterfactual thinking, and mediation analysis. Soc Psychiatry Psychiatr Epidemiol. 2017;52(7):785–93.

Wemrell M, Mulinari S, Merlo J. Intersectionality and risk for ischemic heart disease in Sweden: Categorical and anti-categorical approaches. Soc Sci Med. 2017;177:213–22. 

The majority of intersectionality-informed epidemiological research adopts an intracategorical or intercategorical orientation; much less attention has been directed to developing quantitative methods consistent with anticategorical intersectionality (see McCall, 2005 listed above). Wemrell and colleagues help to fill this gap by introducing an innovative method for conducting quantitative anticategorical intersectionality-informed research based on the epidemiological concept of discriminatory accuracy (DA). Specifically, the authors contend that quantifying the ability of social identity-based categories to distinguish between those with and without a health outcome of interest (as is typically done in epidemiological research) can demonstrate the large degree of heterogeneity within these categories, and thus what anticategorical intersectionality describes as the “simplifying social fictions” (McCall, 2005, p. 1773) of social identities. They outline their analytic approach (using area under the receiver-operating characteristic curves) and provide guidance for intersectionality theory-consistent interpretations.

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Evans CR, Williams DR, Onnela JP, Subramanian SV. A multilevel approach to modeling health inequalities at the intersection of multiple social identities. Soc Sci Med. 2018;203:64–73. 

In this pioneering article, Evans and colleagues introduce a novel modeling approach for intercategorical intersectionality-informed epidemiological research that is referred to in subsequent articles as Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). Briefly, Intersectional MAIHDA models are multilevel models in which individuals are nested within groups defined by intersecting social identities, which enables investigations of within- and between-group heterogeneity and the ability to identify and quantify interaction (i.e., intersectional) effects for all groups. The authors outline methodological and theoretical advantages of the method over “conventional” intersectional models (i.e., fixed effects models with cross-product terms between social identity variables) and provide an illustrative example. 

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Commentaries: 

Merlo J. Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) within an intersectional framework. Soc Sci Med. 2018; 

Evans CR. Adding interactions to models of intersectional health inequalities: Comparing multilevel and conventional methods. Soc Sci Med. 2019;221:95–105. 

Evans further develops the Intersectional MAIHDA method by investigating whether adding additional dimensions of identity into the models reveals or explains away intersectional effects. She also compares findings from Intersectional MAIHDA models to those obtained from conventional intersectional models and discusses plausible statistically-based reasons for the observed differences. Stata code for all analyses are provided in the supplementary materials. 

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Commentaries:

Bauer GR. Math versus meaning in MAIHDA: A commentary on multilevel statistical models for quantitative intersectionality. Soc Sci Med. 2019; 112500. 

Evans CR, Leckie G, Merlo J. Multilevel versus single-level regression for the analysis of multilevel information: The case of quantitative intersectional analysis. Soc Sci Med. 2019;245:112499. 

Evans CR. Reintegrating contexts into quantitative intersectional analyses of health inequalities. Heal Place. 2019;60:102214. 

Another Intersectional MAIHDA methods article, Evans outlines how to incorporate social contexts (e.g., schools and neighborhoods) into the models. She also suggests alternative conceptualizations of contexts for quantitative intersectionality-informed research, with the goal of motivating epidemiologists to better attend to socio-structural processes in their analyses.

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Scheim AI, Bauer GR. The Intersectional Discrimination Index: Development and validation of measures of self-reported enacted and anticipated discrimination for intercategorical analysis. Soc Sci Med. 2019;226:236–45. 

In the only measurement-specific article on this list, Scheim and Bauer develop and validate the Intersectional Discrimination Index (InDI), a measure of attribution-free anticipated and enacted discrimination to be used in intercategorical intersectionality-informed epidemiological research. The authors include the final version of the index within the paper, provide guidance regarding its scoring, and outline an analytic approach for using the index to assess discrimination as social determinant of intersectional health inequities in a companion article (listed below). 

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Commentaries:

Harnois CE, Bastos JL. The promise and pitfalls of intersectional scale development. Soc Sci Med. 2019;223:73–6. 

Bauer GR, Scheim AI. Methods for analytic intercategorical intersectionality in quantitative research: Discrimination as a mediator of health inequalities. Soc Sci Med. 2019;226:236–45. 

In the companion article to that listed above, Bauer and Scheim illustrate a novel method for analytic (i.e., process-oriented) intersectionality-informed epidemiological research. They start with a helpful overview of extant analytic approaches for incorporating intersectionality theory into epidemiological research and highlight persisting methodological challenges, with an emphasis on research aiming to conduct causal analyses. They then introduce their method, a causal mediation analysis (adapted from VanderWeele’s three-way decomposition method and informed by the potential outcomes framework) that allows for heterogeneity of the mediated effect across groups defined by intersecting social identities, and provide an illustrative demonstration complete with equations and SAS code. 

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Commentaries:

Evans CR. Modeling the intersectionality of processes in the social production of health inequalities. Soc Sci Med. 2019;226:249–53.

Jackson JW, VanderWeele TJ. Intersectional decomposition analysis with differential exposure, effects, and construct. Soc Sci Med. 2019;226:254–9. 

Richman LS, Zucker AN. Quantifying intersectionality: An important advancement for health inequality research. Soc Sci Med. 2019;226:246–8. 

Bauer GR, Scheim AI. Advancing quantitative intersectionality research methods: Intracategorical and intercategorical approaches to shared and differential constructs. Soc Sci Med. 2019;226:260–2.

Social identities (i.e., gender, race, ethnicity, class, sexual orientation, age, etc.) are a key construct within epidemiological research, used to define populations and study the patterning of health and disease. And yet, epidemiologists have conceptualized identity in varied, shifting, and oftentimes problematic ways. There have been numerous calls to shift from a discrete and individualized framing of identity to a more nuanced and contextualized one, reflecting broader debates in epidemiology between theories of disease distribution that locate risk within the biology and behaviors of individuals and those that locate risk further upstream (Krieger, 2001). 

Perhaps because of its potential to improve upon extant conceptualizations of identity as well as other core population health constructs, there has been a growing interest in intersectionality theory among epidemiologists. Originating in Black feminist activism and scholarship as a way to explain the unique forms of discrimination experienced by Black women (Collins, 1990; Crenshaw, 1989), intersectionality is a theoretical framework primarily concerned with the multidimensional, interlocking nature of social inequities. Within this framework, social identities are conceptualized as mutually constitutive, jointly shaping lived experience through their historical and ongoing relationships with systems of power. This has several implications for epidemiology: adopting an intersectional perspective could lead to a more nuanced (and valid) understanding of the population patterning of disease, a heightened focus on social determinants, and more structurally oriented interventions. However, there are well-articulated challenges involved in translating a theory aimed at describing complex realities (versus generating hypotheses or predictions) into a quantitative science such as epidemiology, as well as important questions over epistemological consistency.  

Thankfully, epidemiologists and other population health researchers have made considerable developments in bridging intersectionality theory with epidemiological methods over the past decade. The following list attempts to reflect the dynamic and non-linear nature of this endeavor and is organized as follows. The first set of articles are conceptual, reviewing the why and how of incorporating an intersectional lens into epidemiological research. The second set of articles are methodological, presenting new or novel applications of existing analytic approaches to studying the distribution and determinants of health and disease. When possible, I mention published commentaries and responses to these articles to highlight the ongoing challenges involved in “quantifying” intersectionality and to emphasize the fact that no single method is inherently intersectional. Finally, because intersectionality theory is first and foremost a critical theory with the goal of enacting transformative social change, the third set of articles are focused on examining the process of epidemiological knowledge production itself as a means of reinforcing or challenging systems of power, as well as how epidemiological knowledge can be used to promote intersectional health equity. Of course, this list is nowhere near exhaustive, and is influenced by both extant interpretations of intersectionality theory in the field of epidemiology and my own social location, academic training, and worldview. 

Conceptual articles

Bowleg L. The problem with the phrase women and minorities: Intersectionality-an important theoretical framework for public health. Am J Public Health. 2012;102(7):1267–73. 

In one of the first articles to explicitly advocate for an intersectional approach to epidemiological-related research, Bowleg provides a compelling case for adopting intersectionality theory as a critical public health framework, focusing on its potential to “[reframe] how public health scholars conceptualize, investigate, analyze, and address disparities and social inequality in health” (p. 1267). After providing a brief history of intersectionality and outlining its core tenets, she reviews the major theoretical and methodological challenges associated with incorporating the theory into public health research; however, she stresses that having an “intersectionality-informed stance” is more important than methodological refinement. Bowleg concludes by outlining five ways in which this intersectionality-informed stance will benefit public health, ranging from increased understanding of how intersecting social identities influence the patterning and determinants of health disparities to improved interventions and surveillance efforts.     

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Bauer GR. Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance health equity. Soc Sci Med. 2014;110(10–17). 

Another formative article, Bauer starts similarly by articulating the potential benefits of incorporating intersectionality theory into the broader field of population health sciences. She provides a detailed and nuanced description of the associated methodological challenges, with a focus on measurement- and analytic-related concerns, and offers guidelines for addressing them. The most useful aspect is Bauer’s discussion of how tensions within intersectional scholarship may be understood in the context of population health research. For example, questions such as “who” is intersectional (e.g., all social locations or only those with multiple marginalized identities) and whether all identities are relevant/intersectional in all contexts are addressed in terms of their significance for understanding the distribution and determinants of health and disease. She also considers how intersectionality meshes with epidemiologic theories, particularly Nancy Krieger’s ecosocial theory, with implications for the validity and social value of epidemiological research. 

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Gkiouleka A, Huijts T, Beckfield J, Bambra C. Understanding the micro and macro politics of health: Inequalities, intersectionality & institutions – A research agenda. Soc Sci Med. 2018;200:92–8. 

Gkiouleka and colleagues review the theoretical and methodological underpinnings of using intersectionality as an analytic tool for studying health inequities and offer two research approaches relevant to epidemiology: “situational intersectionality” (i.e., focusing on the specific social identities relevant to a given research question, while exploring potential heterogeneity in such relevance across groups) and “institutional imbrication” (i.e., operationalizing institutions and other macro-level factors typically studied discreetly in epidemiology (e.g., neighborhoods and laws/policies) as intersectional). The article concludes with recommendations for measurement and analysis when using these research approaches and outlines seven actions that researchers can take to make their work more intersectional. 

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Bowleg L. When Black + lesbian + woman ≠ Black lesbian woman: The methodological challenges of qualitative and quantitative intersectionality research. Sex Roles. 2008;59:312–25. 

While not specific to epidemiology or even to population or public health, this earlier article by Bowleg nonetheless provides an excellent overview of measurement-, analytic-, and interpretation-related challenges to conducting intersectionality-informed research. For each of these domains, she provides specific guidelines for addressing the noted challenges, although she emphasizes that contextualization is paramount to defining intersectional scholarship. 

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McCall L. The complexity of intersectionality. Signs (Chic). 2005;30:1771–800. 

This seminal article in intersectional scholarship laid the groundwork for how epidemiologists began to operationalize the theory for quantitative research. McCall presents three orientations for intersectionality-informed research, which represent a continuum of approaches to managing the complexity of multiple intersecting identities: “anticategorical intersectionality” (critique and deconstruct social identities), “intracategorical intersectionality” (reveal nuance within a particular social location), and “intercategorical intersectionality” (document inequalities across many social locations). After reviewing the underlying assumptions and analytic guidelines for each orientation, she calls for an expansion of methodologies used to answer intersectionality-informed research questions so as to “fully engage with the topics and issues of intersectionality” (p. 1774); on a personal note, I find this a strong argument for the incorporation of intersectionality theory into epidemiology! 

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Evans CR. Modeling the intersectionality of processes in the social production of health inequalities. Soc Sci Med. 2019;226:249–53. 

This article is a comprehensive overview of the types of intersectionality-informed epidemiological and population health studies. Evans provides a useful classification system for this growing body of research, distinguishing specific/intracategorical from comprehensive/intercategorical and descriptive from analytic studies, providing researchers with a common language to describe their objectives and analyses. She also suggests specific epidemiologic theories that are particularly well-aligned with intersectionality theory (and could thus be used in conjunction, advancing both fields) and outlines future research directions.  

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Directed Acyclic Graphs, or DAGs, are commonly used in Epidemiology to communicate the assumptions that are being made about the structure of the causal network being studied, and can be used to guide our analytic strategy by facilitating the identification of non-causal pathways which could bias the estimates of targeted effect estimands. The following is a list of resources that are useful in learning about DAGs and their applications:

Pearl J. Causal diagrams for empirical research. Biometrika 1995;82:669–88.

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In this article, Pearl introduces a framework utilizing DAGs to identify and communicate what assumptions are necessary to produce causal effect estimates from nonexperimental data.

Greenland S, Pearl J, Robins JM. Causal Diagrams for Epidemiologic Research. Epidemiology 1999; 10:37–48.

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Greenland, Pearl and Robins provide an introduction to the use of DAGs in Epidemiology. This includes an overview of terminology, application, and a discussion of how this framework expands the traditional epidemiologic criteria for confounding.

Glymour MM. Using causal diagrams to understand common problems in social epidemiology. Methods Soc. Epidemiol., 2006, p. 387–422.

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In Chapter 16, Glymour provides an overview for the application of DAGs in Epidemiology with an emphasis on their use in causal inference and in social epidemiology. Glymour also provides discussion of related contemporary issues such as: natural experiments, identification of direct and indirect effects, and the use of indicator variables for handling missing data.

Hernán MA, Robins JM. Graphical Representation of Causal Effects. Causal Inference What If, 2020, p. 69–82.

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Chapter 6 provides an introduction to the use of DAGs in Epidemiology for causal inference, with an emphasis on linking DAGs to the counterfactual framework.

Westreich D, Greenland S. The table 2 fallacy: Presenting and interpreting confounder and modifier coefficients. Am J Epidemiol 2013; 177: 292–8.

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Westreich and Greenland use DAGs to introduce and illustrate the Table 2 Fallacy (i.e., the fallacy that all estimated coefficients for a given model can be interpreted as estimating the same type of effect). They also discuss strategies that can be employed to avoid misinterpretations that can result from the Table 2 Fallacy.

Textor J, Hardt J, Knüppel S. DAGitty: A graphical tool for analyzing causal diagrams. Epidemiology 2011;22:745.

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DAGitty is a free, user-friendly, online tool that allows you to easily build your DAG. The application is then able to identify the minimally sufficient adjustment set necessary for estimating the total effect or direct effect. It can also identify instrumental variables that are available in the causal network, and it provides a list of testable assumptions based on the causal network (http://www.dagitty.net/dags.html).

Segregation of epidemiology into chronic and infectious diseases has led to a neglected area in public health – the interface between chronic disease and infectious disease.

Choi, B. C., Morrison, H., Wong, T., Wu, J., & Yan, Y. P. (2007). Bringing chronic disease epidemiology and infectious disease epidemiology back together. Journal of Epidemiology & Community Health, 61(9), 802-802.

In the past month or so I’ve been seeing a lot of dialogue on #epitwitter about the lack of infectious disease training in many epidemiology programs. In an informal poll of 1000+ it seemed like many in epidemiology programs don’t receive little to no infectious disease training as part of their core training. The name ‘epidemiology’ calls back the era at the founding of the field when infectious diseases were the main health ‘epidemics’ impacting the population at large. But increasingly, infectious diseases have become a relic of a bygone era, as by far most epidemiologist are focused on chronic diseases.

However, we’ve seen in recent decades many infectious diseases not only interact with chronic diseases, but are the underlying causes of chronic diseases. Infectious disease epidemiologists in many ways continue to be at the forefront of epidemiology, integrating some of the most cutting edge tools into their field: predictive modelling and forecasting, complex spatial analysis, genomic mapping etc. As Choi et al. (2007) describe elegantly, many tools developed by infectious disease researchers may be of utility to understand ‘infectiousness’ in other fields in absence bacterial or viral agents.

Fortunately in absence of structured programs, there are a lot of good resources out there. For example, to be able to conduct my research on tuberculosis (TB) transmission, I had to self-teach a lot of infectious disease epidemiology through text-books, online resources, etc. Below I’ve listed ten courses for self-teaching at the introductory level, all freely available online.

Open courses in infectious disease epidemiology

Epidemics in Western Society Since 1600 (Yale)
https://oyc.yale.edu/history/hist-234

A well-rounded understanding of epidemiology requires a dive into the past. After all, many of the infectious diseases prevalent in the world today, such as tuberculosis, have been with us historically– and as look to eradicate these diseases– those who don’t remember the past, as they say, are doomed to repeat it. This course, offered from a historian’s perspective, covers history stretching from the plague and the social reactions to it, the development of the sanitary movement and germ theory, historical impact of infectious diseases (such as TB, malaria, polio) and re-emergence of these diseases in the modern context. Course recordings from 2010 and sample mid-term and final exams are provided.

John Snow and the Cholera Epidemic of 1854 (Harvard University via edX)
https://www.edx.org/course/predictionx-john-snow-and-the-cholera-epidemic-of

This course focuses on the a pivotal point in the history of infectious disease epidemiology: the Cholera epidemic of 1854 that swept through London. The course explores the role of one of the legendary figures of epidemiology, John Snow, in understanding how cholera was being spread and how it could be stopped. John Snow’s endeavor is perhaps the most well-known example of what is called ‘shoe-leather epidemiology’, going into the field with your feet to investigate what is happening. John Snow knocked on doors and created maps, and this course includes interactive mapping tools and timelines that allow students to engage with his investigation as it unfolded.

Epidemiology of Infectious Diseases
http://ocw.jhsph.edu/index.cfm/go/viewCourse/course/EpiInfectiousDisease/…

This is a comprehensive course in infectious disease epidemiology, covering the basics of the field including definitions, the principles of disease surveillance and outbreak investigation, and types of epidemiologic studies used to investigate outbreaks. The course also includes information about transmission dynamics, technical presentations on modelling and molecular epidemiology which many other courses in this list don’t cover quite as well. Although this is a comprehensive offering, one drawback is that the course is from 2006 and may not include the most up to date information. Nevertheless, highly recommend this for any students in epidemiology looking to extend their training.

Outbreaks and Epidemics (Johns Hopkins University via Coursera)
https://www.coursera.org/learn/outbreaks-epidemics

This short course takes a more hands-on approach to epidemic dynamics. Over the course of four modules, students are challenged to apply epidemiological and statistical concepts to infectious disease problems. The course examines concepts such as risk difference and relative difference, attack rates, reproductive numbers and other important metric useful in understanding scale of epidemics. Finally, the course applies these concepts to not just infectious disease outbreaks worldwide as many other courses, but to non-infectious disease epidemics such as the opioid epidemic in the US.

Epidemics – the Dynamics of Infectious Diseases (Penn State University via Coursera)
https://www.coursera.org/learn/epidemics

Over eight modules, this course provides a detailed review of the dynamics of infectious diseases from how they emerge to how they spread through environmental and social networks including concepts such as breaking transmission chains and ‘super-spreaders’. The basic ecology of infectious diseases, including vector-pathogen interaction and how the characteristics of these agents impacts the patterns of disease incidence is examined. The course uses case studies of pandemic influenza, childhood diseases such as measles and malaria as case studies. Finally, the course touches on drug resistance and how it impacts efforts to contain many re-emerging infectious diseases.

Epidemics, Pandemics and Outbreaks (University of Pittsburgh via Coursera)
https://www.coursera.org/learn/epidemic-pandemic-outbreak

While we often think of infectious diseases at the smallest scale—the level of the patient. However, infectious diseases do not recognize borders, and much of how infectious diseases can spread into pandemics is driven by policies at the global, national and local levels. The strength of this course is its focus on policy—how legal and public health systems respond to outbreaks and pandemics. One of the modules on local countermeasures includes extensive discussion ethical and pragmatic issues around disease reporting, travel restrictions, quarantine. Another module focuses on global health security. examining how globalization has altered the patterns of disease transmission, and how nations work together (or not) to detect and respond to infectious disease threats.

Principles of Infectious Disease Epidemiology Online Training Course (Missouri Department of Health)
https://health.mo.gov/training/epi/

For those interested in outbreak investigation, this course developed by the Missouri Department of Health has a fantastic series of modules on the topic. The course covers public health surveillance, including how to evaluate and improve surveillance systems. Then it dives deep into understanding and organizing and interpreting epidemiologic data generated from surveillance and investigation systems. Finally, the course dives into the principles of outbreak investigation—including interviewing skills, preparing investigation reports, with each step taught working through outbreak case studies. To access the course you need to register on the site, but the course is open to public and a great learning experience.

Epidemics I and II (The University of Hong Kong via edX)
https://www.edx.org/course/epidemics-i
https://www.edx.org/course/epidemics-ii

These two courses examine infectious disease epidemiology in a comprehensive manner. Offered by the University of Hong Kong, the courses have a special focus on infectious diseases in the East Asian context. The first course starts by examining the ecology and evolution of infectious disease, moving on to diseases such as Ebola and Zika and how epidemics emerge. Finally using SARS as a model, the first course evaluates identification of novel diseases, and the use of informatics including genetic data to understand the trajectories of epidemics. The second course elaborates and expands on some of these discussions, with a strong module on infectious disease modeling, examining dynamic and structural uncertainty in models of disease spread. The course also includes a supplemental module on COVID-19, as the course designers are involved in active research in this disease. This module will include “study results, final size estimation for super-spreading clusters, the Chinese experience for global preparedness, and more.”

Plagues, Pestilence and Pandemics: Are You Ready? (Griffith University via Futurelearn)
https://www.futurelearn.com/courses/plague-pestilence-pandemic

This very short course is a useful non-technical review of some of the current and emerging global health pandemics (including SARS and Ebola). The course examines outbreak responses and how transmission chains can be broken. The course also explores bacterial resistance, including healthcare associated infections and the threat they present.

Lessons from Ebola: Preventing the Next Pandemic (Harvard University via edX)
https://www.edx.org/course/lessons-from-ebola-preventing-the-next-pandemic-2

Ebola virus disease (EBV), a hemorrhagic fever with mortality rates ranging between 20-90%, was first identified in 1976. The disease re-appeared throughout the next four decades in small clusters. However, in 2014 the WHO declared a major outbreak in Guinea which over the course of the following year expanded to many neighboring countries across West Africa. While that outbreak subsided, newer Ebola outbreaks have appearing in the intervening years and as of last year the WHO has declared Ebola a global health emergency. This course takes a retrospective examination of the 2014 Ebola outbreak, focusing on the many aspects of the disease including the presentation and transmission patterns. As well the course examines the numerous challenges that surfaced in containment efforts at the local level, and failure of international efforts to prevent the spread of the disease. The deep dive into this pandemic provides insight into what went wrong, and how we could avoid the same pitfalls in the future.

Additional: live courses focused on ongoing corona virus pandemic (COVID 19)

COVID-19: Tackling the Novel Coronavirus (The London School of Hygiene & Tropical Medicine via Futurelearn)
https://www.futurelearn.com/courses/covid19-novel-coronavirus

In December of 2019, the WHO was informed about a small cluster of cases of pneumonia of unknown origin in Wuhan, China. In the two months following, the disease agent was identified as a new coronavirus (COVID-19) and rapidly spread across the world, infecting hundreds of thousands in a matter of weeks and declared as a worldwide pandemic. At the moment of writing this summary (3/20/2020) this pandemic has lead to states of emergency across many countries, travel bans, and shut down of non-essential services. This course, starting on Mar 23, 2020 will offer the latest information about COVID-19 presented by experts in the field, including the spread mechanisms for the disease the public health measures necessary to contain it.

Science Matters: Let’s Talk About COVID-19 (Imperial College London via Coursera)
https://www.coursera.org/learn/covid-19

This course (starting on March 19, 2020) is a more in-depth review of COVID-19 covering topics including the basic reproduction number of the disease, its case fatality, phylogenetic analysis, and clinical presentation. The course also offers a review of the economics and social impact of the current outbreak, with the latest materials as events unfold. Finally, the development of a vaccine is the target goal for any new disease, and COVID-19 is no different. this course contains module on the process of vaccine development during real-time epidemics.

If you have suggestions for additional resources, please comment below or email me!

Last updated: 3/21/2020