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Influenza viruses are a persistent threat to global human health. Increased susceptibility to infection and the risk factors associated with progression to severe influenza-related disease are determined by a multitude of viral, host, and environmental conditions. Decades of epidemiologic research have broadly defined high-risk groups, while new genomic association studies have identified specific host factors impacting an individual's response to influenza. Here, we review and highlight both human susceptibility to influenza infection and the conditions that lead to severe influenza disease.

Irritable bowel syndrome (IBS) prevalence rates are based on diagnostic criteria, the basis for case definitions. Diagnostic criteria have a substantial impact on prevalence rates, which are significant for understanding burden of disease, comparing global subpopulations, generating pathophysiologic research, allocating of health care and research resources, and incentivizing and prioritizing new treatments. There are substantial methodological pitfalls in epidemiologic research, so determining regional and global IBS prevalence rates is problematic. The Rome Foundation Global Epidemiology Study was designed to resolve these problems and achieve more valid results. The results of this study are presented in detail; future directions are discussed.

Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.

Record linkage - i.e. the use and linkage of electronic, mainly administrative databases - has been utilizedin epidemiologic research for over three decades now, starting from the system implemented in a fewUS health maintenance organizations (HMO, i.e. the Kaiser Permanente network), a few Nordic countriessystems, or the UK general practice research database (UK GPRD, now clinical practice research datalink,CPRD) [1, 2]...

In the accompanying article, Mosites et al. (Am J Epidemiol. 2021;190(11):2432–2436) evaluate data sources that enumerate people experiencing homelessness in the United States with respect to their strengths and limitations for conducting epidemiologic research in homeless populations. We largely agree with their key arguments, yet offer additional points that provide important context about these data for researchers and other stakeholders. Overall, we believe that it is possible to address many of the noted shortcomings of these data, and once addressed, the data could be more effectively leveraged to improve the health, housing stability, and quality of life of people experiencing homelessness.

Background: The goals of this project were to assess the status of NCI's rare cancer–focused population science research managed by the Division of Cancer Control and Population Sciences (DCCPS), to develop a framework for evaluation of rare cancer research activities, and to review available resources to study rare cancers. Methods: Cancer types with an overall age-adjusted incidence rate of less than 20 cases per 100,000 individuals were identified using NCI Surveillance, Epidemiology and End Results (SEER) Program data. SEER data were utilized to develop a framework based on statistical commonalities. A portfolio analysis of DCCPS-supported active grants and a review of three genomic databases were conducted. Results: For the 45 rare cancer types included in the analysis, 123 active DCCPS-supported rare cancer-focused grants were identified, of which the highest percentage (18.7%) focused on ovarian cancer. The developed framework revealed five clusters of rare cancer types. The cluster with the highest number of grants (n = 43) and grants per cancer type (10.8) was the cluster that included cancer types of higher incidence, average to better survival, and high prevalence (in comparison with other rare cancers). Resource review revealed rare cancers are represented in available genomic resources, but to a lesser extent compared with more common cancers. Conclusions: This article provides an overview of the rare cancer–focused population sciences research landscape as well as information on gaps and opportunities. Impact: The findings of this article can be used to develop efficient and comprehensive strategies to accelerate rare cancer research. See related commentary by James V. Lacey Jr, p. 1300

Quantitative bias analysis comprises the tools used to estimate the direction, magnitude, and uncertainty from systematic errors affecting epidemiologic research. Despite the availability of methods and tools, and guidance for good practices, few reports of epidemiologic research incorporate quantitative estimates of bias impacts. The lack of familiarity with bias analysis allows for the possibility of misuse, which is likely most often unintentional but could occasionally include intentional efforts to mislead. We identified 3 examples of suboptimal bias analysis, one for each common bias. For each, we describe the original research and its bias analysis, compare the bias analysis with good practices, and describe how the bias analysis and research findings might have been improved. We assert no motive to the suboptimal bias analysis by the original authors. Common shortcomings in the examples were lack of a clear bias model, computed example, and computing code; poor selection of the values assigned to the bias model’s parameters; and little effort to understand the range of uncertainty associated with the bias. Until bias analysis becomes more common, community expectations for the presentation, explanation, and interpretation of bias analyses will remain unstable. Attention to good practices should improve quality, avoid errors, and discourage manipulation.

Homelessness is associated with a multitude of poor health outcomes. However, the full extent of the risks associated with homelessness is not possible to quantify without reliable population data. Here, we outline 3 federal, publicly available data sources for estimating the number of people experiencing homelessness in the United States. We describe the appropriate uses and limitations of each data source in the context of infectious disease epidemiology. These data sources provide an opportunity to expand current research and develop actionable analyses.

Colorectal cancer is the third most common cause of cancer in men and women in the world. Epidemiologic research approximates that half of colon cancer risk is preventable by modifiable risk factors, including diet. This article reviews prior studies involving certain food items and their relation to colorectal cancer, to elucidate whether diet can be a potential intervention.

PURPOSE OF REVIEW: We reviewed published studies on menstrual cycle tracking applications (MCTAs) in order to describe the potential of MCTAs for epidemiologic research. RECENT FINDINGS: A search of PubMed, Web of Science, and Scopus for MCTA literature yielded 150 articles. After exclusions, there were 49 articles that addressed the primary interest areas: 1) characteristics of MCTA users in research, 2) reasons women use or continue using MCTAs, 3) accuracy of identifying ovulation and utility at promoting and preventing pregnancy, and 4) quality assessments of MCTAs across several domains. SUMMARY: MCTAs are an important tool for the advancement of epidemiologic research on menstruation. MCTA studies should describe the characteristics of their user-base and missing data patterns. Describing the motivation for using MCTAs throughout a user’s life and validating the data collected should be prioritized in future research.

The health benefits of physical activity have been widely recognized, yet traditional measures of physical activity including questionnaires and category-based assessments of volume and intensity provide only broad estimates of daily activities. Accelerometers have advanced epidemiologic research on physical activity by providing objective and continuous measurement of physical activity in free-living conditions. Wrist-worn accelerometers have become especially popular due to low participant burden. However, the validity and reliability of wrist-worn devices for adults have yet to be summarized. Moreover, accelerometer data provide rich information on how physical activity is accumulated throughout the day, but only a small portion of these rich data have been utilized by researchers. Lastly, new methodological developments that aim to overcome some of the limitations of accelerometers are emerging. The purpose of this review is to provide an overview of accelerometry research, with a special focus on wrist-worn accelerometers. We describe briefly how accelerometers work, summarize the validity and reliability of wrist-worn accelerometers, discuss the benefits of accelerometers including measuring light-intensity physical activity, and discuss pattern metrics of daily physical activity recently introduced in the literature. A summary of large-scale cohort studies and randomized trials that implemented wrist-worn accelerometry is provided. We conclude the review by discussing new developments and future directions of research using accelerometers, with a focus on wrist-worn accelerometers.

Results of epidemiologic reseach of environmental risс factors for multiple sclerosis among Moscow population are given. It was shown, that provoking factors for development or exacerbation this pathology are infections and stress situations. Diet habits, chronic bacterial infections of respiratory tract are significant too. The obtained results can be used in development of a certain diet and preventive measures.

Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questions in clinical and epidemiologic research and inform study design and statistical analysis. DAGs are constructed to depict prior knowledge about biological and behavioral systems related to specific causal research questions. DAG components portray who receives treatment or experiences exposures; mechanisms by which treatments and exposures operate; and other factors that influence the outcome of interest or which persons are included in an analysis. Once assembled, DAGs — via a few simple rules — guide the researcher in identifying whether the causal effect of interest can be identified without bias and, if so, what must be done either in study design or data analysis to achieve this. Specifically, DAGs can identify variables that, if controlled for in the design or analysis phase, are sufficient to eliminate confounding and some forms of selection bias. DAGs also help recognize variables that, if controlled for, bias the analysis (e.g., mediators or factors influenced by both exposure and outcome). Finally, DAGs help researchers recognize insidious sources of bias introduced by selection of individuals into studies or failure to completely observe all individuals until study outcomes are reached. DAGs, however, are not infallible, largely owing to limitations in prior knowledge about the system in question. In such instances, several alternative DAGs are plausible, and researchers should assess whether results differ meaningfully across analyses guided by different DAGs and be forthright about uncertainty. DAGs are powerful tools to guide the conduct of clinical research.

In the article by Nobles et al. (Am J Epidemiol. 2021;190(9):1710–1720), characteristics of those epidemiologists selected for various chair and presentation roles at the annual meetings of the Society for Epidemiologic Research (SER) from 2015 through 2017 were examined. Characteristics that were compared included inferred gender, institutional affiliation, subject area, and h-index. Important disparities were observed between session chairs, speakers, and poster presenters. SER leadership considers diversity and equity to be priorities and is committed to positive change. New programs and processes have been used to broaden participation and improve diversity since 2018, but the SER must continue its efforts to change processes and monitor of the experiences of SER members. A diversity of perspectives within the SER membership and at its meetings will improve all aspects of our practice of epidemiology.

The annual meeting of the Society for Epidemiologic Research is a prominent showcase for epidemiologists to present their research and share their expertise with peers. There are multiple paths to being on a podium at the meeting, and that role has implications for not only the speaker but also the audience. The article by Nobles et al. (Am J Epidemiol. 2021;190(9):1710–1720) represents an innovative investigation of representation among speakers at 3 recent SER annual meetings, with a primary focus on gender. Women were significantly under-represented as symposium speakers, which is an important role at the meeting. Symposium talks are invited talks and thus recognition of stature in the field. However, women were not under-represented as speakers in the concurrent contributed sessions, which are not invited talks and undergo blinded peer review. This gender contrast between the speakers at concurrent contributed sessions and the symposia is likely due to the different submission processes for the 2 types of presentations, but it could also reflect the review processes. Although the symposia are highly informative and enjoyable components of the meetings, some modifications in the submission and evaluation processes may be beneficial.

BACKGROUND From June 2014 to October 2015, the Michigan Department of Health and Human Services reported an outbreak of 90 cases of Legionnaires' disease, including 10 deaths, in Genesee County, Michigan. As Legionnaires' disease is not routinely tested for as a cause of community-acquired pneumonia, we hypothesized that the size of the outbreak was underestimated. METHODS We used Centers for Disease Control and Prevention (CDC) Wide-ranging ONline Data for Epidemiologic Research data to compare pneumonia mortality in Genesee to similar counties from 2011 to 2017. We used data from the Genesee County Vital Records Division to assess geographic overlap of pneumonia mortality with reported Legionnaires disease cases by census tract. RESULTS We estimated 70.0 excess pneumonia deaths (90% uncertainty interval: 36-103) in Genesee County during the outbreak. Areas of high pneumonia mortality overlapped with those with high Legionnaires' disease incidence. CONCLUSIONS These findings are consistent with the hypothesis that the Legionnaires' disease outbreak was larger than reported. Earlier outbreak detection and response may have facilitated identification of additional cases.

Fetal alcohol spectrum disorders (FASD) describe a range of physical, behavioral, and neurologic deficits in individuals exposed to alcohol prenatally. Reduced palpebral fissure length is one of the cardinal facial features of FASD. However, other ocular measurements have not been studied extensively in FASD. Using the Fetal Alcohol Syndrome Epidemiologic Research (FASER) database, we investigated how inner canthal distance (ICD), interpupillary distance (IPD), and outer canthal distance (OCD) centiles differed between FASD and non-FASD individuals. We compared ocular measurement centiles in children with FASD to non-FASD individuals and observed reductions in all three centiles for ICD, IPD, and OCD. However, when our non-FASD children who had various forms of growth deficiency (microcephaly, short-stature, or underweight) were compared to controls, we did not observe a similar reduction in ocular measurements. This suggests that reductions in ocular measurements are a direct effect of alcohol on ocular development independent of its effect on growth parameters, which is consistent with animal models showing a negative effect of alcohol on developing neural crest cells. Interpupillary distance centile appeared to be the most significantly reduced ocular measure we evaluated, suggesting it may be a useful measure to be considered in the diagnosis of FASD.

“The mission of the Diversity and Inclusion Committee (D&I) in the Society for Epidemiologic Research is to foster the diversity of our membership and work towards the engagement of all members, from diverse backgrounds at all stages of their careers, in the Society’s activities, with the intent of enhancing discovery in public health.” As a foundational step in implementing our mission, the D&I Committee conducted a survey of SER membership. Here we report on the efforts we have undertaken to expand the diversity and inclusiveness of our Society and our aspirations for future efforts in support of D&I. Early on, we established the SERvisits program to conduct outreach to institutions and students that have historically been underrepresented at SER; we hope this program continues to grow in its reach and impact. We have also taken steps to increase the inclusiveness of SER activities, for example, by engaging members on issues of D&I through symposia and workshops at SER annual meetings and through social media. DeVilbiss et al. (Am J Epidemiol. 2020;189(10):998–1010) have demonstrated that there is substantial room for improvement with regards to diversity and inclusion within SER. We invite SER members to become involved and collaborate on this long-term goal.

The Society for Epidemiologic Research (SER) has recently taken laudable steps toward increasing diversity, equity, and inclusion within the society, including participation in the annual meeting. In this essay, we argue that there is one critical piece of the diversity and inclusion equation that is, however, overlooked: institution. At the 2019 Annual Meeting, a mere 8 institutions accounted for a disproportionate number of both oral concurrent sessions and symposium speakers. This lack of institutional diversity, unless addressed, will hinder SER’s ability to address other aspects of diversity, equity, and inclusion.

Background To gain a better understanding of nonfatal injuries in Alaska, underutilized data sources such as workers’ compensation claims must be analyzed. The purpose of the current study was to utilize workers’ compensation claims data to estimate the risk of nonfatal, work-related injuries among occupations in Alaska, characterize injury patterns, and prioritize future research. Methods A dataset with information on all submitted claims during 2014–2015 was provided for analysis. Claims were manually reviewed and coded. For inclusion in this study, claims had to represent incidents that resulted in a nonfatal acute traumatic injury, occurred in Alaska during 2014–2015, and were approved for compensation. Results Construction workers had the highest number of injuries (2,220), but a rate lower than the overall rate (34 per 1,000 construction workers, compared to 40 per 1,000 workers overall). Fire fighters had the highest rate of injuries on the job, with 162 injuries per 1,000 workers, followed by law enforcement officers with 121 injuries per 1,000 workers. The most common types of injuries across all occupations were sprains/strains/tears, contusions, and lacerations. Conclusion The successful use of Alaska workers’ compensation data demonstrates that the information provided in the claims dataset is meaningful for epidemiologic research. The predominance of sprains, strains, and tears among all occupations in Alaska indicates that ergonomic interventions to prevent overexertion are needed. These findings will be used to promote and guide future injury prevention research and interventions.

Increasing diversity and inclusion among organizational membership has become a focus for many professional societies, including the Society for Epidemiologic Research (SER). In this issue of the Journal, DeVilbiss et al. (Am J Epidemiol. 2020:189(10):998–1010) assessed dimensions of diversity and inclusion within SER to provide baseline data for future evaluations of Society initiatives. In our response, we note that diversity in SER appears strong but there is lag with regard to inclusion. We also highlight some of the major weaknesses of this study that hinder efforts to accurately evaluate inclusion within SER. There is a need to more concretely define inclusion and think broadly about how measures of inclusion should be operationalized in future surveys. Additional limitations of the study include its limited generalizability to the wider SER membership and the lack of questions about barriers to inclusion in SER activities. We conclude with recommendations for SER and other professional societies based on prior literature evaluating successful diversity and inclusion efforts. We also propose a conceptual model to assist with operationalizing and directing future analyses of inclusion measures. It is essential that SER move beyond efforts around diversity to focus on measuring and enhancing inclusion.

As a result of the extensive integration of technology into the healthcare system, cybersecurity incidents have become an increasing challenge for the healthcare industry. Recent examples include WannaCry, a nontargeted ransomware attack on more than 150 countries worldwide that temporarily crippled parts of the National Health Service in the United Kingdom, and the 2016 ransomware attack on Los Angeles's Hollywood Presbyterian Medical Center. The attacks cost millions of dollars in lost revenue and fines, as well as significant reputational damage. Efforts are needed to devise tools that allow experts to more accurately quantify the actual impact of such events on both individual patients and healthcare systems as a whole. While the United States has robust disaster preparedness and response systems integrated throughout the healthcare and government sectors, the rapidly evolving cybersecurity threat against healthcare entities is outpacing existing countermeasures and challenges in the "all-hazards" disaster preparedness paradigm. Further epidemiologic research of clinical cybersecurity attacks and their effects on patient care and clinical outcomes is necessary to prevent and mitigate future attacks.

An understanding of epidemiologic methods is important for the clinical exercise physiologist to assess the complex relationships between physical activity and health and disease. It is essential to the practice of preventive and rehabilitative care to understand the web of causation and complex interactions among agent (exercise), host (individual), and environment (affects transmission of agent source to host) in relationship to disease/injury and clinical outcomes. Application of the epidemiologic measures of disease/injury occurrence, variations in occurrence, and statistical measures of attributable risk and population attributable risk contribute to the clinician's skill level in assessing potential cause-effect relationships reported in the literature about exercise medicine, physical activity, and public health. By becoming familiar with the study methods used in epidemiology, the clinical exercise physiologist will be better positioned to assess criteria for a cause-effect relationship as well as to critically evaluate the assessment efforts used across a variety of study designs and applications of epidemiology in clinical research and practice.

Unmeasured confounding, selection bias, and measurement error are well-known sources of bias in epidemiologic research. Methods for assessing these biases have their own limitations. Many quantitative sensitivity analysis approaches consider each type of bias individually, while more complex approaches are harder to implement or require numerous assumptions. By failing to consider multiple biases at once, researchers can underestimate -- or overestimate -- their joint impact. We show that it is possible to bound the total composite bias due to these three sources, and to use that bound to assess the sensitivity of a risk ratio to any combination of these biases. We derive bounds for the total composite bias under a variety of scenarios, providing researchers with tools to assess their total potential impact. We apply this technique to a study where unmeasured confounding and selection bias are both concerns, and to another study in which possible differential exposure misclassification and unmeasured confounding are concerns. We also show that a "multi-bias E-value" can describe the minimal strength of joint bias-parameter association necessary for an observed risk ratio to be compatible with a null causal effect (or with other pre-specified effect sizes). This may provide intuition about the relative impacts of each type of bias. The approach we describe is easy to implement with minimal assumptions, and we provide R functions to do so., Comment: 23 pages (main text); 19 pages (appendix)

BACKGROUND AND AIM: Epidemiologists seek to conduct studies that will be influential on hazard identification, risk assessment and policy decisions. Individual studies contribute via various forms ...

A focus of cancer epidemiologic research has become the identification of risk factors that influence specific subtypes of disease, a phenomenon known as etiologic heterogeneity. In previous work we developed a novel strategy to cluster tumor markers and identify disease subtypes that differ maximally with respect to known risk factors for use in the context of case-control studies. The method relies on the premise that unsupervised k-means clustering will find candidate solutions that are closely aligned with the sought-after etiologically distinct clusters, which may not be true in the presence of clusters of tumor markers that are not related to risk of disease. In this article, we investigate in detail the ability of the method to identify the “true” clusters in the presence of clusters that are unrelated to risk factors, what we term “counterfeit” clusters. We find that our method works when the strength of structure is larger in the clusters that truly represent etiologic heterogeneity than in the counterfeit clusters, but when this condition is not met, or when there are many tumor markers that simply represent noise, the method will not find the correct solution without first performing variable selection to identify the tumor markers most strongly related to the risk factors. We illustrate the results using data from a breast cancer case-control study.

Introduction: Studies using electronic health record (EHR) data often have a limited number of years available for analysis. There is a trade off between the look back period length used to define baseline characteristics and follow up duration used to define outcomes. Objective: Quantify the impact of 6, 12, and 24 month look back periods on the association between diabetes (DM) and subsequent cardiovascular (CV) events using EHR data alone and in combination with Medicare claims. Methods: EHR data from an academic health system and a federally qualified health center from 2009-2014 were linked to Medicare claims data. Eligibility criteria were age ≥65 years, Durham County address, 24 months of continuous enrollment after first claim, EHR encounter in the 2011 index year, and no history of cardiovascular disease (CVD) in the 24 months prior to the index date (i.e., look back period). DM was defined using EHR ICD-9 codes, HbA1c ≥6.5%, or glucose lowering medication, and using claims based diagnosis codes or glucose lowering medication. The outcome was a major CV event (myocardial infarction, stroke, or cardiac procedure) defined by diagnosis or procedure codes. Hazard ratios (HR) compared time to the outcome between patients with and without DM. Results: In 5473 patients, mean age was 77 years, 67% were female and 28% were Black. The prevalence of DM using EHR data only increased with a longer look back period (6 months [19%]; 12 months [21%]; 24 months [23%]) but was less than the prevalence using all available data from EHRs and claims together (28%) (Table 1A). Shorter look back periods resulted in higher HRs (6 month HR=1.64) as compared to HRs from longer look back periods (24 month HR=1.41) using EHR data alone or all available data from the EHR and claims together (HR=1.43) (Table 1B). Conclusions: To avoid over estimating associations, studies of CVD using EHR data to identify baseline conditions may want to use 12-24 month look back periods in the absence of additional administrative data. This may also lead to a shorter follow-up period.

Public concern about the impact of endocrine disrupting chemicals (EDCs) on both humans and the environment is growing steadily. Epidemiologic research provides key information towards our understanding of the relationship between environmental exposures like EDCs and human health outcomes. Intended for researchers in disciplines complementary to epidemiology, this paper highlights the importance and challenges of epidemiologic research in order to present the key elements pertaining to the design and interpretation of an epidemiologic study on EDCs. The conduct of observational studies on EDCs derives from a thoughtful research question, which will help determine the subsequent methodological choices surrounding the careful selection of the study population (including the comparison group), the adequate ascertainment of exposure(s) and outcome(s) of interest, and the application of methodological and statistical concepts more specific to epidemiology. The interpretation of epidemiologic results may be arduous due to the latency occurring between EDC exposure and certain outcome(s), the complexity in capturing EDC exposure(s), and traditional methodological and statistical issues that also deserve consideration (e.g., confounding, effect modification, non-monotonic responses). Moving forward, we strongly advocate for an integrative approach of expertise in the fields of epidemiology, exposure science, risk assessment and toxicology to adequately study the health risks associated with EDCs while tackling their challenges.

Scientific conferences provide attendees opportunities to network, share research, learn new skills and ideas, and initiate collaborations. Conference attendance is especially important for students and early-career researchers who are establishing their research careers or looking for jobs. However, attending conferences can be expensive, and the high cost of conference attendance might hit students and early-career researchers the hardest. According to a new member survey from the Society for Epidemiologic Research, early-career members are more racially and ethnically diverse than senior members, meaning that reducing financial barriers to conference participation might be an important consideration for increasing diversity among conference attendees. In this commentary, we discuss how choice of conference location—choosing less expensive cities nearer to more Society for Epidemiologic Research members—could reduce financial and other barriers to conference attendance for all members and improve diversity and inclusion in the Society.

Twin registries have developed as a valuable resource for the study of many aspects of disease and society over the years in many different countries. A number of these registries include large numbers of twins with data collected at varying information levels for twin cohorts over the past several decades. More recent expansion of twin datasets has allowed for the collection of genetic data, together with many other levels of ‘omic’ information along with multiple demographic, physiological, health outcomes and other measures typically used in epidemiologic research. Other twin data sources outside these registries reflect research interests in particular aspects of disease or specific phenotypic assessment. Twin registries have the potential to play a key role in many aspects of the artificial intelligence/machine learning-driven projects of the future and will continue to keep adapting to the changing research landscape.

Introduction:Despite the potential of social media to influence public health and generate insights, the process of monitoring and analyzing the dissemination of health care messages on social media has been described as difficult and in need of improvement.Objectives:The objective of this study was to describe and demonstrate a reproducible methodology for cataloging and analyzing health care–related social media comments and provide insight into how clinicians and members of the general public respond to health care messaging on social media.Methods:We collected social media comments related to the American Dental Association’s 2016 “Evidence-Based Clinical Practice Guideline for the Use of Pit-and-Fissure Sealants” between April 10, 2017, and October 31, 2017, from Facebook, Twitter, LinkedIn, Reddit, and online message boards for the New York Times, FiveThirtyEight, and Dentaltown. Using data provided in the comments, we conducted engagement analysis as well as content, network, and sentiment analysis across 8 categories.Results:We collected 671 comments. Among our findings, Facebook (472 of 671) was the most popular platform among commentators; almost half of all comments (335 of 671) aligned with the recommendations of the 2016 American Dental Association sealants guideline; clinicians were more likely than the general public to like a comment that suggested an improvement to the guideline; and >75% of comments (521 of 671) were supported by anecdotal evidence.Conclusion:As the prevalence of anecdotes on social media suggests, the likelihood of falsehoods spreading on social media is high. Insights gleaned from the methodology described in this research could help combat the spread of such misinformation by providing disseminators of health care messaging with insight into their target audiences. Armed with this knowledge, disseminators can craft health care messages that more effectively engage clinicians and the general public.Knowledge Transfer Statement:The methodology used in this research provides a reproducible strategy for tracking social media engagement with health care messages. Engagement results can assist future delivery of health care messages to key stakeholders and ensure better implementation and adoption of these communications.