A Future for Observational Epidemiology: Clarity, Credibility, Transparency

Validity of individual self-report oral health measures in assessing periodontitis for causal research applications

BACKGROUND

Self-reported measures of periodontitis developed for use in population surveillance are increasingly used in causal research. Numerous studies evaluate the validity of these measures against clinical parameters of periodontitis, yet few include validation parameters outside of multivariable models. Individual item validity is necessary to adequately inform use of these measures in causal research.

METHODS

We used data from the National Health and Nutrition Examination Survey 2011-2014 in which dentate participants completed full-mouth periodontal examinations (N = 6966). We evaluated six self-report questionnaire items related to periodontal disease status against periodontitis case definitions developed by the Centers for Disease Control and Prevention and American Academy of Periodontology (CDC-AAP). We estimated the sensitivity and specificity of individual items using severe and moderate-to-severe periodontitis classifications. We additionally combined items to evaluate the validity of joint measures.

RESULTS

Sensitivity was highest when measures were evaluated against severe periodontitis. Self-rated oral health of fair/poor demonstrated the highest sensitivity for severe (0.60) and moderate-to-severe periodontitis (0.48). Specificity was highest when evaluated against moderate-to-severe periodontitis, with self-reported history of tooth mobility as the most specific measure (0.87 for severe disease; 0.92 for moderate-to-severe) followed by a history of bone loss (0.88 for severe; 0.91 for moderate-to-severe). Combining questions generally improved specificity at the expense of sensitivity.

CONCLUSIONS

Our findings related to item-specific validity and the associated clinical profiles facilitate needed considerations for the use of self-reported measures of periodontitis in causal research. Additionally, item-specific validity can be used to inform assessments of misclassification bias within such investigations.

Quantitative Assessment of Systematic Bias: A Guide for Researchers

Observational research provides valuable opportunities to advance oral health science but is limited by vulnerabilities to systematic bias, including unmeasured confounding, errors in variable measurement, or bias in the creation of study populations and/or analytic samples. The potential influence of systematic biases on observed results is often only briefly mentioned among the discussion of limitations of a given study, despite existing methods that support detailed assessments of their potential effects. Quantitative bias analysis is a set of methodological techniques that, when applied to observational data, can provide important context to aid in the interpretation and integration of observational research findings into the broader body of oral health research. Specifically, these methods were developed to provide quantitative estimates of the potential magnitude and direction of the influence of systematic biases on observed results. We aim to encourage and facilitate the broad adoption of quantitative bias analyses into observational oral health research. To this end, we provide an overview of quantitative bias analysis techniques, including a step-by-step implementation guide. We also provide a detailed appendix that guides readers through an applied example using real data obtained from a prospective observational cohort study of preconception periodontitis in relation to time to pregnancy. Quantitative bias analysis methods are available to all investigators. When appropriately applied to observational studies, findings from such studies can have a greater impact in the broader research context.

Mixed messages: most spinal pain and osteoarthritis observational research is unclear or misaligned

OBJECTIVES

We assessed authors' language and methods to determine alignment between reported aims, methods, intent, and interpretations in observational studies in spinal pain or osteoarthritis.

STUDY DESIGN AND SETTING

We searched five databases for observational studies that included people with spinal pain or osteoarthritis published in the last 5 years. We randomized 100 eligible studies, and classified study intent (aims and methods) and interpretations as causal, non-causal, unclear, or misaligned.

RESULTS

Overall, 38% of studies were aligned regarding their intent and interpretation (either causally (22%) or non-causally (16%)). 29% of studies' aims and 29% of study methods were unclear. Intent was misaligned in 16% of studies (where aim differed to method) and 23% of studies had misaligned interpretations (where there were multiple conflicting claims). The most common kind of aim was non-causal (38%), and the most common type of method (39%), intent (38%), and interpretations (35%) was causal.

CONCLUSIONS

Misalignment and mixed messages are common in observational research of spinal pain and osteoarthritis. More than 6 in 10 observational studies may be uninterpretable, because study intent and interpretations do not align. While causal methods and intent are most common in observational research, authors commonly shroud causal intent in non-causal terminology.

Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level.

Chronic neuropsychiatric sequelae of SARS-CoV-2: Protocol and methods from the Alzheimer's Association Global Consortium

Introduction

Coronavirus disease 2019 (COVID-19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID-19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID-19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID-19 to neurologic illness, both short and long term.

Methods

This article describes what is known so far in terms of links among COVID-19, the brain, neurological symptoms, and Alzheimer's disease (AD) and related dementias. We focus on risk factors and possible molecular, inflammatory, and viral mechanisms underlying neurological injury. We also provide a comprehensive description of the Alzheimer's Association Consortium on Chronic Neuropsychiatric Sequelae of SARS-CoV-2 infection (CNS SC2) harmonized methodology to address these questions using a worldwide network of researchers and institutions.

Results

Successful harmonization of designs and methods was achieved through a consensus process initially fragmented by specific interest groups (epidemiology, clinical assessments, cognitive evaluation, biomarkers, and neuroimaging). Conclusions from subcommittees were presented to the whole group and discussed extensively. Presently data collection is ongoing at 19 sites in 12 countries representing Asia, Africa, the Americas, and Europe.

Discussion

The Alzheimer's Association Global Consortium harmonized methodology is proposed as a model to study long-term neurocognitive sequelae of SARS-CoV-2 infection.

Key Points

The following review describes what is known so far in terms of molecular and epidemiological links among COVID-19, the brain, neurological symptoms, and AD and related dementias (ADRD)The primary objective of this large-scale collaboration is to clarify the pathogenesis of ADRD and to advance our understanding of the impact of a neurotropic virus on the long-term risk of cognitive decline and other CNS sequelae. No available evidence supports the notion that cognitive impairment after SARS-CoV-2 infection is a form of dementia (ADRD or otherwise). The longitudinal methodologies espoused by the consortium are intended to provide data to answer this question as clearly as possible controlling for possible confounders. Our specific hypothesis is that SARS-CoV-2 triggers ADRD-like pathology following the extended olfactory cortical network (EOCN) in older individuals with specific genetic susceptibility.The proposed harmonization strategies and flexible study designs offer the possibility to include large samples of under-represented racial and ethnic groups, creating a rich set of harmonized cohorts for future studies of the pathophysiology, determinants, long-term consequences, and trends in cognitive aging, ADRD, and vascular disease.We provide a framework for current and future studies to be carried out within the Consortium. and offers a "green paper" to the research community with a very broad, global base of support, on tools suitable for low- and middle-income countries aimed to compare and combine future longitudinal data on the topic.The Consortium proposes a combination of design and statistical methods as a means of approaching causal inference of the COVID-19 neuropsychiatric sequelae. We expect that deep phenotyping of neuropsychiatric sequelae may provide a series of candidate syndromes with phenomenological and biological characterization that can be further explored. By generating high-quality harmonized data across sites we aim to capture both descriptive and, where possible, causal associations.

Social Epidemiology: Past, Present, and Future

In a context where epidemiologic research has been heavily influenced by a biomedical and individualistic approach, the naming of “social epidemiology” allowed explicit emphasis on the social production of disease as a powerful explanatory paradigm and as critically important for interventions to improve population health. This review briefly highlights key substantive areas of focus in social epidemiology over the past 30 years, reflects on major advances and insights, and identifies challenges and possible future directions. Future opportunities for social epidemiology include grounding research in theoretically based and systemic conceptual models of the fundamental social drivers of health; implementing a scientifically rigorous yet realistic approach to drawing conclusions about social causes; using complementary methods to generate valid explanations and identify effective actions; leveraging the power of harmonization, replication, and big data; extending interdisciplinarity and diversity; advancing emerging critical approaches to understanding the health impacts of systemic racism and its policy implications; going global; and embracing a broad approach to generating socially useful research.

Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Is the Way Forward to Step Back? Documenting the Frequency with which Study Goals are Misaligned with Study Methods and Interpretations in the Epidemiologic Literature

In any research study, there is an underlying research process that should begin with a clear articulation of the study's goal. The study's goal drives this process; it determines many study features including the estimand of interest, the analytic approaches that can be used to estimate it, and which coefficients, if any, should be interpreted. "Misalignment" can occur in this process when analytic approaches and/or interpretations do not match the study's goal; misalignment is potentially more likely to arise when study goals are ambiguously framed. This study documented misalignment in the observational epidemiologic literature and explored how the framing of study goals contributes to its occurrence. The following misalignments were examined: 1) use of an inappropriate variable selection approach for the goal (a "goal-methods" misalignment) and 2) interpretation of coefficients of variables for which causal considerations were not made (e.g., Table 2 Fallacy, a "goal-interpretation" misalignment). A random sample of 100 articles published 2014-2018 in the top 5 general epidemiology journals were reviewed. Most reviewed studies were causal, with either explicitly stated (13/103, 13%) or associationally-framed (71/103, 69%) aims. Full alignment of goal-methods-interpretations was infrequent (9/103, 9%), although clearly causal studies (5/13, 38%) were more often fully aligned than seemingly causal ones (3/71, 4%). Goal-methods misalignments were common (34/103, 33%), but most frequently, methods were insufficiently reported to draw conclusions (47/103, 46%). Goal-interpretations misalignments occurred in 31% (32/103) of studies and occurred less often when the methods were aligned (2/103, 2%) compared with when the methods were misaligned (13/103, 13%).

Factors that influence data sharing through data sharing platforms: A qualitative study on the views and experiences of cohort holders and platform developers

Background

Infrastructures are being developed to enhance and facilitate the sharing of cohort data internationally. However, empirical studies show that many barriers impede sharing data broadly.

Purpose

Therefore, our aim is to describe the barriers and concerns for the sharing of cohort data, and the implications for data sharing platforms.

Methods

Seventeen participants involved in developing data sharing platforms or tied to cohorts that are to be submitted to platforms were recruited for semi-structured interviews to share views and experiences regarding data sharing.

Results

Credit and recognition, the potential misuse of data, loss of control, lack of resources, socio-cultural factors and ethical and legal barriers are elements that influence decisions on data sharing. Core values underlying these reasons are equality, reciprocity, trust, transparency, gratification and beneficence.

Conclusions

Data generators might use data sharing platforms primarily for collaborative modes of working and network building. Data generators might be unwilling to contribute and share for non-collaborative work, or if no financial resources are provided for sharing data.

A systematic review of quantitative bias analysis applied to epidemiological research

BACKGROUND

Quantitative bias analysis (QBA) measures study errors in terms of direction, magnitude and uncertainty. This systematic review aimed to describe how QBA has been applied in epidemiological research in 2006-19.

METHODS

We searched PubMed for English peer-reviewed studies applying QBA to real-data applications. We also included studies citing selected sources or which were identified in a previous QBA review in pharmacoepidemiology. For each study, we extracted the rationale, methodology, bias-adjusted results and interpretation and assessed factors associated with reproducibility.

RESULTS

Of the 238 studies, the majority were embedded within papers whose main inferences were drawn from conventional approaches as secondary (sensitivity) analyses to quantity-specific biases (52%) or to assess the extent of bias required to shift the point estimate to the null (25%); 10% were standalone papers. The most common approach was probabilistic (57%). Misclassification was modelled in 57%, uncontrolled confounder(s) in 40% and selection bias in 17%. Most did not consider multiple biases or correlations between errors. When specified, bias parameters came from the literature (48%) more often than internal validation studies (29%). The majority (60%) of analyses resulted in >10% change from the conventional point estimate; however, most investigators (63%) did not alter their original interpretation. Degree of reproducibility related to inclusion of code, formulas, sensitivity analyses and supplementary materials, as well as the QBA rationale.

CONCLUSIONS

QBA applications were rare though increased over time. Future investigators should reference good practices and include details to promote transparency and to serve as a reference for other researchers.

Non-genetic risk and protective factors and biomarkers for neurological disorders: a meta-umbrella systematic review of umbrella reviews

BACKGROUND

The etiologies of chronic neurological diseases, which heavily contribute to global disease burden, remain far from elucidated. Despite available umbrella reviews on single contributing factors or diseases, no study has systematically captured non-purely genetic risk and/or protective factors for chronic neurological diseases.

METHODS

We performed a systematic analysis of umbrella reviews (meta-umbrella) published until September 20th, 2018, using broad search terms in MEDLINE, SCOPUS, Web of Science, Cochrane Database of Systematic Reviews, Cumulative Index to Nursing and Allied Health Literature, ProQuest Dissertations & Theses, JBI Database of Systematic Reviews and Implementation Reports, DARE, and PROSPERO. The PRISMA guidelines were followed for this study. Reference lists of the identified umbrella reviews were also screened, and the methodological details were assessed using the AMSTAR tool. For each non-purely genetic factor association, random effects summary effect size, 95% confidence and prediction intervals, and significance and heterogeneity levels facilitated the assessment of the credibility of the epidemiological evidence identified.

RESULTS

We identified 2797 potentially relevant reviews, and 14 umbrella reviews (203 unique meta-analyses) were eligible. The median number of primary studies per meta-analysis was 7 (interquartile range (IQR) 7) and that of participants was 8873 (IQR 36,394). The search yielded 115 distinctly named non-genetic risk and protective factors with a significant association, with various strengths of evidence. Mediterranean diet was associated with lower risk of dementia, Alzheimer disease (AD), cognitive impairment, stroke, and neurodegenerative diseases in general. In Parkinson disease (PD) and AD/dementia, coffee consumption, and physical activity were protective factors. Low serum uric acid levels were associated with increased risk of PD. Smoking was associated with elevated risk of multiple sclerosis and dementia but lower risk of PD, while hypertension was associated with lower risk of PD but higher risk of dementia. Chronic occupational exposure to lead was associated with higher risk of amyotrophic lateral sclerosis. Late-life depression was associated with higher risk of AD and any form of dementia.

CONCLUSIONS

We identified several non-genetic risk and protective factors for various neurological diseases relevant to preventive clinical neurology, health policy, and lifestyle counseling. Our findings could offer new perspectives in secondary research (meta-research).

Why Methodology Is Important: Coffee as a Candidate Treatment for COVID-19?

BACKGROUND

During this pandemic situation, some studies have led to hasty conclusions about Corona Virus Disease-19 (COVID-19) treatment, due to a lack of methodology. This pedagogic study aimed to highlight potential biases in research on COVID-19 treatment.

METHODS

We evaluate the effect of coffee's active part, 1,3,7-trimethylxanthine (TMX) on patients with COVID-19. A cohort of 93 patients, with a diagnosis of COVID-19 is analyzed.

RESULTS

TMX group and control group included, respectively, 26 and 67 patients. In the TMX group, patients had a median length of stay in hospital of 5.5 days shorter than in the control group (9.5 vs. 15 days, p < 0.05). Patients in the control group were more severe than patients in the TMX group with a significantly higher National Early Warning Score 2 (NEWS-2 score) (8 vs. 6, p = 0.002).

CONCLUSIONS

Multiple biases prevents us from concluding to an effect of coffee on COVID-19. Despite an important social pressure during this crisis, methodology and conscientiousness are the best way to avoid hasty conclusions that can be deleterious for patients. Identifier: NCT04395742.

Moving Health Literacy Research and Practice towards a Vision of Equity, Precision and Transparency

Over the past two decades, health literacy research has gained increasing attention in global health initiatives to reduce health disparities. While it is well-documented that health literacy is associated with health outcomes, most findings are generated from cross-sectional data. Along with the increasing importance of health literacy in policy, there is a lack of specificity and transparency about how to improve health literacy in practice. In this study, we are calling for a shift of current research paradigms from judging health literacy levels towards observing how health literacy skills are developed over the life course and practised in the real world. This includes using a life-course approach, integrating the rationale of precision public health, applying open science practice, and promoting actionable knowledge translation strategies. We show how a greater appreciation for these paradigms promises to advance health literacy research and practice towards an equitable, precise, transparent, and actionable vision.

Global epidemiology of Duchenne muscular dystrophy: an updated systematic review and meta-analysis

Background

Duchenne Muscular Dystrophy (DMD) is a rare disorder caused by mutations in the dystrophin gene. A recent systematic review and meta-analysis of global DMD epidemiology is not available. This study aimed to estimate the global overall and birth prevalence of DMD through an updated systematic review of the literature.

Methods

MEDLINE and EMBASE databases were searched for original research articles on the epidemiology of DMD from inception until 1st October 2019. Studies were included if they were original observational research articles written in English, reporting DMD prevalence and/or incidence along with the number of individuals of the underlying population. The quality of the studies was assessed using a STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist adapted for observational studies on rare diseases. To derive the pooled epidemiological prevalence estimates, a meta-analysis was performed using random-effects logistic models for overall and birth prevalence and within two different underlying populations (i.e. all individuals and in males only), separately. Heterogeneity was assessed using Cochran’s Q-test along with its derived measure of inconsistency I².

Results

A total of 44 studies reporting the global epidemiology of DMD were included in the systematic review and only 40 were included in the meta-analysis. The pooled global DMD prevalence was 7.1 cases (95% CI: 5.0–10.1) per 100,000 males and 2.8 cases (95% CI: 1.6–4.6) per 100,000 in the general population, while the pooled global DMD birth prevalence was 19.8 (95% CI:16.6–23.6) per 100,000 live male births. A very high between-study heterogeneity was found for each epidemiological outcome and for all underlying populations (I² > 90%). The test for funnel plot asymmetry suggested the absence of publication bias. Of the 44 studies included in this systematic review, 36 (81.8%) were assessed as being of medium and 8 (18.2%) of low quality, while no study was assessed as being of high quality.

Conclusions

Generating epidemiological evidence on DMD is fundamental to support public health decision-making. The high heterogeneity and the lack of high quality studies highlights the need to conduct better quality studies on rare diseases.

Single-family room neonatal intensive care unit design: do patient outcomes actually change?

OBJECTIVE

This study examined outcomes in a single-family room neonatal intensive care unit (NICU) compared to an open bay within a public safety net hospital.

STUDY DESIGN

We included 9995 NICU encounters over 5 years pre and post move for a predominantly low SES population. Outcomes were length of stay (LOS), growth, time to first oral feeding, and incidence of sepsis. Analysis included regression models, interrupted time series, and growth models.

RESULTS

LOS decreased over time in preterm infants both pre and post move, but increased post move for term/post-term infants (p < 0.001). First oral feeding decreased over time in both periods. A higher incidence of sepsis was found in the post period for term/post-term infants (p = 0.01).

CONCLUSION

Our analysis appropriately accounted for time trends. Few differences were observed. Changes in LOS for term/post-term infants should be further explored as well as the impact of NICU care patterns.

Online survey about the STROBE statement highlighted diverging views about its content, purpose, and value

BACKGROUND AND OBJECTIVE

The endorsement rates of The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) Statement are low and little is known about authors' opinions about this reporting guideline. We conducted an online survey with observational study authors on attitude toward and experiences with the STROBE Statement with the aim of understanding how to effectively implement STROBE.

METHODS

A thematic analysis on the responses to an open-ended question was conducted using inductive coding. Two coders classified responses independently into themes using a codebook. The inter-rater agreement ranged from 87.7 to 99.9%.

RESULTS

15% (n = 150) of survey participants (n = 1,015) shared perceptions and insights on STROBE. We established four themes: 1) perceptions of the checklist, 2) academic confidence, 3) use in education and training, and 4) journal endorsement and use in peer review. Views were diverse and revealed multiple misunderstandings about the checklist's purpose and content, and lack of incentives for its use.

CONCLUSIONS

Better communication efforts are needed when disseminating STROBE and other reporting guidelines. These should focus on content, education for early career researchers, and encouragement of critical self-reflection on one's own work. In addition, results emphasized the need for better incentive and enforcement mechanisms.

The future of activity space and health research

We discuss the future of activity space and health research in the context of a recently published systematic review. Our discussion outlines a number of elements for reflection among the research community. We need to think beyond activity space and reconceptualize exposure in era of high volume, high precision location data. We need to develop standardized methods for understanding global positioning system data. We must adopt replicable scientific computing processes and machine learning models. Finally, we must embrace modern notions of causality in order to contend with the conceptual challenges faced by our research field.

Epidemiology: Back to the Future

In 2018, the Society for Epidemiologic Research and its partner journal, the American Journal of Epidemiology, assembled a working group to develop a set of papers devoted to the "future of epidemiology." These 14 papers covered a wide range of topic areas and perspectives, from thoughts on our profession, teaching, and methods to critical areas of substantive research. The authors of those papers considered current challenges and future opportunities for research and education. In light of past commentaries, 4 papers also include reflections on the discipline at present and in the future.