Estimating alcohol-attributable injury deaths: A comparison of epidemiological methods.

BACKGROUND AND AIMS: Injuries often involve alcohol, but determining the proportion caused by alcohol is difficult. Several approaches have been used to determine the causal role of alcohol, but these methods have not been compared directly with one another. Such a comparison would be useful for understanding the strengths and comparability of different approaches. This study compared estimates of average annual alcohol-attributable deaths in the United States from injuries during 2015-19 using a blood alcohol concentration (BAC) method compared with a population attributable fraction (PAF) approach. METHODS: For the BAC method, we used a direct method involving the proportion of decedents with a high blood alcohol concentration (BAC; e.g. ≥ 0.10%). For the PAF approach, we compared the use of unadjusted survey data with average consumption data adjusted using alcohol sales data to account for underreporting and also accounting for the underreporting of binge drinking. Survey data were from the Behavioral Risk Factor Surveillance System and mortality data were from the National Vital Statistics System. RESULTS: The number of alcohol-attributable injury deaths using the direct method (48 516 deaths annually) was similar to that using PAF methods (47 879 deaths annually), but only when alcohol use measures were adjusted using alcohol sales data. Furthermore, estimates were similar for cause-specific categories of deaths, including non-motor vehicle unintentional injuries and motor vehicle crashes. Among PAF methods, excessive drinking accounted for 38.3% of injury deaths using unadjusted survey data, but 64.8% of injury deaths using adjusted data. CONCLUSIONS: Estimates of alcohol-attributable injury deaths from a direct method and from a population attributable fraction method that adjusts for alcohol use based on alcohol sales data appear to be comparable.

Complexity in Epidemiology and Public Health. Addressing Complex Health Problems Through a Mix of Epidemiologic Methods and Data.

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions-patterns, mechanisms, and dynamics-along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems-emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support the systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.

Online Health Communities: an alternative feasible data registry tool for developing countries.

Given the many challenges facing healthcare access in many developing countries and the added limitations observed in emergencies like COVID-19 pandemic, the authors here discuss an alternative and feasible approach to overcome all these limitations.

Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.

OBJECTIVES: Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data. TARGET AUDIENCE: Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference. SCOPE: We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided.

Response to Tsuda et al. "demonstrating the undermining of science and health policy after the Fukushima nuclear accident by applying the toolkit for detecting misused epidemiological methods".

BACKGROUND: The SHAMISEN (Nuclear Emergency Situations - Improvement of Medical And Health Surveillance) European project was conducted in 2015-2017 to review the lessons learned from the experience of past nuclear accidents and develop recommendations for preparedness and health surveillance of populations affected by a nuclear accident. Using a toolkit approach, Tsuda et al. recently published a critical review of the article by Cléro et al. derived from the SHAMISEN project on thyroid cancer screening after nuclear accident. MAIN BODY: We address the main points of criticism of our publication on the SHAMISEN European project. CONCLUSION: We disagree with some of the arguments and criticisms mentioned by Tsuda et al. We continue to support the conclusions and recommendations of the SHAMISEN consortium, including the recommendation not to launch a mass thyroid cancer screening after a nuclear accident, but rather to make it available (with appropriate information counselling) to those who request it.

Invited Commentary: Some Social Epidemiologic Lessons From the COVID-19 Pandemic.

In their recent article, Dimitris et al. (Am J Epidemiol. 2022;191(6):980-986) presented a series of challenges modern epidemiology has faced during the coronavirus disease 2019 (COVID-19) pandemic, including challenges around the scientific progress, epidemiologic methods, interventions, equity, team science, and training needed to address these issues. Here, 2 social epidemiologists who have been working on COVID-19 inequities reflect on further lessons with an added year of perspective. We focus on 2 key challenges: 1) dominant biomedical individualistic narratives around the production of population health, and 2) the role of profit in policy-making. We articulate a need to consider social epidemiologic approaches, including acknowledging the importance of considering how societal systems lead to health inequities. To address these challenges, future (and current) epidemiologists should be trained in theories of population health distribution and political structures of governance. Last, we close with the need for better investment in public health infrastructure as a crucial step toward achieving population health equity.

Crossing Birth-Weight-Specific Mortality Curves: How a Puzzling Clinical Observation Led to an Important Advance in Epidemiologic Methods.

Throughout the second half of the 20th century, clinicians noted that although African-American neonates were more likely than White neonates to weigh less than 2,500 g at birth (low birth weight), among low-birth-weight infants African Americans were more likely than Whites to survive. However, when born at normal weight, African-American infants were substantially less likely to survive. The observation generated much physiological speculation, and several clever mathematical manipulations were devised to "uncross the mortality curves." With the development and dissemination of directed acyclic graphs in the early 2000s, methodologists focusing on perinatal epidemiology showed graphically, in an early use of directed acyclic graphs, that birth weight was a "collider" and that controlling for birth weight, whether by regression, stratification, or restriction, introduced confounding of the race-mortality association by all unmeasured common causes of birth weight and mortality. These investigations showed that the crossing curves could be explained as an artifact of a conceptually flawed analysis. These results have applicability beyond perinatal epidemiology, including applicability to the "obesity paradox."

Invited Commentary: Use of Epidemiologic Methods to Guide Comprehensive and Equitable Approaches to Policy.

Despite a dramatic reduction in the prevalence of commercial cigarette smoking in the United States, children are still commonly exposed to secondhand smoke (SHS), which is a cause of various pediatric health problems. Further, SHS exposure is patterned by race and class, exacting an inequitable toll on children from families with lesser social and economic advantage. In this issue of the Journal, Titus et al. (Am J Epidemiol. 2023;192(1):25-33) use natural experiment evaluation methods (difference-in-differences) to test whether the recently implemented US Department of Housing and Urban Development policy that forbade smoking in and around New York City Housing Authority buildings affected child respiratory health. The results from their work remind us that policies do not always impact outcomes as we might expect. Given that policy is one of the most potent tools for population health promotion, this work underlines the need for epidemiologists to engage in policy evaluation at all stages of the policy life cycle, in order to discover comprehensive approaches to policy development and implementation that prioritize equity and address structural racism.

Response to Toshihide Tsuda, Yumiko Miyano and Eiji Yamamoto [1].

BACKGROUND: In August 2021, we published in Environmental Health a Toolkit for detecting misused epidemiological methods with the goal of providing an organizational framework for transparently evaluating epidemiological studies, a body of evidence, and resultant conclusions. Tsuda et al., the first group to utilize the Toolkit in a systematic fashion, have offered suggestions for its modification. MAIN BODY: Among the suggested modifications made by Tsuda et al., we agree that rearrangement of Part A of the Toolkit to reflect the sequence of the epidemiological study process would facilitate its usefulness. Expansion or adaptation of the Toolkit to other disciplines would be valuable but would require the input of discipline-specific expertise. We caution against using the sections of the Toolkit to produce a tally or cumulative score, because none of the items are weighted as to importance or impact. Rather, we suggest a visual representation of how a study meets the Toolkit items, such as the heat maps used to present risk of bias criteria for studies included in Cochrane reviews. We suggest that the Toolkit be incorporated in the sub-specialty known as "forensic epidemiology," as well as in graduate training curricula, continuing education programs, and conferences, with the recognition that it is an extension of widely accepted ethics guidelines for epidemiological research. CONCLUSION: We welcome feedback from the research community about ways to strengthen the Toolkit as it is applied to a broader assemblage of research studies and disciplines, contributing to its value as a living tool/instrument. The application of the Toolkit by Tsuda et al. exemplifies the usefulness of this framework for transparently evaluating, in a systematic way, epidemiological research, conclusions relating to causation, and policy decisions. POSTSCRIPT: We note that our Toolkit has, most recently, inspired authors with discipline-specific expertise in the field of Conservation Biology to adapt it for use in the Biological Sciences.

Using the Generalized Synthetic Control Method to Estimate the Impact of Extreme Weather Events on Population Health.

BACKGROUND: Traditional epidemiologic approaches such as time-series or case-crossover designs are often used to estimate the effects of extreme weather events but can be limited by unmeasured confounding. Quasi-experimental methods are a family of methods that leverage natural experiments to adjust for unmeasured confounding indirectly. The recently developed generalized synthetic control method that exploits the timing of an exposure is well suited to estimate the impact of acute environmental events on health outcomes. To demonstrate how this method can be used to study extreme weather events, we examined the impact of the 20-26 October 2007 Southern California wildfire storm on respiratory hospitalizations. METHODS: We used generalized synthetic control to compare the average number of ZIP code-level respiratory hospitalizations during the wildfire storm between ZIP codes that were classified as exposed versus unexposed to wildfire smoke. We considered wildfire exposure eligibility for each ZIP code using fire perimeters and satellite-based smoke plume data. We retrieved respiratory hospitalization discharge data from the Office of Statewide Health Planning and Development. R code to implement the generalized synthetic control method is included for reproducibility. RESULTS: The analysis included 172 exposed and 578 unexposed ZIP codes. We estimated that the average effect of the wildfire storm among the exposed ZIP codes was an 18% (95% confidence interval: 10% to 29%) increase in respiratory hospitalizations. CONCLUSIONS: We illustrate the use of generalized synthetic control to leverage natural experiments to quantify the health impacts of extreme weather events when traditional approaches are unavailable or limited by assumptions.

Spatio-temporal clustering of amyotrophic lateral sclerosis in France: A population-based study.

OBJECTIVE: To assess spatial aggregates of amyotrophic lateral sclerosis (ALS) incident cases, using a solid geo-epidemiological statistical method, in France. METHODS: This population-based study (2003-2011) investigated 47.1 million person-years of follow-up (PYFU). Case ascertainment of incident ALS cases was based on multiple sources (ALS referral centers, hospital centres and health insurance data). Neurologists confirmed all ALS diagnoses. Exhaustiveness was estimated through capture-recapture. Aggregates were investigated in four steps: (a) geographical modelling (standardized incidence ratio (SIR) calculation), (b) analysis of the spatial distribution of incidence (Phothoff-Winttinghill's test, Global Moran's Index, Kulldorf's spatial scan statistic, Local Moran's Index), (c) classification of the level of certainty of spatial aggregates (i.e. definite cluster; probable over-incidence area; possible over-incidence area) and (d) evaluation of the robustness of the results. RESULTS: The standardized incidence of ALS was 2.46/100,000 PYFU (95% CI 2.31-2.63, European population as reference) based on 1199 incident cases. We identified 13 areas of spatial aggregates: one cluster (stable in robustness analysis), five probable over-incidence areas (2 stable in robustness analysis) and seven possible over-incidence areas (including 4 stable areas in robustness analysis). A cluster was identified in the Rhône-Alpes region: 100 observed vs 54.07 expected cases for 2,411,514 PYFU, SIR: 1.85 (95% CI 1.50-2.25). CONCLUSION: We report here one of the largest investigations of incidence and spatial aggregation of ALS ever performed in a western country. Using a solid methodology framework for case ascertainment and cluster analysis, we identified 13 areas that warrant further investigation.

Invited Commentary: Is Bias Towards the Null From Nondifferential Misclassification Wishful Thinking?

In their article, Yland et al. (Am J Epidemiol. 2022;191(8):1485-1495) bring renewed attention to the oft-cited heuristic "nondifferential misclassification biases results towards the null." They make a compelling case, as others have before, that this heuristic is misguided. Herein, I briefly discuss the history of this heuristic, discuss why I believe it became and stayed popular, and note the very limited circumstances for which it may be appropriate.

Illustrating How to Simulate Data From Directed Acyclic Graphs to Understand Epidemiologic Concepts.

Simulation methods are a powerful set of tools that can allow researchers to better characterize phenomena from the real world. As such, the ability to simulate data represents a critical set of skills that epidemiologists should use to better understand epidemiologic concepts and ensure that they have the tools to continue to self-teach even when their formal instruction ends. Simulation methods are not always taught in epidemiology methods courses, whereas causal directed acyclic graphs (DAGs) often are. Therefore, this paper details an approach to building simulations from DAGs and provides examples and code for learning to perform simulations. We recommend using very simple DAGs to learn the procedures and code necessary to set up a simulation that builds on key concepts frequently of interest to epidemiologists (e.g., mediation, confounding bias, M bias). We believe that following this approach will allow epidemiologists to gain confidence with a critical skill set that may in turn have a positive impact on how they conduct future epidemiologic studies.

[Application of epidemiological methods in health impact assessment].

Health impact assessment (HIA) system has been listed in the Outline of the Healthy China 2030 Plan and the Law of Basic Health Care and Health Promotion of the People's Republic of China, however, the technique guideline of HIA needs to be established and improved. This paper summarizes the applications of different epidemiological methods in HIA and focus on the introduction of the application of ecology model of health social determinants as theory basis in the establishment of HIA system along with the introduction of HIA cases in the world. The applications of epidemiological methods in domestic HIA research are limited. Therefore, appropriate applications of epidemiological methods should be strengthened in HIA guideline and system development, especially the applications of big health data, mobile health techniques, systems epidemiology and implementation science, to facilitate data collection and potential health hazard evaluation and surveillance for HIA, establishment and improvement of HIA system and the implementation of Healthy China Strategy.

Misconceptions About the Direction of Bias From Nondifferential Misclassification.

Measurement error is pervasive in epidemiologic research. Epidemiologists often assume that mismeasurement of study variables is nondifferential with respect to other analytical variables and then rely on the heuristic that "nondifferential misclassification will bias estimates towards the null." However, there are many exceptions to the heuristic for which bias towards the null cannot be assumed. In this paper, we compile and characterize 7 exceptions to this rule and encourage analysts to take a more critical and nuanced approach to evaluating and discussing bias from nondifferential mismeasurement.

Constructing and validating a transferable epidemic risk index in data scarce environments using open data: A case study for dengue in the Philippines.

Epidemics are among the most costly and destructive natural hazards globally. To reduce the impacts of infectious disease outbreaks, the development of a risk index for infectious diseases can be effective, by shifting infectious disease control from emergency response to early detection and prevention. In this study, we introduce a methodology to construct and validate an epidemic risk index using only open data, with a specific focus on scalability. The external validation of our risk index makes use of distance sampling to correct for underreporting of infections, which is often a major source of biases, based on geographical accessibility to health facilities. We apply this methodology to assess the risk of dengue in the Philippines. The results show that the computed dengue risk correlates well with standard epidemiological metrics, i.e. dengue incidence (p = 0.002). Here, dengue risk constitutes of the two dimensions susceptibility and exposure. Susceptibility was particularly associated with dengue incidence (p = 0.048) and dengue case fatality rate (CFR) (p = 0.029). Exposure had lower correlations to dengue incidence (p = 0.193) and CFR (p = 0.162). Highest risk indices were seen in the south of the country, mainly among regions with relatively high susceptibility to dengue outbreaks. Our findings reflect that the modelled epidemic risk index is a strong indication of sub-national dengue disease patterns and has therefore proven suitability for disease risk assessments in the absence of timely epidemiological data. The presented methodology enables the construction of a practical, evidence-based tool to support public health and humanitarian decision-making processes with simple, understandable metrics. The index overcomes the main limitations of existing indices in terms of construction and actionability.

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

A curated dataset of peste des petits ruminants virus sequences for molecular epidemiological analyses.

Peste des petits ruminants (PPR) is a highly contagious and devastating viral disease infecting predominantly sheep and goats. Tracking outbreaks of disease and analysing the movement of the virus often involves sequencing part or all of the genome and comparing the sequence obtained with sequences from other outbreaks, obtained from the public databases. However, there are a very large number (>1800) of PPRV sequences in the databases, a large majority of them relatively short, and not always well-documented. There is also a strong bias in the composition of the dataset, with countries with good sequencing capabilities (e.g. China, India, Turkey) being overrepresented, and most sequences coming from isolates in the last 20 years. In order to facilitate future analyses, we have prepared sets of PPRV sequences, sets which have been filtered for sequencing errors and unnecessary duplicates, and for which date and location information has been obtained, either from the database entry or from other published sources. These sequence datasets are freely available for download, and include smaller datasets which maximise phylogenetic information from the minimum number of sequences, and which will be useful for simple lineage identification. Their utility is illustrated by uploading the data to the MicroReact platform to allow simultaneous viewing of lineage date and geographic information on all the viruses for which we have information. While preparing these datasets, we identified a significant number of public database entries which contain clear errors, and propose guidelines on checking new sequences and completing metadata before submission.

A threshold-free approach with age-dependency for estimating malaria seroprevalence.

BACKGROUND: In malaria serology analysis, the standard approach to obtain seroprevalence, i.e the proportion of seropositive individuals in a population, is based on a threshold which is used to classify individuals as seropositive or seronegative. The choice of this threshold is often arbitrary and is based on methods that ignore the age-dependency of the antibody distribution. METHODS: Using cross-sectional antibody data from the Western Kenyan Highlands, this paper introduces a novel approach that has three main advantages over the current threshold-based approach: it avoids the use of thresholds; it accounts for the age dependency of malaria antibodies; and it allows us to propagate the uncertainty from the classification of individuals into seropositive and seronegative when estimating seroprevalence. The reversible catalytic model is used as an example for illustrating how to propagate this uncertainty into the parameter estimates of the model. RESULTS: This paper finds that accounting for age-dependency leads to a better fit to the data than the standard approach which uses a single threshold across all ages. Additionally, the paper also finds that the proposed threshold-free approach is more robust against the selection of different age-groups when estimating seroprevalence. CONCLUSION: The novel threshold-free approach presented in this paper provides a statistically principled and more objective approach to estimating malaria seroprevalence. The introduced statistical framework also provides a means to compare results across studies which may use different age ranges for the estimation of seroprevalence.