Estudios epidemiológicos o cómo tenemos que diseñar nuestra investigación (primera parte) / Epidemiological studies or how should we design our research? (part one)

El conocimiento en metodología es imprescindible para poner en marcha nuestros proyectos de investigación y nuestros diseños epidemiológicos. Plantear adecuadamente un diseño nos permitirá comunicar debidamente los resultados de nuestra experiencia clínica. Existen diferentes tipos de diseño en función de múltiples factores, como la participación activa del investigador, la aleatorización de los pacientes o la formación del grupo control. Conocerlos es fundamental para saber cuál es el más apropiado para comenzar nuestra andadura como investigadores. En este trabajo mostramos las características y la terminología con las que nos referimos a los diferentes diseños epidemiológicos y expondremos algunos de estos diseños, que terminaremos de ver en próximos artículos.(AU)

Methodological knowledge is essential to implement our research projects and epidemiological designs. adequatedesign will allow us to adequately communicate the results of our clinical experience.there are different types of design depending on multiple factors such as the active participation of the investigator,the randomization of patients or the composition of the control group. it is essential to be familiar with them inorder to know which is the most appropriate to start our journey as researchers.in this paper we show the characteristics and terminology with which we refer to the different epidemiologicaldesigns and we will present some of these designs, which we will finish in future papers.(AU)

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.

Análisis mediante variables instrumentales para establecer causalidad en investigaciones observacionales / Analysis through instrumental variables to establish causality in observational research

La identificación de relaciones causales es uno de los problemas fundamentales de la investigación científica en medicina y es necesaria para poder ejercerla en forma efectiva. Sin embargo, desde el punto de vista práctico es difícil establecer la existencia de relaciones causales en estudios de carácter observacional, en gran parte por la presencia de factores de confusión. El análisis a través de variables instrumentales es una de las estrategias que permite controlar el efecto confundidor y documentar la presencia de relaciones causa-efecto en estas situaciones. En este artículo, el autor resume los principales supuestos del análisis a través de variables instrumentales, haciendo foco en la aleatorización mendeliana. (AU)

The identification of causal relationships is one of the fundamental challenges in scientific research in medicine and is necessary for its effective practice. However, from a practical standpoint, establishing the existence of causal relationships in observational studies is difficult, largely due to the presence of confounding factors. Analysis through instrumental variables is one of the strategies that allows to control the confounding effect and documenting the presence of cause-and-effect relationships in these situations. In this article, the author summarizes the main assumptions of analysis through instrumental variables, with a focus on Mendelian randomization. (AU)

Perfil de pacientes com sepse e choque séptico em um hospital de trauma: estudo transversal / Profile of patients with sepsis and septic shock in a trauma hospital: cross-sectional study / Perfil de pacientes con sepsis y shock séptico en un hospital de trauma: estudio transversal

Objetivo: Avaliar o perfil e o desfecho clínico de pacientes com sepse e choque séptico em um hospital de trauma de Belo Horizonte. Métodos: Trata-se de estudo transversal, retrospectivo e descritivo dos casos de sepse e choque séptico. Resultados: Constatou-se que 97 (73%) pacientes eram do sexo masculino, com idade entre 19 e 59 anos, 87 (65%) previamente hígidos. Principal motivo de internação foi queda da própria altura com 23 (17%) casos e 37 (28%) diagnósticos de trauma crânio encefálico. Oitenta e seis (65%) pacientes tiveram diagnóstico de sepse e 47 (35%) choque séptico, o principal foco de infecção foi pulmonar 83 (62%). Obtivemos 88 (66%) resultados de hemoculturas negativas e 45 (34%) positivas, 64 (48%) pacientes receberam antibiótico em até 60 minutos após o diagnóstico e o tempo de internação mais prevalente após o evento séptico foi de 7 dias, representado por 31 (46%) pacientes que já estavam internados em uma Unidade de Terapia Intensiva (UTI). Mortalidade de 37 (28%) e 96 (72%) sobreviventes da população estudada. Conclusão: O evento séptico é um problema de saúde pública e tem alta letalidade em pacientes traumatizados. (AU)

Objective: To evaluate the profile and clinical outcome of patients with sepsis and septic shock in a trauma hospital in Belo Horizonte. Methods: This is a cross-sectional, retrospective and descriptive study of cases of sepsis and septic shock. Results: It was found that 97 (73%) patients were male, aged 19 to 59 years, 87 (65%) were previously healthy. Main reason for hospitalization was a fall from one's own height with 23 (17%) cases and 37 (28%) diagnosis of traumatic brain injury. Eighty-six (65%) patients were diagnosed with sepsis and 47 (35%) septic shock, the main focus of infection was pulmonary 83 (62%). We got 88 (66%) negative blood culture results and 45 (34%) positive, 64 (48%) patients received antibiotics within 60 minutes after diagnosis and the most prevalent hospital stay after septic event was 7 days, represented by 31 (46%) patients who were already hospitalized in an Intensive Care Unit (ICU). Mortality of 37 (28%) and 96 (72%) survivors of the population studied. Conclusion: Septic event is a public health problem and has high lethality in traumatized patients. (AU)

Objetivo: Evaluar el perfil y el resultado clínico de los pacientes con sepsis y shock séptico en un hospital de trauma en Belo Horizonte. Métodos: Este es un estudio transversal, retrospectivo y descriptivo de casos de sepsis y shock séptico. Resultados: Se encontró que 97 (73%) pacientes eran hombres, de 19 a 59 años, 87 (65%) antes estaban sanos. La razón principal de la hospitalización fue una caída desde la propia altura con 23 (17%) casos y 37 (28%) diagnóstico de lesión cerebral traumática. Ochenta y seis (65%) pacientes fueron diagnosticados con sepsis y 47 (35%) shock séptico, el foco principal de infección fue pulmonar 83 (62%). Tenemos 88 (66%) resultados negativos del cultivo sanguíneo y 45 (34%) positivo, 64 (48%) los pacientes recibieron antibióticos dentro de los 60 minutos posteriores al diagnóstico y la estancia hospitalaria más frecuente después del evento séptico fue de 7 días, representados por 31 (46%) pacientes que ya estaban hospitalizados en una Unidad de Cuidados Intensivos (UCI). Mortalidad de 37 (28%) y 96 (72%) sobrevivientes de la población estudiada. Conclusión: El evento séptico es un problema de salud pública y tiene alta letalidad en pacientes traumatizados. (AU)

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.

Curso de Introducción a los principios y métodos epidemiológicos: Orientación Clínica y Orientación Crítica - Bloque de Formación Común 2022

Encuentro de presentación del curso, destinado a residentes de cualquier especialidad del Sistema de Residencias de la Provincia de Buenos Aires, cuyo fundamento es pensar a la epidemiología como "el estudio de la distribución y los determinantes de estados o eventos, (en particular de enfermedades) relacionados con la salud y la aplicación de esos estudios al control de enfermedades y otros problemas de salud". En este sentido y, a modo de introducción de la capacitación, Mario Rovere aborda los siguientes aspectos para la reflexión: dimensión individual - colectiva de la epidemiología, ¿por qué y para qué estudiamos epidemiología?, aportes a la clínica, vigilancia y pensamiento epidemiológico, su relevancia frente a los cambios constantes en los problemas de salud - pandemias visibles e invisibles. Luego, Alicia Lawrynowicz, coordinadora del curso, además de informar sobre cuestiones administrativas y formales de la capacitación, aclara que ésta constará de dos formaciones: Introducción a los Principios y Métodos Epidemiológicos: Orientación Epidemiología Clínica y una segunda con Orientación en Epidemiología Crítica. Sobre el primer curso los contenidos de los diferentes módulos serán: Módulo 0: Familiarización con la propuesta curso: Introducción a los principios y métodos epidemiológicos; Módulo 1: El enfoque epidemiológico de la salud y la enfermedad; Módulo 2: Descripción y búsqueda de las causas de la salud y enfermedad; Módulo 3: Principios básicos de la Epidemiología Clínica. El diagnóstico. En tanto que, en referencia a la Orientación en Epidemiología Crítica, su composición será: Módulo 0: Familiarización con la propuesta curso: Introducción a los principios y métodos epidemiológicos, Módulo 1: El enfoque epidemiológico de la salud y la enfermedad, Módulo 2: Descripción y búsqueda de las causas de la salud y enfermedad, Módulo 3: Bases conceptuales de la Epidemiología Crítica y Determinantes y determinaciones del P-S-E-A-C, Módulo 4: Desigualdades en salud. Espacio territorio.

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.

Método de captura y recaptura: una experiencia de su aplicación para determinar la incidencia de diabetes gestacional / Capture and recapture method: anexperience of itsapplication to estimatetheincidence of gestational diabetes

Los métodos de captura y recaptura (MCR) se emplean en la estimación de poblaciones mediante la utilización de diferentes fuentes de datos, disponibles e incompletas, que registran por separado un mismo evento. En esta metodología, las fuentes son utilizadas para extrapolar el número de individuos no registrados, usando la información recopilada sobre los individuos sí registrados. Este artículo describe todos los pasos de su aplicación práctica, a partir de un ejemplo de estimación de la incidencia de diabetes gestacional en una institución, a partir de cinco fuentes documentales. (AU)

Capture-recapture (CRM) methods are widely used to estimate populations by using different data sources, available and incomplete, that record the same event separately. In these methods, the available sources are used to extrapolate the number of unregistered individuals, using the information collected on the individuals that are registered. This article describes all the steps of its practical application, based on an example of estimating the incidence of gestational diabetes in an institution based on five documentary sources. (AU)

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.