Field Of Epidemiology Research Articles

Environmental Education and Health Promotion: Diagnosis of the Epidemiological Profile of Dengue Cases in the City of Anápolis, Goiás, From 2014 to 2017

Objective: The aim of this study is to investigate the epidemiological profile of dengue cases reported in the city of Anápolis - Goiás, during the years 2014 to 2017, with the aim of providing an overview to support control and prevention actions. Theoretical Framework: Considered a Public Health problem in Brazil, dengue is a disease transmitted by the main arbovirus vector, the Aedes aegypti mosquito, through the bite of an infected female, which causes morbidity in the country and it is re-emerging. In the case of dengue fever in the urban environment, it is necessary to monitor the epidemiology, due to the probability of epidemics due to the conditions that facilitate the formation of the transmission chain. Method: The methodology adopted for this research was an exploratory study, in order to understand the subject to be addressed, and a documentary research to present the data, which were obtained from the Notifiable Diseases Information System – Goiás (SINAN), and in the IT Department of the Brazilian Unified Health System / Health Information System (DATASUS). Results and Discussion: During the period evaluated, 22,592 cases were reported. Data for the years 2014 to 2017 showed that the number of cases of dengue fluctuated over the period studied and the need for more investment in information as a disease prevention strategy. Research Implications: The practical and theoretical implications of this research are discussed below, providing insights into how the results can be applied or influence practices in the field of Epidemiology and Environmental Sciences. These implications can cover the Surveillance and Health Care sectors, as well as directly impact the management sector. Originality/Value: This study contributes to the democratization of information and is relevant for identifying the epidemiological reality of certain geographic areas, identifying certain weaknesses and providing data for managers in light of scientific evidence.

Air pollution’s numerical, spatial, and temporal heterogeneous impacts on childhood hand, foot and mouth disease: a multi-model county-level study from China

BackgroundWhile stationary links between childhood hand, foot and mouth disease (HFMD) and air pollution are known, a comprehensive study on their heterogeneous relationships (nonstationarity), jointly considering numerical, temporal and spatial dimensions, has not been reported.MethodsMonthly HFMD incidence and air pollution data were collected at the county level from Sichuan-Chongqing, China (2009–2011), alongside meteorological and social environmental covariates. Key influential factors were identified using random forest (RF) under the stationary assumption. Factors’ numerically, temporally, and spatially heterogeneous relationships with HFMD were assessed using generalized additive model (GAM) and geographically and temporally weighted regression (GTWR).ResultsOur findings highlighted the relatively higher stationary contributions of fine particulate matter (PM2.5) and ozone (O3) to HFMD incidence across Sichuan-Chongqing counties. We further uncovered heterogeneous impacts of PM2.5 and O3 from three nonstationary perspectives. Numerically, PM2.5 showed an inverse ‘V’-shaped relationship with HFMD incidence, while O3 exhibited a complex pattern, with increased HFMD incidence at low PM2.5 and moderate O3 concentrations. Temporally, PM2.5’s impact peaked in autumn and was weakest in spring, whereas O3’s effect was strongest in summer. Spatially, hotspot mapping revealed high-risk clusters for PM2.5 impact across all seasons, with notable geographical variations, and for O3 in spring, summer, and autumn, concentrated in specific regions of Sichuan-Chongqing.ConclusionsThis study underscores the nuanced and three-perspective heterogeneous influences of air pollution on HFMD in small areas, emphasizing the need for differentiated, localized, and time-sensitive prevention and control strategies to enhance the precision of dynamic early warnings and predictive models for HFMD and other infectious diseases, particularly in the fields of environmental and spatial epidemiology.

Pioneer of epidemiology and anesthesiology – John Snow (1813–1858)

IntroductionJohn Snow, a British physician of the 19th century, played a pivotal role in challenging prevailing medical beliefs during his time. His groundbreaking studies on the cholera epidemic led to the rejection of the miasma theory and the establishment of the link between contaminated water and the spread of the disease.AimThis study aims to explore the contributions of John Snow to the fields of anaesthesiology and epidemiology, focusing on his significant findings and methodologies.Material and methodsThis study utilizes a literature review approach to examine John Snow's contributions to anaesthesiology and epidemiology. Searches were conducted in databases using relevant keywords, focusing on peer-reviewed articles and authoritative sources. The data was qualitatively synthesized to extract key insights into Snow's methodologies and findings, aiming to provide a brief overview of his impact on medical science.Results and discussion:John Snow (1813-1858) is celebrated as a pioneer in epidemiology and anaesthesiology. He identified contaminated water as the source of the 1854 cholera outbreak in London, establishing the basis for modern epidemiology. Snow's innovative mapping of cholera cases was crucial in controlling the epidemic. He also advanced anaesthetic techniques, notably using chloroform on Queen Victoria during childbirth, improving the safety of anaesthesia. His work remains influential in both fields.Conclusions:John Snow's pioneering work significantly impacted the development of anaesthesiology and epidemiology. His insights into cholera transmission and anaesthetic agents, along with his seminal publication On Chloroform and Other Anaesthetic, continue to inspire advancements in medical science. Snow's legacy underscores the importance of evidence-based medicine and the pursuit of knowledge in addressing public health challenges.

Discovery of Ornithodoros tholozani, the Main Vector of Iranian Tick-born Relapsing Fever

Doctor Joseph Désiré Tholozan was a French physician who became the special physician of Nassereddin Shah, King of Persia (Iran) in the 19th century. He studied lots of topics in the field of epidemiology of infectious diseases. His efforts led to the discovery of the main vector of Iranian tick-born relapsing fever, Ornithodoros tholozani. He was also one of the pioneers of the Iranian Sanitary Council, whose efforts led to the foundation of the Ministry of Health of Iran a few decades later. This paper is a brief review of his biography and his roles in promoting health in Iran in the 19th century.

Identifying skills required of new epidemiologists: a content analysis of Canadian job postings and master's programs.

The rise of emerging public health threats has increased the need for qualified epidemiologists in Canada. Our study aimed to identify the knowledge, skills, and abilities (KSAs) required of epidemiologists entering the workforce and determine whether these align with those taught in graduate epidemiology programs. An inductive content analysis of Canadian job postings from May to December 2023 containing the keyword "epidemiology" and requiring master's degrees in epidemiology or related fields was conducted to identify the KSAs required in the workforce. Inductive content analysis of Master of Science (MSc) program descriptions and core course descriptions was completed to discern skills gained through Canadian graduate epidemiology and public health programs. Based on the 295 job postings analyzed, five KSA categories were identified: communication skills (n = 268, 90.8%), analytical skills (n = 267, 90.5%), soft skills (n = 254, 86.1%), research methodology (n = 217, 73.6%), and knowledge of epidemiological concepts (n = 170, 57.6%). Analysis of 18 MSc programs found that that all of them described analytical skills, research methodology, and epidemiological concepts within their curriculum. Communication skills were described in 94.4% (n = 17) of programs, while soft skills were mentioned in 50.0% (n = 9). However, only 66.7% (n = 12) of programs outlined learning objectives or specified the skills acquired from their programs in their descriptions. There was alignment between the needs of the Canadian epidemiology job market and MSc programs, particularly in analytical skills and research methodology. However, development of soft skills should be emphasized within graduate epidemiology programs to better prepare graduates for the job market. Future research should aim to develop competency statements for epidemiologists in training to ensure consistency across graduate programs and promote career readiness.

An evolved interpretation of Austin Bradford Hill's causal viewpoints and their influence on epidemiologic methods.

In 1965, Sir Austin Bradford Hill articulated nine viewpoints for evaluating whether a body of evidence about the relationship between an exposure and outcome should be interpreted causally. In this commentary, we highlight a selection of the ways in which these viewpoints have had an impact on the field of epidemiology in terms of methods development, study design, and interpretation of results. Additionally, we opine on how the viewpoints relate to our understanding of basic epidemiologic concepts - for example, our choice of absolute or relative measures of effect, our evolving understanding of the role of context in the generalizability of study results, and modern epistemologies for causal inference (i.e., the potential outcomes framework and graphical causal models). Hill cautioned his audience that evidence should be weighed according to the policy choice it would inform and the context in which that policy would be implemented. We root our remarks in considerations of the public health impact of our conclusions about the causal nature of an observed relationship.

Fifty Years of Psychiatric Classification and Epidemiology Interactions: What is a Mental Disorder?

Psychiatric clinical diagnostic formulation has evolved over time. The changes alter our understanding and our ability to provide a public health perspective on the epidemiology of mental disorders in large populations. Epidemiology is an important perspective and set of tools to assess prevalence, treated prevalence, untreated prevalence, individual risks for mental disorders, and possible links to the etiology of disorders by following the trails of environmental exposures, biological measures, interpersonal dynamics, and genetic risk factors. As communities develop health-care services to treat individuals with mental disorders, knowledge about their prevalence and treatment requirements is also important. Since severe mental disorders may require institutional care, the diagnostic criteria used may either protect an individual’s liability for dangerous behavior (i.e. the insanity defense) or be used to control political and social dissidents. The criteria may also be used to determine evidence-based treatment options and eligibility for disability benefits. In this paper, using my vantage points as a physician scientist and public health officer, with leadership positions in national federal and professional mental health organizations, I address the developments in these areas over the past 50 years that have influenced institutional positions in the U.S. National Institute of Mental Health, the World Health Organization, the American Psychiatric Association, and the Center for the Study of Traumatic Stress. These perspectives may aid the next generation of investigators to advance the epidemiological and mental disorder classification scientific fields.

Identifying Impacts of Contact Tracing on Epidemiological Inference from Phylogenetic Data.

Robust sampling methods are foundational to inferences using phylogenies. Yet the impact of using contact tracing, a type of non-uniform sampling used in public health applications such as infectious disease outbreak investigations, has not been investigated in the molecular epidemiology field. To understand how contact tracing influences a recovered phylogeny, we developed a new simulation tool called SEEPS (Sequence Evolution and Epidemiological Process Simulator) that allows for the simulation of contact tracing and the resulting transmission tree, pathogen phylogeny, and corresponding virus genetic sequences. Importantly, SEEPS takes within-host evolution into account when generating pathogen phylogenies and sequences from transmission histories. Using SEEPS, we demonstrate that contact tracing can significantly impact the structure of the resulting tree, as described by popular tree statistics. Contact tracing generates phylogenies that are less balanced than the underlying transmission process, less representative of the larger epidemiological process, and affects the internal/external branch length ratios that characterize specific epidemiological scenarios. We also examined real data from a 2007-2008 Swedish HIV-1 outbreak and the broader 1998-2010 European HIV-1 epidemic to highlight the differences in contact tracing and expected phylogenies. Aided by SEEPS, we show that the data collection of the Swedish outbreak was strongly influenced by contact tracing even after downsampling, while the broader European Union epidemic showed little evidence of universal contact tracing, agreeing with the known epidemiological information about sampling and spread. Overall, our results highlight the importance of including possible non-uniform sampling schemes when examining phylogenetic trees. For that, SEEPS serves as a useful tool to evaluate such impacts, thereby facilitating better phylogenetic inferences of the characteristics of a disease outbreak. SEEPS is available at github.com/MolEvolEpid/SEEPS.

Feasibility Study on the Use of NO2 and PM2.5 Sensors for Exposure Assessment and Indoor Source Apportionment at Fixed Locations.

Recent advances in sensor technology for air pollution monitoring open new possibilities in the field of environmental epidemiology. The low spatial resolution of fixed outdoor measurement stations and modelling uncertainties currently limit the understanding of personal exposure. In this context, air quality sensor systems (AQSSs) offer significant potential to enhance personal exposure assessment. A pilot study was conducted to investigate the feasibility of the NO2 sensor model B43F and the particulate matter (PM) sensor model OPC-R1, both from Alphasense (UK), for use in epidemiological studies. Seven patients with chronic obstructive pulmonary disease (COPD) or asthma had built-for-purpose sensor systems placed inside and outside of their homes at fixed locations for one month. Participants documented their indoor activities, presence in the house, window status, and symptom severity and performed a peak expiratory flow test. The potential inhaled doses of PM2.5 and NO2 were calculated using different data sources such as outdoor data from air quality monitoring stations, indoor data from AQSSs, and generic inhalation rates (IR) or activity-specific IR. Moreover, the relation between indoor and outdoor air quality obtained with AQSSs, an indoor source apportionment study, and an evaluation of the suitability of the AQSS data for studying the relationship between air quality and health were investigated. The results highlight the value of the sensor data and the importance of monitoring indoor air quality and activity patterns to avoid exposure misclassification. The use of AQSSs at fixed locations shows promise for larger-scale and/or long-term epidemiological studies.

On the use of reactive multiparticle collision dynamics to gather particulate level information from simulations of epidemic models

This paper discusses the application of reactive multiparticle collision (RMPC) dynamics, a particle-based method, to epidemic models. First, we consider a susceptible-infectious-recovered framework to obtain data on contacts of susceptibles with infectious people in a population. It is found that the number of contacts increases and the contact duration decreases with increases in the disease transmission rate and average population speed. Next, we obtain reinfection statistics for a general infectious disease from RMPC simulations of a susceptible-infectious-recovered-susceptible model. Finally, we simulate a susceptible-exposed-infectious-recovered model and gather the exposure, infection, and recovery time for the individuals in the population under consideration. It is worth mentioning that we can collect data in the form of average contact duration, average initial infection time, etc., from RMPC simulations of these models, which is not possible with population-based stochastic models, or deterministic systems. This study provides quantitative insights on the potential of RMPC to simulate epidemic models and motivates future efforts for its application in the field of mathematical epidemiology.

Effective and inclusive mentorship in the field of epidemiology.

Quality mentoring improves outcomes across career stages, including a sense of belonging, persistence, and productivity. However, the status quo in mentorship culture-including in epidemiology-is an ad hoc approach. This pervasive culture adversely affects individual mentees and the entire scientific research enterprise. Public health disciplines such as epidemiology bear a distinct responsibility to foster an inclusive mentorship culture, ensuring the next generation is equipped not only with methodological expertise but also with a commitment to diversity, equity, inclusion, and the principles of public health. In this commentary, we outline the primary attributes of effective mentors, core competencies, and the evidence base underlying how mentors can improve their skills with comprehensive training. We call on mentors, as well as institutional leaders, to make personal and structural changes, such as requiring mentor training, implementing evidence-based tools (e.g., individual development plans), and regularly evaluating mentorship quality. Institutional leaders can remove barriers (e.g., costs to enroll in mentor training) and facilitate mentorship in non-monetary ways, for example, by making it a formal part of the promotion process. Mentors and leaders must champion these changes, fortifying not only individual career trajectories but also advancing scientific integrity, inclusivity, and justice within the epidemiologic community.

Trends in Physiotherapy of Chronic Low Back Pain Research: Knowledge Synthesis Based on Bibliometric Analysis.

Physiotherapy and chronic low back pain (CLBP) form a broad and quickly developing research area. The aim of this article was to holistically, thematically and chronologically analyze and synthesize the literature production in this research area and identify the most prolific research entities and research themes. This article quantitatively and qualitatively analyzed research literature production harvested from the Scopus bibliometric database, using a triangulation of bibliometric and thematic analysis. For this, Excel 2024, Bibliometrix Biblioshiny 4.1 and VOSviewer version 1.6.20 softwares were used. In the Scopus database, 2843 data sources were found, which were published between 1974 and 26 February 2024. The growth trend has been linearly positive since the beginning of publication, and after 2018 exponential growth began. A review of the most prolific entities showed that the most literature was published in America, Europe and Australasia. The thematic analysis of the information sources identified six main themes (pathophysiology of CLBP and the quantification assessment tools, diagnostics and CLBP treatment, CLBP questionnaires and surveys, quality of life, complementary methods in physiotherapy and psychosocioeconomic aspects), while the chronological analysis revealed three main areas of development: assessment tools, CLBP processing and study methodology. The results of this bibliometric study present a good starting point for further research, providing taxonomy and research landscapes as a holistic framework offering multidisciplinary knowledge about CLBP, while chronological analysis provides a basis for identifying prospective research trends. This article offers an interdisciplinary view of the current issue of public health. The results of this study provide a basis for the development of both the physiotherapy and epidemiological fields.

Specifying a target trial protocol to estimate the effect of preconception treatment of periodontitis on time-to-pregnancy: A commentary and applied example.

The target trial framework was developed as a strategy to design and analyze observational epidemiologic studies with the aim of reducing bias due to analytic decisions. It involves designing a hypothetical randomized trial to answer a question of interest and systematically considering how to use observational data to emulate each trial component. The primary aim of this paper is to provide a detailed example of the application of the target trial framework to a research question in oral epidemiology. We describe the development of a hypothetical target trial and emulation protocol to evaluate the effect of preconception periodontitis treatment on time-to-pregnancy. We leverage data from Pregnancy Study Online (PRESTO), a preconception cohort, to ground our example in existing observational data. We discuss the decision-making process for each trial component, as well as limitations encountered. Our target trial application revealed data limitations that precluded us from carrying out the proposed emulation. Implications for data quality are discussed and we provide recommendations for researchers interested in conducting trial emulations in the field of oral epidemiology. The target trial framework has the potential to improve the validity of observational research in oral health, when properly applied. We encourage the broad adoption of the target trial framework to the field of observational oral health research and demonstrate its value as a tool to identify directions for future research.

Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review.

This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as a crucial tool for the early detection and management of viral outbreaks. This review will explore the application of various modeling techniques that enable the prediction and understanding of virus concentrations and spread patterns in wastewater systems. It highlights the role of spatial analysis in mapping the geographic distribution of viral loads, providing insights into the dynamics of virus transmission within communities. The integration of GIS in wastewater surveillance will be explored, emphasizing the utility of such systems in visualizing data, enhancing sampling site selection, and ensuring equitable monitoring across diverse populations. The review will also discuss the innovative combination of GIS with remote sensing data and predictive modeling, offering a multi-faceted approach to understand virus spread. Challenges such as data quality, privacy concerns, and the necessity for interdisciplinary collaboration will be addressed. This review concludes by underscoring the transformative potential of these analytical tools in public health, advocating for continued research and innovation to strengthen preparedness and response strategies for future viral threats. This article aims to provide a foundational understanding for researchers and public health officials, fostering advancements in the field of wastewater-based epidemiology.

The Evolution of Selection Bias in the Recent Epidemiologic Literature-A Selective Overview.

Selection bias has long been central in methodological discussions across epidemiology and other fields. In epidemiology, the concept of selection bias has been continually evolving over time. In this issue of the Journal, Mathur and Shpitser (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX) present simple graphical rules for using a Single World Intervention Graph (SWIG) to assess the presence of selection bias when estimating treatment effects in both the general population and a selected sample. Notably, the authors examine the setting in which the treatment affects selection, an issue not well-addressed in the existing literature on selection bias. To place the work by Mathur and Shpitser in context, we review the evolution of the concept of selection bias in epidemiology, with a primary focus on the developments in the last 20-30 years since the introduction of causal directed acyclic graphs (DAGs) to epidemiologic research.

Methods for extending inferences from observational studies: considering causal structures, identification assumptions, and estimators.

Most prior work in quantitative approaches to generalizability and transportability emphasizes extending causal effect estimates from randomized trials to target populations. Extending findings from observational studies is also of scientific interest, and identifiability assumptions and estimation methods differ from randomized settings when there is selection on both the exposure and exposure-outcome mediators in combination with exposure-outcome confounders (and both confounders and mediators can modify exposure-outcome effects). We argue that this causal structure is common in observational studies, particularly in the field of lifecourse epidemiology, e.g., when extending estimates of the effect of an early-life exposure on a later-life outcome from a cohort enrolled in mid- to late-life. We describe identifiability assumptions and identification using observed data in such settings, highlighting differences from work extending findings from randomized trials. We describe statistical methods, including weighting, outcome modeling, and doubly robust approaches to estimate potential outcome means and verage treatment effects in the target population and illustrate performance of the methods in a simulation study. We show that in the presence of selection into the study sample on both exposure and confounders, estimators must be able to address confounding in the target population. When there is also selection on mediators of the exposure-outcome relationship, estimators need to be able to use different sets of variables to account for selection (including the mediator), and confounding. We discuss conceptual implications of our results, as well as highlight unresolved practical questions for applied work to extend findings from observational studies to target populations.

John Snow: The Pioneer of Modern Epidemiology and Anesthesia.

John Snow (15 March 1813-16 June 1858) stands as a seminal figure in the fields of epidemiology and anesthesia. His groundbreaking work in tracing the source of cholera outbreaks and advancing the practice of anesthesia has left an indelible mark on modern medicine. Born in York, England, Snow's early passion for science and dedication to medical practice led him to become a pioneering force in his field. His meticulous methods, including the innovative use of spatial analysis and statistical mapping, challenged prevailing theories and laid the groundwork for modern public health initiatives. Snow's contributions to anesthesia, particularly his work with ether and chloroform, revolutionized surgical practices, significantly improving patient care and safety. This article delves into Snow's life, achievements, and the innovative processes he employed, underscoring his enduring impact on human health. By examining his legacy, we aim to enhance our understanding of medical history and inspire both present and future healthcare professionals, honoring the legacy of this medical hero.

Change of measure in a Heston–Hawkes stochastic volatility model

Abstract We consider the stochastic volatility model obtained by adding a compound Hawkes process to the volatility of the well-known Heston model. A Hawkes process is a self-exciting counting process with many applications in mathematical finance, insurance, epidemiology, seismology, and other fields. We prove a general result on the existence of a family of equivalent (local) martingale measures. We apply this result to a particular example where the sizes of the jumps are exponentially distributed. Finally, a practical application to efficient computation of exposures is discussed.

Social epidemiology and time use

Examining disease progression and associated risk and protective factors over time is essential for epidemiology. “Over time” can be understood as discrete events in people's lives that affect their health outcomes, including exposures and behaviors. For social epidemiology, this is of interest because time spent in social situations is required to benefit from those interactions. A wide range of social relationship types has been linked to health outcomes. Social relationships take time to develop and include spending time with others. Spending time with others has been shown to lead people to adopt beliefs, priorities, and behaviors from the group. Therefore time with others influences decisions about how to spend time and, potentially, health trajectory. More research is needed on how time affects the relationships between social influence and health. This commentary explores gaps in the social epidemiology field related to time use and suggests ways to address these gaps through data collection and analyses.

Chrononutrition and Cardiometabolic Health: An Overview of Epidemiological Evidence and Key Future Research Directions.

Chrononutrition is a rapidly evolving field of nutritional epidemiology that addresses the complex relationship between temporal eating patterns, circadian rhythms, and metabolic health, but most prior research has focused on the cardiometabolic consequences of time-restricted feeding and intermittent fasting. The purpose of this topical review is to summarize epidemiological evidence from observational and intervention studies regarding the role of chrononutrition metrics related to eating timing and regularity in cardiometabolic health preservation and cardiovascular disease prevention. Observational studies are limited due to the lack of time-stamped diet data in most population-based studies. Findings from cohort studies generally indicate that breakfast skipping or the later timing of the first eating occasion, a later lunch and dinner, and a greater proportion of caloric intake consumed in the evening are associated with adverse cardiometabolic outcomes, including higher risk for coronary heart disease, hypertension, type 2 diabetes, obesity, dyslipidemia, and systemic inflammation. Randomized controlled trials are also limited, as most in the field of chrononutrition focus on the cardiometabolic consequences of time-restricted feeding. Overall, interventions that shift eating timing patterns to earlier in the day and that restrict evening caloric intake tend to have protective effects on cardiometabolic health, but small sample sizes and short follow-up are notable limitations. Innovation in dietary assessment approaches, to develop low-cost validated tools with acceptable participant burden that reliably capture chrononutrition metrics, is needed for advancing observational evidence. Culturally responsive pragmatic intervention studies with sufficiently large and representative samples are needed to understand the impact of fixed and earlier eating timing schedules on cardiometabolic health. Additional research is warranted to understand the modifiable determinants of temporal eating patterns, to investigate the role of chrononutrition in the context of other dimensions of diet (quantity, quality, and food and nutrition security) in achieving cardiometabolic health equity, and to elucidate underlying physiological mechanisms.