Commentary: Behind the Broad Street pump: aetiology, epidemiology and prevention of cholera in mid-19th century Britain

Mendelian randomization: causal inference leveraging genetic data

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

Pandemics Throughout History

The emergence and spread of infectious diseases with pandemic potential occurred regularly throughout history. Major pandemics and epidemics such as plague, cholera, flu, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) have already afflicted humanity. The world is now facing the new coronavirus disease 2019 (COVID-19) pandemic. Many infectious diseases leading to pandemics are caused by zoonotic pathogens that were transmitted to humans due to increased contacts with animals through breeding, hunting and global trade activities. The understanding of the mechanisms of transmission of pathogens to humans allowed the establishment of methods to prevent and control infections. During centuries, implementation of public health measures such as isolation, quarantine and border control helped to contain the spread of infectious diseases and maintain the structure of the society. In the absence of pharmaceutical interventions, these containment methods have still been used nowadays to control COVID-19 pandemic. Global surveillance programs of water-borne pathogens, vector-borne diseases and zoonotic spillovers at the animal-human interface are of prime importance to rapidly detect the emergence of infectious threats. Novel technologies for rapid diagnostic testing, contact tracing, drug repurposing, biomarkers of disease severity as well as new platforms for the development and production of vaccines are needed for an effective response in case of pandemics.

Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017

Typhoid disease continues to be a global public health burden. Uganda is one of the African countries characterized by high incidences of typhoid disease. Over 80% of the Ugandan districts are endemic for typhoid, largely attributable to lack of reliable knowledge to support disease surveillance. Spatial-temporal studies exploring major characteristics of the disease within the local population have remained limited in Uganda. The main goal of the study was to reveal spatial-temporal trends and distribution patterns of typhoid disease in Uganda for the period 2012 to 2017. Spatial-temporal statistics revealed monthly and annual trends of the disease at both regional and national levels. Results show that outbreaks occurred during 2015 and 2017 in central and eastern regions, respectively. Spatial scan statistic using the discrete Poisson model revealed spatial clusters of the disease for each of the years from 2012 to 2017, together with populations at risk. Most of the disease clustering was in the central region, followed by western and eastern regions (P <0.01). The northern region was the safest throughout the study period. This knowledge helps surveillance teams to i) plan and enforce preventive measures; ii) effectively prepare for outbreaks; iii) make targeted interventions for resource optimization; and iv) evaluate effectiveness of the intervention methods in the study period. This exploratory research forms a foundation of using Geographical Information Systems (GIS) in other related subsequent research studies to discover hidden spatial patterns that are difficult to discover with conventional methods.

A Prehistory of the Diathesis-Stress Model: Predisposing and Exciting Causes of Insanity in the 19th Century

Histories of the diathesis-stress model trace its origins to the 1950s. However, of 26 psychiatric texts published between 1800 and 1910, 17 noted that causes of insanity could be usefully divided into those that predispose to illness and those that excite onset. In this "predisposition-excitation framework" (PEF) for the etiology of insanity, hereditary or constitutional factors were critical predisposing causes, but education, occupation, sex, and marital status were typically included as well. Psychological traumas were key exciting causes, but so were somatic diseases, pregnancy, and substance abuse. The PEF was often used to explain the diversity of individual responses to adversity. While single dramatic events often excited onset, daily repetition of lesser shocks could also bring on insanity. Matching could occur between predisposing and exciting causes in individuals who had "special susceptibilities." Predispositions could lead to "affects, passions, and perverse manner of life," which became exciting causes. Authors emphasized that it was easier to prevent exposures to exciting causes than to reverse predispositions. A thought experiment of an individual "transplanted early into new and different social conditions" anticipated models of primary prevention. Ratings of predisposing and exciting causes were mandated in the United Kingdom from 1878 to 1887 and at several U.S. psychiatric hospitals in the early 20th century. The PEF was far more stable over place and time in the 19th century than any psychiatric nosologic system. Contrary to the doctrinaire schools of psychoanalytic and biological psychiatry that dominated much of 20th-century psychiatry, the PEF proposed a flexible, developmental, and pluralistic view of etiologic pathways to psychiatric illness.

Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs

Background

Directed acyclic graphs (DAGs) are popular tools for identifying appropriate adjustment strategies for epidemiological analysis. However, a lack of direction on how to build them is problematic. As a solution, we propose using a combination of evidence synthesis strategies and causal inference principles to integrate the DAG-building exercise within the review stages of research projects. We demonstrate this idea by introducing a novel protocol: ‘Evidence Synthesis for Constructing Directed Acyclic Graphs’ (ESC-DAGs)’.

Methods

ESC-DAGs operates on empirical studies identified by a literature search, ideally a novel systematic review or review of systematic reviews. It involves three key stages: (i) the conclusions of each study are ‘mapped’ into a DAG; (ii) the causal structures in these DAGs are systematically assessed using several causal inference principles and are corrected accordingly; (iii) the resulting DAGs are then synthesised into one or more ‘integrated DAGs’. This demonstration article didactically applies ESC-DAGs to the literature on parental influences on offspring alcohol use during adolescence.

Conclusions

ESC-DAGs is a practical, systematic and transparent approach for developing DAGs from background knowledge. These DAGs can then direct primary data analysis and DAG-based sensitivity analysis. ESC-DAGs has a modular design to allow researchers who are experienced DAG users to both use and improve upon the approach. It is also accessible to researchers with limited experience of DAGs or evidence synthesis.

From Many to One to Many-the Search for Causes of Psychiatric Illness

The search for the causes of medical and psychiatric disorders has gone through 3 historical phases. First, up until the mid-19th century, causes of illness were anecdotally recorded from individual cases, resulting in long and diverse lists for all disorders. Second, in the latter half of the 19th century, with the use of microbiological methods, single causes were found for many infectious diseases that led to specific diagnostic tests, effective preventions, and, in some cases, treatments. Causal thinking in medicine shifted from the earlier multicausal approaches to monocausal theories of etiology. Indeed, proving monocausal etiology became a way to establish the legitimacy of a disorder. Through the writings of Kahlbaum and Hecker, psychiatry was deeply influenced by this monocausal perspective, the importance of which was substantially amplified by a twist of fate: the increasing clinical importance of general paresis of the insane throughout the 19th century and the eventual proof that it too was a monocausal condition. However, in the mid-20th century, the third phase began. With decreasing deaths from infectious diseases, epidemiology and clinical medicine shifted to a chronic disease model in which paradigmatic disorders, such as cancer and cardiovascular disease, were shown to be highly multicausal. Biostatistics evolved from deterministic to probabilistic models of disease risk factors. Paradoxically, at this time, biological psychiatry, then rising to dominance in American psychiatry, vigorously pursued monocausal theories, first of neurochemical origin and then of genetic origin. We were trying to establish the legitimacy of our field by pursuing an outmoded model-that "real" diseases are monocausal. Despite ample evidence to the contrary, monocausal thinking continues to influence our field, for example, in the popular but improbable view that we can, with a few key advances, move easily from descriptive to etiologically based diagnoses.

Post-Modern Epidemiology: When Methods Meet Matter

In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of noncommunicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of noncommunicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post-Modern Epidemiology world.

Biodiversity and socioeconomics in the city: a review of the luxury effect

The ecological dynamics of cities are influenced not only by geophysical and biological factors, but also by aspects of human society. In cities around the world, a pattern of higher biodiversity in affluent neighbourhoods has been termed ‘the luxury effect'. The luxury effect has been found globally regarding plant diversity and canopy or vegetative cover. Fewer studies have considered the luxury effect and animals, yet it has been recognized in the distributions of birds, bats, lizards and indoor arthropods. Higher socioeconomic status correlates with higher biodiversity resulting from many interacting factors—the creation and maintenance of green space on private and public lands, the tendency of both humans and other species to favour environmentally desirable areas, while avoiding environmental burdens, as well as enduring legacy effects. The luxury effect is amplified in arid cities and as neighbourhoods age, and reduced in tropical areas. Where the luxury effect exists, benefits of urban biodiversity are unequally distributed, particularly in low-income neighbourhoods with higher minority populations. The equal distribution of biodiversity in cities, and thus the elimination of the luxury effect, is a worthy societal goal.

Cordons Sanitaires and the Rationalisation Process in Southern Europe (Nineteenth-Century Majorca)

Never before the nineteenth century had Europeans, especially in the south, adopted cordons sanitaires in such great numbers or at such a fast rate. This article aims to analyse the process of the rationalisation and militarisation of the cordons sanitaires imposed in the fight against epidemics during the nineteenth century on the Mediterranean island of Majorca (Spain). These cordons should be understood as a declaration of war by the authorities on emerging epidemics. Epidemics could generate sudden and intolerably high rises in mortality that the new liberal citizenship found unacceptable. Toleration of this type of measure was the result of a general consensus, with hardly any opposition, which not only obtained the support of scientists (especially in the field of medicine) but also of most of the local and provincial political elite, and even of the population at large.

Cholera

Cholera is an acute disease of the gastrointestinal tract caused by Vibrio cholerae. Cholera was localized in Asia until 1817, when a first pandemic spread from India to several other regions of the world. After this appearance, six additional major pandemics occurred during the 19th and 20th centuries, the latest of which originated in Indonesia in the 1960s and is still ongoing. In 1854, a cholera outbreak in Soho, London, was investigated by the English physician John Snow (1813 to 1858). He described the time course of the outbreak, managed to understand its routes of transmission, and suggested effective measures to stop its spread, giving rise to modern infectious disease epidemiology. The germ responsible for cholera was discovered twice: first by the Italian physician Filippo Pacini during an outbreak in Florence, Italy, in 1854, and then independently by Robert Koch in India in 1883, thus favoring the germ theory over the miasma theory of disease. Unlike many other infectious diseases, such as plague, smallpox, and poliomyelitis, cholera persists as a huge public health problem worldwide, even though there are effective methods for its prevention and treatment. The main reasons for its persistence are socioeconomic rather than purely biological; cholera flourishes where there are unsatisfactory hygienic conditions and where a breakdown of already fragile sanitation and health infrastructure occurs because of natural disasters or humanitarian crises.

Clinical outcomes research in gynecologic oncology

Clinical outcomes research seeks to understand the real-world manifestations of clinical care. In particular, outcomes research seeks to reveal the effects of pharmaceutical, procedural, and structural aspects of healthcare on patient outcomes, including mortality, disease control, toxicity, cost, and quality of life. Although outcomes research can utilize interventional study designs, insightful use of observational data is a defining feature of this field. Many questions in gynecologic oncology are not amenable to investigation in randomized clinical trials due to cost, feasibility, or ethical concerns. When a randomized trial is not practical or has not yet been conducted, well-designed observational studies have the potential to provide the best available evidence about the effects of clinical care. Such studies may use surveys, medical records, disease registries, and a variety of administrative data sources. Even when a randomized trial has been conducted, observational studies can be used to estimate the real-world effect of an intervention, which may differ from the results obtained in the controlled setting of a clinical trial. This article reviews the goals, methodologies, data sources, and limitations of clinical outcomes research, with a focus on gynecologic oncology.

Questioning the application of risk of bias tools in appraising evidence from natural experimental studies: critical reflections on Benton et al., IJBNPA 2016

We recently read the article by Benton et al. which reviewed risk of bias in natural experimental studies investigating the impact of the built environment on physical activity (Benton et al., 2016; Int J Behav Nutr Phys Act 13:107). As a technical exercise in assessing risk of bias to understand study quality, we found the results of this study both interesting and potentially useful. However, it prompted a number of concerns with the use of risk of bias tools for assessing the quality of evidence from studies exploiting natural experiments. As we discuss in this commentary, the rigid application of such tools could have adverse effects on the uptake and use of natural experiments in population health research and practice.

Epidemiology: Then and Now

Twenty-five years ago, on the 75th anniversary of the Johns Hopkins Bloomberg School of Public Health, I noted that epidemiologic research was moving away from the traditional approaches used to investigate "epidemics" and their close relationship with preventive medicine. Twenty-five years later, the role of epidemiology as an important contribution to human population research, preventive medicine, and public health is under substantial pressure because of the emphasis on "big data," phenomenology, and personalized medical therapies. Epidemiology is the study of epidemics. The primary role of epidemiology is to identify the epidemics and parameters of interest of host, agent, and environment and to generate and test hypotheses in search of causal pathways. Almost all diseases have a specific distribution in relation to time, place, and person and specific "causes" with high effect sizes. Epidemiology then uses such information to develop interventions and test (through clinical trials and natural experiments) their efficacy and effectiveness. Epidemiology is dependent on new technologies to evaluate improved measurements of host (genomics), epigenetics, identification of agents (metabolomics, proteomics), new technology to evaluate both physical and social environment, and modern methods of data collection. Epidemiology does poorly in studying anything other than epidemics and collections of numerators and denominators without specific hypotheses even with improved statistical methodologies.

Meddling Vibrio cholerae Murmurs: A Neoteric Advancement in Cholera Research

Cholera, a known diarrheal disease is associated with various risk factors like hypovolemic shock, rice watery stools, and death in developing countries. The overuse of antibiotics to treat cholera imposed a selective pressure for the emergence and spread of multi-drug resistant Vibrio cholerae strains. The failure of conventional antimicrobial therapy urged the researchers to find an alternative therapy that could meddle the cholera murmurs (Quorum Sensing). It seems to effectively overcome the conventional cholera therapies in parallel to decrease the morbidity and mortality rate in the developing countries. The paramount objective of this review essentially focuses on the different Quorum Sensing (QS) regulatory switches governing virulence and pathogenicity of Vibrio cholerae. This review also provides an insight into the plausible QS targets that could be exploited to bring about a breakthrough to the prevailing cholera therapy.

1831: the map that launched the idea of global health

Today we take for granted the idea of global health, of disease as an international event. Increasingly, we assume as well that the international spread of disease can be traced to human travel patterns as well as to recurring environmental conditions. Perversely, the idea of ‘global health’ and its inverse, global disease, owes little to the three-dimensional imaging of the planet and almost everything to the two-dimensional plane of the map. Here the idea of global disease is traced from its beginnings in the 18th century to its 19th-century introduction in maps of the first cholera pandemic. This global perspective, and the responsibilities it promoted among civil officials, can be seen in modern studies of cancer, influenza and other conditions with both environmental foundations and international presence.

John Snow's legacy: epidemiology without borders

This Review provides abstracts from a meeting held at the London School of Hygiene and Tropical Medicine, on April 11-12, 2013, to celebrate the legacy of John Snow. They describe conventional and unconventional applications of epidemiological methods to problems ranging from diarrhoeal disease, mental health, cancer, and accident care, to education, poverty, financial networks, crime, and violence. Common themes appear throughout, including recognition of the importance of Snow's example, the philosophical and practical implications of assessment of causality, and an emphasis on the evaluation of preventive, ameliorative, and curative interventions, in a wide variety of medical and societal examples. Almost all self-described epidemiologists nowadays work within the health arena, and this is the focus of most of the societies, journals, and courses that carry the name epidemiology. The range of applications evident in these contributions might encourage some of these institutions to consider broadening their remits. In so doing, they may contribute more directly to, and learn from, non-health-related areas that use the language and methods of epidemiology to address many important problems now facing the world.

Using natural experiments to evaluate population health interventions: new Medical Research Council guidance

Natural experimental studies are often recommended as a way of understanding the health impact of policies and other large scale interventions. Although they have certain advantages over planned experiments, and may be the only option when it is impossible to manipulate exposure to the intervention, natural experimental studies are more susceptible to bias. This paper introduces new guidance from the Medical Research Council to help researchers and users, funders and publishers of research evidence make the best use of natural experimental approaches to evaluating population health interventions. The guidance emphasises that natural experiments can provide convincing evidence of impact even when effects are small or take time to appear. However, a good understanding is needed of the process determining exposure to the intervention, and careful choice and combination of methods, testing of assumptions and transparent reporting is vital. More could be learnt from natural experiments in future as experience of promising but lesser used methods accumulates.

Statistical cures and other fallacies

Relapse rates and thus the impact of therapies have been decreasing. Why they decline and the impact on our ability to understand which treatments are better require more than simple math. The objective of this review is to discuss the impact of regression to the mean, changes in outcome effects and how we compare outcomes over time and among studies. This paper provides discourse on the topics of regression to the mean, some examples of the pitfalls of changes and some difficulties in the interpretation of the common percentage change in outcomes. The results show that we can often be deceived by what we think we see and they also demonstrate how such confusion evolves in the literature. This article aims to caution against the over-interpretation of changes from baseline, which are helped along by regression towards the mean and other factors. Furthermore, how we interpret changes from baseline requires care and not wishful thinking, coupled with careful digestion of seemingly reasonable explications of results.

Assessing intrauterine influences on offspring health outcomes: can epidemiological studies yield robust findings?

The influence of factors acting during the intrauterine period on health outcomes of offspring is of considerable research and public health interest. There are, however, methodological challenges in establishing robust causal links, because exposures often act many decades before outcomes of interest, may act before it is evident that women are pregnant and would enter pregnancy birth cohorts, and may also be strongly related to other factors, generating considerable degrees of potential confounding. The degree of confounding can sometimes be estimated by comparing the association between exposures experienced by the mother during pregnancy and outcomes among the offspring with the association of exposures experienced by the father during the pregnancy period and offspring outcomes. If the effects are due to an intrauterine exposure, then maternal exposure during pregnancy should have a clearly greater influence than paternal exposure. A different approach is that of Mendelian randomization, which utilizes genetic variants of known functional effect that can proxy for modifiable exposures. If carried by the mother, these variants would influence the intrauterine environment experienced by her offspring. These genetic variants are stable over time and can be assessed after pregnancy is complete or even after outcomes in the offspring have been observed. The variants would also not generally be related to potential confounding factors. Other epidemiological strategies are briefly reviewed. It is concluded that the naïve acceptance of findings utilizing conventional epidemiological methods in this setting is misplaced.

Rethinking John Snow's South London study: a Bayesian evaluation and recalculation

Famously, John Snow attempted to convince a critical professional audience that public water supplied to South London residents by private companies was a principal vector for the transmission of cholera. The result has been called the sine qua non of the "epidemiological imagination," a landmark study still taught today. In fact, Snow twice attempted to prove public water supplies spread cholera to the South London population. His first, published in 1855, suffered from an incomplete data set that limited its descriptive and predictive import. In 1856, armed with new data, Snow published a more definitive study. This paper describes a previously unacknowledged methodological and conceptual problem in Snow's 1856 argument. We review the context of the South London study, identify the problem and then correct it with an empirical Bayes estimation (EBE) approach. The result hopefully revitalizes Snow's research as a teaching case through the application of a contemporary statistical approach.

Indicators of socioeconomic position (part 1)

This glossary presents a comprehensive list of indicators of socioeconomic position used in health research. A description of what they intend to measure is given together with how data are elicited and the advantages and limitation of the indicators. The glossary is divided into two parts for journal publication but the intention is that it should be used as one piece. The second part highlights a life course approach and will be published in the next issue of the journal.

Behavioral science at the crossroads in public health: extending horizons, envisioning the future

The social and behavioral sciences are at a crossroads in public health. In this paper, we attempt to describe a path toward the further integration of the natural and behavioral sciences with respect to the study of behavior and health. Three innovations are proposed. First, we extend and modify the "stream of causation" metaphor along two axes: time, and levels of nested systems of social and biological organization. Second, we address the question of whether 'upstream' features of social context are causes of disease, fundamental or otherwise. Finally, we propose the concept of a risk regulator to advance the study of behavior and health in populations. To illustrate the potential of these innovations, we develop a multilevel framework for the study of health behaviors and obesity in social and biological context.

Advocacy for public health: a primer

Public health advocacy is the strategic use of news media to advance a public policy initiative, often in the face of opposition.

Is income inequality a determinant of population health? Part 2. U.S. National and regional trends in income inequality and age- and cause-specific mortality

This article describes U.S. income inequality and 100-year national and 30-year regional trends in age- and cause-specific mortality. There is little congruence between national trends in income inequality and age- or cause-specific mortality except perhaps for suicide and homicide. The variable trends in some causes of mortality may be associated regionally with income inequality. However, between 1978 and 2000 those regions experiencing the largest increases in income inequality had the largest declines in mortality (r= 0.81, p < 0.001). Understanding the social determinants of population health requires appreciating how broad indicators of social and economic conditions are related, at different times and places, to the levels and social distribution of major risk factors for particular health outcomes.