The future of epidemiology

Semiparametric regression and risk prediction with competing risks data under missing cause of failure

The cause of failure in cohort studies that involve competing risks is frequently incompletely observed. To address this, several methods have been proposed for the semiparametric proportional cause-specific hazards model under a missing at random assumption. However, these proposals provide inference for the regression coefficients only, and do not consider the infinite dimensional parameters, such as the covariate-specific cumulative incidence function. Nevertheless, the latter quantity is essential for risk prediction in modern medicine. In this paper we propose a unified framework for inference about both the regression coefficients of the proportional cause-specific hazards model and the covariate-specific cumulative incidence functions under missing at random cause of failure. Our approach is based on a novel computationally efficient maximum pseudo-partial-likelihood estimation method for the semiparametric proportional cause-specific hazards model. Using modern empirical process theory we derive the asymptotic properties of the proposed estimators for the regression coefficients and the covariate-specific cumulative incidence functions, and provide methodology for constructing simultaneous confidence bands for the latter. Simulation studies show that our estimators perform well even in the presence of a large fraction of missing cause of failures, and that the regression coefficient estimator can be substantially more efficient compared to the previously proposed augmented inverse probability weighting estimator. The method is applied using data from an HIV cohort study and a bladder cancer clinical trial.

Is there a duty to participate in digital epidemiology?

This paper poses the question of whether people have a duty to participate in digital epidemiology. While an implied duty to participate has been argued for in relation to biomedical research in general, digital epidemiology involves processing of non-medical, granular and proprietary data types that pose different risks to participants. We first describe traditional justifications for epidemiology that imply a duty to participate for the general public, which take account of the immediacy and plausibility of threats, and the identifiability of data. We then consider how these justifications translate to digital epidemiology, understood as an evolution of traditional epidemiology that includes personal and proprietary digital data alongside formal medical datasets. We consider the risks imposed by re-purposing such data for digital epidemiology and propose eight justificatory conditions that should be met in justifying a duty to participate for specific digital epidemiological studies. The conditions are then applied to three hypothetical cases involving usage of social media data for epidemiological purposes. We conclude with a list of questions to be considered in public negotiations of digital epidemiology, including the application of a duty to participate to third-party data controllers, and the important distinction between moral and legal obligations to participate in research.

Mapping epidemiology's past to inform its future: metaknowledge analysis of epidemiologic topics in leading journals, 1974-2013

An empiric perspective on what epidemiology has studied over time might inform discussions about future directions for the discipline. We aimed to identify the main areas of epidemiologic inquiry and determine how they evolved over time in 5 high-impact epidemiologic journals. We analyzed the titles and abstracts of 20,895 articles that were published between 1974 and 2013. In 5 time periods that reflected approximately equal numbers of articles, we identified the main topics by clustering terms based on co-occurrence. Infectious disease and cardiovascular disease epidemiology were the prevailing topics over the 5 periods. Cancer epidemiology was a major topic from 1974 to 2001 but disappeared thereafter. Nutritional epidemiology gained relative importance from 1974 to 2013. Environmental epidemiology appeared during 1996-2001 and continued to be important, whereas 2 clusters related to methodology and meta-analysis in genetics appeared during 2008-2013. Several areas of epidemiology, including injury or psychiatric epidemiology, did not make an appearance as major topics at any time. In an ancillary analysis of 6 high-impact general medicine journals, we found patterns of epidemiologic articles that were overall consistent with the findings in epidemiologic journals. This metaknowledge investigation allowed identification of the dominant topics in and conversely those that were absent from 5 major epidemiologic journals. We discuss implications for the field.

Establishing a field epidemiology elective for medical students in Kenya: a strategy for increasing public health awareness and workforce capacity

Medical students have limited exposure to field epidemiology, even though will assume public health roles after graduation. We established a 10-week elective in field epidemiology during medical school. Students attended one-week didactic sessions on epidemiology, and nine weeks in field placement sites. We administered pre- and post-tests to evaluate the training. We enrolled 34 students in 2011 and 2012. In 2011, we enrolled five of 24 applicants from a class of 280 medical students. In 2012, we enrolled 18 of 81 applicants from a class of 360 students; plus 11 who participated in the didactic sessions only. Among the 34 students who completed the didactic sessions, 74% were male, and their median age was 24 years (range: 22-26). The median pre-test score was 64% (range: 47-88%) and the median post-test score was 82% (range: 72-100%). Successful completion of the field projects was 100%. Six (30%) students were not aware of public health as a career option before this elective, 56% rated the field experience as outstanding, and 100% reported it increased their understanding of epidemiology. Implementing an elective in field epidemiology within the medical training is a highly acceptable strategy to increase awareness for public health among medical students.

An argument for a consequentialist epidemiology

Epidemiology is the study of the causes and distributions of diseases in human populations so that we may identify ways to prevent and control disease. Although this definition broadly serves us well, I suggest that in recent decades, our discipline's robust interest in identifying causes has come at the expense of a more rigorous engagement with the second part of our vision for ourselves-the intent for us to intervene-and that this approach threatens to diminish our field's relevance. I argue here for a consequentialist epidemiology, a formalization and recalibration of the philosophical foundations of our discipline. I discuss how epidemiology is, at its core, more comfortably a consequentialist, as opposed to a deontological, discipline. A more consequentialist approach to epidemiology has several implications. It clarifies our research priorities, offers a perspective on the place of novel epidemiologic approaches and a metric to evaluate the utility of new methods, elevates the importance of global health and considerations about equity to the discipline, brings into sharp focus our engagement in implementation and translational science, and has implications for how we teach our students. I intend this article to be a provocation that can help clarify our disciplinary intentions.

Using spatial analysis to predict health care use at the local level: a case study of type 2 diabetes medication use and its association with demographic change and socioeconomic status

Local health status and health care use may be negatively influenced by low local socio-economic profile, population decline and population ageing. To support the need for targeted local health care, we explored spatial patterns of type 2 diabetes mellitus (T2DM) drug use at local level and determined its association with local demographic, socio-economic and access to care variables. We assessed spatial variability in these associations. We estimated the five-year prevalence of T2DM drug use (2005-2009) in persons aged 45 years and older at four-digit postal code level using the University of Groningen pharmacy database IADB.nl. Statistics Netherlands supplied data on potential predictor variables. We assessed spatial clustering, correlations and estimated a multiple linear regression model and a geographically weighted regression (GWR) model. Prevalence of T2DM medicine use ranged from 2.0% to 25.4%. The regression model included the extent of population ageing, proportion of social welfare/benefits, proportion of low incomes and proportion of pensioners, all significant positive predictors of local T2DM drug use. The GWR model demonstrated considerable spatial variability in the association between T2DM drug use and above predictors and was more accurate. The findings demonstrate the added value of spatial analysis in predicting health care use at local level.

A systematic review of the application of spatial analysis in pharmacoepidemiologic research

PURPOSE

Although current reviews of the use of spatial analysis in general epidemiologic research illustrate an important and well-established role in exploring and predicting health, its application has not been reviewed in the subspecialty field of pharmacoepidemiology.

METHODS

We systematically reviewed the scientific literature to assess to what extent spatial analysis has been applied in pharmacoepidemiologic research and explored its potential added value.

RESULTS

A systematic search in PubMed and Embase/MEDLINE yielded 823 potentially relevant articles; 45 articles met our criteria for review. The studies were reviewed on study objective, applied spatial methods and units of analysis, and author-reported added value of the geographic approach used. Of the 45 included studies, 34 (76%) reported a geographic research objective. Comparative spatial methods were most often used (n = 25; 56%). Eleven studies used spatial statistics (32%); cluster analysis (n = 5) and aggregate data analysis (n = 4) being most common. Mapping was done in 15 studies (33%). The most common added value reported was to aid the planning of health policies and interventions (n = 24; 53%). A minority of pharmacoepidemiologic studies used a geographic approach and the applied methods were less advanced compared with the broader field of epidemiology.

CONCLUSIONS

Further advancements are needed to incorporate currently available spatial techniques to impact health care planning.

Bridging the gap between knowledge and health: the epidemiologist as Accountable Health Advocate ("AHA!")

Epidemiology occupies a unique role as a knowledge-generating scientific discipline with roots in the knowledge translation of public health practice. As our fund of incompletely-translated knowledge expands and as budgets for health research contract, epidemiology must rediscover and adapt its historical skill set in knowledge translation. The existing incentive structures of academic epidemiology - designed largely for knowledge generation - are ill-equipped to train and develop epidemiologists as knowledge translators. A useful heuristic is the epidemiologist as Accountable Health Advocate (AHA) who enables society to judge the value of research, develops new methods to translate existing knowledge into improved health, and actively engages with policymakers and society. Changes to incentive structures could include novel funding streams (and review), alternative publication practices, and parallel frameworks for professional advancement and promotion.

Burden of disease, health indicators and challenges for epidemiology in North America

BACKGROUND

Commissioned by the International Epidemiological Association, this article is part of a series on burden of disease, health indicators and the challenges faced by epidemiologists in bringing their discoveries to provide equitable benefit to the populations in their regions and globally. This report covers the health status and epidemiological capacity in the North American region (USA and Canada).

METHODS

We assessed data from country-specific sources to identify health priorities and areas of greatest need for modifiable risk factors. We examined inequalities in health as a function of social deprivation. We also reviewed information on epidemiological capacity building and scientific contributions by epidemiologists in the region.

FINDINGS

The USA and Canada enjoy technologically advanced healthcare systems that, in principle, prioritize preventive services. Both countries experience a life expectancy at birth that is higher than the global mean. Health indicator measures are consistently worse in the USA than in Canada for many outcomes, although typically by only marginal amounts. Socio-economic and racial/ethnic disparities in indicators exist for many diseases and risk factors in the USA. To a lesser extent, these social inequalities also exist in Canada, particularly among the Aboriginal populations. Epidemiology is a well-established discipline in the region, with many degree-granting schools, societies and job opportunities in the public and private sectors. North American epidemiologists have made important contributions in disease control and prevention and provide nearly a third of the global scientific output via published papers.

CONCLUSIONS

Critical challenges for North American epidemiologists include social determinants of disease distribution and the underlying inequalities in access to and benefit from preventive services and healthcare, particularly in the USA. The gains in life expectancy also underscore the need for research on health promotion and prevention of disease and disability in older adults. The diversity in epidemiological subspecialties poses new challenges in training and accreditation and has occurred in parallel with a decrease in research funding.

How epidemiology has contributed to a better understanding of skin disease

Epidemiology literally means "the study of what is upon the people." It puts the individual's condition in a population context and is the path to disease prevention. In the first part of this review, important aspects of epidemiology are discussed. Fundamentals of epidemiologic research include the measurement of occurrence of an event (prevalence and incidence) and the identification of factors that are associated with this event. The main study designs in observational studies are cohort, case-control, and cross-sectional studies, all of which have intrinsic strengths and limitations. These limitations include a variety of biases, which can be regrouped into selection bias, information bias, and confounding. The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist is an important tool to further improve the reporting and quality of epidemiologic studies, and it is introduced. In the second part of this review, practical examples are presented, illustrating how dermatoepidemiology has contributed to an improved understanding of skin diseases and patient care, specifically in the case of melanoma therapy, serious cutaneous adverse reactions, Lyme disease, long-term safety of psoralin plus UVA (PUVA), teratogenicity of isotretinoin, and comorbidities in psoriasis.

Public Health Research Priorities For The Future

The last century of innovative public health discoveries has led most of the world's population to lead longer, healthier lives. Yet, the future holds some of the greatest public health challenges in mankind's history. Global disparities in health; medication safety; climate change; epidemics of obesity and diabetes; an aging world demographic; and emerging infections all represent problems requiring scientific solutions. The solutions to these problems, like the solutions to those in the last century that contributed so greatly to our quality of life, will require paradigm-shifting innovation. To maximize individual innovative potential, one strategy is formal instruction in the methods of innovative thinking. Teaching innovative thinking is rarely integrated into science training. However 40 years of accumulated evidence suggests that formal instruction results in improved thinking skills. I describe here some of the methods integrated into a course for graduate and professional health science students entitled Innovative Thinking. The curriculum consists of three components: recognizing and finding alternatives to habitual cognitive patterns; learning to use tools that enhance idea generation and originality; and harmonizing divergent thinking with the process of convergent thinking that is central to the scientific method. To build more innovative environments, institutions can promote team science, fund staged scientific designs that are heavy on early prototypes, reward and grow the training programs of past innovators, and become less risk averse. Although public health has accomplished much, it must continue to battle major, growing causes of disease and disability. Innovation is the engine of scientific discovery. Releasing the great potential for discovery in all of us must be central to forwarding health and prosperity in the world.

The emergence of translational epidemiology: from scientific discovery to population health impact

Recent emphasis on translational research (TR) is highlighting the role of epidemiology in translating scientific discoveries into population health impact. The authors present applications of epidemiology in TR through 4 phases designated T1-T4, illustrated by examples from human genomics. In T1, epidemiology explores the role of a basic scientific discovery (e.g., a disease risk factor or biomarker) in developing a "candidate application" for use in practice (e.g., a test used to guide interventions). In T2, epidemiology can help to evaluate the efficacy of a candidate application by using observational studies and randomized controlled trials. In T3, epidemiology can help to assess facilitators and barriers for uptake and implementation of candidate applications in practice. In T4, epidemiology can help to assess the impact of using candidate applications on population health outcomes. Epidemiology also has a leading role in knowledge synthesis, especially using quantitative methods (e.g., meta-analysis). To explore the emergence of TR in epidemiology, the authors compared articles published in selected issues of the Journal in 1999 and 2009. The proportion of articles identified as translational doubled from 16% (11/69) in 1999 to 33% (22/66) in 2009 (P = 0.02). Epidemiology is increasingly recognized as an important component of TR. By quantifying and integrating knowledge across disciplines, epidemiology provides crucial methods and tools for TR.