Commentary: Epidemiology in the era of big data

The use of Logic regression in epidemiologic studies to investigate multiple binary exposures: an example of occupation history and amyotrophic lateral sclerosis

Investigating the joint exposure to several risk factors is becoming a key component of epidemiologic studies. Individuals are exposed to multiple factors, often simultaneously, and evaluating patterns of exposures and high-dimension interactions may allow for a better understanding of health risks at the individual level. When jointly evaluating high-dimensional exposures, common statistical methods should be integrated with machine learning techniques that may better account for complex settings. Among these, Logic regression was developed to investigate a large number of binary exposures as they relate to a given outcome. This method may be of interest in several public health settings, yet has never been presented to an epidemiologic audience. In this paper, we review and discuss Logic regression as a potential tool for epidemiological studies, using an example of occupation history (68 binary exposures of primary occupations) and amyotrophic lateral sclerosis in a population-based Danish cohort. Logic regression identifies predictors that are Boolean combinations of the original (binary) exposures, fully operating within the regression framework of interest (e.g. linear, logistic). Combinations of exposures are graphically presented as Logic trees, and techniques for selecting the best Logic model are available and of high importance. While highlighting several advantages of the method, we also discuss specific drawbacks and practical issues that should be considered when using Logic regression in population-based studies. With this paper, we encourage researchers to explore the use of machine learning techniques when evaluating large-dimensional epidemiologic data, as well as advocate the need of further methodological work in the area.

Using mobile location data in biomedical research while preserving privacy

Location data are becoming easier to obtain and are now bundled with other metadata in a variety of biomedical research applications. At the same time, the level of sophistication required to protect patient privacy is also increasing. In this article, we provide guidance for institutional review boards (IRBs) to make informed decisions about privacy protections in protocols involving location data. We provide an overview of some of the major categories of technical algorithms and medical-legal tools at the disposal of investigators, as well as the shortcomings of each. Although there is no "one size fits all" approach to privacy protection, this article attempts to describe a set of practical considerations that can be used by investigators, journal editors, and IRBs.

What Can We Learn About Drug Safety and Other Effects in the Era of Electronic Health Records and Big Data That We Would Not Be Able to Learn From Classic Epidemiology?

As more and more health systems have converted to the use of electronic health records, the amount of searchable and analyzable data is exploding. This includes not just provider or laboratory created data but also data collected by instruments, personal devices, and patients themselves, among others. This has led to more attention being paid to the analysis of these data to answer previously unaddressed questions. This is especially important given the number of therapies previously found to be beneficial in clinical trials that are currently being re-scrutinized. Because there are orders of magnitude more information contained in these data sets, a fundamentally different approach needs to be taken to their processing and analysis and the generation of knowledge. Health care and medicine are drivers of this phenomenon and will ultimately be the main beneficiaries. Concurrently, many different types of questions can now be asked using these data sets. Research groups have become increasingly active in mining large data sets, including nationwide health care databases, to learn about associations of medication use and various unrelated diseases such as cancer. Given the recent increase in research activity in this area, its promise to radically change clinical research, and the relative lack of widespread knowledge about its potential and advances, we surveyed the available literature to understand the strengths and limitations of these new tools. We also outline new databases and techniques that are available to researchers worldwide, with special focus on work pertaining to the broad and rapid monitoring of drug safety and secondary effects.

Ripe for Disruption? Adopting Nurse-Led Data Science and Artificial Intelligence to Predict and Reduce Hospital-Acquired Outcomes in the Learning Health System

Nurse leaders are dually responsible for resource stewardship and the delivery of high-quality care. However, methods to identify patient risk for hospital-acquired conditions are often outdated and crude. Although hospitals and health systems have begun to use data science and artificial intelligence in physician-led projects, these innovative methods have not seen adoption in nursing. We propose the Petri dish model, a theoretical hybrid model, which combines population ecology theory and human factors theory to explain the cost/benefit dynamics influencing the slow adoption of data science for hospital-based nursing. The proliferation of nurse-led data science in health systems may be facing several barriers: a scarcity of doctorally prepared nurse scientists with expertise in data science; internal structural inertia; an unaligned national "precision health" strategy; and a federal reimbursement landscape, which constrains-but does not negate the hard dollar business case. Nurse executives have several options: deferring adoption, outsourcing services, and investing in internal infrastructure to develop and implement risk models. The latter offers the best performing models. Progress in nurse-led data science work has been sluggish. Balanced partnerships with physician experts and organizational stakeholders are needed, as is a balanced PhD-DNP research-practice collaboration model.

Huntington's disease in the United States: Variation by demographic and socioeconomic factors

BACKGROUND

Despite extensive research regarding the etiology of Huntington's disease, relatively little is known about the epidemiology of this rare disorder, particularly in the United States where there are no national-scale estimates of the disease.

OBJECTIVES

To provide national-scale estimates of Huntington's disease in a U.S. population and to test whether disease rates are increasing, and whether frequency varies by race, ethnicity, or other factors.

METHODS

Using an insurance database of over 67 million enrollees, we retrospectively identified a cohort of 3,707 individuals diagnosed with Huntington's disease between 2003 and 2016. We estimated annual incidence, annual diagnostic frequency, and tested for trends over time and differences in diagnostic frequency by sociodemographic characteristics.

RESULTS

During the observation period, the age-adjusted cumulative incidence rate was1.22 per 100,000 persons (95% confidence interval: 1.53, 1.65), and age-adjusted diagnostic frequency was 6.52 per 100,000 persons (95% confidence interval: 5.31, 5.66); both rates remained relatively stable over the 14-year period. We identified several previously unreported differences in Huntington's disease frequency by self-reported sex, income, and race/ethnicity. However, racial/ethnic differences were of lower magnitude than have previously been reported in other country-level studies.

CONCLUSIONS

In these large-scale estimates of U.S. Huntington's disease epidemiology, we found stable disease frequency rates that varied by several sociodemographic factors. These findings suggest that disease patterns may be more driven by social or environmental factors than has previously been appreciated. Results further demonstrate the potential utility of administrative Big Data in rare disease epidemiology when other data sources are unavailable. © 2019 International Parkinson and Movement Disorder Society.

Systems Modeling to Advance the Promise of Data Science in Epidemiology

Systems science models use computer-based algorithms to model dynamic interactions between study units within and across levels and are characterized by nonlinear and feedback processes. They are particularly valuable approaches that complement the traditional epidemiologic toolbox in cases in which real data are not available and in cases in which traditional epidemiologic methods are limited by issues such as interference, spatial dependence, and dynamic feedback processes. In this commentary, we propose 2 key contributions that systems models can make to epidemiology: 1) the ability to test assumptions about underlying mechanisms that give rise to population distributions of disease; and 2) help in identifying the types of interventions that have the greatest potential to reduce population rates of disease in the future or in new sites where they have not yet been implemented. We discuss central challenges in the application of systems science approaches in epidemiology, propose potential solutions, and predict future developments in the role that systems science can play in epidemiology.

Sobre uma política de ciência e tecnologia para a saúde

RESUMO Frente ao conjunto de políticas de ciência e tecnologia existentes no Brasil, o texto reivindica um olhar diferenciado sobre a política de pesquisa em saúde. Isso decorre de sua magnitude física, de sua tradição histórica e de sua articulação com uma política pública de saúde na qual a intersetorialidade é valorizada. O texto se divide em três partes, precedidas de uma advertência sobre o impacto da conjuntura atual do País sobre a política geral de ciência e tecnologia. Em primeiro lugar, propõe uma abordagem metodológica para a definição das fronteiras da pesquisa em saúde. Em seguida, reivindica para o campo da saúde coletiva um papel de protagonismo na construção dessa política. Finalmente, apresenta e discute alguns desafios atuais postos para a política.

Sampling and Sampling Frames in Big Data Epidemiology

Purpose of Review

The 'big data' revolution affords the opportunity to reuse administrative datasets for public health research. While such datasets offer dramatically increased statistical power compared with conventional primary data collection, typically at much lower cost, their use also raises substantial inferential challenges. In particular, it can be difficult to make population inferences because the sampling frames for many administrative datasets are undefined. We reviewed options for accounting for sampling in big data epidemiology.

Recent Findings

We identified three common strategies for accounting for sampling when the data available were not collected from a deliberately constructed sample: 1) explicitly reconstruct the sampling frame, 2) test the potential impacts of sampling using sensitivity analyses, and 3) limit inference to sample.

Summary

Inference from big data can be challenging because the impacts of sampling are unclear. Attention to sampling frames can minimize risks of bias.

Can online self-reports assist in real-time identification of influenza vaccination uptake? A cross-sectional study of influenza vaccine-related tweets in the USA, 2013-2017

INTRODUCTION

The Centers for Disease Control and Prevention (CDC) spend significant time and resources to track influenza vaccination coverage each influenza season using national surveys. Emerging data from social media provide an alternative solution to surveillance at both national and local levels of influenza vaccination coverage in near real time.

OBJECTIVES

This study aimed to characterise and analyse the vaccinated population from temporal, demographical and geographical perspectives using automatic classification of vaccination-related Twitter data.

METHODS

In this cross-sectional study, we continuously collected tweets containing both influenza-related terms and vaccine-related terms covering four consecutive influenza seasons from 2013 to 2017. We created a machine learning classifier to identify relevant tweets, then evaluated the approach by comparing to data from the CDC's FluVaxView. We limited our analysis to tweets geolocated within the USA.

RESULTS

We assessed 1 124 839 tweets. We found strong correlations of 0.799 between monthly Twitter estimates and CDC, with correlations as high as 0.950 in individual influenza seasons. We also found that our approach obtained geographical correlations of 0.387 at the US state level and 0.467 at the regional level. Finally, we found a higher level of influenza vaccine tweets among female users than male users, also consistent with the results of CDC surveys on vaccine uptake.

CONCLUSION

Significant correlations between Twitter data and CDC data show the potential of using social media for vaccination surveillance. Temporal variability is captured better than geographical and demographical variability. We discuss potential paths forward for leveraging this approach.

A Delphi study to build consensus on the definition and use of big data in obesity research

BACKGROUND

'Big data' has great potential to help address the global health challenge of obesity. However, lack of clarity with regard to the definition of big data and frameworks for effectively using big data in the context of obesity research may be hindering progress. The aim of this study was to establish agreed approaches for the use of big data in obesity-related research.

METHODS

A Delphi method of consensus development was used, comprising three survey rounds. In Round 1, participants were asked to rate agreement/disagreement with 77 statements across seven domains relating to definitions of, and approaches to, using big data in the context of obesity research. Participants were also asked to contribute further ideas in relation to these topics, which were incorporated as new statements (n = 8) in Round 2. In Rounds 2 and 3 participants re-appraised their ratings in view of the group consensus.

RESULTS

Ninety-six experts active in obesity-related research were invited to participate. Of these, 36/96 completed Round 1 (37.5% response rate), 29/36 completed Round 2 (80.6% response rate) and 26/29 completed Round 3 (89.7% response rate). Consensus (defined as > 70% agreement) was achieved for 90.6% (n = 77) of statements, with 100% consensus achieved for the Definition of Big Data, Data Governance, and Quality and Inference domains.

CONCLUSIONS

Experts agreed that big data was more nuanced than the oft-cited definition of 'volume, variety and velocity', and includes quantitative, qualitative, observational or intervention data from a range of sources that have been collected for research or other purposes. Experts repeatedly called for third party action, for example to develop frameworks for reporting and ethics, to clarify data governance requirements, to support training and skill development and to facilitate sharing of big data. Further advocacy will be required to encourage organisations to adopt these roles.

Pharmacological therapy and blood pressure control in primary health care sites in China: data from 254,848 hypertensive patients

Background

Studies on pharmacological therapy and blood pressure (BP) control in primary health care sites of China are limited. We aimed to investigate drug use and compliance as well as compare BP control between pharmacological therapies for lowering BP in hypertensive population serviced by these sites.

Methods

This is a 1-year cohort study using electronic health care records from the National Primary Public Health Services of China. For patients with antihypertensive drugs at the first follow-up, we defined compliance with treatment as a continued treatment with the same specified class of agents at next three follow-ups. In those with compliance, BP control was defined as systolic BP <140 mmHg and diastolic BP <90 mmHg in four follow-ups within 1 year.

Results

Primary health care sites of four areas managed 254,848 hypertensive patients aged ≥35 years. At the first follow-up, 50.2% of the patients took medicines for lowering BP. In those, calcium channel antagonist monotherapy was the most common medicine in urban areas (57.1% vs 15.6% in rural areas, P<0.001); however, the most common one was single-pill combinations including diuretics and non-first-line drugs in rural areas (34.4% vs 10.7% in urban areas, P<0.001). Compliance was 79.9% and 53.2% for single- and multiple-pill combinations in first-line drugs; this rate was 69.5% and 45.0% in regimens combined with non-first-line drugs, respectively. Compared with calcium channel antagonists, diuretics monotherapy increased the overall BP control by 11% (risk ratio, 1.11; 95% confidence interval, 1.08 to 1.13), but it was used in few patients (3.3%); first-line multiple-pill combinations significantly decreased BP control by 20% to 28% in three less urbanized areas, but a similar BP control was achieved in the highly urbanized area.

Conclusion

Our study indicated that drug use such as diuretics could be strengthened in primary health care sites and combined therapy may be improved particularly in less urbanized areas.

A review of Big Data analytics and potential for implementation in the delivery of global neurosurgery

Global access to neurosurgical care is still a work in progress, with many patients in low-income countries not able to access potentially lifesaving neurosurgical procedures. “Big Data” is an increasingly popular data collection and analytical technique predicated on collecting large amounts of data across multiple data sources and types for future analysis. The potential applications of Big Data to global outreach neurosurgery are myriad: from assessing the overall burden of neurosurgical disease to planning cost-effective improvements in access to neurosurgical care, and collecting data on conditions which are rare in developed countries. Although some global neurosurgical outreach programs have intelligently implemented Big Data principles in their global neurosurgery initiatives already, there is still significant progress that remains to be made. Big Data has the potential to drive the efficient improvement of access to neurosurgical care across low- and medium-income countries.

Posttraumatic Stress Disorder and Death From Suicide

PURPOSE OF REVIEW

This review summarizes the increasing public health concern about PTSD and suicide, and the population-based studies that have examined this association. Further, we discuss methodological issues that provide important context for the examination of this association.

RECENT FINDINGS

The majority of epidemiologic studies have shown that PTSD is associated with an increased risk of suicide; however, a notable minority of studies have documented a decreased risk of suicide among persons with PTSD. Methodological (e.g., sample size and misclassification) and etiologic issues (e.g., complicated psychiatric comorbidity) may explain the conflicting evidence. PTSD may be associated with an increased risk of suicide, but further research is needed. Increasing the use of appropriate methods (e.g., marginal structural models that can evaluate both confounding and effect modification, machine learning methods, quantification of systematic error) will strengthen the evidence base and advance our understanding.

Measuring social integration and tie strength with smartphone and survey data

Recordings of smartphone use for contacts are increasingly being used as alternative or supplementary measurement methods for social interactions and social relations in the health sciences. Less work has been done to understand how these measures compare with widely used survey-based information. Using data from the Copenhagen Network Study, we investigated whether derived survey and smartphone measures on two widely studied concepts; Social integration and Tie strength were associated. The study population included 737 college students (mean age 21.6 years, Standard deviation: 2.6), who were followed with surveys and continuous recordings of smartphone usage over a one-month period. We derived self-reported and smartphone measures of social integration (social role diversity, social network size), and tie strength (contact frequency, duration and tie reciprocity). Logistic regression models were used to assess the associations between smartphone derived and self-reported measures adjusting for gender, age and co-habitation. Larger call and text message networks were associated with having a high self-reported social role diversity, and a high self-reported social contact frequency was likewise associated with having both frequent call and text message interactions, longer call duration and a higher level of reciprocity in call and text message communication. Self-reported aspects of social relations and smartphone measures of social interactions have considerable overlap supporting a measurement of similar underlying concepts.

Characteristics of large patient-reported outcomes: Where can one million seizures get us?

Objective

To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.

Methods

Zero‐inflated negative binomial mixed‐effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed‐effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero‐inflated negative binomial and logistic mixed‐effects models, respectively.

Results

A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: “Overtired or irregular sleep,” “Bright or flashing lights,” and “Emotional stress” (p < 0.004).

Significance

This study explored a large cohort of patients with self‐reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient‐recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.

Big Data in Public Health: Terminology, Machine Learning, and Privacy

The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.

Big Data's Role in Precision Public Health

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

How Healthy Are Survey Respondents Compared with the General Population?: Using Survey-linked Death Records to Compare Mortality Outcomes

BACKGROUND

National surveys are used to capture US health trends and set clinical guidelines, yet the sampling frame often includes those in noninstitutional households, potentially missing those most vulnerable for poor health. Declining response rates in national surveys also represent a challenge, and existing inputs to survey weights have limitations. We compared mortality rates between those who respond to surveys and the general population over time.

METHODS

Survey respondents from 20 waves of the National Health Interview Survey from 1990 to 2009 who have been linked to death records through 31 December 2011 were included. For each cohort in the survey, we estimated their mortality rates along with that cohort's mortality rate in the census population using vital statistics records, and differences were examined using Poisson models.

RESULTS

In all years, survey respondents had lower mortality rates compared with the general population when data were both weighted and unweighted. Among men, survey respondents in the weighted sample had 0.86 (95% confidence interval = 0.853, 0.868) times the mortality rate of the general population (among women, RR = 0.887; 95% confidence interval, 0.879, 0.895). Differences in mortality are evident along all points of the life course. Differences have remained relatively stable over time.

CONCLUSION

Survey respondents have lower death rates than the general US population, suggesting that they are a systematically healthier source population. Incorporating nonhousehold samples and revised weighting strategies to account for sample frame exclusion and nonresponse may allow for more rigorous estimation of the US population's health.

Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics

The Hippocratic oath and the Belmont report articulate foundational principles for how physicians interact with patients and research subjects. The increasing use of big data and artificial intelligence techniques demands a re-examination of these principles in light of the potential issues surrounding privacy, confidentiality, data ownership, informed consent, epistemology, and inequities. Patients have strong opinions about these issues. Radiologists have a fiduciary responsibility to protect the interest of their patients. As such, the community of radiology leaders, ethicists, and informaticists must have a conversation about the appropriate way to deal with these issues and help lead the way in developing capabilities in the most just, ethical manner possible.

Model-based and design-based inference goals frame how to account for neighborhood clustering in studies of health in overlapping context types

Accounting for non-independence in health research often warrants attention. Particularly, the availability of geographic information systems data has increased the ease with which studies can add measures of the local "neighborhood" even if participant recruitment was through other contexts, such as schools or clinics. We highlight a tension between two perspectives that is often present, but particularly salient when more than one type of potentially health-relevant context is indexed (e.g., both neighborhood and school). On the one hand, a model-based perspective emphasizes the processes producing outcome variation, and observed data are used to make inference about that process. On the other hand, a design-based perspective emphasizes inference to a well-defined finite population, and is commonly invoked by those using complex survey samples or those with responsibility for the health of local residents. These two perspectives have divergent implications when deciding whether clustering must be accounted for analytically and how to select among candidate cluster definitions, though the perspectives are by no means monolithic. There are tensions within each perspective as well as between perspectives. We aim to provide insight into these perspectives and their implications for population health researchers. We focus on the crucial step of deciding which cluster definition or definitions to use at the analysis stage, as this has consequences for all subsequent analytic and interpretational challenges with potentially clustered data.

Translating Big Data into Smart Data for Veterinary Epidemiology

The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing “big” data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having “big data” to create “smart data,” with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.

Invited Commentary: The Tao of Clinical Cohort Analysis-When the Transitions That Can Be Spoken of Are Not the True Transitions

Patterns in risk-related behaviors identified using clinically deployed surveys may hold value for public health surveillance. However, because such surveys assess subjects only when subjects choose to visit clinics, clinical data are subject to variability in observation patterns that is not present in conventional longitudinal data sets in which research teams contact subjects at regular intervals. In this issue of the Journal, Wilkinson et al. (Am J Epidemiol. 2017;185(8):627-635) describe how they applied a latent transition analysis technique to surveillance data collected during clinic visits. In this commentary I discusses the selection bias that may arise in longitudinal analysis of clinical data due to subject-specific observation patterns, with particular focus on issues that may arise due to classifying successive clinical visits as waves. I suggest that quantitative bias analysis and inverse probability weighting may be useful techniques with which to assess and control bias in future latent transition analyses of clinical data.

mHealth and Application Technology Supporting Clinical Trials: Today's Limitations and Future Perspective of smartRCTs

Nowadays, applications (apps) for smartphones and tablets have become indispensable especially for young generations. The estimated number of mobile devices will exceed 2.16 billion in 2016. Over 2.2 million apps are available in the Google Play store^®, and about 1.8 million apps are available in the Apple App Store^®. Google and Apple distribute nearly 70,000 apps each in the category Health and Fitness, and about 33,000 and 46,000 each in medical apps. It seems like the willingness to use mHealth apps is high and the intention to share data for health research is existing. This leads to one conclusion: the time for app-accompanied clinical trials (smartRCTs) has come. In this perspective article, we would like to point out the stones put in the way while trying to implement apps in clinical research. Further, we try to offer a glimpse of what the future of smartRCT research may hold.

Contextual Correlates of Physical Activity among Older Adults: A Neighborhood Environment-Wide Association Study (NE-WAS)

Background: Few older adults achieve recommended physical activity levels. We conducted a "neighborhood environment-wide association study (NE-WAS)" of neighborhood influences on physical activity among older adults, analogous, in a genetic context, to a genome-wide association study.Methods: Physical Activity Scale for the Elderly (PASE) and sociodemographic data were collected via telephone survey of 3,497 residents of New York City aged 65 to 75 years. Using Geographic Information Systems, we created 337 variables describing each participant's residential neighborhood's built, social, and economic context. We used survey-weighted regression models adjusting for individual-level covariates to test for associations between each neighborhood variable and (i) total PASE score, (ii) gardening activity, (iii) walking, and (iv) housework (as a negative control). We also applied two "Big Data" analytic techniques, LASSO regression, and Random Forests, to algorithmically select neighborhood variables predictive of these four physical activity measures.Results: Of all 337 measures, proportion of residents living in extreme poverty was most strongly associated with total physical activity [-0.85; (95% confidence interval, -1.14 to -0.56) PASE units per 1% increase in proportion of residents living with household incomes less than half the federal poverty line]. Only neighborhood socioeconomic status and disorder measures were associated with total activity and gardening, whereas a broader range of measures was associated with walking. As expected, no neighborhood meaZsures were associated with housework after accounting for multiple comparisons.Conclusions: This systematic approach revealed patterns in the domains of neighborhood measures associated with physical activity.Impact: The NE-WAS approach appears to be a promising exploratory technique. Cancer Epidemiol Biomarkers Prev; 26(4); 495-504. ©2017 AACRSee all the articles in this CEBP Focus section, "Geospatial Approaches to Cancer Control and Population Sciences."

Ecoinformatics (Big Data) for Agricultural Entomology: Pitfalls, Progress, and Promise

Ecoinformatics, as defined in this review, is the use of preexisting data sets to address questions in ecology. We provide the first review of ecoinformatics methods in agricultural entomology. Ecoinformatics methods have been used to address the full range of questions studied by agricultural entomologists, enabled by the special opportunities associated with data sets, nearly all of which have been observational, that are larger and more diverse and that embrace larger spatial and temporal scales than most experimental studies do. We argue that ecoinformatics research methods and traditional, experimental research methods have strengths and weaknesses that are largely complementary. We address the important interpretational challenges associated with observational data sets, highlight common pitfalls, and propose some best practices for researchers using these methods. Ecoinformatics methods hold great promise as a vehicle for capitalizing on the explosion of data emanating from farmers, researchers, and the public, as novel sampling and sensing techniques are developed and digital data sharing becomes more widespread.

Toward Greater Implementation of the Exposome Research Paradigm within Environmental Epidemiology

Investigating a single environmental exposure in isolation does not reflect the actual human exposure circumstance nor does it capture the multifactorial etiology of health and disease. The exposome, defined as the totality of environmental exposures from conception onward, may advance our understanding of environmental contributors to disease by more fully assessing the multitude of human exposures across the life course. Implementation into studies of human health has been limited, in part owing to theoretical and practical challenges including a lack of infrastructure to support comprehensive exposure assessment, difficulty in differentiating physiologic variation from environmentally induced changes, and the need for study designs and analytic methods that accommodate specific aspects of the exposome, such as high-dimensional exposure data and multiple windows of susceptibility. Recommendations for greater data sharing and coordination, methods development, and acknowledgment and minimization of multiple types of measurement error are offered to encourage researchers to embark on exposome research to promote the environmental health and well-being of all populations.

Creating a Culture of Accurate and Precise Data

There are many stakeholders for school health data. Each one has a stake in the quality and accuracy of the health data collected and reported in schools. The joint NASN and NASSNC national school nurse data set initiative, Step Up & Be Counted!, heightens the need to assure accurate and precise data. The use of a standardized terminology allows the data on school health care delivered in local schools to be aggregated for use at the local, state, and national levels. The use of uniform terminology demands that data elements be defined and that accurate and reliable data are entered into the database. Barriers to accurate data are misunderstanding of accurate data needs, student caseloads that exceed the national recommendations, lack of electronic student health records, and electronic student health records that do not collect the indicators using the standardized terminology or definitions. The quality of the data that school nurses report and share has an impact at the personal, district, state, and national levels and influences the confidence and quality of the decisions made using that data.

Does case misclassification threaten the validity of studies investigating the relationship between neck manipulation and vertebral artery dissection stroke? Yes

Background

For patients and health care providers who are considering spinal manipulative therapy of the neck, it is crucial to establish if it is a trigger for cervical artery dissection and/or stroke, and if it is, the magnitude of the risk.

Discussion

We discuss the biological plausibility of how neck manipulation could cause cervical artery dissection. We also discuss how case misclassification threatens the validity of influential published studies that have investigated the relationship between neck manipulation and dissection. Our position is supported by the fact that the largest epidemiologic studies of neck manipulation safety with respect to neurological outcomes have relied on International Classification of Diseases-9 codes for case identification. However, the application of these codes in prior studies failed to identify dissections (rather than strokes in general) and so conclusions from those studies are invalid.

Conclusion

There are several methodological challenges to understanding the association between neck manipulation and vertebral artery dissection. Addressing these issues is critical because even a modest association between neck manipulation and cervical artery dissection could translate into a significant number of avoidable dissections given the widespread use of neck manipulation by providers from various backgrounds. We believe that valid case classification, accurate measurement of manipulative procedures, and addressing reverse causation bias should be top priorities for future research.

Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach

Medical research is increasingly becoming data-intensive; sensitive data are being re-used, linked and analysed on an unprecedented scale. The current EU data protection law reform has led to an intense debate about its potential effect on this processing of data in medical research. To contribute to this evolving debate, this paper reviews how the dominant 'consent or anonymise approach' is challenged in a data-intensive medical research context, and discusses possible ways forwards within the EU legal framework on data protection. A large part of the debate in literature focuses on the acceptability of adapting consent or anonymisation mechanisms to overcome the challenges within these approaches. We however believe that the search for ways forward within the consent or anonymise paradigm will become increasingly difficult. Therefore, we underline the necessity of an appropriate research exemption from consent for the use of sensitive personal data in medical research to take account of all legitimate interests. The appropriate conditions of such a research exemption are however subject to debate, and we expect that there will be minimal harmonisation of these conditions in the forthcoming EU Data Protection Regulation. Further deliberation is required to determine when a shift away from consent as a legal basis is necessary and proportional in a data-intensive medical research context, and what safeguards should be put in place when such a research exemption from consent is provided.

Precision medicine in cardiology

The cardiovascular research and clinical communities are ideally positioned to address the epidemic of noncommunicable causes of death, as well as advance our understanding of human health and disease, through the development and implementation of precision medicine. New tools will be needed for describing the cardiovascular health status of individuals and populations, including 'omic' data, exposome and social determinants of health, the microbiome, behaviours and motivations, patient-generated data, and the array of data in electronic medical records. Cardiovascular specialists can build on their experience and use precision medicine to facilitate discovery science and improve the efficiency of clinical research, with the goal of providing more precise information to improve the health of individuals and populations. Overcoming the barriers to implementing precision medicine will require addressing a range of technical and sociopolitical issues. Health care under precision medicine will become a more integrated, dynamic system, in which patients are no longer a passive entity on whom measurements are made, but instead are central stakeholders who contribute data and participate actively in shared decision-making. Many traditionally defined diseases have common mechanisms; therefore, elimination of a siloed approach to medicine will ultimately pave the path to the creation of a universal precision medicine environment.

From Smallpox to Big Data: The Next 100 Years of Epidemiologic Methods

For more than a century, epidemiology has seen major shifts in both focus and methodology. Taking into consideration the explosion of "big data," the advent of more sophisticated data collection and analytical tools, and the increased interest in evidence-based solutions, we present a framework that summarizes 3 fundamental domains of epidemiologic methods that are relevant for the understanding of both historical contributions and future directions in public health. First, the manner in which populations and their follow-up are defined is expanding, with greater interest in online populations whose definition does not fit the usual classification by person, place, and time. Second, traditional data collection methods, such as population-based surveillance and individual interviews, have been supplemented with advances in measurement. From biomarkers to mobile health, innovations in the measurement of exposures and diseases enable refined accuracy of data collection. Lastly, the comparison of populations is at the heart of epidemiologic methodology. Risk factor epidemiology, prediction methods, and causal inference strategies are areas in which the field is continuing to make significant contributions to public health. The framework presented herein articulates the multifaceted ways in which epidemiologic methods make such contributions and can continue to do so as we embark upon the next 100 years.

Using Electronic Health Records for Population Health Research: A Review of Methods and Applications

The use and functionality of electronic health records (EHRs) have increased rapidly in the past decade. Although the primary purpose of EHRs is clinical, researchers have used them to conduct epidemiologic investigations, ranging from cross-sectional studies within a given hospital to longitudinal studies on geographically distributed patients. Herein, we describe EHRs, examine their use in population health research, and compare them with traditional epidemiologic methods. We describe diverse research applications that benefit from the large sample sizes and generalizable patient populations afforded by EHRs. These have included reevaluation of prior findings, a range of diseases and subgroups, environmental and social epidemiology, stigmatized conditions, predictive modeling, and evaluation of natural experiments. Although studies using primary data collection methods may have more reliable data and better population retention, EHR-based studies are less expensive and require less time to complete. Future EHR epidemiology with enhanced collection of social/behavior measures, linkage with vital records, and integration of emerging technologies such as personal sensing could improve clinical care and population health.

ERAIZDA: a model for holistic annotation of animal infectious and zoonotic diseases

There is an urgent need for a unified resource that integrates trans-disciplinary annotations of emerging and reemerging animal infectious and zoonotic diseases. Such data integration will provide wonderful opportunity for epidemiologists, researchers and health policy makers to make data-driven decisions designed to improve animal health. Integrating emerging and reemerging animal infectious and zoonotic disease data from a large variety of sources into a unified open-access resource provides more plausible arguments to achieve better understanding of infectious and zoonotic diseases. We have developed a model for interlinking annotations of these diseases. These diseases are of particular interest because of the threats they pose to animal health, human health and global health security. We demonstrated the application of this model using brucellosis, an infectious and zoonotic disease. Preliminary annotations were deposited into VetBioBase database (http://vetbiobase.igbb.msstate.edu). This database is associated with user-friendly tools to facilitate searching, retrieving and downloading of disease-related information. Database URL: http://vetbiobase.igbb.msstate.edu.