Commentary: Epidemiology in the era of big data

How to harness the power of web scraping for medical and surgical research: An application in estimating international collaboration

The transformative potential of web scraping in surgical research through a comprehensive analysis of its revolutionary applications and profound impact is now within reach. This manuscript unveils the pivotal role of web scraping in driving innovation, enabling more effective management of human capital dynamics, and enhancing patient outcomes in the surgical field. As an example, we demonstrate how web scraping can uncover insights into international collaboration in surgery research revealing limited collaboration between surgeons in developed and developing countries.

Precision Medicine Approaches in Cardiology and Personalized Therapies for Improved Patient Outcomes: A systematic review

Personalized medicine is a novel and rapidly evolving approach to clinical practice that involves making decisions about disease prediction, prevention, diagnosis, and treatment by utilizing modern technologies. The concepts of precision medicine have grown as a result of ongoing developments in genomic analysis, molecular diagnostics, and technology. These advancements have enabled a deeper understanding and interpretation of the human genome, allowing for a personalized approach to clinical care. The primary objective of this research is to assess personalized medicine in terms of its indications, advantages, practical clinical uses, potential future directions, problems, and effects on healthcare. An extensive analysis of the scientific literature regarding this topic demonstrated the new medical approach's relevance and usefulness, as well as the fact that personalized medicine is becoming increasingly prevalent in various sectors. The online, internationally indexed databases PubMed and Cochrane Reviews were used to conduct searches for and critically evaluate the most relevant published research including original papers and reviews in the scientific literature. The findings suggest that precision medicine has a lot of potential and its implementation lowers the incidence of stroke as well as coronary heart disease and improves patient health in cardiology.

Adverse COVID-19 outcomes in American Veterans with age-related macular degeneration: a case-control study

OBJECTIVES

Prior studies suggest that patients with age-related macular degeneration (AMD) have poorer COVID-19 outcomes. This study aims to evaluate whether AMD is associated with adverse COVID-19 outcomes in a large clinical database.

DESIGN

Case-control study.

SETTING

We obtained demographic and clinical data from a national US Veterans Affairs (VA) database for all Veterans aged 50 years or older with positive COVID-19 testing prior to 2 May 2021.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome measure was hospitalisation. Secondary outcome measures were intensive care unit admission, mechanical ventilation and death. Potential associations between AMD and outcome measures occurring within 60 days of COVID-19 diagnosis were evaluated using multiple logistic regression analyses.

RESULTS

Of the 171 325 patients in the study cohort, 7913 (5%) had AMD and 2152 (1%) had severe AMD, defined as advanced atrophic or exudative AMD disease coding. Multiple logistic regression adjusting for age, Charlson Comorbidity Index, sex, race, ethnicity and COVID-19 timing showed that an AMD diagnosis did not significantly increase the odds of hospitalisation (p=0.11). Using a Bonferroni-adjusted significance level of 0.006, AMD and severe AMD also were not significant predictors for the secondary outcomes, except for AMD being modestly protective for death (p=0.002).

CONCLUSIONS

After adjusting for other variables, neither AMD nor severe AMD was a risk factor for adverse COVID-19 outcomes in the VA healthcare system. These findings indicate that an AMD diagnosis alone should not alter recommended ophthalmic management based on COVID-19 adverse outcome risk.

Maternal and child nutrition programme of investigation within the 100 Million Brazilian Cohort: study protocol

INTRODUCTION

There is a limited understanding of the early nutrition and pregnancy determinants of short-term and long-term maternal and child health in ethnically diverse and socioeconomically vulnerable populations within low-income and middle-income countries. This investigation programme aims to: (1) describe maternal weight trajectories throughout the life course; (2) describe child weight, height and body mass index (BMI) trajectories; (3) create and validate models to predict childhood obesity at 5 years of age; (4) estimate the effects of prepregnancy BMI, gestational weight gain (GWG) and maternal weight trajectories on adverse maternal and neonatal outcomes and child growth trajectories; (5) estimate the effects of prepregnancy BMI, GWG, maternal weight and interpregnancy BMI changes on maternal and child outcomes in the subsequent pregnancy; and (6) estimate the effects of maternal food consumption and infant feeding practices on child nutritional status and growth trajectories.

METHODS AND ANALYSIS

Linked data from four different Brazilian databases will be used: the 100 Million Brazilian Cohort, the Live Births Information System, the Mortality Information System and the Food and Nutrition Surveillance System. To analyse trajectories, latent-growth, superimposition by translation and rotation and broken stick models will be used. To create prediction models for childhood obesity, machine learning techniques will be applied. For the association between the selected exposure and outcomes variables, generalised linear models will be considered. Directed acyclic graphs will be constructed to identify potential confounders for each analysis investigating potential causal relationships.

ETHICS AND DISSEMINATION

This protocol was approved by the Research Ethics Committees of the authors' institutions. The linkage will be carried out in a secure environment. After the linkage, the data will be de-identified, and pre-authorised researchers will access the data set via a virtual private network connection. Results will be reported in open-access journals and disseminated to policymakers and the broader public.

Use of Danish National Somatic Cell Count Data to Assess the Need for Dry-Off Treatment in Holstein Dairy Cattle

In Denmark, PCR testing of dairy cattle is commonly used to select animals for the antibacterial treatment of intramammary infection (IMI) during the dry-off period. IMI is associated with a high somatic cell count (SCC), routinely recorded for milk quality control for most commercial dairy herds. This study aimed to compare SCC curves over the lactation among dairy cows with positive vs. negative PCR test results for four major IMI pathogens. Data from 133,877 PCR-tested Holstein cows from 1364 Danish conventional dairy herds were used to fit a nonlinear mixed-effects model using a modified four-parameter Wilmink function. We stratified the data into first, second, third or fourth and later parity and fitted Wilmink curves to all SCC observations between 6 and 305 days in milk. The PCR tests were taken before dry-off at the end of the lactation to investigate which animals qualified for selective dry cow therapy. A PCR Ct-value of 37 and below was used to determine if an animal was PCR positive for any of the following IMI pathogens: Staphylococcus aureus, Streptococcus agalactiae, Str. dysgalactiae and Str. uberis. Our findings showed that mean SCC curve fits were higher for PCR-positive animals in all four parity groups and across lactations. The use of SCC data fitted to the entire lactation for multiple lactations enabled quantification of overall differences in SCC curves between cattle with and without detected IMI, adjusted for parity group and stage of lactation. These findings are relevant to the use of SCC to support treatment decisions.

Human Capital and Productivity in Plastic Surgery Research Across Nations

BACKGROUND

Understanding country differences in production and human capital in plastic surgery research is crucial in identifying current and future leaders in the field. In this study, we document each country's human capital and productivity in plastic surgery research.

METHODS

A web scraping algorithm was deployed on PubMed to retrieve information on every publication and every first author in 10 major research outlets in plastic surgery between 2015 and 2021. Each country's human capital in the field is proxied by the number of first authors affiliated with that country. We compare aggregate patterns and volume trajectories of publications affiliated with 110 countries in the context of their human capital.

RESULTS

We find that over the studied period, two countries, the USA and China, are represented in roughly 50% and 45% of global research output and first authors, respectively, in plastic surgery. Specifically in the USA, California has the highest number of affiliated first authors and publications compared with other States.

CONCLUSIONS

Our findings reveal the clear dominance of the USA in plastic surgery research production. No specific US State stands out in the nation as much as the USA does in the global ranking of plastic surgery publications. This suggests that US plastic surgeons across the nation aim to publish. Our global analysis also suggests that countries with a higher share of first authors relative to their research output may have greater capacity to expand their research output in the future.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Linking Ambient NO2 Pollution Measures with Electronic Health Record Data to Study Asthma Exacerbations

Electronic health record (EHR)-derived data can be linked to geospatially distributed socioeconomic and environmental factors to conduct large-scale epidemiologic studies. Ambient NO2 is a known environmental risk factor for asthma. However, health exposure studies often rely on data from geographically sparse regulatory monitors that may not reflect true individual exposure. We contrasted use of interpolated NO2 regulatory monitor data with raw satellite measurements and satellite-derived ground estimates, building on previous work which has computed improved exposure estimates from remotely sensed data. Raw satellite and satellite-derived ground measurements captured spatial variation missed by interpolated ground monitor measurements. Multivariable analyses comparing these three NO2 measurement approaches (interpolated monitor, raw satellite, and satellite-derived) revealed a positive relationship between exposure and asthma exacerbations for both satellite measurements. Exposure-outcome relationships using the interpolated monitor NO2 were inconsistent with known relationships to asthma, suggesting that interpolated monitor data might yield misleading results in small region studies.

Evaluation of a smartphone-based methodology that integrates long-term tracking of mobility, place experiences, heart rate variability, and subjective well-being

This study presents MyGävle, a smartphone application that merge long-term tracking of mobility data, heart rate variability and subjective and objective well-being records. Developed to address the challenges faced in researching healthy and sustainable lifestyles, this app serves as a pioneering implementation of Real-life Long-term Methodology (ReaLM). After eight months' use by 257 participants from Gävle (Sweden), we evaluate the completeness, accuracy, validity, and consistency of all data collected. MyGävle produced remarkable results as a ReaLM method. On average, it precisely tracked participants daily locations for approximately 8 h and accurately collected heart-rate variability values throughout the day (12 h) and night (6 h). Participants reported 5115 subjective place experiences (ranging from 160 to 120 per week) and seasonal participation, although declining, is accurate. Our findings indicate that the amount of data collected through smartphone sensors, fitness wristbands and in-app questionnaires is consistent enough to be leveraged for integrated assessments of habits, environmental exposure, and subjective and physiological well-being. Yet, considerable variation exists across individuals; thus diagnostic analysis must precede use of these datasets in any particular research endeavors. By doing so we can maximise the potential of ReaLM research to delve into real life conditions conducive to healthy living habits while also considering broader sustainability goals.

Decolonizing Epidemiological Research: A Critical Perspective

Decolonizing epidemiological research is a crucial endeavor. Historically, colonial and imperialistic ideologies have pervaded epidemiology, leading to an emphasis on Western perspectives and the neglect of indigenous and other marginalized communities' needs and experiences. To effectively address health disparities and promote justice and equality, acknowledging and addressing these power imbalances are imperative. In this article, I highlight the need of decolonizing epidemiological research and make recommendations. These include increasing the representation of researchers from underrepresented communities, ensuring that epidemiological research is contextually relevant and responsive to the experiences of these communities, and collaborating with policymakers and advocacy groups to inform policies and practices that benefit all populations. Moreover, I underscore the importance of recognizing and valuing the knowledge and skills of marginalized populations, and integrating traditional knowledge-the distinct, culturally specific understanding unique to a particular group-into research efforts. I also emphasize the need of capacity building and equitable research collaborations and authorship as well as epidemiological journal editorship. Decolonizing epidemiology research is a continual process that requires continuing discourse, collaboration, and education.

To scrape or not to scrape, this is dilemma. The post-API scenario and implications on digital research

Introduction

This article aims to investigate the potential impact of restricted social data access on digital research practices. The 2018 Cambridge Analytica scandal exposed the exploitation of Facebook user data for speculative purposes and led to the end of the so-called "Data Golden Age," characterized by free access to social media user data. As a result, many social platforms have limited or entirely banned data access. This policy shift, referred to as the "APIcalypse," has revolutionized digital research methods.

Methods

To address the impact of this policy shift on digital research, a non-probabilistic sample of Italian researchers was surveyed and the responses were analyzed. The survey was designed to explore how constraints on digital data access have altered research practices, whether we are truly in a post-API era with a radical change in data scraping strategies, and what shared and sustainable solutions can be identified for the post-API scenario.

Results

The findings highlight how limits on social data access have not yet created a "post-Api" scenario as expected, but it is turning research practices upside down, positively and negatively. On the positive side, because researchers are experimenting with innovative forms of scraping. Negatively, because there could be a "mass migration" to the few platforms that freely grant their APIs, with critical consequences for the quality of research.

Discussion

The closure of many social media APIs has not opened up a post-API world, but has worsened the conditions of making research, which is increasingly oriented to "easy-data" environments such as Twitter. This should prompt digital researchers to make a self-reflexive effort to diversify research platforms and especially to act ethically with user data. It would also be important for the scientific world and large platforms to enter into understandings for open and conscious sharing of data in the name of scientific progress.

Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model

Background

Forecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy.

Methods

COVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Multivariable time series ARIMA models were fitted to forecast COVID-19 incidence and Effective Reproductive Number (R _eff ) in the Greater Melbourne region. Five models were fitted to compare and validate predictive models using moving three-day ahead forecasts to test the predictive accuracy for both COVID-19 incidence and R _eff over the Melbourne Delta outbreak.

Results

Case-only ARIMA model resulted in an R squared (R²) value of 0.942, Root Mean Square Error (RMSE) of 141.59, and Mean Absolute Percentage Error (MAPE) of 23.19. The model including transit station mobility (TSM) and maximum temperature (Tmax) had greater predictive accuracy with R² 0.948, RMSE 137.57, and MAPE 21.26.

Conclusion

Multivariable ARIMA modelling for COVID-19 cases and R _eff was useful for predicting epidemic growth, with higher predictive accuracy for models including TSM and Tmax. These results suggest that TSM and Tmax would be useful for further exploration for developing weather-informed early warning models for future COVID-19 outbreaks with potential application for the inclusion of weather and Google data with disease surveillance in developing effective early warning systems for informing public health policy and epidemic response.

Big data in corneal diseases and cataract: Current applications and future directions

The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of "5 Vs"-variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.

Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level.

Big data, machine learning, and population health: predicting cognitive outcomes in childhood

The application of machine learning (ML) to address population health challenges has received much less attention than its application in the clinical setting. One such challenge is addressing disparities in early childhood cognitive development-a complex public health issue rooted in the social determinants of health, exacerbated by inequity, characterised by intergenerational transmission, and which will continue unabated without novel approaches to address it. Early life, the period of optimal neuroplasticity, presents a window of opportunity for early intervention to improve cognitive development. Unfortunately for many, this window will be missed, and intervention may never occur or occur only when overt signs of cognitive delay manifest. In this review, we explore the potential value of ML and big data analysis in the early identification of children at risk for poor cognitive outcome, an area where there is an apparent dearth of research. We compare and contrast traditional statistical methods with ML approaches, provide examples of how ML has been used to date in the field of neurodevelopmental disorders, and present a discussion of the opportunities and risks associated with its use at a population level. The review concludes by highlighting potential directions for future research in this area. IMPACT: To date, the application of machine learning to address population health challenges in paediatrics lags behind other clinical applications. This review provides an overview of the public health challenge we face in addressing disparities in childhood cognitive development and focuses on the cornerstone of early intervention. Recent advances in our ability to collect large volumes of data, and in analytic capabilities, provide a potential opportunity to improve current practices in this field. This review explores the potential role of machine learning and big data analysis in the early identification of children at risk for poor cognitive outcomes.

Incidences of anxiety disorders among active duty service members between 1999 and 2018

PURPOSE

Anxiety disorders can impact the health, performance, and retention of military service members. To inform prevention initiatives and long-term treatment planning, incidence rates across anxiety disorders were evaluated among U.S. active-duty service members over a 20-year period.

METHOD

Data were extracted from the Defense Medical Epidemiological Database to examine incidence rates of generalized anxiety disorder (GAD), panic disorder (PD), agoraphobia (AG), social anxiety disorder (SAD), obsessive compulsive disorder (OCD), agoraphobia with panic disorder (AWPD), agoraphobia without history of panic disorder (AWOPD), and unspecified anxiety disorder (UAD) among 151,844 service members between 1999 and 2018 in relation to sex, age, race, marital status, military pay grade, service branch.

RESULTS

Incidence rates of anxiety disorders increased significantly over the 20-year period. Anxiety disorder incidence rates ranged widely from 0.01 to 23.70 (per 1000 service members). There were significant differences in observed versus expected diagnostic rates across all demographic variables examined (p < 0.001).

CONCLUSION

Incidence rates varied considerably across the anxiety disorders, with UAD being the highest. These data highlight the importance of health care professionals attending to anxiety disorders, in order to plan for service member needs, develop preventative interventions, address early detection, and deliver treatments to improve combat readiness.

Toward Systems Models for Obesity Prevention: A Big Role for Big Data

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.

Characterizing clinical pediatric obesity subtypes using electronic health record data

In this work, we present a study of electronic health record (EHR) data that aims to identify pediatric obesity clinical subtypes. Specifically, we examine whether certain temporal condition patterns associated with childhood obesity incidence tend to cluster together to characterize subtypes of clinically similar patients. In a previous study, the sequence mining algorithm, SPADE was implemented on EHR data from a large retrospective cohort (n = 49 594 patients) to identify common condition trajectories surrounding pediatric obesity incidence. In this study, we used Latent Class Analysis (LCA) to identify potential subtypes formed by these temporal condition patterns. The demographic characteristics of patients in each subtype are also examined. An LCA model with 8 classes was developed that identified clinically similar patient subtypes. Patients in Class 1 had a high prevalence of respiratory and sleep disorders, patients in Class 2 had high rates of inflammatory skin conditions, patients in Class 3 had a high prevalence of seizure disorders, and patients in Class 4 had a high prevalence of Asthma. Patients in Class 5 lacked a clear characteristic morbidity pattern, and patients in Classes 6, 7, and 8 had a high prevalence of gastrointestinal issues, neurodevelopmental disorders, and physical symptoms respectively. Subjects generally had high membership probability for a single class (>70%), suggesting shared clinical characterization within the individual groups. We identified patient subtypes with temporal condition patterns that are significantly more common among obese pediatric patients using a Latent Class Analysis approach. Our findings may be used to characterize the prevalence of common conditions among newly obese pediatric patients and to identify pediatric obesity subtypes. The identified subtypes align with prior knowledge on comorbidities associated with childhood obesity, including gastro-intestinal, dermatologic, developmental, and sleep disorders, as well as asthma.

Area-level racial prejudice and health: A systematic review

BACKGROUND

In recent years, there has been growing interest in "moving beyond the individual" to measure area-level racism as a social determinant of health. Much of this work has aggregated racial prejudice data collected at the individual-level to the area-level.

OBJECTIVE

As this is a rapidly emerging area of research, we conducted a systematic literature review to describe evidence of the relationship between area-level racial prejudice and health, whether results differed by race/ethnicity, and to characterize key conceptual and methodological considerations to guide future research.

METHOD

We searched four interdisciplinary databases for US-based, peer-reviewed articles measuring area level racial prejudice by aggregating individual-level indicators of racial prejudice and examining associations with mental or physical health outcome(s). Data extraction followed PRISMA guidelines and also included theory and conceptualization, pathways to health, and strengths and limitations.

RESULTS

Fourteen of 14,632 identified articles met inclusion criteria and were included in the review. Health outcomes spanned all-cause (n = 4) and cause-specific (n = 4) mortality, birth outcomes (n = 4), cardiovascular outcomes (n = 2), mental health (n = 1), and self-rated health (n = 1). All studies found a positive association between area-level racial prejudice and adverse health outcomes among racial/ethnic minoritized groups, with four studies also showing a similar association among Whites. Engagement with formal theory was limited and exposure conceptualization was mixed. Methodological considerations included unmeasured confounding and trade-offs between generalizability, self-censorship, and specificity of measurement.

CONCLUSIONS

Future research should continue to develop the conceptual and methodological rigor of this work and test hypotheses to inform evidence-based interventions to advance population health and reduce racial health inequities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Mitigating bias in estimating epidemic severity due to heterogeneity of epidemic onset and data aggregation

Outbreaks of infectious diseases, such as influenza, are a major societal burden. Mitigation policies during an outbreak or pandemic are guided by the analysis of data of ongoing or preceding epidemics. The reproduction number, R0, defined as the expected number of secondary infections arising from a single individual in a population of susceptibles is critical to epidemiology. For typical compartmental models such as the Susceptible-Infected-Recovered (SIR) R0 represents the severity of an epidemic. It is an estimate of the early-stage growth rate of an epidemic and is an important threshold parameter used to gain insights into the spread or decay of an outbreak. Models typically use incidence counts as indicators of cases within a single large population; however, epidemic data are the result of a hierarchical aggregation, where incidence counts from spatially separated monitoring sites (or sub-regions) are pooled and used to infer R0. Is this aggregation approach valid when the epidemic has different dynamics across the regions monitored? We characterize bias in the estimation of R0 from a merged data set when the epidemics of the sub-regions, used in the merger, exhibit delays in onset. We propose a method to mitigate this bias, and study its efficacy on synthetic data as well as real-world influenza and COVID-19 data.

Incidence of insomnia and obstructive sleep apnea in active duty United States military service members

STUDY OBJECTIVES

Epidemiologic studies of obstructive sleep apnea (OSA) and insomnia in the U.S. military are limited. The primary aim of this study was to report and compare OSA and insomnia diagnoses in active duty the United States military service members.

METHOD

Data and service branch densities used to derive the expected rates of diagnoses on insomnia and OSA were drawn from the Defense Medical Epidemiology Database. Single sample chi-square goodness of fit tests and independent samples t-tests were conducted to address the aims of the study.

RESULTS

Between 2005 and 2019, incidence rates of OSA and insomnia increased from 11 to 333 and 6 to 272 (per 10,000), respectively. Service members in the Air Force, Navy, and Marines were diagnosed with insomnia and OSA below expected rates, while those in the Army had higher than expected rates (p < .001). Female service members were underdiagnosed in both disorders (p < .001). Comparison of diagnoses following the transition from ICD 9 to 10 codes revealed significant differences in the amounts of OSA diagnoses only (p < .05).

CONCLUSION

Since 2005, incidence rates of OSA and insomnia have markedly increased across all branches of the U.S. military. Despite similar requirements for overall physical and mental health and resilience, service members in the Army had higher rates of insomnia and OSA. This unexpected finding may relate to inherent differences in the branches of the military or the role of the Army in combat operations. Future studies utilizing military-specific data and directed interventions are required to reverse this negative trend.

Alcohol use disorder in active duty service members: Incidence rates over a 19-year period

Background: Alcohol use is a concerning issue for the military given its potential negative impact on human performance. Limited data are available regarding the incidence of alcohol use disorder in the military, which is critical to understand to evaluate force readiness, as well as for preventative initiatives and treatment planning. The aim was to examine the alcohol use disorder incidence rates (overall and across demographics) among active duty service members from 2001 to 2018. Methods: Data on 208,870 active duty service members between 2001 and 2018 from the Defense Medical Epidemiology Database was examined. Incidence rates were analyzed to determine the diagnostic rates of AUD (including both alcohol abuse and dependence), which were then examined by sex, age, service branch, military pay grade, marital status, and race. Results: Incidence rates of AUD in active duty service members (per 1,000 service members) ranged from 6.45 to 10.50 for alcohol abuse and 5.21 to 7.11 for alcohol dependence. Initial diagnoses of new-onset AUD occurred most frequently within 20-24 year-old, white, male, and non-married U.S. Army service members in the enlisted pay grades of E-1 to E-4. Statistically significant differences (p <.001) were found between observed and expected counts across all examined demographic variables. Conclusions: To our knowledge, this is the first study to provide a comprehensive examination of AUD incidence rates in an active-duty military population over an extended 18-year period and during the last decade. Incidence rates were higher than expected for alcohol dependence and lower than expected for alcohol abuse. Given the untoward effects of AUD on overall health and force readiness, active-duty service members may benefit from more advanced preventative interventions to decrease incidence rates of AUD over time. Future research should use these data to develop targeted interventions for the demographics at greatest risk.

Sodium and Health Outcomes: Ascertaining Valid Estimates in Research Studies

PURPOSE OF REVIEW

The dietary reference intake (DRI) for sodium has been highly debated with persuasive and elegant arguments made for both population sodium reduction and for maintenance of the status quo. After the 2015 Dietary Guidelines Advisory Committee (DGAC) report was published, controversy ensued, and by Congressional mandate, the sodium DRIs were updated in 2019. The 2019 DRIs defined adequate intake (AI) levels by age-sex groups that are largely consistent with the DRIs for sodium that were published in 2005. Given the overall similarities between the 2005 and 2019 DRIs, one may wonder how the recently published research on sodium and health outcomes was considered in determining the DRIs, particularly, the recent studies from very large observational cohort studies. We aim to address this concern and outline the major threats to ascertaining valid estimates of the relationship between dietary sodium and health outcomes in observational cohort studies. We use tools from modern epidemiology to demonstrate how unexpected and inconsistent findings in these relationships may emerge. We use directed acyclic graphs to illustrate specific examples in which biases may occur.

RECENT FINDINGS

We identified the following key threats to internal validity: poorly defined target intervention, poorly measured sodium exposure, unmeasured or residual confounding, reverse causality, and selection bias. Researchers should consider these threats to internal validity while developing research questions and throughout the research process. For the DRIs to inform real-world interventions relating to sodium reduction, it is recommended that more specific research questions be asked that can clearly define potential interventions of interest.

Application of big-data for epidemiological studies of refractive error

PURPOSE

To examine whether data sourced from electronic medical records (EMR) and a large industrial spectacle lens manufacturing database can estimate refractive error distribution within large populations as an alternative to typical population surveys of refractive error.

SUBJECTS

A total of 555,528 patient visits from 28 Irish primary care optometry practices between the years 1980 and 2019 and 141,547,436 spectacle lens sales records from an international European lens manufacturer between the years 1998 and 2016.

METHODS

Anonymized EMR data included demographic, refractive and visual acuity values. Anonymized spectacle lens data included refractive data. Spectacle lens data was separated into lenses containing an addition (ADD) and those without an addition (SV). The proportions of refractive errors from the EMR data and ADD lenses were compared to published results from the European Eye Epidemiology (E3) Consortium and the Gutenberg Health Study (GHS).

RESULTS

Age and gender matched proportions of refractive error were comparable in the E3 data and the EMR data, with no significant difference in the overall refractive error distribution (χ2 = 527, p = 0.29, DoF = 510). EMR data provided a closer match to the E3 refractive error distribution by age than the ADD lens data. The ADD lens data, however, provided a closer approximation to the E3 data for total myopia prevalence than the GHS data, up to age 64.

CONCLUSIONS

The prevalence of refractive error within a population can be estimated using EMR data in the absence of population surveys. Industry derived sales data can also provide insights on the epidemiology of refractive errors in a population over certain age ranges. EMR and industrial data may therefore provide a fast and cost-effective surrogate measure of refractive error distribution that can be used for future health service planning purposes.

Big data, observational research and P-value: a recipe for false-positive findings? A study of simulated and real prospective cohorts

BACKGROUND

An increasing number of observational studies combine large sample sizes with low participation rates, which could lead to standard inference failing to control the false-discovery rate. We investigated if the 'empirical calibration of P-value' method (EPCV), reliant on negative controls, can preserve type I error in the context of survival analysis.

METHODS

We used simulated cohort studies with 50% participation rate and two different selection bias mechanisms, and a real-life application on predictors of cancer mortality using data from four population-based cohorts in Northern Italy (n = 6976 men and women aged 25-74 years at baseline and 17 years of median follow-up).

RESULTS

Type I error for the standard Cox model was above the 5% nominal level in 15 out of 16 simulated settings; for n = 10 000, the chances of a null association with hazard ratio = 1.05 having a P-value < 0.05 were 42.5%. Conversely, EPCV with 10 negative controls preserved the 5% nominal level in all the simulation settings, reducing bias in the point estimate by 80-90% when its main assumption was verified. In the real case, 15 out of 21 (71%) blood markers with no association with cancer mortality according to literature had a P-value < 0.05 in age- and gender-adjusted Cox models. After calibration, only 1 (4.8%) remained statistically significant.

CONCLUSIONS

In the analyses of large observational studies prone to selection bias, the use of empirical distribution to calibrate P-values can substantially reduce the number of trivial results needing further screening for relevance and external validity.

Public Opinions about Online Learning during COVID-19: A Sentiment Analysis Approach

The aim of this study was to analyze public opinion about online learning during the COVID-19 (Coronavirus Disease 2019) pandemic. A total of 154 articles from online news and blogging websites related to online learning were extracted from Google and DuckDuckGo. The articles were extracted for 45 days, starting from the day the World Health Organization (WHO) declared COVID-19 a worldwide pandemic, 11 March 2020. For this research, we applied the dictionary-based approach of the lexicon-based method to perform sentiment analysis on the articles extracted through web scraping. We calculated the polarity and subjectivity scores of the extracted article using the TextBlob library. The results showed that over 90% of the articles are positive, and the remaining were mildly negative. In general, the blogs were more positive than the newspaper articles; however, the blogs were more opinionated compared to the news articles.

A primer on quantitative bias analysis with positive predictive values in research using electronic health data

OBJECTIVE

In health informatics, there have been concerns with reuse of electronic health data for research, including potential bias from incorrect or incomplete outcome ascertainment. In this tutorial, we provide a concise review of predictive value-based quantitative bias analysis (QBA), which comprises epidemiologic methods that use estimates of data quality accuracy to quantify the bias caused by outcome misclassification.

TARGET AUDIENCE

Health informaticians and investigators reusing large, electronic health data sources for research.

SCOPE

When electronic health data are reused for research, validation of outcome case definitions is recommended, and positive predictive values (PPVs) are the most commonly reported measure. Typically, case definitions with high PPVs are considered to be appropriate for use in research. However, in some studies, even small amounts of misclassification can cause bias. In this tutorial, we introduce methods for quantifying this bias that use predictive values as inputs. Using epidemiologic principles and examples, we first describe how multiple factors influence misclassification bias, including outcome misclassification levels, outcome prevalence, and whether outcome misclassification levels are the same or different by exposure. We then review 2 predictive value-based QBA methods and why outcome PPVs should be stratified by exposure for bias assessment. Using simulations, we apply and evaluate the methods in hypothetical electronic health record-based immunization schedule safety studies. By providing an overview of predictive value-based QBA, we hope to bridge the disciplines of health informatics and epidemiology to inform how the impact of data quality issues can be quantified in research using electronic health data sources.

Incidence (2008-2015) of post-traumatic headaches in United States military personnel

Objective: To conduct a descriptive transversal study to evaluate the incidence and demographic characteristics of post-traumatic headache using data from the Defense Medical Epidemiology Database.Methods: A retrospective cohort study was conducted of data from 2008 to 2015 based on the International Classification of Diseases codes for both acute and chronic post-traumatic headache.Results: A total of 17,010 new cases of post-traumatic headaches were diagnosed among active duty military personnel. Reported incidence rates of post-traumatic headaches in the military increased 29-fold over the timeframe analyzed. Males enlisted in the Army were more likely to be diagnosed with post-traumatic headaches than females (O/E = 0.76), other branches, and officers.Conclusion: Findings of this study indicate that there is a significant incidence of post-traumatic headaches in the U.S. military. However, the incidence rates of post-traumatic headaches in the military are much lower than what was expected considering the number of traumatic brain injuries in the United States military reported by the Department of Defense for the same period. Additional research is needed to further evaluate these differences and the impact of PTHs on military personnel.

Joint and interactive effects between health comorbidities and environmental exposures in predicting amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a rare yet devastating neurodegenerative condition. The mechanisms leading to ALS are most certainly complex and likely involve a joint contribution of several factors with possible synergistic or antagonistic interactions. To provide a better understanding of the association between non-genetic factors and ALS, we evaluated the joint exposure to multiple health and environmental factors linked with ALS in our previous studies, also screening for high-dimensional interactions.

METHODS

We used data from a nested case-control study within the Danish population, with 1086 ALS cases from 1982 to 2009, jointly investigating 4 hospital-based diagnoses - diabetes, obesity, physical/stress trauma, cardiovascular disease (CVD) during 1977-2009; and 4 environmental exposures - lead, formaldehyde, diesel exhaust, and solvents, assessed from individual occupational history. All covariates were evaluated as ever/never exposed, and we used targeted machine learning techniques to screen for important joint predictors and interactions. These were then evaluated in a final logistic regression model adjusting for potential confounders (age, SES, geography). All analyses were stratified by sex.

RESULTS

Among men, trauma and solvents were associated with higher odds of ALS (OR = 1.55, 95% CI: 1.08-2.23; OR = 1.49, 95% CI: 1.17-1.89, respectively), and presented a negative interaction (OR = 0.49, 95% CI: 0.30-0.80). A positive diesel/CVD interaction was observed (OR = 1.56, 95% CI: 0.94-2.60). Among women, solvents, trauma, lead, and CVD were associated with higher odds of ALS, and a negative lead/solvents interaction was documented (OR = 0.52, 95% CI: 0.42-0.63).

CONCLUSIONS

This study is one of the first attempts to evaluate joint and interactive effects of multiple risk factors on ALS, identifying potential synergistic and antagonistic mechanisms.

Incidence Rates of Posttraumatic Stress Disorder Over a 17-Year Period in Active Duty Military Service Members

Posttraumatic stress disorder (PTSD) affects approximately 8% of the general population. The prevalence of PTSD is twice as high in active duty service members and military veterans. Few studies have investigated the incidence rates of PTSD in active duty military personnel. The present study evaluated the incidence of PTSD diagnoses and the differences between demographic factors for service members between 2001 and 2017. Data on 182,400 active duty service members between 2001 and 2017 were drawn from the Defense Medical Epidemiological Database and examined by sex, age, service branch, military pay grade, marital status, and race. From 2001 to 2017, the incidence rates of PTSD in the active force (per 1,000 service members) steadily climbed, with a low of 1.24 in 2002 to a high of 12.94 in 2016. Service members most often diagnosed with PTSD were in the U.S. Army, with the enlisted pay grades of E-5-E-9, White, married, male, and between 20 and 24 years old. Statistically significant differences, ps < .001, were found between observed and expected counts across all examined demographic variables. The present study is the first to our knowledge to provide a comprehensive examination of PTSD incidence rates in an active duty military population.

Using universal kriging to improve neighborhood physical disorder measurement

Ordinary kriging, a spatial interpolation technique, is commonly used in social sciences to estimate neighborhood attributes such as physical disorder. Universal kriging, developed and used in physical sciences, extends ordinary kriging by supplementing the spatial model with additional covariates. We measured physical disorder on 1,826 sampled block faces across 4 US cities (New York, Philadelphia, Detroit, and San Jose) using Google Street View imagery. We then compared leave-one-out cross-validation accuracy between universal and ordinary kriging and used random subsamples of our observed data to explore whether universal kriging could provide equal measurement accuracy with less spatially dense samples. Universal kriging did not always improve accuracy. However, a measure of housing vacancy did improve estimation accuracy in Philadelphia and Detroit (7.9 and 6.8% lower root mean square error, respectively) and allowed for equivalent estimation accuracy with half the sampled points in Philadelphia. Universal kriging may improve neighborhood measurement.

Big data and artificial intelligence in future patient management. How is it all started? Where are we at now? Quo tendimus?

Background

This article is focused on the understanding of the key points and their importance and impact on the future of early disease predictive models, accurate and fast diagnosis, patient management, optimise treatment, precision medicine, and allocation of resources through the applications of Big Data (BD) and Artificial Intelligence (AI) in healthcare.

Content

BD and AI processes include learning which is the acquisition of information and rules for using the information, reasoning which is using rules to reach approximate or definite conclusions and self-correction. This can help improve the detection of diseases, rare diseases, toxicity, identifying health system barriers causing under-diagnosis. BD combined with AI, Machine Learning (ML), computing and predictive-modelling, and combinatorics are used to interrogate structured and unstructured data computationally to reveal patterns, trends, potential correlations and relationships between disparate data sources and associations.

Summary

Diagnosis-assisted systems and wearable devices will be part and parcel not only of patient management but also in the prevention and early detection of diseases. Also, Big Data will have an impact on payers, devise makers and pharmaceutical companies. BD and AI, which is the simulation of human intelligence processes, are more diverse and their application in monitoring and diagnosis will only grow bigger, wider and smarter.

Outlook

BD connectivity and AI of diagnosis-assisted systems, wearable devices and smartphones are poised to transform patient and to change the traditional methods for patient management, especially in an era where is an explosion in medical data.

A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China

A novel pneumonia-like coronavirus disease (COVID-19) caused by a novel coronavirus named SARS-CoV-2 has swept across China and the world. Public health measures that were effective in previous infection outbreaks (eg, wearing a face mask, quarantining) were implemented in this outbreak. Available multidimensional social network data that take advantage of the recent rapid development of information and communication technologies allow for an exploration of disease spread and control via a modernized epidemiological approach. By using spatiotemporal data and real-time information, we can provide more accurate estimates of disease spread patterns related to human activities and enable more efficient responses to the outbreak. Two real cases during the COVID-19 outbreak demonstrated the application of emerging technologies and digital data in monitoring human movements related to disease spread. Although the ethical issues related to using digital epidemiology are still under debate, the cases reported in this article may enable the identification of more effective public health measures, as well as future applications of such digitally directed epidemiological approaches in controlling infectious disease outbreaks, which offer an alternative and modern outlook on addressing the long-standing challenges in population health.

Grocery purchase data in the study of alcohol use - A validity study

BACKGROUND

Alcohol use epidemiology is facing challenges as survey response rates decline. In addition, population surveys fail to capture a large proportion of alcohol consumed and are expensive to conduct. This study aims to aid in complementing traditional epidemiological methods by validate grocery purchase data in the research on population alcohol use.

METHODS

The LoCard study subjects were loyalty card holders of a grocery retail co-operative, which possessed more than 45 % market share in Finland. One third of those who consented to the analyses of their grocery purchases were presented a questionnaire including a Food Frequency Questionnaire on the web; N = 11,818 responded. The relationship between beer purchase frequency and self-reported beer drinking frequency was studied for association and agreement in different subgroups using crosstabulations and Poisson regression modeling.

RESULTS

The association between beer purchase frequency and self-reported beer drinking frequency was good (Gamma = .556). The agreement between beer purchase frequency and drinking frequency was only fair (Kappa = .189). Limiting the data to those single adult households that reported making at least 61 % of their grocery purchases from this grocery retailer and collapsing the frequency categories to three instead of six increased the agreement to good (Kappa = .463).

CONCLUSIONS

Information on beer purchase frequency from the loyalty card database can be used to rank people according to their drinking frequency and to estimate beer drinking frequency with fair to good accuracy, depending on what share of grocery purchases they make from the grocery retailer in question.

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.

Big data requirements for artificial intelligence

PURPOSE OF REVIEW

To summarize how big data and artificial intelligence technologies have evolved, their current state, and next steps to enable future generations of artificial intelligence for ophthalmology.

RECENT FINDINGS

Big data in health care is ever increasing in volume and variety, enabled by the widespread adoption of electronic health records (EHRs) and standards for health data information exchange, such as Digital Imaging and Communications in Medicine and Fast Healthcare Interoperability Resources. Simultaneously, the development of powerful cloud-based storage and computing architectures supports a fertile environment for big data and artificial intelligence in health care. The high volume and velocity of imaging and structured data in ophthalmology and is one of the reasons why ophthalmology is at the forefront of artificial intelligence research. Still needed are consensus labeling conventions for performing supervised learning on big data, promotion of data sharing and reuse, standards for sharing artificial intelligence model architectures, and access to artificial intelligence models through open application program interfaces (APIs).

SUMMARY

Future requirements for big data and artificial intelligence include fostering reproducible science, continuing open innovation, and supporting the clinical use of artificial intelligence by promoting standards for data labels, data sharing, artificial intelligence model architecture sharing, and accessible code and APIs.

The effect of neighborhood social environment on prostate cancer development in black and white men at high risk for prostate cancer

INTRODUCTION

Neighborhood socioeconomic (nSES) factors have been implicated in prostate cancer (PCa) disparities. In line with the Precision Medicine Initiative that suggests clinical and socioenvironmental factors can impact PCa outcomes, we determined whether nSES variables are associated with time to PCa diagnosis and could inform PCa clinical risk assessment.

MATERIALS AND METHODS

The study sample included 358 high risk men (PCa family history and/or Black race), aged 35-69 years, enrolled in an early detection program. Patient variables were linked to 78 nSES variables (employment, income, etc.) from previous literature via geocoding. Patient-level models, including baseline age, prostate specific antigen (PSA), digital rectal exam, as well as combined models (patient plus nSES variables) by race/PCa family history subgroups were built after variable reduction methods using Cox regression and LASSO machine-learning. Model fit of patient and combined models (AIC) were compared; p-values<0.05 were significant. Model-based high/low nSES exposure scores were calculated and the 5-year predicted probability of PCa was plotted against PSA by high/low neighborhood score to preliminarily assess clinical relevance.

RESULTS

In combined models, nSES variables were significantly associated with time to PCa diagnosis. Workers mode of transportation and low income were significant in White men with a PCa family history. Homeownership (%owner-occupied houses with >3 bedrooms) and unemployment were significant in Black men with and without a PCa family history, respectively. The 5-year predicted probability of PCa was higher in men with a high neighborhood score (weighted combination of significant nSES variables) compared to a low score (e.g., Baseline PSA level of 4ng/mL for men with PCa family history: White-26.7% vs 7.7%; Black-56.2% vs 29.7%).

DISCUSSION

Utilizing neighborhood data during patient risk assessment may be useful for high risk men affected by disparities. However, future studies with larger samples and validation/replication steps are needed.

Scientometric trends for coronaviruses and other emerging viral infections

BACKGROUND

COVID-19 is the most rapidly expanding coronavirus outbreak in the past 2 decades. To provide a swift response to a novel outbreak, prior knowledge from similar outbreaks is essential.

RESULTS

Here, we study the volume of research conducted on previous coronavirus outbreaks, specifically SARS and MERS, relative to other infectious diseases by analyzing >35 million articles from the past 20 years. Our results demonstrate that previous coronavirus outbreaks have been understudied compared with other viruses. We also show that the research volume of emerging infectious diseases is very high after an outbreak and decreases drastically upon the containment of the disease. This can yield inadequate research and limited investment in gaining a full understanding of novel coronavirus management and prevention.

CONCLUSIONS

Independent of the outcome of the current COVID-19 outbreak, we believe that measures should be taken to encourage sustained research in the field.

Motives of contributing personal data for health research: (non-)participation in a Dutch biobank

Background

Large-scale, centralized data repositories are playing a critical and unprecedented role in fostering innovative health research, leading to new opportunities as well as dilemmas for the medical sciences. Uncovering the reasons as to why citizens do or do not contribute to such repositories, for example, to population-based biobanks, is therefore crucial. We investigated and compared the views of existing participants and non-participants on contributing to large-scale, centralized health research data repositories with those of ex-participants regarding the decision to end their participation. This comparison could yield new insights into motives of participation and non-participation, in particular the behavioural change of withdrawal.

Methods

We conducted 36 in-depth interviews with ex-participants, participants, and non-participants of a three-generation, population-based biobank in the Netherlands. The interviews focused on the respondents’ decision-making processes relating to their participation in a large-scale, centralized repository for health research data.

Results

The decision of participants and non-participants to contribute to the biobank was motivated by a desire to help others. Whereas participants perceived only benefits relating to their participation and were unconcerned about potential risks, non-participants and ex-participants raised concerns about the threat of large-scale, centralized public data repositories and public institutes, such as social exclusion or commercialization. Our analysis of ex-participants’ perceptions suggests that intrapersonal characteristics, such as levels of trust in society, participation conceived as a social norm, and basic societal values account for differences between participants and non-participants.

Conclusions

Our findings indicate the fluidity of motives centring on helping others in decisions to participate in large-scale, centralized health research data repositories. Efforts to improve participation should focus on enhancing the trustworthiness of such data repositories and developing layered strategies for communication with participants and with the public. Accordingly, personalized approaches for recruiting participants and transmitting information along with appropriate regulatory frameworks are required, which have important implications for current data management and informed consent procedures.

Characterization and Correction of Bias Due to Nonparticipation and the Degree of Loyalty in Large-Scale Finnish Loyalty Card Data on Grocery Purchases: Cohort Study

Background

To date, the evaluation of diet has mostly been based on questionnaires and diaries that have their limitations in terms of being time and resource intensive, and a tendency toward social desirability. Loyalty card data obtained in retailing provides timely and objective information on diet-related behaviors. In Finland, the market is highly concentrated, which provides a unique opportunity to investigate diet through grocery purchases.

Objective

The aims of this study were as follows: (1) to investigate and quantify the selection bias in large-scale (n=47,066) loyalty card (LoCard) data and correct the bias by developing weighting schemes and (2) to investigate how the degree of loyalty relates to food purchases.

Methods

Members of a loyalty card program from a large retailer in Finland were contacted via email and invited to take part in the study, which involved consenting to the release of their grocery purchase data for research purposes. Participants’ sociodemographic background was obtained through a web-based questionnaire and was compared to that of the general Finnish adult population obtained via Statistics Finland. To match the distributions of sociodemographic variables, poststratification weights were constructed by using the raking method. The degree of loyalty was self-estimated on a 5-point rating scale.

Results

On comparing our study sample with the general Finnish adult population, in our sample, there were more women (65.25%, 30,696/47,045 vs 51.12%, 2,273,139/4,446,869), individuals with higher education (56.91%, 20,684/36,348 vs 32.21%, 1,432,276/4,446,869), and employed individuals (60.53%, 22,086/36,487 vs 52.35%, 2,327,730/4,446,869). Additionally, in our sample, there was underrepresentation of individuals aged under 30 years (14.44%, 6,791/47,045 vs 18.04%, 802,295/4,446,869) and over 70 years (7.94%, 3,735/47,045 vs 18.20%, 809,317/4,446,869), as well as retired individuals (23.51%, 8,578/36,487 vs 31.82%, 1,414,785/4,446,869). Food purchases differed by the degree of loyalty, with higher shares of vegetable, red meat & processed meat, and fat spread purchases in the higher loyalty groups.

Conclusions

Individuals who consented to the use of their loyalty card data for research purposes tended to diverge from the general Finnish adult population. However, the high volume of data enabled the inclusion of sociodemographically diverse subgroups and successful correction of the differences found in the distributions of sociodemographic variables. In addition, it seems that food purchases differ according to the degree of loyalty, which should be taken into account when researching loyalty card data. Despite the limitations, loyalty card data provide a cost-effective approach to reach large groups of people, including hard-to-reach population subgroups.

Glossary for public health surveillance in the age of data science

Public health surveillance is the ongoing systematic collection, analysis and interpretation of data, closely integrated with the timely dissemination of the resulting information to those responsible for preventing and controlling disease and injury. With the rapid development of data science, encompassing big data and artificial intelligence, and with the exponential growth of accessible and highly heterogeneous health-related data, from healthcare providers to user-generated online content, the field of surveillance and health monitoring is changing rapidly. It is, therefore, the right time for a short glossary of key terms in public health surveillance, with an emphasis on new data-science developments in the field.

Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic

We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources.

Scraping the Web for Public Health Gains: Ethical Considerations from a 'Big Data' Research Project on HIV and Incarceration

Web scraping involves using computer programs for automated extraction and organization of data from the Web for the purpose of further data analysis and use. It is frequently used by commercial companies, but also has become a valuable tool in epidemiological research and public health planning. In this paper, we explore ethical issues in a project that "scrapes" public websites of U.S. county jails as part of an effort to develop a comprehensive database (including individual-level jail incarcerations, court records and confidential HIV records) to enhance HIV surveillance and improve continuity of care for incarcerated populations. We argue that the well-known framework of Emanuel et al. (2000) provides only partial ethical guidance for the activities we describe, which lie at a complex intersection of public health research and public health practice. We suggest some ethical considerations from the ethics of public health practice to help fill gaps in this relatively unexplored area.