Protecting Personally Identifiable Information When Using Online Geographic Tools for Public Health Research

STROBE-GEMA: a STROBE extension for reporting of geographically explicit ecological momentary assessment studies

CONTEXT

While a growing body of research has been demonstrating how exposure to social and built environments relate to various health outcomes, specific pathways generally remain poorly understood. But recent technological advancements have enabled new study designs through continuous monitoring using mobile sensors and repeated questionnaires. Such geographically explicit momentary assessments (GEMA) make it possible to link momentary subjective states, behaviors, and physiological parameters to momentary environmental conditions, and can help uncover the pathways linking place to health. Despite its potential, there is currently no review of GEMA studies detailing how location data is used to measure environmental exposure, and how this in turn is linked to momentary outcomes of interest. Moreover, a lack of standard reporting of such studies hampers comparability and reproducibility.

AIMS

The objectives of this research were twofold: 1) conduct a systematic review of GEMA studies that link momentary measurement with environmental data obtained from geolocation data, and 2) develop a STROBE extension guideline for GEMA studies.

METHOD

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Inclusion criteria consisted of a combination of repeated momentary measurements of a health state or behavior with GPS coordinate collection, and use of these location data to derive momentary environmental exposures. To develop the guideline, the variables extracted for the systematic review were compared to elements of the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and CREMAS (CRedibility of Evidence from Multiple Analyses of the Same data) checklists, to provide a new guideline for GEMA studies. An international panel of experts participated in a consultation procedure to collectively develop the proposed checklist items. RESULTS AND DEVELOPED TOOLS: A total of 20 original GEMA studies were included in the review. Overall, several key pieces of information regarding the GEMA methods were either missing or reported heterogeneously. Our guideline provides a total of 27 categories (plus 4 subcategories), combining a total of 70 items. The 22 categories and 32 items from the original STROBE guideline have been integrated in our GEMA guideline. Eight categories and 6 items from the CREMAS guideline have been included to our guideline. We created one new category (namely "Consent") and added 32 new items specific to GEMA studies.

CONCLUSIONS AND RECOMMENDATIONS

This study offers a systematic review and a STROBE extension guideline for the reporting of GEMA studies. The latter will serve to standardize the reporting of GEMA studies, as well as facilitate the interpretation of results and their generalizability. In short, this work will help researchers and public health professionals to make the most of this method to advance our understanding of how environments influence health.

SEnDAE: A resource for expanding research into social and environmental determinants of health

BACKGROUND AND OBJECTIVE

Social and Environmental Determinants of Health (SEDoH) are of increasing interest to researchers in personal and public health. Collecting SEDoH and associating them with patient medical record can be challenging, especially for environmental variables. We announce here the release of SEnDAE, the Social and Environmental Determinants Address Enhancement toolkit, and open-source resource for ingesting a range of environmental variables and measurements from a variety of sources and associated them with arbitrary addresses.

METHODS

SEnDAE includes optional components for geocoding addresses, in case an organization does not have independent capabilities in that area, and recipes for extending the OMOP CDM and the ontology of an i2b2 instance to display and compute over the SEnDAE variables within i2b2.

RESULTS

On a set of 5000 synthetic addresses, SEnDAE was able to geocode 83%. SEnDAE geocodes addresses to the same Census tract as ESRI 98.1% of the time.

CONCLUSION

Development of SEnDAE is ongoing, but we hope that teams will find it useful to increase their usage of environmental variables and increase the field's general understanding of these important determinants of health.

Geoprivacy in Neighbourhoods and Health Research: A Mini-Review of the Challenges and Best Practices in Epidemiological Studies

Neighbourhood and health research often relies on personal location data (e.g., home address, daily itineraries), despite the risks of geoprivacy breaches. Thus, geoprivacy is an important emerging topic, contemplated in international regulations such as the General Data Protection Regulation. In this mini-review, we briefly assess the potential risks associated with the usage of personal location data and provide geoprivacy-preserving recommendations to be considered in epidemiological research. Risks include inference of personal information that the individual does not wish to disclose, reverse-identification and security breaches. Various measures should be implemented at different stages of a project (pre-data collection, data processing, data analysis/publication and data sharing) such as informed consent, pseudo-anonymization and geographical methods.

The Disclosure of Personally Identifiable Information in Studies of Neighborhood Contexts and Patient Outcomes

Clinical epidemiology and patient-oriented health care research that incorporates neighborhood-level data is becoming increasingly common. A key step in conducting this research is converting patient address data to longitude and latitude data, a process known as geocoding. Several commonly used approaches to geocoding (eg, ggmap or the tidygeocoder R package) send patient addresses over the internet to web-based third-party geocoding services. Here, we describe how these approaches to geocoding disclose patients’ personally identifiable information (PII) and how the subsequent publication of the research findings discloses the same patients’ protected health information (PHI). We explain how these disclosures can occur and recommend strategies to maintain patient privacy when studying neighborhood effects on patient outcomes.

Artificial intelligence and the future of life sciences

Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.

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.

Development of a Neighborhood Walkability Index for Studying Neighborhood Physical Activity Contexts in Communities across the U.S. over the Past Three Decades

To examine how urban form shapes physical activity and health over time, a measure of neighborhood walkability is needed that can be linked to cohort studies with participants living across the United States (U.S.) that have been followed over the past decades. The Built Environment and Health-Neighborhood Walkability Index (BEH-NWI), a measure of neighborhood walkability that can be calculated for communities across the United States between 1990 and 2015, was conceptualized, developed, and tested using data from the New York City Tri-State Area. BEH-NWI measures were created for 1990 and 2010 using historical data on population density, street intersection density, density of rail stops, and density of pedestrian trip generating/supporting establishments. BEH-NWI scores were calculated for 1-km buffers around the 1990 residences of NYU Women's Health Study (NYUWHS) participants and NYC Department of Health and Mental Hygiene's Physical Activity and Transit (PAT) survey participants enrolled in 2011. Higher neighborhood BEH-NWI scores were significantly associated with greater self-reported walking per week (+ 0.31 MET-hours/week per unit BEH-NWI, 95% CI 0.23, 0.36) and lower body mass index (- 0.17 BMI units per unit BEH-NWI, 95% - 0.23, - 0.12) among NYUWHS participants. Higher neighborhood BEH-NWI scores were associated with significantly higher accelerometer-measured physical activity among PAT survey participants (39% more minutes of moderate-intensity equivalent activity/week across the interquartile range of BEH-NWI, 95% CI 21%, 60%). The BEH-NWI can be calculated using historical data going back to 1990, and BEH-NWI scores predict BMI, weekly walking, and physical activity in two NYC area datasets.

INTERACT: A comprehensive approach to assess urban form interventions through natural experiments

BACKGROUND

Urban form interventions can result in positive and negative impacts on physical activity, social participation, and well-being, and inequities in these outcomes. Natural experiment studies can advance our understanding of causal effects and processes related to urban form interventions. The INTErventions, Research, and Action in Cities Team (INTERACT) is a pan-Canadian collaboration of interdisciplinary scientists, urban planners, and public health decision makers advancing research on the design of healthy and sustainable cities for all. Our objectives are to use natural experiment studies to deliver timely evidence about how urban form interventions influence health, and to develop methods and tools to facilitate such studies going forward.

METHODS

INTERACT will evaluate natural experiments in four Canadian cities: the Arbutus Greenway in Vancouver, British Columbia; the All Ages and Abilities Cycling Network in Victoria, BC; a new Bus Rapid Transit system in Saskatoon, Saskatchewan; and components of the Sustainable Development Plan 2016-2020 in Montreal, Quebec, a plan that includes urban form changes initiated by the city and approximately 230 partnering organizations. We will recruit a cohort of between 300 and 3000 adult participants, age 18 or older, in each city and collect data at three time points. Participants will complete health and activity space surveys and provide sensor-based location and physical activity data. We will conduct qualitative interviews with a subsample of participants in each city. Our analysis methods will combine machine learning methods for detecting transportation mode use and physical activity, use temporal Geographic Information Systems to quantify changes to urban intervention exposure, and apply analytic methods for natural experiment studies including interrupted time series analysis.

DISCUSSION

INTERACT aims to advance the evidence base on population health intervention research and address challenges related to big data, knowledge mobilization and engagement, ethics, and causality. We will collect ~ 100 TB of sensor data from participants over 5 years. We will address these challenges using interdisciplinary partnerships, training of highly qualified personnel, and modern methodologies for using sensor-based data.

Family Socioeconomic Status, Cortisol, and Physical Health in Early Childhood: The Role of Advantageous Neighborhood Characteristics

OBJECTIVE

Children from families with lower socioeconomic status (SES) evidence greater physiological dysregulation and poorer health. Despite recognition of environmental contributors, little is known about the influence of neighborhood characteristics. The present study examined the moderating role of community-level risks and resources on the relation of family SES to children's daily cortisol output and physical health during the kindergarten year.

METHODS

In fall and spring of kindergarten, children's (N = 338) daily total cortisol was measured and parents and teachers rated children's global physical health. Parents reported family SES. Neighborhood characteristics were assessed using the Child Opportunity Index, a population-level tool that evaluates the quality of multiple domains of neighborhood attributes.

RESULTS

In fall, children reared in lower SES family environments had higher cortisol when residing in lower quality (lower opportunity) neighborhoods (b = -.097, p < .001), but there was no relation between family SES and children's cortisol in more advantaged (higher opportunity) neighborhoods (b = -.023, p = .36). Lower family SES was prospectively associated with poorer physical health in spring (controlling for fall health) only among children living in lower opportunity neighborhoods (b = -.250, p = .018) and was unrelated to physical health among children residing in higher opportunity neighborhoods (b = .042, p = .70).

CONCLUSIONS

Higher opportunity neighborhoods may protect against the negative consequences of low family SES on children's stress physiology and physical health. Public health interventions that bolster neighborhood opportunities may benefit young children reared in socioeconomically disadvantaged family environments.

Determining Burden of Commuting for Treatment Using Online Mapping Services - A Study of Breast Cancer Patients

For patients with breast cancer who must frequent medical centers for care, commuting is a significant burden. This burden could affect their decisions during treatment. We developed a method to use census tracts and zip codes to determine commuting burden for patients with breast cancer with online mapping services, while protecting patient addresses from third parties. We found that patients who lived farther from Vanderbilt had fewer unique appointment days and more appointments scheduled per day. Total burden decreased over time after diagnosis, but advanced stage patients had sustained high levels of commute time until ten months after diagnosis. Additionally, we found that patients who lived far from Vanderbilt were less likely to receive radiotherapy from Vanderbilt. With the amount of work patients put into traveling for care, understanding commuting burden could help healthcare organizations form strategies to improve access to care and compliance with care plans.

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Environmental Domain

BACKGROUND

There is growing interest in how environment is related to adults' weight and activity and eating behaviors. However, little is known about whether environmental factors are related to the individual variability seen in adults' intentional weight loss or maintenance outcomes.

OBJECTIVES

The environmental domain subgroup of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project sought to identify a parsimonious set of objective and perceived neighborhood and social environment constructs and corresponding measures to include in the assessment of response to adult weight-loss treatment.

SIGNIFICANCE

Starting with the home address, the environmental domain subgroup recommended for inclusion in future weight-loss or maintenance studies constructs and measures related to walkability, perceived land use mix, food outlet accessibility (perceived and objective), perceived food availability, socioeconomics, and crime-related safety (perceived and objective) to characterize the home neighborhood environment. The subgroup also recommended constructs and measures related to social norms (perceived and objective) and perceived support to characterize an individual's social environment. The 12 neighborhood and social environment constructs and corresponding measures provide a succinct and comprehensive set to allow for more systematic examination of the impact of environment on adults' weight loss and maintenance.

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.

Ethical implications of location and accelerometer measurement in health research studies with mobile sensing devices

Quantification of individual behaviours using mobile sensing devices, including physical activity and spatial location, is a rapidly growing field in both academic research and the corporate world. In this case study, we summarize the literature examining the ethical aspects of mobile sensing and argue that a robust discussion about the ethical implications of mobile sensing for research purposes has not occurred sufficiently in the literature. Based on our literature summary and guided by basic ethical principles set out in Canadian, US, and International Ethics documents we propose four areas where further discussion should occur: consent, privacy and confidentiality, mitigating risk, and consideration of vulnerable populations. We argue that ongoing consent is crucial for participants to be aware of the precision and volume of data that is collected with mobile sensing devices. Related to privacy we discuss that participants may not agree that anonymized data is sufficient for privacy and confidentiality when mobile sensing data are collected. There has been some discussion about mitigating risk in the literature. We highlight that the researchers' obligations toward mitigating risks that are not directly related to the study purpose are unclear and require considerable discussion. Finally, using mobile sensing devices to study vulnerable populations requires careful consideration, particularly with respect to balancing research needs with participant burden. Based on our discussion, we identify a broad set of unanswered questions about the ethics of mobile sensing that should be addressed by the research community.

The spatial accuracy of geographic ecological momentary assessment (GEMA): Error and bias due to subject and environmental characteristics

BACKGROUND

Geographic ecological momentary assessment (GEMA) combines ecological momentary assessment (EMA) with global positioning systems (GPS) and geographic information systems (GIS). This study evaluates the spatial accuracy of GEMA location data and bias due to subject and environmental data characteristics.

METHODS

Using data for 72 subjects enrolled in a study of urban adolescent substance use, we compared the GPS-based location of EMA responses in which the subject indicated they were at home to the geocoded home address. We calculated the percentage of EMA locations within a sixteenth, eighth, quarter, and half miles from the home, and the percentage within the same tract and block group as the home. We investigated if the accuracy measures were associated with subject demographics, substance use, and emotional dysregulation, as well as environmental characteristics of the home neighborhood.

RESULTS

Half of all subjects had more than 88% of their EMA locations within a half mile, 72% within a quarter mile, 55% within an eighth mile, 50% within a sixteenth of a mile, 83% in the correct tract, and 71% in the correct block group. There were no significant associations with subject or environmental characteristics.

CONCLUSIONS

Results support the use of GEMA for analyzing subjects' exposures to urban environments. Researchers should be aware of the issue of spatial accuracy inherent in GEMA, and interpret results accordingly. Understanding spatial accuracy is particularly relevant for the development of 'ecological momentary interventions' (EMI), which may depend on accurate location information, though issues of privacy protection remain a concern.

Confidentiality considerations for use of social-spatial data on the social determinants of health: Sexual and reproductive health case study

Understanding whether and how the places where people live, work, and play are associated with health behaviors and health is essential to understanding the social determinants of health. However, social-spatial data which link a person and their attributes to a geographic location (e.g., home address) create potential confidentiality risks. Despite the growing body of literature describing approaches to protect individual confidentiality when utilizing social-spatial data, peer-reviewed manuscripts displaying identifiable individual point data or quasi-identifiers (attributes associated with the individual or disease that narrow identification) in maps persist, suggesting that knowledge has not been effectively translated into public health research practices. Using sexual and reproductive health as a case study, we explore the extent to which maps appearing in recent peer-reviewed publications risk participant confidentiality. Our scoping review of sexual and reproductive health literature published and indexed in PubMed between January 1, 2013 and September 1, 2015 identified 45 manuscripts displaying participant data in maps as points or small-population geographic units, spanning 26 journals and representing studies conducted in 20 countries. Notably, 56% (13/23) of publications presenting point data on maps either did not describe approaches used to mask data or masked data inadequately. Furthermore, 18% (4/22) of publications displaying data using small-population geographic units included at least two quasi-identifiers. These findings highlight the need for heightened education for researchers, reviewers, and editorial teams. We aim to provide readers with a primer on key confidentiality considerations when utilizing linked social-spatial data for visualizing results. Given the widespread availability of place-based data and the ease of creating maps, it is critically important to raise awareness on when social-spatial data constitute protected health information, best practices for masking geographic identifiers, and methods of balancing disclosure risk and scientific utility. We conclude with recommendations to support the preservation of confidentiality when disseminating results.