Timely, Granular, and Actionable: Informatics in the Public Health 3.0 Era

Engaging Antiracist And Decolonial Praxis To Advance Equity In Oregon Public Health Surveillance Practices

Public health surveillance and data systems in the US remain an unnamed facet of structural racism. What gets measured, which data get collected and analyzed, and how and by whom are not matters of happenstance. Rather, surveillance and data systems are productions and reproductions of political priority, epistemic privilege, and racialized state power. This has consequences for how communities of color are represented or misrepresented, viewed, and valued and for what is prioritized and viewed as legitimate cause for action. Surveillance and data systems accordingly must be understood as both an instrument of structural racism and an opportunity to dismantle it. Here, we outline a critique of standard surveillance systems and practice, drawing from the social epidemiology, critical theory, and decolonial theory literatures to illuminate matters of power germane to epistemic and procedural justice in the surveillance of communities of color. We then summarize how community partners, academics, and state health department data scientists collaborated to reimagine survey practices in Oregon, engaging public health critical race praxis and decolonial theory to reorient toward antiracist surveillance systems. We close with a brief discussion of implications for practice and areas for continued consideration and reflection.

Public Health Informatics and the Perioperative Physician: Looking to the Future

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations

The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people's homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals' obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people's use of dynamic food environments and links to diet and health.

Tying Overdose Data to Action: North Carolina's Opioid and Substance Use Action Plan Data Dashboard

From 2000 to 2020, more than 28 000 North Carolina (NC) residents died of drug overdose. In response, NC Department of Health and Human Services worked with community partners to develop an Opioid and Substance Use Action Plan (OSUAP), now in its third iteration. The NC OSUAP data dashboard brings together data on 15 public health indicators and 16 local actions across 8 strategies. We share innovations in design, data structures, user tasks, and visual elements over 5 years of dashboard development and maintenance, with a special focus and supplemental material covering the technical details and techniques that dashboard design and implementation teams may benefit from.

Digital dashboards visualizing public health data: a systematic review

Introduction

Public health is not only threatened by diseases, pandemics, or epidemics. It is also challenged by deficits in the communication of health information. The current COVID-19 pandemic demonstrates that impressively. One way to deliver scientific data such as epidemiological findings and forecasts on disease spread are dashboards. Considering the current relevance of dashboards for public risk and crisis communication, this systematic review examines the state of research on dashboards in the context of public health risks and diseases.

Method

Nine electronic databases where searched for peer-reviewed journal articles and conference proceedings. Included articles (n = 65) were screened and assessed by three independent reviewers. Through a methodological informed differentiation between descriptive studies and user studies, the review also assessed the quality of included user studies (n = 18) by use of the Mixed Methods Appraisal Tool (MMAT).

Results

65 articles were assessed in regards to the public health issues addressed by the respective dashboards, as well as the data sources, functions and information visualizations employed by the different dashboards. Furthermore, the literature review sheds light on public health challenges and objectives and analyzes the extent to which user needs play a role in the development and evaluation of a dashboard. Overall, the literature review shows that studies that do not only describe the construction of a specific dashboard, but also evaluate its content in terms of different risk communication models or constructs (e.g., risk perception or health literacy) are comparatively rare. Furthermore, while some of the studies evaluate usability and corresponding metrics from the perspective of potential users, many of the studies are limited to a purely functionalistic evaluation of the dashboard by the respective development teams.

Conclusion

The results suggest that applied research on public health intervention tools like dashboards would gain in complexity through a theory-based integration of user-specific risk information needs.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=200178, identifier: CRD42020200178.

What Data Should Be Included in a Modern Public Health Data System

The public is inundated with data, both in where data are ubiquitously collected and in how organizations are using data to drive public sector and commercial decisions. The public health data system is no exception to this flood of data, both in growing data volume and variety. However, what are collected and analyzed about the health status of the nation, how particular data and measures are prioritized for parsimony, and how those data provide a signal for where to invest to address health inequities are in dire need of a reboot. As with other articles in this supplement, this article builds from a literature review, an environmental scan, and deliberations from the National Commission to Transform Public Health Data Systems. The article summarizes what data should be included and identifies where the technology and data sectors can contribute to fill current gaps to measure equity, positive health, and well-being.

Food and Nutrition Systems Dashboards: A Systematic Review

The rapid expansion of food and nutrition information requires new ways of data sharing and dissemination. Interactive platforms integrating data portals and visualization dashboards have been effectively utilized to describe, monitor, and track information related to food and nutrition; however, a comprehensive evaluation of emerging interactive systems is lacking. We conducted a systematic review on publicly available dashboards using a set of 48 evaluation metrics for data integrity, completeness, granularity, visualization quality, and interactivity based on 4 major principles: evidence, efficiency, emphasis, and ethics. We evaluated 13 dashboards, summarized their characteristics, strengths, and limitations, and provided guidelines for developing nutrition dashboards. We applied mixed effects models to summarize evaluation results adjusted for interrater variability. The proposed metrics and evaluation principles help to improve data standardization and harmonization, dashboard performance and usability, broaden information and knowledge sharing among researchers, practitioners, and decision makers in the field of food and nutrition, and accelerate data literacy and communication.

Developing Dashboards to Address Children's Health Disparities in Ohio

OBJECTIVES

Social determinants of health (SDoH) can be measured at the geographic level to convey information about neighborhood deprivation. The Ohio Children's Opportunity Index (OCOI) is a composite area-level opportunity index comprised of eight health domains. Our research team has documented the design, development, and use cases of a dashboard solution to visualize OCOI.

METHODS

The OCOI is a multidomain index spanning the following eight domains: (1) family stability, (2) infant health, (3) children's health, (4) access, (5) education, (6) housing, (7) environment, and (8) criminal justice. Information on these eight domains is derived from the American Community Survey and other administrative datasets. Our team used the Tableau Desktop visualization software and applied a user-centered design approach to developing the two OCOI dashboards-main OCOI dashboard and OCOI-race dashboard. We also performed convergence analysis to visualize the census tracts where different health indicators simultaneously exist at their worst levels.

RESULTS

The OCOI dashboard has multiple, interactive components as follows: a choropleth map of Ohio displaying OCOI scores for a specific census tract, graphs presenting OCOI or domain scores to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. A case study using the two dashboards for convergence analysis revealed census tracts in neighborhoods with low infant health scores and a high proportion of minority population.

CONCLUSION

The OCOI dashboards could assist health care leaders in making decisions that enhance health care delivery and policy decision-making regarding children's health particularly in areas where multiple health indicators exist at their worst levels.

Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada

OBJECTIVE

Geographic measurement of diets is generally not available at areas smaller than a national or provincial (state) scale, as existing nutrition surveys cannot achieve sample sizes needed for an acceptable statistical precision for small geographic units such as city subdivisions.

DESIGN

Using geocoded Nielsen grocery transaction data collected from supermarket, supercentre and pharmacy chains combined with a gravity model that transforms store-level sales into area-level purchasing, we developed small-area public health indicators of food purchasing for neighbourhood districts. We generated the area-level indicators measuring per-resident purchasing quantity for soda, diet soda, flavoured (sugar-added) yogurt and plain yogurt purchasing. We then provided an illustrative public health application of these indicators as covariates for an ecological spatial regression model to estimate spatially correlated small-area risk of type 2 diabetes mellitus (T2D) obtained from the public health administrative data.

SETTING

Greater Montreal, Canada in 2012.

PARTICIPANTS

Neighbourhood districts (n 193).

RESULTS

The indicator of flavoured yogurt had a positive association with neighbourhood-level risk of T2D (1·08, 95 % credible interval (CI) 1·02, 1·14), while that of plain yogurt had a negative association (0·93, 95 % CI 0·89, 0·96). The indicator of soda had an inconclusive association, and that of diet soda was excluded due to collinearity with soda. The addition of the indicators also improved model fit of the T2D spatial regression (Watanabe-Akaike information criterion = 1765 with the indicators, 1772 without).

CONCLUSION

Store-level grocery sales data can be used to reveal micro-scale geographic disparities and trends of food selections that would be masked by traditional survey-based estimation.

Teasing out Artificial Intelligence in Medicine: An Ethical Critique of Artificial Intelligence and Machine Learning in Medicine

The rapid adoption and implementation of artificial intelligence in medicine creates an ontologically distinct situation from prior care models. There are both potential advantages and disadvantages with such technology in advancing the interests of patients, with resultant ontological and epistemic concerns for physicians and patients relating to the instatiation of AI as a dependent, semi- or fully-autonomous agent in the encounter. The concept of libertarian paternalism potentially exercised by AI (and those who control it) has created challenges to conventional assessments of patient and physician autonomy. The unclear legal relationship between AI and its users cannot be settled presently, an progress in AI and its implementation in patient care will necessitate an iterative discourse to preserve humanitarian concerns in future models of care. This paper proposes that physicians should neither uncritically accept nor unreasonably resist developments in AI but must actively engage and contribute to the discourse, since AI will affect their roles and the nature of their work. One's moral imaginative capacity must be engaged in the questions of beneficence, autonomy, and justice of AI and whether its integration in healthcare has the potential to augment or interfere with the ends of medical practice.

Electronic medical record implementation in tertiary care: factors influencing adoption of an electronic medical record in a cancer centre

Background

Electronic Medical Records (EMRs) are one of a range of digital health solutions that are key enablers of the data revolution transforming the health sector. They offer a wide range of benefits to health professionals, patients, researchers and other key stakeholders. However, effective implementation has proved challenging.

Methods

A qualitative methodology was used in the study. Interviews were conducted with 12 clinical and administrative staff of a cancer centre at one-month pre-launch and eight clinical and administrative staff at 12-months post-launch of an EMR. Data from the interviews was collected via audio recording. Audio recordings were transcribed, de-identified and analysed to identify staff experiences with the EMR.

Results

Data from the pre-implementation interviews were grouped into four categories: 1) Awareness and understanding of EMR; 2) Engagement in launch process; 3) Standardisation and completeness of data; 4) Effect on workload. Data from the post-launch interviews were grouped into six categories: 1) Standardisation and completeness of data; 2) Effect on workload; 3) Feature completeness and functionality; 4) Interaction with technical support; 5) Learning curve; 6) Buy-in from staff. Two categories: Standardisation and completeness of data and effect on workload were common across pre and post-implementation interviews.

Conclusion

Findings from this study contribute new knowledge on barriers and enablers to the implementation of EMRs in complex clinical settings. Barriers to successful implementation include lack of technical support once the EMR has launched, health professional perception the EMR increases workload, and the learning curve for staff adequately familiarize themselves with using the EMR.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-020-06015-6.

Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health

The rapid proliferation of health informatics and digital health innovations has revolutionized clinical and research practices. There is no doubt that these fields will continue to have accelerated growth and a substantial impact on population health. However, there are legitimate concerns about how these promising technological advances can lead to unintended consequences such as perpetuating health and health care disparities for underresourced populations. To mitigate this potential pitfall, it is imperative for the health informatics and digital health scientific communities to understand the challenges faced by disadvantaged groups, including racial and ethnic minorities, which hinder their achievement of ideal health. This paper presents illustrative exemplars as case studies of contextually tailored, sociotechnical mobile health interventions designed with community members to address health inequities using community-engaged research approaches. We strongly encourage researchers and innovators to integrate community engagement into the development of data-driven, modernized solutions for every sector of society to truly achieve health equity for all.

Assessing the potential utility of commercial 'big data' for health research: Enhancing small-area deprivation measures with Experian™ Mosaic groups

In contrast to area-based deprivation measures, commercial datasets remain infrequently used in health research and policy. Experian collates numerous commercial and administrative data sources to produce Mosaic groups which stratify households into 15 groups for marketing purposes. We assessed the potential utility of Mosaic groups for health research purposes by investigating their relationships with Indices of Multiple Deprivation (IMD) for the British population. Mosaic groups showed significant associations with IMD quintiles. Correspondence Analysis revealed variations in patterns of association, with Mosaic groups either showing increasing, decreasing, or some mixed trends with deprivation quintiles. These results suggest that Experian's Mosaics additionally measure other aspects of socioeconomic circumstances to those captured by deprivation measures. These commercial data may provide new insights into the social determinants of health at a small area level.

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Mosaic groups showed a significant association with IMD quintiles.

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Trend patterns varied between different Mosaic groups across IMD quintiles.

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Mosaic groups have potential to enhance routinely used socioeconomic measures in research.

Integrating Social and Behavioral Determinants of Health into Population Health Analytics: A Conceptual Framework and Suggested Road Map

There is growing recognition that social and behavioral risk factors impact population health outcomes. Interventions that target these risk factors can improve health outcomes. This study presents a review of existing literature and proposes a conceptual framework for the integration of social and behavioral data into population health analytics platforms. The authors describe several use cases for these platforms at the patient, health system, and community levels, and align these use cases with the different types of prevention identified by the Centers for Disease Control and Prevention. They then detail the potential benefits of these use cases for different health system stakeholders and explore currently available and potential future sources of social and behavioral domains data. Also noted are several potential roadblocks for these analytic platforms, including limited data interoperability, expense of data acquisition, and a lack of standardized technical terminology for socio-behavioral factors.

Improving Health Promotion Through the Integration of Technology, Crowdsourcing, and Social Media

As Internet accessibility and technological innovations continue to increase communication, new opportunities have emerged to leverage these tools to improve health promotion practice. Advances and utilization of collaborative Internet communication, or social media, have provided global connectivity on an unprecedented scale. Using these innovations to leverage the collective intellect of online communities for specific goals, crowdsourcing is an approach that has the potential to solve complex public health problems. Due to the novelty of crowdsourcing implementations and the relative infancy of its application within public health, it is necessary to examine examples to facilitate practitioner conceptualization and application. This article details the development and application of a crowdsourced effort leveraging social media and technology to assist in relief efforts during Hurricane Harvey. Furthermore, the article presents examples corresponding to a typology of crowdsourcing for public health, including Knowledge Discovery and Management, Distributed Human Intelligence Tasking, Broadcast Search, and Peer-Vetted Creative Production problems. Leveraging these innovative applications has positive implications for health promotion practice, including improved intervention development and evaluation, increased multidisciplinary collaboration, and enhanced facilitation of communication, information exchange, and support.