The imaginary of precision public health

The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets

BACKGROUND

In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients' gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.

OBJECTIVE

This study aimed to explore categorical data's effects on machine learning model outputs, rooted the effects in the data collection and dataset publication processes, and proposed a mixed methods approach to examining datasets' data categories before using them for machine learning training.

METHODS

Against the theoretical background of the social construction of categories, we suggest a mixed methods approach to assess categorical data's utility for machine learning model training. As an example, we applied our approach to a Brazilian dermatological dataset (Dermatological and Surgical Assistance Program at the Federal University of Espírito Santo [PAD-UFES] 20). We first present an exploratory, quantitative study that assesses the effects when including or excluding each of the unique categorical data features of the PAD-UFES 20 dataset for training a transformer-based model using a data fusion algorithm. We then pair our quantitative analysis with a qualitative examination of the data categories based on interviews with the dataset authors.

RESULTS

Our quantitative study suggests scattered effects of including categorical data for machine learning model training across predictive classes. Our qualitative analysis gives insights into how the categorical data were collected and why they were published, explaining some of the quantitative effects that we observed. Our findings highlight the social constructedness of categorical data in publicly available datasets, meaning that the data in a category heavily depend on both how these categories are defined by the dataset creators and the sociomedico context in which the data are collected. This reveals relevant limitations of using publicly available datasets in contexts different from those of the collection of their data.

CONCLUSIONS

We caution against using data features of publicly available datasets without reflection on the social construction and context dependency of their categorical data features, particularly in data-sparse areas. We conclude that social scientific, context-dependent analysis of available data features using both quantitative and qualitative methods is helpful in judging the utility of categorical data for the population for which a model is intended.

Cells and the city: The rise and fall of urban biopolitics in San Francisco, 1970-2020

STS theories of biocapital conceptualize how biomedical knowledge and capital form together. Though these formations of biocapital often are located in large urban centers, few scholars have attended to how they are transforming urban spaces and places. In this paper we argue that the twinned technological development of cells and cities concentrates economic and symbolic capital and sets in motion contentious practices we name urban biopolitics. We draw on archival research and a nearly decade-long ethnography of the expansion of biomedical campuses in a major American city to show how the speculative logics of land development and biomedical innovation become bound together in a process we describe as speculative revitalization. We examine how the logics of speculative revitalization imagine a future in which cities and biomedicine produce wealth and health harmoniously together. However, in practice-as buildings of new biomedical urban campuses get built-the dreams of billionaire philanthrocapitalists to create global cities clash with the plans of biomedical researchers to create global health. We document the reproduction of stratified and racialized biomedical exclusions that result while also highlighting the unlikely opportunities for creating alliances committed to creating equitable biomedical research and healthcare in urban communities.

Precision medicine in primary care: How GPs envision "old" and "new" forms of personalization

Visions of precision or personalized medicine (PM) are gaining currency around the globe. While the potential of PM in specialist medicine has been in focus, primary care is also considered to be a fruitful area for the application of PM. "Low-tech" forms of personalization and attention to individual patients are already central features of primary care practice, and primary care thus constitutes an area in which "old" and "new" forms of personalization (may) come together. Against this backdrop, we explore general practitioners' (GPs) views on PM and how they envision the future of personalization in primary care. We draw on 45 qualitative interviews with GPs from Austria, Denmark, and the United States. Along the lines of major "promises" of PM-tailoring treatment decisions, improving disease prevention, empowering patients-we show that in some areas GPs consider PM to be a continuation or extension of existing practices of personalization, while in other cases, GPs envision that PM may negatively disrupt current forms of personalization in primary care. We suggest that this ambivalent stance towards PM can be understood through the lens of GPs' views on core values and practices of primary care.

Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health

The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research.

Using Precision Public Health to Manage Climate Change: Opportunities, Challenges, and Health Justice

Amid public health concerns over climate change, "precision public health" (PPH) is emerging in next generation approaches to practice. These novel methods promise to augment public health operations by using ever larger and more robust health datasets combined with new tools for collecting and analyzing data. Precision strategies to protecting the public health could more effectively or efficiently address the systemic threats of climate change, but may also propagate or exacerbate health disparities for the populations most vulnerable in a changing climate. How PPH interventions collect and aggregate data, decide what to measure, and analyze data pose potential issues around privacy, neglecting social determinants of health, and introducing algorithmic bias into climate responses. Adopting a health justice framework, guided by broader social and climate justice tenets, can reveal principles and policy actions which may guide more responsible implementation of PPH in climate responses.

Precision, Equity, and Public Health and Epidemiology Informatics - A Scoping Review

OBJECTIVES

This scoping review synthesizes the recent literature on precision public health and the influence of predictive models on health equity with the intent to highlight central concepts for each topic and identify research opportunities for the biomedical informatics community.

METHODS

Searches were conducted using PubMed for publications between 2017-01-01 and 2019-12-31.

RESULTS

Precision public health is defined as the use of data and evidence to tailor interventions to the characteristics of a single population. It differs from precision medicine in terms of its focus on populations and the limited role of human genomics. High-resolution spatial analysis in a global health context and application of genomics to infectious organisms are areas of progress. Opportunities for informatics research include (i) the development of frameworks for measuring non-clinical concepts, such as social position, (ii) the development of methods for learning from similar populations, and (iii) the evaluation of precision public health implementations. Just as the effects of interventions can differ across populations, predictive models can perform systematically differently across subpopulations due to information bias, sampling bias, random error, and the choice of the output. Algorithm developers, professional societies, and governments can take steps to prevent and mitigate these biases. However, even if the steps to avoid bias are clear in theory, they can be very challenging to accomplish in practice.

CONCLUSIONS

Both precision public health and predictive modelling require careful consideration in how subpopulations are defined and access to data on subpopulations can be challenging. While the theory for both topics has advanced considerably, there is much work to be done in understanding how to implement and evaluate these approaches in practice.

Toward a Symbiotic Perspective on Public Health: Recognizing the Ambivalence of Microbes in the Anthropocene

Microbes evolve in complex environments that are often fashioned, in part, by human desires. In a global perspective, public health has played major roles in structuring how microbes are perceived, cultivated, and destroyed. The germ theory of disease cast microbes as enemies of the body and the body politic. Antibiotics have altered microbial development by providing stringent natural selection on bacterial species, and this has led to the formation of antibiotic-resistant bacterial strains. Public health perspectives such as "Precision Public Health" and "One Health" have recently been proposed to further manage microbial populations. However, neither of these take into account the symbiotic relationships that exist between bacterial species and between bacteria, viruses, and their eukaryotic hosts. We propose a perspective on public health that recognizes microbial evolution through symbiotic associations (the hologenome theory) and through lateral gene transfer. This perspective has the advantage of including both the pathogenic and beneficial interactions of humans with bacteria, as well as combining the outlook of the "One Health" model with the genomic methodologies utilized in the "Precision Public Health" model. In the Anthropocene, the conditions for microbial evolution have been altered by human interventions, and public health initiatives must recognize both the beneficial (indeed, necessary) interactions of microbes with their hosts as well as their pathogenic interactions.

The Population Health OutcomEs aNd Information EXchange (PHOENIX) Program - A Transformative Approach to Reduce the Burden of Chronic Disease

This concept article introduces a transformative vision to reduce the population burden of chronic disease by focusing on data integration, analytics, implementation and community engagement. Known as PHOENIX (The Population Health OutcomEs aNd Information EXchange), the approach leverages a state level health information exchange and multiple other resources to facilitate the integration of clinical and social determinants of health data with a goal of achieving true population health monitoring and management. After reviewing historical context, we describe how multilevel and multimodal data can be used to facilitate core public health services, before discussing the controversies and challenges that lie ahead.