Public Health Applications Research Articles

You Are What You Tweet: Analyzing Twitter for Public Health

Analyzing user messages in social media can measure different population characteristics, including public health measures. For example, recent work has correlated Twitter messages with influenza rates in the United States; but this has largely been the extent of mining Twitter for public health. In this work, we consider a broader range of public health applications for Twitter. We apply the recently introduced Ailment Topic Aspect Model to over one and a half million health related tweets and discover mentions of over a dozen ailments, including allergies, obesity and insomnia. We introduce extensions to incorporate prior knowledge into this model and apply it to several tasks: tracking illnesses over times (syndromic surveillance), measuring behavioral risk factors, localizing illnesses by geographic region, and analyzing symptoms and medication usage. We show quantitative correlations with public health data and qualitative evaluations of model output. Our results suggest that Twitter has broad applicability for public health research.

Machine learning and algorithmic fairness in public and population health

Until now, much of the work on machine learning and health has focused on processes inside the hospital or clinic. However, this represents only a narrow set of tasks and challenges related to health; there is greater potential for impact by leveraging machine learning in health tasks more broadly. In this Perspective we aim to highlight potential opportunities and challenges for machine learning within a holistic view of health and its influences. To do so, we build on research in population and public health that focuses on the mechanisms between different cultural, social and environmental factors and their effect on the health of individuals and communities. We present a brief introduction to research in these fields, data sources and types of tasks, and use these to identify settings where machine learning is relevant and can contribute to new knowledge. Given the key foci of health equity and disparities within public and population health, we juxtapose these topics with the machine learning subfield of algorithmic fairness to highlight specific opportunities where machine learning, public and population health may synergize to achieve health equity. Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.

An implementation science approach to evaluating pathogen whole genome sequencing in public health

BackgroundPathogen whole genome sequencing (WGS) is being incorporated into public health surveillance and disease control systems worldwide and has the potential to make significant contributions to infectious disease surveillance, outbreak investigation and infection prevention and control. However, to date, there are limited data regarding (i) the optimal models for integration of genomic data into epidemiological investigations and (ii) how to quantify and evaluate public health impacts resulting from genomic epidemiological investigations.MethodsWe developed the Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework to guide examination of the use of WGS in public health surveillance and disease control. We illustrate the use of this framework with three pathogens as case studies: Listeria monocytogenes, Mycobacterium tuberculosis and SARS-CoV-2.ResultsThe framework utilises an adaptable whole-of-system approach towards understanding how interconnected elements in the public health application of pathogen genomics contribute to public health processes and outcomes. The three phases of the PG-PHASE Framework are designed to support understanding of WGS laboratory processes, analysis, reporting and data sharing, and how genomic data are utilised in public health practice across all stages, from the decision to send an isolate or sample for sequencing to the use of sequence data in public health surveillance, investigation and decision-making. Importantly, the phases can be used separately or in conjunction, depending on the need of the evaluator. Subsequent to conducting evaluation underpinned by the framework, avenues may be developed for strategic investment or interventions to improve utilisation of whole genome sequencing.ConclusionsComprehensive evaluation is critical to support health departments, public health laboratories and other stakeholders to successfully incorporate microbial genomics into public health practice. The PG-PHASE Framework aims to assist public health laboratories, health departments and authorities who are either considering transitioning to whole genome sequencing or intending to assess the integration of WGS in public health practice, including the capacity to detect and respond to outbreaks and associated costs, challenges and facilitators in the utilisation of microbial genomics and public health impacts.

Evaluation of Gene Knockouts by CRISPR as Potential Targets for the Genetic Engineering of the Mosquito Culex quinquefasciatus.

Culex quinquefasciatus mosquitoes are a globally widespread vector of several human and animal pathogens. Their biology and behavior allow them to thrive in proximity to urban areas, rendering them a constant public health threat. Their mixed bird/mammal feeding behavior further offers a vehicle for zoonotic pathogens transmission to people and, separately, poses a threat to the conservation of insular birds. The advent of CRISPR has led to the development of novel technologies for the genetic engineering of wild mosquito populations. Yet, research into Cx. quinquefasciatus has been lagging compared to other disease vectors. Here, we use this tool to disrupt a set of five pigmentation genes in Cx. quinquefasciatus that, when altered, lead to visible, homozygous-viable phenotypes. We further validate this approach in separate laboratories and in two distinct strains of Cx. quinquefasciatus that are relevant to potential future public health and bird conservation applications. We generate a double-mutant line, demonstrating the possibility of sequentially combining multiple such mutations in a single individual. Lastly, we target two loci, doublesex in the sex-determination pathway and proboscipedia, a hox gene, demonstrating the flexibility of these methods applied to novel targets. Our work provides a platform of seven validated loci that could be used for targeted mutagenesis in Cx. quinquefasciatus and the future development of genetic suppression strategies for this species. Furthermore, the mutant lines generated here could have widespread utility to the research community using this model organism, as they could be used as targets for transgene delivery, where a copy of the disrupted gene could be included as an easily scored transgenesis marker.

Malocclusion indices and their applications in public health: a review study

Health indices are important to measurement of the presence and severity of diseases. The objective of this study was to perform a literature review about malocclusion indices and analyze their applications in the public health field. The research was performed by consulting Pubmed, SciELO, Web of Science, Scopus, Bireme and Embase databases, using the following terms: public health, epidemiological methods, indices, epidemiological surveys, preventive dentistry, malocclusion, and orthodontics. Publications on the development and use of malocclusion indices in clinical and epidemiological studies were included, without restrictions of methodology and language. Fifty-two indices and their variations were identified. Of these, most were intended for individual assessments and their use in public health was difficult due to the requirements for their application, including the need for specialists, plaster models analysis, complementary exams such as cephalometric radiographs and photographs, specific equipment, the need for longitudinal monitoring of cases, and exclusively objective or subjective assessments. Some malocclusion indices present positive aspects and partially evaluate physical, functional, psychological, and social conditions. However, it is still challenging to find a unanimous index that fulfills the requirements for assessing the severity, treatment need, and impact of malocclusions on the individual’s quality of life with applicability in public health.

The Uniform Poisson–Ailamujia Distribution: Actuarial Measures and Applications in Biological Science

We propose a new asymmetric discrete model by combining the uniform and Poisson–Ailamujia distributions using the binomial decay transformation method. The distribution, named the uniform Poisson–Ailamujia, due to its flexibility is a good alternative to the well-known Poisson and geometric distributions for real data applications in public health, biology, sociology, medicine, and agriculture. Its main statistical properties are studied, including the cumulative and hazard rate functions, moments, and entropy. The new distribution is considered to be suitable for modeling purposes; its parameter is estimated by eight classical methods. Three applications to biological data are presented herein.

The best possible self-intervention as a viable public health tool for the prevention of type 2 diabetes: A reflexive thematic analysis of public experience and engagement.

BackgroundPublic health initiatives seek to modify lifestyle behaviours associated with risk (e.g., diet, exercise, and smoking), but underpinning psychological and affective processes must also be considered to maximize success.ObjectiveThis study aimed to qualitatively assess how participants engaged with and utilized the best possible self (BPS)‐intervention specifically as a type 2 diabetes (T2D) prevention tool.Design and MethodsFourteen participants engaged with a tailored BPS intervention. Reflexive thematic analysis analysed accounts of participant's experiences and feasibility of use.ResultsAll participants submitted evidence of engagement with the intervention. The analysis considered two main themes: Holistic Health and Control. The analysis highlighted several nuanced ways in which individuals conceptualized their health, set goals, and received affective benefits, offering insights into how people personalized a simple intervention to meet their health needs.ConclusionsTo our knowledge, this is the first study to tailor the BPS intervention as a public health application for the prevention of T2D. The intervention enabled users to identify their best possible selves in a way that encouraged T2D preventive behaviours. We propose that our tailored BPS intervention could be a flexible and brief tool to assist public health efforts in encouraging change to aid T2D prevention.Public ContributionThe format, language and application of the BPS intervention were adapted in response to a public consultation group that developed a version specifically for application in this study.

Human Papillomavirus Detection by Whole-Genome Next-Generation Sequencing: Importance of Validation and Quality Assurance Procedures.

Next-generation sequencing (NGS) yields powerful opportunities for studying human papillomavirus (HPV) genomics for applications in epidemiology, public health, and clinical diagnostics. HPV genotypes, variants, and point mutations can be investigated in clinical materials and described in previously unprecedented detail. However, both the NGS laboratory analysis and bioinformatical approach require numerous steps and checks to ensure robust interpretation of results. Here, we provide a step-by-step review of recommendations for validation and quality assurance procedures of each step in the typical NGS workflow, with a focus on whole-genome sequencing approaches. The use of directed pilots and protocols to ensure optimization of sequencing data yield, followed by curated bioinformatical procedures, is particularly emphasized. Finally, the storage and sharing of data sets are discussed. The development of international standards for quality assurance should be a goal for the HPV NGS community, similar to what has been developed for other areas of sequencing efforts including microbiology and molecular pathology. We thus propose that it is time for NGS to be included in the global efforts on quality assurance and improvement of HPV-based testing and diagnostics.

Development and Application of a United States wide correction for PM2.5 data collected with the PurpleAir sensor.

PurpleAir sensors, which measure particulate matter (PM), are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10,000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12,000 24-hour averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (U.S.), including 16 states. Consistent with previous evaluations, under typical ambient and smoke impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40% in most parts of the U.S. A simple linear regression reduces much of this bias across most U.S. regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 μg m-3 to 3 μg m-3 with an average FRM or FEM concentration of 9 μg m-3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the U.S. in the AirNow Fire and Smoke Map (fire.airnow.gov) and has the potential to be successfully used in other air quality and public health applications.

Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE): Overview, Components, and Public Health Applications.

BackgroundThe Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor health indicators of public health importance for the detection and tracking of disease outbreaks, consequences of severe weather, and other events of concern. The ESSENCE concept began in an internally funded project at the Johns Hopkins University Applied Physics Laboratory, advanced with funding from the State of Maryland, and broadened in 1999 as a collaboration with the Walter Reed Army Institute for Research. Versions of the system have been further developed by Johns Hopkins University Applied Physics Laboratory in multiple military and civilian programs for the timely detection and tracking of health threats.ObjectiveThis study aims to describe the components and development of a biosurveillance system increasingly coordinating all-hazards health surveillance and infectious disease monitoring among large and small health departments, to list the key features and lessons learned in the growth of this system, and to describe the range of initiatives and accomplishments of local epidemiologists using it.MethodsThe features of ESSENCE include spatial and temporal statistical alerting, custom querying, user-defined alert notifications, geographical mapping, remote data capture, and event communications. To expedite visualization, configurable and interactive modes of data stratification and filtering, graphical and tabular customization, user preference management, and sharing features allow users to query data and view geographic representations, time series and data details pages, and reports. These features allow ESSENCE users to gather and organize the resulting wealth of information into a coherent view of population health status and communicate findings among users.ResultsThe resulting broad utility, applicability, and adaptability of this system led to the adoption of ESSENCE by the Centers for Disease Control and Prevention, numerous state and local health departments, and the Department of Defense, both nationally and globally. The open-source version of Suite for Automated Global Electronic bioSurveillance is available for global, resource-limited settings. Resourceful users of the US National Syndromic Surveillance Program ESSENCE have applied it to the surveillance of infectious diseases, severe weather and natural disaster events, mass gatherings, chronic diseases and mental health, and injury and substance abuse.ConclusionsWith emerging high-consequence communicable diseases and other health conditions, the continued user requirement–driven enhancements of ESSENCE demonstrate an adaptable disease surveillance capability focused on the everyday needs of public health. The challenge of a live system for widely distributed users with multiple different data sources and high throughput requirements has driven a novel, evolving architecture design.

Blockchain Applications in Health Care and Public Health: Increased Transparency.

BackgroundAlthough big data and smart technologies allow for the development of precision medicine and predictive models in health care, there are still several challenges that need to be addressed before the full potential of these data can be realized (eg, data sharing and interoperability issues, lack of massive genomic data sets, data ownership, and security and privacy of health data). Health companies are exploring the use of blockchain, a tamperproof and distributed digital ledger, to address some of these challenges.ObjectiveIn this viewpoint, we aim to obtain an overview of blockchain solutions that aim to solve challenges in health care from an industry perspective, focusing on solutions developed by health and technology companies.MethodsWe conducted a literature review following the protocol defined by Levac et al to analyze the findings in a systematic manner. In addition to traditional databases such as IEEE and PubMed, we included search and news outlets such as CoinDesk, CoinTelegraph, and Medium.ResultsHealth care companies are using blockchain to improve challenges in five key areas. For electronic health records, blockchain can help to mitigate interoperability and data sharing in the industry by creating an overarching mechanism to link disparate personal records and can stimulate data sharing by connecting owners and buyers directly. For the drug (and food) supply chain, blockchain can provide an auditable log of a product’s provenance and transportation (including information on the conditions in which the product was transported), increasing transparency and eliminating counterfeit products in the supply chain. For health insurance, blockchain can facilitate the claims management process and help users to calculate medical and pharmaceutical benefits. For genomics, by connecting data buyers and owners directly, blockchain can offer a secure and auditable way of sharing genomic data, increasing their availability. For consent management, as all participants in a blockchain network view an immutable version of the truth, blockchain can provide an immutable and timestamped log of consent, increasing transparency in the consent management process.ConclusionsBlockchain technology can improve several challenges faced by the health care industry. However, companies must evaluate how the features of blockchain can affect their systems (eg, the append-only nature of blockchain limits the deletion of data stored in the network, and distributed systems, although more secure, are less efficient). Although these trade-offs need to be considered when viewing blockchain solutions, the technology has the potential to optimize processes, minimize inefficiencies, and increase trust in all contexts covered in this viewpoint.

Social Media Big Data: The Good, The Bad, and the Ugly (Un)truths.

Social Media Big Data: The Good, The Bad, and the Ugly (Un)truths.

Antibody- and nucleic acid-based lateral flow immunoassay for Listeria monocytogenes detection.

Listeria monocytogenes is an invasive opportunistic foodborne pathogen and its routine surveillance is critical for protecting the food supply and public health. The traditional detection methods are time-consuming and require trained personnel. Lateral flow immunoassay (LFIA), on the other hand, is an easy-to-perform, rapid point-of-care test and has been widely used as an inexpensive surveillance tool. In recent times, nucleic acid-based lateral flow immunoassays (NALFIA) are also developed to improve sensitivity and specificity. A significant improvement in lateral flow-based assays has been reported in recent years, especially the ligands (antibodies, nucleic acids, aptamers, bacteriophage), labeling molecules, and overall assay configurations to improve detection sensitivity, specificity, and automated interpretation of results. In most commercial applications, LFIA has been used with enriched food/environmental samples to ensure detection of live cells thus prolonging the assay time to 24-48h; however, with the recent improvement in LFIA sensitivity, results can be obtained in less than 8h with shortened and improved enrichment practices. Incorporation of surface-enhanced Raman spectroscopy and/or immunomagnetic separation could significantly improve LFIA sensitivity for near-real-time point-of-care detection of L. monocytogenes for food safety and public health applications.

The preparation of antibacterial eco-friendly bio-based PTT-based β-cyclodextrin by complexation of copper and zinc ions

There has been an increasing focus on antibacterial textiles owing to their widespread applications in global public health. In this study, we prepared antibacterial bio-based poly (trimethylene terephthalate) (PTT) by multi-crosslinking with citric acid among PTT and β-cyclodextrin (β-CD), and then adsorbing zinc and copper ions. The results show that β-cyclodextrin was successfully grafted onto the surface of PTT. The adsorption amounts of Zn2+ and Cu2+ on β-CD/PTT were investigated by ICP and FE-SEM, which showed that Zn2+ and Cu2+ penetrated the fabric surface. Moreover, under optimal adsorption conditions, the adsorption capacity of Zn2+ and Cu2+ are about 34 mg/g and 50 mg/g, respectively. During antibacterial test, the bactericidal rates of β-CD/Cu2+/PTT reached 90.75% for E. coli and 85.36% for S. aureus, and the bactericidal rates of β-CD/Zn2+/PTT are 80.48% for E. coli and 73.10% for S. aureus. Functionalized PTT fabrics with eco-environmental and sustainable performance may have promising application in the field of antibacterial fabrics.

A global metagenomic map of urban microbiomes and antimicrobial resistance

SummaryWe present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.

Application of 2D‐nanosheet Graphene Oxide and Engineered DNA‐binding Proteins for Specific Gene Detection

DNA detection technologies play an important role in diagnostic applications in the areas of public health and biomedicine. Zinc finger proteins (ZFPs) are the most common DNA-binding domains and multiple ZF domains can be assembled to bind to any desired DNA sequences of interest. In this work, six finger ZFPs were engineered to bind to 18 base pairs of DNA within the stx2gene that encodes for E. coli O157 shiga toxin and the tetM gene (tetracycline resistance gene) with high specificity. Here, we further optimized a novel sensing technology to detect antibiotic resistance genes (ARGs) in bacteria using a graphene oxide-based biosensor utilizing the engineered ZFPs. Two-dimensional graphene oxide (GO) sheet possesses unique electronic, thermal, and mechanical properties. In this study, we take advantage of the quenching ability of GO to create a novel method for detecting the specific double-stranded (ds) DNA sequences within ARGs. Quantum dot (QD)-labeled ZFPs can be adsorbed onto GO via stacking interactions of aromatic and hydrophobic residues in conjunction with hydrogen bonding interaction between hydroxyl or carboxyl groups of GO and hydroxyl or amine groups of the protein. The fluorescence signal of QD-labeled ZFPs is quenched due to fluorescence resonance energy transfer (FRET) between QDs and GO when they are in close proximity. In the presence of target DNA, the bound protein-DNA complex will be dissociated from the GO surface due to the conformational change, thereby restoring the fluorescence signal. Here, we were able to improve the sensitivity of our system, providing a limit of detection as low as 100 pM. This study could bring new capabilities to molecular diagnostics and broaden the spectrum of GO applications in public health and clinical diagnostics.

Wastewater Monitoring Raises Privacy and Ethical Considerations

Analysis of municipal wastewater, or sewage for public health applications is a rapidly expanding field aimed at understanding emerging epidemiological trends, including human and disease migration. The newly gained ability to extract and analyze genetic material from wastewater poses important societal and ethical questions, including: How to safeguard data? Who owns genetic data recovered from wastewater? and What are the ethical and legal issues surrounding its use? In the U.S., both corporate and legal policies regarding privacy have been historically reactive instead of proactive. In wastewater-based epidemiology (WBE), the pace of innovation has outpaced the ability of social and legal mechanisms to keep up. To address this discrepancy, early and robust discussions of the research, policies, and ethics surrounding WBE analysis and genetics are needed. This article contributes to this discussion by examining ownership issues for human genetic data recovered from wastewater and the uses to which it may be put. We focus particularly on the risks associated with personally identifiable data, highlighting potential risks, relevant privacy-enhancing technologies, and appropriate ethics. This article proposes an approach for people conducting WBE studies to help them systematically consider the ethical and privacy implications of their work.

Using a novel genetic algorithm to assess peer influence on willingness to use pre-exposure prophylaxis in networks of Black men who have sex with men

The DeGroot model for opinion diffusion over social networks dates back to the 1970s and models the mechanism by which information or disinformation spreads through a network, changing the opinions of the agents. Extensive research exists about the behavior of the DeGroot model and its variations over theoretical social networks; however, research on how to estimate parameters of this model using data collected from an observed network diffusion process is much more limited. Existing algorithms require large data sets that are often infeasible to obtain in public health or social science applications. In order to expand the use of opinion diffusion models to these and other applications, we developed a novel genetic algorithm capable of recovering the parameters of a DeGroot opinion diffusion process using small data sets, including those with missing data and more model parameters than observed time steps. We demonstrate the efficacy of the algorithm on simulated data and data from a social network intervention leveraging peer influence to increase willingness to take pre-exposure prophylaxis in an effort to decrease transmission of human immunodeficiency virus among Black men who have sex with men.

Progress in the Application of Geostatistics Downscaling Methods in the Field of Public Health

With the continuous development of the two emerging fields of medical geography and spatial epidemiology, Geostatistical downscaling plays an important role in high-precision disease mapping and incidence prediction in a range of public health application This article combines the application cases of different diseases and lists a series of geostatistical downscaling methods, including Area-to-point and Area-to-Area Kriging, Area-to-point and Area-to-area Poisson Kriging, Area-to-point and Area-to- Area regression Kriging, Area-to-point and Area-to-Area co-kriging, Area-to-point and Area-to-Area binomial kriging, etc. By introducing the application progress of different methods in the field of public health, the advantages and disadvantages are compared. Finally, trends and some basic questions of methods proposed recently are discussed in the conclusion.

A Tale of Two Biologies: Distinctions in Philosophy and Practice between Organismal and Sub-Organismal Science

While the 21st century has been proclaimed as the age of biology, the disciplines of sub-organismal biology have received greater attention, often at a cost to organismal biology. However, the fields of organismal biology – ecology and evolution – are not only fundamental to biology, but are of societal importance in terms of their application in environmental conservation, sustainability and public health. We argue here that organismal and sub-organismal biology differ substantially in their philosophy and practice: while organismal biology focuses on systems and collectives, sub-organismal biology rests on reductionism. Further, we emphasize that these distinctions must be recognized in institutional and funding structures for organismal biology to fully realize its potential.