A Reproducible Python-Based Computational Pipeline for Real-Time Ingestion, Advanced Analysis, and Dynamic Reporting of Public Health Data: A Systems Validation Study.

External service integration and adherence to industry standards has become ever more important for collections data management platforms. External APIs (Application Programming Interfaces), allow for the development of bi-directional data flows critical to service integration. In contrast to service-oriented backend APIs, public APIs must have continually up-to-date, comprehensive documentation that covers common use cases, on-the-fly request validation, and meaningful error messages. OpenAPI (OpenAPI Initiative 2021), a machine-readable API documentation specification can help significantly with testing and maintenance, and libraries can be used to automate common maintenance tasks. Specify 7 is a biological collections data management platform developed by the Specify Collections Consortium (Specify Software Consortium 2021). This presentation summarizes the challenges and lessons learned with publishing the existing backend Specify 7 API to a public-facing external API. Each Specify 7 API is composed of 200 resources. A standard set of CRUD (Create, Read, Update, Delete) operations is provided for each resource for client interaction with a group of service-based endpoints for bulk operations such as file uploads, file-based data imports, and attachment manipulation. To support the migration, we developed a custom library to enhance request validation. Parameter validation is extended through a real-time comparison against the existing schema and data. The library is available to the community under a MIT license on GitHub (https://github.com/specify/open_api_tools/). In this presentation, we will close with an overview of the next steps for the Specify 7 public API. These include: An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects. An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects.

Heterogeneity and Interoperability in Local Public Health Information Systems.

This paper aims to analyze the current context of Application Programming Interface (API) technology, with focus in External API (Public API or Open API) by news organizations. The adoption of API has been considered a technological strategy that introduces innovation processes in media groups. This technological approach allows the public access to data, increase the distribution of journalistic information and transparency which improves the credibility of organizations. However, there is not a pattern strategic in the use of API adopted by news organizations and the external developers have used various technologies, named toolkit, to store, process, analyze and visualize data. The study cases were The New York Times, The Guardian, NPR and, ESPN.

Denmark have unique health informatics databases e.g. "The Children's Database", which since 2009 holds data on all Danish children from birth until 17 years of age. In the current set-up a number of potential sources of errors exist - both technical and human-which means that the data is flawed. This gives rise to erroneous statistics and makes the data unsuitable for research purposes. In order to make the data usable, it is necessary to develop new methods for validating the data generation process at the municipal/regional/national level. In the present ongoing research project, two research areas are combined: Public Health Informatics and Computer Science, and both ethnographic as well as system engineering research methods are used. The project is expected to generate new generic methods and knowledge about electronic data collection and transmission in different social contexts and by different social groups and thus to be of international importance, since this is sparsely documented in the Public Health Informatics perspective. This paper presents the preliminary results, which indicate that health information technology used ought to be subject for redesign, where a thorough insight into the work practices should be point of departure.

In public health computing, artificial intelligence (AI) and big data analytics together provide a wealth of fresh approaches to handle significant public health issues, enhance patient outcomes, and guide choices. Standard approaches of analysis may fail to provide real-time insights that can be utilised to move fast as the volume of data in healthcare systems all across the globe rises. Together, artificial intelligence (AI) and big data analytics can manage enormous volumes of various kinds of health data, including social aspects of health, public health data, and electronic health records (EHR). This combination allows one to build prediction models able to detect emerging illnesses, see health trends approaching, and identify groups of persons at risk. From vast volumes of data, artificial intelligence systems—including deep learning and machine learning—can identify helpful patterns. This clarifies risk factors, forecasts disease outbreaks, and guides choices on the most efficient use of resources. Moreover, Big Data analytics allows us to examine large-scale effects of activities, thereby enabling individuals in decision-making to do so grounded on strong evidence. By anticipating how each patient will do, thus improving treatments, and so reducing variations in access to and outcomes of healthcare, using AI and Big Data combined may also assist to personalise healthcare. Using AI and Big Data in public health informatics presents some challenges even with these advances. Concerns concerning data security, the requirement of uniform data formats, and the possibility that algorithms may produce biassed choices abound, for instance. Dealing with these problems is very crucial if we are to guarantee fair and ethical use of Big Data and artificial intelligence to enhance public health choices. This article discusses how Big Data analytics and artificial intelligence will transform public health informatics going forward. It lists their advantages and drawbacks and offers ideas for improving the responses on the pitch.

Effectively communicating data to a broad range of audiences plays an important role in public health. The COVID-19 pandemic showed how in times of public health crises, people seek out data. Because of this emerging broad interest in public health data, the accessibility and usability of data visualizations and data communication products are critical to reaching not only public health professionals but others, too. At the New York City Department of Health and Mental Hygiene, we've worked to make public health data comprehensible and actionable through an approach that features user-centered design, and incorporates health literacy principles. Key strategies include conducting research with users to understand their needs, ensuring usability in web-based products, and designing data visualization products to be easily understood by both experts and general audience. Explanatory data products, which highlight key insights, play a crucial role in making data accessible. As public health crises continue to demand clear and transparent information sharing, we advocate for a sustained commitment to user-centered design in public health data communication, ensuring that data is not only available but also truly accessible and useful to the public.

With the acceleration of urbanization, the risk of urban population exposure to environmental pollutants is increasing. Protecting public health is the top priority in the construction of smart cities. The purpose of this study is to propose a method for identifying toxicological biological indicators of human exposure in smart cities based on public health data and deep learning to achieve accurate assessment and management of exposure risks. Initially, the study used a network of sensors within the smart city infrastructure to collect environmental monitoring data, including indicators such as air quality, water quality, and soil pollution. Using public health data, a database containing information on types and concentrations of environmental pollutants has been established. Convolutional neural network was used to recognize the pattern of environmental monitoring data, identify the relationship between different indicators, and build the correlation model between health indicators and environmental indicators. Identify biological indicators associated with environmental pollution exposure through training optimization. Experimental analysis showed that the prediction accuracy of the model reached 93.45%, which could provide decision support for the government and the health sector. In the recognition of the association pattern between respiratory diseases, cardiovascular diseases and environmental exposure factors such as PM2.5 and SO2, the fitting degree between the model and the simulation value reached more than 0.90. The research design model can play a positive role in public health and provide new decision-making ideas for protecting public health.

IntroductionIndigenous Peoples are not always engaged in setting health priorities and decry misapplication and misinterpretation of their data. Indigenous data governance and sovereignty can help overcome these issues, but they are variably implemented across Nations. Public health data governance is uniquely challenging, with implementation gaps presenting an exciting opportunity to define and remove barriers at all levels of government.MethodsWe sought to identify barriers (what) and facilitators (how) to implementing a local data governance plan with the goal of increasing Innu participation in public health. Working with two First Nations in Newfoundland and Labrador, Canada, and supported by strong relationships, 18 participants who work with Innu health data across federal, provincial and Innu jurisdictions were interviewed and participated in group discussions to produce rich qualitative data on the subject. The research plan was led by the First Nations.ResultsParticipants identified that size and organisation matter to data governance, and novel partnerships must be considered to achieve the economies of scale necessary for infrastructure. Public health data have multiple custodians and require governance across multiple jurisdictions. Creating a round table (a space for dialogue among equals) to guide the appropriate collection, use and reporting of locally relevant public health data has emerged as an actionable recommendation. In this model, public health data were viewed as co-created by all parties, while Innu led analysis and interpretation. Barriers to data governance included the complexity of accessing data, lack of space to meet as equals, lack of common goals, mistrust and racism. Facilitators included the recognised need for an adequately resourced Innu Health Data Office, communication, trust and good relationships.ConclusionFor Innu, data governance advances self-determination, potentially redressing the current inequitable distribution of power in public health planning. Describing the challenges Innu face in operationalising public health data governance and strategies to overcome them may be generalisable to other Indigenous Nations attempting to do the same, especially those with small populations.

Health Systems Need to Transform Data Collection to Advance Health Equity.

Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.

ABSTRACTWhile many firms in recent years have started to offer public Application Programming Interfaces (APIs), firms struggle with shaping digital platform strategies that align API design with aspired business goals and the demands of external developers. We address the lack of theory that explains the performance impacts of three API archetypes (professional, mediation, and open asset services). We couple survey data from 152 API product managers with manually coded API design classifications. With this data, we conduct cluster and regression analyses that reveal moderating effects of two value creation strategies (economies of scope in production and innovation) on the relationships between API archetype similarity and two API performance outcomes: return on investment and diffusion. We contribute to IS literature by developing a unifying theory that consolidates different theoretical perspectives on API design, by extending current knowledge on the performance effects of API design, and by empirically studying the distinct circumstances under which digital platforms facilitate economies of scope in production or in innovation. Our results provide practical implications on how API providers can align API archetype choice with the value creation strategy and the API’s business objective.

Summary: The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data.Availability and implementation: Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl.Contact:peter@biomatters.com

Objective Government expenditure on and the number of aged care facilities in Australia have increased consistently since 1995. As a result, a range of aged care policy changes have been implemented. Data on demographics and utilisation are important in determining the effects of policy on residential aged care services. Yet, there are surprisingly few statistical summaries in the peer-reviewed literature on the profile of Australian aged care residents or trends in service utilisation. Therefore, the aim of the present study was to characterise the demographic profile and utilisation of a large cohort of residential aged care residents, including trends over a 3-year period. Methods We collected 3 years of data (2011-14) from 77 residential aged care facilities and assessed trends and differences across five demographic and three service utilisation variables. Results The median age at admission over the 3-year period remained constant at 86 years. There were statistically significant decreases in separations to home (z=2.62, P=0.009) and a 1.35% increase in low care admissions. Widowed females made up the majority (44.75%) of permanent residents, were the oldest and had the longest lengths of stay. One-third of permanent residents had resided in aged care for 3 years or longer. Approximately 30% of residents were not born in Australia. Aboriginal residents made up less than 1% of the studied population, were younger and had shorter stays than non-Aboriginal residents. Conclusion The analyses revealed a clear demographic profile and consistent pattern of utilisation of aged care facilities. There have been several changes in aged care policy over the decades. The analyses outlined herein illustrate how community, health services and public health data can be used to inform policy, monitor progress and assess whether intended policy has had the desired effects on aged care services. What is known about the topic? Characterisation of permanent residents and their utilisation of residential aged care facilities is poorly described in the peer-reviewed literature. Further, publicly available government reports are incomplete or characterised using incomplete methods. What does this paper add? The analyses in the present study revealed a clear demographic profile and consistent pattern of utilisation of aged care facilities. The most significant finding of the study is that one-third of permanent residents had resided in an aged care facility for ≥3 years. These findings add to the overall picture of residential aged care utilisation in Australia. What are the implications for practitioners? The analyses outlined herein illustrate how community, health services and public health data can be utilised to inform policy, monitor progress and assess whether or not intended policy has had the desired effects on aged care services.

The quality and timeliness of public health data is a topic of prime concern in this information age. Many epidemiologists, health scientists and researchers in the public health domain have consistently emphasized on the importance of the need for the right timely data for the right decision-making at the right time. In other words, there is an urgent need to ensure that the right data reaches the right people at the right time. However, this urgent need appears to be misleading and not achievable in the current public health practices and workflow processes. The workflow processes in the current healthcare environments enable data collection to be delayed and only to be captured when the events have already occurred. In this paper, a systematic review of relevant scientific literature was used to not only explore the complexity and uniqueness of public health data, but also explain why improving the quality and timeliness of public health data is a challenging endeavor for many epidemiologists, health scientists and researchers. Recommendations for streamlining the public health workflow processes to support the generation of high-quality and timely public health data were also discussed in the paper.

In December 2008, the Centers for Disease Control and Prevention (CDC) convened a meeting of national public health partners to identify priorities for addressing social determinants of human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), viral hepatitis, sexually transmitted diseases (STDs), and tuberculosis (TB). The consultants were divided into four working groups: (1) public health policy, (2) data systems, (3) agency partnerships and prevention capacity building, and (4) prevention research and evaluation. Groups focused on identifying top priorities; describing activities, methods, and metrics to implement priorities; and identifying partnerships and resources required to implement priorities. The meeting resulted in priorities for public health policy, improving data collection methods, enhancing existing and expanding future partnerships, and improving selection criteria and evaluation of evidence-based interventions. CDC is developing a national communications plan to guide and inspire action for keeping social determinants of HIV/AIDS, viral hepatitis, STDs, and TB in the forefront of public health activities.