A systematic approach to public health - Novel application of the human factors analysis and classification system to public health and COVID-19

The application of systematic accident analysis tools to investigate food safety incidents

Effective food safety (FS) management relies on the understanding of the factors that contribute to FS incidents (FSIs) and the means for their mitigation and control. This review aims to explore the application of systematic accident analysis tools to both design FS management systems (FSMSs) as well as to investigate FSI to identify contributive and causative factors associated with FSI and the means for their elimination or control. The study has compared and contrasted the diverse characteristics of linear, epidemiological, and systematic accident analysis tools and hazard analysis critical control point (HACCP) and the types and depth of qualitative and quantitative analysis they promote. Systematic accident analysis tools, such as the Accident Map Model, the Functional Resonance Accident Model, or the Systems Theoretical Accident Model and Processes, are flexible systematic approaches to analyzing FSI within a socio-technical food system which is complex and continually evolving. They can be applied at organizational, supply chain, or wider food system levels. As with the application of HACCP principles, the process is time-consuming and requires skilled users to achieve the level of systematic analysis required to ensure effective validation and verification of FSMS and revalidation and reverification following an FSI. Effective revalidation and reverification are essential to prevent recurrent FSI and to inform new practices and processes for emergent FS concerns and the means for their control.

Contributing Factors to Adverse Events in Long-Term Care Facilities in Ireland, a Content Analysis

OBJECTIVES

There is substantial research on contributing factors to adverse events (AEs) in acute settings. Little is known about AEs in long-term care facilities (LTCFs). Our aim was to identify contributing factors to AEs from LTCFs for older persons and people with disability to inform quality improvement.

DESIGN

Content analysis of statutory notifications of AEs from LTCFs using a modified version of the Human Factors Analysis and Classifications System (HFACS) applied to health care.

SETTING

A sample of high risk-rated notifications of AEs received by the regulator in 2018 and 2019 was drawn from the Database of Statutory Notifications from Social Care in Ireland (n = 156).

METHODS

Two researchers independently analyzed notifications to identify contributing factors using our modified HFACS. The number of factors identified in each level, subcategory, and nanocode of the HFACS was calculated along with percentage representation of factors within notifications and percentage contribution of individual factors to the total number of factors identified. The number and percentage contributions of factors were also calculated, disaggregated by notification type.

RESULTS

Contributing factors from all levels of the HFACS were identified. The most common contributing factor was "Resident factors" followed by "Unsafe acts." No contributing factors were attributed to 68 notifications (43.6%). Multiple contributing factors were attributed to 45 notifications (28.8%). The largest percentage of factors was identified in notifications of serious injuries (27.4%).

CONCLUSION AND IMPLICATIONS

It is the responsibility of LTCFs to account for "Resident factors" in system design, risk management, and care plans. Developing systems and processes that identify where residents have higher risk of harm may reduce the occurrence of high-risk events and thus improve resident safety. The large proportion of notifications where no contributing factors were identified suggests the need for improved reflection and reporting from LTCFs and for more specific questions on notification forms.

Patient safety incidents in endoscopy: a human factors analysis of nonprocedural significant harm incidents from the National Reporting and Learning System (NRLS)

BACKGROUND

Despite advances in understanding and reducing the risk of endoscopic procedures, there is little consideration of the safety of the wider endoscopy service. Patient safety incidents (PSIs) still occur. We sought to identify nonprocedural PSIs (nPSIs) and their causative factors from a human factors perspective and generate ideas for safety improvement.

METHODS

Endoscopy-specific PSI reports were extracted from the National Reporting and Learning System (NRLS). A retrospective, cross-sectional human factors analysis of data was performed. Two independent researchers coded data using a hybrid thematic analysis approach. The Human Factors Analysis and Classification System (HFACS) was used to code contributory factors. Analysis informed creation of driver diagrams and key recommendations for safety improvement in endoscopy.

RESULTS

From 2017 to 2019, 1181 endoscopy-specific PSIs of significant harm were reported across England and Wales, with 539 (45.6%) being nPSIs. Five categories accounted for over 80% of all incidents, with "follow-up and surveillance" being the largest (23.4% of all nPSIs). From the free-text incident reports, 487 human factors codes were identified. Decision-based errors were the most common act prior to PSI occurrence. Other frequent preconditions to incidents were focused on environmental factors, particularly overwhelmed resources, patient factors, and ineffective team communication. Lack of staffing, standard operating procedures, effective systems, and clinical pathways were also contributory. Seven key recommendations for improving safety have been made in response to our findings.

CONCLUSIONS

This was the first national-level human factors analysis of endoscopy-specific PSIs. This work will inform safety improvement strategies and should empower individual services to review their approach to safety.

Decision Support Frameworks in Public Health Emergencies: A Systematic Review of Dynamic Models in Complex Contexts

Public health emergencies are extraordinary events of disease spread, with health, economic, and social consequences, which require coordinated actions by governments and society. This work aims to analyze scopes, application possibilities, challenges, and gaps of decision support frameworks in PHE management, using the components of the Health Emergency and Disaster Risk Management Framework (H-EDRM) and the Preparedness, Prevention, Response and Recovery Model (PPRR Model), providing guidelines for the development of new models. A systematic literature review was carried out using the Web of Science, Scopus, and Pubmed knowledge databases on studies published between 2016 and 2023, and thirty-six articles were selected. The outcomes show a concentration of frameworks on short-term emergency response operations, with a limited emphasis on the political and strategic components that drive actors and responsibilities. Management prioritizes monitoring, evaluation, and information management frameworks. However, the models need to overcome the challenges of multisectoral and interdisciplinary action, different levels of decisions and actors, data sharing, and development of common platforms of evidence for decisions fitted to the various emergencies.

Impact of food safety supervision efficiency on preventing and controlling mass public crisis

Food safety has received unprecedented attention since the COVID-19 outbreak. Exploring food safety regulatory mechanisms in the context of cluster public crises is critical for COVID-19 prevention and control. As a result, using data from a food safety regulation survey in the Bei-jing-Tianjin-Hebei urban cluster, this paper investigates the impact of food safety regulation on the prevention and control of COVID-19. The study found that food safety regulation and cluster public crisis prevention and control have a significant positive relationship, with the ability to integrate regulatory resources acting as a mediator between the two. Second, industry groups argue that the relationship between regulatory efficiency and regulatory resource integration should be moderated in a positive manner. Finally, industry association support positively moderates the mediating role of regulatory re-source integration capacity between food safety regulatory efficiency and cluster public crises, and there is a mediating effect of being moderated. Our findings shed light on the mechanisms underlying the roles of regulatory efficiency, resource integration capacity, and industry association support in food safety, and they serve as a useful benchmark for further improving food safety regulations during the COVID-19 outbreak.

Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS

The risk factors affecting workers' unsafe acts were comprehensively identified by Human Factors Analysis and Classification System (HFACS) and grounded theory based on interview data and accident reports from deep coal mines. Firstly, we collected accident case and field interview data from deep coal mines issued by authoritative institutions. Then, the data were coded according to grounded theory to obtain relevant concepts and types. The HFACS model was used to classify the concepts and categories. Finally, the relationship between core and secondary categories was sorted out by applying a story plot. The results show that risk factors of unsafe acts of deep coal mine workers include environmental factors, organizational influence, unsafe supervision and unsafe state of miners, and the main manifestations of unsafe acts are errors and violations. Among them, the unsafe state of miners is the intermediate variable, and other factors indirectly affect risky actions of coal miners through unsafe sates. Resource management, organizational processes and failure to correct problems are the top three risk factors that occur more frequently in unsafe acts. The three most common types of unsafe act are unreasonable labor organization, failure to enforce rules, and inadequate technical specifications. By combining grounded theory and the HFACS framework to analyze data, risk factors for deep coal miners can be quickly identified, and more precise and comprehensive conceptual models of risk factors in unsafe acts of deep coal miners can be obtained.