Environmental Health Research Research Articles

Advances and perspectives in environmental health research in China

Advances and perspectives in environmental health research in China

Non-Nutritive Suck Parameters Measurements Using a Custom Pressure Transducer System.

The non-nutritive suck (NNS) device is a transportable, user-friendly pressure transducer system that quantifies infants' NNS behavior on a pacifier. Recording and analysis of the NNS signal using our system can provide measures of an infant's NNS burst duration (s), amplitude (cmH2O), and frequency (Hz). Accurate, reliable, and quantitative assessment of NNS has immense value in serving as a biomarker for future feeding, speech-language, cognitive, and motor development. The NNS device has been used in numerous research lines, some of which have included measuring NNS features to investigate the effects of feeding-related interventions, characterizing NNS development across populations, and correlating sucking behaviors with subsequent neurodevelopment. The device has also been used in environmental health research to examine how exposures in utero can influence infant NNS development. Thus, the overarching goal in research and clinical utilization of the NNS device is to correlate NNS parameters with neurodevelopmental outcomes to identify children at risk for developmental delays and provide rapid early intervention.

Critical Environmental Injustice: A Case Study Approach to Understanding Disproportionate Exposure to Toxic Emissions.

Public health research and environmental health research, specifically, have focused on toxic exposure-encompassing individuals or communities that are disproportionately exposed to contaminants that are harmful or potentially harmful to them. Yet, little research has applied critical environmental justice theory-characterized by the idea that marginalized communities need to be treated as indispensable rather than disposable-to the study of toxic exposure. To fill this gap, the current paper offers a case study approach applying critical environmental justice theory to the study of disproportionate and unequal exposure to toxic contaminants. This case study is of Kettleman City, a rural, unincorporated community in the heart of California's Central Valley (USA). This community experiences the co-location of environmental hazards, including residing at the intersection of two major highways and hosting a class I hazardous-waste landfill, which is one of the few licensed to accept PCBs. PCBs are a contaminant that has been linked with several adverse health outcomes, including cancers and low birthweight. Residents may also experience poor air quality from proximity to the highways. This case highlights the uneven distribution of pollution and environmental degradation that may be shouldered by the community, along with their experiences of adverse health and social impacts. This analysis reveals the importance of incorporating a critical environmental justice perspective to unpack experiences of not only disproportionate exposure but also disproportionate procedural and recognitional inequality. This research highlights the untapped potential of environmental justice to catalyze exposure science in challenging the unequal distribution of contaminants.

Comparison of maternal venous blood metabolomics collected as dried blood spots, dried blood microsamplers, and plasma for integrative environmental health research

Use of capillary blood devices for exposome research can deepen our understanding of the intricate relationship between environment and health, and open up new avenues for preventive and personalized medicine, particularly for vulnerable populations. While the potential of these whole blood devices to accurately measure chemicals and metabolites has been demonstrated, how untargeted metabolomics data from these samplers can be integrated with previous and ongoing environmental health studies that have used conventional blood collection approaches is not yet clear. Therefore, we performed a comprehensive comparison between relative-quantitative metabolite profiles measured in venous blood collected with dried whole blood microsamplers (DBM), dried whole blood spots (DBS), and plasma from 54 mothers in an ethnically diverse population. We determined that a majority of the 309 chemicals and metabolites showed similar median intensity rank, moderate correlation, and moderate agreement between participant-quantiled intraclass correlation coefficients (ICCs) for pair-wise comparisons among the three biomatrices. In particular, whole blood sample types, DBM and DBS, were in highest agreement across metabolite comparison metrics, followed by metabolites measured in DBM and plasma, and then metabolites measured in DBS and plasma. We provide descriptive characteristics and measurement summaries as a reference database. This includes unique metabolites that were particularly concordant or discordant in pairwise comparisons. Our results demonstrate that the range of metabolites from untargeted metabolomics data collected with DBM, DBS, and plasma provides biologically relevant information for use in independent exposome investigations. However, before meta-analysis with combined datasets are performed, robust statistical approaches that integrate untargeted metabolomics data collected on different blood matrices need to be developed.

Citizen science in environmental health research: A comparison with conventional approaches and creation of a guidance tool issued from the LILAS initiative

ContextPublic interest for citizen science (CS) in environmental health is growing. The goals of environmental health research projects are diverse, as are the methods used to reach these goals. Opportunities for greater implication of the civil society and related challenges differ at each step of such projects. These methodological aspects need to be widely shared and understood by all stakeholders. The LILAS initiative (acronym for “application of citizen science approaches such as LIving LAbS to research on environmental exposures and chronic risks”) aimed to 1) favor a mutual understanding of the main issues and research methods in environmental health, of their stakes for different actors, but also of the requirements, strengths and limitations of these methods and to 2) identify expected benefits and points of attention related to stronger degrees of participation as part of environmental health research projects. MethodsThe LILAS initiative gathered institutional researchers, academics and civil society representatives interested in environmental exposures. Five meetings allowed to collectively identify different types of environmental health research studies and reflect about the benefits, limitations, and methodological issues related to the introduction of growing citizen participation as part of such studies. An analytic table matrix summarizing these aspects was co-created and filled by participants, as a tool devoted to help stakeholders with the definition of future CS research projects in environmental health. ResultsFor different fields of research (e.g.: studies for assessment of environmental exposures, interventions on these exposures, quantitative risk assessment, epidemiological studies), the matrix lists expected benefits for various stakeholders, the fundamental principles of research methods and related practical constraints, but also advantages and limitations related to the use of CS or conventional research approaches. ConclusionThe LILAS initiative allowed to develop a tool which provides consolidated grounds for the co-creation of research projects on environmental exposures involving CS.

Identifying children’s environmental health risks, needs, misconceptions, and opportunities for research translation using social media

As part of the Advancing Science, Practice, Programming, and Policy in Research Translation for Children’s Environmental Health (ASP3IRE) center, machine learning, geographic information systems (GIS), and natural language processing to analyze more than 650 million posts related to children’s environmental health are being used. Using preliminary analyses as examples, this commentary discusses the potential opportunities, benefits, challenges, and limitations of children’s health social media analytics. Social media contains large volumes of contextually rich data that describe children’s health risks and needs, characteristics of homes and childcare locations important to environmental exposures, and parent and childcare provider perceptions, awareness of, and misconceptions about children’s environmental health. Twenty five million unique conversations mentioning children, with likes, views, and replies from more than 33 million X (formerly Twitter) users were identified. Many of these posts can be linked to traditional environmental and health data. However, social media analytics have several challenges and limitations. Challenges include a need for interdisciplinary collaborations, selectivity and sensitivity of analytical methods, the dynamic, evolving communication methods and platform preferences of social media users, and operational policies. Limitations include data availability, generalizability, and self-report bias. Social media analytics has significant potential to contribute to children’s environmental health research and translation.

Associations of neighborhood greenspace, and active living environments with autism spectrum disorders: A matched case-control study in Ontario, Canada

BackgroundIncreasing evidence links early life residential exposure to natural urban environmental attributes and positive health outcomes in children. However, few studies have focused on their protective effects on the risk of autism spectrum disorder (ASD). The aim of this study was to investigate the associations of neighborhood greenspace, and active living environments during pregnancy with ASD in young children (≤6 years). MethodsWe conducted a population-based matched case-control study of singleton term births in Ontario, Canada for 2012–2016. The ASD and environmental data was generated using the Ontario Autism Spectrum Profile, the Better Outcomes Registry & Network Ontario, and Canadian Urban Environmental Health Research Consortium. We employed conditional logistic regressions to estimate the odds ratio (OR) between ASD and environmental factors characterizing selected greenspace metrics and neighborhoods conducive to active living (i.e., green view index (GVI), normalized difference vegetation index (NDVI), tree canopy, park proximity and active living environments index (ALE)). ResultsWe linked 8643 mother-child pairs, including 1554 cases (18%). NDVI (OR 1.034, 0.944–1.024, per Inter Quartile Range [IQR] = 0.08), GVI (OR 1.025, 95% CI 0.953–1.087, per IQR = 9.45%), tree canopy (OR 0.992, 95% CI 0.903–1.089, per IQR = 6.24%) and the different categories of ALE were not associated with ASD in adjusted models for air pollution. In contrast, living closer to a park was protective (OR 0.888, 0.833–0.948, per 0.06 increase in park proximity index), when adjusted for air pollution. ConclusionsThis study reported mixed findings showing both null and beneficial effects of green spaces and active living environments on ASD. Further investigations are warranted to elucidate the role of exposure to greenspaces and active living environments on the development of ASD.

345 Fostering academic-community research teams to conduct community-engaged research in environmental justice communities: The RISE Communities R25 program

OBJECTIVES/GOALS: Residents of environmental justice (EJ) communities experience significantly higher rates of negative health outcomes associated with poor air quality. Low-cost air sensors may supplement regulatory monitoring to better measure air pollution at local scales, but widespread application of this technology remains limited due to many challenges. METHODS/STUDY POPULATION: To address these obstacles, we designed a training program to equip community and academic research partners with the skills and knowledge to successfully apply low-cost sensors in community-engaged environmental health research. The R esearch Innovations using Sensor Technology in Environmental Justice Communities (RISE Communities) program was established through an NIEHS R25 award in 2022 and has three specific aims: 1) Foster community-academic partnerships through research education, training, and team development activities, 2) Provide technical training in the application of low-cost sensors for indoor, outdoor, and personal air monitoring in EJ communities, and 3) Establish a community of practice to address air quality in communities nationwide. RESULTS/ANTICIPATED RESULTS: We hosted our first cohort in August 2023, training five community-academic research teams in team collaboration, community-engaged research, and technical skills for collecting and analyzing data from PurpleAir sensors. Each team received 12 sensors to take to their home EJ communities to begin their projects. Community of practice development continues through interactive webinars and development of a web-based repository of training videos and discussion board posts. Evaluation data show high participant learning and satisfaction, with mean confidence scores improving on 6/8 metrics. Evaluation data also suggest several areas for improvement such as more time spent in teams for planning and additional opportunities for interaction within the cohort and with program instructors for problem-solving. DISCUSSION/SIGNIFICANCE: Effective training for team-based community-engaged research requires careful planning for team development and study implementation. Longitudinal training and support for the technical aspects of utilizing air sensors is also critical to team success. The RISE Communities program is actively recruiting for future training cohorts.

Coming together as a whole: lessons from connecting across sectors, cultures, and contexts to elevate efforts for planetary health

BackgroundAddressing complex and interconnected ecological, social, and health issues necessitates upstream, solutions-oriented, and whole-systems thinking. Specifically, exploring what it means to live in reciprocity with the planet and all living systems, now and for generations to come, can have a crucial role in advancing planetary health. MethodsIn this presentation, we show findings from four gatherings that we co-designed and co-hosted to connect communities, lands, waters, climate, and health. We draw lessons from two events in the Cowichan watershed (co-hosted by Cowichan Tribes) and the Stellako and Nechako watersheds (co-hosted by Stellat'en First Nation) that were co-designed with the ECHO Network. These gatherings brought together youth, Indigenous leaders, researchers, and health, community, and environmental decision-makers across British Columbia to learn how to address connected health, environmental, and community concerns. We supplement these findings with insights from two co-hosted conversations, that connected five continents and more than 40 countries, each enabling 24 h of continuous dialogue on the theme of working together for a healthy, just, and sustainable planet. FindingsGatherings took place between Oct 12, 2022, and Dec 1, 2023. These gatherings each provided lessons about how Indigenous-led, integrative approaches that connect the health of people to lands, waters, and ecosystems can elevate and enhance our work and scholarship to address ecological, social, and health issues. Specifically, initiatives co-designed in this way help overcome challenges that arise when addressing boundary-crossing, intersectional and intersectoral issues at the nexus of climate, public health, and planetary health. Our findings include insights into strengthening research and public health capacity for integrative approaches to complex issues that are relevant to place-based contexts and have implications and applications for planetary health. InterpretationOur presentation summarises how these gatherings—each of which were fuelled by a sense of love for the planet, each other, and future generations—have progressed intersectoral, intergenerational, boundary-crossing, and transformative approaches to planetary health. These insights help guide future directions for planetary health research, practice, and policy. FundingThe Canadian Institute of Health Research Environment (IP4150712), Michael Smith Health Research BC (RA-2022-2872), and Vancouver Foundation (FOI19-1781).

A novel correction method for modelling parameter-driven autocorrelated time series with count outcome

BackgroundCount time series (e.g., daily deaths) are a very common type of data in environmental health research. The series is generally autocorrelated, while the widely used generalized linear model is based on the assumption of independent outcomes. None of the existing methods for modelling parameter-driven count time series can obtain consistent and reliable standard error of parameter estimates, causing potential inflation of type I error rate.MethodsWe proposed a new maximum significant ρ correction (MSRC) method that utilizes information of significant autocorrelation coefficient ρ estimate within 5 orders by moment estimation. A Monte Carlo simulation was conducted to evaluate and compare the finite sample performance of the MSRC and classical unbiased correction (UB-corrected) method. We demonstrated a real-data analysis for assessing the effect of drunk driving regulations on the incidence of road traffic injuries (RTIs) using MSRC in Shenzhen, China. Moreover, there is no previous paper assessing the time-varying intervention effect and considering autocorrelation based on daily data of RTIs.ResultsBoth methods had a small bias in the regression coefficients. The autocorrelation coefficient estimated by UB-corrected is slightly underestimated at high autocorrelation (≥ 0.6), leading to the inflation of the type I error rate. The new method well controlled the type I error rate when the sample size reached 340. Moreover, the power of MSRC increased with increasing sample size and effect size and decreasing nuisance parameters, and it approached UB-corrected when ρ was small (≤ 0.4), but became more reliable as autocorrelation increased further. The daily data of RTIs exhibited significant autocorrelation after controlling for potential confounding, and therefore the MSRC was preferable to the UB-corrected. The intervention contributed to a decrease in the incidence of RTIs by 8.34% (95% CI, -5.69–20.51%), 45.07% (95% CI, 25.86–59.30%) and 42.94% (95% CI, 9.56–64.00%) at 1, 3 and 5 years after the implementation of the intervention, respectively.ConclusionsThe proposed MSRC method provides a reliable and consistent approach for modelling parameter-driven time series with autocorrelated count data. It offers improved estimation compared to existing methods. The strict drunk driving regulations can reduce the risk of RTIs.

Validating spatio-temporal environmental machine learning models: Simpson’s paradox and data splits

Machine learning has revolutionized environmental sciences by estimating scarce environmental data, such as air quality, land cover type, wildlife population counts, and disease risk. However, current methods for validating these models often ignore the spatial or temporal structure commonly found in environmental data, leading to inaccurate evaluations of model quality. This paper outlines the problems that can arise from such validation methods and describes how to avoid erroneous assumptions about training data structure. In an example on air quality estimation, we show that a poor model with an r 2 of 0.09 can falsely appear to achieve an r 2 value of 0.73 by failing to account for Simpson’s paradox. This same model’s r 2 can further inflate to 0.82 when improperly splitting data. To ensure high-quality synthetic data for research in environmental science, justice, and health, researchers must use validation procedures that reflect the structure of their training data.

The EMPOWER program: a history and guide for increasing diversity using integrated research and education.

The prevalence and pervasive nature of emerging chemicals of concern have created widespread environmental injustice apprehensions in vulnerable communities. To alleviate and address these concerns, identifying, engaging, and training a diverse environmental health research workforce will be critical and necessary steps to combat and prevent the consequences of environmental injustice. While there is an obvious need to enhance diversity in environmental health research, this process is hampered by facets of systemic racism that reduce access to educational resources needed to build interest and knowledge in students and teachers. We present here a historical perspective to offer a guide for building programs and relationships with underserved schools to help overcome limiting factors that have plagued certain public school systems. With the proper training and mentorship, the untapped workforce present within these schools will be empowered to understand and address current and emerging environmental health and safety threats. Through this transformative 8-week high school research program, we will develop well-prepared, ethical researchers committed to scientific inquiry, intensive fieldwork, and collaborative problem solving to address environmental health challenges. Following the four-step risk assessment process, students, teachers, and faculty mentors will work collaboratively to identify toxicants, potential hazards and risks, and environmental disparities in urban neighborhoods, which provides the necessary training to formulate critical thinking skills for use in academic or nonacademic careers.NEW & NOTEWORTHY The Engaging Multidisciplinary Professional Opportunities for Women in Environmental Research (EMPOWER) program is a one-of-a-kind research summer experience for minority female high school students in the state of Georgia. In addition, this program provides high school teachers with hands-on experiences that can be adapted to use in the classrooms. This combination of lab and field research immerses participants in understanding urban environmental exposures and their health effects. The EMPOWER program was established to meet the critical need for increasing diversity in science, technology, engineering, and mathematics.

A bibliometric and scientometric analysis-based review of environmental health and safety research in the construction industry

Purpose The construction business is widely recognised for its inherent complexity and dynamic nature, which stems from the nature of the job involved. The industry is often regarded as one of the most challenging industries globally in terms of implementing environmental, health and safety (EHS) practices. However, in the absence of EHS, the construction industry cannot be considered sustainable. Therefore, this study aims to identify the trends, knowledge gaps and implications of EHS research to enhance construction activities and knowledge. Design/methodology/approach The study adopted a science mapping approach involving bibliometric and scientometric analysis of 407 construction EHS publications from the Scopus database with the VOSviewer software. The study is based on journal articles from the Scopus database without restriction to any time range. Findings The main focus of construction EHS research identified in the study includes sustainability-related studies, risk-related, environmental issues, EHS management, integrated management systems studies, health and safety related and EHS in the construction process. Some emerging areas also identified include productivity, design, culture, social sustainability and machine learning. The most influential and productive publication sources, countries/regions and EHS publications with the highest impact were also determined. Research limitations/implications Documents published in the Scopus database were considered for analysis because of the wider coverage of the database. Journal articles written in English language represent the inclusion criteria, whereas other documents were excluded from the analysis. The study also limited the search to articles with the engineering subject area. Practical implications The research findings will enlighten stakeholders and practitioners on the focal knowledge areas in the EHS research domain, which are vital for enhancing EHS in the industry. Originality/value To the best of the authors’ knowledge, this review-based study is the first attempt to internationally conduct a science mapping on extant literature in the EHS research domain through bibliometric and scientometric assessments.

Practical Aspects for Applying Picard Iterations to the SIR Model Using Actual Data

The updated version of the Picard method for solving systems of differential equations is employed to solve the SIR system. A local performance of the Picard iteration algorithm combined with the Gauss-Seidel approach is applied to the SIR model. The integral form of the SIR model, in addition to the use of Gauss-Seidel philosophy (using the most recent calculated values), achieved more accuracy in the computational work than those obtained using the differential forms. Documented data regarding the spread of corona virus 19 in the Kingdom of Saudi Arabia region from April to the end of December 2020 were used to calculate the corresponding actual values for the parameters and the initial conditions. Due to efficient management and to obtain representable behaviours, we restricted the size of the study to only 1% of the population. The global characteristics of the integral formulation have affected the calculations accurately. The initial conditions and the model’s parameters are established depending on the documented data. The results illustrate the superiority of the updated Picard formulation over the classical Picard within their domain of convergence. The results of this study illuminate and validate the importance of mathematical modeling. These findings can provide valuable insights into mathematical modeling for those involved in environmental health research, especially those responsible for devising strategic plans.

Environmental and social inequalities in health: Neighborhood-level composite indices as a decision-making tool

The environmental health research carried out on a broad scale does not provide decision-makers with tools for managing environmental and social inequalities in health on a fine scale. The goal of the present study was to develop a method for neighborhood-level territorial diagnosis that decision-makers can use to incorporate health issues into their planning policies. The primary objective was to analyze composite spatial vulnerability and resilience indices in the Lille metropolitan area in northern France. The secondary objective was to refine these results by taking account of neighborhood-level characteristics. The study highlights the interest of transdisciplinary research and collaboration with local experts to effectively evaluate and address environmental health issue (more than 30 experts gathered and 12 thematic workshops organized). The results showed that the proposed method allows for the comparison of neighborhoods; however, certain specificities may be underestimated. The real importance of certain variables linked to specific contexts can be minimized, which can distort our understanding of the challenges these neighborhoods face. Analyzing the environmental and social inequalities in health in the metropolitan area's center vs. its belt made it possible to take better account of the characteristics of the areas studied.

First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning

Ground-level ozone (O3), renowned for its adverse impacts on human health and crop production, has garnered significant attention from governmental and public sectors. To address the limitations posed by sparse and uneven ground-level O3 observations, this study proposes an innovative method for hourly full-coverage ground-level O3 estimation using machine learning. Meteorological data from National Centers for Environmental Prediction global forecasting system, satellite data from Fengyun-4 A(FY-4 A) and Ozone Monitoring Instrument, emission inventory from Multi-resolution Emission Inventory for China, and other auxiliary data are utilized as input variables, while ground-based O3 observations serve as the response variable. The method is applied on a monthly basis across China for the year 2022, resulting in the generation of an hourly full-coverage high-resolution (4 km) ground-level O3 estimation, termed ML-derived-O3. Cross-validation results demonstrate the robustness of ML-derived-O3 yielding a coefficient of determination (R 2) of 0.96 (0.91) for sample-based (site-based) evaluations and a root-mean-square error (RMSE) of 9.22 (13.65) µg m−3. However, the date-based evaluation is less satisfactory due to the imbalanced training data, resulting from the pronounced daily variations in ground-level O3 concentrations. Nevertheless, the seasonal and hourly ML-derived-O3 exhibits high prediction accuracy, with R 2 values surpassing 0.95 and RMSE remaining below 7.5 µg m−3. This study marks a significant milestone as the first successful attempt to obtain hourly full-coverage ground-level O3 data across China. The diurnal variation of ML-derived-O3 demonstrates high consistency with ground-based observations, irrespective of clear or cloudy days, effectively capturing ground-level O3 pollution exposure events. This novel estimation method will be employed to establish a long-term high spatial-temporal resolution ground-level O3 dataset, which holds valuable applications for air pollution monitoring and environmental health research in future endeavors.

Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi.

Conceptualizing the Role of the Microbiome as a Mediator and Modifier in Environmental Health Studies: A Scoping Review of Studies of Triclosan and the Microbiome.

Triclosan is an endocrine-disrupting antimicrobial additive that is suspected of contributing to antibiotic resistance and altering the microbiome. In this scoping review, we summarize what is known about the association between triclosan exposure and the microbiome using evidence from in vivo and epidemiologic studies. Our review includes 11 rodent studies, seven fish studies, and five human studies. Evidence from animal studies suggests that triclosan decreases the diversity of the microbiome, although only one epidemiologic study agreed. Most studies suggest that triclosan alters the microbial community beta diversity, but disagree on which taxa contributed to compositional differences. Taxa in the Bacteroidetes, Firmicutes, and Proteobacteria may be more influenced by triclosan than those in other phyla. Studies on triclosan and the microbiome were scarce and were inconclusive as to the effects of triclosan on the microbiome. Additional research is needed to clarify windows of heightened susceptibility of the microbiome to triclosan. We recommend guidelines for future microbiome research in environmental health to increase comparability across studies.

Planning, implementing and governing systems-based co-creation: the DISCOVER framework

BackgroundIncreasingly, public health faces challenges requiring complex, multifaceted and multi-sectoral responses. This calls for systems-based approaches that facilitate the kind of collective and collaborative thinking and working required to address complexity. While the literature on systems thinking, system dynamics and the associated methodologies is extensive, there remains little clear guidance on how to plan, govern and implement participatory systems approaches within a co-creation process.MethodsWe used a three-step process to develop DISCOVER, a framework for implementing, and governing systems-based co-creation:Stage 1: We conducted a literature analysis of key texts to identify well-documented methods and phases for co-creation using a systems approach, as well as areas where gaps existed.Stage 2: We looked for the most appropriate methods and approaches to fill the gaps in the knowledge production chain.Stage 3: We developed the framework, identifying how the different tools and approaches fit together end-to-end, from sampling and recruiting participants all the way through to responding with an action plan.ResultsWe devised DISCOVER to help guide researchers and stakeholders to collectively respond to complex social, health and wider problems. DISCOVER is a strategic research planning and governance framework that provides an actionable, systematic way to conceptualise complex problems and move from evidence to action, using systems approaches and co-creation. In this article, we introduce the eight-step framework and provide an illustrative case study showcasing its potential. The framework integrates complementary approaches and methods from social network analysis, systems thinking and co-creation literature. The eight steps are followed sequentially but can overlap.ConclusionsDISCOVER increases rigour and transparency in system approaches to tackling complex issues going from planning to action. It is being piloted in environmental health research but may be suitable to address other complex challenges and could be incorporated into research proposals and protocols for future projects.

Identifying employee, workplace and population characteristics associated with COVID-19 outbreaks in the workplace: a population-based study

ObjectivesTo identify risk factors that contribute to outbreaks of COVID-19 in the workplace and quantify their effect on outbreak risk.MethodsWe identified outbreaks of COVID-19 cases in the workplace and investigated...