Search

Public interest in where food comes from and how it is produced, processed, and distributed has increased over the last few decades, with even greater focus emerging during the COVID-19 pandemic. Mounting evidence and experience point to disturbing weaknesses in our food systems’ abilities to support human livelihoods and wellbeing, and alarming long-term trends regarding both the environmental footprint of food systems and mounting vulnerabilities to shocks and stressors. How can we tackle the “wicked problems” embedded in a food system? More specifically, how can convergent research programs be designed and resulting knowledge implemented to increase inclusion, sustainability, and resilience within these complex systems, support widespread contributions to and acceptance of solutions to these challenges, and provide concrete benchmarks to measure progress and understand tradeoffs among strategies along multiple dimensions? This article introduces and defines food systems informatics (FSI) as a tool to enhance equity, sustainability, and resilience of food systems through collaborative, user-driven interaction, negotiation, experimentation, and innovation within food systems. Specific benefits we foresee in further development of FSI platforms include the creation of capacity-enabling verifiable claims of sustainability, food safety, and human health benefits relevant to particular locations and products; the creation of better incentives for the adoption of more sustainable land use practices and for the creation of more diverse agro-ecosystems; the wide-spread use of improved and verifiable metrics of sustainability, resilience, and health benefits; and improved human health through better diets.

Current studies on data sharing via data commons or shared vocabularies using ontologies mainly focus on developing the infrastructure for data sharing yet little attention has been paid to the role of power in data sharing among food system stakeholders. Stakeholders within food systems have different interpretations of the types and magnitudes of their own and other’s level of power to solve food system challenges. Politically neutral, yet scientifically/socioeconomically accurate power classification systems are yet to be developed, and must be capable of enumerating and characterizing what power means to each stakeholder, existing power dynamics within the food system, as well as alternative forms of power not currently utilized to their full capacity. This study describes the design and implementation of a workshop, which used methods from community-based participatory modeling, to examine the role of power relative to data sharing and equitable health outcomes. Workshop participants co-created several boundary objects that described the power relationships among food system stakeholders and the changes needed to current power relationships. Our results highlight current imbalances in power relationships among food system stakeholders. The information we collected on specific relationships among broad categories of stakeholders highlighted needs for initiatives and activities to increase the types and varieties of power especially across consumers, farmers, and labor stakeholder groups. Furthermore, by utilizing this workshop methodology, food system stakeholders may be able to envision new power relationships and bring about a fundamental re-orienting of current power relationships capable of valorizing food system sustainability/resiliency, especially the health of its workers and consumers.

A foodshed is a concept analogous to a watershed, describing the catchment of the sources of food for a region. As such, it portrays linkages ranging from local communities out to the global food system. Inefficiencies exist at all stages of the food supply chain, resulting in the challenges of inequitable access to healthy and safe food. Many of these inefficiencies are informational; for instance, food being wasted that could be donated to food banks were there communication of the need. These informational inefficiencies can be ameliorated by a stronger semantic characterization of the links between actors and resources in the food system, allowing for the development of smarter software technologies to facilitate interconnections. We discuss an iterative process to improve informatics frameworks for the foodshed by engaging with regional stakeholders to identify important issues and information needs. Key Words: food systems, ontologies, semantic web, smart foodsheds, stakeholder engagement.

Objective There is limited evidence about prevalence and odds of adverse birth outcomes among Arab American women in the United States. We estimated the prevalence of low birth weight (LBW < 2500 g) and preterm birth (PTB < 37 completed weeks' gestation) among Arab American women in Ohio and studied the association between ethnicity, Arab American nativity (foreign or US born) and odds of LBW and PTB. Methods We identified Arab American women based on birth certificate data from live singleton births from 2007-2010 to 2013-2015 and a name algorithm. We compared the prevalence of LBW and PTB by ethnicity (Arab American vs. non-Hispanic White) and by nativity (foreign-born Arab American vs. US-born Arab American). Logistic regression models were used to estimate the unadjusted and adjusted effects of ethnicity and mother's nativity on study outcomes. Results 31,744 Arab American women (2.5% of all births in Ohio) were identified over a 7-year period. 24,129 Arab American women with complete data were included in the analysis after applying exclusion criteria. Prevalence of LBW was 5.2% (non-Hispanic White), 6.1% (Arab American), 6.4% (US-born Arab American) and 5.6% (foreign-born Arab American). Prevalence of PTB was 7.2% (non-Hispanic White), 7.0% (Arab American), 7.3% (US-born Arab American), and 5.4% (foreign-born Arab American). In adjusted models, which controlled for mother demographics, health behaviors, and pregnancy risk factors, Arab Americans had 33% higher odds of LBW (odds ratio [OR] 1.33; 95% Confidence Intervals[CI] 1.26-1.41) than non-Hispanic Whites. Foreign born Arab American women had 15% lower odds of PTB (OR 0.85; 95% CI 0.75-0.95) than US-born Arab Americans. Conclusions for Practice Our main findings were that LBW is influenced by Arab ethnicity while PTB is influenced by nativity among Arab American women. These findings may be informative for developing and implementing strategies for adverse birth outcomes for a growing US ethnic minority population., Author(s): Ayaz Hyder [sup.1] , Kierra S. Barnett [sup.2] Author Affiliations: (1) grid.261331.4, 0000 0001 2285 7943, College of Public Health, Translational Data Analytics Institute, The Ohio State University, , [...]

Background: Often combined with other traditional and non-traditional types of data, geospatial sensing data have a crucial role in public health studies. We conducted a systematic narrative review to broaden our understanding of the usage of big geospatial sensing, ancillary data, and related spatial data infrastructures in public health studies. Methods: English-written, original research articles published during the last ten years were examined using three leading bibliographic databases (i.e., PubMed, Scopus, and Web of Science) in April 2022. Study quality was assessed by following well-established practices in the literature. Results: A total of thirty-two articles were identified through the literature search. We observed the included studies used various data-driven approaches to make better use of geospatial big data focusing on a range of health and health-related topics. We found the terms 'big' geospatial data and geospatial 'big data' have been inconsistently used in the existing geospatial sensing studies focusing on public health. We also learned that the existing research made good use of spatial data infrastructures (SDIs) for geospatial sensing data but did not fully use health SDIs for research. Conclusions: This study reiterates the importance of interdisciplinary collaboration as a prerequisite to fully taking advantage of geospatial big data for future public health studies. [ABSTRACT FROM AUTHOR]

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature with which to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatiotemporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model with the approach used in Ohio and the approach included in decision support materials from the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policy-makers. [ABSTRACT FROM AUTHOR]