Consumption of unpasteurised milk in the United States has presented a public health challenge for decades because of the increased risk of pathogen transmission causing illness outbreaks. We analysed Foodborne Disease Outbreak Surveillance System data to characterise unpasteurised milk outbreaks. Using Poisson and negative binomial regression, we compared the number of outbreaks and outbreak-associated illnesses between jurisdictions grouped by legal status of unpasteurised milk sale based on a May 2019 survey of state laws. During 2013–2018, 75 outbreaks with 675 illnesses occurred that were linked to unpasteurised milk; of these, 325 illnesses (48%) were among people aged 0–19 years. Of 74 single-state outbreaks, 58 (78%) occurred in states where the sale of unpasteurised milk was expressly allowed. Compared with jurisdictions where retail sales were prohibited (n = 24), those where sales were expressly allowed (n = 27) were estimated to have 3.2 (95% CI 1.4–7.6) times greater number of outbreaks; of these, jurisdictions where sale was allowed in retail stores (n = 14) had 3.6 (95% CI 1.3–9.6) times greater number of outbreaks compared with those where sale was allowed on-farm only (n = 13). This study supports findings of previously published reports indicating that state laws resulting in increased availability of unpasteurised milk are associated with more outbreak-associated illnesses and outbreaks.

International relations scholarship concerns dyads, yet standard modeling approaches fail to adequately capture the data generating process behind dyadic events and processes. As a result, they suffer from biased coefficients and poorly calibrated standard errors. We show how a regression-based approach, the Additive and Multiplicative Effects (AME) model, can be used to account for the inherent dependencies in dyadic data and glean substantive insights in the interrelations between actors. First, we conduct a simulation to highlight how the model captures dependencies and show that accounting for these processes improves our ability to conduct inference on dyadic data. Second, we compare the AME model to approaches used in three prominent studies from recent international relations scholarship. For each study, we find that compared to AME, the modeling approach used performs notably worse at capturing the data generating process. Further, conventional methods misstate the effect of key variables and the uncertainty in these effects. Finally, AME outperforms standard approaches in terms of out-of-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously. Most importantly, by better modeling the data generating process underlying political phenomena, the AME framework improves scholars’ ability to conduct inferential analyses on dyadic data.

Background:Staphylococcus aureus (S. aureus) is the second most common cause of healthcare-acquired infections in neonates. S. aureus colonization is a known risk factor for invasive disease. Aside from healthcare workers (HCWs), recent data suggest that parents are important reservoirs of S. aureus in the neonatal intensive care unit (NICU). S. aureus typically colonizes the nares, but it can also colonize other anatomic locations such as the throat. Objective: Our objectives were to identify and compare S. aureus colonization among HCWs and parents and to identify and compare different sites of S. aureus colonization. Methods: Between April 2015 and July 2016, we performed 4 point-prevalence surveys and collected nares and throat swabs from HCWs (nurses, respiratory therapists, nurse practitioners, and physicians) at a quaternary-care NICU. During an overlapping period, we screened parents of neonates in the NICU for S. aureus colonization using nares, throat, groin, and perianal cultures as a part of an ongoing randomized control trial. Cultures from both studies were collected using standardized methods. ESwabs were used to collect samples, which were inoculated into broth for enrichment and subsequently cultured onto chromogenic agar to differentiate between MSSA and MRSA. Results: The prevalence of methicillin susceptible S. aureus (MSSA) colonization was 46% (105/226) in HCWs and 28% (239/842) in parents. The prevalence of methicillin resistant S. aureus (MRSA) colonization was 2.2% (5/226) in HCWs and 2.2% (19/842) in parents. Of those who were colonized with S. aureus, 35% (79/226) of HCWs and 46.5% (160/344) of parents had nares and throat colonization while 11.5% (26/226) of HCWs and 12.2% (42/344) of parents had only throat colonization but not nares colonization. Of those who were MRSA colonized, 1.3% (3/226) of HCWs and 1.8% (15/842) of parents had a positive nares and throat culture as compared to 0.9% (2/226) of HCWs and 0.2% (2/842) of parents had only positive throat cultures. Additionally, 68% (175/257) were colonized with S. aureus at any swabbed site including nares, throat, groin, or perinanal areas. However, only 30% (77/257) of parents had only nares colonization as compared to 58.8% (151/257) had throat and nares colonization, 38.1% (98/257) had nares and groin colonization, and 37.4% (96/257) had nares and perianal colonization. Conclusions: HCWs had greater prevalence of S. aureus colonization compared to parents. As expected, the nares was the most common site of MSSA and MRSA, but a large proportion of S. aureus colonized HCWs and parents had only throat colonization. Given the prevalence of S. aureus in non-nares sites of HCWs and parents in the NICU, further studies should examine the role of non-nares carriers in the transmission of S. aureus in this population.

Funding: None

Disclosures: Aaron Milstone reports consultancy with Becton Dickinson.

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.