This document introduces and explains common implementation concepts and frameworks relevant to healthcare epidemiology and infection prevention and control and can serve as a stand-alone guide or be paired with the “SHEA/IDSA/APIC Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals: 2022 Updates,” which contain technical implementation guidance for specific healthcare-associated infections. This Compendium article focuses on broad behavioral and socio-adaptive concepts and suggests ways that infection prevention and control teams, healthcare epidemiologists, infection preventionists, and specialty groups may utilize them to deliver high-quality care. Implementation concepts, frameworks, and models can help bridge the “knowing-doing” gap, a term used to describe why practices in healthcare may diverge from those recommended according to evidence. It aims to guide the reader to think about implementation and to find resources suited for a specific setting and circumstances by describing strategies for implementation, including determinants and measurement, as well as the conceptual models and frameworks: 4Es, Behavior Change Wheel, CUSP, European and Mixed Methods, Getting to Outcomes, Model for Improvement, RE-AIM, REP, and Theoretical Domains.

The intent of this document is to highlight practical recommendations in a concise format designed to assist acute-care hospitals in implementing and prioritizing their surgical-site infection (SSI) prevention efforts. This document updates the Strategies to Prevent Surgical Site Infections in Acute Care Hospitals published in 2014.1 This expert guidance document is sponsored by the Society for Healthcare Epidemiology of America (SHEA). It is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America (IDSA), the Association for Professionals in Infection Control and Epidemiology (APIC), the American Hospital Association (AHA), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise.

In total, 50 healthcare facilities completed a survey in 2021 to characterize changes in infection prevention and control and antibiotic stewardship practices. Notable findings include sustained surveillance for multidrug-resistant organisms but decreased use of human resource-intensive interventions compared to previous surveys in 2013 and 2018 conducted prior to the COVID-19 pandemic.

The Intensity Interferometry technique consists of measuring the spatial coherence (visibility) of an object via its intensity fluctuations over a sufficient range of telescope separations (baselines). This allows us to study the size, shape and morphology of stars with an unprecedented resolution. Cherenkov telescopes have a set of characteristics that coincidentally allow for Intensity Interferometry observations: very large reflective surfaces, sensitivity to individual photons, temporal resolution of nanoseconds and the fact that they come in groups of several telescopes. In the recent years, the MAGIC Collaboration has developed a deadtime-free Intensity Interferometry setup for its two 17 m diameter Cherenkov telescopes that includes a 4-channel GPU-based real-time correlator, 410–430 nm filters and new ways of splitting its primary mirrors into submirrors using Active Mirror Control (AMC). With this setup, MAGIC can operate as a long-baseline optical interferometer in the baseline range 40–90 m, which translates into angular resolutions of 0.5-1 mas. Additionally, thanks to its AMC, it can simultaneously measure the zero-baseline correlation or, by splitting into submirrors, access shorter baselines under 17 m in multiple u-v plane orientations. The best candidates to observe with this technique are relatively small and bright stars, in other words, massive stars (O, B and A types). We will present the science cases that are currently being proposed for this setup, as well as the prospects for the future of the system and technique, like the possibility of large-scale implementation with CTA.

We used a survey to characterize contemporary infection prevention and antibiotic stewardship program practices across 64 healthcare facilities, and we compared these findings to those of a similar 2013 survey. Notable findings include decreased frequency of active surveillance for methicillin-resistant Staphylococcus aureus, frequent active surveillance for carbapenem-resistant Enterobacteriaceae, and increased support for antibiotic stewardship programs.

Childhood adversity (CA) increases the risk of subsequent mental health problems. Adolescent social support (from family and/or friends) reduces the risk of mental health problems after CA. However, the mechanisms of this effect remain unclear, and we speculate that they are manifested on neurodevelopmental levels. Therefore, we investigated whether family and/or friendship support at ages 14 and 17 function as intermediate variables for the relationship between CA before age 11 and affective or neural responses to social rejection feedback at age 18. We studied 55 adolescents with normative mental health at age 18 (26 with CA and therefore considered “resilient”), from a longitudinal cohort. Participants underwent a Social Feedback Task in the magnetic resonance imaging scanner. Social rejection feedback activated the dorsal anterior cingulate cortex and the left anterior insula. CA did not predict affective or neural responses to social rejection at age 18. Yet, CA predicted better friendships at age 14 and age 18, when adolescents with and without CA had comparable mood levels. Thus, adolescents with CA and normative mood levels have more adolescent friendship support and seem to have normal mood and neural responses to social rejection.

A study on locally available composts in Austria, Germany, Italy and Switzerland was conducted to investigate the potential of these non-chemical based tools to increase soil health in orchards afflicted by apple replant disease (ARD). A total of 26 different composts (six to seven per country) were chosen for the study. Composts were divided into ten types according to the waste materials used as substrates in the composting process. Growth reduction is the main symptom associated with replant disease; therefore compost performance was evaluated based on the growth responses of apple rootstock plantlets in compost-amended soils in pots. These greenhouse trials were performed in one research station per country, located in an intensive apple-growing area, and soil was taken from an apple orchard affected by replanting disease. Plant growth response was measured as shoot elongation at the end of each greenhouse trial, and results showed increases in growth compared with the respective controls of 2–26% in 20 out of 26 composts evaluated. The heterogeneous nature of the composts most likely attributed to the finding that similar compost types originating from the different countries had varying effects on plant growth. Overall, no significant changes in chemical and biological properties were observed in amended soils as compared with non-amended controls. The high soil resilience was in part expected given the good organic matter content in the original soils (>2%). The bacterial communities of the composts were investigated using the COMPOCHIP microarray, and analyses showed that differences in plant growth response were mainly attributed to the microbial changes introduced into the soil through composts rather than to changes in soil chemical and biological parameters. However, the bacterial communities of composts appeared to be more influenced by geographical origin than by compost type. The results have shown that soil amendment with composts generated from locally produced wastes have the potential to reduce the effects of ARD, although the effects appear to be both compost and soil specific.

Responses of weeds and sugarbeets (Beta vulgaris L.) to postemergence treatments of methyl m-hydroxycarbanilate m-methylcarbanilate (phenmedipham) and two analogues were evaluated in six field studies. Phenmedipham at 1.7 kg/ha controlled foxtail millet (Setaria italica (L.) Beauv.) and kochia (Kochia scoparia (L.) Schrad.) better than 2.2 kg/ha of methyl m-hydroxycarbanilate carbanilate and ethyl m-hydroxycarbanilate carbanilate. Pigweed (Amaranthus spp.) was controlled better by the analogues at 1.1 kg/ha than by phenmedipham. The foliar growth of sugarbeets was generally suppressed more by the analogues than by phenmedipham, but injury was not considered detrimental at 1.1 kg/ha. Yield of sugarbeet roots and sugar was reduced by 7% or less by phenmedipham at rates of 1.1 to 4.5 kg/ha, but these yield reductions were associated primarily with the failure of phenmedipham to completely control all weeds for 5 to 9 weeks after treatment.

In a 2-year study, five densities of kochia (Kochia scoparia (L.) Schrad.) competed with sugarbeets (Beta vulgaris L.) for the entire growing season. The yield of sugarbeet roots and pounds of sucrose per acre decreased as the intensity of competition from kochia plants increased. One kochia plant per 25 ft of row reduced the average yield of roots by 2.6 T/A and sucrose by 960 lb/A. A density of one kochia plant, or more, per 2 ft of row in the first year, or one kochia plant per 1 ft of row in the second year, significantly reduced the sucrose content of the roots. Individual kochia plants significantly reduced the weight of sugarbeet tops and roots within a radius of 31 inches from the center of the sugarbeet row.

In a 2-year study, kochia [Kochia scoparia (L.) Schrad.] competed with sugarbeets (Beta vulgaris L.) for various periods after emergence. Kochia then was removed and plants that emerged subsequently were controlled by hand-weeding. Yield of sugarbeet roots was reduced when kochia competed for more than 5 or 6 weeks. Kochia that competed with sugarbeets for the entire season reduced yields more than 95%. In another study, kochia was controlled by frequent hand-weeding for different periods after sugarbeet emergence. When kochia was controlled initially for 3 to 4 weeks, yield of sugarbeets was not reduced. Some kochia emerged following the 3 to 4 weeks of hand-weeding. However, competition from sugarbeets reduced the weight of this kochia at harvest by 59 and 92%.

The performance of four sequential weed management models that assumed either low or high risk was compared to the performance of two sugarbeet consultants, one who assumed low risk and the other high risk. Weed management recommendations were performed over one growing season at two locations, each with several levels of weed populations. Recommendations for preplant, postemergence, and layby herbicide treatments or late-season handweeding differed among the four weed management levels. The high-risk management level was labor intensive and the low-risk management level was herbicide intensive. Weed populations at harvest, recoverable sucrose, and net return above weed control costs were not different among the four weed management levels. Weeds can be controlled in sugarbeets by employing weed management practices based on bioeconomic modeling.

The question of when to control weeds traditionally has been approached with the calculation of critical periods (CP) based on crop yields. The concept of economic critical period (ECP) and early (EEPT) and late (LEFT) economic period thresholds are presented as a comprehensive approach to answer the same question based on economic losses and costs of control. ECP is defined as the period when the benefit of controlling weeds is greater than its cost. EEPT and LEFT are the limits of the ECP and can be used to determine when first and last weed control measures should be performed. Calculation of EEPT accounts for the economic losses due to weed competition that occur between planting and postemergence weed control. In this way it is possible to better evaluate the economic feasibility of using preplant or preemergence control tactics. The EEPT for DCPA application is analyzed in the context of onion production in Colorado. The EEPT for DCPA application was calculated from an empirical regression model that assessed the impact of weed load and time of weed removal on onion yields. The EEPT was affected by control efficacy, weed-free yield, DCPA cost, and onion price. DCPA application was economically advisable in only one of 20 fields analyzed because of the tow DCPA efficacy (60%).