bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sea-Level Rise in Northern Germany: A GIS-Based Simulation and Visualization
Authors: Schuldt Caroline , Schiewe Jochen , Kröger Johannes 
Published: November 2020

AbstractThe future sea-level rise caused by climate change will lead to coastal regions being flooded and ecological and socio-economic systems being disrupted. This study examines the question of how the sea-level rise in Northern Germany can be simulated on a regional level and visualized as a media map. The simulation is based on the TanDEM-X digital elevation model, IDW interpolated current measurements of the sea level and the vertical land movement, as well as regional sea surface elevation projections for the year 2100. Two different climate scenarios were applied based on IPCC forecasts. Particular attention was paid to transforming elevation systems into orthometric heights. In addition, the uncertainties existing in the simulation of future developments were quantified and visualized. Depending on the applied scenario, an area between 1061 and 9004 km2 will be inundated. Accordingly, the affected population varies between 5477 and 626,880 people. The calculation of the inundated areas reveals serious differences; between the various climate scenarios, as well as between the North and Baltic coasts, but above all between the assumption of a stable coastal protection on the one hand and a dike breach on the other. Based on the requirements of journalistic cartography and the specific requirements of the German broadcasting company Norddeutscher Rundfunk (NDR), static maps were developed, which will be shown as a sequence starting with the least and ending with the most severe possible impact.
Health status and willingness-to-pay estimates for the benefits of improved recycling rates: evidence from Great Britain
Authors: Giovanis Eleftherios , Ozdamar Oznur , Samuk Sahizer 
Published: November 2020

AbstractWaste management is a challenging task around the globe. Waste disposal and recycling have important implications, not only for environmental preservation, but also for the public health, well-being, the economy and sustainable development. However, little is known about the impact of the recycling rates on public health and the willingness to pay to increase recycling. The aim of this study was to examine the relationship among household income, recycling rates and health status and to estimate the marginal willingness-to-pay (MWTP) in Great Britain. The empirical analysis relied on data from the British Household Panel Survey (BHPS) over the period 1999–2009. We estimated the impact of recycling rates and income on health status and we calculated the monetary value for a unit increase in recycling. To solve for the endogeneity issues, coming from possible reverse causality and omitted-variable bias, we implemented two instrumental variables (IV) approaches. First, we applied the Two-Stage Least Squares (2SLS) and second, we estimated a Pooled Ordered Probit model. We found that for one percent increase in recycling rates, the average MWTP was estimated between £290 and £340 per annum. Furthermore, our results show that other determinants play an additional significant role on health status, such as the employment and marital status, the age, education level and meteorological conditions. While the study provides insights about the MWTP, future studies regarding the costs of providing recycling services may offer additional useful information to help the policy makers in the decision-making process.
Allowing for unemployment in productivity measurement
Authors: Gandy Rob , Mulhearn Chris 
Published: November 2020

AbstractThe labour productivity index is a mainstay measure for comparing countries’ relative economic performance, but the Covid-19 pandemic could expose some of its inherent limitations: it focuses on people in work and ignores unemployment, and it is not standardised. In theory, a country’s index value could increase, even though its GDP might fall, because of significant increased unemployment in low-productivity sectors such as tourism and retail. It follows that the index value could fall when these sectors recover. Also, high-performing countries could see their index value fall because of the pandemic’s impact in high-value sectors, such as demand for oil.Consequently, a wider perspective of productivity is necessary. This paper, therefore, proposes a complementary index which adjusts labour productivity for levels of unemployment—the social labour productivity index (SLPI)—and recommends that the labour productivity index itself should be standardised. The relationship between employment and productivity is complex. For example, the UK’s economic performance, involving comparatively low labour productivity and low unemployment, has been deemed a ‘productivity puzzle’. A literature review discusses this relationship, but it is clear that econometric worldwide evaluation requires very large data sets, that are unlikely to be routinely available in practice to monitor international performance. By contrast, data sets on national productivity are small and already available, although they contain little or no data on causal factors. SLPI values were calculated for differing levels of unemployment and relative labour productivity for newly employed workers for countries where data was available; with patterns over the period 1986–2016 established for the G7 countries, Portugal, Ireland, Greece, and Spain. There were marked variations between the two indices for countries with high unemployment. The SLPI presents a practicable measure which can be utilised quickly in these unprecedented times. Using available data to compare countries’ GDP with their total workforce, it arguably provides a better measure of their overall economic and social health. Sensitivity analyses varying assumptions can model differing potential scenarios to sit alongside GDP and labour productivity index predictions.
Reporting Surgical Site Infections (SSIs) Using Different Surveillance Systems— Complexity of Infection Matters
Authors: Ellison Jennifer , Control  , Chakravorty David , Conly John , Kim Joseph , Litvinchuk Stacey , Pokhrel Arun , Shen Ye , Control  , Smith Christopher , Bush Kathryn , Control  
Published: November 2020

Background: In Alberta, Canada, surgical site infections (SSIs) following total hip (THR) and knee replacements (TKR) are reported using 2 data sources: infection prevention and control (IPC), which surveys all THR and TKR using NHSN definitions and the Canadian International Classification of Disease, Tenth Revision (ICD-10-CA) codes, and the National Surgical Quality Improvement Program (NSQIP), which uses a systematic sampling process that involves an 8-day cycle schedule, modified NHSN definitions and current procedural terminology (CPT) codes. We compared the similarities and discrepancies in THR/TKR SSI reporting. Methods: A retrospective multisite cohort study of IPC and NSQIP THR/TKR SSI data at 4 hospitals was performed. SSI data were collected between September 1, 2015, and March 31, 2018. Demographic information and complex and total SSIs reported by IPC and NSQIP were compared for both THR and TKR surgeries. To determine whether both data sources reported similar trends over time, total SSIs by quarter were compared. Univariate analyses using a t test for age and the χ2 test for gender for complex SSIs and total SSIs was performed. The Pearson correlation and the Shapiro-Wilk test were used to assess the THR and TKR trends between the 2 data sources. A P value of <.05 was considered significant. Results: Following the removal of duplicates and missing data, 7,549 IPC and 2,037 NSQIP patients, respectively, were compared. Age, gender, and other demographic parameters were not significantly different. Total THR and TKR SSIs per 100 procedures using NSQIP data were significantly higher than the same rates using IPC data: THR, 2.25 versus 0.92 (P < .05) and TKR, 3.43 versus 1.26 (P < .05). Both IPC and NSQIP data indicated increasing total THR SSI rates over time, but with different magnitudes (r = 0.658). For total TKR SSI, the IPC rate decreased, whereas the NSQIP rate increased over the same period (r = 0.374). When superficial SSIs were excluded, the rates reported between IPC and NSQIP data by hospital and by procedure type were more comparable, with trends toward higher rates reported by NSQIP for THR than for TKR: THR, 1.19 versus 0.68 (P = 0.15) and TKR, 0.92 versus 0.80 (P = .68). Conclusions: Different approaches used to monitor SSIs following surgeries may lead to different results and trend patterns. NSQIP reports total SSI rates that are significantly higher than the IPC Alberta orthopedic population predominantly as a result of increased identification of superficial SSIs. Because the diagnosis of superficial SSIs may be less reliable, SSI reporting should focus on complex infections.Funding: NoneDisclosures: None
Risk Factors Associated With Hospital-Onset MRSA Proportion—National Healthcare Safety Network, 2017–2018
Authors: Zhou Liang , Wu Hsiu , Soe Minn , Pollock Daniel , Edwards Jonathan 
Published: November 2020

Background:Staphylococcus aureus is frequently implicated in healthcare-associated infections in the United States, and a substantial proportion of these infections are attributed to methicillin-resistant Staphylococcus aureus (MRSA). Although MRSA infections have decreased in health care settings, accurate estimates of the rate of decline call for risk-adjusted methods for calculating the resistant proportion (%R), that is, the proportion of S. aureus resistant to cefoxitin or oxacillin. Risk-adjusted %R also enables more accurate interhospital comparisons and can serve as a quantitative guide and evaluation metric for prevention efforts. Methods: To develop a risk-adjusted %R for S. aureus, we analyzed the antimicrobial susceptibility test (AST) results for S. aureus isolates reported to the CDC NHSN Antimicrobial Resistance Option during 2017–2018. Isolates were reported for cerebrospinal fluid (CSF), blood, lower respiratory tract (LRT), and urine. Isolates without cefoxitin and oxacillin test results, or from the facilities that had >10% missing test results were excluded. Test results were differentiated between those associated with community-onset and hospital-onset (HO) infections by defining the latter group as test results for isolates obtained 3 days or more after hospital admission. Logistic regression was used to evaluate the factors associated with oxacillin/cefoxitin resistance. Hospital, patient and isolate-level variables from NHSN annual survey and AR option were assessed as covariates. Variable entry into the models is based on significance level P < .05. Results: Among 9,992 hospital-onset SA isolates from 9,019 patients in 315 facilities, 5,488 (54.9%) were MRSA. Logistic regression showed that a higher proportion of HO-MRSA was significantly associated with older age, female, particular sources of specimen (urine and LRT), and selected hospital characteristics: hospitals not serving as major teaching hospitals, hospitals with a higher proportion of MRSA among community-onset SA isolates, hospitals with lower percentage of beds in intensive care units, and hospitals outsourcing AST service (Table 1). Conclusions: HO-MRSA is independently associated with community burden of MRSA, older and female patient populations, and hospital teaching status and AST practices, which highlights the importance of public health engagement and regional collaborations to prevent MRSA. To provide a standardized MRSA proportion for public health surveillance, taking some of these factors into account in MRSA proportion standardization should be considered.Funding: NoneDisclosures: None
Risk Factors for Surgical Site Infection After Orthopedic Trauma Surgery: A Two-Year Prospective Multicenter Analysis
Authors: Abreu Milena Reis , Paiva Larissa , Mendes Tamires Costa , Maia Barbara Cristiny , Rodrigues Ana Luiza , Moreira cia , Souza Victor de , Couto Braulio , Starling Carlos 
Published: November 2020

Background: Trauma is defined by the NHSN as “blunt or penetrating traumatic injury.” Therefore, if the surgery was performed because of a recent fall, for example, then it is a trauma surgery. Here, we investigated which preoperative and operative parameters are associated with surgical site infection (SSI) after orthopedic trauma surgery. Objective: We aimed to answer 3 main questions: What is the risk of wound infection for patients undergoing trauma surgery? What are the main etiologic agents of SSI after trauma surgery? And what are the risk factors associated with SSI after trauma surgery? Methods: This prospective multicenter cohort study included 2,035 patients undergoing trauma surgery between July 2016 and June 2018 in 4 hospitals in Belo Horizonte, Brazil. Outcome variables were SSI, hospital mortality, and length of hospital stay. The following preoperative and operative parameters were evaluated: age, length of hospital stay before surgery, duration of surgery, number of professionals at surgery, number of hospital admissions, surgical wound classification, American Society of Anesthesiologists (ASA) preoperative assessment score, type of surgery (elective, emergency), general anesthesia (yes, no), trauma surgery (yes, no), and the 3-point prediction Nosocomial Infections Surveillance (NNIS) risk index. Results: The overall estimated SSI risk was 2.8% (95% CI, 2.0%–3.6%). Hospital mortality risk after trauma surgery was 3.4% (95% CI, 2.8%–4.4%). Hospital length of stay parameters in noninfected patients were as follows: mean, 8 days; median, 3 days; SD, 12 days. Hospital length of stay parameters in infected patients were mean, 30 days; median, 23 days; with SD, 31 days. The parameters for hospital stay in infected patients were mean, 10 days; median, 3 days, and SD, 15.9 (P < .001). Trauma orthopedic surgery lasting >2 hours was associated with approximately twice the risk (RR, 2.2) of developing an SSI compared to ≤2 hours of surgery: 27 of 739 (3.7%) versus 21 of 1,290 (1.6%), respectively, (P = .005) (Fig. 1). The NNIS risk index predicts the risk of SSI after trauma surgery (P = .003): 13 of 737 SSIs (1.8%) had an NNIS risk index of 0; 20 of 736 SSIs (2.7%) had an NNIS risk index of 1; 8 of 211 SSIs (3.8%) had an NNIS risk index of 2; and 2 of 11 SSIs (18.2%) had an NNIS risk index of 3 (Fig. 2). Conclusions: We identified intrinsic risk factors for SSI after orthopedic trauma surgery. The identification of the actual SSI incidence after trauma surgery in developing country hospitals and associated risk factors may support actions to minimize the complications caused by SSI.Funding: NoneDisclosures: None
The Microbiome Analysis of Hospital Mobile Phones: Hidden Contaminants Revealed
Authors: Simmonds Rebecca 
Published: November 2020

Background: The undisputed versatility of smart devices makes them integral to modern-day society, especially within our National Health System. Despite the benefits, there are increasing concerns regarding their contamination and the associated infection risk. Bacteria under antimicrobial selective pressure can rapidly acquire resistant mechanisms leading to the assumption; mobile phones used within clinical environments may harbor bacteria associated with a higher infection mortality rate. Using next-generation sequencing technology, we characterized the true extent of bacterial contamination on mobile devices of hospital staff to determine the level of antimicrobial resistance within Staphylococcus aureus and Enterococcus faecalis. Smart phones of 250 hospital staff and 191 control group participants were swabbed over a 6-month period. The microbiome of devices were characterized using Illinima MiSeq metabarcoding pipeline. Cultured isolates were quantified and underwent Kirby-Bauer disc diffusion. Primer version 6 and SPSS version 23 software were used to analyze the statistics. Nearly all mobile devices were contaminated with potential pathogens regardless of environment. Metabarcoding revealed far greater bacterial diversity and abundance of gram-negative bacterial contamination than did culture-based methods. In total, 198 bacterial genera of were discovered across both groups, of which 34 were unique to the hospital. Bacillus was significantly higher within the hospital group (P = .036). Differences were also detected within the hospital (high-risk vs low-risk areas, P = .048). Methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcusfaecalis were only isolated from hospital mobile phones (P < .001 and P = .038, respectively). Our results indicate that traditional culture-dependent swabbing methods do not provide an accurate picture of contamination. Metabarcoding reinforces the need for mobile phone infection control practices to mitigate the risks associated with the increase use of smart device technology in clinical environments. These devices are currently exposing immunocompromised patients to unknown levels of pathogenic and multidrug-resistant bacteria. Departmental differences may suggest that the mobile phone is not just an extension to its owners but to their environment and that routine decontamination should be required to prevent the undermining of hand hygiene and the transmission of pathogenic bacteria.Funding: NoneDisclosures: None
Trends in Staphylococcus aureus Bloodstream Infections in Nursing Homes in Monroe County, New York
Authors: Tracy Marissa , Felsen Christina B. , Gellert Anita , Dumyati Ghinwa 
Published: November 2020

Background: Methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs) are common in hospitals and nursing homes. Infection prevention efforts reduced MRSA BSI in hospitals but the trend in nursing homes is not well described. In addition, the contribution of methicillin sensitive S. aureus (MSSA) to the total burden of invasive S. aureus (iSA) in nursing homes remains unknown. Methods: As part of the CDC Emerging Infections Program, we conduct population-based surveillance for iSA infections in Monroe County, New York. Case patients were county residents with S. aureus isolated from a sterile site. Our analysis was limited to data from 2009–2018 for MRSA and 2015–2018 for MSSA and to cases classified as hospital-onset (HO, positive culture ≥3 calendar days after admission) or nursing home-onset (NHO, positive culture in nursing homes or within 3 days of hospital admission from a nursing home). Risk factors for iSA BSI in nursing homes were compared using the χ2 and Student t tests in SAS version 9.4 software. Results: During 2009–2014, 664 MRSA cases occurred and 427 (64%) were BSIs. Of these, 228 (53%) were NHO and 199 (47%) were HO. The BSI incidence per 100,000 population of NHO cases declined from 7.9 in 2009 to 2.8 in 2014, mirroring the decline in HO incidence from 8.7 in 2009 to 3.1 in 2014 (Fig. 1). During 2015–2018, 203 MRSA cases (163 BSIs, 80%) and 235 MSSA cases (163 BSIs, 69%) occurred. Of the 163 MRSA BSIs, 94 (58%) were NHO and 69 (42%) were HO, whereas of the 235 MSSA BSIs, only 56 (34%) were NHO and 107 (66%) were HO. MRSA BSI incidence per 100,000 population in both settings plateaued during 2015–2018 (Fig. 1) and MSSA NHO BSI incidence was lower than HO (1.9 NHO vs 3.6 HO). The total iSA BSI incidence was similar in both settings (5.9 vs 5.0 per 100,000 population in HO and NHO, respectively). NHO MSSA and MRSA cases have similar risk factors for BSI; 45 (30%) had decubitus ulcers, 34 (23%) were on chronic dialysis, 41 (27%) had a CVC in place within 2 days of BSI onset, and 63% had prior healthcare exposures. Most of these developed within 4 weeks of hospital discharge (Fig. 2). Conclusions: The incidence of MRSA BSI in nursing homes has declined since 2009 but plateaued starting in 2015. Compared to MRSA, MSSA caused fewer BSIs in nursing homes; however, iSA risk factors, including previous healthcare exposure, were similar. Continued study is needed to identify interventions effective against all iSA infections in nursing homes.Funding: NoneDisclosures: None
Tuberculosis Exposure and Conversion Rates Can Guide Deimplementation of Annual Tuberculosis Screening
Authors: Meacham Holly , Kobayashi Takaaki , Alsuhaibani Mohammed , Holley Stephanie , Edmond Michael , Diekema Daniel , Dains Angie , Abosi Oluchi , Kukla Mary , Jenn Kyle , Salinas Jorge 
Published: November 2020

Background: The CDC recently updated recommendations on tuberculosis (TB) screening in healthcare facilities, suggesting the discontinuation of annual TB screening. However, hospitals may opt to continue based on their local TB epidemiology. We assessed TB infection control parameters in our facility to guide the implementation of the new CDC recommendations. Methods:We retrieved data for patients with an International Classification of Disease, Tenth Revision (ICD-10) code for TB treated at the University of Iowa Hospitals and Clinics during 2016–2019. We supplemented our search with microbiology data: culture or PCR for Mycobacterium tuberculosis. Based on manual chart review, we adjudicated each patient as active TB, latent TB, previously treated TB, unclear history, or no TB. We further labeled active TB cases based on their risk of transmission (pulmonary or extrapulmonary cases that underwent an aerosol generating procedure). We then calculated the number of exposure events associated with those patients and tuberculin skin test (TST) conversion rates among the exposed. Results: During 2016–2019, we identified 197 patients based on ICD-10 codes. In total, 10 additional patients were detected by microbiology data review. Of these 207 patients, 48 (23.2%) had active TB: lung, n = 24 (50%); lymph node, n = 9 (19%); bone or spine, n = 5 (10%); eye, n = 3 (6%); disseminated, n = 2 (4%); pleura, n = 2 (4%); skin abscess, n = 2 (4%); and meningitis, n = 1 (2%). Of the 24 pulmonary patients, 6 (25%) had either a positive smear or a cavity on imaging. In total, 159 patients were excluded: no TB, n = 22 (14%); latent TB, n = 27 (17%); old or treated TB, n = 93 (58%); and unclear history, n = 9 (6%). Of the 48 cases with active TB, 31 (65%) were deemed potentially infectious. Also, 10 cases (32%) led to the exposure of 204 healthcare workers (HCWs). Baseline and postexposure TST were available for 179 HCWs (88%); 72 (35%) followed up in the employee health clinic within the 8–12 weeks after exposure. Of 161 HCWs with a negative TST at baseline, no conversions occurred. Of 18 HCWs with positive TST at baseline, no HCW developed symptoms during the observation period. Conclusions: Nearly one-third of infectious TB cases led to HCW exposures in a low-incidence setting. However, no TST conversions or active TB infections were seen. Exposure and conversion rates are useful indicators of TB infection control in healthcare facilities and may help guide implementation of the new CDC TB control recommendations.Funding: NoneDisclosures: None