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Healthcare personnel with severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection were interviewed to describe activities and practices in and outside the workplace. Among 2,625 healthcare personnel, workplace-related factors that may increase infection risk were more common among nursing-home personnel than hospital personnel, whereas selected factors outside the workplace were more common among hospital personnel.
Background: Pneumonia (PNA) is an important cause of morbidity and mortality among nursing home residents. The McGeer surveillance definitions were revised in 2012 to help NHs better monitor infections for quality improvement purposes. However, the concordance between surveillance definitions and clinically diagnosed PNA has not been well studied. Our objectives were to identify nursing home residents who met the revised McGeer PNA definition, to compare them with residents with clinician documented PNA, and determine whether modifications to the surveillance criteria could increase concordance. Methods: We analyzed respiratory tract infection (RTI) data from 161 nursing homes in 10 states that participated in a 1-day healthcare-associated infection point-prevalence survey in 2017. Trained surveillance officers from the CDC Emerging Infections Program collected data on residents with clinician documentation, signs, symptoms, and diagnostic testing potentially indicating an RTI. Clinician-documented pneumonia was defined as any resident with a diagnosis of pneumonia identified in the medical chart. We identified the proportion of residents with clinician documented PNA who met the revised McGeer PNA definition. We evaluated the criteria reported to develop 3 modified PNA surveillance definitions (Box), and we compared them to residents with clinician documented PNA.
Results: Among the 15,296 NH residents surveyed, 353 (2%) had >1 signs and/or symptoms potentially indicating RTI. Among the 353 residents, the average age was 76 years, 105 (30%) were admitted to postacute care or rehabilitation, and 108 (31%) had clinician-documented PNA. Among those with PNA, 28 (26%) met the Revised McGeer definition. Among 81 residents who did not meet the definition, 39 (48%) were missing the chest x-ray requirement, and among the remaining 42, only 3 (7%) met the constitutional criteria requirement (Fig. 1). Modification of the constitutional criteria requirement increased the detection of clinically documented PNA from 28 (26%) to 36 (33%) using modified definition 1; to 51 (47%) for modified definition 2; and to 55 (51%) for modified definition 3. Conclusions: Tracking PNA among nursing home residents using a standard definition is essential to improving detection and, therefore, informing prevention efforts. Modifying the PNA criteria increased the identification of clinically diagnosed PNA. Better concordance with clinically diagnosed PNA may improve provider acceptance and adoption of the surveillance definition, but additional research is needed to test its validity.
Background: With the emergence of antibiotic resistant threats and the need for appropriate antibiotic use, laboratory microbiology information is important to guide clinical decision making in nursing homes, where access to such data can be limited. Susceptibility data are necessary to inform antibiotic selection and to monitor changes in resistance patterns over time. To contribute to existing data that describe antibiotic resistance among nursing home residents, we summarized antibiotic susceptibility data from organisms commonly isolated from urine cultures collected as part of the CDC multistate, Emerging Infections Program (EIP) nursing home prevalence survey. Methods: In 2017, urine culture and antibiotic susceptibility data for selected organisms were retrospectively collected from nursing home residents’ medical records by trained EIP staff. Urine culture results reported as negative (no growth) or contaminated were excluded. Susceptibility results were recorded as susceptible, non-susceptible (resistant or intermediate), or not tested. The pooled mean percentage tested and percentage non-susceptible were calculated for selected antibiotic agents and classes using available data. Susceptibility data were analyzed for organisms with ≥20 isolates. The definition for multidrug-resistance (MDR) was based on the CDC and European Centre for Disease Prevention and Control’s interim standard definitions. Data were analyzed using SAS v 9.4 software. Results: Among 161 participating nursing homes and 15,276 residents, 300 residents (2.0%) had documentation of a urine culture at the time of the survey, and 229 (76.3%) were positive. Escherichia coli, Proteus mirabilis, Klebsiella spp, and Enterococcus spp represented 73.0% of all urine isolates (N = 278). There were 215 (77.3%) isolates with reported susceptibility data (Fig. 1). Of these, data were analyzed for 187 (87.0%) (Fig. 2). All isolates tested for carbapenems were susceptible. Fluoroquinolone non-susceptibility was most prevalent among E. coli (42.9%) and P. mirabilis (55.9%). Among Klebsiella spp, the highest percentages of non-susceptibility were observed for extended-spectrum cephalosporins and folate pathway inhibitors (25.0% each). Glycopeptide non-susceptibility was 10.0% for Enterococcus spp. The percentage of isolates classified as MDR ranged from 10.1% for E. coli to 14.7% for P. mirabilis. Conclusions: Substantial levels of non-susceptibility were observed for nursing home residents’ urine isolates, with 10% to 56% reported as non-susceptible to the antibiotics assessed. Non-susceptibility was highest for fluoroquinolones, an antibiotic class commonly used in nursing homes, and ≥ 10% of selected isolates were MDR. Our findings reinforce the importance of nursing homes using susceptibility data from laboratory service providers to guide antibiotic prescribing and to monitor levels of resistance.
Background: With an aging population, increasingly complex care, and frequent re-admissions, prevention of healthcare-associated infections (HAIs) in nursing homes (NHs) is a federal priority. However, few contemporary sources of HAI data exist to inform surveillance, prevention, and policy. Prevalence surveys (PSs) are an efficient approach to generating data to measure the burden and describe the types of HAI. In 2017, the Centers for Disease Control and Prevention (CDC) performed its first large-scale HAI PS through the Emerging Infections Program (EIP) to measure the prevalence and describe the epidemiology of HAI in NH residents. Methods: NHs from several states (CA, CO, CT, GA, MD, MN, NM, NY, OR, & TN) were randomly selected and asked to participate in a 1-day HAI PS between April and October 2017; participation was voluntary. EIP staff reviewed available medical records for NH residents present on the survey date to collect demographic and basic clinical information and infection signs and symptoms. HAIs with onset on or after NH day 3 were identified using revised McGeer infection definitions applied to data collected by EIP staff and were reported to the CDC through a web-based system. Data were reviewed by CDC staff for potential errors and to validate HAI classifications prior to analysis. HAI prevalence, number of residents with >1 HAI per number of surveyed residents ×100, and 95% CIs were calculated overall (pooled mean) and for selected resident characteristics. Data were analyzed using SAS v9.4 software. Results: Among 15,296 residents in 161 NHs, 358 residents with 375 HAIs were identified. The most common HAI sites were skin (32%), respiratory tract (29%), and urinary tract (20%). Cellulitis, soft-tissue or wound infection, symptomatic UTI, and cold or pharyngitis were the most common individual HAIs (Fig. 1). Overall HAI prevalence was 2.3 per 100 residents (95% CI, 2.1–2.6); at the NH level, the median HAI prevalence was 1.8 and ranged from 0 to 14.3 (interquartile range, 0–3.1). At the resident level (Fig. 2), HAI prevalence was significantly higher in persons admitted for postacute care with diabetes, with a pressure ulcer, receiving wound care, or with a device. Conclusions: In this large-scale survey, 1 in 43 NH residents had an HAI on a given day. Three HAI types comprised >80% of infections. In addition to identifying characteristics that place residents at higher risk for HAIs, these findings provide important data on HAI epidemiology in NHs that can be used to expand HAI surveillance and inform prevention policies and practices.
Background: Certain nursing home (NH) resident care tasks have a higher risk for multidrug-resistant organisms (MDRO) transfer to healthcare personnel (HCP), which can result in transmission to residents if HCPs fail to perform recommended infection prevention practices. However, data on HCP-resident interactions are limited and do not account for intrafacility practice variation. Understanding differences in interactions, by HCP role and unit, is important for informing MDRO prevention strategies in NHs. Methods: In 2019, we conducted serial intercept interviews; each HCP was interviewed 6–7 times for the duration of a unit’s dayshift at 20 NHs in 7 states. The next day, staff on a second unit within the facility were interviewed during the dayshift. HCP on 38 units were interviewed to identify healthcare personnel (HCP)–resident care patterns. All unit staff were eligible for interviews, including certified nursing assistants (CNAs), nurses, physical or occupational therapists, physicians, midlevel practitioners, and respiratory therapists. HCP were asked to list which residents they had cared for (within resident rooms or common areas) since the prior interview. Respondents selected from 14 care tasks. We classified units into 1 of 4 types: long-term, mixed, short stay or rehabilitation, or ventilator or skilled nursing. Interactions were classified based on the risk of HCP contamination after task performance. We compared proportions of interactions associated with each HCP role and performed clustered linear regression to determine the effect of unit type and HCP role on the number of unique task types performed per interaction. Results: Intercept-interviews described 7,050 interactions and 13,843 care tasks. Except in ventilator or skilled nursing units, CNAs have the greatest proportion of care interactions (interfacility range, 50%–60%) (Fig. 1). In ventilator and skilled nursing units, interactions are evenly shared between CNAs and nurses (43% and 47%, respectively). On average, CNAs in ventilator and skilled nursing units perform the most unique task types (2.5 task types per interaction, Fig. 2) compared to other unit types (P < .05). Compared to CNAs, most other HCP types had significantly fewer task types (0.6–1.4 task types per interaction, P < .001). Across all facilities, 45.6% of interactions included tasks that were higher-risk for HCP contamination (eg, transferring, wound and device care, Fig. 3). Conclusions: Focusing infection prevention education efforts on CNAs may be most efficient for preventing MDRO transmission within NH because CNAs have the most HCP–resident interactions and complete more tasks per visit. Studies of HCP-resident interactions are critical to improving understanding of transmission mechanisms as well as target MDRO prevention interventions.
Funding: Centers for Disease Control and Prevention (grant no. U01CK000555-01-00)
Disclosures: Scott Fridkin, consulting fee, vaccine industry (spouse)
Background: Antibiotics are among the most commonly prescribed drugs in nursing homes; urinary tract infections (UTIs) are a frequent indication. Although there is no gold standard for the diagnosis of UTIs, various criteria have been developed to inform and standardize nursing home prescribing decisions, with the goal of reducing unnecessary antibiotic prescribing. Using different published criteria designed to guide decisions on initiating treatment of UTIs (ie, symptomatic, catheter-associated, and uncomplicated cystitis), our objective was to assess the appropriateness of antibiotic prescribing among NH residents. Methods: In 2017, the CDC Emerging Infections Program (EIP) performed a prevalence survey of healthcare-associated infections and antibiotic use in 161 nursing homes from 10 states: California, Colorado, Connecticut, Georgia, Maryland, Minnesota, New Mexico, New York, Oregon, and Tennessee. EIP staff reviewed resident medical records to collect demographic and clinical information, infection signs, symptoms, and diagnostic testing documented on the day an antibiotic was initiated and 6 days prior. We applied 4 criteria to determine whether initiation of treatment for UTI was supported: (1) the Loeb minimum clinical criteria (Loeb); (2) the Suspected UTI Situation, Background, Assessment, and Recommendation tool (UTI SBAR tool); (3) adaptation of Infectious Diseases Society of America UTI treatment guidelines for nursing home residents (Crnich & Drinka); and (4) diagnostic criteria for uncomplicated cystitis (cystitis consensus) (Fig. 1). We calculated the percentage of residents for whom initiating UTI treatment was appropriate by these criteria. Results: Of 248 residents for whom UTI treatment was initiated in the nursing home, the median age was 79 years [IQR, 19], 63% were female, and 35% were admitted for postacute care. There was substantial variability in the percentage of residents with antibiotic initiation classified as appropriate by each of the criteria, ranging from 8% for the cystitis consensus, to 27% for Loeb, to 33% for the UTI SBAR tool, to 51% for Crnich and Drinka (Fig. 2). Conclusions: Appropriate initiation of UTI treatment among nursing home residents remained low regardless of criteria used. At best only half of antibiotic treatment met published prescribing criteria. Although insufficient documentation of infection signs, symptoms and testing may have contributed to the low percentages observed, adequate documentation in the medical record to support prescribing should be standard practice, as outlined in the CDC Core Elements of Antibiotic Stewardship for nursing homes. Standardized UTI prescribing criteria should be incorporated into nursing home stewardship activities to improve the assessment and documentation of symptomatic UTI and to reduce inappropriate antibiotic use.
Background: The NHSN collects data on mucosal barrier injury, laboratory-confirmed, bloodstream infections (MBI-LCBIs) as part of bloodstream infection (BSI) surveillance. Specialty care areas (SCAs), which include oncology patient care locations, tend to report the most MBI-LCBI events compared to other location types. During the update of the NSHN aggregate data and risk models in 2015, MBI-LCBI events were excluded from central-line–associated BSI (CLABSI) model calculations; separate models were generated for MBI-LCBIs, resulting in MBI-specific standardized infection ratios (SIRs). This is the first analysis to describe risk-adjusted incidence of MBI-LCBIs at the national level. Methods: Data were analyzed for MBI-LCBIs attributed to oncology locations conducting BSI surveillance from January 2015 through December 2018. We generated annual national MBI-LCBI SIRs using risk models developed from 2015 data and compared the annual SIRs to the baseline (2015) using a mid-P exact test. To account for the impact of an expansion in the MBI-LCBI organism list in 2017 from 489 organisms (32 genera) to 1,003 organisms (89 genera), we removed the MBI-LCBI events that met the newly added MBI organisms and generated additional MBI SIRs for 2017 and 2018. Results: The annual SIRs remained above 1 since 2015, indicating a greater number of MBI-LCBIs identified than were predicted based on the 2015 national data (Fig. 1). Each year’s SIR was significantly different than the national baseline, and the highest SIR was observed in 2017 (SIR, 1.377). In 2017, 12% of MBI events were attributed to an organism that was added to the MBI organism list, and in 2018 it was 10%. After removal of MBIs attributed to the expanded organisms, the 2017 and 2018 SIRs remained higher than those of previous years (1.241 and 1.232, respectively). Conclusions: The distinction of MBI-LCBIs from all other CLABSIs provides an opportunity to assess the burden of this infection type within specific patient populations. Since 2015, the increase of these events in the oncology population highlights the need for greater attention on prevention strategies pertinent to MBI-LCBI in this vulnerable population.
Antibiotic resistance (AR) is a growing and highly prevalent problem in nursing homes. We describe selected AR phenotypes from pathogens causing urinary tract infections (UTIs) reported by nursing homes to the National Healthcare Safety Network (NHSN).
Pathogens and antibiotic susceptibility testing results for UTI events in nursing homes between January 2013 and December 2017 were analyzed. The pathogen distribution and pooled mean proportion of isolates that tested resistant to select antibiotic agents are reported.
Setting and Participants:
US nursing homes voluntarily participating in the Long-Term Care Facility component of the NHSN.
Overall, 243 nursing homes reported 1 or more UTIs: 121 (50%) were nonprofit facilities, median bed size was 91 (range: 9–801), and average occupancy was 87%. In total, 6,157 pathogens were reported for 5,485 UTI events. Moreover, 9 pathogens accounted for 90% of all reported UTIs; the 3 most frequently identified were Escherichia coli (41%), Proteus species (14%), and Klebsiella pneumoniae/oxytoca (13%). Among E. coli, fluoroquinolone, and extended-spectrum cephalosporin resistance were most prevalent (50% and 20%, respectively). Although Staphylococcus aureus and Enterococcus faecium represented <5% of pathogens reported, they had the highest rates of resistance (67% methicillin resistant and 60% vancomycin resistant, respectively). Multidrug resistance was most common in Pseudomonas aeruginosa (11%). For the resistant phenotypes we assessed, 36% of all UTIs reported were associated with a resistant pathogen.
This is the first summary of AR among common pathogens causing UTIs reported to NHSN by nursing homes. Improved understanding of the resistance burden among common infections helps inform facility infection prevention and antibiotic stewardship efforts.
Acute change in mental status (ACMS), defined by the Confusion Assessment Method, is used to identify infections in nursing home residents. A medical record review revealed that none of 15,276 residents had an ACMS documented. Using the revised McGeer criteria with a possible ACMS definition, we identified 296 residents and 21 additional infections. The use of a possible ACMS definition should be considered for retrospective nursing home infection surveillance.
A complex interaction exists between age, body mass index, medical conditions, polypharmacotherapy, smoking, alcohol use, education, nutrition, depressive symptoms, functioning and quality of life (QoL). We aimed to examine the inter-relationships among these variables, test whether depressive symptomology plays a central role in a large sample of adults, and determine the degree of association with life-style and health variables.
Regularised network analysis was applied to 3532 North-American adults aged ⩾45 years drawn from the Osteoarthritis Initiative. Network stability (autocorrelation after case-dropping), centrality of nodes (strength, M, the sum of weight of the connections for each node), and edges/regularised partial correlations connecting the nodes were assessed.
Physical and mental health-related QoL (M = 1.681; M = 1.342), income (M = 1.891), age (M = 1.416), depressive symptoms (M = 1.214) and education (M = 1.173) were central nodes. Depressive symptoms’ stronger negative connections were found with mental health-related QoL (−0.702), income (−0.090), education (−0.068) and physical health-related QoL (−0.354). This latter was a ‘bridge node’ that connected depressive symptoms with Charlson comorbidity index, and number of medications. Physical activity and Mediterranean diet adherence were associated with income and physical health-related QoL. This latter was a ‘bridge node’ between the former two and depressive symptoms. The network was stable (stability coefficient = 0.75, i.e. highest possible value) for all centrality measures.
A stable network exists between life-style behaviors and social, environmental, medical and psychiatric variables. QoL, income, age and depressive symptoms were central in the multidimensional network. Physical health-related QoL seems to be a ‘bridge node’ connecting depressive symptoms with several life-style and health variables. Further studies should assess such interactions in the general population.
Among young Samoan children, diet may not be optimal: in 2015, 16·1 % of 24–59-month-olds were overweight/obese, 20·3 % stunted and 34·1 % anaemic. The present study aimed to identify dietary patterns among 24–59-month-old Samoan children and evaluate their association with: (i) child, maternal and household characteristics; and (ii) nutritional status indicators (stunting, overweight/obesity, anaemia).
A community-based, cross-sectional study. Principal component analysis on 117 FFQ items was used to identify empirical dietary patterns. Distributions of child, maternal and household characteristics were examined by factor score quintiles. The regression of nutritional status indicators v. these quintiles was performed using logistic regression models.
Ten villages on the Samoan island of Upolu.
A convenience sample of mother–child pairs (n 305).
Two dietary patterns, modern and neo-traditional, emerged. The modern pattern was loaded with ‘westernized’ foods (red meat, condiments and snacks). The neo-traditional pattern included vegetables, local starches, coconuts, fish and poultry. Following the modern diet was associated with urban residence, greater maternal educational attainment, higher socio-economic status, lower vitamin C intake and higher sugar intake. Following the neo-traditional diet was associated with rural residence, lower socio-economic status, higher vitamin C intake and lower sugar intake. While dietary patterns were not related to stunting or anaemia, following the neo-traditional pattern was positively associated with child overweight/obesity (adjusted OR=4·23, 95 % CI 1·26, 14·17, for the highest quintile, P-trend=0·06).
Further longitudinal monitoring and evaluation of early childhood growth and development are needed to understand the influences of early diet on child health in Samoa.
Arterial wall thickening, stimulated by low-grade systemic inflammation, underlies many cardiovascular events. As diet is a significant moderator of systemic inflammation, the dietary inflammatory index (DIITM) has recently been devised to assess the overall inflammatory potential of an individual’s diet. The primary objective of this study was to assess the association of the DII with common carotid artery–intima-media thickness (CCA–IMT) and carotid plaques. To substantiate the clinical importance of these findings we assessed the relationship of DII score with atherosclerotic vascular disease (ASVD)-related mortality, ischaemic cerebrovascular disease (CVA)-related mortality and ischaemic heart disease (IHD)-related mortality more. The study was conducted in Western Australian women aged over 70 years (n 1304). Dietary data derived from a validated FFQ (completed at baseline) were used to calculate a DII score for each individual. In multivariable-adjusted models, DII scores were associated with sub-clinical atherosclerosis: a 1 sd (2·13 units) higher DII score was associated with a 0·013-mm higher mean CCA–IMT (P=0·016) and a 0·016-mm higher maximum CCA–IMT (P=0·008), measured at 36 months. No relationship was seen between DII score and carotid plaque severity. There were 269 deaths during follow-up. High DII scores were positively associated with ASVD-related death (per sd, hazard ratio (HR): 1·36; 95 % CI 1·15, 1·60), CVA-related death (per sd, HR: 1·30; 95 % CI 1·00, 1·69) and IHD-related death (per sd, HR: 1·40; 95 % CI 1·13, 1·75). These results support the hypothesis that a pro-inflammatory diet increases systemic inflammation leading to development and progression of atherosclerosis and eventual ASVD-related death.
We assessed the appropriateness of initiating antibiotics in 49 nursing home (NH) residents receiving antibiotics for urinary tract infection (UTI) using 3 published algorithms. Overall, 16 residents (32%) received prophylaxis, and among the 33 receiving treatment, the percentage of appropriate use ranged from 15% to 45%. Opportunities exist for improving UTI antibiotic prescribing in NH.
Young children are particularly vulnerable to malnutrition as nutrition transition progresses. The present study aimed to document the prevalence, coexistence and correlates of nutritional status (stunting, overweight/obesity and anaemia) in Samoan children aged 24–59 months.
A cross-sectional community-based survey. Height and weight were used to determine prevalence of stunting (height-for-age Z-score <−2) and overweight/obesity (BMI-for-age Z-score >+2) based on WHO growth standards. Anaemia was determined using an AimStrip Hemoglobin test system (Hb <110 g/l).
Ten villages on the Samoan island of Upolu.
Mother–child pairs (n 305) recruited using convenience sampling.
Moderate or severe stunting was apparent in 20·3 % of children, 16·1 % were overweight/obese and 34·1 % were anaemic. Among the overweight/obese children, 28·6 % were also stunted and 42·9 % anaemic, indicating dual burden of malnutrition. Stunting was significantly less likely among girls (OR=0·41; 95 % CI 0·21, 0·79, P<0·01) than boys. Overweight/obesity was associated with higher family socio-economic status and decreased sugar intake (OR per 10 g/d=0·89, 95 % CI 0·80, 0·99, P=0·032). The odds of anaemia decreased with age and anaemia was more likely in children with an anaemic mother (OR=2·20; 95 % CI 1·22, 3·98, P=0·007). No child, maternal or household characteristic was associated with more than one of the nutritional status outcomes, highlighting the need for condition-specific interventions in this age group.
The observed prevalences of stunting, overweight/obesity and anaemia suggest that it is critical to invest in nutrition and develop health programmes targeting early childhood growth and development in Samoa.
To facilitate surveillance and describe the burden of healthcare-associated infection (HAI) in nursing homes (NHs), we compared the quality of resident-level data collected by NH personnel and external staff.
A 1-day point-prevalence survey
SETTING AND PARTICIPANTS
Overall, 9 nursing homes among 4 Centers for Disease Control and Prevention (CDC) Emerging Infection Program (EIP) sites were included in this study.
NH personnel collected data on resident characteristics, clinical risk factors for HAIs, and the presence of 3 HAI screening criteria on the day of the survey. Trained EIP surveillance officers collected the same data elements via retrospective medical chart review for comparison; surveillance officers also collected available data to identify HAIs (using revised McGeer definitions). Overall agreement was calculated among residents identified by both teams with selected risk factors and HAI screening criteria. The impact of using NH personnel to collect screening criteria on HAI prevalence was assessed.
The overall prevalence of clinical risk factors among the 1,272 residents was similar between NH personnel and surveillance officers, but the level of positive agreement (residents with factors identified by both teams) varied between 39% and 87%. Surveillance officers identified 253 residents (20%) with ≥1 HAI screening criterion, resulting in 67 residents with an HAI (5.3 per 100 residents). The NH personnel identified 152 (12%) residents with ≥1 HAI screening criterion; 42 residents had an HAI (3.5 per 100 residents).
We identified discrepancies in resident-level data collection between surveillance officers and NH personnel, resulting in varied estimates of the HAI prevalence. These findings have important implications for the design and implementation of future HAI prevalence surveys.
Illegal killing/taking of birds is a growing concern across the Mediterranean. However, there are few quantitative data on the species and countries involved. We assessed numbers of individual birds of each species killed/taken illegally in each Mediterranean country per year, using a diverse range of data sources and incorporating expert knowledge. We estimated that 11–36 million individuals per year may be killed/taken illegally in the region, many of them on migration. In each of Cyprus, Egypt, Italy, Lebanon and Syria, more than two million birds may be killed/taken on average each year. For species such as Blackcap Sylvia atricapilla, Common Quail Coturnix coturnix, Eurasian Chaffinch Fringilla coelebs, House Sparrow Passer domesticus and Song Thrush Turdus philomelos, more than one million individuals of each species are estimated to be killed/taken illegally on average every year. Several species of global conservation concern are also reported to be killed/taken illegally in substantial numbers: Eurasian Curlew Numenius arquata, Ferruginous Duck Aythya nyroca and Rock Partridge Alectoris graeca. Birds in the Mediterranean are illegally killed/taken primarily for food, sport and for use as cage-birds or decoys. At the 20 worst locations with the highest reported numbers, 7.9 million individuals may be illegally killed/taken per year, representing 34% of the mean estimated annual regional total number of birds illegally killed/taken for all species combined. Our study highlighted the paucity of data on illegal killing/taking of birds. Monitoring schemes which use systematic sampling protocols are needed to generate increasingly robust data on trends in illegal killing/taking over time and help stakeholders prioritise conservation actions to address this international conservation problem. Large numbers of birds are also hunted legally in the region, but specific totals are generally unavailable. Such data, in combination with improved estimates for illegal killing/taking, are needed for robustly assessing the sustainability of exploitation of birds.
Outpatient hemodialysis bloodstream infection rates, now used for performance measurement and were significantly higher for manual compared with automated surveillance (P<.001), largely owing to the absence of blood culture data in the dialysis electronic health record. Improvement in data sharing between hospitals and outpatient dialysis centers is necessary.
Infect. Control Hosp. Epidemiol. 2016;37(4):472–474