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To estimate the incidence, duration and risk factors for diagnostic delays associated with pertussis.
We used longitudinal retrospective insurance claims from the Marketscan Commercial Claims and Encounters, Medicare Supplemental (2001–2020), and Multi-State Medicaid (2014–2018) databases.
Inpatient, emergency department, and outpatient visits.
The study included patients diagnosed with pertussis (International Classification of Diseases [ICD] codes) and receipt of macrolide antibiotic treatment.
We estimated the number of visits with pertussis-related symptoms before diagnosis beyond that expected in the absence of diagnostic delays. Using a bootstrapping approach, we estimated the number of visits representing a delay, the number of missed diagnostic opportunities per patient, and the duration of delays. Results were stratified by age groups. We also used a logistic regression model to evaluate potential factors associated with delay.
We identified 20,828 patients meeting inclusion criteria. On average, patients had almost 2 missed opportunities prior to diagnosis, and delay duration was 12 days. Across age groups, the percentage of patients experiencing a delay ranged from 29.7% to 37.6%. The duration of delays increased considerably with age from an average of 5.6 days for patients aged <2 years to 13.8 days for patients aged ≥18 years. Factors associated with increased risk of delays included emergency department visits, telehealth visits, and recent prescriptions for antibiotics not effective against pertussis.
Diagnostic delays for pertussis are frequent. More work is needed to decrease diagnostic delays, especially among adults. Earlier case identification may play an important role in the response to outbreaks by facilitating treatment, isolation, and improved contact tracing.
The purpose of this document is to highlight practical recommendations to assist acute-care hospitals in prioritization and implementation of strategies to prevent healthcare-associated infections through hand hygiene. This document updates the Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals through Hand Hygiene, published in 2014. This expert guidance document is sponsored by the Society for Healthcare Epidemiology (SHEA). It is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America, the Association for Professionals in Infection Control and Epidemiology, the American Hospital Association, and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise.
Previous studies have suggested that a hospital patient's risk of developing healthcare facility-onset (HCFO) Clostridioides difficile infections (CDIs) increases with the number of concurrent spatially proximate patients with CDI, termed CDI pressure. However, these studies were performed either in a single institution or in a single state with a very coarse measure of concurrence. We conducted a retrospective case-control study involving over 17.5 million inpatient visits across 700 hospitals in eight US states. We built a weighted, directed network connecting overlapping inpatient visits to measure facility-level CDI pressure. We then matched HCFO-CDIs with non-CDI controls on facility, comorbidities and demographics and performed a conditional logistic regression to determine the odds of developing HCFO-CDI given the number of coincident patient visits with CDI. On average, cases' visits coincided with 9.2 CDI cases, which for an individual with an average length of stay corresponded to an estimated 17.7% (95% CI 12.9–22.7%) increase in the odds of acquiring HCFO-CDI compared to an inpatient visit without concurrent CDI cases or fully isolated from both direct and indirect risks from concurrent CDI cases. These results suggest that, either directly or indirectly, hospital patients with CDI lead to CDIs in non-infected patients with temporally overlapping visits.
Pediatric antimicrobial stewardship programs (ASPs) improve antibiotic use for hospitalized children. Prescriber surveys indicate acceptance of ASPs, but data on infectious diseases (ID) physician opinions of ASPs are lacking. We conducted a survey of pediatric ID physicians, ASP and non-ASP, and their perceptions of ASP practices and outcomes.
We performed a survey of adult infectious diseases (ID) physicians to explore unintended consequences of antimicrobial stewardship programs (ASP). ID physicians worried about disagreement with colleagues, provider autonomy, and remote recommendations. Non-ASP ID physicians expressed more concern regarding ASPs focus on costs, provider efficiency, and unintended consequences of ASP guidance.
Although a growing number of healthcare facilities are implementing healthcare personnel (HCP) coronavirus disease 2019 (COVID-19) vaccination requirements, vaccine exemption request management as a part of such programs is not well described.
Infectious disease (ID) physician members of the Emerging Infections Network with infection prevention or hospital epidemiology responsibilities.
Eligible persons were sent a web-based survey focused on hospital plans and practices around exemption allowances from HCP COVID-19 vaccine requirements.
Of the 695 ID physicians surveyed, 263 (38%) responded. Overall, 160 respondent institutions (92%) allowed medical exemptions, whereas 132 (76%) allowed religious exemptions. In contrast, only 14% (n = 24) allowed deeply held personal belief exemptions. The types of medical exemptions allowed varied considerably across facilities, with allergic reactions to the vaccine or its components accepted by 145 facilities (84%). For selected scenarios commonly used as the basis for religious and deeply held personal belief exemption requests, 144 institutions (83%) would not approve exemptions focused on concerns regarding right of consent or violations of freedom of personal choice, and 140 institutions (81%) would not approve exemptions focused on introducing foreign substances into one’s body or the sanctity of the body. Most respondents noted plans for additional infection prevention interventions for HCP who received an exemption for COVID-19 vaccination.
Although many respondent institutions allowed exemptions from HCP COVID-19 vaccination requirements, the types of exemptions allowed and how the exemption programs were structured varied widely.
We surveyed infectious disease specialists about early coronavirus disease 2019 (COVID-19) vaccination preparedness. Almost all responding institutions rated their facility’s preparedness plan as either excellent or adequate. Vaccine hesitancy and concern about adverse reactions were the most commonly anticipated barriers to COVID-19 vaccination. Only 60% believed that COVID-19 vaccination should be mandatory.
Background: Simulations based on models of healthcare worker (HCW) mobility and contact patterns with patients provide a key tool for understanding spread of healthcare-acquired infections (HAIs). However, simulations suffer from lack of accurate model parameters. This research uses Microsoft Kinect cameras placed in a patient room in the medical intensive care unit (MICU) at the University of Iowa Hospitals and Clinics (UIHC) to obtain reliable distributions of HCW visit length and time spent by HCWs near a patient. These data can inform modeling efforts for understanding HAI spread. Methods: Three Kinect cameras (left, right, and door cameras) were placed in a patient room to track the human body (ie, left/right hands and head) at 30 frames per second. The results reported here are based on 7 randomly selected days from a total of 308 observation days. Each tracked body may have multiple raw segments over the 2 camera regions, which we “stitch” up by matching features (eg, direction, velocity, etc), to obtain complete trajectories. Due to camera noise, in a substantial fraction of the frames bodies display unnatural characteristics including frequent and rapid directional and velocity change. We use unsupervised learning techniques to identify such “ghost” frames and we remove from our analysis bodies that have 20% or more “ghost” frames. Results: The heat map of hand positions (Fig. 1) shows that high-frequency locations are clustered around the bed and more to the patient’s right in accordance with the general medical practice of performing patient exams from their right. HCW visit frequency per hour (mean, 6.952; SD, 2.855) has 2 peaks, 1 during morning shift and 1 during the afternoon shift, with a distinct decrease after midnight. Figure 2 shows visit length (in minutes) distribution (mean, 1.570; SD, 2.679) being dominated by “check in visits” of <30 seconds. HCWs do not spend much time at touching distance from patients during short-length visits, and the fraction of time spent near the patient’s bed seems to increase with visit length up to a point. Conclusions: Using fine-grained data, this research extracts distributions of these critical parameters of HCW–patient interactions: (1) HCW visit length, (2) HCW visit frequency as a function of time of day, and (3) time spent by HCW within touching distance of patient as a function of visit length. To the best of our knowledge, we provide the first reliable estimates of these parameters.
Background: Hospital-acquired infections are commonly spread through the movement of healthcare professionals (HCPs). Computational simulations provide a powerful tool for understanding how HCP behavior contributes to these infections, but how well they reflect the real world rests on a number of critical parameters. Our goal is to provide accurate, fine-grained estimates of real HCP movement and interaction parameters suitable for simulating the potential spread of pathogens over different types of inpatient facilities. Methods: We obtained a commercial data set with 44 million deidentified elements compiled from >27,000 HCPs from >30 job types. The data were collected over 27 months from >20 facilities of varying size using a proprietary electronic sensor system. Each observation recorded an HCP visiting 1 of 12,000 rooms (38% being patient rooms) and consisted of the entry and exit time stamps, hand hygiene behavior, and for many rooms, their (x, y) geometric coordinates within the facility. From these data, we can reconstruct the behavior (including location and hand-hygiene adherence) of each instrumented HCP across multiple shifts. Results: Distributions describing various aspects of HCP behavior (eg, arrival rates and dwell times) were derived using HCP job function, department or unit assignment, type of shift (day vs night), time of day, facility size, and staffing of facility. In a similar fashion, we constructed HCP cross-table transition probabilities using job type, room type, department type, unit type, and facility type. These distributions were used to generate reasonable HCP movement and behavior patterns in a simulation environment. Distributions of dwell time were, for the most part, heavy tailed, but they varied by type of job and facility: dwell times over all facilities, job types, and room types averaged ∼339 seconds (SD, 495 seconds), with a mean of maximums by job type of ∼37,168 seconds. However, these distributions differ within job type but across facilities (ie, nurses in 1 facility averaged 397 seconds, but 277 seconds in another) and within facility but across job type. For example, physicians averaged 292 seconds, whereas nurses averaged 397 seconds and physical therapists averaged 861 seconds. Conclusions: Our results provide a unique resource for disease modelers who wish to build meaningful simulations of the transmission of hospital-acquired infections. The scale and diversity of the data gave us the unique capability to provide, with confidence, distinct parameter sets for different types and sizes of healthcare facilities across a wide range of situations.
Background: Mobility patterns of healthcare workers (HCWs) (ie, the spatiotemporal distribution of patient rooms they visit) have a significant impact on the spread of healthcare acquired infections (HAIs). Objective: In this project, we used fine-grained data from a sensor deployment at the medical intensive care unit (MICU) in the University of Iowa Hospitals and Clinics (UIHC) to study the mobility patterns of HCWs and their impact on HAI spread. Methods: We analyzed 10 days of data from a 20-bed MICU sensor deployment. For parameters t1 and t2, each pair of rooms i and j is assigned a weight W(i, j) representing the number of times an HCW spends at least t1 seconds in room i followed by at least t1 seconds in room j, within t2 seconds of each other. W(i, j) is a measure of HCW traffic going from room i to room j; we study the correlation between W(i, j) and the distance between rooms i and j. Additionally, we perform 2 disease-spread simulations: (1) a base simulation, obtained by replaying observed HCW mobility traces and (2) a perturbed simulation, which is the same as the base simulation, except that we replace each HCW who visits a room by a random available HCW. Thus, the perturbed simulation removes correlations in the observed HCW mobility traces. Results: We computed W(i, j) for all room pairs i, j for parameters t1 = 30 seconds and t2 = 1,800 and 3,600 seconds. For nurses, there was a strong negative correlation of between pairwise room distance and the weights W(i, j) (−0.768 for t2 = 1,800; −0.711 for t2 = 3,600), The more distant 2 rooms were, the less they shared nurse traffic. This was not true for physicians (correlation = −0.027 for t2 = 1,800; −0.014 for t2 = 3,600). Figure 1 shows a weight versus distance scatter plot for nurses for t1 = 30 and t2 = 1,800. This spatial correlation has positive implications for disease spread; the base simulation, which preserves these spatial correlations, has between 12% and 55% fewer mean infected patients (>100 replicates) for different simulation parameters compared to the perturbed simulation. Conclusions: Our results, based on fine-grained data, show a “naturally emerging” cohorting behavior of nurses, where nurses are more likely to visit rooms close to each other within a 30–60 minute time window, than rooms further away. Through simulations, this behavior provides substantial protection against disease spread.
To determine whether Clostridioides difficile infection (CDI) exhibits spatiotemporal interaction and clustering.
Retrospective observational study.
The University of Iowa Hospitals and Clinics.
This study included 1,963 CDI cases, January 2005 through December 2011.
We extracted location and time information for each case and ran the Knox, Mantel, and mean and maximum component size tests for time thresholds (T = 7, 14, and 21 days) and distance thresholds (D = 2, 3, 4, and 5 units; 1 unit = 5–6 m). All tests were implemented using Monte Carlo simulations, and random CDI cases were constructed by randomly permuting times of CDI cases 20,000 times. As a counterfactual, we repeated all tests on 790 aspiration pneumonia cases because aspiration pneumonia is a complication without environmental factors.
Results from the Knox test and mean component size test rejected the null hypothesis of no spatiotemporal interaction (P < .0001), for all values of T and D. Results from the Mantel test also rejected the hypothesis of no spatiotemporal interaction (P < .0003). The same tests showed no such effects for aspiration pneumonia. Our results from the maximum component size tests showed similar trends, but they were not consistently significant, possibly because CDI outbreaks attributable to the environment were relatively small.
Our results clearly show spatiotemporal interaction and clustering among CDI cases and none whatsoever for aspiration pneumonia cases. These results strongly suggest that environmental factors play a role in the onset of some CDI cases. However, our results are not inconsistent with the possibility that many genetically unrelated CDI cases occurred during the study period.
Presenteeism, or working while ill, by healthcare personnel (HCP) experiencing influenza-like illness (ILI) puts patients and coworkers at risk. However, hospital policies and practices may not consistently facilitate HCP staying home when ill.
Objective and methods:
We conducted a mixed-methods survey in March 2018 of Emerging Infections Network infectious diseases physicians, describing institutional experiences with and policies for HCP working with ILI.
Of 715 physicians, 367 (51%) responded. Of 367, 135 (37%) were unaware of institutional policies. Of the remaining 232 respondents, 206 (89%) reported institutional policies regarding work restrictions for HCP with influenza or ILI, but only 145 (63%) said these were communicated at least annually. More than half of respondents (124, 53%) reported that adherence to work restrictions was not monitored or enforced. Work restrictions were most often not perceived to be enforced for physicians-in-training and attending physicians. Nearly all (223, 96%) reported that their facility tracked laboratory-confirmed influenza (LCI) in patients; 85 (37%) reported tracking ILI. For employees, 109 (47%) reported tracking of LCI and 53 (23%) reported tracking ILI. For independent physicians, not employed by the facility, 30 (13%) reported tracking LCI and 11 (5%) ILI.
More than one-third of respondents were unaware of whether their institutions had policies to prevent HCP with ILI from working; among those with knowledge of institutional policies, dissemination, monitoring, and enforcement of these policies was highly variable. Improving communication about work-restriction policies, as well as monitoring and enforcement, may help prevent the spread of infections from HCP to patients.
A nationwide survey indicated that screening for asymptomatic carriers of C. difficile is an uncommon practice in US healthcare settings. Better understanding of the role of asymptomatic carriage in C. difficile transmission, and of the measures available to reduce that risk, are needed to inform best practices regarding the management of carriers.
To estimate the burden of Clostridium difficile infections (CDIs) due to interfacility patient sharing at regional and hospital levels.
Retrospective observational study.
We used data from the Healthcare Cost and Utilization Project California State Inpatient Database (2005–2011) to identify 26,878,498 admissions and 532,925 patient transfers. We constructed a weighted, directed network among the hospitals by defining an edge between 2 hospitals to be the monthly average number of patients discharged from one hospital and admitted to another on the same day. We then used a network autocorrelation model to study the effect of the patient sharing network on the monthly average number of CDI cases per hospital, and we estimated the proportion of CDI cases attributable to the network.
We found that 13% (95% confidence interval [CI], 7.6%–18%) of CDI cases were due to diffusion through the patient-sharing network. The network autocorrelation parameter was estimated at 5.0 (95% CI, 3.0–6.9). An increase in the number of patients transferred into and/or an increased CDI rate at the hospitals from which those patients originated led to an increase in the number of CDIs in the receiving hospital.
A minority but substantial burden of CDI infections are attributable to hospital transfers. A hospital’s infection control may thus be nontrivially influenced by its neighboring hospitals. This work adds to the growing body of evidence that intervention strategies designed to minimize HAIs should be done at the regional rather than local level.
To characterize healthcare provider diagnostic testing practices for identifying Clostridioides (Clostridium) difficile infection (CDI) and asymptomatic carriage in children.
An 11-question survey was sent by e-mail or facsimile to all pediatric infectious diseases (PID) members of the Infectious Diseases Society of America’s Emerging Infections Network (EIN).
Among 345 eligible respondents who had ever responded to an EIN survey, 196 (57%) responded; 162 of these (83%) were aware of their institutional policies for CDI testing and management. Also, 159 (98%) respondents knew their institution’s C. difficile testing method: 99 (62%) utilize NAAT without toxin testing and 60 (38%) utilize toxin testing, either as a single test or a multistep algorithm. Of 153 respondents, 10 (7%) reported that formed stools were tested for C. difficile at their institution, and 76 of 151 (50%) reported that their institution does not restrict C. difficile testing in infants and young children. The frequency of symptom- and age-based testing restrictions did not vary between institutions utilizing NAAT alone compared to those utilizing toxin testing for C. difficile diagnosis. Of 143 respondents, 26 (16%) permit testing of neonatal intensive care unit patients and 12 of 26 (46%) treat CDI with antibiotics in this patient population.
These data suggest that there are opportunities to improve CDI diagnostic stewardship practices in children, including among hospitals using NAATs alone for CDI diagnosis in children.
To delineate the timing of, indications for, and assessment of visitor restriction policies and practices (VRPP) in pediatric facilities.
An electronic survey to characterize VRPP in pediatric healthcare facilities.
The Infectious Diseases Society of America Emerging Infections Network surveyed 334 pediatric infectious disease consultants via an electronic link. Descriptive analyses were performed.
A total of 170 eligible respondents completed a survey between 12 July and August 15, 2016, for a 51% response rate. Of the 104 respondents (61%) familiar with their VRPP, 92 (88%) had VRPP in all inpatient units. The respondents reported age-based VRPP (74%) symptom-based VRPP (97%), and outbreak-specific VRPP (75%). Symptom-based VRPP were reported to be seasonal by 24% of respondents and to be implemented year-round according to 70% of respondents. According to the respondents, communication of VRPP to families occurred at admission (87%) and through signage in care areas (64%), while communication of VRPP to staff occurred by email (77%), by meetings (55%), and by signage in staff-only areas (49%). Respondents reported that enforcement of VRPP was the responsibility of nursing (80%), registration clerks (58%), unit clerks (53%), the infection prevention team (31%), or clinicians 16 (16%). They also reported that the effectiveness of VRPP was assessed through active surveillance of hospital acquired respiratory infections (62%), through active surveillance of healthcare worker exposures (28%) and through patient/family satisfaction assessments (29%).
Visitor restriction policies and practices vary in scope, implementation, enforcement, and physician awareness in pediatric facilities. A prospective multisite evaluation of outcomes would facilitate the adoption of uniform guidance.
To determine whether the seasonality of surgical site infections (SSIs) can be explained by changes in temperature.
Retrospective cohort analysis.
The National Inpatient Sample database.
All hospital discharges with a primary diagnosis of SSI from 1998 to 2011 were considered cases. Discharges with a primary or secondary diagnoses of specific surgeries commonly associated with SSIs from the previous and current month served as our “at risk” cohort.
We modeled the national monthly count of SSI cases both nationally and stratified by region, sex, age, and type of institution. We used data from the National Climatic Data Center to estimate the monthly average temperatures for all hospital locations. We modeled the odds of having a primary diagnosis of SSI as a function of demographics, payer, location, patient severity, admission month, year, and the average temperature in the month of admission.
SSI incidence is highly seasonal, with the highest SSI incidence in August and the lowest in January. During the study period, there were 26.5% more cases in August than in January (95% CI, 23.3–29.7). Controlling for demographic and hospital-level characteristics, the odds of a primary SSI admission increased by roughly 2.1% per 2.8°C (5°F) increase in the average monthly temperature. Specifically, the highest temperature group, >32.2°C (>90°F), was associated with an increase in the odds of an SSI admission of 28.9% (95% CI, 20.2–38.3) compared to temperatures <4.4°C (<40°F).
At population level, SSI risk is highly seasonal and is associated with warmer weather.
To investigate the scale of antimicrobial prescribing without a corresponding visit, and to compare the attributes of patients who received antimicrobials with a corresponding visit with those who did not have a visit.
We followed up 185,010 Medicare patients for 1 year after an acute myocardial infarction. For each antimicrobial prescribed, we determined whether the patient had an inpatient, outpatient, or provider claim in the 7 days prior to the antimicrobial prescription being filled. We compared the proportions of patient characteristics for those prescriptions associated with a visit and without a visit (ie, phantom prescriptions). We also compared the rates at which different antimicrobials were prescribed without a visit.
We found that of 356,545 antimicrobial prescriptions, 14.75% had no evidence of a visit in the week prior to the prescription being filled. A higher percentage of patients without a visit were identified as white (P<.001) and female (P<.001). Patients without a visit had a higher likelihood of survival and fewer additional cardiac events (acute myocardial infarction, cardiac arrest, stroke, all P<.001). Among the antimicrobials considered, amoxicillin, penicillin, and agents containing trimethoprim and methenamine were much more likely to be prescribed without a visit. In contrast, levofloxacin, metronidazole, moxifloxacin, vancomycin, and cefdinir were much less likely to be prescribed without a visit.
Among this cohort of patients with chronic conditions, phantom prescriptions of antimicrobials are relatively common and occurred more frequently among those patients who were relatively healthy.
Recently, the US Food and Drug Administration requested that a “maximal use” trial be conducted to ensure the safety of frequent use of alcohol-based hand rubs (ABHRs) by healthcare workers.
To establish how frequently volunteers should be exposed to ABHR during a maximal use trial.
Retrospective review of literature and analysis of 2 recent studies that utilized hand hygiene electronic compliance monitoring (ECM) systems.
We reviewed PubMed for articles published between 1970 and December 31, 2015, containing the terms hand washing, hand hygiene, hand hygiene compliance, and alcohol-based hand rubs. Article titles, abstracts, or text were reviewed to determine whether the frequency of ABHR use by healthcare workers was reported. Two studies using hand hygiene ECM systems were reviewed to determine how frequently nurses used ABHR per shift and per hour.
Of 3,487 citations reviewed, only 10 reported how frequently individual healthcare workers used ABHR per shift or per hour. Very conservative estimates of the frequency of ABHR use were reported owing to shortcomings of the methods utilized. The greatest frequency of ABHR use was recorded by an ECM system in a medical intensive care unit. In 95% of nursing shifts, individual nurses used ABHR 141 times or less per shift, and 15 times or less per hour.
Hand hygiene ECM systems established that the frequency of exposure to ABHRs varies substantially among nurses. Our findings should be useful in designing how frequently individuals should be exposed to ABHR during a maximal use trial.