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We performed a systematic literature review and meta-analysis on the effectiveness of coronavirus disease 2019 (COVID-19) vaccination against post-COVID conditions (long COVID) among fully vaccinated individuals.
Systematic literature review/meta-analysis.
We searched PubMed, Cumulative Index to Nursing and Allied Health, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 1, 2019, to June 2, 2023, for studies evaluating the COVID-19 vaccine effectiveness (VE) against post-COVID conditions among fully vaccinated individuals who received two doses of COVID-19 vaccine. A post-COVID condition was defined as any symptom that was present four or more weeks after COVID-19 infection. We calculated the pooled diagnostic odds ratio (DOR) (95% confidence interval) for post-COVID conditions between fully vaccinated and unvaccinated individuals. Vaccine effectiveness was estimated as 100% x (1-DOR).
Thirty-two studies with 775,931 individuals evaluated the effect of vaccination on post-COVID conditions, of which, twenty-four studies were included in the meta-analysis. The pooled DOR for post-COVID conditions among fully vaccinated individuals was 0.680 (95% CI: 0.523–0.885) with an estimated VE of 32.0% (11.5%–47.7%). Vaccine effectiveness was 36.9% (23.1%–48.2%) among those who received two doses of COVID-19 vaccine before COVID-19 infection and 68.7% (64.7%–72.2%) among those who received three doses before COVID-19 infection. The stratified analysis demonstrated no protection against post-COVID conditions among those who received COVID-19 vaccination after COVID-19 infection.
Receiving a complete COVID-19 vaccination prior to contracting the virus resulted in a significant reduction in post-COVID conditions throughout the study period, including during the Omicron era. Vaccine effectiveness demonstrated an increase when supplementary doses were administered.
Although multiple studies have revealed that coronavirus disease 2019 (COVID-19) vaccines can reduce COVID-19–related outcomes, little is known about their impact on post–COVID-19 conditions. We performed a systematic literature review and meta-analysis on the effectiveness of COVID-19 vaccination against post–COVID-19 conditions (ie, long COVID).
We searched PubMed, CINAHL, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 1, 2019, to April 27, 2022, for studies evaluating COVID-19 vaccine effectiveness against post–COVID-19 conditions among individuals who received at least 1 dose of Pfizer/BioNTech, Moderna, AstraZeneca, or Janssen vaccine. A post–COVID-19 condition was defined as any symptom that was present 3 or more weeks after having COVID-19. Editorials, commentaries, reviews, study protocols, and studies in the pediatric population were excluded. We calculated the pooled diagnostic odds ratios (DORs) for post–COVID-19 conditions between vaccinated and unvaccinated individuals. Vaccine effectiveness was estimated as 100% × (1 − DOR).
In total, 10 studies with 1,600,830 individuals evaluated the effect of vaccination on post–COVID-19 conditions, of which 6 studies were included in the meta-analysis. The pooled DOR for post–COVID-19 conditions among individuals vaccinated with at least 1 dose was 0.708 (95% confidence interval (CI), 0.692–0.725) with an estimated vaccine effectiveness of 29.2% (95% CI, 27.5%–30.8%). The vaccine effectiveness was 35.3% (95% CI, 32.3%–38.1%) among those who received the COVID-19 vaccine before having COVID-19, and 27.4% (95% CI, 25.4%–29.3%) among those who received it after having COVID-19.
COVID-19 vaccination both before and after having COVID-19 significantly decreased post–COVID-19 conditions for the circulating variants during the study period although vaccine effectiveness was low.
We describe COVID-19 cases among nonphysician healthcare personnel (HCP) by work location. The proportion of HCP with coronavirus disease 2019 (COVID-19) was highest in the emergency department and lowest among those working remotely. COVID-19 and non–COVID-19 units had similar proportions of HCP with COVID-19 (13%). Cases decreased across all work locations following COVID-19 vaccination.
Although multiple studies revealed high vaccine effectiveness of coronavirus disease 2019 (COVID-19) vaccines within 3 months after the completion of vaccines, long-term vaccine effectiveness has not been well established, especially after the δ (delta) variant became prominent. We performed a systematic literature review and meta-analysis of long-term vaccine effectiveness.
We searched PubMed, CINAHL, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 2019 to November 15, 2021, for studies evaluating the long-term vaccine effectiveness against laboratory-confirmed COVID-19 or COVID-19 hospitalization among individuals who received 2 doses of Pfizer/BioNTech, Moderna, or AstraZeneca vaccines, or 1 dose of the Janssen vaccine. Long-term was defined as >5 months after the last dose. We calculated the pooled diagnostic odds ratio (DOR) with 95% confidence interval for COVID-19 between vaccinated and unvaccinated individuals. Vaccine effectiveness was estimated as 100% × (1 − DOR).
In total, 16 studies including 17,939,172 individuals evaluated long-term vaccine effectiveness and were included in the meta-analysis. The pooled DOR for COVID-19 was 0.158 (95% CI: 0.157-0.160) with an estimated vaccine effectiveness of 84.2% (95% CI, 84.0- 84.3%). Estimated vaccine effectiveness against COVID-19 hospitalization was 88.7% (95% CI, 55.8%–97.1%). Vaccine effectiveness against COVID-19 during the δ variant period was 61.2% (95% CI, 59.0%–63.3%).
COVID-19 vaccines are effective in preventing COVID-19 and COVID-19 hospitalization across a long-term period for the circulating variants during the study period. More observational studies are needed to evaluate the vaccine effectiveness of third dose of a COVID-19 vaccine, the vaccine effectiveness of mixing COVID-19 vaccines, COVID-19 breakthrough infection, and vaccine effectiveness against newly emerging variants.
Healthcare workers (HCWs) are at risk of COVID-19 due to high levels of SARS-CoV-2 exposure. Thus, effective vaccines are needed. We performed a systematic literature review and meta-analysis on COVID-19 short-term vaccine effectiveness among HCWs.
We searched PubMed, CINAHL, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 2019 to June 11, 2021, for studies evaluating vaccine effectiveness against symptomatic COVID-19 among HCWs. To meta-analyze the extracted data, we calculated the pooled diagnostic odds ratio (DOR) for COVID-19 between vaccinated and unvaccinated HCWs. Vaccine effectiveness was estimated as 100% × (1 − DOR). We also performed a stratified analysis for vaccine effectiveness by vaccination status: 1 dose and 2 doses of the vaccine.
We included 13 studies, including 173,742 HCWs evaluated for vaccine effectiveness in the meta-analysis. The vast majority (99.9%) of HCWs were vaccinated with the Pfizer/BioNTech COVID-19 mRNA vaccine. The pooled DOR for symptomatic COVID-19 among vaccinated HCWs was 0.072 (95% confidence interval [CI], 0.028–0.184) with an estimated vaccine effectiveness of 92.8% (95% CI, 81.6%–97.2%). In stratified analyses, the estimated vaccine effectiveness against symptomatic COVID-19 among HCWs who had received 1 dose of vaccine was 82.1% (95% CI, 46.1%–94.1%) and the vaccine effectiveness among HCWs who had received 2 doses was 93.5% (95% CI, 82.5%–97.6%).
The COVID-19 mRNA vaccines are highly effective against symptomatic COVID-19, even with 1 dose. More observational studies are needed to evaluate the vaccine effectiveness of other COVID-19 vaccines, COVID-19 breakthrough after vaccination, and vaccine efficacy against new variants.
We described the epidemiology of bat intrusions into a hospital and subsequent management of exposures during 2018–2020. Most intrusions occurred in older buildings during the summer and fall months. Hospitals need bat intrusion surveillance systems and protocols for bat handling, exposure management, and intrusion mitigation.
Background: COVID-19 in hospitalized patients may be the result of community acquisition or in-hospital transmission. Molecular epidemiology can help confirm hospital COVID-19 transmission and outbreaks. We describe large COVID-19 clusters identified in our hospital and apply molecular epidemiology to confirm outbreaks. Methods: The University of Iowa Hospitals and Clinics is an 811-bed academic medical center. We identified large clusters involving patients with hospital onset COVID-19 detected during March–October 2020. Large clusters included ≥10 individuals (patients, visitors, or HCWs) with a laboratory confirmed COVID-19 diagnosis (RT-PCR) and an epidemiologic link. Epidemiologic links were defined as hospitalization, work, or visiting in the same unit during the incubation or infectious period for the index case. Hospital onset was defined as a COVID-19 diagnosis ≥14 days from admission date. Admission screening has been conducted since May 2020 and serial testing (every 5 days) since July 2020. Nasopharyngeal swab specimens were retrieved for viral whole-genome sequencing (WGS). Cluster patients with a pairwise difference in ≤5 mutations were considered part of an outbreak. WGS was performed using Oxford Nanopore Technology and protocols from the ARTIC network. Results: We identified 2 large clusters involving patients with hospital-onset COVID-19. Cluster 1: 2 hospital-onset cases were identified in a medical-surgical unit in June 2020. Source and contact tracing revealed 4 additional patients, 1 visitor, and 13 employees with COVID-19. Median age for patients was 62 (range, 38–79), and all were male. In total, 17 samples (6 patients, 1 visitor, and 10 HCWs) were available for WGS. Cluster 2: A hospital-onset case was identified via serial testing in a non–COVID-19 intensive care unit in September 2020. Source investigation, contact tracing, and serial testing revealed 3 additional patients, and 8 HCWs. One HCW also had a community exposure. Patient median age was 60 years (range, 48–68) and all were male. In total, 11 samples (4 patients and 7 HCWs) were sequenced. Using WGS, cluster 1 was confirmed to be an outbreak: WGS showed 0–5 mutations in between samples. Cluster 2 was also an outbreak: WGS showed less diversity (0–3 mutations) and ruled out the HCW with a community exposure (20 mutations of difference). Conclusion: Whole-genome sequencing confirmed the outbreaks identified using classic epidemiologic methods. Serial testing allowed for early outbreak detection. Early outbreak detection and implementation of control measures may decrease outbreak size and genetic diversity.
Background: Bats are recognized as important vectors in disease transmission. Frequently, bats intrude into homes and buildings, increasing the risk to human health. We describe bat intrusions and exposure incidents in our hospital over a 3-year period. Methods: The University of Iowa Hospitals and Clinics (UIHC) is an 811-bed academic medical center in Iowa City, Iowa. Established in 1928, UIHC currently covers 209,031.84 m2 (~2,250,000 ft2) and contains 6 pavilions built between 1928 and 2017. We retrospectively obtained bat intrusion calls from the infection prevention and control program call database at UIHC during 2018–2020. We have also described the event management for intrusions potentially associated with patient exposures. Results: In total, 67 bat intrusions occurred during 2018–2020. The most frequent locations were hallways or lounges 28 (42%), nonclinical office spaces 19 (14%), and stairwells 8 (12%). Most bat intrusions (65%) occurred during the summer and fall (June–November). The number of events were 15 in 2018, 28 in 2019, and 24 in 2020. We observed that the number of intrusions increased with the age of each pavilion (Figure 1). Of 67 intrusions, 2 incidents (3%) were associated with potential exposure to patients. In the first incident, reported in 2019, the bat was captured in a patient care area and released before an investigation of exposures was completed and no rabies testing was available. Also, 10 patients were identified as having had potential exposure to the bat. Among them, 9 patients (90%) received rabies postexposure prophylaxis. In response to this serious event, we provided facility-wide education on our bat control policy, which includes the capture and safe handling of the bat, assessment of potential exposures, and potential need for rabies testing. We also implemented a bat exclusion project focused on the exterior of the oldest hospital buildings. The second event, 1 patient was identified to have potential exposure to the bat. The bat was captured, tested negative for rabies, no further action was needed. Conclusions: Bat intrusions can be an infection prevention and control challenge in facilities with older buildings. Hospitals may need animal intrusion surveillance systems, management protocols, and remediation efforts.
Background: Hospital semiprivate rooms may lead to coronavirus disease 2019 (COVID-19) patient exposures. We investigated the risk of COVID-19 patient-to-patient exposure in semiprivate rooms and the subsequent risk of acquiring COVID-19. Methods: The University of Iowa Hospitals & Clinics is an 811-bed tertiary care center. Overall, 16% of patient days are spent in semiprivate rooms. Most patients do not wear masks while in semiprivate rooms. Active COVID-19 surveillance included admission and every 5 days nasopharyngeal SARS-CoV-2 polymerase chain reaction (PCR) testing. We identified inpatients with COVID-19 who were in semiprivate rooms during their infectious periods during July–December 2020. Testing was repeated 24 hours after the first positive test. Cycle threshold (Ct) values of the two tests (average Ct <30), SARS-CoV-2 serology results, clinical assessment, and COVID-19 history were used to determine patient infectiousness. Roommates were considered exposed if in the same semiprivate room with an infectious patient. Exposed patients were notified, quarantined (private room), and follow-up testing was arranged (median seven days). Conversion was defined as having a negative test followed by a subsequent positive within 14 days after exposure. We calculated the risk of exposure: number of infectious patients in semiprivate rooms/number of semiprivate patient-days (hospitalization days in semiprivate rooms). Results: There were 16,427 semiprivate patient days during July–December 2020. We identified 43 COVID-19 inpatients who roommates during their infectious periods. Most infectious patients (77%) were male; the median age was 67 years; and 22 (51%) were symptomatic. Most were detected during active surveillance: admission testing (51%) and serial testing (28%). There were 57 exposed roommates. The risk of exposure was 3 of 1,000 semiprivate patient days. In total, 16 roommates (28%) did not complete follow-up testing. Of 41 exposed patients with follow-up data, 8 (20%) converted following their exposure. Median time to conversion was 5 days. The risk of exposure and subsequent conversion was 0.7 of 1,000 semiprivate patient days. Median Ct value of the source patient was 20 for those who converted and 23 for those who did not convert. Median exposure time was 45 hours (range, 3–73) for those who converted and 12 hours (range, 1–75) for those who did not convert. Conclusions: The overall risk of exposure in semiprivate rooms was low. The conversion rate was comparable to that reported for household exposures. Lower Ct values and lengthier exposures may be associated with conversion. Active COVID-19 surveillance helps early detection and decreases exposure time.
Background: Hospitalized patients may unknowingly carry severe acute respiratory coronavirus virus 2 (SARS-CoV-2), even if they are admitted for other reasons. Because SARS-CoV-2 may remain positive by reverse-transcriptase polymerase chain reaction (RT-PCR) for months after infection, patients with a positive result may not necessarily be infectious. We aimed to determine the frequency of SARS-CoV-2 infections in patients admitted for reasons unrelated to coronavirus disease 2019 (COVID-19). Methods: The University of Iowa Hospitals and Clinics is an 811-bed tertiary-care center. We use a nasopharyngeal SARS-CoV-2 RT-PCR to screen admitted patients without signs or symptoms compatible with COVID-19. Patients with positive tests undergo a repeat test to assess cycle threshold (Ct) value kinetics. We reviewed records for patients with positive RT-PCR screening admitted during July–October 2020. We used a combination of history, serologies, and RT-PCR Ct values to assess and qualify likelihood of infectiousness: (1) likely infectious, if Ct values were <29, or (2) likely not infectious, if 1 or both samples had Cts <30 with or without a positive SARS-CoV-2 antinucleocapsid IgG/IgM test or history of a positive result in the past 90 days. Contact tracing was only conducted for patients likely to be infectious. We describe the isolation duration and contact tracing data. Results: In total, 6,447 patients were tested on hospital admission for any reason (persons under investigation or admitted for reasons other than COVID-19). Of these, 240 (4%) had positive results, but 65 (27%) of these were admitted for reasons other than COVID-19. In total, 55 patients had Ct values available and were included in this analysis. The median age was 56 years (range, 0–91), 28 (51%) were male, and 12 (5%) were children. The most frequent admission syndromes were neurological (36%), gastrointestinal (16%), and trauma (16%). Our assessment revealed 23 likely infections (42%; 14 definite, 9 possible) and 32 cases likely not infectious (58%). The mean Ct for patients who were likely infectious was 22; it was 34 for patients who were likely not infectious. Mean duration of in-hospital isolation was 6 days for those who were likely infectious and 2 days for those who were likely not infectious. We detected 8 individuals (1 healthcare worker and 7 patients) who were exposed to a likely infectious patient. Conclusions: SARS-CoV-2 infection in patients hospitalized for other reasons was infrequent. An assessment of the likelihood of infectiousness including history, RT-PCR Cts, and serology may help prioritize patients in need of isolation and contact investigations.
Background: Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 RNA can be detected by real-time reverse-transcription polymerase chain reaction (RT-PCR) for several weeks after infection. Discerning persistent RT-PCR positivity versus reinfection is challenging and the frequency of COVID-19 reinfections is unknown. We aimed to determine the frequency of clinically suspected reinfection in our center and confirm reinfection using viral whole-genome sequencing (WGS). Methods: The University of Iowa Hospitals and Clinics (UIHC) is an 811-bed academic medical center. Patients with respiratory complaints undergo COVID-19 RT-PCR using nasopharyngeal swabs. The RT-PCR (TaqPath COVID-19 Combo kit) uses 3 targets (ORF1ab, S gene, and N gene). We identified patients with previous laboratory-confirmed COVID-19 who sought care for new respiratory complaints and underwent a repeated SARS-CoV-2 test at least 45 days from their first positive test. We then identified patients with median RT-PCR cycle threshold (Ct) values. Results: During the study period, 13,603 patients had a SARS-CoV-2– positive RT-PCR. Of these, 296 (2.2%) had a clinical visit for new onset of symptoms and a repeated RT-PCR assay >45 days from the first test. Moreover, 29 patients (9.8%) had a positive RT-PCR assay in the repeated testing. Ct values were available for samples from 25 patients; 7 (28%) had Ct values. Conclusions: In patients with a recent history of COVID-19 infection, repeated testing for respiratory symptoms was infrequent. Some had a SARS-CoV-2–positive RT-PCR assay on repeated testing, but only 1 in 4 had Ct values suggestive of a reinfection. We confirmed 1 case of reinfection using WGS.
Background: The COVID-19 pandemic has affected healthcare systems worldwide, but the impact on infection prevention and control (IPC) programs has not been fully evaluated. We assessed the impact of the COVID-19 pandemic on IPC consultation requests. Methods: The University of Iowa Hospitals & Clinics comprises an 811-bed hospital that admits >36,000 patients yearly and >200 outpatient clinics. Questions about IPC can be addressed to the Program of Hospital Epidemiology via e-mail, in person, or through our phone line. We routinely record date and time, call source, reason for the call, and estimated time to resolve questions for all phone line requests. We defined calls during 2018–2019 as the pre–COVID-19 period and calls from January to December 2020 as the COVID-19 period. Results: In total, 6,564 calls were recorded from 2018 to 2020. In the pre–COVID-19 period (2018–2019), we received a median of 71 calls per month (range, 50–119). The most frequent call sources were inpatient units (n = 902; 50%), department of public health (n = 357; 20%), laboratory (n = 171; 9%), and outpatient clinics (n = 120; 7%) (Figure 1). The most common call topics were isolation and precautions (n = 606; 42%), outside institutions requests (n = 324; 22%), environmental and construction (n = 148; 10%), and infection exposures (n = 149; 10%). The most frequent infection-related calls were about tuberculosis (17%), gram-negative organisms (14%), and influenza (9%). During the COVID-19 period, the median monthly call volume increased 500% to 368 per month (range, 149–829). Most (83%) were COVID-19 related. The median monthly number of COVID-19 calls was 302 (range, 45–674). The median monthly number of non–COVID-19 calls decreased to 56 (range, 36–155). The most frequent call sources were inpatient units (57%), outpatient clinics (16%), and the department of public health (5%). Most calls concerned isolation and precautions (50%) and COVID-19 testing (20%). The mean time required to respond to each question was 10 minutes (range, 2–720). The biggest surges in calls during the COVID-19 period were at the beginning of the pandemic (March 2020) and during the hospital peak COVID-19 census (November 2020). Conclusions: In addition to supporting a proactive COVID-19 response, our IPC program experienced a 500% increase in consultation requests. Planning for future bioemergencies should include creative strategies to provide additional resources to increase response capacity within IPC programs.
The incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure in shared patient rooms was low at our institution: 1.8 per 1,000 shared-room patient days. However, the secondary attack rate (21.6%) was comparable to that reported in household exposures. Lengthier exposures were associated with SARS-CoV-2 conversion. Hospitals should implement measures to decrease shared-room exposures.
Patients admitted to the hospital may unknowingly carry severe acute respiratory coronavirus virus 2 (SARS-CoV-2), and hospitals have implemented SARS-CoV-2 admission screening. However, because SARS-CoV-2 reverse-transcription polymerase chain reaction (RT-PCR) assays may remain positive for months after infection, positive results may represent active or past infection. We determined the prevalence and infectiousness of patients who were admitted for reasons unrelated to COVID-19 but tested positive for SARS-CoV-2 on admission screening.
We conducted an observational study at the University of Iowa Hospitals & Clinics from July 7 to October 25, 2020. All patients admitted without suspicion of COVID-19 were included, and medical records of those with a positive admission screening test were reviewed. Infectiousness was determined using patient history, PCR cycle threshold (Ct) value, and serology.
In total, 5,913 patients were screened and admitted for reasons unrelated to COVID-19. Of these, 101 had positive admission RT-PCR results; 36 of these patients were excluded because they had respiratory signs/symptoms on admission on chart review. Also, 65 patients (1.1%) did not have respiratory symptoms. Finally, 55 patients had Ct values available and were included in this analysis. The median age of the final cohort was 56 years and 51% were male. Our assessment revealed that 23 patients (42%) were likely infectious. The median duration of in-hospital isolation was 5 days for those likely infectious and 2 days for those deemed noninfectious.
SARS-CoV-2 was infrequent among patients admitted for reasons unrelated to COVID-19. An assessment of the likelihood of infectiousness using clinical history, RT-PCR Ct values, and serology may help in making the determination to discontinue isolation and conserve resources.
Healthcare-associated infections (HAIs) remain a major challenge. Various strategies have been tried to prevent or control HAIs. Positive deviance, a strategy that has been used in the last decade, is based on the observation that a few at-risk individuals follow uncommon, useful practices and that, consequently, they experience better outcomes than their peers who share similar risks. We performed a systematic literature review to measure the impact of positive deviance in controlling HAIs.
A systematic search strategy was used to search PubMed, CINAHL, Scopus, and Embase through May 2020 for studies evaluating positive deviance as a single intervention or as part of an initiative to prevent or control healthcare-associated infections. The risk of bias was evaluated using the Downs and Black score.
Of 542 articles potentially eligible for review, 14 articles were included for further analysis. All studies were observational, quasi-experimental (before-and-after intervention) studies. Hand hygiene was the outcome in 8 studies (57%), and an improvement was observed in association with implementation of positive deviance as a single intervention in all of them. Overall HAI rates were measured in 5 studies (36%), and positive deviance was associated with an observed reduction in 4 (80%) of them. Methicillin-resistant Staphylococcus aureus infections were evaluated in 5 studies (36%), and positive deviance containing bundles were successful in all of them.
Positive deviance may be an effective strategy to improve hand hygiene and control HAIs. Further studies are needed to confirm this effect.
Background:Stenotrophomonas maltophilia (S. maltophilia) is an opportunistic and nosocomial pathogen that can cause an invasive and fatal infection, particularly in hospitalized and immunocompromised patients. However, little is known about the impact of S. maltophilia bacteremia in pediatric patients. Therefore, we aimed to identify risk factors for mortality, antibiotic susceptibility of S. maltophilia, and mortality rates in pediatric patients with S. maltophilia bacteremia. Methods: We conducted a retrospective cohort study by identifying all S. maltophilia–positive blood cultures in the microbiology laboratory database between January 2007 and December 2018 from hospitalized pediatric patients (age, 1–14 years) at King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia. After identifying patients with S. maltophilia bacteremia, medical charts were reviewed for demographics, clinical data, and outcome within 7 days of bacteremia diagnosis. Risk factors associated with mortality in S. maltophilia bacteremia patients were determined using univariate and multivariate analyses. Results: Overall, 68% of pediatric patients with S. maltophilia bacteremia were identified. The most common underlying primary diagnoses were malignancy (29.4%), congenital heart diseases (16.2%), anemia (14.7%), and primary immunodeficiency (11.8%). All infections were nosocomial infections, and (88.2%) bacteremia cases were central-line–associated bloodstream infections. The risk factors associated with mortality as determined by univariate analysis were ICU admission (P < .001), intubation (P = .001), neutropenia (P = .008), prior use of carbapenem (P = .002), thrombocytopenia (P = .006), and respiratory colonization (P < .001). On multivariate analysis, ICU admission (P = .007; 95% CI, 0.003–0.406) and neutropenia (P = .009; 95% CI, 0.013–0.537) were the major risk factors associated with mortality. S. maltophilia was the most susceptible to trimethoprim and sulfamethoxazole (TMP/SMX, 94.1%), followed by levofloxacin (85.7%). In addition, 36 patients received TMP/SMX as monotherapy, and 11 patients received it in combination with other antibiotics (fluoroquinolone, ceftazidime, or aminoglycoside). Hence, no statistically significant difference was observed in patient mortality. The overall mortality rate within 7 days of S. maltophilia bacteremia diagnosis was 33.8%. Conclusions:S. maltophilia bacteremia is a devastating emerging infection associated with high mortality among hospitalized children. Therefore, early diagnosis and prompt management based on local susceptibility data are crucial. Various risk factors, especially ICU admission and neutropenia, are associated with S. maltophilia bacteremia mortality.
Background: Surveillance for surgical site infections (SSI) is recommended by the CDC. Currently, colon and abdominal hysterectomy SSI rates are publicly available and impact hospital reimbursement. However, the CDC NHSN allows surgical procedures to be abstracted based on International Classification of Diseases, Tenth Revision (ICD-10) or current procedural terminology (CPT) codes. We assessed the impact of using ICD and/or CPT codes on the number of cases abstracted and SSI rates. Methods: We retrieved administrative codes (ICD and/or CPT) for procedures performed at the University of Iowa Hospitals & Clinics over 1 year: October 2018–September 2019. We included 10 procedure types: colon, hysterectomy, cesarean section, breast, cardiac, craniotomy, spinal fusion, laminectomy, hip prosthesis, and knee prosthesis surgeries. We then calculated the number of procedures that would be abstracted if we used different permutations in administration codes: (1) ICD codes only, (2) CPT codes only, (3) both ICD and CPT codes, and (4) at least 1 code from either ICD or CPT. We then calculated the impact on SSI rates based on any of the 4 coding permutations. Results: In total, 9,583 surgical procedures and 180 SSIs were detected during the study period using the fourth method (ICD or CPT codes). Denominators varied according to procedure type and coding method used. The number of procedures abstracted for breast surgery had a >10-fold difference if reported based on ICD only versus ICD or CPT codes (104 vs 1,109). Hip prosthesis had the lowest variation (638 vs 767). For SSI rates, cesarean section showed almost a 3-fold increment (2.6% when using ICD only to 7.32% with both ICD & CPT), whereas abdominal hysterectomy showed nearly a 2-fold increase (1.14% when using CPT only to 2.22% with both ICD & CPT codes). However, SSI rates remained fairly similar for craniotomy (0.14% absolute difference), hip prosthesis (0.24% absolute difference), and colon (0.09% absolute difference) despite differences in the number of abstracted procedures and coding methods. Conclusions: Denominators and SSI rates vary depending on the coding method used. Variations in the number of procedures abstracted and their subsequent impact on SSI rates were not predictable. Variations in coding methods used by hospitals could impact interhospital comparisons and benchmarking, potentially leading to disparities in public reporting and hospital penalties.
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.
We performed a retrospective analysis of the impact of using the International Classification of Diseases, Tenth Revision procedure coding system (ICD-10) or current procedural terminology (CPT) codes to calculate surgical site infection (SSI) rates. Denominators and SSI rates vary depending on the coding method used. The coding method used may influence interhospital performance comparisons.