Mental health disorders among post graduate residents in Kenya during the COVID-19 pandemic

Background: Healthcare workers, including residents, are prone to various mental health disorders especially given the context of the COVID-19 pandemic. Residents, particularly, are already under undue stress due to their respective training program demands. Methods: This cross-sectional, online survey-based study from August to November 2020 collected demographic and mental health measurements from all residents at the Aga Khan University Hospital, Nairobi. The questionnaire investigated demographic variables, information regarding direct care of COVID-19 patients, prior history of mental health and mental health outcomes using the Patient Health Questionnaire, Generalized Anxiety Disorder scale, Insomnia Severity Index, Impact of Event Scale–Revised Questionnaire and Stanford Professional Fulfillment Index Questionnaire. Results: A total of 100 residents completed the survey (participation rate 77.5%). Participants were about equal in gender (women [53%]), with a median age of 31.28 years, and majority were single (66.7%). A total of 66 participants (66%) were directly engaged in COVID-19 care. Depression: 64.3%, anxiety: 51.5%, insomnia: 40.5%, distress: 35.4%, and burnout: 51.0% were reported in all participants. Statistical significance was found in median depression, professional fulfillment and interpersonal disengagement when comparing frontline resident directly involved in care of COVID-19 patient versus second line residents. Conclusion: Residents directly involved with caring for COVID-19 patients had statistically higher incidences of depression and interpersonal disengagement and lower professional fulfillment compared to second line residents. Keeping in mind the limited resources in sub-Saharan Africa, urgent and geographically specific strategies are needed to help combat mental health disorders in this specific population

Which comorbid conditions should we be analyzing as risk factors for healthcare-associated infections?

OBJECTIVETo determine which comorbid conditions are considered causally related to central-line associated bloodstream infection (CLABSI) and surgical-site infection (SSI) based on expert consensus.DESIGNUsing the Delphi method, we administered an iterative, 2-round survey to 9 infectious disease and infection control experts from the United States.METHODSBased on our selection of components from the Charlson and Elixhauser comorbidity indices, 35 different comorbid conditions were rated from 1 (not at all related) to 5 (strongly related) by each expert separately for CLABSI and SSI, based on perceived relatedness to the outcome. To assign expert consensus on causal relatedness for each comorbid condition, all 3 of the following criteria had to be met at the end of the second round: (1) a majority (&gt;50%) of experts rating the condition at 3 (somewhat related) or higher, (2) interquartile range (IQR)≤1, and (3) standard deviation (SD)≤1.RESULTSFrom round 1 to round 2, the IQR and SD, respectively, decreased for ratings of 21 of 35 (60%) and 33 of 35 (94%) comorbid conditions for CLABSI, and for 17 of 35 (49%) and 32 of 35 (91%) comorbid conditions for SSI, suggesting improvement in consensus among this group of experts. At the end of round 2, 13 of 35 (37%) and 17 of 35 (49%) comorbid conditions were perceived as causally related to CLABSI and SSI, respectively.CONCLUSIONSOur results have produced a list of comorbid conditions that should be analyzed as risk factors for and further explored for risk adjustment of CLABSI and SSI.Infect Control Hosp Epidemiol 2017;38:449–454</jats:sec

The effect of adding comorbidities to current centers for disease control and prevention central-line–associated bloodstream infection risk-adjustment methodology

BACKGROUNDRisk adjustment is needed to fairly compare central-line–associated bloodstream infection (CLABSI) rates between hospitals. Until 2017, the Centers for Disease Control and Prevention (CDC) methodology adjusted CLABSI rates only by type of intensive care unit (ICU). The 2017 CDC models also adjust for hospital size and medical school affiliation. We hypothesized that risk adjustment would be improved by including patient demographics and comorbidities from electronically available hospital discharge codes.METHODSUsing a cohort design across 22 hospitals, we analyzed data from ICU patients admitted between January 2012 and December 2013. Demographics and International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) discharge codes were obtained for each patient, and CLABSIs were identified by trained infection preventionists. Models adjusting only for ICU type and for ICU type plus patient case mix were built and compared using discrimination and standardized infection ratio (SIR). Hospitals were ranked by SIR for each model to examine and compare the changes in rank.RESULTSOverall, 85,849 ICU patients were analyzed and 162 (0.2%) developed CLABSI. The significant variables added to the ICU model were coagulopathy, paralysis, renal failure, malnutrition, and age. The C statistics were 0.55 (95% CI, 0.51–0.59) for the ICU-type model and 0.64 (95% CI, 0.60–0.69) for the ICU-type plus patient case-mix model. When the hospitals were ranked by adjusted SIRs, 10 hospitals (45%) changed rank when comorbidity was added to the ICU-type model.CONCLUSIONSOur risk-adjustment model for CLABSI using electronically available comorbidities demonstrated better discrimination than did the CDC model. The CDC should strongly consider comorbidity-based risk adjustment to more accurately compare CLABSI rates across hospitals.Infect Control Hosp Epidemiol 2017;38:1019–1024</jats:sec

The impact of universal glove and gown use on Clostridioides difficile acquisition: A cluster-randomized trial

BACKGROUND: Clostridioides difficile is the most common cause of healthcare-associated infections in the United States. It is unknown whether universal gown and glove use in intensive care units (ICUs) decreases acquisition of C. difficile. METHODS: This was a secondary analysis of a cluster-randomized trial in 20 medical and surgical ICUs in 20 US hospitals from 4 January 2012 to 4 October 2012. After a baseline period, ICUs were randomized to standard practice for glove and gown use versus the intervention of all healthcare workers being required to wear gloves and gowns for all patient contact and when entering any patient room (contact precautions). The primary outcome was acquisition of toxigenic C. difficile determined by surveillance cultures collected on admission and discharge from the ICU. RESULTS: A total of 21 845 patients had both admission and discharge perianal swabs cultured for toxigenic C. difficile. On admission, 9.43% (2060/21 845) of patients were colonized with toxigenic C. difficile. No significant difference was observed in the rate of toxigenic C. difficile acquisition with universal gown and glove use. Differences in acquisition rates in the study period compared with the baseline period in control ICUs were 1.49 per 100 patient-days versus 1.68 per 100 patient-days in universal gown and glove ICUs (rate difference, -0.28; generalized linear mixed model, P = .091). CONCLUSIONS: Glove and gown use for all patient contact in medical and surgical ICUs did not result in a reduction in the acquisition of C. difficile compared with usual care. CLINICAL TRIALS REGISTRATION: NCT01318213

Influence of real-world characteristics on outcomes for patients with methicillin-resistant Staphylococcal skin and soft tissue infections:a multi-country medical chart review in Europe

BACKGROUND: Patient-related (demographic/disease) and treatment-related (drug/clinician/hospital) characteristics were evaluated as potential predictors of healthcare resource use and opportunities for early switch (ES) from intravenous (IV)-to-oral methicillin-resistant Staphylococcus aureus (MRSA)-active antibiotic therapy and early hospital discharge (ED). METHODS: This retrospective observational medical chart study analyzed patients (across 12 European countries) with microbiologically confirmed MRSA complicated skin and soft tissue infections (cSSTI), ≥3 days of IV anti-MRSA antibiotics during hospitalization (July 1, 2010-June 30, 2011), and discharged alive by July 31, 2011. Logistic/linear regression models evaluated characteristics potentially associated with actual resource use (length of IV therapy, length of hospital stay [LOS], IV-to-oral antibiotic switch), and ES and ED (using literature-based and expert-verified criteria) outcomes. RESULTS: 1542 patients (mean ± SD age 60.8 ± 16.5 years; 61.5% males) were assessed with 81.0% hospitalized for MRSA cSSTI as the primary reason. Several patient demographic, infection, complication, treatment, and hospital characteristics were predictive of length of IV therapy, LOS, IV-to-oral antibiotic switch, or ES and ED opportunities. Outcomes and ES and ED opportunities varied across countries. Length of IV therapy and LOS (r = 0.66, p < 0.0001) and eligibilities for ES and ED (r = 0.44, p < 0.0001) showed relatively strong correlations. IV-to-oral antibiotic switch patients had significantly shorter length of IV therapy (−5.19 days, p < 0.001) and non-significantly shorter LOS (−1.86 days, p > 0.05). Certain patient and treatment characteristics were associated with increased odds of ES (healthcare-associated/ hospital-acquired infection) and ED (patient living arrangements, healthcare-associated/ hospital-acquired infection, initiating MRSA-active treatment 1–2 days post cSSTI index date, existing ED protocol), while other factors decreased the odds of ES (no documented MRSA culture, ≥4 days from admission to cSSTI index date, IV-to-oral switch, IV line infection) and ED (dementia, no documented MRSA culture, initiating MRSA-active treatment ≥3 days post cSSTI index date, existing ES protocol). CONCLUSIONS: Practice patterns and opportunity for further ES and ED were affected by several infection, treatment, hospital, and geographical characteristics, which should be considered in identifying ES and ED opportunities and designing interventions for MRSA cSSTI to reduce IV days and LOS while maintaining the quality of care. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2334-14-476) contains supplementary material, which is available to authorized users

Significant regional differences in antibiotic use across 576 US hospitals and 11 701 326 adult admissions, 2016-2017

BACKGROUND: Quantifying the amount and diversity of antibiotic use in United States hospitals assists antibiotic stewardship efforts but is hampered by limited national surveillance. Our study aimed to address this knowledge gap by examining adult antibiotic use across 576 hospitals and nearly 12 million encounters in 2016-2017. METHODS: We conducted a retrospective study of patients aged ≥ 18 years discharged from hospitals in the Premier Healthcare Database between 1 January 2016 and 31 December 2017. Using daily antibiotic charge data, we mapped antibiotics to mutually exclusive classes and to spectrum of activity categories. We evaluated relationships between facility and case-mix characteristics and antibiotic use in negative binomial regression models. RESULTS: The study included 11 701 326 admissions, totaling 64 064 632 patient-days, across 576 hospitals. Overall, patients received antibiotics in 65% of hospitalizations, at a crude rate of 870 days of therapy (DOT) per 1000 patient-days. By class, use was highest among β-lactam/β-lactamase inhibitor combinations, third- and fourth-generation cephalosporins, and glycopeptides. Teaching hospitals averaged lower rates of total antibiotic use than nonteaching hospitals (834 vs 957 DOT per 1000 patient-days; P \u3c .001). In adjusted models, teaching hospitals remained associated with lower use of third- and fourth-generation cephalosporins and antipseudomonal agents (adjusted incidence rate ratio [95% confidence interval], 0.92 [.86-.97] and 0.91 [.85-.98], respectively). Significant regional differences in total and class-specific antibiotic use also persisted in adjusted models. CONCLUSIONS: Adult inpatient antibiotic use remains high, driven predominantly by broad-spectrum agents. Better understanding reasons for interhospital usage differences, including by region and teaching status, may inform efforts to reduce inappropriate antibiotic prescribing

Electronically available patient claims data improve models for comparing antibiotic use across hospitals: Results from 576 US facilities

BACKGROUND: The Centers for Disease Control and Prevention (CDC) uses standardized antimicrobial administration ratios (SAARs)-that is, observed-to-predicted ratios-to compare antibiotic use across facilities. CDC models adjust for facility characteristics when predicting antibiotic use but do not include patient diagnoses and comorbidities that may also affect utilization. This study aimed to identify comorbidities causally related to appropriate antibiotic use and to compare models that include these comorbidities and other patient-level claims variables to a facility model for risk-adjusting inpatient antibiotic utilization. METHODS: The study included adults discharged from Premier Database hospitals in 2016-2017. For each admission, we extracted facility, claims, and antibiotic data. We evaluated 7 models to predict an admission\u27s antibiotic days of therapy (DOTs): a CDC facility model, models that added patient clinical constructs in varying layers of complexity, and an external validation of a published patient-variable model. We calculated hospital-specific SAARs to quantify effects on hospital rankings. Separately, we used Delphi Consensus methodology to identify Elixhauser comorbidities associated with appropriate antibiotic use. RESULTS: The study included 11 701 326 admissions across 576 hospitals. Compared to a CDC-facility model, a model that added Delphi-selected comorbidities and a bacterial infection indicator was more accurate for all antibiotic outcomes. For total antibiotic use, it was 24% more accurate (respective mean absolute errors: 3.11 vs 2.35 DOTs), resulting in 31-33% more hospitals moving into bottom or top usage quartiles postadjustment. CONCLUSIONS: Adding electronically available patient claims data to facility models consistently improved antibiotic utilization predictions and yielded substantial movement in hospitals\u27 utilization rankings