Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

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.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

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.

Clostridioides difficile infection (CDI) is the most frequently reported hospital-acquired infection in the United States. Bioaerosols generated during toilet flushing are a possible mechanism for the spread of this pathogen in clinical settings.

To measure the bioaerosol concentration from toilets of patients with CDI before and after flushing.

In this pilot study, bioaerosols were collected 0.15 m, 0.5 m, and 1.0 m from the rims of the toilets in the bathrooms of hospitalized patients with CDI. Inhibitory, selective media were used to detect C. difficile and other facultative anaerobes. Room air was collected continuously for 20 minutes with a bioaerosol sampler before and after toilet flushing. Wilcoxon rank-sum tests were used to assess the difference in bioaerosol production before and after flushing.

Rooms of patients with CDI at University of Iowa Hospitals and Clinics.

Bacteria were positively cultured from 8 of 24 rooms (33%). In total, 72 preflush and 72 postflush samples were collected; 9 of the preflush samples (13%) and 19 of the postflush samples (26%) were culture positive for healthcare-associated bacteria. The predominant species cultured were Enterococcus faecalis, E. faecium, and C. difficile. Compared to the preflush samples, the postflush samples showed significant increases in the concentrations of the 2 large particle-size categories: 5.0 µm (P = .0095) and 10.0 µm (P = .0082).

Bioaerosols produced by toilet flushing potentially contribute to hospital environmental contamination. Prevention measures (eg, toilet lids) should be evaluated as interventions to prevent toilet-associated environmental contamination in clinical settings.

To ascertain opinions regarding etiology and preventability of hospital-onset bacteremia and fungemia (HOB) and perspectives on HOB as a potential outcome measure reflecting quality of infection prevention and hospital care.

Cross-sectional survey.

Hospital epidemiologists and infection preventionist members of the Society for Healthcare Epidemiology of America (SHEA) Research Network.

A web-based, multiple-choice survey was administered via the SHEA Research Network to 133 hospitals.

A total of 89 surveys were completed (67% response rate). Overall, 60% of respondents defined HOB as a positive blood culture on or after hospital day 3. Central line-associated bloodstream infections and intra-abdominal infections were perceived as the most frequent etiologies. Moreover, 61% thought that most HOB events are preventable, and 54% viewed HOB as a measure reflecting a hospital’s quality of care. Also, 29% of respondents’ hospitals already collect HOB data for internal purposes. Given a choice to publicly report central-line–associated bloodstream infections (CLABSIs) and/or HOB, 57% favored reporting either HOB alone (22%) or in addition to CLABSI (35%) and 34% favored CLABSI alone.

Among the majority of SHEA Research Network respondents, HOB is perceived as preventable, reflective of quality of care, and potentially acceptable as a publicly reported quality metric. Further studies on HOB are needed, including validation as a quality measure, assessment of risk adjustment, and formation of evidence-based bundles and toolkits to facilitate measurement and improvement of HOB rates.

Recent studies have shown that using no-touch disinfection technologies (ie, ultraviolet light [UVL] or hydrogen peroxide vapor [HPV] systems) can limit the transmission of nosocomial pathogens and prevent healthcare-associated infections (HAIs). To investigate these findings further, we performed a systematic literature review and meta-analysis on the impact of no-touch disinfection methods to decrease HAIs.

We searched PubMed, CINAHL, CDSR, DARE and EMBASE through April 2017 for studies evaluating no-touch disinfection technology and the nosocomial infection rates for Clostridium difficile, methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), and other multidrug-resistant organisms (MDROs). We employed random-effect models to obtain pooled risk ratio (pRR) estimates. Heterogeneity was evaluated with I2 estimation and the Cochran Q statistic. Pooled risk ratios for C. difficile, MRSA, VRE, and MDRO were assessed separately.

In total, 20 studies were included in the final review: 13 studies using UVL systems and 7 studies using HPV systems. When the results of the UVL studies were pooled, statistically significant reduction ins C. difficile infection (CDI) (pRR, 0.64; 95% confidence interval [CI], 0.49–0.84) and VRE infection rates (pRR, 0.42; 95% CI, 0.28–0.65) were observed. No differences were found in rates of MRSA or gram-negative multidrug-resistant pathogens.

Ultraviolet light no-touch disinfection technology may be effective in preventing CDI and VRE infection.

Infect Control Hosp Epidemiol 2018;39:20–31

Distinguishing bipolar disorder (BP) from major depressive disorder (MDD) has important relevance for prognosis and treatment. Prior studies have identified clinical features that differ between these two diseases but have been limited by heterogeneity and lack of replication. We sought to identify depression-related features that distinguish BP from MDD in large samples with replication.

Using a large, opportunistically ascertained collection of subjects with BP and MDD we selected 34 depression-related clinical features to test across the diagnostic categories in an initial discovery dataset consisting of 1228 subjects (386 BPI, 158 BPII and 684 MDD). Features significantly associated with BP were tested in an independent sample of 1000 BPI cases and 1000 MDD cases for classifying ability in receiver operating characteristic (ROC) analysis.

Seven clinical features showed significant association with BPI compared with MDD: delusions, psychomotor retardation, incapacitation, greater number of mixed symptoms, greater number of episodes, shorter episode length, and a history of experiencing a high after depression treatment. ROC analyses of a model including these seven factors showed significant evidence for discrimination between BPI and MDD in an independent dataset (area under the curve = 0.83). Only two features (number of mixed symptoms, and feeling high after an antidepressant) showed an association with BPII versus MDD.

Our study suggests that clinical features distinguishing depression in BPI versus MDD have important classification potential for clinical practice, and should also be incorporated as ‘baseline’ features in the evaluation of novel diagnostic biomarkers.

Co-morbidity of mood and anxiety disorders is common and often associated with greater illness severity. This study investigates clinical correlates and familiality of four anxiety disorders in a large sample of bipolar disorder (BP) and major depressive disorder (MDD) pedigrees.

The sample comprised 566 BP families with 1416 affected subjects and 675 MDD families with 1726 affected subjects. Clinical characteristics and familiality of panic disorder, social phobia, specific phobia and obsessive-compulsive disorder (OCD) were examined in BP and MDD pedigrees with multivariate modeling using generalized estimating equations.

Co-morbidity between mood and anxiety disorders was associated with several markers of clinical severity, including earlier age of onset, greater number of depressive episodes and higher prevalence of attempted suicide, when compared with mood disorder without co-morbid anxiety. Familial aggregation was found with co-morbid panic and OCD in both BP and MDD pedigrees. Specific phobia showed familial aggregation in both MDD and BP families, although the findings in BP were just short of statistical significance after adjusting for other anxiety co-morbidities. We found no evidence for familiality of social phobia.

Our findings suggest that co-morbidity of MDD and BP with specific anxiety disorders (OCD, panic disorder and specific phobia) is at least partly due to familial factors, which may be of relevance to both phenotypic and genetic studies of co-morbidity.

Severity of illness is an important confounder in outcome studies involving infectious diseases. However, it is unclear whether the time at which severity of illness is measured is important.

We performed a retrospective study of 328 episodes of gram-negative bacteremia in adult patients to assess the impact of the time of measurement of severity of illness on the association between empirical antimicrobial therapy received and in-hospital mortality. Using a modified Acute Physiology Score (APS), severity of illness was measured at 2 time points: (1) hospital admission and (2) 24 hours before the first culture-positive blood sample was collected. Multivariate logistic regression was used to estimate the impact of adjusting for the APS on the relationship between empirical therapy received (ie, the exposure) and in-hospital mortality (ie, the outcome).

The mean APS ( ± standard deviation) of patients with bacteremia increased during their hospital stay (from 19.2 ± 11.6 at admission to 24.2 ± 13.6 at the second time point; P < .01). When examining the association between empirical antimicrobial therapy received and in-hospital mortality, and controlling for the APS, there was a trend toward a decreased impact of appropriate therapy received on in-hospital mortality. The unadjusted odds ratio (OR) for the association between appropriate therapy received and in-hospital mortality was 0.83 (95% confidence interval [CI], 0.51-1.34). After controlling for the APS at admission, this association was attenuated (OR, 0.94 [95% CI, 0.57-1.55]), and when a change in the APS was also included in the multivariate logistic regression model, the association was further attenuated (OR, 0.99 [95% CI, 0.58-1.69]).

The magnitude of the association between appropriate antimicrobial therapy received and in-hospital mortality among patients with gram-negative bacteremia was sensitive to the timing of adjustment for severity of illness.