The role of lay health workers in data collection for clinical and translational research studies is not well described. We explored lay health workers as data collectors in clinical and translational research studies. We also present several methods for examining their work, i.e., qualitative interviews, fidelity checklists, and rates of unusable/missing data.

We conducted 2 randomized, controlled trials that employed lay health research personnel (LHR) who were employed by community-based organizations. In one study, n = 3 Latina LHRs worked with n = 107 Latino diabetic participants. In another study, n = 6 LHR worked with n = 188 Cambodian American refugees with depression. We investigated proficiency in biological, behavioral, and psychosocial home-based data collection conducted by LHR. We also conducted in-depth interviews with lay LHR to explore their experience in this research role. Finally, we described the training, supervision, and collaboration for LHR to be successful in their research role.

Independent observers reported a very high degree of fidelity to technical data collection protocols (>95%) and low rates of missing/unusable data (1.5%–11%). Qualitative results show that trust, training, communication, and supervision are key and that LHR report feeling empowered by their role. LHR training included various content areas over several weeks with special attention to LHR and participant safety. Training and supervision from both the academic researchers and the staff at the community-based organizations were necessary and had to be well-coordinated.

Carefully selected, trained, and supervised LHRs can collect sophisticated data for community-based clinical and translational research.

Routine patient care data are increasingly used for biomedical research, but such “secondary use” data have known limitations, including their quality. When leveraging routine care data for observational research, developing audit protocols that can maximize informational return and minimize costs is paramount.

For more than a decade, the Latin America and East Africa regions of the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium have been auditing the observational data drawn from participating human immunodeficiency virus clinics. Since our earliest audits, where external auditors used paper forms to record audit findings from paper medical records, we have streamlined our protocols to obtain more efficient and informative audits that keep up with advancing technology while reducing travel obligations and associated costs.

We present five key lessons learned from conducting data audits of secondary-use data from resource-limited settings for more than 10 years and share eight recommendations for other consortia looking to implement data quality initiatives.

After completing multiple audit cycles in both the Latin America and East Africa regions of the IeDEA consortium, we have established a rich reference for data quality in our cohorts, as well as large, audited analytical datasets that can be used to answer important clinical questions with confidence. By sharing our audit processes and how they have been adapted over time, we hope that others can develop protocols informed by our lessons learned from more than a decade of experience in these large, diverse cohorts.

To measure the impact of an automated hand hygiene monitoring system (AHHMS) and an intervention program of complementary strategies on hand hygiene (HH) performance in both acute-care and long-term care (LTC) units.

Prospective, nonrandomized, before-and-after intervention study.

Single Veterans Affairs Medical Center (VAMC), with 2 acute-care units and 6 LTC units.

An AHHMS that provides group HH performance rates was implemented on 8 units at a VAMC from March 2021 through April 2022. After a 4-week baseline period and 2.5-week washout period, the 52-week intervention period included multiple evidence-based components designed to improve HH compliance. Unit HH performance rates were expressed as the number of dispenses (events) divided by the number of patient room entries and exits (opportunities) × 100. Statistical analysis was performed with a Poisson general additive mixed model.

During the 4-week baseline period, the median HH performance rate was 18.6 (95% CI, 16.5–21.0) for all 8 units. During the intervention period, the median HH rate increased to 21.6 (95% CI, 19.1–24.4; P < .0001), and during the last 4 weeks of the intervention period (exactly 1 year after baseline), the 8 units exhibited a median HH rate of 25.1 (95% CI, 22.2–28.4; P < .0001). The median HH rate increased from 17.5 to 20.0 (P < .0001) in LTC units and from 22.9 to 27.2 (P < .0001) in acute-care units.

The intervention was associated with increased HH performance rates for all units. The performance of acute-care units was consistently higher than LTC units, which have more visitors and more mobile veterans.

It has been previously identified that levels of peripheral inflammatory proteins, such as cytokines, are altered in people with schizophrenia spectrum disorders (SSD).

As there is considerable inconsistency in the literature with respect to how inflammatory profiles differ between acute and chronic stages of SSD, a systematic review and network meta-analysis was performed.

Records from CINAHL, the Cochrane Central Register of Controlled Trials, EMBASE, PubMed, and PsycINFO were systematically searched from inception until 31 March 2022 for published studies that had measured levels of inflammatory proteins in cases of SSD and healthy controls. Pairwise and network meta-analyses were performed to determine whether there were significant differences in mean peripheral protein concentrations between acute SSD, chronic SSD, and healthy controls.

After application of the screening process, 215 articles were included for data-analysis. One group of markers were consistently elevated (p<0·05) in both acute and chronic SSD, relative to healthy controls; this group comprised interleukin (IL)-1β, IL-1 receptor antagonist (IL-1RA), soluble interleukin-2 receptor (sIL-2R), IL-6, IL-8, IL-10, tumor necrosis factor (TNF)-α, and high sensitivity C-reactive protein (hsCRP). A second group of markers were inconsistently altered between illness stages: IL-2 and interferon (IFN)-γ were significantly elevated (p<0·05) in acute SSD, whilst IL-4, IL-12 and IFN-γ were significantly decreased (p<0·05) in chronic SSD.

These results indicate that a baseline level of inflammatory protein alteration occurs in SSD throughout the course of illness. This was evident from the group of markers that were consistently elevated in acute and chronic SSD (e.g., IL-6), representing possible trait markers. Moreover, superimposed immune activity may occur in acute SSD, given the group of possible state markers that were increased only in acute illness (e.g., IFN-γ). Further research is required to elucidate whether these peripheral changes are reflected within the central nervous system.

None Declared

Although clozapine is the most efficacious medication for treatment-refractory schizophrenia, not all patients will have an adequate response. Optimising clozapine dose using therapeutic drug monitoring could therefore maximise response.

Using individual patient data, we undertook a receiver operating characteristic (ROC) curve analysis to determine an optimal therapeutic range for clozapine levels to guide clinical practice.

We conducted a systematic review of PubMed, PsycINFO and Embase for studies that provided individual participant level data on clozapine levels and response. These data were analysed using ROC curves to determine the prediction performance of plasma clozapine levels for treatment response.

We included data on 294 individual participants from nine studies. ROC analysis yielded an area under the curve of 0.612. The clozapine level at the point of optimal diagnostic benefit was 372 ng/mL; at this level, the response sensitivity was 57.3%, and specificity 65.7%. The interquartile range for treatment response was 223–558 ng/mL. There was no improvement in ROC performance with mixed models including patient gender, age or length of trial. Clozapine dose and clozapine concentration to dose ratio did not provide significantly meaningful prediction of response to clozapine.

Clozapine dose should be optimised based on clozapine therapeutic levels. We found that a range between 250 and 550 ng/mL could be recommended, while noting that a level of >350 ng/mL is the most optimal for response. Although some patients may not respond without clozapine levels >550 ng/mL, the benefits should be weighed against the increased risk of adverse drug reactions.

To determine how engagement of the hospital and/or vendor with performance improvement strategies combined with an automated hand hygiene monitoring system (AHHMS) influence hand hygiene (HH) performance rates.

Prospective, before-and-after, controlled observational study.

The study was conducted in 58 adult and pediatric inpatient units located in 10 hospitals.

HH performance rates were estimated using an AHHMS. Rates were expressed as the number of soap and alcohol-based hand rub portions dispensed divided by the number of room entries and exits. Each hospital self-assigned to one of the following intervention groups: AHHMS alone (control group), AHHMS plus clinician-based vendor support (vendor-only group), AHHMS plus hospital-led unit-based initiatives (hospital-only group), or AHHMS plus clinician-based vendor support and hospital-led unit-based initiatives (vendor-plus-hospital group). Each hospital unit produced 1–2 months of baseline HH performance data immediately after AHHMS installation before implementing initiatives.

Hospital units in the vendor-plus-hospital group had a statistically significant increase of at least 46% in HH performance compared with units in the other 3 groups (P ≤ .006). Units in the hospital only group achieved a 1.3% increase in HH performance compared with units that had AHHMS alone (P = .950). Units with AHHMS plus other initiatives each had a larger change in HH performance rates over their baseline than those in the AHHMS-alone group (P < 0.001).

AHHMS combined with clinician-based vendor support and hospital-led unit-based initiatives resulted in the greatest improvements in HH performance. These results illustrate the value of a collaborative partnership between the hospital and the AHHMS vendor.

To determine how pharmacists with formal antimicrobial stewardship program (ASP) responsibilities prioritize their time and pharmacists without formal antimicrobial stewardship program responsibilities contribute to ASP activities.

A nationwide survey.

Members of the American College of Clinical Pharmacy who subscribe to the following practice and research network e-mail listservs: infectious diseases, adult medicine, cardiology, critical care, hematology–oncology, immunology and transplantation, and pediatrics.

A survey was distributed via listservs. Respondents were asked about their personal and institutional demographics and ASP activities.

In total, 245 pharmacists responded: 135 pharmacists with formal antimicrobial stewardship program responsibilities; 110 pharmacists without formal antimicrobial stewardship program responsibilities. Although most respondents had completed a general pharmacy residency (85%), only 20% had completed an infectious diseases (ID) specialty residency. Among pharmacists with formal antimicrobial stewardship program responsibilities, one-third had no formal training or certification in ID or ASP. Pharmacists without formal antimicrobial stewardship program responsibilities spent ∼12.5% of their time per week on ASP activities, whereas pharmacists with formal antimicrobial stewardship program responsibilities spent 28% of their time performing non-ASP activities. Pharmacists with formal antimicrobial stewardship program responsibilities were more likely than pharmacists without formal antimicrobial stewardship program responsibilities to perform antibiotic guideline development (P < .001), antibiotic-related education (P = .002), and direct notification of rapid diagnostic results (P = .018). Pharmacists with formal antimicrobial stewardship program responsibilities without formal ID training or certification spent less time on ASP activities and were more likely to perform lower-level interventions.

Many ASP activities are being performed by pharmacists without formal ID training. To ensure the future success of ASPs, pharmacists with formal antimicrobial stewardship program responsibilities should have adequate training to meet more advanced metrics, and more pharmacists without formal antimicrobial stewardship program responsibilities should be included in basic interventions.

Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors.

Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used.

All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN).

Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.