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Abstract A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples. We reproduce the differential expression analysis simulation results and identify a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested in the previous study.

Background Exposure to second-generation antipsychotics (SGAs) carries a risk of type 2 diabetes, but questions remain about the diabetogenic effects of SGAs. Aims To assess the diabetes risk associated with two frequently used SGAs. Method This was a retrospective cohort study of adults with schizophrenia, bipolar I disorder or severe major depressive disorder (MDD) exposed during 2008–2013 to continuous monotherapy with aripiprazole or olanzapine for up to 24 months, with no pre-period exposure to other antipsychotics. Newly diagnosed type 2 diabetes was quantified with targeted minimum loss-based estimation; risk was summarised as the restricted mean survival time (RMST), the average number of diabetes-free months. Sensitivity analyses were used to evaluate potential confounding by indication. Results Aripiprazole-treated patients had fewer diabetes-free months compared with olanzapine-treated patients. RMSTs were longer in olanzapine-treated patients, by 0.25 months [95% CI: 0.14, 0.36], 0.16 months [0.02, 0.31] and 0.22 months [0.01, 0.44] among patients with schizophrenia, bipolar I disorder and severe MDD, respectively. Although some sensitivity analyses suggest a risk of unobserved confounding, E-values indicate that this risk is not severe. Conclusions Using robust methods and accounting for exposure duration effects, we found a slightly higher risk of type 2 diabetes associated with aripiprazole compared with olanzapine monotherapy regardless of diagnosis. If this result was subject to unmeasured selection despite our methods, it would suggest clinician success in identifying olanzapine candidates with low diabetes risk. Confirmatory research is needed, but this insight suggests a potentially larger role for olanzapine in the treatment of well-selected patients, particularly for those with schizophrenia, given the drug's effectiveness advantage among them.

Abstract Background Over 1.6 million adolescents in the United States meet criteria for substance use disorders (SUDs). While there are promising treatments for SUDs, adolescents respond to these treatments differentially in part based on the setting in which treatments are delivered. One way to address such individualized response to treatment is through the development of adaptive interventions (AIs): sequences of decision rules for altering treatment based on an individual’s needs. This protocol describes a project with the overarching goal of beginning the development of AIs that provide recommendations for altering the setting of an adolescent’s substance use treatment. This project has three discrete aims: (1) explore the views of various stakeholders (parents, providers, policymakers, and researchers) on deciding the setting of substance use treatment for an adolescent based on individualized need, (2) generate hypotheses concerning candidate AIs, and (3) compare the relative effectiveness among candidate AIs and non-adaptive interventions commonly used in everyday practice. Methods This project uses a mixed-methods approach. First, we will conduct an iterative stakeholder engagement process, using RAND’s ExpertLens online system, to assess the importance of considering specific individual needs and clinical outcomes when deciding the setting for an adolescent’s substance use treatment. Second, we will use results from the stakeholder engagement process to analyze an observational longitudinal data set of 15,656 adolescents in substance use treatment, supported by the Substance Abuse and Mental Health Services Administration, using the Global Appraisal of Individual Needs questionnaire. We will utilize methods based on Q-learning regression to generate hypotheses about candidate AIs. Third, we will use robust statistical methods that aim to appropriately handle casemix adjustment on a large number of covariates (marginal structural modeling and inverse probability of treatment weights) to compare the relative effectiveness among candidate AIs and non-adaptive decision rules that are commonly used in everyday practice. Discussion This project begins filling a major gap in clinical and research efforts for adolescents in substance use treatment. Findings could be used to inform the further development and revision of influential multi-dimensional assessment and treatment planning tools, or lay the foundation for subsequent experiments to further develop or test AIs for treatment planning.

Data are limited on the attributable outcomes of Clostridium difficile–associated disease (CDAD), particularly in CDAD-endemic settings. We conducted a retrospective cohort study of nonsurgical inpatients admitted for >48 hours in 2003 (N = 18,050). The adjusted hazard ratios for readmission (hazard ratio 2.19, 95% confidence interval [CI] 1.87–2.55) and deaths within 180 days (hazard ratio 1.23, 95% CI 1.03–1.46) were significantly different among CDAD case-patients and noncase patients. In a propensity score matched-pairs analysis that used a nested subset of the cohort (N = 706), attributable length of stay attributable to CDAD was 2.8 days, attributable readmission at 180 days was 19.3%, and attributable death at 180 days was 5.7%. CDAD patients were significantly more likely than controls to be discharged to a long-term-care facility or outside hospital. Even in a nonoutbreak setting, CDAD had a statistically significant negative impact on patient illness and death, and the impact of CDAD persisted beyond hospital discharge.

BACKGROUND:Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study. METHODS AND RESULTS:We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p

Background A considerable number of Gram-negative bacteraemias occur outside intensive care units (ICUs). Inadequate antibiotic therapy in ICUs has been associated with adverse outcomes; however, there are no prospective studies in non-ICU patients. Methods A 6 month (1 August 2006–31 January 2007), prospective cohort study of non-ICU patients with Gram-negative bacteraemia in a tertiary-care hospital was performed. Inadequate empirical antibiotic therapy was defined as no antibiotic or starting a non-susceptible antibiotic within 24 h after the initial positive blood culture. Results Two hundred and fifty non-ICU patients had Gram-negative bacteraemia. The mean age was 56.4 (±16.1) years. The predominant bacteria in monomicrobial infections were Escherichia coli (24%), Klebsiella pneumoniae (18%) and Pseudomonas aeruginosa (8%). Sixty-one (24%) patients had polymicrobial bacteraemia. Seventy patients (28%) required ICU transfer and 35 (14%) died. Seventy-nine (31.6%) received inadequate empirical antibiotic therapy. These patients were more likely to have a hospital-acquired infection [odds ratio (OR) = 1.99, 95% confidence interval (CI) = 1.11–3.56, P = 0.02] and less likely to have E. coli monomicrobial bacteraemia [OR 0.40 (95% CI 0.19–0.86), P = 0.02]. There were no differences in occurrence of sepsis [72 (91.1%) patients with inadequate versus 159 (93.0%) with adequate therapy; P = 0.6], ICU transfer [20 (25.3%) versus 50 (29.2%); P = 0.5], post-bacteraemia length of stay (median = 6.8 versus 6.1 days; P = 0.09) or death [11 (13.9%) versus 24 (14.0%); P = 1.0]. Conclusions Nearly one-third of the non-ICU patients with Gram-negative bacteraemia received inadequate empirical antibiotic therapy. There was no difference in adverse outcomes between patients receiving inadequate or adequate therapy in this study. [ABSTRACT FROM AUTHOR]

Fluctuations in patient volume during the COVID-19 pandemic may have been particularly concerning for rural hospitals. We examined hospital discharge data from the Healthcare Cost and Utilization Project State Inpatient Databases to compare data from the COVID-19 pandemic period (March 8, 2020-December 31, 2021) with data from the prepandemic period (January 1, 2017-March 7, 2020). Changes in average daily medical volume at rural hospitals showed a dose-response relationship with community COVID-19 burden, ranging from a 13.2 percent decrease in patient volume in periods of low transmission to a 16.5 percent increase in volume in periods of high transmission. Overall, about 35 percent of rural hospitals experienced fluctuations exceeding 20 percent (in either direction) in average daily total volume, in contrast to only 13 percent of urban hospitals experiencing similar magnitudes of changes. Rural hospitals with a large change in average daily volume were more likely to be smaller, government-owned, and critical access hospitals and to have significantly lower operating margins. Our findings suggest that rural hospitals may have been more vulnerable operationally and financially to volume shifts during the pandemic, which warrants attention because of the potential impact on these hospitals' long-term sustainability.

Quality measurement is an important tool for incentivizing improvement in the quality of health care. Most quality measurement efforts do not explicitly target health equity. Although some measurement approaches may intend to realign incentives to focus quality improvement efforts on underserved groups, the extent to which they accomplish this goal is understudied. We posit that tying incentives to approaches on the basis of stratification or disparities may have unintended consequences or limited effects. Such approaches might not reduce existing disparities because addressing one aspect of equity may be in competition with addressing others. We propose equity weighting, a new measurement framework to advance equity on multiple fronts that addresses the shortcomings of existing approaches and explicitly calibrates incentives to align with equity goals. We use colorectal cancer screening data derived from 2017 Medicare claims to illustrate how equity weighting fixes unintended consequences in other methods and how it can be adapted to policy goals.

Algorithms are currently used to assist in a wide array of health care decisions. Despite the general utility of these health care algorithms, there is growing recognition that they may lead to unintended racially discriminatory practices, raising concerns about the potential for algorithmic bias. An intuitive precaution against such bias is to remove race and ethnicity information as an input to health care algorithms, mimicking the idea of "race-blind" decisions. However, we argue that this approach is misguided. Knowledge, not ignorance, of race and ethnicity is necessary to combat algorithmic bias. When race and ethnicity are observed, many methodological approaches can be used to enforce equitable algorithmic performance. When race and ethnicity information is unavailable, which is often the case, imputing them can expand opportunities to not only identify and assess algorithmic bias but also combat it in both clinical and nonclinical settings. A valid imputation method, such as Bayesian Improved Surname Geocoding, can be applied to standard data collected by public and private payers and provider entities. We describe two applications in which imputation of race and ethnicity can help mitigate potential algorithmic biases: equitable disease screening algorithms using machine learning and equitable pay-for-performance incentives.

Objectives. To describe the types of social services provided at community health centers (CHCs), characteristics of CHCs providing these services, and the association between on-site provision and health care quality. Methods. We surveyed CHCs in 12 US states and the District of Columbia during summer 2017 (n = 208) to identify referral to and provision of services to address 8 social needs. Regression models estimated factors associated with the provision of social services by CHCs and the association between providing services and health care quality (an 8-item composite). Results. CHCs most often offered on-site assistance for needs related to food or nutrition (43%), interpersonal violence (32%), and housing (30%). Participation in projects with community-based organizations was associated with providing services on-site (odds ratio = 2.48; P  = .018). On-site provision was associated with better performance on measures of health care quality (e.g., each additional social service was associated with a 4.3 percentage point increase in colorectal cancer screenings). Conclusions. Some CHCs provide social services on-site, and this was associated with better performance on measures of health care quality. Public Health Implications. Health care providers are increasingly seeking to identify and address patients' unmet social needs, and on-site provision of services is 1 strategy to consider.