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Although generic oral contraceptives (OCPs) can improve adherence and reduce health care expenditures, use of generic OCPs remains low, and the factors that affect generic prescribing are not well understood. We aimed to understand the barriers and facilitators of generic OCP prescribing and potential solutions to increase generic OCP prescribing, as well as pilot an educational module to address clinician misconceptions about generic OCPs. We developed focus group scripts using the 4D model of appreciative inquiry. A total of four focus groups occurred, two at the American Association of Nurse Practitioners (AANP) national conference and two at the American College of Physicians (ACP) Internal Medicine meeting. Focus group transcripts were analyzed using a constant comparative method with no a priori hypothesis to generate emerging and reoccurring themes. Findings from these focus groups were used to develop an educational module promoting generic OCP prescribing. Participants were recruited from the AANP Network for Research and the ACP Research Panel. This study demonstrates that health system factors, workflow factors, clinician factors, and patient factors were the main barriers to and facilitators of generic OCP prescribing. Nurse practitioners were responsive to an educational module and reported increased willingness to discuss and prescribe generic OCPs after completing the module. Interventions to increase generic OCP prescribing must address clinician and patient factors within the context of workflow and larger health system factors.

Past studies describe numerous endophenotypes associated with schizophrenia (SZ), but many endophenotypes may overlap in information they provide, and few studies have investigated the utility of a multivariate index to improve discrimination between SZ and healthy community comparison subjects (CCS). We investigated 16 endophenotypes from the first phase of the Consortium on the Genetics of Schizophrenia, a large, multi-site family study, to determine whether a subset could distinguish SZ probands and CCS just as well as using all 16. Participants included 345 SZ probands and 517 CCS with a valid measure for at least one endophenotype. We used both logistic regression and random forest models to choose a subset of endophenotypes, adjusting for age, gender, smoking status, site, parent education, and the reading subtest of the Wide Range Achievement Test. As a sensitivity analysis, we re-fit models using multiple imputations to determine the effect of missing values. We identified four important endophenotypes: antisaccade, Continuous Performance Test-Identical Pairs 3-digit version, California Verbal Learning Test, and emotion identification. The logistic regression model that used just these four endophenotypes produced essentially the same results as the model that used all 16 (84% vs. 85% accuracy). While a subset of endophenotypes cannot replace clinical diagnosis nor encompass the complexity of the disease, it can aid in the design of future endophenotypic and genetic studies by reducing study cost and subject burden, simplifying sample enrichment, and improving the statistical power of locating those genetic regions associated with schizophrenia that may be the easiest to identify initially.

The impaired ability to make correct antisaccades (i.e., antisaccade performance) is well documented among schizophrenia subjects, and researchers have successfully demonstrated that antisaccade performance is a valid schizophrenia endophenotype that is useful for genetic studies. However, it is unclear how the ascertainment biases that unavoidably result from recruitment differences in schizophrenia subjects identified in family versus case-control studies may influence patient-control differences in antisaccade performance. To assess the impact of ascertainment bias, researchers from the Consortium on the Genetics of Schizophrenia (COGS) compared antisaccade performance and antisaccade metrics (latency and gain) in schizophrenia and control subjects from COGS-1, a family-based schizophrenia study, to schizophrenia and control subjects from COGS-2, a corresponding case-control study. COGS-2 schizophrenia subjects were substantially older; had lower education status, worse psychosocial function, and more severe symptoms; and were three times more likely to be a member of a multiplex family than COGS-1 schizophrenia subjects. Despite these variations, which were likely the result of ascertainment differences (as described in the introduction to this special issue), the effect sizes of the control-schizophrenia differences in antisaccade performance were similar in both studies (Cohen's d effect size of 1.06 and 1.01 in COGS-1 and COGS-2, respectively). This suggests that, in addition to the robust, state-independent schizophrenia-related deficits described in endophenotype studies, group differences in antisaccade performance do not vary based on subject ascertainment and recruitment factors.

The children of older fathers have increased risks of developing schizophrenia spectrum disorders, and among those who develop these disorders, those with older fathers present with more severe clinical symptoms. However, the influence of advanced paternal age on other important domains related to schizophrenia, such as quantitative endophenotype deficit levels, remains unknown. This study investigated the associations between paternal age and level of endophenotypic impairment in a well-characterized family-based sample from the Consortium on the Genetics of Schizophrenia (COGS). All families included at least one affected subject and one unaffected sibling. Subjects met criteria for schizophrenia (probands; n = 293) or were unaffected first-degree siblings of those probands (n = 382). Paternal age at the time of subjects birth was documented. Subjects completed a comprehensive clinical assessment and a battery of tests that measured 16 endophenotypes. After controlling for covariates, potential paternal age-endophenotype associations were analyzed using one model that included probands alone and a second model that included both probands and unaffected siblings. Endophenotype deficits in the Identical Pairs version of the 4-digit Continuous Performance Test and in the Penn Computerized Neurocognitive Battery verbal memory test showed significant associations with paternal age. However, after correcting for multiple comparisons, no endophenotype was significantly associated with paternal age. These findings suggest that factors other than advanced paternal age at birth may account for endophenotypic deficit levels in schizophrenia.

The application of natural language processing and machine learning (ML) in electronic health records (EHRs) may help reduce dementia underdiagnosis, but models that are not designed to reflect minority populations may instead perpetuate underdiagnosis. To improve the identification of undiagnosed dementia, particularly in Black Americans (BAs), we developed support vector machine (SVM) ML models to assign dementia risk scores based on features identified in unstructured EHR data (via latent Dirichlet allocation and stable topic extraction in n = 1 M notes) and structured EHR data. We hypothesized that separate models would show differentiation between racial groups, so the models were fit separately for BAs (n = 5 K with dementia ICD codes, n = 5 K without) and White Americans (WAs; n = 5 K with codes, n = 5 K without). To validate our method, scores were generated for separate samples of BAs (n = 10 K) and WAs (n = 10 K) without dementia codes, and the EHRs of 1.2 K of these patients were reviewed by dementia experts. All subjects were age 65+ and drawn from the VA, which meant that the samples were disproportionately male. A strong positive relationship was observed between SVM-generated risk scores and undiagnosed dementia. BAs were more likely than WAs to have undiagnosed dementia per chart review, both overall (15.3% vs. 9.5%) and among Veterans with >90th percentile cutoff scores (25.6% vs. 15.3%). With chart reviews as the reference standard and varied cutoff scores, the BA model performed slightly better than the WA model (AUC = 0.86 with negative predictive value [NPV] = 0.98, positive predictive value [PPV] = 0.26, sensitivity = 0.61, specificity = 0.92 and accuracy = 0.91 at >90th percentile cutoff vs. AUC = 0.77 with NPV = 0.98, PPV = 0.15, sensitivity = 0.43, specificity = 0.91 and accuracy = 0.89 at >90th). Our findings suggest that race-specific ML models can help identify BAs who may have undiagnosed dementia. Future studies should examine model generalizability in settings with more females and test whether incorporating these models into clinical settings increases the referral of undiagnosed BAs to specialists. [ABSTRACT FROM AUTHOR]