Most privately insured patients do not receive federally recommended abdominal aortic aneurysm screening.

OBJECTIVE: Since 2005, the United States Preventative Services Task Force has recommended abdominal aortic aneurysm (AAA) ultrasound screening for 65- to 75-year-old male ever-smokers. Integrated health systems such as Kaiser Permanente and the Veterans Affairs (VA) health care system report 74% to 79% adherence, but compliance rates in the private sector are unknown. METHODS: The IBM Marketscan Commercial and Medicare Supplemental databases (2006-2017) were queried for male ever-smokers continuously enrolled from age 65 to 75 years. Exclusion criteria were previous history of AAA, connective tissue disorder, and aortic surgery. Patients with abdominal computed tomographic or magnetic resonance imaging from ages 65 to 75 years were also excluded. Screening was defined as a complete abdominal, retroperitoneal, or aortic ultrasound. A logistic mixed-effects model utilizing state as a random intercept was used to identify patient characteristics associated with screening. RESULTS: Of 35,154 eligible patients, 13,612 (38.7%) underwent screening. Compliance varied by state, ranging from 24.4% in Minnesota to 51.6% in Montana (P < .05). Screening activity increased yearly, with 0.7% of screening activity occurring in 2008 vs 22.2% in 2016 (P <.05). In a logistic mixed-effects model adjusting for state as a random intercept, history of hypertension (odds ratio [OR], 1.07; 95% confidence interval [CI], 1.03-1.13), coronary artery disease (OR, 1.17; 95% CI, 1.10-1.22), congestive heart failure (OR, 1.14; 95% CI, 1.01-1.22), diabetes (OR, 1.1; 95% CI, 1.06-1.16), and chronic kidney disease (OR, 1.4; 95% CI, 1.24-1.53) were associated with screening. Living outside of a census-designated metropolitan area was negatively associated with screening (OR, 0.92; 95% CI, 0.87-0.97). CONCLUSIONS: In a private claims database representing 250 million claimants, 38.7% of eligible patients received United States Preventative Services Task Force-recommended AAA screening. Compliance was nearly one-half that of integrated health systems and was significantly lower for patients living outside of metropolitan areas. Efforts to improve early detection of AAA should include targeting non-metropolitan areas and modifying Medicare reimbursement and incentivization strategies to improve guideline adherence.

Interruptive Electronic Alerts for Choosing Wisely Recommendations: A Cluster Randomized Controlled Trial.

OBJECTIVE: To assess the efficacy of interruptive electronic alerts in improving adherence to the American Board of Internal Medicine's Choosing Wisely recommendations to reduce unnecessary laboratory testing. MATERIALS AND METHODS: We administered 5 cluster randomized controlled trials simultaneously, using electronic medical record alerts regarding prostate-specific antigen (PSA) testing, acute sinusitis treatment, vitamin D testing, carotid artery ultrasound screening, and human papillomavirus testing. For each alert, we assigned 5 outpatient clinics to an interruptive alert and 5 were observed as a control. Primary and secondary outcomes were the number of postalert orders per 100 patients at each clinic and number of triggered alerts divided by orders, respectively. Post hoc analysis evaluated whether physicians experiencing interruptive alerts reduced their alert-triggering behaviors. RESULTS: Median postalert orders per 100 patients did not differ significantly between treatment and control groups; absolute median differences ranging from 0.04 to 0.40 for PSA testing. Median alerts per 100 orders did not differ significantly between treatment and control groups; absolute median differences ranged from 0.004 to 0.03. In post hoc analysis, providers receiving alerts regarding PSA testing in men were significantly less likely to trigger additional PSA alerts than those in the control sites (Incidence Rate Ratio 0.12, 95% CI [0.03-0.52]). DISCUSSION: Interruptive point-of-care alerts did not yield detectable changes in the overall rate of undesired orders or the order-to-alert ratio between active and silent sites. Complementary behavioral or educational interventions are likely needed to improve efforts to curb medical overuse. CONCLUSION: Implementation of interruptive alerts at the time of ordering was not associated with improved adherence to 5 Choosing Wisely guidelines. TRIAL REGISTRATION: NCT02709772.

An exploratory study on the social and genotypic clustering of HIV infection in men having sex with men.

OBJECTIVE: To explore the clustering of HIV infected men having sex with men (MSM) using social network approach in conjunction with the phylogenetic relationship of the virus strains. DESIGN: An exploratory study incorporating social network and phylogenetic analysis. METHODS: Recently diagnosed HIV-infected MSM attending one major HIV specialist clinic in Hong Kong were recruited in the study involving the administration of a self-administered questionnaire on behaviours and partnership patterns using a Likert Scale, the results of which were assessed using social network analysis and in context of the phylogenetic analysis from sequencing the HIV-1 pol gene, as part of the clinical investigation for genotypic resistance. Clusters were defined using social and molecular methods. RESULTS: An 'Internet-centred' cluster and 'Sauna-centred' cluster could be delineated using correspondence analysis and network diagrams. The main distinguishing features of MSM in the 'Internet-centred' social cluster were: younger age, higher education level, and multiple partner types. Three genetic clusters could be identified in the phylogenetic tree, two of which associated with Internet use and one with sauna for sex partnership. There were partial overlaps between social and genetic clusters. Characteristically, the virus strains in sauna users were more disperse compared with the closely knit configuration of those using Internet. CONCLUSION: The principle of the duality of place and person can be strategically applied in epidemiologic investigation. The characterization of MSM cluster using anonymized network data provides a potentially powerful tool for informing public health intervention.

One- to four-dimensional kernels for virtual screening and the prediction of physical, chemical, and biological properties.

Many chemoinformatics applications, including high-throughput virtual screening, benefit from being able to rapidly predict the physical, chemical, and biological properties of small molecules to screen large repositories and identify suitable candidates. When training sets are available, machine learning methods provide an effective alternative to ab initio methods for these predictions. Here, we leverage rich molecular representations including 1D SMILES strings, 2D graphs of bonds, and 3D coordinates to derive efficient machine learning kernels to address regression problems. We further expand the library of available spectral kernels for small molecules developed for classification problems to include 2.5D surface and 3D kernels using Delaunay tetrahedrization and other techniques from computational geometry, 3D pharmacophore kernels, and 3.5D or 4D kernels capable of taking into account multiple molecular configurations, such as conformers. The kernels are comprehensively tested using cross-validation and redundancy-reduction methods on regression problems using several available data sets to predict boiling points, melting points, aqueous solubility, octanol/water partition coefficients, and biological activity with state-of-the art results. When sufficient training data are available, 2D spectral kernels in general tend to yield the best and most robust results, better than state-of-the art. On data sets containing thousands of molecules, the kernels achieve a squared correlation coefficient of 0.91 for aqueous solubility prediction and 0.94 for octanol/water partition coefficient prediction. Averaging over conformations improves the performance of kernels based on the three-dimensional structure of molecules, especially on challenging data sets. Kernel predictors for aqueous solubility (kSOL), LogP (kLOGP), and melting point (kMELT) are available over the Web through: http://cdb.ics.uci.edu.