Medicaid Costs and Utilization of Collaborative Versus Colocation Care for Patients With Depression.

OBJECTIVE: The authors examined cost and utilization metrics for racially diverse Medicaid primary care patients with depression receiving care through either a collaborative care model (CoCM) of integration or the standard colocation model. METHODS: Data from a retrospective cohort of Medicaid patients screening positive for clinically significant depression during January 2016-December 2017 were analyzed to assess health care costs and selected utilization measures. Seven primary care clinics providing CoCM were compared with 16 clinics providing colocated behavioral health care. Data for the first year and second year after a patient received an initial Patient Health Questionnaire-9 score ≥10 were analyzed. RESULTS: In the first year, compared with patients receiving colocated care (N=3,061), CoCM patients (N=4,315) had significantly lower odds of emergency department (ED) visits (OR=0.95) and medical specialty office visits (OR=0.92), with slightly higher odds of primary care provider (PCP) visits (OR=1.03) and behavioral health office visits (OR=1.03). In year 2, CoCM patients (N=2,623) had significantly lower odds of inpatient medical admissions (OR=0.87), ED visits (OR=0.84), medical specialty office visits (OR=0.89), and PCP visits (OR=0.94) than the colocated care patients (N=1,838). The two groups did not significantly differ in total cost in both years. CONCLUSIONS: Access to CoCM treatment in primary care for racially diverse Medicaid patients with depression was associated with more positive health care utilization outcomes than for those accessing colocated treatment. As organizations continue to seek opportunities to integrate behavioral health care into primary care, consideration of health care costs and utilization may be helpful in the selection and implementation of integration models.

Contribution of systemic and airway immune responses to pediatric obesity-related asthma.

Childhood obesity contributes to many diseases, including asthma. Although the precise mechanism by which obesity causes asthma is not known, there is literature to suggest that innate and adaptive systemic and airway immune responses in obese children with asthma differ from those in normal-weight children with asthma. Both non-allergic or non-T2 phenotype with systemic T helper (Th)1 polarization and allergic Th cell responses have been reported in childhood obesity-related asthma. There is preliminary evidence to suggest that genetic and epigenetic mechanisms contribute to these immune responses. Initial investigations into the biology of non-T2 immune responses have identified upregulation of genes in the CDC42 pathway. CDC42 is a RhoGTPase that plays a key role in Th cell physiology, including preferential naïve Th cell differentiation to Th1 cells, as well as cytokine production and exocytosis. These novel pathways are promising findings to direct targeted therapy development for obesity-related asthma to address the disease burden.

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.

Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning.

Survey of Radiation Oncologists to Assess Interest and Potential Use of a Genetic Test Predicting Susceptibility for the Development of Toxicities After Prostate Cancer Radiation Therapy.

PURPOSE: A genetic test predicting susceptibility for the development of toxicities after prostate cancer radiation therapy is in development. This test intends to help physicians with treatment decision making. METHODS AND MATERIALS: Radiation oncologists were surveyed using a web-based questionnaire to gauge their interest in using a genetic test predictive of increased risk of radiation therapy toxicities as an aid in determining therapy for men with prostate cancer. Responses were summarized using frequencies, and a χ2 test compared responses among participants. Multivariable ordinal regression identified factors associated with anticipated adoption or nonadoption of such a genetic test by radiation oncologists. RESULTS: Among 204 radiation oncologists (64% from the United States, 36% from other countries), 86.3% would order a genetic test and 80.2% said the test would be useful for treatment discussions. There was wide acceptance (76.7%) to offer a genetic test to all patients considering radiation therapy for prostate cancer. Additionally, 98.1% indicated that patients would be receptive to the test information. There were no significant differences in the likelihood of ordering a genetic test based on practice setting, familiarity with scientific literature, time spent on research, or geographic location (all P > .05). CONCLUSIONS: Radiation oncologists who treat prostate cancer are interested in and willing to order a genetic test predictive of susceptibility to radiation therapy toxicity to aid their treatment decision making.