A one health approach for monitoring antimicrobial resistance: developing a national freshwater pilot effort.

Antimicrobial resistance (AMR) is a world-wide public health threat that is projected to lead to 10 million annual deaths globally by 2050. The AMR public health issue has led to the development of action plans to combat AMR, including improved antimicrobial stewardship, development of new antimicrobials, and advanced monitoring. The National Antimicrobial Resistance Monitoring System (NARMS) led by the United States (U.S) Food and Drug Administration along with the U.S. Centers for Disease Control and U.S. Department of Agriculture has monitored antimicrobial resistant bacteria in retail meats, humans, and food animals since the mid 1990's. NARMS is currently exploring an integrated One Health monitoring model recognizing that human, animal, plant, and environmental systems are linked to public health. Since 2020, the U.S. Environmental Protection Agency has led an interagency NARMS environmental working group (EWG) to implement a surface water AMR monitoring program (SWAM) at watershed and national scales. The NARMS EWG divided the development of the environmental monitoring effort into five areas: (i) defining objectives and questions, (ii) designing study/sampling design, (iii) selecting AMR indicators, (iv) establishing analytical methods, and (v) developing data management/analytics/metadata plans. For each of these areas, the consensus among the scientific community and literature was reviewed and carefully considered prior to the development of this environmental monitoring program. The data produced from the SWAM effort will help develop robust surface water monitoring programs with the goal of assessing public health risks associated with AMR pathogens in surface water (e.g., recreational water exposures), provide a comprehensive picture of how resistant strains are related spatially and temporally within a watershed, and help assess how anthropogenic drivers and intervention strategies impact the transmission of AMR within human, animal, and environmental systems.

The Impact of Social Determinants of Maternal Mental Health in Marginalized Mothers.

Background: A deeper understanding of the key determinants of maternal mental health is important for improving care for women, especially women who are at an economic disadvantage. Objectives: To explore the associations of select social determinants: access, social support, and stress, with the onset of antepartum depression in low-income mothers. Participants: The Pregnancy Risk Assessment Monitoring System (PRAMS) is an ongoing, population-based surveillance system with selected data from 2016 to 2019, establishing a randomly selected sample of women with a recent live birth at most 4 months postdelivery, between 1,300 and 3,400 women per state (n ∼ 162,558). Methods: In this cross-sectional study, the phase 8 PRAMS was adapted to measure social support, access, stress, and their relationships with the onset of antepartum depression in low-income mothers. To assess low-income marginalization, a threshold was established based on income levels within 130% of the federal poverty level; antepartum (n ∼ 41,289). Results: The defined access, social support, and stress factors showed a statistically significant association with the onset of antepartum depression among low-income mothers. Of women in this sample, 22.6% indicated antepartum depression (p < 0.001; R2 = 0.066). Negative social support indicators were associated with an increased likelihood of antepartum depression; 3.71 increased odds of depression for abuse during pregnancy, and 0.79 decreased odds with positive acknowledgment of paternity. Access indicator terms showed an association with the decreased likelihood of antepartum depression through breastfeeding information support (Info from Baby Doc, odds ratio [OR] = 0.86), prenatal care utilization (12+ visits, OR = 0.82), and specific insurance type (insurance by job, OR = 0.82). All instances of stressful life events showed an increased likelihood of depression during pregnancy (for majority of stressful life events: OR >1.12). Conclusions: Economically marginalized mothers face unmet social and health care needs leading to poorer outcomes during pregnancy. These findings provide additional support for improved policy and public health efforts, such as assessment, education, and interventions, to decrease prevalence and improve treatment for antepartum depression among marginalized mothers.

Analysis of Participant Stigma and Associated Costs of a Peer-Led Social Media HIV Intervention.

HIV-related stigma is a primary barrier to seeking HIV care. Online social media interventions utilizing peer-led approaches provide an opportunity to revolutionize HIV health behavior change. Secondary analysis of the UCLA HOPE Study (6 waves) was done to examine the effectiveness of an online peer-led intervention in reducing HIV-related internalized stigma (IS), association between IS and sexual risk behaviors (SRB), and associated costs for changing the likelihood of HIV testing. Among 897 participants, an inverse relationship between IS (Discomfort with people with HIV, Stereotypes, Moral Judgment) and SRB (Number of Sexual Partners, Sexual Encounters) factors was identified over time (p < .05). Engagement in stigma conversations increased participant likelihood to request HIV tests (B = 0.02, Wald = 8.10, p = .004) when made in group versus one-on-one contact. Innovative technology has potential to improve HIV-care efforts through expanded reach to at-risk populations, improved communication maintenance, ease of accessibility, and user anonymity.

The Physiologic Role and Use of Estriol.

Obtaining estrogen balance with a physiologic estriol and estradiol ratio is an important aspect of physiologic bioidentical hormone restoration therapy. Risks, including that of breast cancer, should be minimized while attempting to obtain the protective benefits and symptom management with therapy. Estriol plays a central role in protecting against breast cancer and should be considered an integral part of therapy for any patient with lower than normal physiologic levels.

In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects.

INTRODUCTION: Pharmacovigilance plays a pivotal role in monitoring adverse events (AEs) related to chemical substances in human/animal populations. With increasing spontaneous-reporting systems, researchers turned to in-silico approaches to efficiently analyze drug safety profiles. Here, we review in-silico methods employed for assessing multiple drug-drug/drug-disease AEs covered by comparative analyses and visualization strategies. AREAS COVERED: Disproportionality, involving multi-stage statistical methodologies and data processing, identifies safety signals among drug-AE pairs. By stratifying data based on disease indications/demographics, researchers address confounders and assess drug safety. Comparative analyses, including clustering techniques and visualization techniques, assess drug similarities, patterns, and trends, calculate correlations, and identify distinct toxicities. Furthermore, we conducted a thorough Scopus search on 'pharmacovigilance,' yielding 5,836 publications spanning 2003 to 2023. EXPERT OPINION: Pharmacovigilance relies on diverse data sources, presenting challenges in the integration of in-silico approaches and requiring compliance with regulations and AI adoption. Systematic use of statistical analyses enables identifications of potential risks with drugs. Frequentist and Bayesian methods are used in disproportionalities, each with its strengths and weaknesses. Integration of pharmacogenomics with pharmacovigilance enables personalized medicine, with AI further enhancing patient engagement. This multidisciplinary approach holds promise, improving drug efficacy and safety, and should be a core mission of One-Health studies.

Mosquito control exposures and breast cancer risk: analysis of 1071 cases and 2096 controls from the Ghana Breast Health Study.

Epidemiologic data on insecticide exposures and breast cancer risk are inconclusive and mostly from high-income countries. Using data from 1071 invasive pathologically confirmed breast cancer cases and 2096 controls from the Ghana Breast Health Study conducted from 2013 to 2015, we investigated associations with mosquito control products to reduce the spread of mosquito-borne diseases, such as malaria. These mosquito control products were insecticide-treated nets, mosquito coils, repellent room sprays, and skin creams for personal protection against mosquitos. Multivariable and polytomous logistic regression models were used to estimate odds ratios (ORadj) and 95% confidence intervals (CI) with breast cancer risk-adjusted for potential confounders and known risk factors. Among controls, the reported use of mosquito control products were mosquito coils (65%), followed by insecticide-treated nets (56%), repellent room sprays (53%), and repellent skin creams (15%). Compared to a referent group of participants unexposed to mosquito control products, there was no significant association between breast cancer risk and mosquito coils. There was an association in breast cancer risk with reported use of insecticide-treated nets; however, that association was weak and not statistically significant. Participants who reported using repellent sprays were at elevated risks compared to women who did not use any mosquito control products, even after adjustment for all other mosquito control products (OR = 1.42, 95% CI=1.15-1.75). We had limited power to detect an association with repellent skin creams. Although only a few participants reported using repellent room sprays weekly/daily or < month-monthly, no trends were evident with increased frequency of use of repellent sprays, and there was no statistical evidence of heterogeneity by estrogen receptor (ER) status (p-het > 0.25). Our analysis was limited when determining if an association existed with repellent skin creams; therefore, we cannot conclude an association. We found limited evidence of risk associations with widely used mosquito coils and insecticide-treated nets, which are reassuring given their importance for malaria prevention. Our findings regarding specific breast cancer risk associations, specifically those observed between repellent sprays, require further study.

Pharmacokinetics and tumor delivery of nanoparticles.

Nanoparticles (NPs) have been widely used in different areas, including consumer products and medicine. In terms of biomedical applications, NPs or NP-based drug formulations have been extensively investigated for cancer diagnostics and therapy in preclinical studies, but the clinical translation rate is low. Therefore, a thorough and comprehensive understanding of the pharmacokinetics of NPs, especially in drug delivery efficiency to the target therapeutic tissue tumor, is important to design more effective nanomedicines and for proper assessment of the safety and risk of NPs. This review article focuses on the pharmacokinetics of both organic and inorganic NPs and their tumor delivery efficiencies, as well as the associated mechanisms involved. We discuss the absorption, distribution, metabolism, and excretion (ADME) processes following different routes of exposure and the mechanisms involved. Many physicochemical properties and experimental factors, including particle type, size, surface charge, zeta potential, surface coating, protein binding, dose, exposure route, species, cancer type, and tumor size can affect NP pharmacokinetics and tumor delivery efficiency. NPs can be absorbed with varying degrees following different exposure routes and mainly accumulate in liver and spleen, but also distribute to other tissues such as heart, lung, kidney and tumor tissues; and subsequently get metabolized and/or excreted mainly through hepatobiliary and renal elimination. Passive and active targeting strategies are the two major mechanisms of tumor delivery, while active targeting tends to have less toxicity and higher delivery efficiency through direct interaction between ligands and receptors. We also discuss challenges and perspectives remaining in the field of pharmacokinetics and tumor delivery efficiency of NPs.

Effects of Mental Illness Amongst Adults in the United States Living With Diabetes Mellitus on Hospital Admissions.

OBJECTIVE: To examine the influence of comorbid mental illness on hospitalization among adults reporting diabetes mellitus. METHODS: This cross-sectional observational study used National Health Interview Survey (NHIS) data from 2000-2018 to examine hospitalization. Mental illness was defined as no to low psychological distress (NLPD), moderate psychological distress (MPD), and serious psychological distress (SPD) as per the Kessler-6 scale. Socio-demographic factors and health status were added as covariates in binary logistic regression. RESULTS: This study involved 48,807 survey participants and reflected an estimated population of 17,524,418 adults with diabetes in the United States, of whom 19.9% were hospitalized in the year prior to the survey. Among those who were hospitalized, 71.5% exhibited None to Low Psychological Distress (NLPD), 17.7% reported Moderate Psychological Distress (MPD), and 10.8% reported Serious Psychological Distress (SPD). Conversely, among non-hospitalized individuals, the percentages were as follows: 83.2% had NLPD, 11.4% had MPD, and 5.3% had SPD. The odds ratio (OR) for hospitalization was found to be OR=1.31 (95% CI: 1.20, 1.43, p<0.0001) for MPD and OR=1.42 (95% CI: 1.28, 1.58, p<0.0001) for SPD, in comparison to those with no or low psychological distress. CONCLUSION: Among adults with diabetes mellitus, those with mental illness were more likely to be hospitalized than those without mental illness. Programs and policies to improve care among adults with both mental illness and diabetes may help to reduce hospitalizations.

Understanding multi-level barriers to medication adherence among adults living with sickle cell disease.

There is limited research that identifies and examines multi-level barriers to medication adherence among adults with Sickle Cell Disease (SCD); Identify multi-level barriers to medication adherence among adults with SCD; and Examine the relationship between multi-level barriers and medication adherence levels. A cross-sectional study included 130 adults (ages ≥ 18 years old) living with SCD who receive treatment/care from one of the 10 adult SCD clinics within the Networking California for sickle cell care initiative. Study measures included the medication adherence report scale (Professor Rob Horne), Beliefs about Medicine Questionnaire (Professor Rob Horne), and patient reported outcomes measurement information system. Participants reported barriers to medication adherence across 3 levels: Community-level barriers (e.g., COVID-19 pandemic); Institutional-level barriers (e.g., bad experiences with the health care system); and Individual-level barriers (e.g., beliefs and depression severity). Depression severity and patient concerns about SCD medication were inversely correlated with medication adherence (rs = -0.302, P < .001; rs = -0.341, P < .001 respectively). Patient beliefs about the necessity of SCD medication were insignificantly correlated with medication adherence (rs = 0.065, P = .464). Medication adherence was higher among patients who had fewer adherence barriers than multiple adherence barriers (Median medication adherence: fewer barriers = 22 vs multiple barrier = 20.50, P = .085), suggesting clinical significance although statistically insignificant. Identifying multi-level adherence barriers and examining their relationship with medication adherence will help develop targeted public health strategies to promote improved medication adherence and wellness among adults with SCD.

Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice.

Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of "Nano-Tumor Database", which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded "Nano-Tumor Database" and several statistical models that may help nanomedicine design in the future.

A comparison of methods to detect low levels of Salmonella enterica in surface waters to support antimicrobial resistance surveillance efforts performed in multiple laboratories.

Developing effective and sensitive detection methods for antimicrobial resistant Salmonella enterica from surface water is a goal of the National Antimicrobial Resistance Monitoring System (NARMS). There are no specified methods for recovery of S. enterica in surface waters in the U.S. A multi-laboratory evaluation of four methods - bulk water enrichment (BW), vertical Modified Moore Swab (VMMS), modified Standard Method 9260.B2 (SM), and dead-end ultrafiltration (DEUF) - was undertaken to recover S. enterica from surface water. In Phase 1, one-liter volumes of water were collected from the same site on five different dates. Water was shipped and analyzed at four different laboratory locations (A, B, C, and D) for recovery of 1) inoculated fluorescent S. Typhimurium strain (ca. 30 CFU/L) and 2) Salmonella present in the water sampled. At each location, BW, VMMS, or SM recovery was performed on five separate 1 L water samples. Twenty 1 L water samples were subjected to each recovery method, and overall, sixty 1 L samples were assayed for Salmonella. Inoculated, fluorescent Salmonella Typhimurium and environmental Salmonella spp. were recovered from 65 % (39/60) and 45 % (27/60) of water samples, respectively. BW, VMMS, and SM recovered fluorescent S. Typhimurium from 60 %, 60 %, and 75 % of inoculated samples, respectively. Analysis by Chi-squared test determined laboratory location had a significant (p < 0.05) effect on fluorescent S. Typhimurium recovery compared to method or date of water collection. In Phase 2, recovery of inoculated fluorescent S. Typhimurium from 1 L samples by SM and DEUF was compared at laboratory locations B and D. SM and DEUF recovered fluorescent S. Typhimurium from 100 % (20/20) and 95 % (19/20) of inoculated water samples, respectively; laboratory location (p > 0.05) did not affect Salmonella recovery. Uniform laboratory methodology and training should be prioritized in conducting Salmonella recovery from surface water in laboratories.

Development and application of an interactive generic physiologically based pharmacokinetic (igPBPK) model for adult beef cattle and lactating dairy cows to estimate tissue distribution and edible tissue and milk withdrawal intervals for per- and polyfluoroalkyl substances (PFAS).

Humans can be exposed to per- and polyfluoroalkyl substances (PFAS) through dietary intake from milk and edible tissues from food animals. This study developed a physiologically based pharmacokinetic (PBPK) model to predict tissue and milk residues and estimate withdrawal intervals (WDIs) for multiple PFAS including PFOA, PFOS and PFHxS in beef cattle and lactating dairy cows. Results showed that model predictions were mostly within a two-fold factor of experimental data for plasma, tissues, and milk with an estimated coefficient of determination (R2) of >0.95. The predicted muscle WDIs for beef cattle were <1 day for PFOA, 449 days for PFOS, and 69 days for PFHxS, while the predicted milk WDIs in dairy cows were <1 day for PFOA, 1345 days for PFOS, and zero day for PFHxS following a high environmental exposure scenario (e.g., 49.3, 193, and 161 ng/kg/day for PFOA, PFOS, and PFHxS, respectively, for beef cattle for 2 years). The model was converted to a web-based interactive generic PBPK (igPBPK) platform to provide a user-friendly dashboard for predictions of tissue and milk WDIs for PFAS in cattle. This model serves as a foundation for extrapolation to other PFAS compounds to improve safety assessment of cattle-derived food products.

Competitive performance as a discriminator of doping status in elite athletes.

As the aim of any doping regime is to improve sporting performance, it has been suggested that analysis of athlete competitive results might be informative in identifying those at greater risk of doping. This research study aimed to investigate the utility of a statistical performance model to discriminate between athletes who have a previous anti-doping rule violation (ADRV) and those who do not. We analysed performances of male and female 100 and 800 m runners obtained from the World Athletics database using a Bayesian spline model. Measures of unusual improvement in performance were quantified by comparing the yearly change in athlete's performance (delta excess performance) to quantiles of performance in their age-matched peers from the database population. The discriminative ability of these measures was investigated using the area under the ROC curve (AUC) with the 55%, 75% and 90% quantiles of the population performance. The highest AUC values across age were identified for the model with a 75% quantile (AUC = 0.78-0.80). The results of this study demonstrate that delta excess performance was able to discriminate between athletes with and without ADRVs and therefore could be used to assist in the risk stratification of athletes for anti-doping purposes.

Development of machine learning algorithms to estimate maximum residue limits for veterinary medicines.

Establishing maximum-residue limits (MRLs) for veterinary medicine helps to protect the human food supply. Guidelines for establishing MRLs are outlined by regulatory authorities that drug sponsors follow in each country. During the drug approval process, residue limits are targeted for specific animal species and matrices. Therefore, MRLs are commonly not established for other species. This study demonstrates unestablished MRLs can be reliably predicted for under-represented food commodity groups using machine learning (ML). Classification methods with imbalanced data were used to analyze MRL data from multiple countries by implementing resampling techniques in different ML classifiers. Afterward, we developed and evaluated a data-mining method for predicting unestablished MRLs. Seven different ML classifiers such as support vector classifier, multi-layer perceptron (MLP), random forest, decision tree, k-neighbors, Gaussian NB, and AdaBoost have been selected in this baseline study. Among these, the neural network MLP classifier reliably scored the highest average-weighted F1 score (accuracy >99% with markers and ≈88% without markets) in predicting unestablished MRLs. This provides the first study to apply ML algorithms in regulatory food animal medicine. By predicting and estimating MRLs, we can potentially decrease the use and cost of live animals and the overall research burden of determining new MRLs.

An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice.

The critical barrier for clinical translation of cancer nanomedicine stems from the inefficient delivery of nanoparticles (NPs) to target solid tumors. Rapid growth of computational power, new machine learning and artificial intelligence (AI) approaches provide new tools to address this challenge. In this study, we established an AI-assisted physiologically based pharmacokinetic (PBPK) model by integrating an AI-based quantitative structure-activity relationship (QSAR) model with a PBPK model to simulate tumor-targeted delivery efficiency (DE) and biodistribution of various NPs. The AI-based QSAR model was developed using machine learning and deep neural network algorithms that were trained with datasets from a published "Nano-Tumor Database" to predict critical input parameters of the PBPK model. The PBPK model with optimized NP cellular uptake kinetic parameters was used to predict the maximum delivery efficiency (DEmax) and DE at 24 (DE24) and 168 h (DE168) of different NPs in the tumor after intravenous injection and achieved a determination coefficient of R2 = 0.83 [root mean squared error (RMSE) = 3.01] for DE24, R2 = 0.56 (RMSE = 2.27) for DE168, and R2 = 0.82 (RMSE = 3.51) for DEmax. The AI-PBPK model predictions correlated well with available experimentally-measured pharmacokinetic profiles of different NPs in tumors after intravenous injection (R2 ≥ 0.70 for 133 out of 288 datasets). This AI-based PBPK model provides an efficient screening tool to rapidly predict delivery efficiency of a NP based on its physicochemical properties without relying on an animal training dataset.

An Automated Customizable Live Web Crawler for Curation of Comparative Pharmacokinetic Data: An Intelligent Compilation of Research-Based Comprehensive Article Repository.

Data curation has significant research implications irrespective of application areas. As most curated studies rely on databases for data extraction, the availability of data resources is extremely important. Taking a perspective from pharmacology, extracted data contribute to improved drug treatment outcomes and well-being but with some challenges. Considering available pharmacology literature, it is necessary to review articles and other scientific documents carefully. A typical method of accessing articles on journal websites is through long-established searches. In addition to being labor-intensive, this conventional approach often leads to incomplete-content downloads. This paper presents a new methodology with user-friendly models to accept search keywords according to the investigators' research fields for metadata and full-text articles. To accomplish this, scientifically published records on the pharmacokinetics of drugs were extracted from several sources using our navigating tool called the Web Crawler for Pharmacokinetics (WCPK). The results of metadata extraction provided 74,867 publications for four drug classes. Full-text extractions performed with WCPK revealed that the system is highly competent, extracting over 97% of records. This model helps establish keyword-based article repositories, contributing to comprehensive databases for article curation projects. This paper also explains the procedures adopted to build the proposed customizable-live WCPK, from system design and development to deployment phases.

Darobactin B Stabilises a Lateral-Closed Conformation of the BAM Complex in E. coli Cells.

The ß-barrel assembly machinery (BAM complex) is essential for outer membrane protein (OMP) folding in Gram-negative bacteria, and represents a promising antimicrobial target. Several conformational states of BAM have been reported, but all have been obtained under conditions which lack the unique features and complexity of the outer membrane (OM). Here, we use Pulsed Electron-Electron Double Resonance (PELDOR, or DEER) spectroscopy distance measurements to interrogate the conformational ensemble of the BAM complex in E. coli cells. We show that BAM adopts a broad ensemble of conformations in the OM, while in the presence of the antibiotic darobactin B (DAR-B), BAM's conformational equilibrium shifts to a restricted ensemble consistent with the lateral closed state. Our in-cell PELDOR findings are supported by new cryoEM structures of BAM in the presence and absence of DAR-B. This work demonstrates the utility of PELDOR to map conformational changes in BAM within its native cellular environment.

A retrospective study of adverse drug events in anticoagulant administration with relevance to COVID-19.

Initial studies in COVID-19 patients reported lower mortality rates associated with the use of the drug heparin, a widely used anticoagulant. The objective of this analysis was to determine whether there are adverse events associated with the administration of anticoagulants, and specifically how this might apply in patients known to have COVID-19. Data for this study were obtained from the Food and Drug Administration's Adverse Event Reporting System (FAERS) public database and from the NIH's clinical trials website. Proportional Reporting Ratios (PRR) with lower 95% confidence intervals (lower CI) and empirical Bayes geometric mean (EBGM) scores with lower 95% confidence limits were calculated for data from the FAERS database where the adverse events studied mimicked COVID-19 symptoms.

Risk Factors for Serious Psychological Distress among Pregnant Women and Mothers with Children less than One Year of Age: An Evaluation of National Health Interview Survey Data (1997-2016).

OBJECTIVE: This study examined individual factors associated with serious psychological distress (SPD) in mothers. METHODS: The study used National Health Interview Survey data (1997-2016), with analysis being restricted to pregnant women and non-pregnant mothers whose youngest child was 12 months old or younger. The Andersen framework, a reliable tool to study health services, was used to examine the effect of individual predisposing, enabling, and need factors. RESULTS: Of 5,210 women, 13.3% had SPD, as determined by the Kessler-6 scale. Compared to those without SPD, those with SPD were more likely to be 18 to 24 years old (39.0% vs. 31.7%; all P values < .001), never have been married (45.5% vs. 33.3%), not have completed high school (34.4% vs. 21.1%), have a family income below 100% of the federal poverty level (52.5% vs. 32.0%), and have public insurance (51.9% vs. 36.3%). Furthermore, women with SPD had a lower proportion of "excellent" health status (17.5% vs. 32.7%). Multivariable regression found that having any formal education was associated with a lower likelihood of perinatal SPD than was not completing high school. For example, the bachelor's degree odds ratio was 0.48 (95% CI: 0.30, 0.76). A receiver operator curve analysis revealed that individual predisposing factors (e.g. age, marital status, and education), accounted for more explained variation than did enabling or need factors. CONCLUSION: There are high levels of poor maternal mental health. Prevention and clinical services should focus on mothers with less than a high school education and those reporting poor physical health.

Comfort in seeking support from sexual violence prevention education health services among college women.

Objective: Explores racial differences of sexual violence-(SV) health service-(HS) outcomes among college women: (1) seeking support from a confidential-resource-(CR) and (2) reporting SV to the Title IX office. Participants: Data was collected from all ages of women (N = 583) and grade levels from one-large university on the Pacific-coast. Methods: Logistic-regression of HS outcomes were performed using the Fall 2016 American College Health Association-NCHA-II-survey. Results: The following variables increased the likelihood of women seeking support from the two health-services: (#1CR) relationship-status, race, and experiencing sexual-violence. WOC were 7x more likely to seek support if physically-assaulted, and WW were 3.9x more likely to seek support if a graduate student. (#2Title IX) year in school, physical-assault, and receiving prevention-education after the first-year in college. Overall, there were significant differences by race in the variables that influenced WW and WOC's comfort or likelihood to seek support. Conclusion: Colleges need to consider the disproportionate impact of SV on WOC.