The Mission Continues: Engaging Post-9/11 Disabled Military Veterans in Civic Service

The Mission Continues: Engaging Post-9/11 Disabled Military Veterans in Civic Servic

Impacts of the Mission Continues Fellowship Program on Post-9/11 Disabled Military Veterans, Their Families, and Their Communities

Impacts of the Mission Continues Fellowship Program on Post-9/11 Disabled Military Veterans, Their Families, and Their Communitie

Saying It in Graphics: From Intentions to Visualizations

We propose a methodology for automatically realizing communicative goals in graphics. It features a task model that mediates the communicative intent and the selection of graphical techniques. The methodology supports the following functions: isolating assertions presentable in graphics; mapping such assertions into tasks for the potential reader, and selecting graphical techniques that support those tasks. We illustrate the methodology by redesigning a textual argument into a multimedia one with the same rhetorical and content structures but employing graphics to achieve some of the intentions. 1

An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene Interactions on risk of myocardial infarction: The importance of model validation

BACKGROUND: To examine interactions among the angiotensin converting enzyme (ACE) insertion/deletion, plasminogen activator inhibitor-1 (PAI-1) 4G/5G, and tissue plasminogen activator (t-PA) insertion/deletion gene polymorphisms on risk of myocardial infarction using data from 343 matched case-control pairs from the Physicians Health Study. We examined the data using both conditional logistic regression and the multifactor dimensionality reduction (MDR) method. One advantage of the MDR method is that it provides an internal prediction error for validation. We summarize our use of this internal prediction error for model validation. RESULTS: The overall results for the two methods were consistent, with both suggesting an interaction between the ACE I/D and PAI-1 4G/5G polymorphisms. However, using ten-fold cross validation, the 46% prediction error for the final MDR model was not significantly lower than that expected by chance. CONCLUSIONS: The significant interaction initially observed does not validate and may represent a type I error. As data-driven analytic methods continue to be developed and used to examine complex genetic interactions, it will become increasingly important to stress model validation in order to ensure that significant effects represent true relationships rather than chance findings

The PRogram In Support of Moms (PRISM): study protocol for a cluster randomized controlled trial of two active interventions addressing perinatal depression in obstetric settings

BACKGROUND: Perinatal depression, the most common pregnancy complication, is associated with negative maternal-offspring outcomes. Despite existence of effective treatments, it is under-recognized and under-treated. Professional organizations recommend universal screening, yet multi-level barriers exist to ensuring effective diagnosis, treatment, and follow-up. Integrating mental health and obstetric care holds significant promise for addressing perinatal depression. The overall study goal is to compare the effectiveness of two active interventions: (1) the Massachusetts Child Psychiatry Access Program (MCPAP) for Moms, a state-wide, population-based program, and (2) the PRogram In Support of Moms (PRISM) which includes MCPAP for Moms plus a proactive, multifaceted, practice-level intervention with intensive implementation support. METHODS: This study is conducted in two phases: (1) a run-in phase which has been completed and involved practice and patient participant recruitment to demonstrate feasibility for the second phase, and (2) a cluster randomized controlled trial (RCT), which is ongoing, and will compare two active interventions 1:1 with ten Ob/Gyn practices as the unit of randomization. In phase 1, rates of depressive symptoms and other demographic and clinical features among patients were examined to inform practice randomization. Patient participants to be recruited in phase 2 will be followed longitudinally until 13 months postpartum; they will have 3-5 total study visits depending on whether their initial recruitment and interview was at 4-24 or 32-40 weeks gestation, or 1-3 months postpartum. Sampling throughout pregnancy and postpartum will ensure participants with different depressive symptom onset times. Differences in depression symptomatology and treatment participation will be compared between patient participants by intervention arm. DISCUSSION: This manuscript describes the full two-phase study protocol. The study design is innovative because it combines effectiveness with implementation research designs and integrates critical components of participatory action research. Our approach assesses the feasibility, acceptance, efficacy, and sustainability of integrating a stepped-care approach to perinatal depression care into ambulatory obstetric settings; an approach that is flexible and can be tailored and adapted to fit unique workflows of real-world practices. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02760004, registered prospectively on May 3, 2016

Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings

BACKGROUND: To describe quantitative data quality monitoring and performance metrics adopted by the Global Network´s (GN) Maternal Newborn Health Registry (MNHR), a maternal and perinatal population-based registry (MPPBR) based in low and middle income countries (LMICs). METHODS: Ongoing prospective, population-based data on all pregnancy outcomes within defined geographical locations participating in the GN have been collected since 2008. Data quality metrics were defined and are implemented at the cluster, site and the central level to ensure data quality. Quantitative performance metrics are described for data collected between 2010 and 2013. RESULTS: Delivery outcome rates over 95% illustrate that all sites are successful in following patients from pregnancy through delivery. Examples of specific performance metric reports illustrate how both the metrics and reporting process are used to identify cluster-level and site-level quality issues and illustrate how those metrics track over time. Other summary reports (e.g. the increasing proportion of measured birth weight compared to estimated and missing birth weight) illustrate how a site has improved quality over time. CONCLUSION: High quality MPPBRs such as the MNHR provide key information on pregnancy outcomes to local and international health officials where civil registration systems are lacking. The MNHR has measures in place to monitor data collection procedures and improve the quality of data collected. Sites have increasingly achieved acceptable values of performance metrics over time, indicating improvements in data quality, but the quality control program must continue to evolve to optimize the use of the MNHR to assess the impact of community interventions in research protocols in pregnancy and perinatal health.Fil: Goudar, Shivaprasad S.. KLE University. Jawaharlal Nehru Medical College; IndiaFil: Stolka, Kristen B.. Research Triangle Institute International; Estados UnidosFil: Koso Thomas, Marion. Eunice Kennedy Shriver National Institute of Child Health and Human Development; Estados UnidosFil: Honnungar, Narayan V.. KLE University. Jawaharlal Nehru Medical College; IndiaFil: Mastiholi, Shivanand C.. KLE University. Jawaharlal Nehru Medical College; IndiaFil: Ramadurg, Umesh Y.. S. Nijalingappa Medical College; IndiaFil: Dhaded, Sangappa M.. KLE University. Jawaharlal Nehru Medical College; IndiaFil: Pasha, Omrana. Aga Khan University; PakistánFil: Patel, Archana. Indira Gandhi Government Medical College and Lata Medical Research Foundation; IndiaFil: Esamai, Fabian. University School of Medicine; KeniaFil: Chomba, Elwyn. University of Zambia; ZambiaFil: Garces, Ana. Universidad de San Carlos; GuatemalaFil: Althabe, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Carlo, Waldemar A.. University of Alabama at Birmingahm; Estados UnidosFil: Goldenberg, Robert L.. Columbia University; Estados UnidosFil: Hibberd, Patricia L.. Massachusetts General Hospital for Children; Estados UnidosFil: Liechty, Edward A.. Indiana University; Estados UnidosFil: Krebs, Nancy F.. University of Colorado School of Medicine; Estados UnidosFil: Hambidge, Michael K.. University of Colorado School of Medicine; Estados UnidosFil: Moore, Janet L.. Research Triangle Institute International; Estados UnidosFil: Wallace, Dennis D.. Research Triangle Institute International; Estados UnidosFil: Derman, Richard J. Christiana Care Health Services; Estados UnidosFil: Bhalachandra, Kodkany S.. KLE University. Jawaharlal Nehru Medical College; IndiaFil: Bose, Carl L.. University of North Carolina; Estados Unido

The relationship between processing speed and verbal and non-verbal new learning and memory in progressive multiple sclerosis

Objective: Processing speed (PS) deficits are the most common cognitive deficits in multiple sclerosis (MS), followed by learning and memory deficits, and are often an early cognitive problem. It has been argued that impaired PS is a primary consequence of MS, which in turn decreases learning. The current analysis examined the association between PS and learning in a large cohort of individuals with progressive MS. Methods: Baseline data from a randomized clinical trial on rehabilitation taking place at 11 centers across North America and Europe were analyzed. Participants included 275 individuals with clinically definite progressive MS (primary, secondary) consented into the trial. Results: Symbol Digit Modalities Test (SDMT) significantly correlated with California Verbal Learning Test-II (CVLT-II) (r = 0.21, p = 0.0003) and Brief Visuospatial Memory Test–Revised (BVMT-R) (r = 0.516, p < 0.0001). Receiver operating characteristic (ROC) analysis of the SDMT z score to distinguish between impaired and non-impaired CVLT-II performance demonstrated an area under the curve (AUC) of 0.61 (95% confidence interval (CI): 0.55–0.68) and a threshold of −1.62. ROC analysis between SDMT and BVMT-R resulted in an AUC of 0.77 (95% CI: 0.71–0.83) and threshold of −1.75 for the SDMT z score to predict impaired BVMT-R. Conclusion: Results indicate little ability beyond chance to predict CVLT-II from SDMT (61%), albeit statistically significant. In contrast, there was a 77% chance that the model could distinguish between impaired and non-impaired BVMT-R. Several potential explanations are discussed