Trauma-informed care with women diagnosed with postpartum depression: a conceptual framework.

Postpartum depression (PPD) is a mental health disorder that affects approximately 20% of all new mothers. PPD frequently co-occurs with and is exacerbated by trauma, particularly for women from vulnerable populations. Trauma-informed care (TIC) is a best practice that recognizes the importance of, and takes steps to promote recovery from, trauma while preventing retraumatization. Despite its potential utility, there is limited research published on TIC, including how TIC is operationalized across practice settings. Further, despite the prevalence and negative effects of untreated PPD, to date there have been limited articles published on TIC and PPD. The purpose of this article is to provide a TIC framework for service delivery for women diagnosed with PPD including explicit strategies for how TIC should be structured across roles, settings, and systems. Implications for health practice, policy, and future research are provided.

Integrating the social determinants of health into two interprofessional courses: Findings from a pilot study.

Five colleges and universities in Upstate New York, United States, created the 'Route-90 Collaborative' to support faculty implementing the Institute of Medicine's (IOM) Framework for Educating Health Professionals to Address the Social Determinants of Health. The two courses described herein used a flipped classroom approach in which students from 14 different nations were responsible for facilitating individual classes. This descriptive study used an educational intervention in two interprofessional courses - reproductive health and global health - based on the IOM Framework into two courses. The evaluation used quantitative and open-ended text response data from students. Course evaluations indicated the students found the courses helped them to learn more about health issues and service delivery in various countries, expand their knowledge base on sociocultural and ecological influences on health care, and broaden their perspectives on various health topics so they will be able to provide higher quality healthcare. Although this is the first effort of our Collaborative to implement the Framework, given the student feedback, we believe implementing the Framework in various courses has the potential to enhance healthcare service delivery and reduce the negative impact of social determinants of health.

Advancing Social Work Education for Health Impact.

Social work education plays a critical role in preparing social workers to lead efforts that improve health. Because of the dynamic health care landscape, schools of social work must educate students to facilitate health care system improvements, enhance population health, and reduce medical costs. We reviewed the existing contributions of social work education and provided recommendations for improving the education of social workers in 6 key areas: aging, behavioral health, community health, global health, health reform, and health policy. We argue for systemic improvement in the curriculum at every level of education, including substantive increases in content in health, health care, health care ethics, and evaluating practice outcomes in health settings. Schools of social work can further increase the impact of the profession by enhancing the curricular focus on broad content areas such as prevention, health equity, population and community health, and health advocacy.

Hazards in Motion: Development of Mobile Geofences for Use in Logging Safety.

Logging is one of the most hazardous occupations in the United States. Real-time positioning that uses global navigation satellite system (GNSS) technology paired with radio frequency transmission (GNSS-RF) has the potential to reduce fatal and non-fatal accidents on logging operations through the use of geofences that define safe work areas. Until recently, most geofences have been static boundaries. The aim of this study was to evaluate factors affecting mobile geofence accuracy in order to determine whether virtual safety zones around moving ground workers or equipment are a viable option for improving situational awareness on active timber sales. We evaluated the effects of walking pace, transmission interval, geofence radius, and intersection angle on geofence alert delay using a replicated field experiment. Simulation was then used to validate field results and calculate the proportion of GNSS error bearings resulting in early alerts. The interaction of geofence radius and intersection angle affected safety geofence alert delay in the field experiment. The most inaccurate alerts were negative, representing early warning. The magnitude of this effect was largest at the greatest intersection angles. Simulation analysis supported these field results and also showed that larger GNSS error corresponded to greater variability in alert delay. Increasing intersection angle resulted in a larger proportion of directional GNSS error that triggered incorrect, early warnings. Because the accuracy of geofence alerts varied greatly depending on GNSS error and angle of approach, geofencing for occupational safety is most appropriate for general situational awareness unless real-time correction methods to improve accuracy or higher quality GNSS-RF transponders are used.

Filling out the forms: Health literacy among the uninsured.

This study addresses health literacy among patients at a free clinic in Syracuse, NY. Researchers conducted chart reviews of 600 patients and qualitative interviews with 22 patients and 7 providers. Most clinic patients had a high school, or higher, level education and no difficulty with reading comprehension. Nevertheless, a majority had at least some difficulty with comprehending and following through on health information. For many interviewees, the greatest difficulty was completing the required forms for entitlement programs. The findings demonstrate that low health literacy may occur even among well-educated patients and recommend that health literacy be assessed for all patients.

Community health agency administrators' access to public health data for program planning, evaluation, and grant preparation.

The Affordable Care Act mandates that public health data be made available for community agency use. Having access to such data allows community agencies to tailor interventions, evaluations, and funding requests more effectively. This study, jointly undertaken by Syracuse University faculty and students with the New York State Perinatal Association, sought to understand community agencies' access to requests for governmental data, as well as to identify areas for improving data access. Results from this survey of administrators from 43 agencies in New York State found that only one-half of their requests for data were successful. Difficulties in obtaining access to needed data included fiscal and staffing constraints of the state-level agencies that house the data, as well as possible overinterpretation of confidentiality policies. In addition, some of community agency respondents reported that their staff lacked skills in data analysis and would benefit from training in epidemiology and quantitative evaluation.

Accuracy of WAAS-Enabled GPS-RF Warning Signals When Crossing a Terrestrial Geofence.

Geofences are virtual boundaries based on geographic coordinates. When combined with global position system (GPS), or more generally global navigation satellite system (GNSS) transmitters, geofences provide a powerful tool for monitoring the location and movements of objects of interest through proximity alarms. However, the accuracy of geofence alarms in GNSS-radio frequency (GNSS-RF) transmitter receiver systems has not been tested. To achieve these goals, a cart with a GNSS-RF locator was run on a straight path in a balanced factorial experiment with three levels of cart speed, three angles of geofence intersection, three receiver distances from the track, and three replicates. Locator speed, receiver distance and geofence intersection angle all affected geofence alarm accuracy in an analysis of variance (p = 0.013, p = 2.58 × 10(-8), and p = 0.0006, respectively), as did all treatment interactions (p < 0.0001). Slower locator speed, acute geofence intersection angle, and closest receiver distance were associated with reduced accuracy of geofence alerts.

Addressing access barriers to services for mothers at risk for perinatal mood disorders: A social work perspective.

This article identifies variables at the micro/individual, mezzo/partner/spouse and family, and macro/health care-system levels that inhibit mothers at risk for perinatal mood disorders from accessing health and mental health care services. Specific recommendations are made for conducting thorough biopsychosocial assessments that address the mothers' micro-, mezzo-, and macro-level contexts. Finally, the authors provide suggestions for how to intervene at the various levels to remove access barriers for mothers living with perinatal mood disorders as well as their families.

Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety.

Real-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little formal evaluation of the network connectivity of these devices. The objectives of this study were to 1) characterize the connectivity of mesh networks in variable forest and topographic conditions; 2) evaluate the abilities of lidar and satellite remote sensing data to predict connectivity; and 3) assess the relative importance of the predictive metrics. A large field experiment was conducted to test the connectivity of a network of one mobile and five stationary goTenna Pro mesh radios on 24 Public Land Survey System sections approximately 260 ha in area in northern Idaho. Dirichlet regression was used to predict connectivity using 1) both lidar- and satellite-derived metrics (LIDSAT); 2) lidar-derived metrics only (LID); and 3) satellite-derived metrics only (SAT). On average the full network was connected only 32.6% of the time (range: 0% to 90.5%) and the mobile goTenna was disconnected from all other devices 18.2% of the time (range: 0% to 44.5%). RMSE for the six connectivity levels ranged from 0.101 to 0.314 for the LIDSAT model, from 0.103 to 0.310 for the LID model, and from 0.121 to 0.313 for the SAT model. Vegetation-related metrics affected connectivity more than topography. Developed models may be used to predict the connectivity of real-time mesh networks over large spatial extents using remote sensing data in order to forecast how well similar networks are expected to perform for wildland firefighting, forestry, and public safety applications. However, safety professionals should be aware of the impacts of vegetation on connectivity.

A novel smartphone-based activity recognition modeling method for tracked equipment in forest operations.

Activity recognition modelling using smartphone Inertial Measurement Units (IMUs) is an underutilized resource defining and assessing work efficiency for a wide range of natural resource management tasks. This study focused on the initial development and validation of a smartphone-based activity recognition system for excavator-based mastication equipment working in Ponderosa pine (Pinus ponderosa) plantations in North Idaho, USA. During mastication treatments, sensor data from smartphone gyroscopes, accelerometers, and sound pressure meters (decibel meters) were collected at three sampling frequencies (10, 20, and 50 hertz (Hz)). These data were then separated into 9 time domain features using 4 sliding window widths (1, 5, 7.5 and 10 seconds) and two levels of window overlap (50% and 90%). Random forest machine learning algorithms were trained and evaluated for 40 combinations of model parameters to determine the best combination of parameters. 5 work elements (masticate, clear, move, travel, and delay) were classified with the performance metrics for individual elements of the best model (50 Hz, 10 second window, 90% window overlap) falling within the following ranges: area under the curve (AUC) (95.0% - 99.9%); sensitivity (74.9% - 95.6%); specificity (90.8% - 99.9%); precision (81.1% - 98.3%); F1-score (81.9% - 96.9%); balanced accuracy (87.4% - 97.7%). Smartphone sensors effectively characterized individual work elements of mechanical fuel treatments. This study is the first example of developing a smartphone-based activity recognition model for ground-based forest equipment. The continued development and dissemination of smartphone-based activity recognition models may assist land managers and operators with ubiquitous, manufacturer-independent systems for continuous and automated time study and production analysis for mechanized forest operations.

Use of electronic technologies to promote community and personal health for individuals unconnected to health care systems.

Ensuring health care services for populations outside the mainstream health care system is challenging for all providers. But developing the health care infrastructure to better serve such unconnected individuals is critical to their health care status, to third-party payers, to overall cost savings in public health, and to reducing health disparities. Our increasingly sophisticated electronic technologies offer promising ways to more effectively engage this difficult to reach group and increase its access to health care resources. This process requires developing not only newer technologies but also collaboration between community leaders and health care providers to bring unconnected individuals into formal health care systems. We present three strategies to reach vulnerable groups, outline benefits and challenges, and provide examples of successful programs.

Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations.

Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work activities in greater detail than traditional time study analysis. The objective of this study was to evaluate whether smartwatch-based activity recognition models could quantify the activities of rigging crew workers setting and disconnecting log chokers on cable logging operations. Four productive cycle elements (travel to log, set choker, travel away, clear) were timed for choker setters and four productive cycle elements (travel to log, unhook, travel away, clear) were timed for chasers working at five logging sites in North Idaho. Each worker wore a smartwatch that recorded accelerometer data at 25 Hz. Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window sizes (1 to 15 s) and five window overlap levels (0%, 25%, 50%, 75%, and 90%). Models were compared using multiclass area under the Receiver Operating Characteristic (ROC) curve, or AUC. The best choker setter model was created using a 3-s window with 90% overlap and had sensitivity values ranging from 76.95% to 83.59% and precision values ranging from 41.42% to 97.08%. The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions.

Selecting a Cell Engineering Methodology During Cell Therapy Product Development.

When considering the development pathway for a genetically modified cell therapy product, it is critically important that the product is engineered consistent with its intended human use. For scientists looking to develop and commercialize a new technology, the decision to select a genetic modification method depends on several practical considerations. Whichever path is chosen, the developer must understand the key risks and potential mitigations of the cell engineering approach. The developer should also understand the clinical implications: permanent/memory establishment versus transient expression, and clinical manufacturing considerations when dealing with transplantation of genetically engineered cells. This review covers important topics for mapping out a strategy for developers of new cell-based therapeutics. Biological, technological, manufacturing, and clinical considerations are all presented to map out development lanes for the initiation and risk management of new gene-based cell therapeutic products for human use.

Active ground optical remote sensing for improved monitoring of seedling stress in nurseries.

Active ground optical remote sensing (AGORS) devices mounted on overhead irrigation booms could help to improve seedling quality by autonomously monitoring seedling stress. In contrast to traditionally used passive optical sensors, AGORS devices operate independently of ambient light conditions and do not require spectral reference readings. Besides measuring red (590-670 nm) and near-infrared (>760 nm) reflectance AGORS devices have recently become available that also measure red-edge (730 nm) reflectance. We tested the hypothesis that the additional availability of red-edge reflectance information would improve AGORS of plant stress induced chlorophyll breakdown in Scots pine (Pinus sylvestris). Our results showed that the availability of red-edge reflectance information improved AGORS estimates of stress induced variation in chlorophyll concentration (r2>0.73, RMSE<1.69) when compared to those without (r2=0.57, RMSE=2.11).

Potent induction of B- and T-cell immunity against human carcinoembryonic antigen-expressing tumors in human carcinoembryonic antigen transgenic mice mediated by direct lentivector injection.

The applicability of immunotherapy would be dramatically broadened to a greater number of recipients if direct "off-the-shelf" products could be engineered to engender functionally potent immune responses against true "self"-tumor antigens. This would obviate the need for ex vivo culture of dendritic cells or T cells on a patient-by-patient basis, for example. The carcinoembryonic antigen (CEA) is a glycoprotein expressed in normal gut epithelium that is up-regulated in the majority of colon cancers, non-small cell lung cancers, and half of all breast cancers. Such properties make CEA an excellent and important target for cancer immunotherapy. In this study, we show stabilization of 14-day established s.c. mGC4CEA tumors in human CEA (huCEA) transgenic mice following two direct low-dose injections of 0.15x10(6) transducing units of a lentiviral vector (LV) that directs expression of huCEA (LV-huCEA). This stabilization result was reproducible and detailed analyses including antibody assays, multiplex cytokine analyses on unstimulated splenocytes, lymph node cell characterizations, tetramer staining, and immunofluorescence staining of tumor sections showed that this outcome correlated with both a cellular and humoral immune response. Similar tumor outcomes were not seen when mice were vaccinated with a control LV that engineered expression of enGFP only. The long-term potency of this vaccination strategy was also studied and revealed the requirement for maintenance of tumor antigen-specific immunity for efficient tumor control. These data support the use of direct injections of low doses of LV-huCEA for enhancement of tumor immunotherapy directed against CEA.

Risk Factors for Failure in Hammertoe Surgery.

BACKGROUND: Hammertoe correction is perhaps the most common elective surgery performed in the foot, yet rates of symptomatic recurrence and revision surgery can be high. In this study, we aimed to identify patient and provider risk factors associated with failure after hammertoe surgery. METHODS: Consecutive patients with a minimum of 6 months' follow-up undergoing hammertoe surgery within a single, urban foot and ankle practice between January 1, 2011, and December 31, 2013, served as the basis of this retrospective cohort study. Cox regression analysis was used to identify important predictor variables obtained through chart and radiographic review. One hundred fifty-two patients (311 toes) with a mean age of 60.8 ± 11.2 years and mean follow-up of 29.5 ± 21.2 months were included. RESULTS: Statistically significant predictors of failure were having a larger preoperative transverse plane deviation of the digit (hazard ratio [HR], 1.03 for each degree; P < .001; 95% CI, 1.02, 1.04), operating on the second toe (vs third or fourth) (HR, 2.23; P = .003; 95% CI, 1.31, 3.81), use of a phalangeal osteotomy to reduce the proximal interphalangeal (PIP) joint (HR, 2.77; P = .005; 95% CI, 1.36, 5.64), and using less common/conventional operative techniques to reduce the PIP joint (HR, 2.62; P = .03; 95% CI, 1.09, 6.26). Concomitant performance of first ray surgery reduced hammertoe recurrence by 50% (HR, 0.51; P = .01; 95% CI, 0.30, 0.87). CONCLUSION: We identified risk factors that may provide guidance for surgeons during preoperative hammertoe surgery consultations. This information may better equip patients with appropriate postoperative expectations when contemplating surgery. LEVEL OF EVIDENCE: Level III, retrospective case series.

Public Health Social Work as a Unifying Framework for Social Work's Grand Challenges.

Introduced in 2013 by the American Academy of Social Work and Social Welfare, the Grand Challenges for Social Work (GCSW) implicitly embrace a public health perspective. However, the lack of a specific overarching conceptual framework creates a challenge for moving the GCSW from concept to practice. In this article, authors propose that public health social work (PHSW) provides a unifying framework for moving the GCSW from concept to practice. Authors undertook a review of the literature, including a review of published literature and all Web sites and other Web materials focused on the GCSW. Three GCSW were selected to illustrate the utility of PHSW and the social work health impact model (SWHIM): (1) stopping family violence, (2) eradicating social isolation, and (3) achieving opportunity and justice. Using a wide-lens PHSW approach, the illustrations focus on actions that can influence populations through strengthened environments and multilevel interventions. The public health field reflects the rigorous science behind the theoretical models, community-based approaches, and attention to effects of social determinants of health at the population level. Because health and inequalities are the focus of many of the GCSW, incorporating both public and population health, together with the SWHIM, can help provide structure to achieve collective goals.

Positioning Methods and the Use of Location and Activity Data in Forests.

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms location-based services (LBS), geofences, wearable technology, activity recognition, mesh networking, the Internet of Things (IoT), and big data. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.

Evaluating the effectiveness of air quality interventions.

Evaluating the extent to which air quality regulations improve public health--sometimes referred to as accountability--is part of an emerging effort to assess the effectiveness of environmental regulatory policies. Air quality has improved substantially in the United States and Western Europe in recent decades, with far less visible pollution and decreasing concentrations of several major pollutants. In large part, these gains were achieved through increasingly stringent air quality regulations. The costs associated with compliance and, importantly, the need to ensure that the regulations are achieving the intended public health benefits underscore the importance of accountability research. To date, accountability research has emphasized measuring the effects of actions already taken to improve air quality. Such research may also contribute to estimating the burden of disease that might be avoided in the future if certain actions are taken. The Health Effects Institute (HEI) currently funds eight ongoing studies on accountability, which cover near-term interventions to improve air quality including (1) a ban on the sale of coal, (2) replacing old wood stoves with cleaner ones, (3) decreasing sulfur content in fuel, (4) measures to reduce traffic, and (5) longer term, wide-ranging actions or events (such as complex changes associated with the reunification of Germany). HEI is also funding the development of methods and research to assess regulations that are implemented incrementally over extended periods of time, such as Title IV of the 1990 Clean Air Act Amendments, which reduces sulfur dioxide emissions from power plants in the eastern United States.