Talking about health: Community ambassadors as a health promotion strategy to increase breast and bowel cancer screening in regional Australia.

ISSUE ADDRESSED: In Australia, cancer is the leading contributor to disease burden, with breast and bowel cancer among the most commonly diagnosed cancers. Despite the presence of community-wide health promotion activities and screening programs, people living in regional and rural locations experience a number of factors that reduce breast and bowel cancer survival outcomes. This study investigates the ways that high-risk community members in a regional area of Australia interact with health messaging about breast and bowel cancer screening. METHODS: A qualitative research method was used to conduct 31 in-depth one-on-one interviews with community members, leaders and essential service providers. A thematic approach was used to analyse data. RESULTS: Findings provided insight to the ways that health is spoken about within the community, what prompts discussion of health, trustworthy sources of health information and the significance of peer-to-peer communication. CONCLUSIONS: Existing community communication lines can be used to assist in delivering public health messages among high-risk and vulnerable population groups. Utilising community ambassadors is identified as a health promotion method for hard-to-reach populations. SO WHAT?: Conversations about health and screening amongst community members, and led by community members, play a key role in shaping engagement with cancer screening programs and represent an important site for health promotion activities. These findings have implications for future public health initiatives amongst high-risk groups in regional locations.

Rubbing behavior of European brown bears: factors affecting rub tree selectivity and density.

Scent-mediated communication is considered the principal communication channel in many mammal species. Compared with visual and vocal communication, odors persist for a longer time, enabling individuals to interact without being in the same place at the same time. The brown bear (Ursus arctos), like other mammals, carries out chemical communication, for example, by means of scents deposited on marking (or rub) trees. In this study, we assessed rub tree selectivity of the brown bear in the predominantly deciduous forests of the Cantabrian Mountains (NW Spain). We first compared the characteristics of 101 brown bear rub trees with 263 control trees. We then analyzed the potential factors affecting the density of rub trees along 35 survey routes along footpaths. We hypothesized that: (1) bears would select particular trees, or tree species, with characteristics that make them more conspicuous; and (2) that bears would select trees located in areas with the highest presence of conspecifics, depending on the population density or the position of the trees within the species' range. We used linear models and generalized additive models to test these hypotheses. Our results showed that brown bears generally selected more conspicuous trees with a preference for birches (Betula spp.). This choice may facilitate the marking and/or detection of chemical signals and, therefore, the effectiveness of intraspecific communication. Conversely, the abundance of rub trees along footpaths did not seem to depend on the density of bear observations or their relative position within the population center or its border. Our results suggest that Cantabrian brown bears select trees based on their individual characteristics and their location, with no influence of characteristics of the bear population itself. Our findings can be used to locate target trees that could help in population monitoring.

La comunicación olfativa se considera el principal canal de comunicación en muchas especies de mamíferos. En comparación con la comunicación visual y la vocal, los olores persisten durante más tiempo, lo que permite a los individuos interactuar sin estar en el mismo lugar al mismo tiempo. El oso pardo (Ursus arctos), al igual que otros mamíferos, emplea la comunicación química, por ejemplo, por medio de olores depositados en árboles a través del marcaje o rascado. En este estudio, evaluamos la selección de árboles de marcaje por el oso pardo en los bosques predominantemente caducifolios de la Cordillera Cantábrica (noroeste de España). En primer lugar, comparamos las características individuales de 101 árboles de marcaje de oso pardo con 263 árboles control. Después, analizamos los factores potenciales que afectan la densidad de árboles de marcaje en 35 trayectos de prospección a lo largo de caminos y pistas forestales. Planteamos las hipótesis que: (1) los osos seleccionan árboles particulares, o especies de árboles, con características que los hacen más conspicuos; y (2) que los osos seleccionan árboles ubicados en áreas con mayor presencia de conespecíficos, dependiendo de la densidad de población osera o de la posición de los árboles dentro del rango de distribución de la especie. Usamos modelos lineales y modelos aditivos generalizados para probar estas hipótesis. Nuestros resultados mostraron que los osos pardos generalmente seleccionaron árboles más conspicuos, con preferencia por los abedules (Betula spp.). Esta elección puede facilitar el marcaje y/o detección de señales químicas y, por tanto, la eficacia de la comunicación intraespecífica. Por el contrario, la abundancia de marcaje a lo largo de los trayectos no parece depender de la densidad de las observaciones de osos o de su posición relativa con respecto al centro o los límites del rango de la población. Nuestros resultados sugieren que los osos pardos cantábricos seleccionan árboles en función de sus características individuales y de su ubicación, sin que influyan en ello las características de la población osera. Nuestros hallazgos pueden servir para localizar árboles específicos que podrían ayudar al monitoreo de la población.

Reconceptualising Person-Centered Service Models as Social Ecology Networks in Supporting Integrated Care.

Efforts to address problems such as mental health, poverty, social exclusion, and chronic disease have often proven resistant to traditional policies or interventions. In this paper, we take up the challenge and present a pioneering new method of analysis in drawing on theoretical and methodological extensions of two prominent approaches, namely, social network analysis and developmental social ecology. Considered in combination, these two seemingly disparate approaches frame a powerful new way of thinking about person-centred care, as well as offer a methodologically more rigorous set of analytical tools. The conceptual model developed from this combination offers to bridge the apparent disconnect between service integration levels and patient needs in such a way as to direct optimal effort to interventions at the individual level and to provide a new innovative approach to the delivery of integrated care.

A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage.

OBJECTIVES: Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization. Earlier identification of need for hospital-level care could triage patients more efficiently to high- or low-resource ED tracks. Existing tools to predict disposition for pediatric asthma use only clinical data, perform best several hours into the ED stay, and are static or score-based. Machine learning offers a population-specific, dynamic option that allows real-time integration of available nonclinical data at triage. Our objective was to compare the performance of four common machine learning approaches, incorporating clinical data available at the time of triage with information about weather, neighborhood characteristics, and community viral load for early prediction of the need for hospital-level care in pediatric asthma. METHODS: Retrospective analysis of patients ages 2 to 18 years seen at two urban pediatric EDs with asthma exacerbation over 4 years. Asthma exacerbation was defined as receiving both albuterol and systemic corticosteroids. We included patient features, measures of illness severity available in triage, weather features, and Centers for Disease Control and Prevention influenza patterns. We tested four models: decision trees, LASSO logistic regression, random forests, and gradient boosting machines. For each model, 80% of the data set was used for training and 20% was used to validate the models. The area under the receiver operating characteristic (AUC) curve was calculated for each model. RESULTS: There were 29,392 patients included in the analyses: mean (±SD) age of 7.0 (±4.2) years, 42% female, 77% non-Hispanic black, and 76% public insurance. The AUCs for each model were: decision tree 0.72 (95% confidence interval [CI] = 0.66-0.77), logistic regression 0.83 (95% CI = 0.82-0.83), random forests 0.82 (95% CI = 0.81-0.83), and gradient boosting machines 0.84 (95% CI = 0.83-0.85). In the lowest decile of risk, only 3% of patients required hospitalization; in the highest decile this rate was 100%. After patient vital signs and acuity, age and weight, followed by socioeconomic status (SES) and weather-related features, were the most important for predicting hospitalization. CONCLUSIONS: Three of the four machine learning models performed well with decision trees preforming the worst. The gradient boosting machines model demonstrated a slight advantage over other approaches at predicting need for hospital-level care at the time of triage in pediatric patients presenting with asthma exacerbation. The addition of weight, SES, and weather data improved the performance of this model.

Palliation or Prolongation? The Impact of a Peer-Review Intervention on Shortening Radiotherapy Schedules for Bone Metastases.

PURPOSE: Shorter fractionation radiation regimens for palliation of bone metastases result in lower financial and social costs for patients and their caregivers and have similar efficacy as longer fractionation schedules, although practice patterns in the United States show poor adoption. We investigated whether prospective peer review can increase use of shorter fractionation schedules. METHODS: In June 2016, our practice mandated peer review of total dose and fractionation for all patients receiving palliative treatment during our weekly chart rounds. We used descriptive statistics and Fisher's exact test to compare lengths of treatment of uncomplicated bone metastases before and after implementation of the peer review process. RESULTS: Between July 2015 and December 2016, a total of 242 palliative treatment courses were delivered, including 105 courses before the peer review intervention and 137 after the intervention. We observed greater adoption of shorter fractionation regimens after the intervention. The use of 8 Gy in one fraction increased from 2.8% to 13.9% of cases postadoption. Likewise, the use of 20 Gy in five fractions increased from 25.7% to 32.8%. The use of 30 Gy in 10 fractions decreased from 55.2% to 47.4% ( P = .002), and the use of ≥ 11 fractions decreased from 16.2% before the intervention to 5.8% after ( P = .006). CONCLUSION: Prospective peer review of palliative regimens for bone metastases can lead to greater adoption of shorter palliative fractionation schedules in daily practice, in accordance with national guidelines. This simple intervention may therefore benefit patients and their caregivers as well as provide value to the health care system.

Provider variability in intensity modulated radiation therapy utilization among Medicare beneficiaries in the United States.

BACKGROUND: In this study, we sought to examine the variation in intensity modulated radiation therapy (IMRT) use among radiation oncology providers. METHODS AND MATERIALS: The Medicare Physician and Other Supplier Public Use File was queried for radiation oncologists practicing during 2014. Healthcare Common Procedural Coding System code 77301 was designated as IMRT planning with metrics including number of total IMRT plans, rate of IMRT utilization, and number of IMRT plans per distinct beneficiary. RESULTS: Of 2759 radiation oncologists, the median number of total IMRT plans was 26 (mean, 33.4; standard deviation, 26.2; range, 11-321) with a median IMRT utilization rate of 36% (mean, 43%; standard deviation, 25%; range, 4% to 100%) and a median number of IMRT plans per beneficiary of 1.02 (mean, 1.07; range, 1.00-3.73). On multivariable analysis, increased IMRT utilization was associated with male sex, academic practice, technical fee billing, freestanding practice, practice in a county with 21 or more radiation oncologists, and practice in the southern United States (P < .05). The top 1% of users (28 providers) billed a mean 181 IMRT plans with an IMRT utilization rate of 66% and 1.52 IMRT plans per beneficiary. Of these 28 providers, 24 had billed technical fees, 25 practiced in freestanding clinics, and 20 practiced in the South. CONCLUSIONS: Technical fee billing, freestanding practice, male sex, and location in the South were associated with increased IMRT use. A small group of outliers shared several common demographic and practice-based characteristics.

Classification of voluntary coughs applied to the screening of respiratory disease.

Pulmonary and respiratory diseases (e.g. asthma, COPD, allergies, pneumonia, tuberculosis, etc.) represent a large proportion of the global disease burden, mortality, and disability. In this context of creating automated diagnostic tools, we explore how the analysis of voluntary cough sounds may be used to screen for pulmonary disease. As a clinical study, voluntary coughs were recorded using a custom mobile phone stethoscope from 54 patients, of which 7 had COPD, 15 had asthma, 11 had allergic rhinitis, 17 had both asthma and allergic rhinitis, and four had both COPD and allergic rhinitis. Data were also collected from 33 healthy subjects. These patients also received full auscultation at 11 sites, given a clinical questionnaire, and underwent full pulmonary function testing (spirometer, body plethysmograph, DLCO) which culminated in a diagnosis provided by an experienced pulmonologist. From machine learning analysis of these data, we show that it is possible to achieve good classification of cough sounds in terms of Wet vs Dry, yielding an ROC curve with AUC of 0.94, and show that voluntary coughs can serve as an effective test for determining Healthy vs Unhealthy (sensitivity=35.7% specificity=100%). We also show that the use of cough sounds can enhance the performance of other diagnostic tools such as a patient questionnaire and peak flow meter; however voluntary coughs alone provide relatively little value in determining specific disease diagnosis.

Making Sense of a Negative Clinical Trial Result: A Bayesian Analysis of a Clinical Trial of Lorazepam and Diazepam for Pediatric Status Epilepticus.

STUDY OBJECTIVE: We demonstrate the application of a Bayesian approach to a recent negative clinical trial result. A Bayesian analysis of such a trial can provide a more useful interpretation of results and can incorporate previous evidence. METHODS: This was a secondary analysis of the efficacy and safety results of the Pediatric Seizure Study, a randomized clinical trial of lorazepam versus diazepam for pediatric status epilepticus. We included the published results from the only prospective pediatric study of status in a Bayesian hierarchic model, and we performed sensitivity analyses on the amount of pooling between studies. We evaluated 3 summary analyses for the results: superiority, noninferiority (margin <-10%), and practical equivalence (within ±10%). RESULTS: Consistent with the original study's classic analysis of study results, we did not demonstrate superiority of lorazepam over diazepam. There is a 95% probability that the true efficacy of lorazepam is in the range of 66% to 80%. For both the efficacy and safety outcomes, there was greater than 95% probability that lorazepam is noninferior to diazepam, and there was greater than 90% probability that the 2 medications are practically equivalent. The results were largely driven by the current study because of the sample sizes of our study (n=273) and the previous pediatric study (n=61). CONCLUSION: Because Bayesian analysis estimates the probability of one or more hypotheses, such an approach can provide more useful information about the meaning of the results of a negative trial outcome. In the case of pediatric status epilepticus, it is highly likely that lorazepam is noninferior and practically equivalent to diazepam.

Can videoconferencing affect older people's engagement and perception of their social support in long-term conditions management: a social network analysis from the Telehealth Literacy Project.

Social support is a key component in managing long-term conditions. As people age in their homes, there is a greater risk of social isolation, which can be ameliorated by informal support networks. This study examined the relationship between changes in social support networks for older people living in a regional area following weekly videoconference groups delivered to the home. Between February and June 2014, we delivered 44 weekly group meetings via videoconference to participants in a regional town in Australia. The meetings provided participants with education and an opportunity to discuss health issues and connect with others in similar circumstances. An uncontrolled, pre-post-test methodology was employed. A social network tool was completed by 45 (87%) participants either pre- or post-intervention, of which 24 (46%) participants completed the tool pre- and post-intervention. In addition, 14 semi-structured interviews and 4 focus groups were conducted. Following the intervention, participants identified increased membership of their social networks, although they did not identify individuals from the weekly videoconference groups. The most important social support networks remained the same pre- and post-intervention namely, health professionals, close family and partners. However, post-intervention participants identified friends and wider family as more important to managing their chronic condition compared to pre-intervention. Participants derived social support, in particular, companionship, emotional and informational support as well as feeling more engaged with life, from the weekly videoconference meetings. Videoconference education groups delivered into the home can provide social support and enhance self-management for older people with chronic conditions. They provide the opportunity to develop a virtual social support network containing new and diverse social connections.

A mobile platform for automated screening of asthma and chronic obstructive pulmonary disease.

Chronic Obstructive Pulmonary Disease (COPD) and asthma each represent a large proportion of the global disease burden; COPD is the third leading cause of death worldwide and asthma is one of the most prevalent chronic diseases, afflicting over 300 million people. Much of this burden is concentrated in the developing world, where patients lack access to physicians trained in the diagnosis of pulmonary disease. As a result, these patients experience high rates of underdiagnosis and misdiagnosis. To address this need, we present a mobile platform capable of screening for Asthma and COPD. Our solution is based on a mobile smart phone and consists of an electronic stethoscope, a peak flow meter application, and a patient questionnaire. This data is combined with a machine learning algorithm to identify patients with asthma and COPD. To test and validate the design, we collected data from 119 healthy and sick participants using our custom mobile application and ran the analysis on a PC computer. For comparison, all subjects were examined by an experienced pulmonologist using a full pulmonary testing laboratory. Employing a two-stage logistic regression model, our algorithms were first able to identify patients with either asthma or COPD from the general population, yielding an ROC curve with an AUC of 0.95. Then, after identifying these patients, our algorithm was able to distinguish between patients with asthma and patients with COPD, yielding an ROC curve with AUC of 0.97. This work represents an important milestone towards creating a self-contained mobile phone-based platform that can be used for screening and diagnosis of pulmonary disease in many parts of the world.

Application of semi-supervised deep learning to lung sound analysis.

The analysis of lung sounds, collected through auscultation, is a fundamental component of pulmonary disease diagnostics for primary care and general patient monitoring for telemedicine. Despite advances in computation and algorithms, the goal of automated lung sound identification and classification has remained elusive. Over the past 40 years, published work in this field has demonstrated only limited success in identifying lung sounds, with most published studies using only a small numbers of patients (typically N<;20) and usually limited to a single type of lung sound. Larger research studies have also been impeded by the challenge of labeling large volumes of data, which is extremely labor-intensive. In this paper, we present the development of a semi-supervised deep learning algorithm for automatically classify lung sounds from a relatively large number of patients (N=284). Focusing on the two most common lung sounds, wheeze and crackle, we present results from 11,627 sound files recorded from 11 different auscultation locations on these 284 patients with pulmonary disease. 890 of these sound files were labeled to evaluate the model, which is significantly larger than previously published studies. Data was collected with a custom mobile phone application and a low-cost (US$30) electronic stethoscope. On this data set, our algorithm achieves ROC curves with AUCs of 0.86 for wheeze and 0.74 for crackle. Most importantly, this study demonstrates how semi-supervised deep learning can be used with larger data sets without requiring extensive labeling of data.

Implementation of smart phone video plethysmography and dependence on lighting parameters.

The remote measurement of heart rate (HR) and heart rate variability (HRV) via a digital camera (video plethysmography) has emerged as an area of great interest for biomedical and health applications. While a few implementations of video plethysmography have been demonstrated on smart phones under controlled lighting conditions, it has been challenging to create a general scalable solution due to the large variability in smart phone hardware performance, software architecture, and the variable response to lighting parameters. In this context, we present a selfcontained smart phone implementation of video plethysmography for Android OS, which employs both stochastic and deterministic algorithms, and we use this to study the effect of lighting parameters (illuminance, color spectrum) on the accuracy of the remote HR measurement. Using two different phone models, we present the median HR error for five different video plethysmography algorithms under three different types of lighting (natural sunlight, compact fluorescent, and halogen incandescent) and variations in brightness. For most algorithms, we found the optimum light brightness to be in the range 1000-4000 lux and the optimum lighting types to be compact fluorescent and natural light. Moderate errors were found for most algorithms with some devices under conditions of low-brightness (<;500 lux) and highbrightness (>4000 lux). Our analysis also identified camera frame rate jitter as a major source of variability and error across different phone models, but this can be largely corrected through non-linear resampling. Based on testing with six human subjects, our real-time Android implementation successfully predicted the measured HR with a median error of -0.31 bpm, and an inter-quartile range of 2.1bpm.

Clinical utility of the modified segmental boost technique for treatment of the pelvis and inguinal nodes.

PURPOSE: Low-lying pelvic malignancies often require simultaneous radiation to pelvis and inguinal nodes. We previously reported improved homogeneity with the modified segmental boost technique (MSBT) compared to that with traditional methods, using phantom models. Here we report our institutional clinical experience with MSBT. METHODS AND MATERIALS: MSBT patients from May 2001 to March 2007 were evaluated. Parameters analyzed included isocenter/multileaf collimation shifts, time per fraction (four fields), monitor units (MU)/fraction, femoral doses, maximal dose relative to body mass index, and inguinal node depth. In addition, a dosimetric comparison of the MSBT versus intensity modulated radiation therapy (IMRT) was conducted. RESULTS: Of the 37 MSBT patients identified, 32 were evaluable. Port film adjustments were required in 6% of films. Median values for each analyzed parameter were as follows: MU/fraction, 298 (range, 226-348); delivery time, 4 minutes; inguinal depth, 4.5 cm; volume receiving 45 Gy (V45), 7%; V27.5, 87%; body mass index, 25 (range, 16.0-33.8). Inguinal dose was 100% in all cases; in-field inhomogeneity ranged from 111% to 118%. IMRT resulted in significantly decreased dose to normal tissue but required more time for treatment planning and a higher number of MUs (1,184 vs. 313 MU). CONCLUSIONS: In our clinical experience, the mono-isocentric MSBT provides a high degree of accuracy, improved homogeneity compared with traditional techniques, ease of simulation, treatment planning, treatment delivery, and acceptable femoral doses for pelvic/inguinal radiation fields requiring 45 to 50.4 Gy. In addition, the MSBT delivers a relatively uniform dose distribution throughout the treatment volume, despite varying body habitus. Clinical scenarios for the use of MSBT vs. intensity-modulated radiation therapy are discussed. To our knowledge, this is the first study reporting the utility of MSBT in the clinical setting.