PSYCHOSOCIAL FACTORS AND MOBILE HEALTH INTERVENTION: IMPACT ON LONG-TERM OUTCOMES AFTER LUNG TRANSPLANTATION

Identifying and intervening on modifiable risk factors may improve outcomes in lung transplantation (LTx), which, despite recent improvements, remain suboptimal. Evidence suggests that two modifiable risk factors, psychiatric disorders and nonadherence, may improve LTx outcomes in the short-term; however, neither has been explored in the long-term. Therefore, the overarching goal of this dissertation was to determine the long-term impact of these modifiable risk factors and intervention to attenuate them. First, we examined the relationship of pre- and early post-transplant psychiatric disorders on LTx-related morbidity and mortality for up to 15 years post-LTx. Our sample included 155 1-year LTx survivors enrolled in a prospective study of mental health post- LTx. We found that depression during the first year post-LTx increased risk of BOS, mortality and graft loss by nearly twofold, and that pre-transplant depression and pre- and post-transplant anxiety were not associated with clinical outcomes. Next, we examined the impact of a mobile health intervention designed to promote adherence to the post-LTx regimen, PocketPATH, on long-term LTx-related morbidity, mortality and nonadherence. We conducted two follow-up studies to the original yearlong randomized controlled trial in which participants assigned to PocketPATH showed improved adherence to the regimen, relative to usual care. Among the 182 LTx recipients (LTxRs) who survived the original trial, we found that PocketPATH had a protective indirect effect on mortality by promoting LTxRs’ communication with the LTx team during the first year. Among the 104 LTxRs who completed the follow-up assessment, we found that PocketPATH’s adherence benefits over the first year were not sustained into the long-term, although LTxRs assigned to PocketPATH were more likely than LTxRs assigned to usual care to perform the home self-care tasks of the regimen at follow-up. Median time since LTx for participants in both follow-up studies was 4.2 years (range, 2.8-5.7 years). This dissertation presents an important first step toward identifying and intervening on modifiable risk factors to improve long-term LTx outcomes. Mobile health technologies offer limitless potential to target these risk factors and others. More work is needed to determine specific features and long-term patient engagement strategies that will optimize and sustain intervention effectiveness

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Psychiatric disorders as risk factors for adverse medical outcomes after solid organ transplantation

PURPOSE OF REVIEW: Given that the prevalence of psychiatric disorders in transplant candidates and recipients is substantially higher than in the general population, and that linkages between psychiatric disorders and medical outcomes for nontransplant-related diseases have been established, it is important to determine whether psychiatric disorders predict posttransplant medical outcomes. RECENT FINDINGS: Most research has focused on the association between depression (both pretransplant and posttransplant) and posttransplant mortality. Some research has examined transplant-related morbidity outcomes, such as graft rejection, posttransplant malignancies, and infection. However, methodological limitations make it difficult to compare existing studies in this literature directly. Overall, the studies presented in this review indicate that psychiatric distress occurring in the early transplant aftermath bears a stronger relationship to morbidity and mortality outcomes than psychiatric distress occurring before transplant. SUMMARY: The literature on the impact of psychiatric conditions on the morbidity and mortality of solid organ transplant recipients remains inconclusive. More research is needed in order to investigate these associations among a broader range of psychiatric predictors, morbidity outcomes, and recipient populations. Until evidence suggests otherwise, we recommend frequent monitoring of psychiatric symptoms during the first year after transplantation to aid in early identification and treatment during this critical period of adjustment. © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins

Re-emerging Infectious Disease (RED) Alert tool

Objective: Although relying on verbal definitions of "re-emergence", descriptions that classify a “re-emergence” event as any significant recurrence of a disease that had previously been under public health control, and subjective interpretations of these events is currently the conventional practice, this has the potential to hinder effective public health responses. Defining re-emergence in this manner offers limited ability for ad hoc analysis of prevention and control measures and facilitates non-reproducible assessments of public health events of potentially high consequence. Re-emerging infectious disease alert (RED Alert) is a decision-support tool designed to address this issue by enhancing situational awareness by providing spatiotemporal context through disease incidence pattern analysis following an event that may represent a local (country-level) re-emergence. The tool’s analytics also provide users with the associated causes (socioeconomic indicators) related to the event, and guide hypothesis-generation regarding the global scenario.Introduction: Definitions of “re-emerging infectious diseases” typically encompass any disease occurrence that was a historic public health threat, declined dramatically, and has since presented itself again as a significant health problem. Examples include antimicrobial resistance leading to resurgence of tuberculosis, or measles re-appearing in previously protected communities. While the language of this verbal definition of “re-emergence” is sensitive enough to capture most epidemiologically relevant resurgences, its qualitative nature obfuscates the ability to quantitatively classify disease re-emergence events as such.Methods: Our tool automatically computes historic disease incidence and performs trend analyses to help elucidate events which a user may considered a true re-emergence in a subset of pertinent infectious diseases (measles, cholera, yellow fever, and dengue). The tool outputs data visualizations that illustrate incidence trends in diverse and informative ways. Additionally, we categorize location and incidence-specific indicators for re-emergence to provide users with associated indicators as well as justifications and documentation to guide users’ next steps. Additionally, the tool also houses interactive maps to facilitate global hypothesis-generation.Results: These outputs provide historic trend pattern analyses as well as contextualization of the user’s situation with similar locations. The tool also broadens users' understanding of the given situation by providing related indicators of the likely re-emergence, as well as the ability to investigate re-emergence factors of global relevance through spatial analysis and data visualization.Conclusions: The inability to categorically name a re-emergence event as such is due to lack of standardization and/or availability of reproducible, data-based evidence, and hinders timely and effective public health response and planning. While the tool will not explicitly call out a user scenario as categorically re-emergent or not, by providing users with context in both time and space, RED Alert aims to empower users with data and analytics in order to substantially enhance their contextual awareness; thus, better enabling them to formulate plans of action regarding re-emerging infectious disease threats at both the country and global level

Spatial temporal cluster analysis to enhance awareness of disease re-emergence on a global scale

ObjectiveThe application of spatial analysis to improve the awareness and use of surveillance data.IntroductionThe re-emergence of an infectious disease is dependent on social, political, behavioral, and disease-specific factors. Global disease surveillance is a requisite of early detection that facilitates coordinated interventions to these events. Novel informatics tools developed from publicly available data are constantly evolving with the incorporation of new data streams. Re-emerging Infectious Disease (RED) Alert is an open-source tool designed to help analysts develop a contextual framework when planning for future events, given what has occurred in the past. Geospatial methods assist researchers in making informed decisions by incorporating the power of place to better explain the relationships between variables.MethodsDisease incidence and indicator data derived for the RED Alert project were analyzed for spatial associations. Using aggregate country-level data, spatial and spatiotemporal clusters were identified in ArcMap 10.5.1. The identified clusters were then used as the outcome for a series of binary logistic regression models to determine significant covariates that help explain global hotspots. These methods will continue to evolve and be incorporated into the RED Alert decision support ecosystem to provide analysts with a global perspective on potential re-emergence.ResultsHotspots of high disease incidence in relation to neighboring countries were identified for measles, cholera, dengue, and yellow fever between 2000 and 2014. Disease-specific predictors were identified using aggregate estimates from World Bank indicator dataset. Data was imputed where possible to enhance the validity of the Gi * statistic for clustering. In the future, as data streams become more readily available, hotspot modeling at a finer resolution will help to improve the precision of spatial epidemiology.ConclusionsSpatial methods enhance the capability of understanding complex population and disease relationships, which in turn improves surveillance and the ability to predict re-emergence. With tools like RED Alert, public health analysts can better prepare to respond rapidly to future re-emerging disease threats. 

Post-operative Psychosocial Predictors of Outcome in Bariatric Surgery

Although there are several recent reviews of the pre-operative factors that influence treatment outcome for bariatric surgery, commensurate efforts to identify and review the predictive validity of post-operative variables are lacking. This review describes the post-operative psychosocial predictors of weight loss in bariatric surgery. Results suggest empirical support for post-operative binge eating, uncontrolled eating/grazing, and presence of a depressive disorder as negative predictors of weight loss outcomes; whereas, adherence to dietary and physical activity guidelines emerged as positive predictors of weight loss. With the exception of depression, psychological comorbidities were not consistently associated with weight loss outcomes. Results highlight the need for post-operative assessment of disordered eating and depressive disorder, further research on the predictive value of post-operative psychosocial factors, and development of targeted interventions