Forest Conversion and Degradation in Papua New Guinea 1972-2002

Quantifying forest change in the tropics is important because of the role these forests play in the conservation of biodiversity and the global carbon cycle. One of the world's largest remaining areas of tropical forest is located in Papua New Guinea. Here we show that change in its extent and condition has occurred to a greater extent than previously recorded. We assessed deforestation and forest degradation in Papua New Guinea by comparing a land-cover map from 1972 with a land-cover map created from nationwide high-resolution satellite imagery recorded since 2002. In 2002 there were 28,251,967 ha of tropical rain forest. Between 1972 and 2002, a net 15 percent of Papua New Guinea's tropical forests were cleared and 8.8 percent were degraded through logging. The drivers of forest change have been concentrated within the accessible forest estate where a net 36 percent were degraded or deforested through both forestry and nonforestry processes. Since 1972, 13 percent of upper montane forests have also been lost. We estimate that over the period 1990–2002, overall rates of change generally increased and varied between 0.8 and 1.8 percent/yr, while rates in commercially accessible forest have been far higher—having varied between 1.1 and 3.4 percent/yr. These rates are far higher than those reported by the FAO over the same period. We conclude that rapid and substantial forest change has occurred in Papua New Guinea, with the major drivers being logging in the lowland forests and subsistence agriculture throughout the country with comparatively minor contributions from forest fires, plantation establishment, and mining

On estimating tropical forest carbon dynamics in Papua New Guinea

One of the few initiatives to address ongoing global warming that did not completely stall at the UNFCCC climate change negotiations was the reduction emissions from deforestation and forest degradation (REDD). REDD has a focus on the forests of the tropics. Unfortunately forest mensuration in most tropical countries has been inadequate to accurately determine forest carbon stocks, much less the effects of land use and changes in land use on them (Houghton et al. 2009; Bryan et al. 2010a). Whilst tropical logging is known to be widespread, the exact areas of tropical forest subject to logging have not been accurately mapped (Asner et al. 2009) or mapped with sufficient regularity to provide adequate data on the areas subject to this activity. Biomass losses due to logging have usually been derived from limited plot data, or derived via various models from estimates of regional biomass and timber extraction volumes (Houghton et al. 2009) and thus encapsulate considerable uncertainty. For these reasons the carbon impact of tropical logging remains an open question, and one that needs to be closed before any international institutional arrangement considers promoting forms of timber extraction as a tool for controlling carbon emissions. Here, we examine the current state of forest carbon research in Papua New Guinea (PNG) to illustrate the problems that can arise by developing forest management policy prematurely from incomplete forest research

Does the quantity of enteral nutrition affect outcomes in critically ill trauma patients?

Abstract from Clinical Nutrition Week, Orlando, FL, January 29-February 2, 2005

Patient- and Hospital-level Predictors of 30-day Readmission after Acute Coronary Syndrome: A Systematic Review

Background: Readmissions following acute myocardial infarction (AMI) are costly and may be partly due to poor care. A previous systematic review examined the literature through 2007. Since then, health policy has changed and additional articles examining predictors of readmission have appeared. We sought to conduct a systematic review of the literature after 2007 regarding socio-demographic, clinical, psychosocial, and hospital level predictors of 30-day readmissions after acute coronary syndrome. Methods: A systematic search of the literature using Pubmed, OVID, ISI web of science, CINAHL, ACP and the Cochrane Library was conducted, including a quality assessment using Downs and Black criteria. Articles reporting on 30-day readmission rate and examining at least one patient-level predictor of readmission at 30 days were included; articles examining interventions to reduce readmissions were excluded. Results: Twenty-two studies were included in this review from which more than 60 predictors of 30-day readmission were identified. Age, co-morbidity, COPD, diabetes, hypertension and having had a previous AMI were all consistently associated with higher risk of readmission. However, no studies reported psychosocial factors as predictors of readmission at 30 days. Conclusion: Studies of readmission should adjust for age and co-morbidity, consistent predictors of readmission at 30-days. Patients with these risk factors for readmission should be targeted for more-intensive follow-up after discharge. Psychosocial predictors of readmission remains a relatively unexplored area of research

Psychosocial Factors Predict Patient Ratings of Care Transition Quality: Results from Transitions, Risks, and Actions in Coronary Events – Center for Outcomes Research and Education (TRACE-CORE)

Background: Short hospital stays and fragmented care make the transition following hospitalization a high-risk period for ACS patients. Identified risks for rehospitalization and complications associated with transitions include demographic (e.g., older age), clinical (e.g., co-morbidities), and psychosocial (e.g., depression) factors. Thus, one might expect high-risk patients to receive better quality transitional care to minimize negative outcomes; alternatively, the quality of care may be yet another outcome influenced by the same risk factors. Little is known about the predictors of quality of care transitions from the patients’ perspective. Methods: We studied 1,545 TRACE-CORE patients (mean age = 62, 34% female, 78% non-Hispanic white) admitted with an ACS who completed in-hospital interviews and the Care Transition Measure (CTM) at 1 month after discharge. High quality transitions were indicated by a CTM-15 score \u3e74. Using logistic regression models we examined the association between in-hospital demographic, clinical, and psychosocial characteristics, generic and disease specific quality of life, health literacy and numeracy, and cognitive status with high quality transitions. Results: Over one-third (36%) of participants (n=552) reported high quality transitions after an ACS. Most variables of interest were associated (p \u3c .20) with care transition quality in bivariate analyses. After adjustment, in-hospital cognitive impairment (Odds Ratio (OR) 0.68; 95% CI 0.46, 0.98) and older age (OR 0.99; CI 0.98, 1.00) were negatively associated with reporting high care transition quality, while high levels of social support (OR 1.06; CI 1.03, 1.10) and patient activation (OR 1.46; CI 1.02, 2.09) increased the chance of reporting high care transition quality in a multivariable model. Conclusions: Older patients, those with cognitive impairment, low social support, or lower patient activation may be at risk for lower-quality transitions following hospitalization for ACS, and may benefit from extra attention and support during the transition from hospital to home

Stories of Young Dropouts : A Social Survey of Success and Failure

The present report covers the research, methodology, fieldwork activities, analysis of data and conclusions regarding Output 1 - Social Survey of the Below 10 Project. It focuses on the causes and characteristics of school failure and early school leaving (ESL hereafter). It was carried out by all partners of the Below 10 project on its first phase, and contains data collected locally in the following countries: Croatia, France, Italy, Portugal, Romania and United Kingdom. The research sought to answer the following questions: 1) what are the causes of early school leaving and dropping out at a local level; 2) how can ESL and dropping out be reduced? Educational outcomes currently assume an unprecedented weighting in the process of individual development and self-representation. The "school trajectory, generalised, merges with the actual biographic construction of the young person" (Vieira, 2010, pp. 278)1 and school inscribes "institutional self-confidence” in individuals (Martucelli, 2006, pp.45)2, rewriting experiences based upon academic evaluations through actions of confirmation, certification or refutation and doubt. Education processes may have emancipative or vulnerability effects, generating exclusion. Schools are for this reason both environments reproducing the prevailing social inequalities as well as producing new inequalities (Bourdieu, Passeron, 19703; DuruBellat, 20024)

Learning by doing: Local Communities work and learn together to prevent and reduce ESL and drop-out

Following the initial research conducted as part of IO1 and carried out by CIES-UIL (Mateus et al, 2018) on factors leading to ESL, the team at the University of Northampton developed guidelines for the implementation of IO2 (Devecchi, et al, 2018). This document is the final report of the IO2’s activities and includes the following: • The IO2 framework; • Report on training, its development and implementation; • Report on the pilot activities carried out by each partner; • Evaluation of training; • Evaluation of pilot activities; • Conclusion and recommendations. Part 1 of this report focuses on the work carried out in preparation for the implementation of the training and pilot initiatives. It outlines the IO2 framework and the support measure (management and leadership) put in place to ensure the effective and successful fulfilment of the IO2’s aims and objectives

Angina Characteristics as Predictors of Trajectories of Quality of Life Following Acute Coronary Syndrome in the Transitions, Risks and Actions in Coronary Events-Center for Outcomes Research and Education cohort (TRACE-CORE)

BACKGROUND: To describe longitudinal trajectories of health-related quality of life (HRQoL) after hospitalization with an acute coronary syndrome (ACS), their associations with baseline angina characteristics, and associations with anxiety, depression, and cognitive impairment. METHODS: TRACE-CORE participants (N=1,613) completed the SF-36 during hospitalization for ACS and 1, 3, & 6 months post-discharge. Latent growth curves identified trajectories of physical and mental components of HRQOL (MCS and PCS) and sequential multiple logistic regression estimated associations between trajectories and angina characteristics. RESULTS: Participants (N=1613) had mean age 63.3 (SD 11.4) years, 33.0% female, and 78.2% non-Hispanic white. We identified 2 MCS trajectories: AVERAGE and IMPAIRED HRQoL. The majority of participants (81.0%) had AVERAGE MCS at baseline (mean MCS 53.6) and slight improvement in scores over time. A minority (19.0%) had IMPAIRED HRQoL at baseline (mean MCS 36.7) and slight improvement in scores over time. We identified 2 similar PCS trajectories with similar patterns of scores over time: AVERAGE (71.1%) and IMPAIRED (28.9%) HRQoL at baseline. Adjusting for demographics & comorbidities, patients with less severe baseline angina were more likely to have AVERAGE MCS (odds ratio [OR]/10 unit change in severity 1.1) and PCS (OR 1.1) trajectories, and similarly for less frequent angina (MCS OR 1.2; PCS OR 1.3). The associations of MCS trajectory with severity and frequency lost significance after adjusting for psychosocial factors, whereas the PCS associations remained significant [All p \u3c 0.05 unless noted]. CONCLUSIONS: About 1/3 of patients exhibited impaired 6-month HRQoL trajectories, which can be predicted by angina characteristics. Psychosocial factors may explain the prediction of mental, not physical, trajectories. Interventions to improve HRQoL after ACS should consider psychosocial factors and angina

Blood glucose concentration does not correlate with organ failure or outcome in trauma patients receiving enteral nutrition

Abstract from American College of Clinical Pharmacy 2005 Spring Practice and Research Forum/Updates in Therapeutics, Myrtle Beach, SC, April 10-13, 2005

Quantitative proteomics of plasma vesicles identify novel biomarkers for Hemoglobin E/ β-Thalassemic patients

Key PointsChaperones, antioxidants, iron-sequestering proteins, and cathepsin S exhibited increased abundance in thalassemic EVs. Haptoglobin and hemopexin are reduced in thalassemic patients’ EVs, reflecting hemolysis. These could be used as clinical biomarkers.</jats:p