Process-Based Modeling to Assess the Effects of Recent Climatic Variation on Site Productivity and Forest Function across Western North America

A process-based forest growth model, 3-PG (Physiological Principles Predicting Growth), parameterized with values of soil properties constrained by satellite-derived estimates of maximum leaf area index (LAI[subscript max]), was run for Douglas-fir (Pseudotsuga menziesii) to contrast the extent to which site growth potential might vary across western North America between a cool, wet period (1950–1975) and a more recent, generally warmer and drier one (2000–2009). LAI[subscript max] represents a surrogate for overall site growth potential, as demonstrated from a strong correlation between the two variables, with the latter based on the culmination of mean annual increment estimates made at 3356 ground-based U.S. Forest Service survey plots across the states of Oregon and Washington. Results indicate that since 2000, predicted LAI[subscript max] has decreased more than 20% in portions of the Southwest USA and for much of the forested area in western Alberta. Similar percentage increases in LAI[subscript max] were predicted for parts of British Columbia, Idaho and Montana. The modeling analysis included an assessment of changes in seasonal constraints on gross primary production (GPP). A general reduction in limitations caused by spring frost occurred across the entire study area. This has led to a longer growing season, along with notable increases in summer evaporative demand and soil drought for much of the study area away from the maritime influence of the Pacific Ocean.Keywords: Douglas-fir, FIA survey plots, Climate change, Leaf area index, 3-PG model, Productivit

In-hospital Depression Predicts Early Hospital Readmission after an Acute Coronary Syndrome: Preliminary Data from TRACE-CORE

Background: Hospital systems, patients and providers seek to avert rehospitalizations within 30 days for patients admitted with an acute coronary syndrome (ACS). Rehospitalizations within 30 days of discharge are often considered preventable and to reflect poor in-hospital management or discharge practices. However, independent associations of psychosocial factors with early rehospitalization in patients admitted with an ACS have not been examined. Methods: A multi-racial cohort of 1,540 patients admitted with an ACS reported psychosocial factors via standardized questionnaires in an in-hospital interview. One month following discharge, patients were interviewed via phone and reported hospital readmissions. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of the association between in-hospital psychosocial characteristics (depression, anxiety, and perceived stress), health literacy and numeracy, and cognitive status, with self-reported readmission within 30 days. Results: Participants were 34% female and 17% non-white, with a mean age of 62 years and a mean length of stay of 4.1 days. Rehospitalization was reported for 14% (n=208) of participants, 77% of which were due to CVD. In univariate analyses, in-hospital severe depression, anxiety, and high stress were associated with higher odds of early readmission, whereas low health numeracy was associated with lower odds of early readmission. Severe depression remained associated with higher odds and low health numeracy remained associated with lower odds of early readmission in a multivariable model including covariates associated on univariate testing with rehospitalization. Conclusions: Early readmission after hospitalization for an ACS was common and associated with in-hospital depression and health numeracy. Notably, depression and health numeracy were the only predictors independently associated with readmission in multivariable analyses. We speculate that the lower likelihood of readmission for those with low numeracy may be related to less engagement with the healthcare system. In-hospital screening for depression and characterization of health numeracy may help stratify risk for early rehospitalization after an ACS

Modeling water uptake on coniferous forest Oregon watershed 10 synthesis

In the Coniferous Forest Biome, many of the understory as well as dominant plants are evergreen and thus water uptake is a year around process. The flow of water from the soil through plants to the atmosphere affects the entire forest ecosystem. As water is evaporated, it absorbs heat and influences the energy budget; as it is conducted through vascular plants, it carries nutrients; and as it is removed from the soil, it reduces seepage and water available to free-living soil organisms. It is important in an ecosystem model that the hydrologic, biologic, and meteorological processes be coupled in a realistic manner. It is the objective of this report to suggest a coupling that is both practical and theoretically sound

Documentation of body mass index and control of associated risk factors in a large primary care network

<p>Abstract</p> <p>Background</p> <p>Body mass index (BMI) will be a reportable health measure in the United States (US) through implementation of Healthcare Effectiveness Data and Information Set (HEDIS) guidelines. We evaluated current documentation of BMI, and documentation and control of associated risk factors by BMI category, based on electronic health records from a 12-clinic primary care network.</p> <p>Methods</p> <p>We conducted a cross-sectional analysis of 79,947 active network patients greater than 18 years of age seen between 7/05 - 12/06. We defined BMI category as normal weight (NW, 18-24.9 kg/m²), overweight (OW, 25-29.9), and obese (OB, ≥ 30). We measured documentation (yes/no) and control (above/below) of the following three risk factors: blood pressure (BP) ≤130/≤85 mmHg, low-density lipoprotein (LDL) ≤130 mg/dL (3.367 mmol/L), and fasting glucose <100 mg/dL (5.55 mmol/L) or casual glucose <200 mg/dL (11.1 mmol/L).</p> <p>Results</p> <p>BMI was documented in 48,376 patients (61%, range 34-94%), distributed as 30% OB, 34% OW, and 36% NW. Documentation of all three risk factors was higher in obesity (OB = 58%, OW = 54%, NW = 41%, p for trend <0.0001), but control of all three was lower (OB = 44%, OW = 49%, NW = 62%, p = 0.0001). The presence of cardiovascular disease (CVD) or diabetes modified some associations with obesity, and OB patients with CVD or diabetes had low rates of control of all three risk factors (CVD: OB = 49%, OW = 50%, NW = 56%; diabetes: OB = 42%, OW = 47%, NW = 48%, p < 0.0001 for adiposity-CVD or diabetes interaction).</p> <p>Conclusions</p> <p>In a large primary care network BMI documentation has been incomplete and for patients with BMI measured, risk factor control has been poorer in obese patients compared with NW, even in those with obesity and CVD or diabetes. Better knowledge of BMI could provide an opportunity for improved quality in obesity care.</p