Hospital-related differences in breast cancer management

A retrospective review is presented of 1353 consecutivepatients with histopathologically confirmed invasive breast carcinoma treatedradically with curative intent during the decade 1980–89.None had received adjuvant systemic therapy with hormonesor prolonged chemotherapy. The distribution of lymph-node negative(N–) and lymph-node positive (N+) patients was 75%and 25%, respectively.The treatment and outcome were analysed as regardsconventional prognostic parameters, in particular considering the axillarylymph-node status and the responsible hospital category (GeneralMunicipal Hospitals (MH)) versus Comprehensive Cancer Center (CC)).The most striking difference was detected as regardsthe number of examined lymph nodes. The mediannumber of nodes described at the MH was7, as compared to 14 at the CC(p < 0.001). In patients with pT1 tumoursthe highest rate of lymph-node positivity was observedwhen 10 or more axillary nodes were removed.Adjuvant radiotherapy reduced the loco-regional recurrence rate inthe N– patients, whereas only the regional recurrenceswere reduced among the N+ patients. The five-and 10-year tumor-related survival rates were 86% and76%, respectively, with no difference between the MHand the CC.As life-prolonging adjuvant hormone therapy and chemotherapy isnow available for patients with axillary lymph nodemetastases, it is important that patients with breastcancer are operated adequately with the aim toremove at least 10 axillary lymph nodes. Athorough examination of the axillary content should beperformed by the pathologist, and the number ofresected lymph nodes and metastases should be reported.The establishment of nation-wide standard criteria for themanagement of breast cancer is recommended.     

Pharmacokinetics and safety of intravitreally delivered etanercept

Background The anti-inflammatory drug etanercept may be an effective therapeutic agent in diabetic retinopathy. In order to further evaluate its potential, the pharmacokinetics and safety of this drug after intravitreal delivery were investigated.Methods After intravitreal administration of etanercept in rabbits, clinical examination, electroretinography (ERG), visually evoked potentials (VEP) and histology were evaluated. The pharmacokinetics and distribution of etanercept were analyzed using fluorescence-coupled protein at 0, 2, 4, and 8 weeks after injection in vitreous, retina, and choroid.Results No adverse effects and signs of toxicity were found. Etanercept showed peak concentrations after 4 weeks in the retina and choroid.Conclusions Intravitreally delivered etanercept is safe and results in high concentrations in the retina and choroid over a long period of time.     

Software-Automated Implant Detection for Intraoperative 3D Imaging—First Clinical Evaluation on 214 Data Sets

Previous studies have demonstrated a frequent occurrence of screw/K-wire malpositioning during surgical fracture treatment under 2D fluoroscopy and a correspondingly high revision rate as a result of using intraoperative 3D imaging. In order to facilitate and accelerate the diagnosis of implant malpositioning in 3D data sets, this study investigates two versions of an implant detection software for mobile 3D C-arms in terms of their detection performance based on comparison with manual evaluation. The 3D data sets of patients who had received surgical fracture treatment at five anatomical regions were extracted from the research database. First, manual evaluation of the data sets was performed, and the number of implanted implants was assessed. For 25 data sets, the time required by four investigators to adjust each implant was monitored. Subsequently, the evaluation was performed using both software versions based on the following detection parameters: true-positive-rate, false-negative-rate, false-detection-rate and positive predictive value. Furthermore, the causes of false positive and false negative detected implants depending on the anatomical region were investigated. Two hundred fourteen data sets with overall 1767 implants were included. The detection parameters were significantly improved (p<.001) from version 1 to version 2 of the implant detection software. Automatic evaluation required an average of 4.1±0.4 s while manual evaluation was completed in 136.15±72.9 s (p<.001), with a statistically significant difference between experienced and inexperienced users (p=.005). In summary, version 2 of the implant detection software achieved significantly better results. The time saved by using the software could contribute to optimizing the intraoperative workflow.     

Mental disorders into adulthood among adolescents placed in residential care: A prospective 10-year follow-up study

BackgroundChild welfare and juvenile justice placed youths show high levels of psychosocial burden and high rates of mental disorders. It remains unclear how mental disorders develop into adulthood in these populations. The aim was to present the rates of mental disorders in adolescence and adulthood in child welfare and juvenile justice samples and to examine their mental health trajectories from adolescence into adulthood.MethodsSeventy adolescents in shared residential care, placed by child welfare (n = 52, mean age = 15 years) or juvenile justice (n = 18, mean age = 17 years) authorities, were followed up into adulthood (child welfare: mean age = 25 years; juvenile justice: mean age = 27 years). Mental disorders were assessed based on the International Classification of Diseases 10th Revision diagnoses at baseline and at follow-up. Epidemiological information on mental disorders was presented for each group. Bivariate correlations and structural equation modeling for the relationship of mental disorders were performed.ResultsIn the total sample, prevalence rates of 73% and 86% for any mental disorder were found in adolescence (child welfare: 70%; juvenile justice: 83%) and adulthood (child welfare: 83%; juvenile justice: 94%) respectively. General psychopathology was found to be stable from adolescence into adulthood in both samples.ConclusionsOur findings showed high prevalence rates and a high stability of general psychopathology into adulthood among child welfare and juvenile justice adolescents in Swiss residential care. Therefore, continuity of mental health care and well-prepared transitions into adulthood for such individuals is highly warranted.     

Only Hyperuricemia with Crystalluria,but not Asymptomatic Hyperuricemia,Drives Progression of Chronic Kidney Disease

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Evaluating sustainable development policies in rural coastal economies

Sustainable development (SD) policies targeting marine economic sectors, designed to alleviate poverty and conserve marine ecosystems, have proliferated in recent years. Many developing countries are providing poor fishing households with new fishing boats (fishing capital) that can be used further offshore as a means to improve incomes and relieve fishing pressure on nearshore fish stocks. These kinds of policies are a marine variant of traditional SD policies focused on agriculture. Here, we evaluate ex ante economic and environmental impacts of provisions of fishing and agricultural capital, with and without enforcement of fishing regulations that prohibit the use of larger vessels in nearshore habitats. Combining methods from development economics, natural resource economics, and marine ecology, we use a unique dataset and modeling framework to account for linkages between households, business sectors, markets, and local fish stocks. We show that the policies investing capital in local marine fisheries or agricultural sectors achieve income gains for targeted households, but knock-on effects lead to increased harvest of nearshore fish, making them unlikely to achieve conservation objectives in rural coastal economies. However, pairing an agriculture stimulus with increasing enforcement of existing fisheries’ regulations may lead to a win–win situation. While marine-based policies could be an important tool to achieve two of the United Nations Sustainable Development Goals (alleviate poverty and protect vulnerable marine resources), their success is by no means assured and requires consideration of land and marine socioeconomic linkages inherent in rural economies.

Coastal and island nations are adopting “blue growth” sustainable development (SD) policies to alleviate poverty and conserve vulnerable ocean resources. Generally speaking, SD policies manage resources and direct investments in order to meet current and future human needs and aspirations, without endangering the natural systems (1). The feasibility and potential of SD has been the focus of decades of academic research; many regard the consideration of economic, social, environmental, and institutional needs and linkages as fundamental to successful policy design and implementation (2–6). Given their novelty, what constitutes a blue growth policy is not universal (7), but like traditional SD policies that focus on land-based sectors such as agriculture, manufacturing, and energy sectors (8, 9), blue growth policies seek to achieve social, economic, and environmental goals simultaneously (10). Blue growth policies attempt to achieve these goals by supporting marine-based industries such as offshore fishing, aquaculture, shipping, and tourism (11, 12). The marine focus has reinvigorated SD efforts of international organizations (including the Global Environmental Facility, United Nations Food and Agriculture Organization, the European Union, and The World Bank) that have collectively invested hundreds of millions of dollars into the development and monitoring of blue growth programs (12–16).While small-scale artisanal fishers consider a variety of factors when making fishing decisions (17), evidence suggests allocation of time is, in part, based on relative returns to labor (18–21). Thus, some blue growth policies attempt to alter returns to fishing relative to alternative income-generating activities as a way to achieve both poverty alleviation and conservation objectives. For example, if poor fishing households are incentivized to participate in offshore fishing, it may lead to increased household incomes and reduced fishing pressure on overexploited nearshore fish stocks (22, 23). Policies enforcing and increasing the regulation of fishing activities are also considered important to achieving blue growth objectives (10, 24).Many SD policies are designed to reduce upfront costs of switching to more sustainable livelihoods. Historically, large-scale fisheries receive the majority of subsidy benefits (25), arguably to the detriment of small-scale fisheries who are often outcompeted by industrialized operations (26). Recently, however, developing countries including Kenya, the Philippines, India, Tanzania, Vietnam, and Indonesia have been investing in programs that bolster the fishing capacity of small-scale and artisanal fishermen (27–33). These programs are designed to help small-scale fishers access larger or better vessels and gear, allowing them to reach more plentiful fishing grounds, compete with commercial vessels, and relieve pressure on vulnerable nearshore fisheries.Despite the recent surge in popularity, we lack evidence that these marine-based SD policies will achieve both poverty reduction and conservation objectives when implemented in rural economies. The complexity of coupled natural–human systems makes it difficult to measure the ex post performance of SD policies, especially in marine environments (34, 35). Additionally, local market failures due to high transportation costs and poorly developed marketing infrastructure (36) can lead to locally defined prices that fluctuate with changes in local supply and demand; local prices may distort household responses to policies, leading to unintended environmental consequences (37–40). Because local market failures are more common in rural economies in developing countries, methods and lessons learned from studies of industrialized fisheries in developed countries may not be relevant. Rather, management of fisheries in rural coastal economies may be more successful if market imperfections, alternative livelihood options, and ecological feedback are considered (20, 41–47).Recent studies explore the causal impacts of land-based SD policy instruments in developing countries (48–56). A key finding of these studies is that community heterogeneity is an important factor in policy performance. However, findings from forestry research do not necessarily carry over to marine settings because fish resources are mobile and regenerate relatively quickly, and, typically, access rights to fisheries are not well defined. This study begins to address the need for research examining responses to SD policy in rural coastal economies.We use a coupled natural–human modeling framework to estimate the ex ante impacts of common SD policies. Our ex ante mechanistic approach that includes a general equilibrium local economy model captures important dynamic feedback between the economy and health of the fish stocks. Indeed, other researchers have studied the correlations between markets and ecosystems in coastal communities in developing countries (44, 57–59). However, the theoretical structure of our analysis approach allows us to examine the causal mechanisms between policy and its outcomes. Our model captures the feedback between economic sectors and households within the economy (Fig. 1, details in SI Appendix). This broad scope is necessary to estimate the extent to which policies targeting poor households in a community also impact nontargeted households (knock-on effects). For example, a policy supporting a subset of fishing households could be detrimental to other households that harvest from the same fish stocks and compete in the same input and output markets.Open in a separate windowFig. 1.A conceptual framework for the bioeconomic local general equilibrium model. Households are represented by four representative groups and may produce goods and services (e.g., agricultural, offshore and nearshore fish, retail, and restaurants) available in local, and possibly global, markets. The simulated policies provide different types of capital to poor households and may also restrict use of fishing capital.Here, we estimate the impacts of two common marine fisheries policies (provision of offshore fishing vessels and increased enforcement of fishing regulations) and an alternative agricultural policy (provision of agricultural capital) in a rural coastal economy. To estimate the impacts of these SD policies in coastal economies, we use a modeling approach that has been developed using theory from development economics, natural resource economics, and marine ecology (37, 60). Introducing new features to the framework, we develop a model of a rural economy capable of disentangling fisher participation in two distinct fishing activities and household consumption of two fish goods. We use microeconomic data collected from household and business surveys to parameterize and calibrate our model, allowing us to realistically estimate policy impacts. An inherent strength to our methodology is the ability to adjust the structure of the model to represent alternative economies. We demonstrate how the model can be used to predict policy outcomes for a typology of rural coastal economies.Although combining policies that simultaneously target marine and agricultural sectors is currently not part of the dialogue on the adoption of blue SD policies around the world (e.g., see The World Bank’s strategy document for its Blue Economy Program and PROBLUE (13)), we find that pairing policy instruments that target both sectors—increased enforcement of vessel regulations and capital investments in the agricultural sector—is better able to achieve both conservation and poverty reduction goals.Why is an agricultural policy combined with enforcement capabilities of marine fishing regulations able to achieve a win–win while marine-focused SD policies are not? Our coupled natural–human modeling framework highlights the mechanisms leading to this counterintuitive outcome. That is, investing in the agricultural sector increases the returns to agricultural labor, which in turn creates upward pressure on wages and encourages a reduction in labor allocated to nearshore fishing. At the same time, the increased wealth in the local economy due to greater agricultural productivity drives up demand for nearshore fish. Although higher prices of fish draw some labor back into the fishery, increased enforcement of vessel regulations prevents fishers from illegally using larger boats in the nearshore habitat as a means to increase harvests. Without coupling increased enforcement and agricultural subsidy, the higher demand for fish would lead to increased harvests in the nearshore environment and lower fish stocks over time.