The deadly dozen of chest trauma

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The challenges of managing breast cancer in the developing world- a perspective from sub- Saharan Africa

PKCommunicable diseases are the major cause of mortality in lower-income countries. Consequently, local and international resources are channelled mainly into addressing the impact of these conditions. HIV, however, is being successfully treated, people are living longer, and disease patterns are changing. As populations age, the incidence of cancer inevitably increases. The World Health Organization has predicted a dramatic increase in global cancer cases during the next 15 years, the majority of which will occur in low- and middle-income countries. Cancer treatment is expensive and complex and in the developing world 5% of global cancer funds are spent on 70% of cancer cases. This paper reviews the challenges of managing breast cancer in the developing world, using sub-Saharan Africa as a model

A climate change simulation starting from 1935

Due to restrictions in the available computing resources and a lack of suitable observational data, transient climate change experiments with global coupled ocean-atmosphere models have been started from an initial state at equilibrium with the present day forcing. The historical development of greenhouse gas forcing from the onset of industrialization until the present has therefore been neglected. Studies with simplified models have shown that this "cold start" error leads to a serious underestimation of the anthropogenic global warming. In the present study, a 150-year integration has been carried out with a global coupled ocean-atmosphere model starting from the greenhouse gas concentration observed in 1935, i.e., at an early time of industrialization. The model was forced with observed greenhouse gas concentrations up to 1985, and with the equivalent C02 concentrations stipulated in Scenario A ("Business as Usual") of the Intergovernmental Panel on Climate Change from 1985 to 2085. The early starting date alleviates some of the cold start problems. The global mean near surface temperature change in 2085 is about 0.3 K (ca. 10) higher in the early industrialization experiment than in an integration with the same model and identical Scenario A greenhouse gas forcing, but with a start date in 1985. Comparisons between the experiments with early and late start dates show considerable differences in the amplitude of the regional climate change patterns, particularly for sea level. The early industrialization experiment can be used to obtain a first estimate of the detection time for a greenhouse-gas-induced near-surface temperature signal. Detection time estimates are obtained using globally and zonally averaged data from the experiment and a long control run, as well as principal component time series describing the evolution of the dominant signal and noise modes. The latter approach yields the earliest detection time (in the decade 1990-2000) for the time-evolving near-surface temperature signal. For global-mean temperatures or for temperatures averaged between 45°N and 45°S, the signal detection times are in the decades 2015-2025 and 2005-2015, respectively. The reduction of the "cold start" error in the early industrialization experiment makes it possible to separate the near-surface temperature signal from the noise about one decade earlier than in the experiment starting in 1985. We stress that these detection times are only valid in the context of the coupled model's internally-generated natural variability, which possibly underestimates low frequency fluctuations and does not incorporate the variance associated with changes in external forcing factors, such as anthropogenic sulfate aerosols, solar variability or volcanic dust. © 1995 Springer-Verlag

Ocean variability and its influence on the detectability of greenhouse warming signals

Recent investigations have considered whether it is possible to achieve early detection of greenhouse-gas-induced climate change by observing changes in ocean variables. In this study we use model data to assess some of the uncertainties involved in estimating when we could expect to detect ocean greenhouse warming signals. We distinguish between detection periods and detection times. As defined here, detection period is the length of a climate time series required in order to detect, at some prescribed significance level, a given linear trend in the presence of the natural climate variability. Detection period is defined in model years and is independent of reference time and the real time evolution of the signal. Detection time is computed for an actual time-evolving signal from a greenhouse warming experiment and depends on the experiment's start date. Two sources of uncertainty are considered: those associated with the level of natural variability or noise, and those associated with the time-evolving signals. We analyze the ocean signal and noise for spatially averaged ocean circulation indices such as heat and fresh water fluxes, rate of deep water formation, salinity, temperature, transport of mass, and ice volume. The signals for these quantities are taken from recent time-dependent greenhouse warming experiments performed by the Max Planck Institute for Meteorology in Hamburg with a coupled ocean-atmosphere general circulation model. The time-dependent greenhouse gas increase in these experiments was specified in accordance with scenario A of the Intergovernmental Panel on Climate Change. The natural variability noise is derived from a 300-year control run performed with the same coupled atmosphere-ocean model and from two long (>3000 years) stochastic forcing experiments in which an uncoupled ocean model was forced by white noise surface flux variations. In the first experiment the stochastic forcing was restricted to the fresh water fluxes, while in the second experiment the ocean model was additionally forced by variations in wind stress and heat fluxes. The mean states and ocean variability are very different in the three natural variability integrations. A suite of greenhouse warming simulations with identical forcing but different initial conditions reveals that the signal estimated from these experiments may evolve in noticeably different ways for some ocean variables. The combined signal and noise uncertainties translate into large uncertainties in estimates of detection time. Nevertheless, we find that ocean variables that are highly sensitive indicators of surface conditions, such as convective overturning in the North Atlantic, have shorter signal detection times (35?65 years) than deep-ocean indicators (≥100 years). We investigate also whether the use of a multivariate detection vector increases the probability of early detection. We find that this can yield detection times of 35?60 years (relative to a 1985 reference date) if signal and noise are projected onto a common ?fingerprint? which describes the expected signal direction. Optimization of the signal-to-noise ratio by (spatial) rotation of the fingerprint in the direction of low-noise components of the stochastic forcing experiments noticeably reduces the detection time (to 10?45 years). However, rotation in space alone does not guarantee an improvement of the signal-to-noise ratio for a time-dependent signal. This requires an ?optimal fingerprint? strategy in which the detection pattern (fingerprint) is rotated in both space and time

Monte Carlo climate change forecasts with a global coupled ocean-atmosphere model

Four time-dependent greenhouse warming experiments were performed with the same global coupled atmosphere-ocean model, but with each simulation using initial conditions from different ''snapshots'' of the control run climate. The radiative forcing - the increase in equivalent CO2 concentrations from 19852035 specified in the Intergovernmental Panel on Climate Change (IPCC) scenario A - was identical in all four 50-year integrations. This approach to climate change experiments is called the Monte Carlo technique and is analogous to a similar experimental set-up used in the field of extended range weather forecasting. Despite the limitation of a very small sample size, this approach enables the estimation of both a mean response and the ''between-experiment'' variability, information which is not available from a single integration. The use of multiple realizations provides insights into the stability of the response, both spatially, seasonally and in terms of different climate variables. The results indicate that the time evolution of the global mean warming signal is strongly dependent on the initial state of the climate system. While the individual members of the ensemble show considerable variation in the pattern and amplitude of near-surface temperature change after 50 years, the ensemble mean climate change pattern closely resembles that obtained in a 100-year integration performed with the same model. In global mean terms, the climate change signals for near surface temperature, the hydrological. cycle and sea level significantly exceed the variability among the members of the ensemble. Due to the high internal variability of the modelled climate system, the estimated detection time of the global mean temperature change signal is uncertain by at least one decade. While the ensemble mean surface temperature and sea level fields show regionally significant responses to greenhouse-gas forcing, it is not possible to identify a significant response in the precipitation and soil moisture fields, variables which are spatially noisy and characterized by large variability between the individual integrations

Down-staging of breast cancer in the pre-screening era: Experiences from Chris Hani Baragwaneth Academic Hospital, Soweto, South Africa

We aimed to investigate the stage of breast cancer at first diagnosis and assess possible determinants of late-stage presentation. A consecutive series of women with newly diagnosed breast cancer at Chris Hani Baragwanath Academic Hospital (CHBAH), Soweto, South Africa were analysed. We retrospectively reviewed electronic patient records. Data were extracted for: (i) stage and year at diagnosis; (ii) travel distance (estimated straight-line distance from GPS-coded residential address to CHBAH); (iii) receptor subtypes; and (iv) age of patient. Generalised linear models were applied to estimate risk ratios for late- v. early-stage disease.Of the patients (N=1 071) studied, the mean age was 55 years and 90% were black Africans. Patients who lived >20 km from the hospital (n=347; 61.8%) presented with late-stage disease (stage 3/4) compared with 50.2% who lived ≤20 km from the hospital (n=724; p=0.02). The majority of patients (74%) >70 years of age who lived >20 km away presented with advanced breast cancer. However, in younger patients, age showed no clear association with stage at presentation. Travel distance was an important predictor of later-stage disease at diagnosis, which was more noticeable in elderly patients. Patients with more aggressive triple-negative and HER2+ tumours presented with later-stage disease

Signal-to-noise analysis of time-dependent greenhouse warming experiments. Part 1: Pattern analysis

Results from a control integration and time-dependent greenhouse warming experiments performed with a coupled ocean-atmosphere model are analysed in terms of their signal-to-noise properties. The aim is to illustrate techniques for efficient description of the space-time evolution of signals and noise and to identify potentially useful components of a multivariate greenhouse-gas ''fingerprint''. The three 100-year experiments analysed here simulate the response of the climate system to a step-function doubling Of CO2 and to the time-dependent greenhouse-gas increases specified in Scenarios A (''Business as Usual'') and D (''Draconian Measures'') of the Intergovernmental Panel on Climate Change (IPCC). If signal and noise patterns are highly similar, the separation of the signal from the natural variability noise is difficult. We use the pattern correlation between the dominant Empirical Orthogonal Functions (EOFs) of the control run and the Scenario A experiment as a measure of the similarity of signal and noise patterns. The EOF 1 patterns of signal and noise are least similar for near-surface temperature and the vertical structure of zonal winds, and are most similar for sea level pressure (SLP). The dominant signal and noise modes of precipitable water and stratospheric/tropospheric temperature contrasts show considerable pattern similarity. Despite the differences in forcing history, a highly similar EOF 1 surface temperature response pattern is found in all three greenhouse warming experiments. A large part of this similarity is due to a common land-sea contrast component of the signal. To determine the degree to which the signal is contaminated by the natural variability (and/or drift) of the control run, we project the Scenario A data onto EOFs 1 and 2 of the control. Signal contamination by the EOF 1 and 2 modes of the noise is lowest for near-surface temperature, a situation favorable for detection. The signals for precipitable water, SLP, and the vertical structure of zonal temperature and zonal winds are significantly contaminated by the dominant noise modes. We use cumulative explained spatial variance, principal component time series, and projections onto EOFs in order to investigate the time evolution of the dominant signal and noise modes. In the case of near-surface temperature, a single pattern emerges as the dominant signal component in the second half of the Scenario A experiment. The projections onto EOFs 1 and 2 of the control run indicate that Scenario D has a large common variability and/or drift component with the control run. This common component is also apparent between years 30 and 50 of the Scenario A experiment, but is small in the 2 x CO2 integration. The trajectories of the dominant Scenario A and control run modes evolve differently, regardless of the basis vectors chosen for projection, thus making it feasible to separate signal and noise within the first two decades of the experiments. For Scenario D it may not be possible to discriminate between the dominant signal and noise modes until the final 2-3 decades of the 100-year integration

Prevalence of comorbidities in women with and without breast cancer in Soweto South Africa: Results from the SABC study

Background. Comorbidities occurring concurrently in breast cancer patients can be burdensome, as they may negatively influence time and stage of presentation.Objectives. To describe the comorbid health conditions among South African (SA) black women with and without breast cancer and to determine factors associated with advanced-stage presentation of breast cancer.Methods. A population-based case-control study on breast cancer was conducted in black women in Soweto, SA, the SABC (South Africa Breast Cancer) study. Lifestyle information and blood samples were collected from 399 women with histologically confirmed new cases of invasive primary breast cancer, recruited prior to any therapy, and 399 age- and neighbourhood-matched controls without breast cancer. We compared self-reported metabolic diseases, depression, anthropometric measurements, blood pressure, HIV status and point-of-care lipid and glucose levels between patients with breast cancer and the control group.Results. In the whole population, the mean (standard deviation) age was 54.6 (12.9) years, the majority (81.2%) of the participants were overweight or obese, 85.3% had abdominal adiposity, 61.3% were hypertensive, 47.1% had impaired fasting plasma glucose, 8.4% had elevated total cholesterol, 74.8% had low high-density lipoprotein and 10.9% were assessed to be depressed. Ninety-one percent of the whole cohort had at least one metabolic disease. In the breast cancer group, 72.2% had one or more metabolic diseases only (HIV-negative and no evidence of depression), compared with 64.7% of the control group. From a multivariate logistic regression adjusted model, higher household socioeconomic status conferred a 19% reduction in the odds of having advanced-stage breast cancer at diagnosis, while hypertension, dyslipidaemia and HIV were not significantly associated with stage at breast cancer diagnosis in the adjusted model.Conclusions. A large proportion of women experience several comorbidities, highlighting the need to address the chronic non-communicable disease epidemic in SA and to co-ordinate multidisciplinary primary-, secondary- and tertiary-level care in the country’s complex healthcare system for better outcome.Â