OBJECTIVE: To examine changes in multiple myeloma incidence and mortality rates during 1982-2018, and to estimate its incidence, mortality, and prevalence for 2019-2043.
METHODS: Population-based statistical modelling study; analysis of and projections based on Australian Institute of Health and Welfare multiple myeloma incidence, mortality, and survival data.
METHODS: Australia, 1982-2018 (historical data) and projections to 2043.
METHODS: Changes in multiple myeloma incidence and mortality rates, 1982-2018, determined by joinpoint regression analysis (age-standardised to 2021 Australian population); projection of rates to 2043 based on age-period-cohort models; estimated 5- and 30-year prevalence of multiple myeloma (modified counting method).
RESULTS: The incidence of multiple myeloma increased during 1982-2018 (eg, annual percentage change [APC], 2006-2018, 1.9%; 95% confidence interval [CI], 1.7-2.2%), but the mortality rate declined during 1990-2018 (APC, -0.4%; 95% CI, -0.5% to -0.2%). The age-standardised incidence rate was projected to increase by 14.9% during 2018-2043, from 8.7 in 2018 to 10.0 (95% CI, 9.4-10.7) new cases per 100 000 population in 2043; the mortality rate was projected to decline by 27.5%, from 4.0 to 2.9 (95% CI, 2.6-3.3) deaths per 100 000 population. The annual number of people newly diagnosed with multiple myeloma was estimated to increase by 89.2%, from 2120 in 2018 to 4012 in 2043; the number of deaths from multiple myeloma was projected to increase by 31.7%, from 979 to 1289. The number of people living with multiple myeloma up to 30 years after initial diagnosis was projected to increase by 163%, from 10 288 in 2018 to 27 093 in 2043, including 13 019 people (48.1%) diagnosed during the preceding five years.
CONCLUSIONS: Although the decline in the mortality rate was projected to continue, the projected increases in the incidence and prevalence of multiple myeloma in Australia over the next 25 years indicate that investment in prevention and early detection research, and planning for prolonged treatment and care, are needed.

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We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand.

暂无摘要。

The realisation of the low-carbon transition of the energy system in resource-intensive regions, as embodied by Shanxi Province, depends on a thorough understanding of the factors impacting the power sector\'s carbon emissions and an accurate prediction of the peak trend. Because of this, the power industry\'s carbon emissions in Shanxi province are measured in this article from 1995 to 2020 using data from the Intergovernmental Panel on Climate Change (IPCC). To obtain a deeper understanding of the factors impacting carbon emissions in the power sector, factor decomposition is performed using the Logarithmic Mean Divisia Index (LMDI). Second, in order to precisely mine the relationship between variables and carbon emissions, the Sparrow Search Algorithm (SSA) aids in the optimisation of the Long Short-Term Memory (LSTM). In order to implement SSA-LSTM-based carbon peak prediction in the power industry, four development scenarios are finally built up. The findings indicate that: (1) There has been a fluctuating upward trend in Shanxi Province\'s total carbon emissions from the power industry between 1995 and 2020, with a cumulative growth of 372.10 percent. (2) The intensity of power consumption is the main factor restricting the rise of carbon emissions, contributing -65.19%, while the per capita secondary industry contribution factor, contributing 158.79%, is the main driver of the growth in emissions. (3) While the baseline scenario and the rapid development scenario fail to peak by 2030, the low carbon scenario and the green development scenario peak at 243,991,100 tonnes and 258,828,800 tonnes, respectively, in 2025 and 2028. (4) Based on the peak performance and the decomposition results, resource-intensive cities like Shanxi\'s power industry should concentrate on upgrading and strengthening the industrial structure, getting rid of obsolete production capacity, and encouraging the faster development of each factor in order to help the power sector reach peak carbon performance.

暂无摘要。

The traditional method for power load forecasting is susceptible to various factors, including holidays, seasonal variations, weather conditions, and more. These factors make it challenging to ensure the accuracy of forecasting results. Additionally, there is a limitation in extracting meaningful physical signs from power data, which ultimately reduces prediction accuracy. This paper aims to address these issues by introducing a novel approach called VCAG (Variable Mode Decomposition-Convolutional Neural Network-Attention Mechanism-Gated Recurrent Unit) for combined power load forecasting. In this approach, we integrate Variable Mode Decomposition (VMD) with Convolutional Neural Network (CNN). VMD is employed to decompose power load data, extracting valuable time-frequency features from each component. These features then serve as input for the CNN. Subsequently, an attention mechanism is applied to give importance to specific features generated by the CNN, enhancing the weight of crucial information. Finally, the weighted features are fed into a Gated Recurrent Unit (GRU) network for time series modeling, ultimately yielding accurate load forecasting results.To validate the effectiveness of our proposed model, we conducted experiments using two publicly available datasets. The results of these experiments demonstrate that our VCAG method achieves high accuracy and stability in power load forecasting, effectively overcoming the limitations associated with traditional forecasting techniques. As a result, this approach holds significant promise for broad applications in the field of power load forecasting.

The vegetable sector is a vital pillar of society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. In this paper, we exploit the average daily price data of six distinct types of vegetables sourced from seven key wholesale markets in Beijing, spanning from 2009 to 2023. Upon training an LSTM model, we discovered that it exhibited exceptional performance on the test dataset. Demonstrating robust predictive performance across various vegetable categories, the LSTM model shows commendable generalization abilities. Moreover, LSTM model has a higher accuracy compared to several machine learning methods, including CNN-based time series forecasting approaches. With R2 score of 0.958 and MAE of 0.143, our LSTM model registers an enhancement of over 5% in forecast accuracy relative to conventional machine learning counterparts. Therefore, by predicting vegetable prices for the upcoming week, we envision this LSTM model application in real-world settings to aid growers, consumers, and policymakers in facilitating informed decision-making. The insights derived from this forecasting research could augment market transparency and optimize supply chain management. Furthermore, it contributes to the market stability and the balance of supply and demand, offering a valuable reference for the sustainable development of the vegetable industry.

Introduction Acute appendicitis is a common reason for acute abdominal pain. It has a high perforation rate of 20%. Diagnosis of acute appendicitis is usually through well-known clinical signs and symptoms. Radiologic imaging is by and large carried out in peculiar cases with indistinct signs and symptoms. Although various scoring methods are available for screening and diagnosis, those have inadequate validity to accurately predict the severity of acute appendicitis. From the differential counts, the neutrophil-to-lymphocyte ratio (NLR) is an economical and straightforward measure of subclinical inflammation. NLR may be a useful marker for predicting the onset and severity of appendicitis because of the insight it gives into immunological and inflammatory pathways. In this study, we aimed to determine the association between NLR and acute appendicitis among adult patients to differentiate between perforated and non-perforated appendicitis in a tertiary care hospital in Tamil Nadu, India. Methods This was a cross-sectional study conducted in the Department of General Surgery of a deemed university in Chennai, Tamil Nadu. The study was conducted from March 2022 to December 2022. Patients aged 18 years and above undergoing appendicectomy surgery were included in the study. Patients with hematology disorders, chronic kidney disease, chronic liver disease, chronic obstructive pulmonary disease, asthma, cancer, or auto-immune diseases, and any viral, bacterial, or parasitic infections were excluded. Pregnant women were also excluded from the study. After obtaining informed consent from the patients, blood samples were collected as and when they were diagnosed as acute appendicitis. Laboratory analysis for complete hemogram including white blood cell (WBC) count, neutrophil, and lymphocyte count was carried out using an automated hematology analyzer. Prevalence of perforated appendicitis was reported as a percentage. The receiver-operating characteristic (ROC) curve was developed for NLR in differentiating perforated and non-perforated appendicitis. Data were entered in Microsoft Excel 2023. These analyses were carried out in STATA 12.0 (StataCorp, College Station, Texas, USA). Results A total of 212 patients aged 18 years and above were included in the study. Among them 93 (43.9%) were male and 119 (56.1%) were female. Prevalence of perforated appendicitis observed intra-operatively was 29.7% and non-perforated appendicitis was 70.3%. The mean (SD) of NLR among patients with perforated appendicitis was 8.8 (5.1) and non-perforated appendicitis was 3.2 (2.4) with a statistically significant difference (p-value < 0.0001). ROC curve with a cut-off value of 3.78 NLR, had sensitivity of 65.9% and specificity of 93.1% in differentiating perforated and non-perforated appendicitis. The positive predictive value (PPV) and negative predictive values (NPV) were reported as 85.7% and 81.2%, respectively. Conclusion NLR has a reasonable validity in differentiating perforated and non-perforated appendicitis. NLR may be useful in low-resource settings where routine confirmatory radiological procedures like computed tomography scans are not available.

Accurate predictive modeling of pandemics is essential for optimally distributing biomedical resources and setting policy. Dozens of case prediction models have been proposed but their accuracy over time and by model type remains unclear. In this study, we systematically analyze all US CDC COVID-19 forecasting models, by first categorizing them and then calculating their mean absolute percent error, both wave-wise and on the complete timeline. We compare their estimates to government-reported case numbers, one another, as well as two baseline models wherein case counts remain static or follow a simple linear trend. The comparison reveals that around two-thirds of models fail to outperform a simple static case baseline and one-third fail to outperform a simple linear trend forecast. A wave-by-wave comparison of models revealed that no overall modeling approach was superior to others, including ensemble models and errors in modeling have increased over time during the pandemic. This study raises concerns about hosting these models on official public platforms of health organizations including the US CDC which risks giving them an official imprimatur and when utilized to formulate policy. By offering a universal evaluation method for pandemic forecasting models, we expect this study to serve as the starting point for the development of more accurate models.