Meta-análisis: Intervalos de confianza e Intervalos de predicción / Meta-analysis: Confidence intervals and Prediction intervals

En los informes meta-analíticos se suelen reportar varios tipos de intervalos, hecho que ha generado cierta confusión a la hora de interpretarlos. Los intervalos de confianza reflejan la incertidumbre relacionada con un número, el tamaño del efecto medio paramétrico. Los intervalos de predicción reflejan el tamaño paramétrico probable en cualquier estudio de la misma clase que los incluidos en un meta-análisis. Su interpretación y aplicaciones son diferentes. En este artículo explicamos su diferente naturaleza y cómo se pueden utilizar para responder preguntas específicas. Se incluyen ejemplos numéricos, así como su cálculo con el paquete metafor en R.(AU)

Several types of intervals are usually employed in meta-analysis, a fact that has generated some confusion when interpreting them. Confidence intervals reflect the uncertainty related to a single number, the parametric mean effect size. Prediction intervals reflect the probable parametric effect size in any study of the same class as those included in a meta-analysis. Its interpretation and applications are different. In this article we explain in de-tail their different nature and how they can be used to answer specific ques-tions. Numerical examples are included, as well as their computation with the metafor Rpackage.(AU)

Combining forecasts from advisors: The impact of advice independence and verbal versus numeric format.

Past research on advice-taking has suggested that people are often insensitive to the level of advice independence when combining forecasts from advisors. However, this has primarily been tested for cases in which people receive numeric forecasts. Recent work by Mislavsky and Gaertig (2022) shows that people sometimes employ different strategies when combining verbal versus numeric forecasts about the likelihood of future events. Specifically, likelihood judgments based on two verbal forecasts (e.g., "rather likely") are more often extreme (relative to the forecasts) than are likelihood judgments based on two numeric forecasts (e.g., "70% probability"). The goal of the present research was to investigate whether advice-takers' use of combination strategies can be sensitive to advice independence when differences in independence are highly salient and whether sensitivity to advice independence depends on the format in which advice is given. In two studies, we found that advice-takers became more extreme with their own likelihood estimate when combining forecasts from advisors who use separate evidence, as opposed to the same evidence. We also found that two verbal forecasts generally resulted in more extreme combined likelihood estimates than two numeric forecasts. However, the results did not suggest that sensitivity to advice independence depends on the format of advice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Trend and projection of the prevalence and burden of near vision loss in China and globally from 1990 to 2030: A Bayesian age-period-cohort modeling study.

Background: Few studies have investigated near vision loss (NVL) in China. To address this gap, we aimed to explore trends in the prevalence and disease burden of NVL from 1990 to 2019 and to predict trends over the next decade. Methods: Using data from the Global Burden of Disease 2019 study, we calculated the age-standardised prevalence rate (ASPR), age-specific disability-adjusted life years (DALYs), and annual percentage change (EAPC) in China and different regions. We then used the Bayesian age-period-cohort (BAPC) predictive model to predict the prevalence trends from 2020 to 2030 in both contexts. Results: At the global level, ASPRs increased from 5613.27 in 1990 to 5937.81 per 100 000 population in 2019, with an EAPC of 0.06. The ASPR in China specifically decreased from 7538.14 in 1990 to 7392.86 per 100 000 population in 2019 (EAPC = -0.02). The age-standardised DALY rate was higher in women than in men, both globally and in China. The NVL burden was relatively higher in low-income regions, low sociodemographic index regions, and the South-East Asia Region compared to other regions. The predictive model indicated that the ASR trend for NVL slowly increased at a global level after 2020, yet decreased in China. Conclusions: Despite a decline in the age-standardised prevalence of NVL in China over the next decade, the current burden remains substantial. To alleviate this burden, decision-makers should adopt inclusive approaches by involving all stakeholders.

[Translated article] Hospital pharmacy towards 2030.

Hospital Pharmacy is today a profession marked by therapeutic advances, with a proactive attitude, focussed on people and their health. The evolution of processes is constant, with the full presence of digitalisation, robotisation, and even artificial intelligence, in an environment that also requires the efficient and sustainable use of these tools. In this context, it is necessary to have a roadmap that guides the advancement of the profession and Hospital Pharmacy Services. Continuing with the philosophy of the 2020 initiative which, with the slogan "Towards the future, safely", defined the strategic lines to advance in the improvement of Hospital Pharmacy practice, the Spanish Society of Hospital Pharmacy wanted to raise the challenges the profession is currently facing and with a view to 2030. With this strategic planning objective, 20 challenges have been identified and developed, which cover the different areas of action and involvement of Hospital Pharmacy and which cover clinical activities, transversal aspects, training, and research, as well as areas related to people and to the organisations or health systems. For each of them, the objectives, standards, tools, and resources have been defined. It is also planned to provide tools that facilitate monitoring of implementation and the impact on the profession, patients, and the environment.

[Translated article] Conquering the future in hospital pharmacy: Training as a pillar of success.

The training of hospital pharmacists in the coming years must adapt and respond to constant current and future social and technological challenges, without neglecting the basic areas of the profession. It is necessary to acquire knowledge in what is known as digital comprehensive health: artificial intelligence, technology and automation, digital skills, and new forms of communication with patients, such as telemedicine and telepharmacy that are already a reality in many hospitals. We must provide knowledge in automated systems for the distribution and dispensing of medicines, robots for preparing sterile preparations, traceability systems, the use of drones in clinical care, etc. as well as training in the application of technology in pharmaceutical care, through devices and applications that help identify patients who require specific care early and effectively. In this digital scenario, new risks and challenges must be faced, such as cybersecurity and cyber resilience, which makes the training and education of healthcare professionals in general, and hospital pharmacists in particular, inexcusable. On the other hand, the appearance of increasingly complex and innovative therapies has a great impact not only on health population but also on economic and environmental issues, which makes new competencies and skills essential to develop and implement disruptive and competent financing, equity, and sustainability strategies. In this demanding and hyper-connected environment, it is understandable that the well-known "burned out worker syndrome" appears, which prevents the correct personal and professional development of the team and highlights the importance of quality training for its prevention and management. In short, in the next decade, the training of hospital pharmacists must be aimed at providing knowledge in innovation and in basic skills needed to adapt and succeed to current demands and changes.

Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases.

Introduction: Heatstroke is a serious clinical condition caused by exposure to high temperature and high humidity environment, which leads to a rapid increase of the core temperature of the body to more than 40°C, accompanied by skin burning, consciousness disorders and other organ system damage. This study aims to analyze the effect of meteorological factors on the incidence of heatstroke using machine learning, and to construct a heatstroke forecasting model to provide reference for heatstroke prevention. Methods: The data of heatstroke incidence and meteorological factors in a city in South China from May to September 2014-2019 were analyzed in this study. The lagged effect of meteorological factors on heatstroke incidence was analyzed based on the distributed lag non-linear model, and the prediction model was constructed by using regression decision tree, random forest, gradient boosting trees, linear SVRs, LSTMs, and ARIMA algorithm. Results: The cumulative lagged effect found that heat index, dew-point temperature, daily maximum temperature and relative humidity had the greatest influence on heatstroke. When the heat index, dew-point temperature, and daily maximum temperature exceeded certain thresholds, the risk of heatstroke was significantly increased on the same day and within the following 5 days. The lagged effect of relative humidity on the occurrence of heatstroke was different with the change of relative humidity, and both excessively high and low environmental humidity levels exhibited a longer lagged effect on the occurrence of heatstroke. With regard to the prediction model, random forest model had the best performance of 5.28 on RMSE and dropped to 3.77 after being adjusted. Discussion: The incidence of heatstroke in this city is significantly correlated with heat index, heatwave, dew-point temperature, air temperature and zhongfu, among which the heat index and dew-point temperature have a significant lagged effect on heatstroke incidence. Relevant departments need to closely monitor the data of the correlated factors, and adopt heat prevention measures before the temperature peaks, calling on citizens to reduce outdoor activities.

A forecasting model for suitable dental implantation in canine mandibular premolar region based on finite element analysis.

In recent years, dental implants have become a trend in the treatment of human patients with missing teeth, which may also be an acceptable method for companion animal dentistry. However, there is a gap challenge in determining appropriate implant sizes for different dog breeds and human. In this study, we utilized skull computed tomography data to create three-dimensional models of the mandibles of dogs in different sizes. Subsequently, implants of various sizes were designed and subjected to biomechanical finite element analysis to determine the optimal implant size. Regression models were developed, exploring the relationship between the average weight of dogs and the size of premolar implants. Our results illustrated that the regression equations for mean body weight (x, kg) and second premolar (PM2), third premolar (PM3), and fourth premolar (PM4) implant length (y, mm) in dogs were: y = 0.2785x + 7.8209, y = 0.2544x + 8.9285, and y = 0.2668x + 10.652, respectively; the premolar implant diameter (mm) y = 0.0454x + 3.3506, which may provide a reference for determine suitable clinical implant sizes for dogs.

[Prediction of the clinical supply of blood components in Xi'an City from 2023 to 2025].

Objective: To construct a prediction model for the clinical supply of blood components in Xi'an City from 2023 to 2025. Methods: Based on the blood supply data of the Blood Management Information System of Shaanxi Provincial Blood Center from January 2013 to December 2022, a gray prediction model and an exponential curve fitting model were used to construct the prediction model, and the optimal prediction model was determined according to the error parameters of the relevant indicators of the model. The supply of blood components in Xi'an from 2023 to 2025 was predicted. Results: The fitting equations of the exponential curve fitting model to predict the supply of suspended red blood cells, platelets and cryoprecipitate in Xi'an were, x（1）（t+1）=1.16e0.04t，x（1）（t+1）=1.04e0.12t and x（1）（t+1）=1.01e1.10t, respectively. The mean absolute errors (mean relative errors) of the exponential curve fitting model in predicting the supply of suspended red blood cells, platelets and cryoprecipitate in Xi'an were 10 488.7 (0.05%), 2 114.9 (0.08%) and 3 089.6 (0.07%), respectively, which were lower than those of the gray prediction model, about 10 488.7 (3.44%), 2 152.78 (8.20%) and 3 441.35 (7.92%), respectively. The exponential curve fitting model predicted that the clinical supply of blood components in Xi'an would increase year by year from 2023 to 2025, and the clinical supply of suspended red blood cells, platelets, and cryoprecipitate in Xi'an would increase to 409 467 U, 69 818 therapeutic volume and 94 724 U, respectively by 2025. Conclusion: The exponential curve fitting model can make a good prediction of the clinical supply of blood components in Xi'an City.

[Future Developments in Trauma Care in Germany]. / Zukunft der Traumaversorgung in Deutschland.

Trauma surgical care in Germany faces major challenges. The increasing number of cases due to demographic change, combined with reduced bed capacity, requires a rethink in many areas. In order to continue to ensure basic and standard care at a high level and across the board in the future, economic incentives must be created to maintain sufficient locations for trauma care. At the same time, there is a shortage of skilled workers that will worsen in the coming years if appropriate measures are not taken to counteract it. Structural changes will also be needed to improve cross-sector networking between outpatient and inpatient care. With the increase in outpatient care, future shortages of both bed capacity and staff shortages may be buffered.

Effects of global change on snakebite envenoming incidence up to 2050: a modelling assessment.

BACKGROUND: Human activities are driving climate, land cover, and population change (global change), and shifting the baseline geographical distribution of snakebite. The interacting effects of global change on snakes and communities at risk of snakebite are poorly understood, limiting capacity to anticipate and manage future changes in snakebite risk. METHODS: In this modelling study, we projected how global change will affect snakebite envenoming incidence in Sri Lanka, as a model system that has a high incidence of snakebite. We used the shared socioeconomic pathway (SSP) scenario analysis framework to integrate forecasts across the domains of: climate change (historical trend from WorldClim plus three underlying regional circulation models [RCMs] in the Coordinated Regional Downscaling Experiment-South Asia repository, with two emissions pathways [representative concentration pathways RCP4.5 and RCP8.5]); land cover change (Dyna-CLUE model); and human population density change (based on Gridded Population of the World data) from Jan 1, 2010 to Dec 31, 2050. Forecasts were integrated under three different development scenarios: a sustainability pathway (SSP1 and no further emissions), a middle-of-the-road pathway (SSP2 and RCP4.5), and a fossil-fuelled pathway (SSP5 and RCP8.5). For SSP2 and SSP5, we nested three different RCMs (CNRM-CM5, GFDL-CCM3, and MPI-ESM-LR; mean averaged to represent consensus) to account for variability in climate predictions. Data were used as inputs to a mechanistic model that predicted snakebite envenoming incidence based on human-snake contact patterns. FINDINGS: From 2010 to 2050, at the national level, envenoming incidence in Sri Lanka was projected to decrease by 12·0-23·0%, depending on the scenario. The rate of decrease in envenoming incidence was higher in SSP5-RCP8.5 than in SSP1 and SSP2-RCP4.5. Change in envenoming incidence was heterogenous across the country. In SSP1, incidence decreased in urban areas expected to have population growth, and with land cover changes towards anthropised classes. In SSP2-RCP4.5 and SSP5-RCP8.5, most areas were projected to have decreases in incidence (SSP5-RCP8.5 showing the largest area with incidence reductions), while areas such as the central highlands and the north of the country showed localised increases. In the model, decreases occurred with human population growth, land use change towards anthropised classes (potentially shifting occupational risk factors), and decreasing abundance of some snake species, potentially due to global warming and reduced climatic and habitat suitability, with displacement of some snake species. INTERPRETATION: Snakebite envenoming incidence was projected to decrease overall in the coming decades in Sri Lanka, but with an apparent emerging conflict with sustainability objectives. Therefore, efforts to mitigate snakebite envenoming incidence will need to consider the potential impacts of sustainability interventions, particularly related to climate and land use change and in areas where increases in incidence are projected. In view of global change, neglected tropical diseases and public health issues related to biodiversity, such as snakebite, should be managed collaboratively by both environment and health stakeholders. FUNDING: UK Medical Research Council.

Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model.

BACKGROUND: Influenza, an acute infectious respiratory disease, presents a significant global health challenge. Accurate prediction of influenza activity is crucial for reducing its impact. Therefore, this study seeks to develop a hybrid Convolution Neural Network-Long Short Term Memory neural network (CNN-LSTM) model to forecast the percentage of influenza-like-illness (ILI) rate in Hebei Province, China. The aim is to provide more precise guidance for influenza prevention and control measures. METHODS: Using ILI% data from 28 national sentinel hospitals in the Hebei Province, spanning from 2010 to 2022, we employed the Python deep learning framework PyTorch to develop the CNN-LSTM model. Additionally, we utilized R and Python to develop four other models commonly used for predicting infectious diseases. After constructing the models, we employed these models to make retrospective predictions, and compared each model's prediction performance using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and other evaluation metrics. RESULTS: Based on historical ILI% data from 28 national sentinel hospitals in Hebei Province, the Seasonal Auto-Regressive Indagate Moving Average (SARIMA), Extreme Gradient Boosting (XGBoost), Convolution Neural Network (CNN), Long Short Term Memory neural network (LSTM) models were constructed. On the testing set, all models effectively predicted the ILI% trends. Subsequently, these models were used to forecast over different time spans. Across various forecasting periods, the CNN-LSTM model demonstrated the best predictive performance, followed by the XGBoost model, LSTM model, CNN model, and SARIMA model, which exhibited the least favorable performance. CONCLUSION: The hybrid CNN-LSTM model had better prediction performances than the SARIMA model, CNN model, LSTM model, and XGBoost model. This hybrid model could provide more accurate influenza activity projections in the Hebei Province.

A novel hybrid optimization model for evaluating and forecasting air quality grades.

Air pollution has a significant global impact on natural resources and public health. Accurate prediction of air pollution is crucial for effective prevention and control measures. However, due to regional variations, different cities may have varying primary pollutants, posing new challenges for accurate prediction. In this paper, we propose a novel method called FP-RF, which integrates clustering algorithms to categorize multiple cities according to their air quality index values. Subsequently, we apply functional principal component analysis to extract the primary components of air pollution within each cluster. Furthermore, an enhanced random forest algorithm is utilized to predict air quality grades for each city. We conduct experimental evaluations using authentic historical data from Anhui Province spanning from 2018 to 2023. The results unequivocally establish the effectiveness of our model, with an average accuracy rate of 98.6% in forecasting six pollution grades and 96.04% accuracy in predicting 16 prefecture-level cities, surpassing performance compared to other baseline models.

[New Radiation Therapy Concepts in Non-Metastatic Lung Cancer]. / Neue radioonkologische Therapiekonzepte beim nicht metastasierten Lungenkarzinom.

Radiotherapy plays a critical role in the management of non-metastatic lung cancer, offering curative potential and symptom relief. It serves as a primary treatment modality or adjuvant therapy post-surgery, enhancing local control and survival rates. Modern techniques like Stereotactic Body Radiotherapy (SBRT) enable precise tumor targeting, minimizing damage to healthy tissue and reducing treatment duration. The synergy between radiotherapy and systemic treatments, including immunotherapy, holds promise in improving outcomes. Immunotherapy augments the immune response against cancer cells, potentially enhancing radiotherapy's efficacy. Furthermore, radiotherapy's ability to modulate the tumor microenvironment complements the immunotherapy's mechanism of action. As a result, the combination of radiotherapy and immunotherapy may offer superior tumor control and survival benefits. Moreover, the integration of radiotherapy with surgery and chemotherapy in multidisciplinary approaches maximizes treatment efficacy while minimizing toxicity. Herein we present an overview on modern radiotherapy and potential developments in the close future.

Willingness to trade-off years of life for an HIV cure - an experimental exploration of affective forecasting.

BACKGROUND: In the US, 1.2 million people live with HIV (PWH). Despite having near-normal life expectancies due to antiretroviral therapy (ART), many PWH seek an HIV cure, even if it means risking their lives. This willingness to take risks for a cure raises questions about "affective forecasting biases," where people tend to overestimate the positive impact of future events on their well-being. We conducted a study to test two interventions to mitigate affective forecasting in the decisions of PWH about taking HIV cure medication. METHODS: We recruited PWH to complete a 30-minute survey about their current quality of life (QoL) and the QoL they anticipate after being cured of HIV, and assigned them to either no additional intervention, to one of two interventions intended to reduce affective forecasting bias, or to both interventions: (1) a defocusing intervention designed to broaden the number of life domains people consider when imagining life changes associated with new circumstances (e.g. HIV cure); and (2) an adaptation intervention to help them gauge fading of strong emotions over time. The study design included a 2 × 2 design: defocusing (yes/no) x adaptation (yes/no) intervention. We assessed PWH's willingness to take hypothetical HIV sterilizing cure medication using the Time Trade-Off (TTO) and their quality of life predictions with WHOQOL-HIV. RESULTS: 296 PWH participated. Counter to what we had hypothesized, neither intervention significantly reduced PWH's willingness to trade time for a cure. Instead, the defocusing intervention increased their willingness to trade time (IRR 1.77, p = 0.03). Exploratory analysis revealed that PWH with lower current quality of life who received the defocusing intervention were more willing to trade time for a cure. CONCLUSION: These negative findings suggest that either these biases are difficult to overcome in the settings of HIV curative medication or other factors beyond affective forecasting biases influence willingness to participate in HIV curative studies, such as respondents' current quality of life.

Forecasting rheumatoid arthritis patient arrivals by including meteorological factors and air pollutants.

The burden of rheumatoid arthritis (RA) has gradually elevated, increasing the need for medical resource redistribution. Forecasting RA patient arrivals can be helpful in managing medical resources. However, no relevant studies have been conducted yet. This study aims to construct a long short-term memory (LSTM) model, a deep learning model recently developed for novel data processing, to forecast RA patient arrivals considering meteorological factors and air pollutants and compares this model with traditional methods. Data on RA patients, meteorological factors and air pollutants from 2015 to 2022 were collected and normalized to construct moving average (MA)- and autoregressive (AR)-based and LSTM models. After data normalization, the root mean square error (RMSE) was adopted to evaluate models' forecast ability. A total of 2422 individuals were enrolled. Not using the environmental data, the RMSEs of the MA- and AR-based models' test sets are 0.131, 0.132, and 0.117 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they are 0.110, 0.130, and 0.112 for the univariate LSTM models. Considering meteorological factors and air pollutants, the RMSEs of the MA- and AR-based model test sets were 0.142, 0.303, and 0.164 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they were 0.108, 0.119, and 0.109 for the multivariable LSTM models. Our study demonstrated that LSTM models can forecast RA patient arrivals more accurately than MA- and AR-based models for datasets of all three sizes. Considering the meteorological factors and air pollutants can further improve the forecasting ability of the LSTM models. This novel method provides valuable information for medical management, the optimization of medical resource redistribution, and the alleviation of resource shortages.