Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction.

In this paper, we address the critical task of 24-h streamflow forecasting using advanced deep-learning models, with a primary focus on the transformer architecture which has seen limited application in this specific task. We compare the performance of five different models, including persistence, long short-term memory (LSTM), Seq2Seq, GRU, and transformer, across four distinct regions. The evaluation is based on three performance metrics: Nash-Sutcliffe Efficiency (NSE), Pearson's r, and normalized root mean square error (NRMSE). Additionally, we investigate the impact of two data extension methods: zero-padding and persistence, on the model's predictive capabilities. Our findings highlight the transformer's superiority in capturing complex temporal dependencies and patterns in the streamflow data, outperforming all other models in terms of both accuracy and reliability. Specifically, the transformer model demonstrated a substantial improvement in NSE scores by up to 20% compared to other models. The study's insights emphasize the significance of leveraging advanced deep learning techniques, such as the transformer, in hydrological modeling and streamflow forecasting for effective water resource management and flood prediction.

Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso.

With the widespread application of machine learning in various fields, enhancing its accuracy in hydrological forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on daily-scale streamflow and explores the application of the Lasso feature selection method alongside three machine learning models (long short-term memory, LSTM; transformer for time series, TTS; random forest, RF) in short-term streamflow prediction. Through comparative experiments, we found that the Lasso method significantly enhances the model's performance, with a respective increase in the generalization capabilities of the three models by 21, 12, and 14%. Among the selected features, lagged streamflow and precipitation play dominant roles, with streamflow closest to the prediction date consistently being the most crucial feature. In comparison to the TTS and RF models, the LSTM model demonstrates superior performance and generalization capabilities in streamflow prediction for 1-7 days, making it more suitable for practical applications in hydrological forecasting in the Haihe River Basin and similar regions. Overall, this study deepens our understanding of feature selection and machine learning models in hydrology, providing valuable insights for hydrological simulations under the influence of complex human activities.

Exploring dental professionals' outlook on the future of dental care amidst the integration of artificial intelligence in dentistry: a pilot study in Pakistan.

OBJECTIVE: The purpose of this study is to explore the perspectives, familiarity, and readiness of dental faculty members regarding the integration and application of artificial intelligence (AI) in dentistry, with a focus on the possible effects on dental education and clinical practice. METHODOLOGY: In a mix-method cross-sectional quantitative and quantitative study conducted between June 1st and August 30th, 2023, the perspectives of faculty members from a public sector dental college in Pakistan regarding the function of AI were explored. This study used qualitative as well as quantitative techniques to analyse faculty's viewpoints on the subject. The sample size was comprised of twenty-three faculty members. The quantitative data was analysed using descriptive statistics, while the qualitative data was analysed using theme analysis. RESULTS: Position-specific differences in faculty familiarity underscore the value of individualized instruction. Surprisingly few had ever come across AI concepts in their professional lives. Nevertheless, many acknowledged that AI had the potential to improve patient outcomes. The majority thought AI would improve dentistry education. Participants suggested a few dental specialties where AI could be useful. CONCLUSION: The study emphasizes the significance of addressing in dental professionals' knowledge gaps about AI. The promise of AI in dentistry calls for specialized training and teamwork between academic institutions and AI developers. Graduates of dentistry programs who use AI are better prepared to navigate shifting environments. The study highlights the positive effects of AI and the value of faculty involvement in maximizing its potential for better dental education and practice.

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition.

Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5.

Prediction model for future OHCAs based on geospatial and demographic data: An observational study.

This study used demographic data in a novel prediction model to identify areas with high risk of out-of-hospital cardiac arrest (OHCA) in order to target prehospital preparedness. We combined data from the nationwide Danish Cardiac Arrest Registry with geographical- and demographic data on a hectare level. Hectares were classified in a hierarchy according to characteristics and pooled to square kilometers (km2). Historical OHCA incidence of each hectare group was supplemented with a predicted annual risk of at least 1 OHCA to ensure future applicability. We recorded 19,090 valid OHCAs during 2016 to 2019. The mean annual OHCA rate was highest in residential areas with no point of public interest and 100 to 1000 residents per hectare (9.7/year/km2) followed by pedestrian streets with multiple shops (5.8/year/km2), areas with no point of public interest and 50 to 100 residents (5.5/year/km2), and malls with a mean annual incidence per km2 of 4.6. Other high incidence areas were public transport stations, schools and areas without a point of public interest and 10 to 50 residents. These areas combined constitute 1496 km2 annually corresponding to 3.4% of the total area of Denmark and account for 65% of the OHCA incidence. Our prediction model confirms these areas to be of high risk and outperforms simple previous incidence in identifying future risk-sites. Two thirds of out-of-hospital cardiac arrests were identified in only 3.4% of the area of Denmark. This area was easily identified as having multiple residents or having airports, malls, pedestrian shopping streets or schools. This result has important implications for targeted intervention such as automatic defibrillators available to the public. Further, demographic information should be considered when implementing such interventions.

Global burden of liver cirrhosis 1990-2019 and 20 years forecast: results from the global burden of disease study 2019.

BACKGROUND: Cirrhosis is a disease that imposes a heavy burden worldwide, but its incidence varies widely by region. Therefore, we analysed data on the incidence and mortality of cirrhosis in 204 countries and territories from 1990-2019 and projected the disease development from 2019-2039. METHODS: Data on the incidence and mortality of liver cirrhosis from 1990 to 2019 were acquired from the public Global Burden of Disease (GBD) study. In addition, the average annual percentage change (AAPC) and estimated annual percentage change (EAPC) of the age-standardized rate (ASR) of cirrhosis in different regions were calculated. The estimates of risk factor exposure were summarized, and the proportion of causes and risk factors of liver cirrhosis and their relationship with the human development index (HDI) and socio-demographic index (SDI) were analysed. Trends in the incidence of cirrhosis in 2019-2039 were predicted using Nordpred and BAPC models. RESULTS: Globally, the ASR of cirrhosis incidence decreased by 0.05% per year from 25.7/100,000 in 1990 to 25.3/100,000 in 2019. The mortality risk associated with cirrhosis is notably lower in females than in males (13 per 100,000 vs 25 per 100,000). The leading cause of cirrhosis shifted from hepatitis B to C. Globally, alcohol use increased by 14%. In line, alcohol use contributed to 49.3% of disability-adjusted life years (DALYs) and 48.4% of global deaths from liver cirrhosis. Countries with a low ASR in 1990 experienced a faster increase in cirrhosis, whereas in 2019, the opposite was observed. In countries with high SDI, the ASR of cirrhosis is generally lower. Finally, projections indicate that the number and incidence of cirrhosis will persistently rise from 2019-2039. CONCLUSIONS: Cirrhosis poses an increasing health burden. Given the changing etiology, there is an imperative to strengthen the prevention of hepatitis C and alcohol consumption, to achieve early reduce the incidence of cirrhosis.

This study is an updated assessment of liver cirrhosis prevalence trends in 204 countries worldwide and the first to project trends over the next 20 years.The disease burden of cirrhosis is still increasing, and despite the decline in ASR, the number and prevalence of cirrhosis will continue to increase over the next two decades after 2019.It is alarming that the global surge in alcohol use is accompanied by an increase in DALYs and deaths due to liver cirrhosis.Liver cirrhosis remains a noteworthy public health event, and our study can further guide the development of national healthcare policies and the implementation of related interventions.

Forecasting municipal solid plastic waste generation and management policy using system dynamics: a case study of Khulna City in Bangladesh.

A comprehensive analysis of municipal solid plastic waste (MSPW) management while emphasizing plastic pollution severity in coastal cities around the world is mandatory to alleviate the augmenting plastic waste footprint in nature. Thus, decision-makers' persuasion for numerous management solutions of MSPW flow-control can be met through meditative systematic strategies at the regional level. To forecast solutions focused on systematic policies, an agent-based system dynamics (ASD) model has been developed and simulated from 2023 to 2040 while considering significant knit parameters for MSPW management of Khulna City in Bangladesh. Baseline simulation results show that per-capita plastic waste generation will increase to 11.6 kg by 2040 from 8.92 kg in 2023. Eventually, the landfilled quantity of plastic waste has accumulated to 70,000 tons within 18 years. Moreover, the riverine discharge has increased to 834 tons in 2040 from a baseline quantity of 512 tons in 2023. So the plastic waste footprint index (PWFI) value rises to 24 by 2040. Furthermore, the absence of technological initiatives is responsible for the logarithmic rise of non-recyclable plastic waste to 1.35*1000=1350 tons. Finally, two consecutive policy scenarios with baseline factors such as controlled riverine discharge, increased collection and separation of plastic waste, expansion of recycle business, and locally achievable plastic conversion technologies have been simulated. Therefore, policy 2, with 69% conversion, 80% source separation, and 50% riverine discharge reduction of MSPW, has been found adequate from a sustainability perspective with the lowest PWFI ranges of 3.97 to 1.07 alongside a per-capita MSPW generation of 7.63 to 10 kg from 2023 till 2040.

A Geospatial Analysis of the Availability, Distribution, and Accessibility of Neurosurgical Facilities, Workforce, and Infrastructure in Nigeria; and Projection Towards 2050.

OBJECTIVE: There has been a modest but progressive increase in the neurosurgical workforce, training, and service delivery in Nigeria in the last 2 decades. However, these resources are unevenly distributed. This study aimed to quantitatively assess the availability and distribution of neurosurgical resources in Nigeria while projecting the needed workforce capacity up to 2050. METHODS: An online survey of Nigerian neurosurgeons and residents assessed the country's neurosurgical infrastructure, workforce, and resources. The results were analyzed descriptively, and geospatial analysis was used to map their distribution. A projection model was fitted to predict workforce targets for 2022-2050. RESULTS: Out of 86 neurosurgery-capable health facilities, 65.1% were public hospitals, with only 17.4% accredited for residency training. Dedicated hospital beds and operating rooms for neurosurgery make up only 4.0% and 15.4% of the total, respectively. The population disease burden is estimated at 50.2 per 100,000, while the operative coverage was 153.2 cases per neurosurgeon. There are currently 132 neurosurgeons and 114 neurosurgery residents for a population of 218 million (ratio 1:1.65 million). There is an annual growth rate of 8.3%, resulting in a projected deficit of 1113 neurosurgeons by 2030 and 1104 by 2050. Timely access to neurosurgical care ranges from 21.6% to 86.7% of the population within different timeframes. CONCLUSIONS: Collaborative interventions are needed to address gaps in Nigeria's neurosurgical capacity. Investments in training, infrastructure, and funding are necessary for sustainable development and optimized outcomes.

Artificial intelligence and future perspectives in Forensic Medicine: a systematic review.

Objective: Artificial intelligence (AI) is the ability of a computer machine to display human capabilities such as reasoning, learning, planning, and creativity. Such processing technology receives the data (already prepared or collected), processes them, using models and algorithms, and answers questions about forecasting and decision-making. AI systems are also able to adapt their behavior by analyzing the effects of previous actions and working then autonomously. Artificial intelligence is already present in our lives, even if it often goes unnoticed (shopping networked, home automation, vehicles). Even in the medical field, artificial intelligence can be used to analyze large amounts of medical data and discover matches and patterns to improve diagnosis and prevention. In forensic medicine, the applications of AI are numerous and are becoming more and more valuable. Method: A systematic review was conducted, selecting the articles in one of the most widely used electronic databases (PubMed). The research was conducted using the keywords "AI forensic" and "machine learning forensic". The research process included about 2000 Articles published from 1990 to the present. Results: We have focused on the most common fields of use and have been then 6 macro-topics were identified and analyzed. Specifically, articles were analyzed concerning the application of AI in forensic pathology (main area), toxicology, radiology, Personal identification, forensic anthropology, and forensic psychiatry. Conclusion: The aim of the study is to evaluate the current applications of AI in forensic medicine for each field of use, trying to grasp future and more usable applications and underline their limitations.

Early warning of systemic risk in stock market based on EEMD-LSTM.

With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market.

Study protocol for a scoping review of Lyme disease prediction methodologies.

INTRODUCTION: In the temperate world, Lyme disease (LD) is the most common vector-borne disease affecting humans. In North America, LD surveillance and research have revealed an increasing territorial expansion of hosts, bacteria and vectors that has accompanied an increasing incidence of the disease in humans. To better understand the factors driving disease spread, predictive models can use current and historical data to predict disease occurrence in populations across time and space. Various prediction methods have been used, including approaches to evaluate prediction accuracy and/or performance and a range of predictors in LD risk prediction research. With this scoping review, we aim to document the different modelling approaches including types of forecasting and/or prediction methods, predictors and approaches to evaluating model performance (eg, accuracy). METHODS AND ANALYSIS: This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review guidelines. Electronic databases will be searched via keywords and subject headings (eg, Medical Subject Heading terms). The search will be performed in the following databases: PubMed/MEDLINE, EMBASE, CAB Abstracts, Global Health and SCOPUS. Studies reported in English or French investigating the risk of LD in humans through spatial prediction and temporal forecasting methodologies will be identified and screened. Eligibility criteria will be applied to the list of articles to identify which to retain. Two reviewers will screen titles and abstracts, followed by a full-text screening of the articles' content. Data will be extracted and charted into a standard form, synthesised and interpreted. ETHICS AND DISSEMINATION: This scoping review is based on published literature and does not require ethics approval. Findings will be published in peer-reviewed journals and presented at scientific conferences.

Digital Health Education for the Future: The SaNuRN (Santé Numérique Rouen-Nice) Consortium's Journey.

SaNuRN is a five-year project by the University of Rouen Normandy (URN) and the Côte d'Azur University (CAU) consortium to optimize digital health education for medical and paramedical students, professionals, and administrators. The project includes a skills framework, training modules, and teaching resources. In 2027, SaNuRN is expected to train a significant portion of the 400,000 health and paramedical professions students at the French national level. Our purpose is to give a synopsis of the SaNuRN initiative, emphasizing its novel educational methods and how they will enhance the delivery of digital health education. Our goals include showcasing SaNuRN as a comprehensive program consisting of a proficiency framework, instructional modules, and educational materials and explaining how SaNuRN is implemented in the participating academic institutions. SaNuRN is a project aimed at educating and training health-related and paramedics students in digital health. The project results from a cooperative effort between URN and CAU, covering four French departments. The project is based on the French National Referential on Digital Health (FNRDH), which defines the skills and competencies to be acquired and validated by every student in the health, paramedical, and social professions curricula. The SaNuRN team is currently adapting the existing URN and CAU syllabi to FNRDH and developing short-duration video capsules of 20 to 30 minutes to teach all the relevant material. The project aims to ensure that the largest student population earns the necessary skills, and it has developed a two-tier system involving facilitators who will enable the efficient expansion of the project's educational outreach and support the students in learning the needed material efficiently. With a focus on real-world scenarios and innovative teaching activities integrating telemedicine devices and virtual professionals, SaNuRN is committed to enabling continuous learning for healthcare professionals in clinical practice. The SaNuRN team introduced new ways of evaluating healthcare professionals by shifting from a knowledge-based to a competencies-based evaluation, aligning with the Miller teaching pyramid and using the Objective Structured Clinical Examination and Script Concordance Test in digital health education. Drawing on the expertise of URN, CAU, and their public health and digital research laboratories and partners, the SaNuRN project represents a platform for continuous innovation, including telemedicine training and living labs with virtual and interactive professional activities. The SaNuRN project provides a comprehensive, personalized 30-hour training package for health and paramedical students, addressing all 70 FNRDH competencies. The program is enhanced using AI and NLP to create virtual patients and professionals for digital healthcare simulation. SaNuRN teaching materials are open-access. The project collaborates with academic institutions worldwide to develop educational material in digital health in English and multilingual formats. SaNuRN offers a practical and persuasive training approach to meet the current digital health education requirements.

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend.

BACKGROUND: Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. METHODS: We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. RESULTS: Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. CONCLUSION: Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

Dermatology workforce projections in the United States, 2021 to 2036.

BACKGROUND: There has been a growing imbalance between supply of dermatologists and demand for dermatologic care. To best address physician shortages, it is important to delineate supply and demand patterns in the dermatologic workforce. The goal of this study was to explore dermatology supply and demand over time. METHODS: We conducted a cross-sectional analysis of workforce supply and demand projections for dermatologists from 2021 to 2036 using data from the Health Workforce Simulation Model from the National Center for Health Workforce Analysis. Estimates for total workforce supply and demand were summarized in aggregate and stratified by rurality. Scenarios with status quo demand and improved access were considered. RESULTS: Projected total supply showed a 12.45% increase by 2036. Total demand increased 12.70% by 2036 in the status quo scenario. In the improved access scenario, total supply was inadequate for total demand in any year, lagging by 28% in 2036. Metropolitan areas demonstrated a relative supply surplus up to 2036; nonmetropolitan areas had at least a 157% excess in demand throughout the study period. In 2021 adequacy was 108% and 39% adequacy for metropolitan and nonmetropolitan areas, respectively; these differences were projected to continue through 2036. CONCLUSIONS: The findings suggest that the dermatology physician workforce is inadequate to meet the demand for dermatologic services in nonmetropolitan areas. Furthermore, improved access to dermatologic care would bolster demand and especially exacerbate workforce inadequacy in nonmetropolitan areas. Continued efforts are needed to address health inequities and ensure access to quality dermatologic care for all.

The Future for Women's and Gender-Related Healthcare for Women's Health Nurse Practitioners.

This commentary examines the future of women's health and gender-related healthcare for Women's Health Nurse Practitioners (WHNPs) within the framework of National Association of Nurse Practitioners in Women's Health's (NPWH's) mission and vision. Emphasizing the importance of addressing menopause, maternal health, and reproductive health, it discusses the significance of WHNP education, certification, and workforce contributions. Despite their critical role, challenges including recognition as maternity care providers and disseminating WHNP-specific outcomes remain. WHNPs play a vital role in providing comprehensive healthcare for women and gender diverse individuals. Guided by the mission and vision of the NPWH, WHNPs address key priority areas including menopause, maternal health, and reproductive health. However, challenges such as recognition as maternity care providers, publishing outcomes specific to WHNP practice, and collecting comprehensive workforce data persist. To advance women's and gender-related healthcare, concerted efforts are needed to address challenges faced by WHNPs. This includes advocating for recognition within maternity care, promoting the dissemination of WHNP-specific research, and improving workforce data collection. By overcoming these challenges, WHNPs can continue to play a pivotal role in promoting the health and well-being of women and gender diverse individuals, shaping the future of women's health and gender-related healthcare delivery.

Projections of Single-level and Multilevel Spinal Instrumentation Procedure Volume and Associated Costs for Medicare Patients to 2050.

BACKGROUND: Instrumented spinal fusions can be used in the treatment of vertebral fractures, spinal instability, and scoliosis or kyphosis. Construct-level selection has notable implications on postoperative recovery, alignment, and mobility. This study sought to project future trends in the implementation rates and associated costs of single-level versus multilevel instrumentation procedures in US Medicare patients aged older than 65 years in the United States. METHODS: Data were acquired from the Centers for Medicare & Medicaid Services from January 1, 2000, to December 31, 2019. Procedure costs and counts were abstracted using Current Procedural Terminology codes to identify spinal level involvement. The Prophet machine learning algorithm was used, using a Bayesian Inference framework, to generate point forecasts for 2020 to 2050 and 95% forecast intervals (FIs). Sensitivity analyses were done by comparing projections from linear, log-linear, Poisson and negative-binomial, and autoregressive integrated moving average models. Costs were adjusted for inflation using the 2019 US Bureau of Labor Statistics' Consumer Price Index. RESULTS: Between 2000 and 2019, the annual spinal instrumentation volume increased by 776% (from 7,342 to 64,350 cases) for single level, by 329% (from 20,319 to 87,253 cases) for two-four levels, by 1049% (from 1,218 to 14,000 cases) for five-seven levels, and by 739% (from 193 to 1,620 cases) for eight-twelve levels (P < 0.0001). The inflation-adjusted reimbursement for single-level instrumentation procedures decreased 45.6% from $1,148.15 to $788.62 between 2000 and 2019, which is markedly lower than for other prevalent orthopaedic procedures: total shoulder arthroplasty (-23.1%), total hip arthroplasty (-39.2%), and total knee arthroplasty (-42.4%). By 2050, the number of single-level spinal instrumentation procedures performed yearly is projected to be 124,061 (95% FI, 87,027 to 142,907), with associated costs of $93,900,672 (95% FI, $80,281,788 to $108,220,932). CONCLUSIONS: The number of single-level instrumentation procedures is projected to double by 2050, while the number of two-four level procedures will double by 2040. These projections offer a measurable basis for resource allocation and procedural distribution.