Cancer is a leading cause of morbidity and mortality in the U.S., with significant financial implications for patients, especially those with limited access to resources. Particularly, the rising costs of cancer treatment have placed considerable strain on cancer survivors. This study evaluates the impact of the Affordable Care Act (ACA) on healthcare costs for cancer survivors. Using data from the Medical Expenditure Panel Survey (2010–2019), we analyzed data for 9,041 U.S. adult cancer survivors aged 18–64. ACA implementation (post- vs. pre-ACA period) was the primary exposure. We examined changes in out-of-pocket (OOP) healthcare spending and its proportion to total healthcare spending and family income of cancer survivors. Our findings indicate that post-ACA, mean OOP spending decreased significantly across various income groups: by 48.3

ObjectivesAmong patients undergoing heart transplantation, BTT-tMCS therapy demonstrates superior clinical efficacy compared to nonbridged HTx; however, its cost-effectiveness among Chinese patients remain uncertain. To evaluate the cost-effectiveness of BTT-tMCS therapy compared with nonbridged HTx among transplant-eligible Chinese patients from the healthcare payer’s perspective.MethodsThe cost-effectiveness analysis for this economic evaluation was conducted at a qualified heart transplantation center in Central China. Participants included patients admitted since 2018 who underwent either BTT-tMCS or nonbridged HTx, identified retrospectively from local electronic medical records and supplemented by corresponding questionnaires. Propensity score matching (PSM) was employed to obtain a homogeneous group of HTx patients to generate model input parameters. A Markov model simulates lifetime disease progression and associated costs for HTx patients, with monetary values standardized to 2023. Sensitivity analyses were performed to test the internal validity of the model’s conclusions. The model evaluated two competing treatment strategies: BTT-tMCS and standard care. Lifetime health care cost and quality-adjusted life-years (QALYs) of the simulated cohort.ResultsPSM identified 40 matched subjects who met the predefined criteria, creating a homogeneous cohort for analysis. Compared to nonbridged HTx, BTT-tMCS yielded higher lifetime incremental costs (510,361 RMB) and effects (5.88 QALYs), resulting in an ICER of 28966.88 RMB/QALY. Sensitivity analyses demonstrated the robustness of these findings, with a 90% probability of cost-effectiveness achieved at a willingness-to-pay threshold exceeding 80422.20 RMB/QALY.ConclusionIn this economic evaluation study, BTT-tMCS therapy was more likely to be cost-effective compared with non-bridged HTx.

Importance:Primary brain and central nervous system cancer (collectively referred to as CNS cancer) comprises 2% of all human cancers and poses significant health and economic challenges in the United States. Objective:To analyze CNS cancer burden in the US, stratified by time, location (state and division), sex, age group, and Sociodemographic Index (SDI). Design, Setting, and Participants:This cross-sectional study involved a repeated analysis of Global Burden of Disease Study (GBD) 2021 data in 2024. Using data from 183 sources, CNS cancer metrics in the US were estimated across states and years. US CNS cancer metrics across all sexes and age groups were included in the GBD. Exposure:CNS cancer diagnosis. Main Outcomes and Measures:Overall and age-standardized estimates of the incidence, prevalence, mortality, disability-adjusted life-years (DALYs), years of life lost, and years lived with disability per 100 000 population, including 95% uncertainty intervals (UIs), and time trends. Results:In 2021, for all age groups and sexes across the US, there were 31 780 incident cases (95% UI, 29971.1 to 32843.9). Age-standardized incidence, DALYs, and mortality rates per 100 000 population were 6.91 (95% UI, 6.58 to 7.12), 134.38 (95% UI, 129.83 to 137.95), and 4.1 (95% UI, 3.87 to 4.22), respectively. Despite no significant change observed in the overall incidence between 1990 and 2021, DALY and mortality rates decreased by 15.77% (95% UI, -17.75% to -13.68%) and 8.41% (95% UI, -11.09% to -6.22%), respectively. Substantial geographic variability was noted. Mississippi, Alabama, Kentucky, and Kansas (West North Central and East South Central divisions) and West Virginia faced persistently high burdens over the past 30 years. Sex differences were evident; disease burden was consistently higher in males compared with females. Age-specific estimates showed a bimodal distribution: the youngest group (<5 years) showed a significant decrease in incidence rate (-34.42% to -11.56%), whereas older age groups (>70 years) experienced increasing trends. DALYs and mortality rates were negatively correlated with SDI (ρ = -0.6860 and ρ = -0.6391; P < .001). Conclusions and Relevance:These findings provide valuable insights into the CNS cancer burden across the US by age, sex, location, and SDI, enabling better public health status assessments, health care policy restructuring, and resource redistribution for improved care.

Introduction Social media (SM) has drastically improved communication and information dissemination among various demographics. Purpose This systematic review study aims to evaluate the use of social media as a health promotion tool in Saudi Arabia. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed in carrying out this research. Different databases were used to search for relevant articles from 2015 to 2024. Results Eighteen studies met the inclusion criteria. Multiple social media platforms were utilized in these efforts, including YouTube, WhatsApp, Snapchat, and the X platform. The articles related to diabetic patients showed that the majority used SM to gather information about disease management. Advantages of using SM include improving patient education and awareness of diabetes, as well as facilitating patient-doctor communication. Barriers included breaching patient privacy, unreliable data, and a lack of easy access for the elderly and those with lower educational levels. YouTube, the X platform, and WhatsApp were especially popular. Moreover, 47% of participants in diabetic study and 96% of participants with celiac disease were using social media as a source of information for disease management. Conclusion SM has been found to have a drastic impact on the knowledge of public health and specific diseases. Decision-makers need to support more studies on SM as a health promotion tool. Moreover, multiple media platforms could complement each other to provide audio-visual learning experiences, such as the X platform and YouTube, for easier access.

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.

While numerous studies over the last decade have highlighted the important influence of environmental factors on mental health, globally applicable data on physical surroundings are still limited. Access to such data and the possibility to link them to epidemiological studies is critical to unlocking the relationship of environment, brain and behaviour and promoting positive future mental health outcomes. The Adolescent Brain Cognitive Development (ABCD) Study is the largest ongoing longitudinal and observational study exploring brain development and child health among children from 21 sites across the United States. Here we describe the linking of the ABCD study data with satellite-based “Urban-Satellite” (UrbanSat) variables consisting of 11 satellite-data derived environmental indicators associated with each subject’s residential address at their baseline visit, including land cover and land use, nighttime lights, and population characteristics. We present these UrbanSat variables and provide a review of the current literature that links environmental indicators with mental health, as well as key aspects that must be considered when using satellite data for mental health research. We also highlight and discuss significant links of the satellite data variables to the default mode network clustering coefficient and cognition. This comprehensive dataset provides the foundation for large-scale environmental epidemiology research.

Although numerous studies over the past decade have highlighted the influence of environmental factors on mental health, globally applicable data on physical surroundings such as land cover and urbanicity are still limited. The urban environment is complex and composed of many interacting factors. To understand how urban living affects mental health, simultaneous measures of multiple environmental factors need to be related to symptoms of mental illness, while considering the underlying brain structure and function. So far, most studies have assessed individual urban environmental factors, such as greenness, in isolation and related them to individual symptoms of mental illness. We have refined the satellite-based ‘Urban Satellite’ (UrbanSat) measures, consisting of 11 satellite-data-derived environmental indicators, and linked them through residential addresses with participants of the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD Study is the largest ongoing longitudinal and observational study exploring brain development and child health, involving 11,800 children, assessed at 9–16 years of age, from 21 sites across the USA. Here we describe linking of the ABCD Study data with UrbanSat variables, including each subject’s residential address at their baseline visit, including land cover and land use, nighttime lights and population characteristics. We also highlight and discuss important links of the satellite-data variables to the default mode network clustering coefficient and cognition. This comprehensive dataset provides an important tool for advancing neurobehavioral research on urbanicity during the critical developmental periods of childhood and adolescence. In this Perspective, the authors present a model of assessing urban environmental factors’ impact on mental health by using UrbanSat measures and data from adolescents in the ABCD Study.

Introduction: The global mental health crisis, compounded by the challenges of the COVID-19 pandemic, underscores the urgent need for accessible mental health care solutions. Telehealth services have emerged as a promising technology to address barriers to access mental health services. However, population-based studies examining telehealth utilization among individuals with depression are limited. Methods: Using data from the National Cancer Institute's Health Information National Trends Survey (HINTS) of 2022 (n = 4502), we investigated telehealth utilization among individuals diagnosed with depression in the United States. We employed multivariable logistic regression analysis to assess the association, adjusting for demographics, health behaviors, health status, trust in the medical system, and access to transportation. We also studied the factors that motivated the use of telehealth among individuals diagnosed with depression. Results: In the multivariable adjusted logistic regression models, individuals diagnosed with depression (AOR 2.59, 95% CI 1.96-3.42) were significantly more likely to use telehealth services relative to individuals with no depression diagnosis. Other factors associated with increased telehealth use included women (AOR 1.36, 95% CI 1.07-1.72), Hispanic ethnicity (AOR 1.78, 95% CI 1.28-2.48), being married or living with a partner (AOR 1.30, 95% CI 1.05-1.62), frequent healthcare visits (AOR 2.31, 95% CI 1.71-3.11), health insurance coverage (AOR 1.86, 95% CI 1.04-3.34), confidence in self-care (AOR 1.38, 95% CI 1.07-1.78), and lack of reliable transportation (AOR 1.57, 95% CI 1.01-2.42). Major motivation factors that influenced telehealth use among individuals with depression primarily included convenience, such as reduced travel times, as well as clinicians’ recommendations. Conclusion: Telehealth is a promising option for accessing mental health care, particularly for those with depression. Further research is needed to understand how well telehealth works and how it can be combined with traditional care, ensuring fair costs and keeping information safe.

Objective: To examine the relationship between knowing that a friend or family member became ill with, or died from, COVID-19 and receiving a vaccine dose within four months of the FDA's Emergency Use Authorization.Methods: A national sample of 1,517 respondents were surveyed from April 7 to April 12, 2021, 1,193 of whom were eligible for the vaccine when the data were collected.Results: Respondents who knew someone who became ill with COVID-19 (AOR = 2.32, 95 % CI 1.74-3.09) or knew someone who died (AOR = 2.29, 95 % CI 1.32-3.99) from COVID-19 were more likely to receive at least a single COVID-19 vaccine dose.Conclusion: Encouraging people to share their COVID-19 illness and bereavement experiences with their local network such as friends, families, social-networks and via social media might help increase vaccine uptake.(c) 2023 Elsevier Ltd. All rights reserved.

Background Antimicrobial resistance (AMR) is an urgent global health challenge and a critical threat to modern health care. Quantifying its burden in the WHO Region of the Americas has been elusive-despite the region's long history of resistance surveillance. This study provides comprehensive estimates of AMR burden in the Americas to assess this growing health threat.Methods Weestimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with AMR for 23 bacterial pathogens and 88 pathogen-drug combinations for countries in the WHO Region of the Americas in 2019. We obtained data from mortality registries, surveillance systems, hospital systems, systematic literature reviews, and other sources, and applied predictive statistical modelling to produce estimates of AMR burden for all countries in the Americas. Five broad components were the backbone of our approach: the number of deaths where infection had a role, the proportion of infectious deaths attributable to a given infectious syndrome, the proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of pathogens resistant to an antibiotic class, and the excess risk of mortality (or duration of an infection) associated with this resistance. We then used these components to estimate the disease burden by applying two counterfactual scenarios: deaths attributable to AMR (compared to an alternative scenario where resistant infections are replaced with susceptible ones), and deaths associated with AMR (compared to an alternative scenario where resistant infections would not occur at all). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. Findings We estimated 569,000 deaths (95% UI 406,000-771,000) associated with bacterial AMR and 141,000 deaths (99,900-196,000) attributable to bacterial AMR among the 35 countries in the WHO Region of the Americas in 2019. Lower respiratory and thorax infections, as a syndrome, were responsible for the largest fatal burden of AMR in the region, with 189,000 deaths (149,000-241,000) associated with resistance, followed by bloodstream infections (169,000 deaths [94,200-278,000]) and peritoneal/intra-abdominal infections (118,000 deaths [78,600-168,000]). The six leading pathogens (by order of number of deaths associated with resistance) were Staphylococcus aureus , Escherichia coli , Klebsiella pneumoniae , Streptococcus pneumoniae , Pseudomonas aeruginosa , and Acinetobacter baumannii. Together, these pathogens were responsible for 452,000 deaths (326,000-608,000) associated with AMR. Methicillin-resistant S. aureus predominated as the leading pathogen-drug combination in 34 countries for deaths attributable to AMR, while aminopenicillin-resistant E. coli was the leading pathogen-drug combination in 15 countries for deaths associated with AMR. Interpretation Given the burden across different countries, infectious syndromes, and pathogen-drug combinations, AMR represents a substantial health threat in the Americas. Countries with low access to antibiotics and basic health-care services often face the largest age-standardised mortality rates associated with and attributable to AMR in the region, implicating specific policy interventions. Evidence from this study can guide mitigation efforts that are tailored to the needs of each country in the region while informing decisions regarding funding and resource allocation. Multisectoral and joint cooperative efforts among countries will be a key to success in tackling AMR in the Americas. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.Copyright (c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

BackgroundAssistive technology (AT) refers to assistive products (AP) and associated systems and services that are relevant for function, independence, well-being, and quality of life for individuals with disabilities. There is a high unmet need for AT for persons with disabilities and this is worse for persons with cognitive and mental or psychosocial disabilities (PDs). Further, information and knowledge on AT for PDs is limited. ObjectiveThe aim of this review was to explore the pattern of AT use among persons with PDs and its associated socioeconomic and health benefits. MethodsThe review was reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), and we conducted systematic searches in the 4 databases: PubMed, Embase.com, APA PsycInfo (Ebsco), and Web of Science (Core Collection) with the following index terms: “Assistive Technology,” “Self-Help Devices,” “Quality of Life,” “Activities of Daily Living,” “Mental Disorders.” We included only AT individuals with PDs can independently use without reliance on a provider. Identified papers were exported to EndNote (Clarivate) and we undertook a narrative synthesis of the included studies. ResultsIn total, 5 studies were included in the review which reported use of different AT for schizophrenia, bipolar disorder, depression and anxiety disorders. The APs described in the included studies are Palm tungsten T3 handheld computer, MOBUS, personal digital assistant, automated pill cap, weighted chain blankets, and smartphone function. All the AT products identified in the studies were found to be easily usable by individuals with PDs. The APs reported in the included studies have broad impact and influence on social function, productivity, and treatment or management. The studies were heterogeneous and were all conducted in high-income countries. ConclusionsOur study contributes to and strengthens existing evidence on the relevance of AT for PDs and its potential to support socioeconomic participation and health. Although AT has the potential to improve function and participation for individuals with PDs; this review highlights that research on the subject is limited. Further research and health policy changes are needed to improve research and AT service provision for individuals with PDs especially in low-income settings. Trial RegistrationPROSPERO CRD42022343735; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=343735

Background:The rapid increase in electronic nicotine product (ENP) use among young people has been a global public health challenge, given the potential harm of ENPs and nicotine dependence. Many countries have recently introduced legislations to regulate ENPs, but the impacts of these policies are poorly understood. This systematic review aims to critically synthesise empirical studies on the effects of global regulations regarding ENPs on the prevalence of use, health outcomes and their determinants, using the 4A marketing mix framework (acceptability, affordability, accessibility and awareness).Methods:Following the PRISMA guideline, we searched PubMed, Embase, Scopus, Web of Science, Academic Search Complete, Business Source Complete, and APA PsycINFO databases from inception until June 14, 2022 and performed citation searches on the included studies. Reviewed literature was restricted to peer-reviewed, English-language articles. We included all pre-post and quasi-experimental studies that evaluated the impacts of e-cigarette policies on the prevalence of ENP use and other health outcomes. A modified Joanna Briggs Institute (JBI) Critical Appraisal checklist for quasi-experimental studies was used for quality assessment. Due to heterogeneity of the included studies, we conducted a narrative synthesis of evidence.Results:Of 3991 unduplicated records screened, 48 (1.2%) met the inclusion criteria, most were from high-income countries in North America and Europe and 26 studies measured self-reported ENPs use. Flavour restrictions significantly decreased youth ENP use and taxation reduced adult use; mixed results were found for the impacts of age restrictions. Indoor vaping restrictions and the European Tobacco Products Directive (TPD) did not seem to reduce ENP use based on existing studies. Changes in determinants such as sales and perceptions corroborated our conclusions. Few studies assessed the impacts of other regulations such as advertising restrictions and retail licensing requirements.Conclusions:Flavour restrictions and taxes have the strongest evidence to support effective control of ENPs, while others need powerful enforcement and meaningful penalties to ensure their effectiveness. Future research should focus on under-examined policies and differential impacts across sociodemographic characteristics and countries.Registration:PROSPERO CRD42022337361.

Background The global burden of lower respiratory infections (LRIs) and corresponding risk factors in children older than 5 years and adults has not been studied as comprehensively as it has been in children younger than 5 years. We assessed the burden and trends of LRIs and risk factors across a groups by sex, for 204 countries and territories. Methods In this analysis of data for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we used dinician-diagnosed pneumonia or bronchiolitis as our case definition for LRIs. We included International Classification of Diseases 9th edition codes 079.6, 466-469, 470.0, 480-482.8, 483.0-483.9, 484.1-484.2, 484.6-484.7, and 487-489 and International Classification of Diseases 10th edition codes A48.1, A70, B97.4 B97.6, 109-115.8, J16 J16.9, J20-121.9, J91.0, P23.0 P23.4, and U04 U04.9. We used the Cause of Death Ensemble modelling strategy to analyse 23109 site-years of vital r *stration data, 825 site-years of sample vital registration data, 1766 site-years of verbal autopsy data, and 681 site-years of mortality surveillance data. We used DisMod-MR 2.1, a Bayesian metaregression tool, to analyse age sex-specific incidence and prevalence data identified via systematic reviews of the literature, population-based survey data, and daims and inpatient data. Additio y, we estimated age sex-specific LRI mortality that is attributable to the independent effects of 14 risk factors. Findings Globally, in 2019, we estimated that there were 257 million (95% uncertainty interval [UI] 240-275) LRI incident episodes in males and 232 million (217-248) in females. In the same year, LRIs accounted for 1.30 million (95% UI 1.18-1.42) male deaths and 1.20 million (1.07-1.33) female deaths. Age-standardised incidence and mortality rates were 1.17 times (95% UI 1.16-1.18) and 1.31 times (95% UI 1.23-1.41) greater in males than in fe es in 2019. Between 1990 and 2019, LRI incidence and mortality rates declined at different rates across age groups and an increase in LRI episodes and deaths was estimated among all adult age groups, with males aged 70 years and older having the highest increase in LRI episodes (126.0% [95% UI 121.4-131.1]) and deaths (100.0% [83.4-115.9]). During the same period, LRI episodes and deaths in children younger than 15 years were estimated to have decreased, and the greatest dedine was observed for LRI deaths in males younger than 5 years (-70.7% [-77.2 to 61.8]). The leading risk factors for LRI mortality varied across age groups and sex. More than half of global LRI deaths in children younger than 5 years were attributable to child wasting (population attributable fraction [PAF] 53.0% [95% UI 37.7-61.8] in males and 56.4% [40.7-65.1] in females), and more than a quarter of LRI deaths among those aged 5-14 years were attributable to household air pollution (PAF 26.0% [95% UI 16.6-35.5] for males and PAF 25.8% [16.3-35.4] for females). PAFs of male LRI deaths attributed to smoking were 20.4% (95% UI 15.4-25.2) in those aged 15-49 years, 305% (24.1-36. 9) in those aged 50-69 years, and 21.9% (16. 8-27. 3) in those aged 70 years and older. PAFs of female LRI deaths attributed to household air pollution were 21.1% (95% UI 14.5-27.9) in those aged 15-49 years and 18 " 2% (12.5-24.5) in those aged 50-69 years. For females aged 70 years and older, the leading risk factor, ambient particulate matter, was responsible for 11-7% (95% UI 8.2-15.8) of LRI deaths. Interpretation The patterns and progress in reducing the burden of LRIs and key risk factors for mortality varied across age groups and sexes. The progress seen in children you - than 5 years was dearly a result of targeted interventions, such as vaccination and reduction of exposure to risk factors. Similar interventions for other age groups could contribute to the achievement of multiple Sustainable Development Goals targets, induding promoting wellbeing at all ages and reducing health inequalities. Interventions, including addressing risk factors such as child wasting, smoking, ambient particulate matter pollution, and household air pollution, would prevent deaths and reduce health disparities. Copyright 2022 The Author(s). Published by Elsevier Ltd.

Background Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. We set out to investigate important attributes and train popular machine learning (ML) algorithms to correctly classify neonates with a low Apgar scores from an imbalanced learning perspective. Methods We analyzed 7716 induced vaginal deliveries from the electronic birth registry of the Kilimanjaro Christian Medical Centre (KCMC). 733 (9.5%) of which constituted of low (< 7) Apgar score neonates. The ‘extra-tree classifier’ was used to assess features’ importance. We used Area Under Curve (AUC), recall, precision, F-score, Matthews Correlation Coefficient (MCC), balanced accuracy (BA), bookmaker informedness (BM), and markedness (MK) to evaluate the performance of the selected six (6) machine learning classifiers. To address class imbalances, we examined three widely used resampling techniques: the Synthetic Minority Oversampling Technique (SMOTE) and Random Oversampling Examples (ROS) and Random undersampling techniques (RUS). We applied Decision Curve Analysis (DCA) to evaluate the net benefit of the selected classifiers. Results Birth weight, maternal age, and gestational age were found to be important predictors for the low Apgar score following induced vaginal delivery. SMOTE, ROS and and RUS techniques were more effective at improving “recalls” among other metrics in all the models under investigation. A slight improvement was observed in the F1 score, BA, and BM. DCA revealed potential benefits of applying Boosting method for predicting low Apgar scores among the tested models. Conclusion There is an opportunity for more algorithms to be tested to come up with theoretical guidance on more effective rebalancing techniques suitable for this particular imbalanced ratio. Future research should prioritize a debate on which performance indicators to look up to when dealing with imbalanced or skewed data.

Active commuting may hold a potential for preventing adverse health outcomes. However, evidence of the association of active commuting and the risk of health outcomes remains debatable. The current study systematically and quantitatively summarised research findings on the association between active commuting and the risk of the mentioned health outcomes. We comprehensively searched four databases (PubMed, EMBASE, Web of Science and Open Grey) from inception to 2 August 2020 for observational studies investigating the associations among adult population. Summary relative risks (RRs) and 95% CIs were estimated for the association. Heterogeneity was investigated using Cochran’s Q test and the I2 statistic. Restricted cubic splines were used to evaluate linear and nonlinear relations. The search yielded 7581 initial references. We included 28 articles in the meta-analysis. Compared with inactive commuting, active commuting reduced the risk of obesity (RR=0.88, 95% CI 0.83 to 0.94, I2=69.1%), hypertension (RR=0.95, 95% CI 0.87 to 1.04, I2=82.2%) and diabetes (RR=0.82, 95% CI 0.76 to 0.90, I2=44.5%). Restricted cubic splines showed linear associations between active commuting and obesity, hypertension and diabetes (Pnonlinearity=0.640; Pnonlinearity=0.886; Pnonlinearity=0.099). As compared with the lowest active commuting group, the risk of obesity, hypertension and diabetes in the highest active commuting group were reduced by 13% (95% CI 0.82 to 0.93, I2=65.2%); 6% (95% CI 0.86 to 1.02, I2=75.2%) and 19% (95% CI 0.73 to 0.91, I2=49.8%) respectively. Active commuting seemed to be associated with lower risk of obesity, hypertension and diabetes. However, the results should be interpreted cautiously because this meta-analysis was based solely on observational studies.PROSPERO registration numberCRD42020202723.

1Department of Epidemiology and Health Statistics, Zhengzhou University, Zhengzhou, People’s Republic of China; 2Department of Science and Laboratory Technology, Dar es Salaam Institute of Technology, Dar es Salaam, Tanzania; 3Edward J. Bloustein School of Planning and Public Policy, Rutgers University, New Brunswick, NJ, USA; 4College of Sanquan, Xinxiang Medical University, Xinxiang, People’s Republic of China; 5Department of Epidemiology and Applied Biostatistics, Kilimanjaro Christian Medical University College, Moshi, Tanzania Objective: The goal of this study was to establish the most efficient boosting method in predicting neonatal low Apgar scores following labor induction intervention and to assess whether resampling strategies would improve the predictive performance of the selected boosting algorithms. Methods: A total of 7716 singleton births delivered from 2000 to 2015 were analyzed. Cesarean deliveries following labor induction, deliveries with abnormal presentation, and deliveries with missing Apgar score or delivery mode information were excluded. We examined the effect of resampling approaches or data preprocessing on predicting low Apgar scores, specifically the synthetic minority oversampling technique (SMOTE), borderline-SMOTE, and the random undersampling (RUS) technique. Sensitivity, specificity, precision, area under receiver operating curve (AUROC), F-score, positive predicted values (PPV), negative predicted values (NPV) and accuracy of the three (3) boosting-based ensemble methods were used to evaluate their discriminative ability. The ensemble learning models tested include adoptive boosting (AdaBoost), gradient boosting (GB) and extreme gradient boosting method (XGBoost). Results: The prevalence of low (<7) Apgar scores was 9.5% (n = 733). The prediction models performed nearly similar in their baseline mode. Following the application of resampling techniques, borderline-SMOTE significantly improved the predictive performance of all the boosting-based ensemble methods under observation in terms of sensitivity, F1-score, AUROC and PPV. Conclusion: Policymakers, healthcare informaticians and neonatologists should consider implementing data preprocessing strategies when predicting a neonatal outcome with imbalanced data to enhance efficiency. The process may be more effective when borderline-SMOTE technique is deployed on the selected ensemble classifiers. However, future research may focus on testing additional resampling techniques, performing feature engineering, variable selection and optimizing further the ensemble learning hyperparameters.

Objective The goal of this study was to establish the most efficient boosting method in predicting neonatal low Apgar scores following labor induction intervention and to assess whether resampling strategies would improve the predictive performance of the selected boosting algorithms. Methods A total of 7716 singleton births delivered from 2000 to 2015 were analyzed. Cesarean deliveries following labor induction, deliveries with abnormal presentation, and deliveries with missing Apgar score or delivery mode information were excluded. We examined the effect of resampling approaches or data preprocessing on predicting low Apgar scores, specifically the synthetic minority oversampling technique (SMOTE), borderline-SMOTE, and the random undersampling (RUS) technique. Sensitivity, specificity, precision, area under receiver operating curve (AUROC), F-score, positive predicted values (PPV), negative predicted values (NPV) and accuracy of the three (3) boosting-based ensemble methods were used to evaluate their discriminative ability. The ensemble learning models tested include adoptive boosting (AdaBoost), gradient boosting (GB) and extreme gradient boosting method (XGBoost). Results The prevalence of low (<7) Apgar scores was 9.5% (n = 733). The prediction models performed nearly similar in their baseline mode. Following the application of resampling techniques, borderline-SMOTE significantly improved the predictive performance of all the boosting-based ensemble methods under observation in terms of sensitivity, F1-score, AUROC and PPV. Conclusion Policymakers, healthcare informaticians and neonatologists should consider implementing data preprocessing strategies when predicting a neonatal outcome with imbalanced data to enhance efficiency. The process may be more effective when borderline-SMOTE technique is deployed on the selected ensemble classifiers. However, future research may focus on testing additional resampling techniques, performing feature engineering, variable selection and optimizing further the ensemble learning hyperparameters.

Introduction: Following an increased use of labor induction procedure to prevent adverse maternal and fetal outcomes in Sub-Saharan Africa, hitting the best algorithm that accurately classify subjects in need of the intervention is of paramount importance. This study aimed at comparing the potential benefits of applying machine learning (ML) algorithms over the conventional logistic regression model in predicting the use of labor induction intervention in pregnant women attending one of the tertiary hospitals in north Tanzania for delivery. Methods: We conducted a secondary data analysis of the Kilimanjaro Christian Medical Centre (KCMC) birth registry database for women with uncomplicated pregnancies from the year 2000 to 2015. We excluded observations with non-vertex presentation and those with missing information on labor induction status. Model accuracy and Area under the receiver operating characteristic curve (AUC - ROC) were used to assess the discriminative ability of the selected models. We plotted the decision curve analysis (DCA) to assess the clinical utility of the models under observation. Results: A total of 21,578 deliveries were analyzed. Among these, 8814 (41%) were induced during the study period. Among the selected machine learning models, Random forest algorithm exhibited the best performance in terms of accuracy [0.75; 95%CI (0.73 – 0.76)] and AUC-ROC [AUC-ROC: 0.75; 95% CI (0.74 – 0.76)] compared to other models including logistic regression. Among assessed maternal attributes, parity, maternal age, body mass index, gestational age and birthweight were deemed most important predictors for labor induction intervention. Conclusion: The selected machine learning methods offered better computational performance compared to the conventional logistic regression model in predicting the use of labor induction intervention. The current study lends substantial support to the use of machine learning models in predicting the use of labor induction intervention.

Objectives We aimed at identifying the important variables for labour induction intervention and assessing the predictive performance of machine learning algorithms. Setting We analysed the birth registry data from a referral hospital in northern Tanzania. Since July 2000, every birth at this facility has been recorded in a specific database. Participants 21 578 deliveries between 2000 and 2015 were included. Deliveries that lacked information regarding the labour induction status were excluded. Primary outcome Deliveries involving labour induction intervention. Results Parity, maternal age, body mass index, gestational age and birth weight were all found to be important predictors of labour induction. Boosting method demonstrated the best discriminative performance (area under curve, AUC=0.75: 95% CI (0.73 to 0.76)) while logistic regression presented the least (AUC=0.71: 95% CI (0.70 to 0.73)). Random forest and boosting algorithms showed the highest net-benefits as per the decision curve analysis. Conclusion All of the machine learning algorithms performed well in predicting the likelihood of labour induction intervention. Further optimisation of these classifiers through hyperparameter tuning may result in an improved performance. Extensive research into the performance of other classifier algorithms is warranted.