Health data science最新文献

Background: Cancer is a major health problem worldwide. A growing number of cancer patients travel to hospitals outside their residential cities due to unbalanced medical resources. We aimed to evaluate the association between patterns of patient mobility and survival among patients with cancer. Methods: Data of patients hospitalized for cancer between January 2015 and December 2017 were collected from the regional data platform of an eastern coastal province of China. According to the cities of hospitalization and residency, 3 mobility patterns including intra-city, local center, and national center pattern were defined. Patients with intra-city pattern were sequentially matched to patients with the other 2 patterns on demographics, marital status, cancer type, comorbidity, and hospitalization frequency, using propensity score matching. We estimated 5-year survival and the associations between all-cause mortality and patient mobility. Results: Among 20,602 cancer patients, there were 17,035 (82.7%) patients with intra-city pattern, 2,974 (14.4%) patients with local center pattern, and 593 (2.9%) patients with national center pattern. Compared to patients with intra-city pattern, higher survival rates were observed in patients with local center pattern [5-year survival rate, 69.3% versus 65.4%; hazard ratio (HR), 0.85; 95% confidence interval (CI), 0.77 to 0.95] and in patients with national center pattern (5-year survival rate, 69.3% versus 64.5%; HR, 0.80; 95% CI, 0.67 to 0.97). Conclusions: We found significant survival disparities among different mobility patterns of patients with cancer. Improving the quality of cancer care is crucial, especially for cities with below-average healthcare resources.

Background: Previous studies suggested that smoking behavior (e.g., smoking status) was associated with an elevated risk of chronic kidney disease (CKD), yet whether this association is causal remains uncertain. Methods: We used data for half million participants aged 40 to 69 years from the UK Biobank cohort. In the traditional observational study, we used Cox proportional hazards models to calculate the associations between 2 smoking indices-smoking status and lifetime smoking index and incident CKD stages 3 to 5. Mendelian randomization (MR) approaches were used to estimate a potential causal effect. In one-sample MR, genetic variants associated with lifetime smoking index were used as instrument variables to examine the causal associations with CKD stages 3 to 5, among 344,255 UK Biobank participants with white British ancestry. We further validated our findings by a two-sample MR analysis using information from the Chronic Kidney Disease Genetics Consortium genome-wide association study. Results: In the traditional observational study, both smoking status [hazard ratio (HR): 1.26, 95% confidence interval (CI): 1.22 to 1.30] and lifetime smoking index (HR: 1.22, 95% CI: 1.20 to 1.24) were positively associated with a higher risk of incident CKD. However, both our one-sample and two-sample MR analyses showed no causal association between lifetime smoking index and CKD (all P > 0.05). The genetic instruments were validated by several statistical tests, and all sensitivity analyses showed similar results with the main model. Conclusion: Evidence from our analyses does not suggest a causal effect of smoking behavior on CKD risk. The positive association presented in the traditional observational study is possibly a result of confounding.

Importance: Heart sound auscultation is a routinely used physical examination in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized training and experience, which limits its generalizability. Deep learning, a subset of machine learning, involves training artificial neural networks to learn from large datasets and perform complex tasks with intricate patterns. Over the past decade, deep learning has been successfully applied to heart sound analysis, achieving remarkable results and accumulating substantial heart sound data for model training. Although several reviews have summarized deep learning algorithms for heart sound analysis, there is a lack of comprehensive summaries regarding the available heart sound data and the clinical applications. Highlights: This review will compile the commonly used heart sound datasets, introduce the fundamentals and state-of-the-art techniques in heart sound analysis and deep learning, and summarize the current applications of deep learning for heart sound analysis, along with their limitations and areas for future improvement. Conclusions: The integration of deep learning into heart sound analysis represents a significant advancement in clinical practice. The growing availability of heart sound datasets and the continuous development of deep learning techniques contribute to the improvement and broader clinical adoption of these models. However, ongoing research is needed to address existing challenges and refine these technologies for broader clinical use.

Importance: Climate change mitigation policies aimed at limiting greenhouse gas (GHG) emissions would bring substantial health co-benefits by directly alleviating climate change or indirectly reducing air pollution. As one of the largest developing countries and GHG emitter globally, China's carbon-peaking and carbon neutrality goals would lead to substantial co-benefits on global environment and therefore on human health. This review summarized the key findings and gaps in studies on the impact of China's carbon mitigation strategies on human health.

Highlights: There is a wide consensus that limiting the temperature rise well below 2 °C would markedly reduce the climate-related health impacts compared with high emission scenario, although heat-related mortalities, labor productivity reduction rates, and infectious disease morbidities would continue increasing over time as temperature rises. Further, hundreds of thousands of air pollutant-related mortalities (mainly due to PM_2.5 and O₃) could be avoided per year compared with the reference scenario without climate policy. Carbon reduction policies can also alleviate morbidities due to acute exposure to PM_2.5. Further research with respect to morbidities attributed to nonoptimal temperature and air pollution, and health impacts attributed to precipitation and extreme weather events under current carbon policy in China or its equivalent in other developing countries is needed to improve our understanding of the disease burden in the coming decades.

Conclusions: This review provides up-to-date evidence of potential health co-benefits under Chinese carbon policies and highlights the importance of considering these co-benefits into future climate policy development in both China and other nations endeavoring carbon reductions.

Background: The burden of neonatal infections in low- and middle-income countries and territories (LMICs) is a critical public health challenge, while our understanding of specific burden and secular trends remains limited. Methods: We gathered annual data on 15 types of neonatal infections in LMICs from 1990 to 2019 from the Global Burden of Disease 2019. Numbers, rates, percent changes, and estimated annual percentage changes of incidence and deaths were calculated. We also explored the association between disease burden, socio-demographic index (SDI), and universal health coverage index (UHCI). Results: Enteric infections and upper respiratory infections owned the top highest incidence rates for neonates in 2019. Neonatal sepsis and other neonatal infections, as well as otitis media, demonstrated an increasing trend of incidence across all 3 low- and middle-income regions. The top 3 causes of neonatal mortality in 2019 were neonatal sepsis and other neonatal infections, lower respiratory infections, and enteric infections. Between 1990 and 2019, all of the neonatal infection-related mortality rates suggested an overall decline. Sex differences could be found in the incidence and mortality of some neonatal infections, but most disease burdens decreased more rapidly in males. SDI and UHCI were both negatively associated with most of the disease burden, but there were exceptions. Conclusions: Our study serves as a vital exploration into the realities of neonatal infectious diseases in LMICs. The identified trends and disparities not only provide a foundation for future research but also underscore the critical need for targeted policy initiatives to alleviate on a global scale.

Background: The COVID-19 pandemic has caused a disproportionate impact on the sexual and gender-diverse (SGD) community. Compared with non-SGD populations, their social relations and health status are more vulnerable, whereas public health data regarding SGD are scarce. Methods: To analyze the concerns and health status of SGD individuals, this cohort study leveraged 471,371,477 tweets from 251,455 SGD and 22,644,411 non-SGD users, spanning from 2020 February 1 to 2022 April 30. The outcome measures comprised the distribution and dynamics of COVID-related topics, attitudes toward vaccines, and the prevalence of symptoms. Results: Topic analysis revealed that SGD users engaged more frequently in discussions related to "friends and family" (20.5% vs. 13.1%, P < 0.001) and "wear masks" (10.1% vs. 8.3%, P < 0.001) compared to non-SGD users. Additionally, SGD users exhibited a marked higher proportion of positive sentiment in tweets about vaccines, including Moderna, Pfizer, AstraZeneca, and Johnson & Johnson. Among 102,464 users who self-reported COVID-19 diagnoses, SGD users disclosed significantly higher frequencies of mentioning 61 out of 69 COVID-related symptoms than non-SGD users, encompassing both physical and mental health challenges. Conclusion: The results provide insights into an understanding of the unique needs and experiences of the SGD community during the pandemic, emphasizing the value of social media data in epidemiological and public health research.

Background: Disease prediction models often use statistical methods or machine learning, both with their own corresponding application scenarios, raising the risk of errors when used alone. Integrating machine learning into statistical methods may yield robust prediction models. This systematic review aims to comprehensively assess current development of global disease prediction integration models. Methods: PubMed, EMbase, Web of Science, CNKI, VIP, WanFang, and SinoMed databases were searched to collect studies on prediction models integrating machine learning into statistical methods from database inception to 2023 May 1. Information including basic characteristics of studies, integrating approaches, application scenarios, modeling details, and model performance was extracted. Results: A total of 20 eligible studies in English and 1 in Chinese were included. Five studies concentrated on diagnostic models, while 16 studies concentrated on predicting disease occurrence or prognosis. Integrating strategies of classification models included majority voting, weighted voting, stacking, and model selection (when statistical methods and machine learning disagreed). Regression models adopted strategies including simple statistics, weighted statistics, and stacking. AUROC of integration models surpassed 0.75 and performed better than statistical methods and machine learning in most studies. Stacking was used for situations with >100 predictors and needed relatively larger amount of training data. Conclusion: Research on integrating machine learning into statistical methods in prediction models remains limited, but some studies have exhibited great potential that integration models outperform single models. This study provides insights for the selection of integration methods for different scenarios. Future research could emphasize on the improvement and validation of integrating strategies.

The 5th annual Beijing Health Data Science Summit, organized by the National Institute of Health Data Science at Peking University, recently concluded with resounding success. This year, the summit aimed to foster collaboration among researchers, practitioners, and stakeholders in the field of health data science to advance the use of data for better health outcomes. One significant highlight of this year's summit was the introduction of the Abstract Competition, organized by Health Data Science, a Science Partner Journal, which focused on the use of cutting-edge data science methodologies, particularly the application of artificial intelligence in the healthcare scenarios. The competition provided a platform for researchers to showcase their groundbreaking work and innovations. In total, the summit received 61 abstract submissions. Following a rigorous evaluation process by the Abstract Review Committee, eight exceptional abstracts were selected to compete in the final round and give presentations in the Abstract Competition. The winners of the Abstract Competition are as follows:•First Prize: "Interpretable Machine Learning for Predicting Outcomes of Childhood Kawasaki Disease: Electronic Health Record Analysis" presented by researchers from the Chinese Academy of Medical Sciences, Peking Union Medical College, and Chongqing Medical University (presenter Yifan Duan).•Second Prize: "Survival Disparities among Mobility Patterns of Patients with Cancer: A Population-Based Study" presented by a team from Peking University (presenter Fengyu Wen).•Third Prize: "Deep Learning-Based Real-Time Predictive Model for the Development of Acute Stroke" presented by researchers from Beijing Tiantan Hospital (presenter Lan Lan). We extend our heartfelt gratitude to the esteemed panel of judges whose expertise and dedication ensured the fairness and quality of the competition. The judging panel included Jiebo Luo from the University of Rochester (chair), Shenda Hong from Peking University, Xiaozhong Liu from Worcester Polytechnic Institute, Liu Yang from Hong Kong Baptist University, Ma Jianzhu from Tsinghua University, Ting Ma from Harbin Institute of Technology, and Jian Tang from Mila-Quebec Artificial Intelligence Institute. We wish to convey our deep appreciation to Zixuan He and Haoyang Hong for their invaluable assistance in the meticulous planning and execution of the event. As the 2023 Beijing Health Data Science Summit comes to a close, we look forward to welcoming all participants to join us in 2024. Together, we will continue to advance the frontiers of health data science and work toward a healthier future for all.