Global Epidemiology最新文献_第4页

Mortality trends associated with hypertension and atrial fibrillation: A CDC WONDER data analysis 高血压和房颤相关的死亡率趋势：CDC WONDER数据分析

Global Epidemiology

Pub Date : 2025-09-26 DOI: 10.1016/j.gloepi.2025.100217

Saad Khan , Usama Idrees , Safa Nasir , Fatima Naveed , Aqsa Munir , Muhammad Junaid Iqbal , Rizwan Ahmad , Muhammad Ubaid Hussain , Fathimathul henna , Khansha Saeed , Amin ul Haq , Ali Ahmed

Background

Hypertension (HTN) and atrial fibrillation (AF) are prevalent cardiovascular disorders that significantly increase the risk of serious complications such as stroke and heart failure, leading to elevated mortality rates. Despite the established relationship between HTN and AF, there is a lack of comprehensive evidence on mortality trends and disparities across various demographic groups in the United States.

Objectives

This study aims to analyze the nationwide mortality trends due to HTN-AF from 1999 to 2020 and to identify disparities across different demographics. The goals include understanding the impact of HTN-AF on public health and informing targeted screening and therapeutic strategies.

Methods

Data on mortality figures related to HTN and AF were obtained from the Centers for Disease Control and Prevention's (CDC) Wide-Ranging Online Data for Epidemiologic Research (WONDER) database. The analysis included age-adjusted mortality rates (AAMR) and crude mortality rates (CMR) stratified by gender, age, race/ethnicity, and geographic regions. The Joinpoint software was used to analyze temporal trends in age-adjusted mortality rate (AAMR). Data were obtained from publicly available multiple causes of death records via the CDC WONDER database.

Results

From 1999 to 2020, the AAMR due to hypertension-attributable factors rose sharply from 2.89 to 23.98 per 100,000 (APC: 4.8 %). Males had higher AAMRs than females, with Black or African American populations seeing the steepest increases. Regionally, the West had the highest AAMR, and rural areas experienced the most significant rise, with micropolitan areas showing the highest APC.

Conclusion

HTN-AF mortality has been increasing steadily across all genders, races, and regions. The study underscores the importance of improving healthcare policies, bridging coverage gaps, and enhancing education and awareness to curb these alarming trends. Addressing the disparities in healthcare access and promoting cardiovascular health initiatives are crucial for reducing HTN-AF-related mortality.

背景：高血压（HTN）和心房颤动（AF）是常见的心血管疾病，可显著增加中风和心力衰竭等严重并发症的风险，导致死亡率升高。尽管HTN和房颤之间存在既定的关系，但缺乏关于美国不同人口群体死亡率趋势和差异的综合证据。目的分析1999年至2020年全国HTN-AF死亡率趋势，并确定不同人口统计学差异。目标包括了解HTN-AF对公共卫生的影响，并告知有针对性的筛查和治疗策略。方法HTN和AF相关的死亡率数据来自美国疾病控制与预防中心（CDC）广泛的流行病学研究在线数据（WONDER）数据库。分析包括按性别、年龄、种族/民族和地理区域分层的年龄调整死亡率（AAMR）和粗死亡率（CMR）。使用Joinpoint软件分析年龄调整死亡率（AAMR）的时间趋势。数据来自CDC WONDER数据库中公开的多种死因记录。结果1999 - 2020年，高血压归因因素导致的AAMR由2.89 / 10万急剧上升至23.98 / 10万（APC: 4.8%）。男性的aamr高于女性，其中黑人或非裔美国人的增幅最大。从地区上看，西部地区的AAMR最高，农村地区的APC上升最为显著，而小城市地区的APC最高。结论htn - af死亡率在所有性别、种族和地区均呈稳步上升趋势。该研究强调了改善医疗保健政策、缩小覆盖差距以及加强教育和意识以遏制这些令人担忧的趋势的重要性。解决医疗保健获取方面的差异和促进心血管健康举措对于降低htn - af相关死亡率至关重要。

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引用次数: 0

Mathematical modelling and time series clustering of Mpox outbreak: A comparative study of the top 10 affected countries and implications for future outbreak management m痘暴发的数学建模和时间序列聚类：十大受影响国家的比较研究及其对未来暴发管理的影响

Global Epidemiology

Pub Date : 2025-07-27 DOI: 10.1016/j.gloepi.2025.100214

Mark-Daniels Tamakloe , Ametus Kuuwill , Ibrahim Osumanu , Helina Siripi

The 2022 Mpox outbreak, characterized by its rapid cross-continental spread beyond traditionally endemic regions, presented a renewed threat to global health security. This study presents a comparative epidemiological analysis of the ten countries most affected by Mpox, integrating mathematical modelling with time series clustering, the first of its kind to analyze the 2022 WHO Mpox data. By applying an SIR-based model to estimate the effective transmission rate, basic reproduction number, time of first infection, and initial susceptible population, the study captures both the pace and persistence of Mpox spread, while critically assessing the effectiveness of national public health responses. Key findings reveal a paradox in North America: Canada exhibited a high transmission rate but a low reproduction number, indicating an elevated transmission potential per contact alongside limited secondary spread. This is likely due to concurrent containment measures or behavioral factors. In contrast, the United States, despite having a lower initial transmission rate, recorded a higher reproduction number. Similarly, Germany exhibited a similar risk trajectory, with elevated reproductive numbers despite robust infrastructure. The cases in the USA and Germany are likely due to systemic health and socio-political policy gaps and delayed behavior-targeted interventions, particularly in the population of men having sex with men (MSM). In Latin America, countries such as Peru and Mexico suffered disproportionately, likely due to limited access to healthcare, which compounded transmission dynamics and reproductive potential. Our study demonstrates that effective Mpox control is not solely dependent on health infrastructure, but also on behavioral targeting, equity, and adaptive health governance. This calls for cross-country and intercontinental collaborations towards combating current and future health shocks, including epidemics.

2022年麻疹疫情的特点是迅速跨大陆传播，超出了传统流行地区，对全球卫生安全构成了新的威胁。本研究对受麻疹影响最严重的10个国家进行了比较流行病学分析，将数学模型与时间序列聚类相结合，这是首次对2022年世卫组织麻疹数据进行分析。通过应用基于免疫特性的模型来估计有效传播率、基本繁殖数、首次感染时间和初始易感人群，该研究捕捉到了痘传播的速度和持续时间，同时严格评估了国家公共卫生应对措施的有效性。主要发现揭示了北美的一个悖论：加拿大表现出高传播率，但繁殖数量低，表明每次接触的传播潜力升高，同时继发传播有限。这可能是由于同时采取的遏制措施或行为因素造成的。相比之下，尽管美国的初始传播率较低，但其繁殖数量却较高。同样，德国也表现出类似的风险轨迹，尽管基础设施健全，但该国的生育数量仍在上升。美国和德国的病例可能是由于系统性的卫生和社会政治政策差距以及延迟的针对行为的干预，特别是在男男性行为人群中。在拉丁美洲，秘鲁和墨西哥等国家受到的影响尤为严重，可能是由于获得医疗保健的机会有限，这加剧了传播动态和生殖潜力。我们的研究表明，有效的麻疹控制不仅依赖于卫生基础设施，还依赖于行为目标、公平和适应性卫生治理。这就要求开展跨国和洲际合作，以应对当前和未来的健康冲击，包括流行病。

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引用次数: 0

Statistical inference and effect measures in abstracts of major HIV and AIDS journals, 1987–2022: A systematic review 1987-2022年主要HIV和AIDS期刊摘要的统计推断和效应度量：系统回顾

Global Epidemiology

Pub Date : 2025-07-25 DOI: 10.1016/j.gloepi.2025.100213

Andreas Stang , Henning Schäfer , Ahmad Idrissi-Yaghir , Christoph M. Friedrich , Matthew P. Fox

Objectives

With the emergence of HIV/AIDS journals, the development of the reporting of statistical inference and effect measures in published abstracts can be examined from the beginning in a new field. The aim of this study was to describe time trends of statistical inference and effect measure reporting of major HIV/AIDS journals

Methods

We included 10 major HIV/AIDS journals and analyzed all available PubMed entries for the period 1987 through 2022. We applied rule-based text mining and machine learning methodology to detect the presence of confidence intervals, numerical p-values or comparisons of p-values with thresholds, language describing statistical significance, and effect measures for dichotomous outcomes

Results

Among 41,730 PubMed entries from the major HIV/AIDS journals, 31,665 contained an abstract. In the early years, most abstracts reporting statistical inference contained only significance terminology without confidence intervals and p-values. From 1988 to 2005, each year 30 % of all abstracts contained p-values without confidence intervals. Thereafter, this reporting style continued to decline. The reporting of confidence intervals increased steadily from 1988 (11 %) to 2022 (56 %). Of the 17 % of abstracts in 2017–2022 that included any effect measure, half reported odds ratios (51 %), followed by hazard ratios (28 %) and risk ratios (16 %). Difference measures and number needed to treat or harm were very uncommon

Conclusions

Within the HIV/AIDS literature, there has been widespread use of confidence intervals. Most of the journals that we reviewed had a decrease in reporting only statistical significance without confidence intervals over time

目的随着HIV/AIDS期刊的出现，可以在一个新的领域从头开始考察发表摘要的统计推断和效果度量报告的发展。本研究的目的是描述主要HIV/AIDS期刊的统计推断和效果测量报告的时间趋势。方法我们纳入了10种主要HIV/AIDS期刊，并分析了1987年至2022年期间所有可获得的PubMed条目。我们应用基于规则的文本挖掘和机器学习方法来检测置信区间、数值p值或p值与阈值的比较、描述统计显著性的语言以及二分类结果的效果度量的存在。结果在来自主要HIV/AIDS期刊的41,730篇PubMed条目中，31,665篇包含摘要。在早期，大多数报告统计推断的摘要只包含显著性术语，没有置信区间和p值。从1988年到2005年，每年有30%的摘要包含没有置信区间的p值。此后，这种报道方式继续减少。从1988年（11%）到2022年（56%），置信区间的报告稳步增加。2017-2022年，17%的摘要包含了任何影响测量，其中一半报告了优势比（51%），其次是风险比（28%）和风险比（16%）。治疗或伤害所需的不同措施和数量非常罕见。结论在艾滋病毒/艾滋病文献中，广泛使用置信区间。我们回顾的大多数期刊随着时间的推移，报告的统计意义没有置信区间都有所减少

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引用次数: 0

Do certain blood groups increase COVID-19 severity and mortality? 某些血型会增加COVID-19的严重程度和死亡率吗？

Global Epidemiology

Pub Date : 2025-07-08 DOI: 10.1016/j.gloepi.2025.100212

Tegene Atamenta kitaw, Ribka Nigatu Haile

Background

The existence of a relationship between the ABO blood group and COVID-19 severity and mortality is still an unresolved concern. Some studies report that groups O and A show a lower and higher risk of developing severe COVID-19 and mortality, respectively. Some studies also report the reverse. There are inconclusive results from different studies. Thus, this study sought to determine the possible associations of ABO blood type with COVID-19 severity and mortality.

Methods

A retrospective study was conducted among 570 adults with real-time reverse transcription-polymerase chain reaction confirmed positive COVID-19 patients attending Eka Kotebe General Hospital, COVID-19 Treatment Center. A Kaplan-Meier survival curve was computed to examine the difference in survival experience between ABO blood groups. Multinomial and binary logistic regression models were fitted to determine the association between ABO blood group with COVID-19 severity and mortality, respectively.

Result

238 (41.8 %) COVID-19 patients had blood group B, followed by 201 (35.3 %) A, 82 (14.4 %) O, and 49 (8.6 %) AB blood type. 23.68 % of participants develop severe COVID-19. Overall, 15.26 % COVID-19-related mortality was found. No difference in survival experience was observed between ABO blood types. There was no statistically significant association between ABO blood type and COVID-19 severity, and mortality.

Conclusion

We found no relationship between ABO blood group differences and COVID-19 severity, and mortality. Further, well-design-controlled studies are suggested to explore the potential link of ABO blood group with COVID-19 severity and mortality.

ABO血型与COVID-19严重程度和死亡率之间是否存在关系仍是一个未解决的问题。一些研究报告称，O组和A组分别表现出较低和较高的发生严重COVID-19和死亡的风险。一些研究也报告了相反的情况。不同的研究都有不确定的结果。因此，本研究试图确定ABO血型与COVID-19严重程度和死亡率的可能关联。方法回顾性分析在Eka Kotebe总医院COVID-19治疗中心就诊的570例成人实时逆转录聚合酶链反应阳性患者。计算Kaplan-Meier生存曲线来检查ABO血型之间生存经验的差异。拟合多项和二元logistic回归模型，分别确定ABO血型与COVID-19严重程度和死亡率之间的关系。结果B型238例（41.8%），A型201例（35.3%），O型82例（14.4%），AB型49例（8.6%）。23.68%的参与者发展为重症COVID-19。总体而言，与covid -19相关的死亡率为15.26%。ABO血型间生存经验无差异。ABO血型与COVID-19严重程度和死亡率之间无统计学意义的关联。结论ABO血型差异与COVID-19严重程度和死亡率无相关性。此外，建议进行设计良好的对照研究，以探索ABO血型与COVID-19严重程度和死亡率的潜在联系。

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引用次数: 0

Epidemiology of stroke in Pakistan and its provinces, 1990–2021: Findings from the global burden of disease study 2021 1990-2021年巴基斯坦及其各省中风流行病学：来自2021年全球疾病负担研究的结果

Global Epidemiology

Pub Date : 2025-07-05 DOI: 10.1016/j.gloepi.2025.100211

Sufyan Shahid , Ali Dheyaa Marsool , Maha Sajjad , Muneeb Saifullah , Muhammad Awais Alam , Syed Ijlal Ahmed , Raheel Ahmed , Fahd Sultan

Background

Despite stroke being a leading cause of death in Pakistan, no study has comprehensively analyzed national and regional trends over time. This gap limits understanding of the evolving burden and underlying risk factors.

Methods

We analyzed regional stroke trends in Pakistan from 1990 to 2021 using Global Burden of Disease (GBD) 2021 data. Age-standardized incidence, prevalence, mortality, disability-adjusted life years (DALYs), and 18 modifiable risk factors were analyzed across provinces. Joinpoint regression identified temporal trends through annual percent changes (APCs), and population attributable fractions (PAFs) quantified risk contributions. All estimates incorporated 95 % uncertainty intervals and underwent sensitivity testing to ensure robustness.

Findings

In 2021, stroke was the second leading cause of death in Pakistan, responsible for 99,759 deaths, with an incidence of 153 and a prevalence of 1088 per 100,000 population. Age-standardized rates for incidence, prevalence, mortality, and DALYs declined overall from 1990 to 2021 (ASIR AAPC: −0.15 %), though reductions were modest. Regional trends showed the steepest declines in Islamabad and Punjab, while Balochistan experienced an increase in mortality (ASMR AAPC: +0.03 %) and DALYs (+0.05 %). Sex-specific trends showed greater declines in incidence and mortality among females, while DALY reductions were modest in both sexes, reflecting ongoing disability. High systolic blood pressure was the leading risk factor for stroke mortality (25.2 per 100,000) and DALYs (674.9 per 100,000), followed by household air pollution, ambient particulate matter, and high fasting glucose. These findings reflect the combined influence of cardiometabolic, environmental, and behavioral risks on Pakistan's evolving stroke burden.

Interpretation

Despite modest improvements, stroke remains a major health challenge in Pakistan, with rising long-term disability and stark regional inequalities. Strengthening prevention, equitable access, and post-stroke care is essential to curb the growing burden.

尽管中风是巴基斯坦的主要死亡原因，但没有一项研究全面分析了国家和地区的长期趋势。这一差距限制了对不断变化的负担和潜在风险因素的理解。方法：我们使用全球疾病负担（GBD） 2021数据分析1990年至2021年巴基斯坦区域卒中趋势。对各省的年龄标准化发病率、患病率、死亡率、残疾调整生命年（DALYs）和18个可改变的危险因素进行分析。接合点回归通过年百分比变化（APCs）确定时间趋势，而人口归因分数（PAFs）量化风险贡献。所有的估计都包含95%的不确定区间，并经过敏感性测试以确保稳健性。2021年，中风是巴基斯坦第二大死因，造成99,759人死亡，发病率为153人，患病率为每10万人1088人。从1990年到2021年，年龄标准化的发病率、患病率、死亡率和DALYs总体下降（ASIR AAPC: - 0.15%），尽管下降幅度不大。区域趋势显示，伊斯兰堡和旁遮普的下降幅度最大，而俾路支省的死亡率和伤残调整生命年分别上升了0.03%和0.05%。按性别区分的趋势显示，女性的发病率和死亡率下降幅度较大，而男女伤残津贴年的下降幅度不大，反映出持续的残疾。高收缩压是中风死亡率（25.2 / 10万）和伤残调整生命年（674.9 / 10万）的主要危险因素，其次是家庭空气污染、环境颗粒物和空腹血糖过高。这些发现反映了心脏代谢、环境和行为风险对巴基斯坦不断变化的中风负担的综合影响。尽管有所改善，但中风仍然是巴基斯坦面临的主要健康挑战，长期残疾人数不断上升，地区不平等现象严重。加强预防、公平获取和卒中后护理对于遏制日益加重的负担至关重要。

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引用次数: 0

Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers 利用生态气候触发器预测孟加拉国登革热疫情的可解释人工智能

Global Epidemiology

Pub Date : 2025-06-05 DOI: 10.1016/j.gloepi.2025.100210

Md. Siddikur Rahman, Md. Abu Bokkor Shiddik

Background

Dengue represents a significant public health threat in Bangladesh, characterized by its complex ecological transmission dynamics. To improve dengue prevention and control efforts, firstly, we employ state-of-the-art artificial intelligence (AI) methods to identify the roles of eco-climatic factors in predicting dengue outbreaks in Bangladesh.

Methods

We utilize high-performance machine learning (ML) models, XGBoost and LightGBM, combined with explainable AI (XAI) methodologies to evaluate the predictive performance and impact of various dengue determinants in Bangladesh from 2000 to 2023. The LightGBM and XGBoost models were also utilized to predict dengue cases and early warning trends from 2024 to 2030. Climatic, socio-demographic, and landscape features were used to train these models; SHapley Additive Explanations (SHAP) values and LIME (Local Interpretable Model-agnostic Explanations) were used to interpret the results.

Findings

Between 2000 and 2023, Bangladesh experienced the highest number of dengue cases in August, while November saw the most fatalities. The XGBoost model excelled in predicting dengue outbreaks, achieving an AUC score of 0.89, a Log Loss of 0.64. Key predictors identified by the model include population density, precipitation, temperature, and land-use patterns. Additionally, Local Interpretable Model-agnostic Explanations (LIME) provided insights into the model's predictions, highlighting the significance of population density, relative humidity, and minimum temperature in dengue outbreaks.

Interpretation

This study showcases the potential of XAI in uncovering the complexities of dengue outbreaks, providing a robust tool for public health interventions. Our AI-driven framework can be utilized to generate prompt and timely alerts to prevent imminent dengue and other infectious disease outbreaks.

登革热在孟加拉国是一个重大的公共卫生威胁，其特点是其复杂的生态传播动态。为了改善登革热预防和控制工作，首先，我们采用最先进的人工智能（AI）方法来确定生态气候因素在预测孟加拉国登革热疫情中的作用。方法利用高性能机器学习（ML）模型，XGBoost和LightGBM，结合可解释人工智能（XAI）方法，评估2000年至2023年孟加拉国各种登革热决定因素的预测性能和影响。利用LightGBM和XGBoost模型预测2024 - 2030年登革热病例和预警趋势。使用气候、社会人口和景观特征来训练这些模型；使用SHapley加性解释（SHAP）值和LIME （Local Interpretable Model-agnostic Explanations）来解释结果。在2000年至2023年期间，孟加拉国8月份的登革热病例最多，而11月份的死亡人数最多。XGBoost模型在预测登革热暴发方面表现出色，AUC得分为0.89，Log Loss为0.64。该模型确定的关键预测因子包括人口密度、降水、温度和土地利用模式。此外，局部可解释模型不可知解释（LIME）为模型预测提供了见解，强调了人口密度、相对湿度和最低温度在登革热暴发中的重要性。本研究展示了XAI在揭示登革热暴发复杂性方面的潜力，为公共卫生干预提供了强有力的工具。我们的人工智能驱动框架可用于产生迅速和及时的警报，以预防即将发生的登革热和其他传染病暴发。

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引用次数: 0

Artificial intelligence for advancing eye care in resource-poor settings: Assessing the predictive accuracy of an AI-model for diabetic retinopathy screening in India 在资源贫乏环境中推进眼科保健的人工智能：评估印度糖尿病视网膜病变筛查人工智能模型的预测准确性

Global Epidemiology

Pub Date : 2025-06-01 DOI: 10.1016/j.gloepi.2025.100209

Rohan Chawla , Prachi Karkhanis , Malay Shah , Aritra Das , Rishabh Sharma , Dhwani Almaula , Pradeep Venkatesh , Harsh Vardhan Singh , Mukul Kumar , Ramanuj Samanta , Vinod Kumarl , Amar Shah , Bhavin Vadera , Nakul Jain , Akanksha Sen , Shyamsundar Shreedhar , Vipin Garg , Soma Dhaval , Kowshik Ganesh , Srinivas Rana , Radhika Tandon

Background

Timely identification and treatment of Diabetic Retinopathy (DR) is critical in avoiding vision loss. DR screening is challenging, especially in resource-limited areas where trained ophthalmologists are scarce. AI solutions show promise in addressing this challenge. In this study, the performance metrics of an AI solution (MadhuNetrAI) developed in India was evaluated for referring and grading DR.

Methods

MadhuNetrAI was developed de novo by the All India Institute of Medical Sciences (AIIMS) and Wadhwani AI (WIAI). It was tested on 1078 fundus images (from AIIMS Delhi and an unannotated subset of publicly available EyePACS images) against two ophthalmologists and an adjudicator serving as independent gold-standard annotators, wherein the disease status of the patients remained unknown.

Findings

MadhuNetrAI demonstrated high sensitivity (93·2 %; CI: 89·5 %–95·6 %) and specificity (95·3 %; CI: 93·7 %–96·6 %) in detecting referable DR (moderate, severe, proliferative DR). The area-under-the-curve for referring DR against the gold standard was 0·97 (CI: 0·95–0·99) indicating excellent diagnostic performance. The agreement in grading DR severity was high (kappa = 0·89, CI: 0·86–0·91). The model performed comparably in detecting DR too.

Interpretation

MadhuNetrAI's ability to grade DR severity and identify referrable cases could bring DR patients to care much earlier. Further research and clinical trials are needed to ensure its reliability and generalizability across diverse populations and image qualities.

Funding

MadhuNetrAI was developed by technical and programmatic teams at WIAI, with inputs and contributions by the clinical team at AIIMS, and funded by USAID. The authors have no financial or non-financial conflicts of interest to disclose.

背景及时发现和治疗糖尿病视网膜病变（DR）是避免视力丧失的关键。DR筛查具有挑战性，特别是在资源有限、训练有素的眼科医生稀缺的地区。人工智能解决方案有望解决这一挑战。在本研究中，对印度开发的人工智能解决方案（MadhuNetrAI）的性能指标进行评估，以参考和评分博士。方法MadhuNetrAI由全印度医学科学研究所（AIIMS）和Wadhwani AI （WIAI）重新开发。在1078张眼底图像（来自AIIMS Delhi和公开可用的EyePACS图像的未注释子集）上，对两名眼科医生和一名作为独立金标准注释者的审稿人进行了测试，其中患者的疾病状态仍然未知。发现smadhunetrai具有高灵敏度(93.2%；CI: 89.5% - 95.6%)和特异性(95.3%；CI: 93.7% - 96%)在发现可参考DR（中度、重度、增殖性DR）方面。参照金标准的DR曲线下面积为0.97 (CI: 0.95 ~ 0.99)，诊断效果良好。对DR严重程度分级的一致性较高（kappa = 0.89, CI: 0.86 ~ 0.91）。该模型在检测DR方面也有相当的效果。madhunetrai分级DR严重程度和识别转诊病例的能力可以使DR患者更早接受治疗。需要进一步的研究和临床试验来确保其在不同人群和图像质量中的可靠性和普遍性。madhunetrai由WIAI的技术和项目团队开发，AIIMS的临床团队提供投入和贡献，并由美国国际开发署资助。作者没有财务或非财务利益冲突要披露。

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引用次数: 0

Epidemiological and histopathological profile of lung Cancer: Insights from a 15-year cross-sectional study at a tertiary care centre in South India 肺癌的流行病学和组织病理学特征：来自印度南部三级保健中心15年横断面研究的见解

Global Epidemiology

Pub Date : 2025-06-01 DOI: 10.1016/j.gloepi.2025.100208

Asmita A. Mehta , Keechilat Pavithran , Prem Kumar Nair , Vishnu Vazhoor , Georg Gutjahr , V.P. Lakshmi Priya

Abstract

Background

In India, lung cancer accounts for 5.9 % of all cancers and 8.1 % of all cancer-related deaths, with adenocarcinoma emerging as the most common histopathological subtype in developing countries.

Aims

To analyze the shifting trends in the epidemiology and histopathology of lung cancer over 15 years, with a focus on gender differences in the prevalence of adenocarcinoma.

Method

This observational, cross-sectional study was conducted at a tertiary care center in Southern India to evaluate the trends in the epidemiology and histopathology of lung cancer over a 15-year period (2008–2022). Data were gathered from patients aged ≥18 diagnosed with primary lung carcinoma. The annual distribution of patients was documented based on age, sex, and tumor histopathology. The analysis was conducted using SPSS software.

Results

A total of 4466 newly diagnosed primary lung cancer cases were analyzed over a 15-year period. The median age at diagnosis was 64 years, with a shift in age distribution over time. The proportion of female cases rose from 20.1 % to 28.4 %, while male cases declined from 79 % to 71 %, indicating a significant gender shift. Adenocarcinoma was the most common histopathology subtype, increasing from 22 % to 40 % in men and from 32 % to 55 % in women. Significant associations were observed between histopathology subtype and age group, gender, and year of diagnosis.

Conclusion

The study revealed evolving trends in the lung cancer profile over the last 15 years. A significant increase in the prevalence of adenocarcinoma was observed, with a more pronounced rise among women compared to men.

在印度，肺癌占所有癌症的5.9%，占所有癌症相关死亡的8.1%，而腺癌是发展中国家最常见的组织病理学亚型。目的分析近15年来肺癌流行病学和组织病理学的变化趋势，重点分析腺癌患病率的性别差异。方法本观察性横断面研究在印度南部的一家三级保健中心进行，旨在评估15年间（2008-2022年）肺癌的流行病学和组织病理学趋势。数据来自年龄≥18岁诊断为原发性肺癌的患者。患者的年度分布是根据年龄、性别和肿瘤组织病理学来记录的。采用SPSS软件进行分析。结果15年间共分析了4466例新发原发性肺癌病例。诊断时的中位年龄为64岁，年龄分布随时间而变化。女性病例的比例从20.1%上升到28.4%，而男性病例从79%下降到71%，显示出明显的性别变化。腺癌是最常见的组织病理学亚型，男性从22%增加到40%，女性从32%增加到55%。组织病理学亚型与年龄、性别和诊断年份之间存在显著相关性。结论：该研究揭示了过去15年来肺癌概况的演变趋势。观察到腺癌的发病率显著增加，与男性相比，女性的发病率上升更为明显。

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引用次数: 0

An AI assistant for critically assessing and synthesizing clusters of journal articles 用于批判性地评估和综合期刊文章集群的人工智能助手

Global Epidemiology

Pub Date : 2025-05-23 DOI: 10.1016/j.gloepi.2025.100207

Louis Anthony Cox Jr.

Current large language models (LLMs) face significant challenges in attempting to synthesize and critically assess conflicting causal claims in scientific literature about exposure-associated health effects. This paper examines the design and performance of AIA2, an experimental AI system (freely available at http://cloud.cox-associates.com/) designed to help explore and illustrate potential applications of current AI in assisting analysis of clusters of related scientific articles, focusing on causal claims in complex domains such as epidemiology, toxicology, and risk analysis. Building on an earlier AI assistant, AIA1, which critically reviewed causal claims in individual papers, AIA2 advances the approach by systematically comparing multiple studies to identify areas of agreement and disagreement, suggest explanations for differences in conclusions, flag methodological gaps and inconsistencies, synthesize and summarize well-supported conclusions despite conflicts, and propose recommendations to help resolve knowledge gaps. We illustrate these capabilities with a case study of formaldehyde exposure and leukemia using a cluster of four papers that feature very different approaches and partly conflicting conclusions. AIA2 successfully identifies major points of agreement and contention, discusses the robustness of the evidence for causal claims, and recommends future research directions to address current uncertainties. AIA2's outputs suggest that current AI can offer a promising, practicable approach to AI-assisted review of clusters of papers, promoting methodological rigor, thoroughness, and transparency in review and synthesis, notwithstanding current limitations of LLMs. We discuss the implications of AI-assisted literature review systems for improving evidence-based decision-making, resolving conflicting scientific claims, and promoting rigor and reproducibility in causal research and health risk analysis.

目前的大型语言模型（llm）在试图综合和批判性评估科学文献中关于暴露相关健康影响的相互矛盾的因果关系主张方面面临重大挑战。本文研究了AIA2的设计和性能，AIA2是一个实验性人工智能系统（可在http://cloud.cox-associates.com/免费获得），旨在帮助探索和说明当前人工智能在辅助分析相关科学文章集群方面的潜在应用，重点关注流行病学、毒理学和风险分析等复杂领域的因果关系主张。在早期人工智能助手AIA1的基础上，AIA2通过系统地比较多个研究来确定一致和不一致的领域，提出结论差异的解释，标记方法上的差距和不一致，综合和总结有充分支持的结论，尽管存在冲突，并提出建议来帮助解决知识差距。我们用甲醛暴露和白血病的案例研究说明了这些能力，使用四篇论文的集群，具有非常不同的方法和部分矛盾的结论。AIA2成功地识别了主要的共识和争论点，讨论了因果主张证据的稳健性，并建议未来的研究方向以解决当前的不确定性。AIA2的产出表明，尽管目前法学硕士存在局限性，但目前的人工智能可以提供一种有前途的、可行的方法来辅助人工智能对论文群的审查，促进审查和综合的方法严谨性、彻全性和透明度。我们讨论了人工智能辅助文献综述系统在改善循证决策、解决科学主张冲突、促进因果研究和健康风险分析的严谨性和可重复性方面的意义。

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引用次数: 0

Detecting spatial clusters of Crimean Congo hemorrhagic fever in Iraq in 2023 2023年伊拉克克里米亚刚果出血热空间聚集性检测

Global Epidemiology

Pub Date : 2025-05-10 DOI: 10.1016/j.gloepi.2025.100205

Hanan Abdulghafoor Khaleel , Riyadh Abdulameer Alhilfi , Sabrina Brown

Background

Since the start of the first large outbreak of Crimean Congo Hemorrhagic Fever (CCHF) in Iraq in 2022, there has been no assessment of clustering of cases by district. The aim of this study is to identify clusters of high and low incidences of human CCHF to guide preventive and control measures, and distribute limited resources.

Methods

This is a cross-sectional study of reported and confirmed CCHF cases in Iraq from January 1, 2023 to December 11, 2023. We used a retrospective purely spatial Poisson scan statistic model to identify clusters of high and low incidences of CCHF at the district level (p < 0.05).

Findings

There were 580 confirmed CCHF cases, distributed in 149 districts. The incidence was 1.3 per 100,000. There were eight statistically significant clusters (three high-incidence and five low-incidence). The three high-incidence clusters were in the southeast while the five low-incidence clusters were mostly in the north and middle-east Iraq.

Interpretation

There is evidence of CCHF clustering in 40 districts in six governorates in south and mid-east Iraq. Additionally, there is evidence of low-incidence clustering of CCHF in 17 governorates, in north and central Iraq, and a risk for future outbreaks. Identifying clusters allows for focused preventive activities, such as insecticide spraying to reduce the tick population, controlling the spread of ticks by treating animals with repellents and other chemicals, and modifying landscapes. Distributing educational materials about handling meat and livestock products and engaging the community can help reduce exposure to ticks and the spread of disease.

背景：自2022年伊拉克克里米亚刚果出血热首次大规模暴发以来，未对地区聚集性病例进行过评估。本研究的目的是确定人类CCHF的高发和低发聚集性，以指导预防和控制措施，并分配有限的资源。方法对2023年1月1日至2023年12月11日在伊拉克报告和确诊的CCHF病例进行横断面研究。我们使用回顾性的纯空间泊松扫描统计模型来确定地区一级CCHF的高发病率和低发病率集群(p <；0.05)。发现有580例确诊的刚果出血热病例，分布在149个县。发病率为每10万人中有1.3人。有8个具有统计学意义的聚类（3个高发病率和5个低发病率）。3个高发病聚集区位于伊拉克东南部，5个低发病聚集区主要位于伊拉克北部和中东。有证据表明，在伊拉克南部和中东6个省的40个地区聚集了霍乱。此外，有证据表明，在伊拉克北部和中部的17个省份出现了低发病率聚集性病例，未来有暴发的风险。识别集群有助于开展重点预防活动，例如喷洒杀虫剂以减少蜱虫数量，通过使用驱虫剂和其他化学物质治疗动物来控制蜱虫的传播，以及改变景观。分发有关处理肉类和牲畜产品的教育材料，并让社区参与进来，可以帮助减少接触蜱虫和疾病传播。

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引用次数: 0