The Spatial-Temporal Dimension of Oncological Prevalence and Mortality in Romania

被引:3
|
作者
Peptenatu, D. [1 ]
Nedelcu, I. D. [1 ]
Pop, C. S. [2 ]
Simion, A. G. [1 ]
Furtunescu, F. [2 ]
Burcea, M. [3 ]
Andronache, I. [1 ]
Radulovic, M. [4 ]
Jelinek, H. F. [5 ,6 ]
Ahammer, H. [7 ]
Gruia, A. K. [3 ]
Grecu, A. [3 ]
Popa, M. C. [1 ]
Militaru, V. [8 ]
Draghici, C. C. [1 ]
Pintilii, R. D. [1 ]
机构
[1] Univ Bucharest, Fac Geog, Res Ctr Integrated Anal & Terr Management CAIMT, Bucharest, Romania
[2] Carol Davila Univ Med & Pharm, Bucharest, Romania
[3] Univ Bucharest, Fac Adm & Business, Bucharest, Romania
[4] Inst Oncol & Radiol Serbia, Dept Expt Oncol, Belgrade, Serbia
[5] Khalifa Univ, Dept Biomed Engn, Abu Dhabi, U Arab Emirates
[6] Khalifa Univ, Healthcare Engn Innovat Ctr, Abu Dhabi, U Arab Emirates
[7] Med Univ Graz, Div Med Phys & Biophys, GSRC, Graz, Austria
[8] Iuliu Hatieganu Univ Med & Pharm Cluj Napoca, Fac Med, Cluj Napoca, Romania
来源
GEOHEALTH | 2023年 / 7卷 / 10期
关键词
oncological prevalence; oncological mortality; persistence; continuity of persistence; ENVIRONMENTAL RISK-FACTORS; FRACTAL CHARACTERISTICS; CANCER-EPIDEMIOLOGY; LYME-DISEASE; AUTOCORRELATION; FRAGMENTATION; STATISTICS; COUNTRIES; REGIONS; DEATHS;
D O I
10.1029/2023GH000901
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The objective of this study was to identify spatial disparities in the distribution of cancer hotspots within Romania. Additionally, the research aimed to track prevailing trends in cancer prevalence and mortality according to a cancer type. The study covered the timeframe between 2008 and 2017, examining all 3,181 territorial administrative units. The analysis of spatial distribution relied on two key parameters. The first parameter, persistence, measured the duration for which cancer prevalence exceeded the 75th percentile threshold. Cancer prevalence refers to the total number of individuals in a population who have been diagnosed with cancer at a specific time point, including both newly diagnosed cases (occurrence) and existing cases. The second parameter, the time continuity of persistence, calculated the consecutive months during which cancer prevalence consistently surpassed the 75th percentile threshold. Notably, persistence of elevated values was also evident in lowland regions, devoid of any discernible direct connection to environmental conditions. In conclusion, this work bears substantial relevance to regional health policies, by aiding in the formulation of prevention strategies, while also fostering a deeper comprehension of the socioeconomic and environmental factors contributing to cancer. This study presents the inaugural spatial geographical map of cancer fatality and the distribution of high cancer prevalence across all types of cancer in Romania. Conducted between 2008 and 2017, this research aimed to identify patterns in the territorial distribution for all reported cancer within the country's territorial administrative units, by calculation of the two main parameters, namely cancer persistence and the time continuity of its persistence. The findings reveal a decline in total cancer cases, while certain subcategories displayed an upward trend. The analysis of mortality demonstrated a pronounced increase in overall prevalence. By emphasizing the connection between cancer prevalence, outcomes, and geographical factors in Romania, this study provides crucial insights for refining national and European policies in the fight against cancer. This study reveals geographical disparities in the prevalence of all cancer types in RomaniaThe analysis spans the period from 2008 to 2017It was observed that cancer prevalence increased in correlation with anthropogenic interventions in the region
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Spatial-temporal analysis of mortality using splines
    vanderLinde, A
    Witzko, KH
    Jockel, KH
    BIOMETRICS, 1995, 51 (04) : 1352 - 1360
  • [2] Multivariate analysis of spatial-temporal scales in melanoma prevalence
    Valachovic, Edward
    Zurbenko, Igor
    CANCER CAUSES & CONTROL, 2017, 28 (07) : 733 - 743
  • [3] Spatial-temporal Graph Transformer Network for Spatial-temporal Forecasting
    Dao, Minh-Son
    Zetsu, Koji
    Hoang, Duy-Tang
    Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024, 2024, : 1276 - 1281
  • [4] Spatial-temporal Variability of Coastline in Bohai Rim Based on Fractal Dimension
    Xu, Ning
    Gao, Zhiqiang
    Ning, Jicai
    Liu, Xiangyang
    REMOTE SENSING AND MODELING OF ECOSYSTEMS FOR SUSTAINABILITY XI, 2014, 9221
  • [5] A Spatial-Temporal Analysis of Health Care and Mortality Inequalities in China
    Li, Yingru
    Wei, Y. H. Dennis
    EURASIAN GEOGRAPHY AND ECONOMICS, 2010, 51 (06) : 767 - 787
  • [6] Towards a Spatial-Temporal Model of Prevalence of Nodding Syndrome and Epilepsy
    Ongaya, Kizito
    Ssemalullu, Paul
    Oyo, Benedict
    Maiga, Gilbert
    Aturinde, Augustus
    E-INFRASTRUCTURE AND E-SERVICES FOR DEVELOPING COUNTRIES, AFRICOMM 2018, 2019, 275 : 67 - 77
  • [7] Spatial-temporal Data Interpolation Based on Spatial-temporal Kriging Method
    Xu M.-L.
    Xing T.
    Han M.
    Zidonghua Xuebao/Acta Automatica Sinica, 2020, 46 (08): : 1681 - 1688
  • [8] Spatial-temporal antialiasing
    Sung, K
    Pearce, A
    Wang, CY
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2002, 8 (02) : 144 - 153
  • [9] Paratactic Spatial-temporal Tow Dimension Data Fusion for Traffic Volume Prediction
    Chen, Liang
    Li, Qiaoru
    Liu, Siming
    Li, Xiaoxiao
    Li, Wei
    ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL III, PROCEEDINGS, 2009, : 656 - 660
  • [10] Drought risk assessment in the coupled spatial-temporal dimension of the Sichuan Basin, China
    Tan Huizhi
    Lu Xiaoning
    Yang Shiqi
    Wang Yongqian
    Li Feng
    Liu Jinbao
    Chen Jun
    Huang Yue
    NATURAL HAZARDS, 2022, 114 (03) : 3205 - 3233