Daneshvar Mojtaba
Tehran university of medical sciences, Tehran, Iran.
BMC Res Notes. 2025 Mar 25;18(1):124. doi: 10.1186/s13104-025-07187-2.
Understanding the relationship between obesity and female-specific cancers (FSCs) is crucial for public health planning and policy development. The current data paper presented a dataset that includes obesity prevalence and incidence rates of breast, ovarian, cervical, and uterine cancers among women in Middle Eastern countries. This dataset could be used for time-trend analysis and different forecasting models. Moreover, exploring the relationship between obesity and FSCs is important to develop preventive healthcare services, especially among developing countries.
The dataset comprises official statistics obtained from reputable sources including the world bank and global burden of disease (GBD) database. The data include a total of 405 observations across 15 middle-eastern countries, from 1990 to 2016. Key variables are obesity prevalence and incidence rate of four major cancers in women including breast cancer, ovarian cancer, cervical cancer, and uterine cancer. This panel data is mainly prepared to investigate the temporal relationship between obesity prevalence and FSC incidence rates, and also performing Counterfactual analysis. Moreover, this data could be utilized for advanced machine learning techniques to estimate future shifts in trend and patterns over time.
了解肥胖与女性特定癌症（FSCs）之间的关系对于公共卫生规划和政策制定至关重要。当前的数据论文展示了一个数据集，其中包括中东国家女性肥胖患病率以及乳腺癌、卵巢癌、宫颈癌和子宫癌的发病率。该数据集可用于时间趋势分析和不同的预测模型。此外，探索肥胖与女性特定癌症之间的关系对于制定预防保健服务至关重要，尤其是在发展中国家。
该数据集包含从包括世界银行和全球疾病负担（GBD）数据库等可靠来源获取的官方统计数据。数据涵盖1990年至2016年期间15个中东国家的总共405条观测数据。关键变量包括女性四种主要癌症（乳腺癌、卵巢癌、宫颈癌和子宫癌）的肥胖患病率和发病率。该面板数据主要用于研究肥胖患病率与女性特定癌症发病率之间的时间关系，并进行反事实分析。此外，该数据可用于先进的机器学习技术，以估计未来趋势和模式随时间的变化。
