在基于人口的癌症登记计划之前,采用贝叶斯方法纠正癌症登记统计数据的计算不足。

Hadis Barati, Mohamad Amin Pourhoseingholi, Gholamreza Roshandel, Seyed Saeed Hashemi Nazari, Esmaeil Fattahi
{"title":"在基于人口的癌症登记计划之前,采用贝叶斯方法纠正癌症登记统计数据的计算不足。","authors":"Hadis Barati, Mohamad Amin Pourhoseingholi, Gholamreza Roshandel, Seyed Saeed Hashemi Nazari, Esmaeil Fattahi","doi":"10.22037/ghfbb.v16i4.2843","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>This study aims to correct undercounts in cancer data before initiating a population-based cancer registry program, employing an innovative Bayesian methodology.</p><p><strong>Background: </strong>Underestimation is a widespread issue in cancer registries within developing countries.</p><p><strong>Methods: </strong>This secondary study utilized cancer registry data. We employed the Bayesian approach to correct undercounting in cancer data from 2005 to 2010, using the ratio of pathology to population-based data in the Golestan province as the initial value.</p><p><strong>Results: </strong>The results of this study showed that the lowest percentage of undercounting belonged to Khorasan Razavi province with an average of 21% and the highest percentage belonged to Sistan and Baluchestan province with an average of 38%.The average age-standardized incidence rate (ASR) for all provinces of the country except Golestan province was equal to 105.72 (Confidence interval (CI) 95% 105.35-106.09) per 100,000 and after Bayesian correction was 137.17 (CI 95% 136.74-137.60) per 100,000. In 2010 the amount of ASR before Bayesian correction was 100.28 (CI 95% 124.39-127.09) per 100,000 for women and 136.49 (CI 95% 171.20-174.38) per 100,000 for men. Also, after implementing the Bayesian correction, ASR increased to 125.74 per 100,000 for women and 172.79 per 100,000 for men.</p><p><strong>Conclusion: </strong>The study demonstrates the effectiveness of the Bayesian approach in correcting undercounting in cancer registries. By utilizing the Bayesian method, the average ASR after Bayesian correction with a 29.74 percent change was 137.17 per 100,000. These corrected estimates provide more accurate information on cancer burden and can contribute to improved public health programs and policy evaluation. Furthermore, this research emphasizes the suitability of the Bayesian method for addressing underestimation in cancer registries. It also underscores its pivotal role in shaping the trajectory of future investigations in this field.</p>","PeriodicalId":12636,"journal":{"name":"Gastroenterology and Hepatology From Bed to Bench","volume":"16 4","pages":"421-431"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10835089/pdf/","citationCount":"0","resultStr":"{\"title\":\"A Bayesian approach to correct the under-count of cancer registry statistics before population-based cancer registry program.\",\"authors\":\"Hadis Barati, Mohamad Amin Pourhoseingholi, Gholamreza Roshandel, Seyed Saeed Hashemi Nazari, Esmaeil Fattahi\",\"doi\":\"10.22037/ghfbb.v16i4.2843\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aim: </strong>This study aims to correct undercounts in cancer data before initiating a population-based cancer registry program, employing an innovative Bayesian methodology.</p><p><strong>Background: </strong>Underestimation is a widespread issue in cancer registries within developing countries.</p><p><strong>Methods: </strong>This secondary study utilized cancer registry data. We employed the Bayesian approach to correct undercounting in cancer data from 2005 to 2010, using the ratio of pathology to population-based data in the Golestan province as the initial value.</p><p><strong>Results: </strong>The results of this study showed that the lowest percentage of undercounting belonged to Khorasan Razavi province with an average of 21% and the highest percentage belonged to Sistan and Baluchestan province with an average of 38%.The average age-standardized incidence rate (ASR) for all provinces of the country except Golestan province was equal to 105.72 (Confidence interval (CI) 95% 105.35-106.09) per 100,000 and after Bayesian correction was 137.17 (CI 95% 136.74-137.60) per 100,000. In 2010 the amount of ASR before Bayesian correction was 100.28 (CI 95% 124.39-127.09) per 100,000 for women and 136.49 (CI 95% 171.20-174.38) per 100,000 for men. Also, after implementing the Bayesian correction, ASR increased to 125.74 per 100,000 for women and 172.79 per 100,000 for men.</p><p><strong>Conclusion: </strong>The study demonstrates the effectiveness of the Bayesian approach in correcting undercounting in cancer registries. By utilizing the Bayesian method, the average ASR after Bayesian correction with a 29.74 percent change was 137.17 per 100,000. These corrected estimates provide more accurate information on cancer burden and can contribute to improved public health programs and policy evaluation. Furthermore, this research emphasizes the suitability of the Bayesian method for addressing underestimation in cancer registries. It also underscores its pivotal role in shaping the trajectory of future investigations in this field.</p>\",\"PeriodicalId\":12636,\"journal\":{\"name\":\"Gastroenterology and Hepatology From Bed to Bench\",\"volume\":\"16 4\",\"pages\":\"421-431\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10835089/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Gastroenterology and Hepatology From Bed to Bench\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22037/ghfbb.v16i4.2843\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gastroenterology and Hepatology From Bed to Bench","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22037/ghfbb.v16i4.2843","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

摘要

目的:本研究旨在采用创新的贝叶斯方法,在启动基于人口的癌症登记计划之前纠正癌症数据中的低估:低估是发展中国家癌症登记中的一个普遍问题:这项二次研究利用了癌症登记数据。我们采用贝叶斯方法,以戈勒斯坦省的病理数据与人口数据之比作为初始值,纠正了 2005 年至 2010 年癌症数据的低估:研究结果表明,计算不足比例最低的是呼罗珊拉扎维省,平均为 21%,最高的是锡斯坦和俾路支斯坦省,平均为 38%。除戈勒斯坦省外,全国各省的平均年龄标准化发病率(ASR)为每 10 万人 105.72 例(置信区间 (CI) 95% 105.35-106.09 例),贝叶斯校正后为每 10 万人 137.17 例(CI 95% 136.74-137.60 例)。2010 年,贝叶斯校正前的女性 ASR 为每 10 万人 100.28 例(CI 95% 124.39-127.09),男性 ASR 为每 10 万人 136.49 例(CI 95% 171.20-174.38)。此外,在实施贝叶斯校正后,女性的 ASR 增加到每 10 万人 125.74 例,男性增加到每 10 万人 172.79 例:该研究证明了贝叶斯方法在纠正癌症登记中的计算不足方面的有效性。通过使用贝叶斯方法,贝叶斯校正后的平均 ASR 为每 10 万人 137.17 例,变化率为 29.74%。这些修正后的估计值提供了更准确的癌症负担信息,有助于改进公共卫生计划和政策评估。此外,这项研究还强调了贝叶斯方法适用于解决癌症登记中的低估问题。它还强调了贝叶斯方法在塑造该领域未来调查轨迹方面的关键作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A Bayesian approach to correct the under-count of cancer registry statistics before population-based cancer registry program.

Aim: This study aims to correct undercounts in cancer data before initiating a population-based cancer registry program, employing an innovative Bayesian methodology.

Background: Underestimation is a widespread issue in cancer registries within developing countries.

Methods: This secondary study utilized cancer registry data. We employed the Bayesian approach to correct undercounting in cancer data from 2005 to 2010, using the ratio of pathology to population-based data in the Golestan province as the initial value.

Results: The results of this study showed that the lowest percentage of undercounting belonged to Khorasan Razavi province with an average of 21% and the highest percentage belonged to Sistan and Baluchestan province with an average of 38%.The average age-standardized incidence rate (ASR) for all provinces of the country except Golestan province was equal to 105.72 (Confidence interval (CI) 95% 105.35-106.09) per 100,000 and after Bayesian correction was 137.17 (CI 95% 136.74-137.60) per 100,000. In 2010 the amount of ASR before Bayesian correction was 100.28 (CI 95% 124.39-127.09) per 100,000 for women and 136.49 (CI 95% 171.20-174.38) per 100,000 for men. Also, after implementing the Bayesian correction, ASR increased to 125.74 per 100,000 for women and 172.79 per 100,000 for men.

Conclusion: The study demonstrates the effectiveness of the Bayesian approach in correcting undercounting in cancer registries. By utilizing the Bayesian method, the average ASR after Bayesian correction with a 29.74 percent change was 137.17 per 100,000. These corrected estimates provide more accurate information on cancer burden and can contribute to improved public health programs and policy evaluation. Furthermore, this research emphasizes the suitability of the Bayesian method for addressing underestimation in cancer registries. It also underscores its pivotal role in shaping the trajectory of future investigations in this field.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.30
自引率
0.00%
发文量
29
期刊最新文献
Posterior tibial nerve electrical stimulation in chronic constipation: a systematic review and meta-analysis. Small fiber neuropathy in irritable bowel syndrome. Biguanides and glucagon like peptide 1 receptor agonists in the amelioration of post liver transplant weight gain; a scoping review of the mechanism of action, safety and efficacy. Design and development of a self-care application for patients with liver cirrhosis. Evaluation strategy of anti-mitochondrial antibodies M2-negative: the role of multiplex rodent tissues and related clinical implications.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1