Time tracking and multidimensional influencing factors analysis on female breast cancer mortality: Evidence from urban and rural China between 1994 to 2019
BackgroundThere are huge differences in female breast cancer mortality between urban and rural China. In order to better prevent breast cancer equally in urban and rural areas, it is critical to trace the root causes of past inequities and predict how future differences will change. Moreover, carcin...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-09-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.1000892/full |
| _version_ | 1811258575615426560 |
|---|---|
| author | Xiaodan Bai Xiyu Zhang Wenjing Xiang Yanjie Wang Yu Cao Guihong Geng Bing Wu Yongqiang Lai Ye Li Baoguo Shi |
| author_facet | Xiaodan Bai Xiyu Zhang Wenjing Xiang Yanjie Wang Yu Cao Guihong Geng Bing Wu Yongqiang Lai Ye Li Baoguo Shi |
| author_sort | Xiaodan Bai |
| collection | DOAJ |
| description | BackgroundThere are huge differences in female breast cancer mortality between urban and rural China. In order to better prevent breast cancer equally in urban and rural areas, it is critical to trace the root causes of past inequities and predict how future differences will change. Moreover, carcinogenic factors from micro-individual to macro-environment also need to be analyzed in detail. However, there is no systematic research covering these two aspects in the current literature.MethodsBreast cancer mortality data in urban and rural China from 1994 to 2019 are collected, which from China Health Statistical Yearbook. The Age-Period-Cohort model is used to examine the effects of different age groups, periods, and birth cohorts on breast cancer mortality. Nordpred project is used to predict breast cancer mortality from 2020 to 2039.ResultsThe age effect gradually increases and changes from negative to positive at the age of 40–44. The period effect fluctuates very little and shows the largest difference between urban and rural areas in 2019. The birth cohort effect gradually decreases with urban-rural effects alternating between strong and weak. In the predicted results, the urban-rural mortality gap becomes first narrow and then wide and shows a trend of younger death.ConclusionsFrom the perspective of a temporal system, the changing trend of breast cancer mortality is highly consistent with the history of social and economic structural changes in China. From the perspective of the theory of social determinants of health, individuals, families, institutions and governments need to participate in the prevention of breast cancer. |
| first_indexed | 2024-04-12T18:16:31Z |
| format | Article |
| id | doaj.art-14fe750bcd724b429da0884abf9d21b3 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-12T18:16:31Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-14fe750bcd724b429da0884abf9d21b32022-12-22T03:21:34ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-09-011010.3389/fpubh.2022.10008921000892Time tracking and multidimensional influencing factors analysis on female breast cancer mortality: Evidence from urban and rural China between 1994 to 2019Xiaodan Bai0Xiyu Zhang1Wenjing Xiang2Yanjie Wang3Yu Cao4Guihong Geng5Bing Wu6Yongqiang Lai7Ye Li8Baoguo Shi9Department of Economics, School of Economics, Minzu University of China, Beijing, ChinaResearch Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, ChinaDepartment of Economics, School of Economics, Minzu University of China, Beijing, ChinaDepartment of Economics, School of Economics, Minzu University of China, Beijing, ChinaDepartment of Economics, School of Economics, Minzu University of China, Beijing, ChinaDepartment of Economics, School of Economics, Minzu University of China, Beijing, ChinaResearch Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, ChinaResearch Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, ChinaResearch Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, ChinaDepartment of Economics, School of Economics, Minzu University of China, Beijing, ChinaBackgroundThere are huge differences in female breast cancer mortality between urban and rural China. In order to better prevent breast cancer equally in urban and rural areas, it is critical to trace the root causes of past inequities and predict how future differences will change. Moreover, carcinogenic factors from micro-individual to macro-environment also need to be analyzed in detail. However, there is no systematic research covering these two aspects in the current literature.MethodsBreast cancer mortality data in urban and rural China from 1994 to 2019 are collected, which from China Health Statistical Yearbook. The Age-Period-Cohort model is used to examine the effects of different age groups, periods, and birth cohorts on breast cancer mortality. Nordpred project is used to predict breast cancer mortality from 2020 to 2039.ResultsThe age effect gradually increases and changes from negative to positive at the age of 40–44. The period effect fluctuates very little and shows the largest difference between urban and rural areas in 2019. The birth cohort effect gradually decreases with urban-rural effects alternating between strong and weak. In the predicted results, the urban-rural mortality gap becomes first narrow and then wide and shows a trend of younger death.ConclusionsFrom the perspective of a temporal system, the changing trend of breast cancer mortality is highly consistent with the history of social and economic structural changes in China. From the perspective of the theory of social determinants of health, individuals, families, institutions and governments need to participate in the prevention of breast cancer.https://www.frontiersin.org/articles/10.3389/fpubh.2022.1000892/fullbreast cancerurban areasrural areasAge-Period-Cohort modelpredictionthe theory of social determinants of health |
| spellingShingle | Xiaodan Bai Xiyu Zhang Wenjing Xiang Yanjie Wang Yu Cao Guihong Geng Bing Wu Yongqiang Lai Ye Li Baoguo Shi Time tracking and multidimensional influencing factors analysis on female breast cancer mortality: Evidence from urban and rural China between 1994 to 2019 Frontiers in Public Health breast cancer urban areas rural areas Age-Period-Cohort model prediction the theory of social determinants of health |
| title | Time tracking and multidimensional influencing factors analysis on female breast cancer mortality: Evidence from urban and rural China between 1994 to 2019 |
| title_full | Time tracking and multidimensional influencing factors analysis on female breast cancer mortality: Evidence from urban and rural China between 1994 to 2019 |
| title_fullStr | Time tracking and multidimensional influencing factors analysis on female breast cancer mortality: Evidence from urban and rural China between 1994 to 2019 |
| title_full_unstemmed | Time tracking and multidimensional influencing factors analysis on female breast cancer mortality: Evidence from urban and rural China between 1994 to 2019 |
| title_short | Time tracking and multidimensional influencing factors analysis on female breast cancer mortality: Evidence from urban and rural China between 1994 to 2019 |
| title_sort | time tracking and multidimensional influencing factors analysis on female breast cancer mortality evidence from urban and rural china between 1994 to 2019 |
| topic | breast cancer urban areas rural areas Age-Period-Cohort model prediction the theory of social determinants of health |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.1000892/full |
| work_keys_str_mv | AT xiaodanbai timetrackingandmultidimensionalinfluencingfactorsanalysisonfemalebreastcancermortalityevidencefromurbanandruralchinabetween1994to2019 AT xiyuzhang timetrackingandmultidimensionalinfluencingfactorsanalysisonfemalebreastcancermortalityevidencefromurbanandruralchinabetween1994to2019 AT wenjingxiang timetrackingandmultidimensionalinfluencingfactorsanalysisonfemalebreastcancermortalityevidencefromurbanandruralchinabetween1994to2019 AT yanjiewang timetrackingandmultidimensionalinfluencingfactorsanalysisonfemalebreastcancermortalityevidencefromurbanandruralchinabetween1994to2019 AT yucao timetrackingandmultidimensionalinfluencingfactorsanalysisonfemalebreastcancermortalityevidencefromurbanandruralchinabetween1994to2019 AT guihonggeng timetrackingandmultidimensionalinfluencingfactorsanalysisonfemalebreastcancermortalityevidencefromurbanandruralchinabetween1994to2019 AT bingwu timetrackingandmultidimensionalinfluencingfactorsanalysisonfemalebreastcancermortalityevidencefromurbanandruralchinabetween1994to2019 AT yongqianglai timetrackingandmultidimensionalinfluencingfactorsanalysisonfemalebreastcancermortalityevidencefromurbanandruralchinabetween1994to2019 AT yeli timetrackingandmultidimensionalinfluencingfactorsanalysisonfemalebreastcancermortalityevidencefromurbanandruralchinabetween1994to2019 AT baoguoshi timetrackingandmultidimensionalinfluencingfactorsanalysisonfemalebreastcancermortalityevidencefromurbanandruralchinabetween1994to2019 |