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Estimation of Cancer Burden in Guangdong Province, China in 2009

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Estimation of Cancer Burden in Guangdong Province, China in 2009 Cao et al. Chin J Cancer (2015) 34:58 DOI 10.1186/s40880-015-0060-4 ORIGINAL ARTICLE Open Access Estimation of cancer burden in Guangdong Province, China in 2009 Su‑Mei Cao1,2, Yan‑Jun Xu3, Guo‑Zhen Lin4, Qi‑Hong Huang5, Kuang‑Rong Wei6, Shang‑Hang Xie1,2 and Qing Liu1,2* Abstract Background: Surveying regional cancer incidence and mortality provides significant data that can assist in making health policy for local areas; however, the province- and region-based cancer burden in China is seldom reported. In this study, we estimated cancer incidence and mortality in Guangdong Province, China and presented basic informa‑ tion for making policies related to health resource allocation and disease control. Methods: A log-linear model was used to calculate the sex-, age-, and registry-specific ratios of incidence to mortal‑ ity (I/M) based on cancer registry data from Guangzhou, Zhongshan, and Sihui between 2004 and 2008. The cancer incidences in 2009 were then estimated according to representative I/M ratios and the mortality records from eight death surveillance sites in Guangdong Province. The cancer incidences in each city were estimated by the corre‑ sponding sex- and age-specific incidences from cancer registries or death surveillance sites in each area. Finally, the total and region-based cancer incidences and mortalities for the entire population of Guangdong Province were summarized. Results: The estimated I/M ratios in Guangzhou (3.658), Zhongshan (2.153), and Sihui (1.527) were significantly dif‑ ferent (P < 0.001), with an average I/M ratio of 2.446. Significant differences in the estimated I/M ratios were observed between distinct age groups and the three cancer registries. The estimated I/M ratio in females was significantly higher than that in males (2.864 vs. 2.027, P < 0.001). It was estimated that there were 163,376 new cancer cases (99,689 males and 63,687 females) in 2009; it was further estimated that 115,049 people (75,054 males and 39,995 females) died from cancer in Guangdong Province in 2009. The estimated crude and age-standardized rate of inci‑ dences (ASRI) in Guangdong Province were 231.34 and 246.87 per 100,000 males, respectively, and 156.98 and 163.57 per 100,000 females, respectively. The estimated crude and age-standardized rate of mortalities (ASRM) in Guangdong Province were 174.17 and 187.46 per 100,000 males, respectively, and 98.59 and 102.00 per 100,000 females, respec‑ tively. In comparison with the western area and the northern mountain area, higher ASRI and ASRM were recorded in the Pearl River Delta area and the eastern area in both males and females. Conclusions: Cancer imposes a heavy disease burden, and cancer patterns are unevenly distributed throughout Guangdong Province. More health resources should be allocated to cancer control, especially in the western and northern mountain areas. Keywords: Disease burden, Incidence-to-mortality ratio, Log-linear model Background valuable information for collecting etiological data and Cancer is the leading cause of death in China [1–4]. Ana- developing cancer control policies. In China, social and lyzing the cancer burden in a population can provide economic changes and an aging population have con- tributed to rapid increases in the morbidity and mortal- *Correspondence: [email protected] ity of most cancers. It has been observed that different 2 Department of Cancer Prevention, Sun Yat-sen University Cancer Center, provinces/areas in China have uneven trends in cancer Guangzhou 510060, Guangdong, P.R. China development because of special risk factors [5, 6]. Thus, Full list of author information is available at the end of the article © 2015 Cao et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/ zero/1.0/) applies to the data made available in this article, unless otherwise stated. Cao et al. Chin J Cancer (2015) 34:58 Page 2 of 8 surveying regional cancer incidence and mortality trends the cancer burden in European and other countries [2, 9, can provide valuable data to assist in making health pol- 10]. Chen et al. [11] used a generalization of this method icy for local areas. The National Central Cancer Registry to estimate the cancer burden in China. They fitted gen- has published Chinese national cancer incidence and eralized linear mixed models and estimated the param- mortality statistics since 2008 [7]; however, the province- eters of models by a Bayesian Markov chain Monte Carlo and region-based cancer burden is rarely reported. method. In the present study, we estimated the total and Guangdong Province has a highly developed economy regional cancer burden in Guangdong Province in 2009 and a population of approximately 100 million; however, using aggregate data from the cancer registry and death the level of economic development varies throughout the surveillance sites. province. The Pearl River Delta area (including Guang- zhou, Shenzhen, Zhuhai, Foshan, Zhongshan, Dongguan, Methods Huizhou, Zhaoqing, and Jiangmen), which is a floodplain Data source in the center of Guangdong Province, and the eastern Incidence and mortality area (including Shantou, Chaozhou, Jieyang, and Shan- Since 1997, four cancer registry sites have existed in wei) of Guangdong Province are economically well devel- Guangzhou, Shenzhen, Zhongshan, and Sihui in Guang- oped (i.e., average income), industrialized, populous, dong Province. We collected incidence and mortality and polluted. In comparison, in the northern mountain data between 2004 and 2008 from each registry site. After area (including Shaoguan, Qingyuan, Meizhou, Heyuan, evaluating the quality of the cancer registries according and Yunfu) and the western area (including Zhanjiang, to the standards defined by the International Agency for Maoming, and Yangjiang) of the province, the level of Research on Cancer [12], we determined that the com- economic development is lower, and the population is pleteness and reliability of mortality data from Shenz- sparse, but the environmental pollution is lower. Dif- hen did not meet the standards; therefore, the data from ferences in population, economy, industrialization, and Shenzhen were excluded from the development of an lifestyle in various areas of Guangdong Province may estimation model for the I/M ratio. Thus, data from three influence the risk of cancer. cancer registries were included in the estimation model. No provincial cancer incidence data exist for Guang- The reported cancer spectrum in the cancer registries dong Province up to now, which limits the evidence avail- contained all types of cancer (ICD-10 C00-C90). able for making cancer control policies. Since 1997, four Cancer mortality data were collected from the Guang- cancer registry sites have been established in Guangdong dong Provincial Center of Disease Control and Preven- Province. These sites cover a population of approxi- tion. The data were from eight death surveillance sites mately 20 million, including two big cities (Guangzhou in Guangdong Province, located in the Yuexiu district of and Shenzhen) and two medium-sized cities (Zhongshan Guangzhou, Zhuhai, Taishan, Nanxiong, Yunfu, Shan- and Sihui); however, these cities are concentrated in the wei, Sihui, and Wuhua. The mortality data from Yuexiu Pearl River Delta area. The uneven distribution of cancer of Guangzhou and Sihui overlapped the data from the registry sites means that the cancer registry data cannot cancer registries. Therefore, only one set of data was depict a representative picture of the provincial cancer included in the analysis (Table 1). burden. Recently, three new cancer registry sites were built in Guangdong Province, but they lack precision and Population data completeness with the cancer incidence too low or the Population and age distribution data were obtained incidence-to-mortality (I/M) ratio extremely high. Fortu- from the Guangdong statistics annual report, published nately, eight death surveillance sites in Guangdong Prov- by the Guangdong Statistics Bureau [13]. The estimated ince are distributed in more areas, including the northern provincial population in 2009 was 83,659,800, includ- mountain, western, and eastern areas of the province. ing 43,091,000 males and 40,568,800 females. Regional Estimating the incidence and mortality of cancer using population data in 2009 were extracted by sex and age combined data from the cancer registries and death sur- (grouped into 5-year age ranges, e.g., 0–4, 5–9, 10–14, veillance sites in Guangdong Province will provide more … 80–84, and 85+ years). The estimated populations accurate data. were 29,670,200 in the Pearl River Delta area, 17,583,400 Jensen et al. [8] developed a statistical technique for in the eastern area, 17,741,200 in the western area, and estimating the incidence of cancer using mortality data. 18,665,000 in the northern mountain area [13]. They used a model of mortality as a Poisson regression with the log incidence as an offset to estimate the inci- Statistical methods dence of cancer based on the number of cancer deaths. Since the I/M ratio is a fairly stable value in a large, This model has been applied comprehensively to estimate defined population, it is used as an index to evaluate the Cao et al. Chin J Cancer (2015) 34:58 Page 3 of 8 Table 1 The economic partitions and covered areas of the For each city in Guangdong Province that was studied, cancer registry and death surveillance data sources the number of cancer cases and deaths in 2009 were esti- in Guangdong Province, China mated by multiplying the predicted incidence and mor- Economic area City Cancer registry Death surveillance tality by the corresponding city populations.
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