Small-Area Lung Cancer Incidence and Mortality: Cross-Sectional Population-Based Study Using Hospital Discharge and Death Registration Data.

IF 3.9 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

JMIR Public Health and Surveillance Pub Date : 2025-09-29 DOI:10.2196/74062

Yu He, Xinxin Xia, Qing Wang, Yaoyun Zhang, Ying Meng, Xiaokang Ji, Qingbo Zhao, Yongchao Wang, Yifu Zhao, Fuzhong Xue, Jin Xu

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引用次数: 0

Abstract

Background: Despite rapid development, cancer registries in low- and middle-income countries, such as China, have the persistent problems with up to 6-year delay and a lack of reported details about small areas.

Objective: This study aimed to develop an approach to provide more up-to-date localized cancer surveillance using linked administrative data. We used lung cancer as an example.

Methods: Based on data of hospitalization record front pages (HRFPs) between 2013 and 2022 from all the secondary and tertiary hospitals in Shandong Province, China, we identified incident cases of lung cancer in 2022 with 2013-2021 being the washout period. Deaths from lung cancer were ascertained for 2022 using linked HRFPs and death registration data. We estimated age-standardized incidence and mortality rates (ASIR and ASMR) of lung cancer in 2022 using Segi world standard population, age-specific incidence and mortality rates by sex, and county-level ASIR and ASMR to illustrate regional disparity. We grouped the counties by municipalities and calculated the Theil indices for within-municipality inequality and between-municipality inequality.

Results: The HRFPs captured 79,672 incident cases of lung cancer in Shandong in 2022 (45,527 males, 34,145 females). The ASIR of lung cancer in Shandong was 42.46 per 100,000 in both sexes (49.19/100,000 in males vs 36.67/100,000 in females). A total of 40,626 lung cancer-specific deaths were ascertained (28,185 men and 12,441 women). The ASMR was 19.76/100,000 in both sexes, 26.29/100,000 and 11.38/100,000 in males and females, respectively. The IQR of county-level ASIR and ASMR were 17.13/100,000 and 10.41/100,000, respectively. The inequality was primarily due to within-municipality disparities, with within-municipality Theil T indices higher than between-municipality Theil T indices (0.0572 vs 0.0033 for ASIR, 0.0824 vs 0.0011 for ASMR).

Conclusions: The cancer surveillance approach based on linked administrative data could provide up-to-date small-area estimates of cancer burden, when cancer registry data are not yet reported and for areas not covered by cancer registries. It could reveal disparity of cancer epidemiology, which provides leads for further investigation into the underlying causes and potential solutions for equity improvement.

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小区域肺癌发病率和死亡率：使用出院和死亡登记数据的横断面人群研究

背景：尽管发展迅速，但在中低收入国家，如中国，癌症登记仍存在长达6年的延迟和缺乏小区域详细报告的问题。目的：本研究旨在开发一种方法，利用相关的管理数据提供更多最新的局部癌症监测。我们以肺癌为例。方法：基于山东省所有二、三级医院2013- 2022年住院记录首页（HRFPs）数据，确定2022年肺癌发病病例，2013-2021年为洗脱期。使用相关的hrfp和死亡登记数据确定2022年肺癌死亡人数。我们使用Segi世界标准人口、按性别划分的年龄特异性发病率和死亡率以及县级ASIR和ASMR来估计2022年肺癌的年龄标准化发病率和死亡率（ASIR和ASMR），以说明地区差异。我们将县按市分组，计算了市内不平等和市间不平等的泰尔指数。结果：2022年山东省hrfp共捕获肺癌病例79672例（男性45527例，女性34145例）。山东省肺癌的男女ASIR为42.46 /10万（男性49.19/10万，女性36.67/10万）。总共确定了40,626例肺癌特异性死亡（28,185名男性和12,441名女性）。男女ASMR分别为19.76/10万、26.29/10万、11.38/10万。县级ASIR和ASMR的IQR分别为17.13/10万和10.41/10万。这种不平等主要是由于城市内部的差异，城市内部的T指数高于城市之间的T指数（ASIR为0.0572 vs 0.0033， ASMR为0.0824 vs 0.0011）。结论：基于相关行政数据的癌症监测方法可以在癌症登记数据尚未报告和癌症登记未涵盖的地区提供最新的小区域癌症负担估计。它可以揭示癌症流行病学的差异，为进一步研究癌症流行病学差异的根本原因和潜在的解决方案提供线索。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

JMIR Public Health and Surveillance Multiple-

CiteScore

13.70

自引率

2.40%

发文量

136

审稿时长

12 weeks

期刊介绍： JMIR Public Health & Surveillance (JPHS) is a renowned scholarly journal indexed on PubMed. It follows a rigorous peer-review process and covers a wide range of disciplines. The journal distinguishes itself by its unique focus on the intersection of technology and innovation in the field of public health. JPHS delves into diverse topics such as public health informatics, surveillance systems, rapid reports, participatory epidemiology, infodemiology, infoveillance, digital disease detection, digital epidemiology, electronic public health interventions, mass media and social media campaigns, health communication, and emerging population health analysis systems and tools.