Abstract
目的
通过监测、流行病学和最终结果(SEER)数据库了解美国复杂社区水平的社会经济学地位(SES)与盲肠腺癌患者死亡率之间的关系。
方法
通过SEER数据库对2011~2015年确诊的美国盲肠腺癌患者的信息进行查询。采用因子分析、聚类分析、单变量和多变量COX比例风险模型,确立了5个社会保障因素:因素1:经济和教育劣势;因素2:与移民有关的特征(语言隔离高,国外出生率高,住宅拥挤率高);因素3:县内搬迁率高;因素4:州内搬迁率高;因素5:国内搬迁率高。确定了5个SES定义的集群。
结果
在17 185例患者中,全因死亡人数为5948例,存活人数为11 237例。在多变量COX回归分析中,以集群1(贫困率低、受教育程度高)为参考,集群3(县内流动率高)的HR值为1.13(95% CI=1.04-1.21,P < 0.05),风险比集群1高13%。集群4(语言隔离率低、国外出生率低和住宅拥挤率低、国内流动率低)的HR值为1.15(95% CI=1.07-1.24,P < 0.001),风险比集群1高15%。集群5(经济和教育劣势、与移民有关特征、国内流动率低)的HR值为1.11(95% CI=1.03-1.20,P < 0.01),风险比集群1高11%。相关SES指标的因素基于盲肠腺癌患者的死亡率显示出显著的统计学意义,表明经济水平和受教育程度低的是盲肠腺癌患者死亡的危险因素。
结论
较低的社会经济学地位与美国盲肠腺癌患者的死亡风险的增加相关,且人群分布存在差异,完善医保政策和加强心理治疗可为盲肠腺癌患者的预后改善提供指导。
Keywords: SEER, 社会经济学地位, 盲肠腺癌, 生存率
Abstract
Objective
To explore the relationship between socioeconomic status (SES) and disease mortality in patients with cecal adenocarcinoma in America through the Surveillance, Epidemiology, and End results (SEER) database.
Methods
The SEER database was queried for patients with cecal adenocarcinoma in America diagnosed from 2011 to 2015. Factor analysis, cluster analysis, and univariate and multivariate Cox proportional hazard models were used for data analysis. Five social security factors were identified: factor 1, economic and educational disadvantage; factor 2, characteristics related to immigration (language isolation and foreign birth); factor 3, high relocation rate in the county; factor 4, high intra-state relocation rate; and factor 5, high domestic relocation rate. Five clusters defined by SES were identified.
Results
The number of all-cause deaths among 17 185 patients was 5948, and the number of survivors was 11, 237. In the multivariate Cox regression analysis, with cluster 1 (low poverty rate and high education level) as the reference, the hazard ratio (HR) of cluster 3 (high intra-county mobility rate) was 1.13 (95% CI: 1.04-1.21, P < 0.05), and the risk was 13% higher than that of cluster 1. The HR of cluster 4 (low language isolation, foreign birth, housing overcrowding, and intra-country mobility rates) was 1.15 (95% CI: 1.07- 1.24, P < 0.001) with a 15% higher risk than cluster 1. The HR of cluster 5 (economic and educational disadvantages, immigration-related characteristics, and low intra-country mobility) was 1.11 (95% CI: 1.03-1.20, P < 0.01) with a 11% higher risk. The factors related to SES indicators were based on the mortality of patients with cecal adenocarcinoma, indicating that low economic and education levels are risk factors for cecal adenocarcinoma.
Conclusion
Low socioeconomic status is associated with an increased risk of death in patients with cecal adenocarcinoma in the United States and show different distribution patterns based on population. Improving health insurance policies and strengthening psychotherapy can provide guidance for improving prognosis f cecal adenocarcinoma patients.
Keywords: SEER, socioeconomic status, cecal adenocarcinoma, survival rate
盲肠腺癌常常被归为结直肠肿瘤研究[1, 2]。结直肠癌(CRC)是一种常见的恶性胃肠道肿瘤。CRC美国是第2常见的恶性肿瘤[3]。早期结直肠癌患者常因无明显症状而在临床上易被诊断为其他的消化道疾病。CRC患者病情发展至错过最佳治疗阶段后方被确诊是导致CRC预后不良的原因[4]。绝大多数晚期CRC患者的预后不超过2年[5]。2020年美国CRC新增确诊147 950例,并有53 200人死于CRC[3]。同一份报告中显示CRC中的近端结肠癌症的发病率最高,甚至超过半数的65岁以上CRC患者的癌变发生在近端结肠和盲肠。
因此,我们着重探究结肠近端,即盲肠腺癌的死亡危险因素。社会经济学地位对盲肠腺癌的预后有重要影响[6, 7]。我们可以基于社区层面的社会经济学地位因素对癌症患者的死亡风险进行探究[8-10]。芬兰的一项调查显示,生活在贫困率高、学历低的社区的人群的盲肠腺癌的发生率更高,且健康预估更差[7]。探究社会经济学地位对盲肠腺癌的影响所需的指标几乎都被SEER数据库囊括在内,包括年龄、性别、种族、学历、收入等[11, 12]。在美国,结直肠镜等检查的自付费用高昂,由于缺乏接受的结直肠镜等检查的机会,SES较低的人群,即没有保险、教育水平较低、收入较低的人群的盲肠腺癌的发现率较低,从而可能导致以往的研究忽略了对SES水平较低人群盲肠腺癌预后的研究[12, 13]。此外,美国的一项研究表明复合的SES因素可能影响盲肠腺癌患者的健康状况[14]。复合的SES指标可能为教育和经济劣势、移民特征、居住不稳定等复合因素[10]。因此,我们可以通过复合的SES指标更全面地探究盲肠腺癌患者的死亡风险的影响因素。
在以往对盲肠腺癌患者生存率的研究中,常常着重于探究诊断年龄、性别、种族、分化程度、分期、治疗方式等因素对生存率的影响[15-17],而对社会经济学地位因素的影响的研究不够充分。在以往的研究中,SES作为盲肠腺癌患者的预后指标常常单一片面,无法全面地体现盲肠腺癌患者真实的SES水平。此外,把右半结肠癌而非其中的盲肠癌作为研究对象,会导致结果缺乏特异性。
本研究借助SEER数据库,提取盲肠腺癌患者的数据,分析影响生存时间的因素。SEER数据库记录了美国部分地区上百万名恶性肿瘤患者的发病信息,有着样本量大、统计学能效高的特点。本研究的目的是开展一项基于大规模人群的研究,通过提取全面的社会经济学地位因素并对各因素进一步整合,开发出一种指标全面、特异性高的模型用于精准分析盲肠腺癌患者的预后水平。社会经济学地位对盲肠腺癌患者的预后评估在治疗方案制定上有重要的指导作用,在社会层面对降低盲肠腺癌的发生发展至关重要。
1. 资料和方法
1.1. 资料来源
使用SEER*Stat软件从SEER数据库提取2011年至2015年盲肠腺癌患者的数据,包括了17185例盲肠腺癌患者。SEER数据库提供患者基本人口学资料、肿瘤相关资料、社会经济学地位资料,如患者所在地区、肿瘤分化程度、患者的受教育程度等。SEER数据库的用途包括随着时间推移对死亡率进行流行病学研究[6]。我们已经取得SEER数据库的使用权限并已签署相关协议,无需再签署相关伦理文件。
1.2. 对象与方法
1.2.1. 纳入与排除标准
纳入标准:第一原发部位为盲肠,国际疾病分类肿瘤学专辑第3版(ICD-O-3/WHO 2008)解剖学编码为C18.0;经组织病理学确诊,病理类型为腺癌,形态学编码为(M814-M838);年龄范围为19~85岁。随访上报时间为12月31日。
排除标准:肿瘤大小情况不明;生存时间未知;病理信息不完整;SES指标信息不完整。
1.2.2. 纳入变量
纳入的变量包括年龄、种族、性别、婚姻状况、分化程度、扩散程度和SES指标。
其中,从2011~2015年ACS县级属性数据中提取以下23个SES指标进行处理:教育程度(3个变量):9年级以下百分比、高中以下学历的百分比、本科以上学历的百分比;贫困(5个变量):个人低于100%贫困人口百分比、家庭低于100%贫困水平百分比、个人低于150% 贫困水平百分比、个人贫困低于200%水平百分比、每间房间一人以上的人口百分比;收入和经济水平(3个变量):失业群体的百分比、中产家庭的百分比、中等收入共同生活户口的百分比;移民(4个变量):外国出生人口的百分比、语言隔离率、移民至美国的百分比(>5年)、移民至美国的百分比(>1年);居住稳定性(8个变量):本县未搬迁、在本县内搬迁、在本州内搬迁、在全国内搬迁的百分比(>5年),本县未搬迁、在本县内搬迁、在本州内搬迁、在全国内搬迁的百分比(>1年)。
1.2.3. 终点
主要终点为总体生存周期,定义为从确诊到死亡的时间,即本研究采取的预后指标为全因死亡。
1.3. 统计学分析
本研究使用R软件4.03版本进行统计学分析。计算23个县级社会经济变量的因子系数,并对其进行因子分析和聚类分析,绘制因子载荷图。样本中的每一个患者都被分配了一个因子得分,通过因子聚类可以得到5个集群。绘制因子和聚类关系的气泡图来观察每个集群所展示出的因子特征。定量变量按平均值和标准差(SD)进行适当总结。分类变量通过相应组中的患者的频率和百分比进行总结。然后用多变量Cox回归分析探究除SES外的因素(年龄、种族、性别、婚姻状况、分化程度、扩散程度等)与盲肠腺癌死亡率的关系。最后,使用多因素Cox回归分析来探究SES因素对盲肠腺癌死亡率的关系。以上分析均采用双侧检验,P < 0.05为差异具有统计学意义。
2. 结果
2.1. 国家经济社会地位变量的共现:经济社会地位因素的特征
对23个SES变量进行因子分析,得到了一个5因素的解释。变量和因素之间的相关性可见图 1。因素1捕捉到了与经济水平和受教育程度低有关的变量:家庭和个人贫困率高,受教育程度低,同时失业率高,家庭收入水平低。因素2捕捉到了与移民有关的变量:语言隔离率高,国外出生率高,住宅拥挤率高。因素3捕捉到了与居住不稳定相关的变量:五年内和一年内的县内搬迁率高。因素4反映了五年内和一年内的州内搬迁率高。因素5反映了5年内和1年内的国内搬迁率高(图 1)。
图 1.
因素(主成分)负荷图
Factor (principal component) load diagram.
2.2. SES集群的形成和描述
对SES指标进行汇合,并对SES因子得分进行聚类分析,得到5个集群。图 2显示了所有集群的因子得分模式,表 1展示了分配至这五个集群中每个集群的盲肠腺癌患者的县级社会经济特征(表 1)。集群1的特点是贫困率低、受教育程度高(因素1颠倒)。集群2的特点是语言隔离率低、国外出生率低和住宅拥挤率低(因素2颠倒)、国内流动率高(因素5)。集群3的特点县内流动率高(因素3)。集群4有语言隔离率低、国外出生率低和住宅拥挤率低(因素2颠倒)和国内流动率低(因素5颠倒)的特征。集群5有着经济和教育劣势(因素1)、与移民有关(因素2)和国内流动率低(因素5颠倒)的特征。表 2显示了每个群组中患者的人口统计和临床特征。在年龄、性别、种族、婚姻状况、分化程度和扩散程度等方面,患者的分布在不同组之间存在显著差异(P < 0.001,表 2)。
图 2.
SES因素在SES集群上的得分模式
Pattern of SES Factor ScoresAcross SES Clusters.
表 1.
县级社会经济变量的集群特征
Characterization of county-level socioeconomic variables by cluster
Variables Mean(SD) |
Cluster1 Mean(SD) |
Cluster2 Mean(SD) |
Cluster3 Mean(SD) |
Cluster4 Mean(SD) |
cluster5 Mean(SD) |
Note: All SES variables with percentages represent the percentage of persons living within a county with that characteristics calculated by the Census American Community Survey (ACS) 2011-2015; Median Family Income and Median Household income are in dollars; ANOVA P < 0.001 for all variables. SD: Standard deviation. Mean: Mean value. | |||||
Education | |||||
% < 9th grade | 654(191) | 455(264) | 461(223) | 539(308) | 1182 (362) |
% < Highschool | 1229(290) | 1164(494) | 1158 (390) | 1417 (620) | 2212 (389) |
% at least bachelors | 4152.95(659) | 2981.58 (770) | 3043.85(897) | 2314.20(918) | 2426.28 (615) |
Poverty | |||||
% Families below poverty | 802(215) | 1292(571) | 1137 (398) | 1170 (546) | 1595 (302) |
% Persons below poverty | 1133(259) | 1736(644) | 1613 (500) | 1537 (638) | 1999 (345) |
% Persons < 150% poverty | 1878 (396) | 2727 (863) | 2553 (652) | 2475 (909) | 3189 (409) |
% Persons < 200% poverty | 2616 (524) | 3711 (969) | 3458 (763) | 3400 (1087) | 4253 (437) |
% Crowding | 549 (238) | 359 (259) | 293 (169) | 209 (129) | 893 (329) |
Income | |||||
% Unemployed | 787 (107) | 856 (274) | 878 (238) | 831 (240) | 1188 (204) |
% Median % family ($) | 9202.27 (1383) | 6268.48 (1399) | 6795.89 (1181) | 6592.94 (1768) | 5918.16 (618) |
% Median household ($) | 7766.80 (1101) | 5199.81 (1292) | 5468.24 (1013) | 5361.33 (1514) | 5213.91 (646) |
Immigration | |||||
% Foreign born | 2709 (647) | 939 (590) | 1014 (578) | 606 (524) | 2563 (935) |
% Move to US(5+ years) | 102 (32.9) | 90.8 (51.5) | 54.6 (29.9) | 29.5 (19.4) | 59.1 (15.9) |
% Move to US(1+ years) | 103 (33.2) | 90.7 (50.2) | 54.5 (30.1) | 29.6 (20.0) | 58.7 (15.5) |
% Language isolation | 840 (232) | 346 (270) | 308 (182) | 224 (228) | 1006 (417) |
Relocation | |||||
Age 5+ years | |||||
% No moving | 8656.87 (258) | 8258.94 (363) | 8295.01 (290) | 8879.15 (224) | 8622.22 (167) |
% Move within county | 811 (214) | 912 (206) | 1026 (173) | 594 (150) | 981 (115) |
% Move within state | 247 (96.3) | 285 (163) | 411 (164) | 330 (149) | 229 (119) |
% Move within country | 183(67.9) | 453(212) | 214 (81.7) | 167(80.9) | 108 (27.2) |
Age 1+ years | |||||
% No moving | 8636.56 (255) | 8224.92 (364) | 8264.30(283) | 8848.60 (230) | 8590.90 (176) |
% Move within county | 827(217) | 936(208) | 1051 (174) | 615(157) | 1010 (119) |
% Move within state | 248(96.0) | 288(165) | 414(164) | 336(151) | 231 (122) |
% Move within country | 185(68.3) | 460(215) | 217 (82.5) | 170(82.7) | 109 |
表 2.
已知影响盲肠腺癌生存率的预后因素在不同集群患者的分布
Distribution of known prognostic factors for cecal adenocarcinoma survival in different clusters of cecal adenocarcinoma patients
Variables | Cluster 1 n (within) |
Cluster 2 n (within) |
Cluster 3 n (within) |
Cluster 4 n (within) |
cluster 5 n (within) |
Note: Not all columns round to 100% due to rounding. N: Number. | |||||
Number | n=4055 | n=1197 | n=3753 | n=4347 | n=3833 |
Race | |||||
Black | 346 (8.53%) | 248 (20.7%) | 428 (11.4%) | 500 (11.5%) | 594 (15.5%) |
Other (American Indian/AK Native,Asian/Pacific Islander) | 426 (10.5%) | 181 (15.1%) | 112 (2.98%) | 44 (1.01%) | 298 (7.77%) |
White | 3283 (81.0%) | 768 (64.2%) | 3213 (85.6%) | 3803 (87.5%) | 2941 (76.7%) |
Gender | |||||
Female | 2267 (55.9%) | 650 (54.3%) | 2137 (56.9%) | 2357 (54.2%) | 2088 (54.5%) |
Male | 1788 (44.1%) | 547 (45.7%) | 1616 (43.1%) | 1990 (45.8%) | 1745 (45.5%) |
Marital | |||||
Married | 2778 (68.5%) | 1142 (95.4%) | 3531 (94.1%) | 3755 (86.4%) | 3406 (88.9%) |
Unmarried | 1277 (31.5%) | 55 (4.59%) | 222 (5.92%) | 592 (13.6%) | 427 (11.1%) |
Grade | |||||
Well differentiated; Grade I | 250 (6.17%) | 69 (5.76%) | 234 (6.24%) | 238 (5.48%) | 285 (7.44%) |
Moderately differentiated; Grade II | 2668 (65.8%) | 839 (70.1%) | 2589 (69.0%) | 2971 (68.3%) | 2611 (68.1%) |
Poorly differentiated; Grade III | 977 (24.1%) | 240 (20.1%) | 735 (19.6%) | 946 (21.8%) | 824 (21.5%) |
Undifferentiated; anaplastic; Grade IV | 160 (3.95%) | 49 (4.09%) | 195 (5.20%) | 192 (4.42%) | 113 (2.95%) |
Stage | |||||
Distant | 726 (17.9%) | 225 (18.8%) | 754 (20.1%) | 830 (19.1%) | 655 (17.1%) |
Localized | 1469 (36.2%) | 371 (31.0%) | 1211 (32.3%) | 1522 (35.0%) | 1320 (34.4%) |
Regional | 1860 (45.9%) | 601 (50.2%) | 1788 (47.6%) | 1995 (45.9%) | 1858 (48.5%) |
2.3. 其他重要预后指标的识别
表 3提供了风险比(HR)、95%可信区间(CI)和P值,以说明Cox比例风险模型中已知预后因素与盲肠腺癌死亡率之间的关系。确诊年份、性别、扩散程度与死亡率相关(表 3)。
表 3.
各种预后因素的盲肠腺癌的分布与危险性的联系,SEER2011-2015
Prognostic factors for deaths of cecal adenocarcinoma patients and their hazard risks (SEER 2011-2015)
Variable | HR | 95% CI | P |
Note: HR: Hazard Ratio; CI: Confidence Interval. | |||
Age | 1.04 | 1.041 | < 0.001 |
Gender | |||
Male | Reference | ||
Female | 0.89 | 0.85-0.95 | < 0.001 |
Race | |||
White | Reference | ||
Black | 1.20 | 1.21-1.30 | < 0.001 |
Other | 0.96 | 0.86-1.08 | 0.51 |
Marital | |||
Married | Reference | ||
Unmarried | 0.94 | 0.88-1.01 | 0.11 |
Grade | |||
Well differentiated; Grade I | Reference | ||
Moderately differentiated; Grade II | 1.01 | 0.89-1.14 | 0.92 |
Poorly differentiated; Grade III | 1.39 | 1.23-1.59 | < 0.001 |
Undifferentiated; anaplastic; Grade IV | 1.51 | 1.28-1.77 | < 0.001 |
Stage | |||
Localized | Reference | ||
Regional | 1.83 | 1.70-1.96 | < 0.001 |
Distant | 7.35 | 6.83-7.92 | < 0.001 |
2.4. SES因素、SES集群和盲肠腺癌死亡率
本研究中共17185个样本,死亡人数为5948,存活人数为11237。表 4用多因素cox回归分析显示了每个SES因素和各个群集的盲肠腺癌死亡风险。因素1的HR值为1.05(P < 0.001),说明具有经济水平和受教育程度低的特征是危险因素(表 4)。
表 4.
盲肠腺癌死亡的风险与经济因素和集群的关联,SEER2011-2015
Hazard risk of the factors for cecal adenocarcinoma mortality and in different socioeconomic clusters (SEER 2011-2015)
Variables | HR | 95% CI | P |
Note: HR: Hazard Ratio; CI: Confidence Interval. | |||
SES factors | |||
Factors 1 | 1.05 | 1.03-1.08 | < 0.001 |
Factors 2 | 0.98 | 0.95-1.00 | 0.06 |
Factors 3 | 1.00 | 0.98-1.03 | 0.74 |
Factors 4 | 1.00 | 0.97-1.03 | 0.98 |
Factors 5 | 1.00 | 0.97-1.02 | 0.80 |
SES Clusters | |||
Clusters 1 | 1.00 | - | - |
Clusters 2 | 1.10 | 0.98-1.13 | 0.10 |
Clusters 3 | 1.13 | 1.04-1.21 | 0.02 |
Clusters 4 | 1.15 | 1.70-1.24 | < 0.001 |
Clusters 5 | 1.11 | 1.03-1.20 | 0.007 |
以集群1为参考,集群3(县内流动率高)的HR值为1.13(95% CI=1.04-1.21,P < 0.05),风险比集群1高13%。集群4(语言隔离率低、国外出生率低和住宅拥挤率低、国内流动率低)的HR值为1.15,(95% CI=1.07-1.24,P < 0.001),风险比集群1高15%。集群5(经济和教育劣势,与移民有关特征和国内流动率低)的HR值为1.11(95% CI=1.03-1.20,P < 0.01),风险比集群1高11%。
3. 讨论
盲肠腺癌在特定的社区、时间段内发生时,其共同的发生模式(即所含因素相同)可能会促使盲肠腺癌患者承担着更大的死亡风险。
本研究通过美国国家癌症研究所收集的具有代表型的大样本数据库探究了社区水平的SES指标与盲肠腺癌患者死亡风险的关系,研究结果对于未来治疗盲肠腺癌和改善我们的护理质量等方面具有重要意义。本研究采取了综合性更好的SES指标分类方法,比以往的单一指标评估SES对盲肠腺癌生存率的影响的研究方法更加全面[9]。
本研究中具有经济水平和受教育程度低的特征会导致盲肠患者的全因死亡率增加,并显示出显著的统计学意义(P < 0.001)。意大利的一项研究表明受教育程度和盲肠腺癌风险之间相关性不显著[18]。因此我们主要从经济水平低下特征展开讨论。鉴于资源有限和基础设施的缺乏,低收入地区的癌症发病率和死亡率上升尤为明显[19]。美国一项研究指出,收入直接影响到盲肠腺癌治疗后的生存率[20]。低收入家庭需要依赖于政府的福利政策,但常常由于住房福利服务的总体质量不足,导致这类人群的心理健康程度和社会关系满意度下降,从而使得其抑郁症患病率上升和健康状况下降[21]。一般情况下,抑郁症既可以被认为是发生癌症的元凶,也是引起癌症患者的生存率下降的原因[22]。抑郁症的诱因往往是心理压力过大、焦虑症等因素,有证据说明心理因素(如压力过大)会对SES-健康的关系造成影响[23]。此外,我们推测受教育程度较低的群体可能对癌症筛查的重要性认识不足,即使确诊了盲肠腺癌,也可能因为症状出现过晚、发现时间过迟而导致患者错过最佳治疗时间[13]。
经济水平和教育程度较低的社区若同时具有外来移民率高的特征(集群5),则会出现社区内盲肠腺癌的预后较差的情况。一项关于美国移民的研究表明,胃肠道等疾病在美国移民中十分常见,这往往与移民的心理创伤或心理健康问题相关[24]。此外,由于语言不通可能导致的病情陈述不准确、诉求表达不完整等情况会导致盲肠腺癌患者的医疗护理水平低于正常和预后较差[25]。增设医院翻译设备或人员可能是未来改善语言隔离情况的有效手段。另外,经常搬迁的人群面临着难以获得或推迟医疗服务的窘境,这与搬迁后的生活地点存在着交通不畅与未投保等原因有关[7, 26-28]。有人提出,护理和治疗的差异可能与社会经济学地位的差异有关,提高低SES群体的护理水平可能对降低盲肠腺癌患者的死亡风险有益[29]。由此可以推测,上述因素会对盲肠腺癌患者的健康造成较大影响并增加他们的全因死亡率。
通过以上证明可以推测,移民的种族、饮食和搬迁因素同时也会对盲肠腺癌患者的健康造成较大影响并增加他们的全因死亡率。移民者包含着许多种族、饮食习惯和信仰,这些都需要更进一步的研究去剖析其中的关联[30, 31]。因此移民相关特征对盲肠腺癌预后的影响难以在本研究中展开讨论。除此以外,州内流动率低或县内流动率高成为了盲肠腺癌死亡率高的危险因素[28, 32]。生活在经常搬迁、社会网络不稳定的家庭的孩子身体健康状况较差。这可能是搬迁导致了青少年的幸福感降低、行为情感问题更显著和非法使用毒品的概率提升[33]。这些因素都是提高盲肠腺癌的发生率与死亡率的重要原因[34, 35]。但是,县内流动率高的社区(集群3)的盲肠腺癌患者相比流动稳定性高的社区(集群4)并没有增加死亡风险。这可能与两个社区的性别、癌症转移情况的差异有关。性别差异和癌症转移对患者的死亡风险尚有影响。本研究结果显示,女性盲肠腺癌患者的全因死亡率比男性低11%(P < 0.001)。同时有研究指出,尤其在中老年群体中,男性结直肠肿瘤发病风险明显高于女性[36]。具体机制尚未有定论,这可能与二者的激素分泌水平不同有关。研究结果显示,易发生转移的盲肠腺癌患者的死亡风险为未发生转移患者4倍左右。美国一项研究表明,盲肠腺癌一旦发生转移,患者的病情将难以逆转[37]。
本研究需要在一定的局限性的背景下进行分析。我们的样本年龄范围比较宽泛,无法将结果应用于对于特定年龄段(例如儿童、青年、中年、老年等患者)的分析,对此可以在样本量足够的前提下,针对某特定年龄段的盲肠腺癌患者进行关于该年龄段发病特点的研究。最后我们采用的结局指标仅仅为死亡或生存,并未追溯死亡是否由疾病或者其他原因造成,未来可在SEER数据库完善患者死亡原因信息后对此进行探究。
我们的研究结果表明,大部分SEER县级SES指标可以被五个潜在因素涵盖。这五个因素的因子分析结果揭示了盲肠腺癌死亡风险概况。具有经济水平和受教育程度低的特征的患者,县内流动率高的社区,移民特征不明显、国内流动率低的社区,以及有经济和教育劣势、移民特征明显、国内流动率低的社区的盲肠腺癌患者死亡风险更高。这些患者和社区可能需要更加完善的医保政策和医疗基础设施来降低盲肠腺癌死亡风险。我们可以在未来着重研究SES因素与社区的医疗保健政策之间的关系。同时我们应该多维度地去为患者制定医疗计划,在原有的药物治疗和物理治疗的基础上加强心理治疗[38]。
Biography
邵子安,E-mail: sza3190901085@163.com
Funding Statement
广东省科技计划项目(2021B1212040007)
Contributor Information
邵 子安 (Zi'an SHAO), Email: sza3190901085@163.com.
吕 军 (Jun LYU), Email: lyujun2020@jnu.edu.cn.
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