Osong Public Health and Research Perspectives

Influence of Socioeconomic Status, Comorbidity, and Disability on Late-stage Cancer Diagnosis

Bo Ram Park, So Young Kim, Dong Wook Shin, Hyung Kook Yang, Jong Hyock Park

Additional article information

Abstract

Objectives

Understanding factors affecting advanced stage at diagnosis is vital to improve cancer outcomes and overall survival. We investigated the factors affecting later-stage cancer diagnosis.

Methods

Patients completed self-reported questionnaires. We collected cancer stage data from medical records review. Logistic regression analyses were performed to identify factors associated with later stage cancer at diagnosis by gender.

Results

In total, 1,870 cancer patients were included in the study; 55.8% were men, 31.1% had more than one comorbid condition, and 63.5% had disabilities. About half of the patients were smokers, and drank alcohol, and 58.0% were diagnosed at an advanced stage. By cancer type, lung and liver cancers (both genders), prostate (men), colorectal, cervical, and thyroid cancer (women) were more likely to be diagnosed at a later stage. After controlling for socioeconomic factors, comorbidity (odds ratio [OR], 1.48 in men) and disability (OR, 1.64 in men and 1.52 in women) remained significantly associated with late-stage diagnosis.

Conclusion

In this nationwide study, using combined information from patients and medical records, we found that male patients with comorbidities or disabilities, and female patients with disabilities were more likely to have advanced stage cancer at diagnosis. Targeted approaches by cancer type and health conditions are recommended.

Keywords: neoplasms, diagnosis, stage, early detection of cancer

INTRODUCTION

One-third of the population in Korea has been at risk of getting cancer since 2008 [1]. Over 200,000 new cancer cases are diagnosed annually, and one in four deaths is cancer related [2]. Despite advances in screening and treatment, stage at diagnosis remains the most important predictor of cancer mortality [3]. According to World Health Organization, a third of cancers could be cured if detected early enough and treated adequately [4]. Based on the analysis of data from the Korea National Cancer Incidence Database survival rates for patients with distant-stage cancers for the eight most common cancers ranged from 2.5% to 69.1%; patients with liver cancer showed the lowest relative survival rates (RSRs). The 5-year RSRs for localized-stage cancers of the stomach, colorectum, female breast, cervix uteri, prostate, and thyroid were > 90%. Conversely, the 5-year RSRs for liver and lung cancers were 42.8% and 46.3%, respectively [5]. Understanding factors that contribute to advanced stage at diagnosis is vital to improve cancer outcomes and overall survival [3]. Previous studies have reported associations between various patient characteristics and cancer stage at diagnosis, such as age [6,7], gender, race/ethnicity [8,9], household income, level of education, employment, marital status, health insurance type/status, and residence [1012]. Health-related characteristics have also been considered major reasons for advanced stage at diagnosis. Additionally, comorbidities [1315] and disabilities [16] are also known to have negative influences on the delivery of stage-appropriate treatment.

Access to health insurance and healthcare services has also been associated with social inequalities in cancer survival and late stage at diagnosis [17]. Previous studies have demonstrated that individuals without health insurance are less likely to have a steady source of healthcare and preventive services, such as cancer screening, and are more likely to be diagnosed at a later stage of cancer for cancers that are readily detectable by screening or via the early symptoms [18].

As early detection is associated with a better outcome in cancers and the burden of cancer continues to increase, the National Cancer Screening Program (NCSP) was designed to provide free screening services for low-income Medicaid recipients in 1999. Since then, the NCSP has expanded its target population. In 2004, the program targeted the five most common cancers in Korea, such as stomach, liver, colorectal, breast, and cervical cancers [19].

Although previous studies have shed some light on the relationships between socioeconomic status and stage at diagnosis in Korea, these studies have been limited to partial data or registry data [2,2022]. Our study extends previous research by using comprehensive information from patients’ self-report and the medical records. The aim of this study was to assess the associations of not only socioeconomic factors but also health-related characteristics with later stage at diagnosis for major types of cancers.

MATERIALS AND METHODS

1. Study design and subjects

This study was performed as a part of the Cancer Patient Experience (CaPE) Study, an annual nationwide survey of cancer patient experience, conducted from 2008 to 2014 to develop a comprehensive supportive care [23]. The National Cancer Center and the nine Regional Cancer Centers participated in the survey. This study was approved by the Institutional Review Board of the National Cancer Center (NCCNCS-08-150).

The target population comprised of all cancer patients aged 18 years and older, 4 months after the first diagnosis who visited or admitted to the 10 cancer centers nationwide between July and September 2008. Cancer patients who agreed to participate were interviewed by trained interviewers. In total, 2,661 cancer patients completed the interview process. After collecting socioeconomic and health-related data, patient medical records were reviewed at each cancer center to collect evident staging data, using the Surveillance, Epidemiology, and End Results Program (SEER).

We used quota sampling according to cancer incidence rates in Korea; 80% for the six major cancers in Korea, namely stomach, lung, liver, colon, breast, and cervical cancer, and 20% of other cancers. We excluded from this analysis the patients with unknown stage at diagnosis or missing information regarding major characteristics.

2. Measures

The clinical factors included the types of cancer and the patients’ SEER stage. The Korean National Cancer Registration (KCCR) collected information on the stage at diagnosis, using the SEER summary stage [2]. Stage at diagnosis, the main outcome variable, was defined using the SEER Site-Specific Summary Staging Guide. For these analyses, stage at diagnosis was reclassified as early stage (in situ or local) or late stage (regional or distant).

Socioeconomic factors, such as gender, age (< 50, 50–60, 60–70, and > 70 years old), education (< middle school, < high school, ≥ college), marital status (never married, married, divorced/widowed), living arrangement (live alone, live with others), monthly household income before diagnosis (< 200 million, 200–400 million, and > 400 million Korean Won [KRW]), health, social security/insurance status (medical insurance, medical aid, uninsured), private insurance (no/yes), residence (metropolitan, city, rural), job (homemaker, office worker, non-office worker, self-employed, agricultural/forestry/fishery workers, unemployed) were grouped.

Comorbidity status was defined as patients having one or more chronic disease or condition. The category of comorbidity including 22 conditions and diseases, such as heart disease, diabetes mellitus, and pulmonary diseases were based on the Charlson comorbidity index (CCI). We used self-reported comorbidity data. Traditionally, medical record reviews and administrative data have been used to calculate the CCI, but a CCI generated via patient self-report, using a simple 1-minute survey performed comparably to CCI measures generated from administrative data [24].

The EQ-5D was used to estimate quality of life. This measure has been validated in Korean populations in previous research. The EQ-5D includes single-item measures of mobility, self-care, the ability to perform usual daily activities, pain/discomfort, and anxiety/depression. Each item was coded, using a three-point scoring system (no problem, some problems, and severe problems). If a cancer patient had at least one problem for any single item measure, then he/she was classified as having a disability [25].

Regarding health behaviors, we included information on smoking and alcohol use as these lead to higher risks of cancer.

3. Statistical analyses

We used multiple logistic regression modelling to calculate odds ratios (OR) and corresponding 95% confidence intervals (95% CI). All of the logistic regression analyses were adjusted by age, education level, marital status, living arrangement, income, insurance type, residence, job, cancer type, comorbidity, disability, smoking, and drinking. Analyses were divided by gender: stomach, lung, liver, colorectal, thyroid, breast, and cervical cancer in women and stomach, lung, liver, colorectal, thyroid, and prostate cancer in men. Stomach cancer was used as a reference because it was the most frequent cancer with the highest number of patients in the NCSP. All analyses were performed using the SAS software (ver. 9.4; SAS Institute, Inc., Cary, NC, USA).

RESULTS

1. Patient characteristics

Of the 2,661 patients enrolled in this survey, the final study population was 1,870 patients after excluding those with incomplete staging and survey data. The distribution of cancer types was in accordance with the distribution of the cancer population in 2008. There were 346 (18.5%) patients with stomach cancer, 278 (14.9%) with breast cancer, 257 (13.7%) with lung cancer, and 307 (16.4%) with other cancers. The most frequent cancer type was stomach cancer (13.3%) in men and breast cancer (14.9%) in women (Table 1).

Table 1

Of the 1,870 patients in this sample, 55.8% of the cancer patients were men; most patients were married (84.2%) or lived with others; less than 10% were living alone; 56.7% had monthly household incomes below 200 million KRW, almost all of the patients were covered by National Health Insurance, and only 1.6% were uninsured. In this study population, 31.1% had more than one comorbid condition, and 63.5% had disabilities. About half of the patients were smokers, and almost half drank alcohol (51.2% and 44.4%, respectively). The stage at diagnosis was fairly evenly divided; 1,085 (58.0%) patients were diagnosed in advanced stages. The proportion of cases with late stage at diagnosis differed significantly by gender, comorbidities, disabilities, and smoking. Male patients who had comorbidities or disabilities, and those who smoked were significantly more likely to have late-stage cancers at diagnosis (Table 2).

Table 2

2. Multivariate analyses

Multivariate logistic regression by gender showed that none of the socioeconomic factors was related to later stage at diagnosis, except being 60 to 70 years old (OR, 0.52; 95% CI, 0.31–0.88).

Compared with stomach cancer, lung cancer had a higher risk for later stage diagnosis (OR, 1.78; 95% CI, 1.15–2.75) among male patients, whereas liver (OR, 0.26; 95% CI, 0.16–0.41) and prostate cancer (OR, 0.26; 95% CI, 0.16–0.41) had lower risks of later stage diagnosis.

Cancers that had higher risks of later stage diagnosis in women were lung cancer (OR, 2.34; 95% CI, 1.11–4.91) and colorectal cancer (OR, 2.74; 95% CI, 1.46–5.14). In contrast, liver cancer (OR, 0.18; 95% CI, 0.07–0.49), thyroid cancer (OR, 0.52; 95% CI, 0.27–0.99), and cervical cancer (OR, 0.40; 95% CI, 0.21–0.78) showed lower risks of advanced cancer stage at diagnosis.

The presence of comorbidities at the time of diagnosis increased the odds of late-stage diagnosis in men (OR, 1.48; 95% CI, 1.11–1.97), but these odds were decreased in women, although it was not statistically significant (OR, 0.72; 95% CI, 0.50–1.04).

Patients with disabilities were at a higher risk of having cancers diagnosed at a later stage in both men (OR, 1.64; 95% CI, 1.23–2.17) and women (OR, 1.52; 95% CI, 1.09–0.10).

Neither smoking nor drinking alcohol was related to a later stage at diagnosis (Table 3).

Table 3

DISCUSSION

In this nationwide study that combined information from patients and medical records, we found that male patients with comorbidities, and both male and female patients with disabilities were more likely to be diagnosed with advanced-stage cancers.

Contrary to the ‘surveillance effect,’ which suggests that increased contact with health services owing to the presence of comorbidities may result in earlier diagnosis, this study found no pattern of earlier stage at diagnosis with higher comorbidity levels. Indeed, some of our findings support the so-called ‘competing demands’ hypothesis, which suggests that the presence of comorbidities can distract patients to the extent that the early symptoms of tumor growth may go unnoticed [26]. For example, male patients with more comorbidities had higher odds of being diagnosed with distant metastases. Additionally, there may be interactions between specific cancers and specific comorbid conditions. Some studies have shown that more severe (or ‘unstable’) comorbid conditions are associated with later stage at diagnosis, whereas less severe comorbid conditions are associated with earlier diagnosis [27].

The presence of disabilities, as well as difficulties in accessing care, may result in lack of availability for appropriate care. Severity of disability has also been found to affect receiving preventive care. Additionally, women with mobility impairments were less likely to receive cancer-screening services [28].

However, no significant effect of socioeconomic characteristics, such as age, marital status, education level, income level, type of health insurance, residence, or occupation was found. Some studies in Korea on the cancer stage and survival of cancer patients revealed that income level and occupation were not related to the stage at diagnosis [22]. Cancer patients who participated in NCSP always showed a higher early stage rate than that of non-participants [21]. Considering these results, it seems that the population-based NCSP has contributed to reducing differences in accessibility by income level because it guarantees that those in the lower 50% of income levels are covered at no charge. To carry out a more effective cancer-screening program, it seems that it is necessary to develop more targeted approaches aimed at those with comorbidities and disabilities.

Comparing results by cancer type, lung (both male and female) and colorectal (female) cancer had a higher risk of later-stage diagnosis as compared to stomach cancer, whereas liver and prostate cancer in men and liver, thyroid, and cervical cancer in women had lower risks of advanced stage at diagnosis. Lung cancer is not a part of NCSP. Insurance reimbursement for cancer screening can have significant effects on early detection of colorectal cancer [29]. As reported by Hong [20], using KCCR data for six major cancers (stomach, lung, liver, colorectal, breast, cervix) in 2004 (before colon and liver cancers were included in NCSP), colorectal and liver cancer patients with lower income levels were at higher risk of advanced stage at diagnosis.

Thus, advanced stage at diagnosis in low-income patients was probably because of the differences in access to colon and liver cancer screenings.

Our study has some limitations. We used self-reported comorbidity and income level information and based our conclusions on the EQ-5D definition of disability. These data have not been consistent with medical or social security data. However, it may reflect perceived disability status. The study sample was recruited only at designated national cancer centers, which did not include some major cancer hospitals in Korea. Thus, sample biases may potentially have influenced our findings. We used both self-reported and clinical information, including data on five major cancers from the national cancer-screening program in Korea. We collected information on health characteristics from the patient health questionnaire, and accurate stage information from a review of medical records by trained medical record administrators.

Increasing awareness of the signs and symptoms of cancer had contributed to detection of cancers in earlier stages. With early detection, there is a greater chance that curative treatment will be successful. Thus, it is important that people are taught to recognize early warning signs of the disease, especially those at a higher risk due to their health conditions. This can be promoted by public health education campaigns and training primary healthcare workers.

Article information

Osong Public Health and Research Perspectives.Aug 31, 2017; 8(4): 264-270.
Published online 2017-08-31. doi:  10.24171/j.phrp.2017.8.4.06
aCollege of Medicine/Graduate Scool of Health Science Business Convergence, Chungbuk National University, Cheongju, Korea
bBig Data Steering Department, National Health Insurance Service, Wonju, Korea
cChungbuk Regional Cardiocerebrovascular Center, Chungbuk National University Hospital, Cheongju, Korea
dCancer Policy Branch, National Cancer Control Research Institute, National Cancer Center, Goyang, Korea
eDepartment of Family Medicine and Supportive Care Center, Samsung Medical Center, Seoul, Korea
Corresponding author: Jong Hyock Park, E-mail: jonghyock@gmail.com
Received: March 6, 2017.
Revised: August 9, 2017.
Accepted: August 14, 2017.
Articles from Osong Public Health and Research Perspectives are provided here courtesy of Osong Public Health and Research Perspectives

References

  • National Cancer Center, Ministry of Health & Welfare in Korea, editors. Cancer facts & figures 2010 in the Republic of Korea. Goyang: National Cancer Center, Ministry of Health & Welfare; 2010.
  • Jung KW, Won YJ, Kong HJ, et al. Cancer statistics in Korea: incidence, mortality, survival and prevalence in 2010, editors. Cancer Res Treat 2013;45:1-14. https://doi.org/10.4143/crt.2013.45.1.1
  • Zafar SY, Abernethy AP, Abbott DH, et al. Comorbidity, age, race and stage at diagnosis in colorectal cancer: a retrospective, parallel analysis of two health systems, editors. BMC Cancer 2008;8:345. https://doi.org/10.1186/1471-2407-8-345
  • World Health Organization, editors. 10 facts about cancer [Internet]. Geneva: WHO; 2008. [cited 2017 Mar 1]. Available from: http://edition.cnn.com/2009/HEALTH/01/29/cancer.facts/index.html
  • Jung KW, Won YJ, Kong HJ, et al. Survival of Korean adult cancer patients by stage at diagnosis, 2006–2010: national cancer registry study, editors. Cancer Res Treat 2013;45:162-71. https://doi.org/10.4143/crt.2013.45.3.162
  • Karami S, Young HA, Henson DE. Earlier age at diagnosis: another dimension in cancer disparity?, editors. Cancer Detect Prev 2007;31:29-34. https://doi.org/10.1016/j.cdp.2006.11.004
  • Schwartz KL, Crossley-May H, Vigneau FD, et al. Race, socioeconomic status and stage at diagnosis for five common malignancies, editors. Cancer Causes Control 2003;14:761-6.
  • Suarez L, Pulley L. Comparing acculturation scales and their relationship to cancer screening among older Mexican-American women, editors. J Natl Cancer Inst Monogr 1995:41-7.
  • Hegarty V, Burchett BM, Gold DT, et al. Racial differences in use of cancer prevention services among older Americans, editors. J Am Geriatr Soc 2000;48:735-40. https://doi.org/10.1111/j.1532-5415.2000.tb04746.x
  • Singh GK, Miller BA, Hankey BF, editors. Area socioeconomic variations in U.S. cancer incidence, mortality, stage, treatment, and survival, 1975–1999. Bethesda, MD: National Cancer Institute; 2003.
  • Baquet CR, Commiskey P. Socioeconomic factors and breast carcinoma in multicultural women, editors. Cancer 2000;88:1256-64.
  • Breen N, Figueroa JB. Stage of breast and cervical cancer diagnosis in disadvantaged neighborhoods: a preventive policy perspective, editors. Am J Prev Med 1996;12:319-26.
  • West DW, Satariano WA, Ragland DR, et al. Comorbidity and breast cancer survival: a comparison between black and white women, editors. Ann Epidemiol 1996;6:413-9. https://doi.org/10.1016/S1047-2797(96)00096-8
  • Fleming ST, Rastogi A, Dmitrienko A, et al. A comprehensive prognostic index to predict survival based on multiple comorbidities: a focus on breast cancer, editors. Med Care 1999;37:601-14.
  • Moritz DJ, Satariano WA. Factors predicting stage of breast cancer at diagnosis in middle aged and elderly women: the role of living arrangements, editors. J Clin Epidemiol 1993;46:443-54. https://doi.org/10.1016/0895-4356(93)90021-R
  • McCarthy EP, Ngo LH, Roetzheim RG, et al. Disparities in breast cancer treatment and survival for women with disabilities, editors. Ann Intern Med 2006;145:637-45. https://doi.org/10.7326/0003-4819-145-9-200611070-00005
  • Halpern MT, Bian J, Ward EM, et al. Insurance status and stage of cancer at diagnosis among women with breast cancer, editors. Cancer 2007;110:403-11. https://doi.org/10.1002/cncr.22786
  • Ward E, Halpern M, Schrag N, et al. Association of insurance with cancer care utilization and outcomes, editors. CA Cancer J Clin 2008;58:9-31. https://doi.org/10.3322/CA.2007.0011
  • National Cancer Center in Korea, editors. National Cancer Center home page [Internet]. Goyang: National Cancer Center. Available from: https://ncc.re.kr
  • Hong DH. Income status and stage at diagnosis of six cancer, editors. PhD dissertation. Seoul: Seoul National University; 2004.
  • Jung HM, Lee JS, Lairson DR, et al. The effect of National Cancer Screening on disparity reduction in cancer stage at diagnosis by income level, editors. PLoS One 2015;10:e0136036. https://doi.org/10.1371/journal.pone.0136036
  • Park JW. Stage at diagnosis and deaths among cancer patients across socioeconomic status on one cancer center, editors. Master thesis. Seoul: Korea University; 2013.
  • Shin DW, Cho J, Kim SY, et al. Discordance among patient preferences, caregiver preferences, and caregiver predictions of patient preferences regarding disclosure of terminal status and end-of-life choices, editors. Psychooncology 2015;24:212-9. https://doi.org/10.1002/pon.3631
  • Chaudhry S, Jin L, Meltzer D. Use of a self-report-generated Charlson Comorbidity Index for predicting mortality, editors. Med Care 2005;43:607-15. https://doi.org/10.1097/01.mlr.0000163658.65008.ec
  • Park JH, Park JH, Kim SG, et al. Changes in employment status and experience of discrimination among cancer patients: findings from a nationwide survey in Korea, editors. Psychooncology 2010;19:1303-12. https://doi.org/10.1002/pon.1694
  • Fleming ST, Pursley HG, Newman B, et al. Comorbidity as a predictor of stage of illness for patients with breast cancer, editors. Med Care 2005;43:132-40.
  • Gurney J, Sarfati D, Stanley J. The impact of patient comorbidity on cancer stage at diagnosis, editors. Br J Cancer 2015;113:1375-80. https://doi.org/10.1038/bjc.2015.355
  • Wisdom JP, McGee MG, Horner-Johnson W, et al. Health disparities between women with and without disabilities: a review of the research, editors. Soc Work Public Health 2010;25:368-86. https://doi.org/10.1080/19371910903240969
  • Halpern MT, Pavluck AL, Ko CY, et al. Factors associated with colon cancer stage at diagnosis, editors. Dig Dis Sci 2009;54:2680-93. https://doi.org/10.1007/s10620-008-0669-0

Table 1

Distribution of cases by the type of cancer

Type of cancer Male Female Total
Stomach 249 (23.9) 97 (11.7) 346 (18.5)
Lung 193 (18.5) 64 (7.7) 257 (13.7)
Liver 145 (13.9) 29 (3.5) 174 (9.3)
Colorectal 175 (16.8) 105 (12.7) 280 (15.0)
Thyroid 17 (1.6) 82 (9.9) 99 (5.3)
Breast NA 278 (33.6) 278 (14.9)
Cervical NA 77 (9.3) 77 (4.1)
Prostate 52 (5.0) NA 52 (2.8)
Others 212 (20.3) 95 (11.5) 307 (16.4)
Total 1,043 (100.0) 827 (100.0) 1,870 (100.0)

Values are presented as number (%).

NA, not available.

Table 2

Characteristics of the study population

Variable Late stagea Total
Socio-economic characteristics
 Gender
  Male** 633 (33.9) 1,043 (55.8)
  Female 452 (24.2) 827 (44.2)
 Age (y)
  < 50 238 (12.7) 429 (22.9)
  50–60 284 (15.2) 486 (26.0)
  60–70 355 (19.0) 586 (31.3)
  > 70 208 (11.1) 369 (19.7)
 Education
  < Middle school 597 (31.9) 995 (53.2)
  < High school 315 (16.8) 574 (30.7)
  > College 173 (9.3) 301 (16.1)
 Marital status
  Never married 33 (1.8) 58 (3.1)
  Married 916 (49.0) 1,574 (84.2)
  Divorced, widowed 136 (7.3) 238 (12.7)
 Living arrangement
  Live alone 91 (4.9) 167 (8.9)
  Live with others 994 (53.2) 1,703 (91.1)
 Income before diagnosis (Korean Won)
  < 200 million 622 (33.3) 1,060 (56.7)
  200–400 million 320 (17.1) 555 (29.7)
  > 400 million 143 (7.6) 255 (13.6)
 Social security
  Medical insurance 953 (51.0) 1,636 (87.5)
  Medical aid 117 (6.3) 204 (10.9)
  Uninsured 15 (0.8) 30 (1.6)
 Private insurance
  Yes 421 (22.5) 719 (38.4)
  No 664 (35.5) 1,151 (61.6)
 Residence
  Metropolitan 357 (19.1) 625 (33.4)
  City 501 (26.8) 877 (46.9)
  Rural 227 (12.1) 368 (19.7)
 Job
  Housewife 217 (11.6) 401 (21.4)
  Office worker 121 (6.5) 197 (10.5)
  Non-office worker 250 (13.4) 421 (22.5)
  Self-employed 82 (4.4) 142 (7.6)
  Agricultural/forestry/fishery workers 217 (11.6) 367 (19.6)
  Unemployed 198 (10.6) 342 (18.3)
Health characteristics
 Type of cancer
  Stomach** 212 (11.3) 346 (18.5)
  Lung 197 (10.5) 257 (13.7)
  Liver 52 (2.8) 174 (9.3)
  Colorectal 202 (10.8) 280 (15.0)
  Breast 136 (7.3) 278 (14.9)
  Cervical 29 (1.6) 77 (4.1)
  Thyroid 40 (2.1) 99 (5.3)
  Prostate 20 (1.1) 52 (2.8)
  Others 197 (10.5) 307 (16.4)
 Comorbidity
  Yes** 314 (16.8) 581 (31.1)
  No 771 (41.2) 1,289 (68.9)
 Disability
  Yes** 740 (39.6) 1,187 (63.5)
  No 345 (18.4) 683 (36.5)
Health behavior
 Smoking
  Yes** 518 (27.7) 957 (51.2)
  No 567 (30.3) 913 (48.8)
 Alcohol use
  Yes 462 (24.7) 822 (44.0)
  No 623 (33.3) 1,048 (56.0)
 Stage at diagnosis
  In situ 5 (0.3)
  Local 780 (41.7)
  Regional 720 (38.5)
  Distant 365 (19.5)
Total 1,085 (58.0) 1,870 (100.0)

Values are presented as number (%).

aDistribution of later stage (regional and distant) patients in the total study population.

Significant at

*p < 0.05,
**p < 0.01.

Table 3

Predictors of later stage cancer at diagnosis

Variable Malea Femalea
Socio-economic characteristics
 Age (y)
  < 50 1.00 1.00
  50–60 0.86 (0.52–1.44) 0.82 (0.54–1.23)
  60–70 1.11 (0.65–1.91) 0.52* (0.31–0.88)
  > 70 0.76 (0.42–1.36) 0.52 (0.25–1.04)
 Education
  < Middle school 1.00 1.00
  < High school 0.96 (0.68–1.34) 0.68 (0.45–1.02)
  > College 1.01 (0.64–1.59) 0.73 (0.42–1.26)
 Marital status
  Never married 1.00 1.00
  Married 0.90 (0.38–2.11) 1.01 (0.38–2.69)
  Divorced/widowed 0.76 (0.30–1.90) 1.11 (0.40–3.06)
 Living arrangement
  Live alone 1.00 1.00
  Live with others 1.51 (0.79–2.89) 1.03 (0.56–1.91)
 Income before diagnosis (Korean Won)
  < 200 million 1.00 1.00
  200–400 million 1.11 (0.79–1.57) 0.98 (0.67–1.43)
  > 400 million 0.89 (0.54–1.47) 0.79 (0.48–1.30)
 Social security
  Medical insurance 1.00 1.00
  Medical aid 0.87 (0.55–1.36) 0.94 (0.55–1.59)
  Uninsured 0.76 (0.28–2.06) 1.21 (0.31–4.74)
 Private insurance
  No 1.00 1.00
  Yes 1.46* (1.02–2.08) 0.86 (0.61–1.21)
 Residence
  Metropolitan 1.00 1.00
  City 0.86 (0.62–1.18) 1.16 (0.82–1.62)
  Rural 0.93 (0.61–1.40) 1.40 (0.88–2.23)
 Job
  Housewife 1.00
  Office worker 1.00 0.99 (0.55–1.81)
  Non-office worker 0.76 (0.46–1.25) 1.09 (0.71–1.69)
  Self-employed 1.04 (0.55–1.94) 0.61 (0.32–1.15)
  Agricultural/forestry/fishery workers 0.76 (0.44–1.30) 1.05 (0.62–1.80)
  Unemployed 0.78 (0.46–1.29) 1.07 (0.62–1.86)
Health characteristics
 Type of cancer
  Stomach 1.00** 1.00**
  Lung 1.78** (1.15–2.75) 2.34* (1.11–4.91)
  Liver 0.26** (0.16–0.41) 0.18** (0.07–0.49)
  Colorectal 1.26 (0.82–1.94) 2.74** (1.46–5.14)
  Thyroid 0.52 (1.18–1.47) 0.52* (0.27–0.99)
  Prostate 0.48* (0.25–0.93)
  Others 1.06 (0.71–1.59) 1.20 (0.66–2.18)
  Breast 0.70 (0.42–1.15)
  Cervix 0.40** (0.21–0.78)
 Comorbidity
  No 1.00 1.00
  Yes 1.48** (1.11–1.97) 0.72 (0.50–1.04)
 Disability
  No 1.00 1.00
  Yes 1.64** (1.23–2.17) 1.52* (1.09–2.10)
Health behavior
 Smoking
  No 1.00 1.00
  Yes 1.38 (0.96–1.99) 0.83 (0.45–1.53)
 Alcohol use
  No 1.00 1.00
  Yes 1.01 (0.72–1.43) 1.10 (0.77–1.56)

Values are presented as odds ratio (95% confidence interval).

aAdjusted for other factors shown in table.

Significant at

*p < 0.05,
**p < 0.01.