Association between Socioeconomic Status and Digestive Tract Cancers: A Case-Control Study
by
, , , ,
Yukino Kawakatsu
1,2
,
Yuriko N. Koyanagi
3,
Isao Oze
1,
Yumiko Kasugai
1,
Hisayoshi Morioka
2,
Rui Yamaguchi
4,5,
Hidemi Ito
3,6 and
Keitaro Matsuo
1,7,*
1
Division of Cancer Epidemiology and Prevention, Aichi Cancer Center, Nagoya 464-8681, Japan
2
Department of Public Health, Tokushima University Graduate School of Biomedical Sciences, Tokushima 770-8501, Japan
3
Division of Cancer Information and Control, Aichi Cancer Center, Nagoya 464-8681, Japan
4
Division of Cancer Systems Biology, Aichi Cancer Center, Nagoya 464-8681, Japan
5
Department of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
6
Department of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
7
Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
*
Author to whom correspondence should be addressed.
Cancers 2020, 12(11), 3258; https://doi.org/10.3390/cancers12113258
Submission received: 12 October 2020
/
Revised: 2 November 2020
/
Accepted: 3 November 2020
/
Published: 4 November 2020
(This article belongs to the Special Issue Social and Molecular Basis of Cancer Health Disparities)
Abstract
:Simple Summary
An association between socioeconomic status (SES) and cancer risk has been reported, but little is known in Asia. We revealed an association between SES, including education level and areal deprivation index (ADI), and digestive tract cancers in Japan. Lower SES was associated with an increased risk of digestive cancers. For stomach cancer, the positive association with ADI disappeared following an additional adjustment of Helicobacter pylori infection and/or atrophic gastritis status. Cancer prevention policy should consider both individual and regional perspectives by the integration of SES in the target population.
Abstract
Although socioeconomic status (SES) has been associated with cancer risk, little research on this association has been done in Japan. To evaluate the association between SES and digestive tract cancer risk, we conducted a case-control study for head and neck, esophageal, stomach, and colorectal cancers in 3188 cases and the same number of age- and sex-matched controls within the framework of the Hospital-based Epidemiological Research Program at Aichi Cancer Center III (HERPACC III). We employed the education level and areal deprivation index (ADI) as SES indicators. The association was evaluated with odds ratios (ORs) and 95% confidence intervals (CIs) by conditional logistic models adjusted for potential confounders. Even after allowance for known cancer risk factors, the education level showed linear inverse associations with head and neck, stomach, and colorectal cancers. Compared to those educated to junior high school, those with higher education showed statistically significantly lower risks of cancer (0.43 (95% CI: 0.27–0.68) for head and neck, 0.52 (0.38–0.69) for stomach, and 0.52 (0.38–0.71) for colorectum). Consistent with these results for the educational level, the ADI in quintiles showed positive associations with head and neck, esophageal, and stomach cancers (p-trend: p = 0.035 for head and neck, p = 0.02 for esophagus, and p = 0.013 for stomach). Interestingly, the positive association between ADI and stomach cancer risk disappeared in the additional adjustment for Helicobacter pylori infection and/or atrophic gastritis status. In conclusion, a lower SES was associated with an increased risk of digestive cancers in Japan and should be considered in cancer prevention policies for the target population.
1. Introduction
Digestive tract cancers are among the most common tumor types, affecting over 4.1 million people worldwide and causing 2.6 million deaths in 2018 [1]. They are particularly common in Asia [1,2]. Reducing the adverse health impacts of these cancers will require the accumulation of knowledge that leads to feasible preventive actions. Numerous epidemiologic studies have been conducted, and associations with modifiable risk factors have been identified, such as smoking or alcohol drinking [3,4,5,6,7,8]. Recently, an association with socioeconomic status (SES) was reported in Western countries [9,10,11,12]. However, the interpretation of this new evidence is complicated by the correlation between SES and these modifiable risk factors [13,14]. Therefore, there is a need for research that can disentangle the effects of these socioeconomic factors and preventable factors on digestive tract cancer risks. Given that a variety of individual exposures may have common socioeconomic causes at the population level, it is important to discuss cancer prevention from both an individual and regional perspective, with a particular focus on lower SES regions. To date, however, few studies have examined the association between socioeconomic differences and cancer risks in Japan.
To evaluate the association between the risk of digestive tract cancers, including head and neck, esophageal, stomach, and colorectal cancers, and SES with regard to the levels of individual education and neighborhood deprivation, we conducted a case-control study to examine the association with consideration to known risk factors.
2. Results
Table 1 shows the background characteristics of cases and matched controls by the four sites of cancer. Age and sex were appropriately matched. Heavier alcohol consumption was more prevalent in all sites of cancer compared with the matched controls, especially for esophageal cancer (51.1% in cases, and 13.5% in controls). Current smoking was more prevalent in the cases, and cumulative exposure to cigarettes was clearly higher in the cases (38.5% in cases and 23.5% in controls for head and neck, 46.5% in cases and 23.3% in controls for esophagus, 30.3% in cases and 22.6% in controls for stomach, and 22.7% in cases and 20.4% in controls for colorectal). The body mass index (BMI) was lower in the cases than controls in head and neck (percentage of underweight: 14.7% in cases and 7.0% in controls), esophageal (15.5% in cases and 3.0% in controls), and stomach cancers (10.4% in cases and 6.3% in controls). We observed no remarkable difference in BMI for colorectal cancer. Diabetes was significantly more common in the cases than controls for head and neck cancer (12.6% in cases and 10.1% in controls) and was significantly more common in the controls than cases for esophageal cancer (7.4% in cases and 12.5% in controls) but not for the other types of cancer. A family history of the subject cancer was common in stomach cancer (26.3% in cases and 17.6% in controls) but not in the other cancers. Regarding physical activity, lower activity was more common in cases for all types of cancers. Vegetable/fruit intakes were lower in the cases than in controls, except for esophageal cancer. No clear difference in the intake of meat and processed meat was observed between the cases and controls across all sites.
Table 2 shows the association between educational level and risk of cancer by site in multivariable conditional logistic regression models. In the crude models, we observed a linear inverse association between educational level and risk of cancer for all sites of cancer. We explored two multivariable models and observed a consistent linear inverse association in a crude analysis in all sites of cancer except esophageal cancer (p-trend in Model 2: p = 1.4 × 10−4 for head and neck, p = 0.172 for esophagus, p = 5.5 × 10−7 for stomach, and p = 1.2 × 10−5 for colorectum). Compared to participants with education to junior high school, those with higher education showed a statistically significantly lower risk of cancer (0.43 (95% CI: 0.27–0.68) for head and neck, 0.52 (0.38–0.69) for stomach, and 0.52 (0.38–0.71) for colorectum). We did not observe a statistically significant association with esophageal cancer; however, the point estimates for higher education and moderate education levels for esophageal cancer were 0.64 and 0.63, respectively, indicating a modest but consistent inverse association between educational level and risk. To further explore the possible heterogeneity of this association by age, we conducted an additional stratified analysis by age group for Model 2 (Table S3). We did not observe a clear heterogeneity between younger and older groups.
Table 3 shows the association between the deprivation index and risk of each cancer by site. In the crude model, we observed a statistically significant or marginally significant trend in all sites of cancer. Consistent with the crude model, the two multivariable models also showed positive associations with head and neck, esophageal, and stomach cancers (p for trends of 0.035, 0.02, and 0.013 for head and neck, esophagus, and stomach, respectively). In contrast, the positive association observed in the crude model for colorectal cancer disappeared after an adjustment for confounders. Of note, a statistically higher risk for stomach cancer was seen in all quintile levels except Q1 as reference.
Table S1 shows that the association between education level and stomach cancer was consistent regardless of Helicobacter pylori infection and atrophic gastritis status. Table S2 shows that, after adjustment for H. pylori and atrophic gastritis status, the association between the deprivation index and stomach cancer risk was attenuated. Although we observed significant linear trends in the crude and two multivariable analyses for overall stomach cancer (Table 3), we did not see any significant trend in the analyses that considered H. pylori infection and atrophic gastritis status.
3. Discussion
In this case–control study, we identified an association between SES and digestive tract cancer risks in 3188 cases and the same number of age- and sex-matched controls. Despite adjustments for known cancer risk factors, the education level showed a linear inverse association with head and neck, stomach, and colorectal cancers, while the areal deprivation index (ADI) showed a positive association with head and neck, esophageal, and stomach cancers. This association between poor SES and a higher risk of digestive tract cancer was accordingly consistent across two different measures.
Previous studies have indicated an inverse association between SES and cancer risk [9,10,12]. Among the digestive tract cancers, inverse associations have been reported for head and neck, esophageal, and stomach cancers [12,15,16]. For colorectal cancer, in contrast, both positive and inverse associations have been reported [11,17,18], with lower SES associated with an increased incidence of colorectal cancer in the United States versus an inverse association among Europeans [17]. A population-based cohort study in Japan showed that the risk of colorectal cancer incidence was lower in men and women with a higher neighborhood deprivation index than in those in lower index categories [19]. In China, positive associations were seen for per capita GDP and disposable income for area-level SES, household income, and the number of assets for individual-level SES; in contrast, however, education showed no association with colorectal cancer [18]. In the present study, we found an inverse association with all sites of digestive tract cancer. This discrepancy may be explained by differences in the associations between SES indicators and lifestyle risk factors or cancer screening rates across countries and times. The reasons for this across-study or -regional difference is not clear but is likely explained in part by social and environmental differences.
Eliminating health disparities will require attention to all SES components and the pathways by which they influence health [20]. Redressing fundamental economic and social inequality is no simple matter. In general, we need to educate people about risk reduction, especially via modifiable risk factors. Furthermore, in this study, disparities due to SES exist even after adjusting for known risk factors. Therefore, it is desirable to conduct further researches to reveal hidden factors behind SES and cancer.
A unique finding of this study was that the positive association between ADI and stomach cancer risk disappeared following the additional adjustment for H. pylori infection and/or atrophic gastritis status. Given that previous studies have shown associations between SES and H. pylori/atrophic gastritis [21], we speculate that SES may be a strong surrogate for H. pylori/atrophic gastritis status. Supporting this, a similar phenomenon was reported in a prospective study conducted in Europe: a significant inverse association between the higher educational level and risk of gastric cancer disappeared after an adjustment for H. pylori seroprevalence [16]. In our study, however, we observed this phenomenon with ADI only and not with the education level. This difference between Europe and Japan might be attributable to differences in social context between the ADI and education level between the two areas. To prevent against stomach cancers in those with low SES, interventions targeting H. pylori infection such as eradication or prevention may be effective. Although the way H. pylori is transmitted remains still not fully clear, the level of contamination is strongly dependent on the familial and environmental context, with a drastic impact of living conditions with poor hygiene and sanitation [22]. According to many epidemiologic studies, water could be an important source of H. pylori contamination [23]. Improving water and sewerage systems, especially in rural area, is likely to reduce the risk of H. pylori infection for low SES. This possibility warrants further evaluation.
The strengths of this study include its large number of participants and case-control design, with consideration for the relevant confounding variables, including H. pylori infection. Both the education level and ADI showed an association with digestive tract cancers, with consideration of the known risk factors. Given that the ADI does not include the educational level in its estimation, the consistency of our results across the two measures supports the robustness of our findings. Among the limitations, the study used a hospital-based case-control design that might have had a degree of selection bias. However, subjects were recruited before the diagnosis of cancer or noncancer and before we had knowledge of their ADI and educational levels, suggesting that this bias was unlikely. Second, it is difficult to completely avoid a degree of information bias, as lifestyle factors were collected via a self-reported questionnaire. We minimized this possibility by asking about lifestyle factors in the year before onset of disease. Educational levels are less likely to be biased due to the timing of the questionnaire. Since the ADI of areas with a small number of households are considered statistically unstable [24], we conducted a sensitivity analysis by excluding areas with a small number of households and confirmed that similar results were obtained (Table S4). Finally, this study was a single hospital-based study, and accessibility to our hospital might have caused a degree of bias by case status. However, most of our cases and controls were from the Nagoya metropolitan area, and public and private transportation to the hospital are widely available. In addition, as shown in Figures S1 and S2, the physical distances between the residential addresses and our hospital did not differ by either the ADI or educational level. This bias therefore seems unlikely.
4. Materials and Methods
4.1. Study Population
All first-visit outpatients at the Aichi Cancer Center Hospital (ACCH) between December 2005 and March 2013 were asked to participate in the Hospital-based Epidemiologic Research Program at Aichi Cancer Center (HERPACC)-3. The framework of HERPACC has been described elsewhere [25]. We conducted a case-control study for each eligible type of cancer among the participants of HERPACC-3. Cases were incident cases of 587 head and neck cancer, 503 esophageal cancer, 1146 stomach cancer, and 952 colorectal cancer. Controls were randomly selected from among HERPACC-3 participants who were confirmed to have no cancer or history of neoplasm and individually matched with cases by age (±5 years) and sex at a case-control ratio of 1:1. We defined noncancer first-visit outpatients as a population in which cases may arise, under the assumption that they will likely visit the ACCH if they develop cancer in the future [25]. All participants gave written informed consent to participate, and 66.4% of participants responded to a self-administered questionnaire, and 62% of participants provided a peripheral blood sample [26]. The study was approved by the Institutional Ethics Committee at Aichi Cancer Center (approval number: ACC-2019-2-33).
4.2. Site Classification
The following International Classification of Diseases for Oncology, Third Edition (ICD-O-3) codes were used [27]: head and neck cancer, C00–C06, C09–C14, and C30–C32; esophageal cancer, C15; stomach cancer, C16; and colorectal cancer, C18–C20.
4.3. Evaluation of Socioeconomic Status
Information on educational levels was collected from first-visit outpatients using a self-administered questionnaire. Educational status was classified into the three categories of junior high school, high school, and higher education.
Regarding deprivation, we used the areal deprivation index (ADI), which consists of weighted sums of a number of census-based variables. The concept and procedures for the Japanese deprivation index are detailed elsewhere [24,28]. We calculated ADI at the “Cho-Aza (CA)” level—the smallest administrative unit provided for in the index—using data from the 2005 Census of Japan. We categorized the deprivation level into five groups by quintiles of the participants’ deprivation index values.
For calculations, residence was considered the residential address at the time of enrollment. This was geocoded to identify living areas in CA units of the 2005 Census of Japan. We excluded 15 participants whose addresses could not be geocoded and 8 whose census information regarding CA was not provided by the statistical bureau [24].
4.4. Evaluation of Environmental Factors
Information on the environmental risk factors was collected using a self-administered questionnaire. At first-visit to our hospital, each participant was asked about their lifestyle before the development of the symptoms that made them visit the hospital. As detailed elsewhere [29], daily alcohol intake (g/day) was used as a measure of drinking intensity and calculated using information on the frequency of alcohol drinking and the total amount of pure alcohol consumed during each drinking session. Alcohol consumption was classified into the four groups of never, low (0–23 g/day), moderate (23–46 g/day), and heavy (≥46 g/day). Smoking status was classified into the three groups of never, former, and current. Smoking dose was evaluated as pack-years (PY), calculated by multiplying the number of packs consumed per day by the number of years of smoking. Participants were categorized into the five groups of never, PY <20, PY <40, PY <60, and PY 60 or more. Body mass index (BMI) was calculated as the weight in kilograms divided by the height in meters squared and divided into the four groups of <18.5, 18.5–21.9, 22–24.9, and ≥25.0. History of diabetes and family history of each cancer were obtained as “yes” or “no”. Physical activity was evaluated as metabolic equivalent (MET) hours per week [30], calculated by the frequency, intensity, and amount of time per session and classified into the four groups of never, 0–10, 10–20, and ≥20 MET hours per week. Energy-adjusted fruit/vegetable intake was estimated by the residual method [31,32] using information from a validated food frequency questionnaire [33]. For fruit/vegetable intake and total energy intake, participants were classified into four groups according to the distributions of the respective factors among the controls (quartiles). The frequencies of meat (beef or pork) intake and processed meat intake were classified into the three categories of <1, 1–4, and ≥5 times/week. For H. pylori infection status, plasma or serum IgG (immunoglobulin G) antibody levels for H. pylori were measured using a commercially available direct enzyme-linked immunosorbent assay kit (E Plate “Eiken” H. pylori Antibody; Eiken Kagaku, Tokyo, Japan), with a positive infection status defined as anti-H. pylori IgG >10 U/mL. For atrophic gastritis status, plasma/serum pepsinogens (PGs) were measured by chemiluminescence enzyme immunoassay, with a positive gastric mucosal atrophy defined as PG I ≤70 ng/mL and PG I/PG II ≤3.
4.5. Statistical Analysis
Differences in the distribution of risk factors between cases and controls were evaluated using the χ2 test. The exposure of interest in this study was SES, for which we used educational level and ADI in quintiles as indices. To evaluate the association between SES and risk of each cancer, we estimated the odds ratios (OR) and corresponding 95% confidence intervals (CIs) by the conditional logistic regression models. We used three models: a crude model and Model 1 and Model 2. The crude model was age- and sex-matched; Model 1 was a multivariable model that adjusted for alcohol consumption (never, 0–23 g, 23–46 g, and ≥46 g ethanol/day); pack-years (0, <20, 20–40, and ≥40 PYs); and family history of each cancer (yes/no). Model 2 further adjusted for BMI (kg/m2); history of diabetes (yes/no); physical activity (0, 0–10, 10–20, and ≥20 MET hours/week); fruit/vegetable intake (quartile); total energy intake (kcal/day); frequency of meat intake (<1, 1–4, and ≥5 times/week); frequency of processed meat intake (<1, 1–4, and ≥5 times/week); and family history of each cancer (yes/no), in addition to the items adjusted for in Model 1. As H. pylori infection is a well-established risk factor for stomach cancer, with a well-known correlation with socioeconomic status [22,34], to estimate the effect of H. pylori infection in our evaluation of SES on cancer risk, we evaluated multivariable models that added the H. pylori status (positive or negative) and gastric atrophy (positive or negative) to subgroups with available blood samples within each of the three models.
All statistical analyses were carried out using Stata version 15 (Stata Corporation, College Station, TX, USA). Two-sided p-values < 0.05 were considered to show statistical significance.
5. Conclusions
We found an association between SES and digestive tract cancers. With regard to stomach cancer, however, the association with ADI was attenuated after adjustment for the H. pylori/atrophic gastritis status. Cancer prevention policy should consider both individual and regional perspectives by the integration of SES in the target population.
Supplementary Materials
The following are available online at https://www.mdpi.com/2072-6694/12/11/3258/s1, Table S1: Association between educational level and risk of subject cancers by site, Table S2: Association between the quintiles of the areal deprivation index and risk of subject cancers by sites, Table S3: Association between educational level and risk of subject cancers by site stratified by age, Table S4: Association between the quintiles of the deprivation index and risk of subject cancers by sites, Figure S1: The physical distances between the residential addresses and our hospital by education level, Figure S2: The physical distances between the residential addresses and our hospital by areal deprivation index.
Author Contributions
Conceptualization, K.M.; methodology, K.M.; formal analysis, Y.K. (Yukino Kawakatsu), Y.N.K., and R.Y.; investigation, H.I. and K.M.; data curation, Y.K. (Yukino Kawakatsu), Y.N.K., I.O., Y.K. (Yumiko Kasugai), H.I., and K.M.; writing—original draft preparation, Y.K. (Yukino Kawakatsu); writing—review and editing, K.M.; visualization, Y.K. (Yukino Kawakatsu); supervision, H.M. and K.M.; project administration, K.M.; and funding acquisition, K.M. All authors have read and agreed to the published version of the manuscript.
Funding
This study is supported by Grants-in-Aid for Scientific Research from the Ministry of Education, Science, Sports, Culture and Technology of Japan (17015018, 221S0001, JP16H06277(CoBiA), JP18H03045, and JP19H03913), and a Grant-in-Aid for the Third Term Comprehensive 10-year Strategy for Cancer Control from the Ministry of Health, Labour and Welfare of Japan.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef] [Green Version]
- Ferlay, J.; Colombet, M.; Soerjomataram, I.; Mathers, C.; Parkin, D.M.; Pineros, M.; Znaor, A.; Bray, F. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int. J. Cancer 2019, 144, 1941–1953. [Google Scholar] [CrossRef] [Green Version]
- Boffetta, P.; Hashibe, M. Alcohol and cancer. Lancet Oncol. 2006, 7, 149–156. [Google Scholar] [CrossRef]
- Sasco, A.J.; Secretan, M.B.; Straif, K. Tobacco smoking and cancer: A brief review of recent epidemiological evidence. Lung Cancer 2004, 45 (Suppl. 2), S3–S9. [Google Scholar] [CrossRef]
- Calle, E.E.; Rodriguez, C.; Walker-Thurmond, K.; Thun, M.J. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N. Engl. J. Med. 2003, 348, 1625–1638. [Google Scholar] [CrossRef] [Green Version]
- Trock, B.; Lanza, E.; Greenwald, P. Dietary fiber, vegetables, and colon cancer: Critical review and meta-analyses of the epidemiologic evidence. J. Natl. Cancer Inst. 1990, 82, 650–661. [Google Scholar] [CrossRef]
- Norat, T.; Bingham, S.; Ferrari, P.; Slimani, N.; Jenab, M.; Mazuir, M.; Overvad, K.; Olsen, A.; Tjønneland, A.; Clavel, F.; et al. Meat, fish, and colorectal cancer risk: The European Prospective Investigation into cancer and nutrition. J. Natl. Cancer Inst. 2005, 97, 906–916. [Google Scholar] [CrossRef]
- Keum, N.; Bao, Y.; Smith-Warner, S.A.; Orav, J.; Wu, K.; Fuchs, C.S.; Giovannucci, E.L. Association of Physical Activity by Type and Intensity With Digestive System Cancer Risk. JAMA Oncol. 2016, 2, 1146–1153. [Google Scholar] [CrossRef] [Green Version]
- Clegg, L.X.; Reichman, M.E.; Miller, B.A.; Hankey, B.F.; Singh, G.K.; Lin, Y.D.; Goodman, M.T.; Lynch, C.F.; Schwartz, S.M.; Chen, V.W.; et al. Impact of socioeconomic status on cancer incidence and stage at diagnosis: Selected findings from the surveillance, epidemiology, and end results: National Longitudinal Mortality Study. Cancer Causes Control 2009, 20, 417–435. [Google Scholar] [CrossRef] [Green Version]
- Coleman, M.P.; Rachet, B.; Woods, L.M.; Mitry, E.; Riga, M.; Cooper, N.; Quinn, M.J.; Brenner, H.; Esteve, J. Trends and socioeconomic inequalities in cancer survival in England and Wales up to 2001. Br. J. Cancer 2004, 90, 1367–1373. [Google Scholar] [CrossRef] [Green Version]
- Doubeni, C.A.; Laiyemo, A.O.; Major, J.M.; Schootman, M.; Lian, M.; Park, Y.; Graubard, B.I.; Hollenbeck, A.R.; Sinha, R. Socioeconomic status and the risk of colorectal cancer: An analysis of more than a half million adults in the National Institutes of Health-AARP Diet and Health Study. Cancer 2012, 118, 3636–3644. [Google Scholar] [CrossRef] [Green Version]
- Faggiano, F.; Partanen, T.; Kogevinas, M.; Boffetta, P. Socioeconomic differences in cancer incidence and mortality. IARC Sci. Publ. 1997, 65–176. [Google Scholar]
- Hanson, M.D.; Chen, E. Socioeconomic status and health behaviors in adolescence: A review of the literature. J. Behav. Med. 2007, 30, 263–285. [Google Scholar] [CrossRef]
- Wardle, J.; Jarvis, M.J.; Steggles, N.; Sutton, S.; Williamson, S.; Farrimond, H.; Cartwright, M.; Simon, A.E. Socioeconomic disparities in cancer-risk behaviors in adolescence: Baseline results from the Health and Behaviour in Teenagers Study (HABITS). Prev. Med. 2003, 36, 721–730. [Google Scholar] [CrossRef]
- Hwang, E.; Johnson-Obaseki, S.; McDonald, J.T.; Connell, C.; Corsten, M. Incidence of head and neck cancer and socioeconomic status in Canada from 1992 to 2007. Oral. Oncol. 2013, 49, 1072–1076. [Google Scholar] [CrossRef]
- Nagel, G.; Linseisen, J.; Boshuizen, H.C.; Pera, G.; Del Giudice, G.; Westert, G.P.; Bueno-de-Mesquita, H.B.; Allen, N.E.; Key, T.J.; Numans, M.E.; et al. Socioeconomic position and the risk of gastric and oesophageal cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC-EURGAST). Int. J. Epidemiol. 2007, 36, 66–76. [Google Scholar] [CrossRef] [Green Version]
- Manser, C.N.; Bauerfeind, P. Impact of socioeconomic status on incidence, mortality, and survival of colorectal cancer patients: A systematic review. Gastrointest. Endosc. 2014, 80, 42–60.e9. [Google Scholar] [CrossRef] [PubMed]
- Pang, Y.; Kartsonaki, C.; Guo, Y.; Chen, Y.; Yang, L.; Bian, Z.; Bragg, F.; Millwood, I.Y.; Lv, J.; Yu, C.; et al. Socioeconomic Status in Relation to Risks of Major Gastrointestinal Cancers in Chinese Adults: A Prospective Study of 0.5 Million People. Cancer Epidemiol. Biomark. Prev. 2020, 29, 823–831. [Google Scholar] [CrossRef] [Green Version]
- Miki, Y.; Inoue, M.; Ikeda, A.; Sawada, N.; Nakaya, T.; Shimazu, T.; Iwasaki, M.; Yamaji, T.; Sasazuki, S.; Shibuya, K.; et al. Neighborhood deprivation and risk of cancer incidence, mortality and survival: Results from a population-based cohort study in Japan. PLoS ONE 2014, 9, e106729. [Google Scholar] [CrossRef]
- Adler, N.E.; Newman, K. Socioeconomic disparities in health: Pathways and policies. Health Aff. (Millwood) 2002, 21, 60–76. [Google Scholar] [CrossRef]
- Hooi, J.K.Y.; Lai, W.Y.; Ng, W.K.; Suen, M.M.Y.; Underwood, F.E.; Tanyingoh, D.; Malfertheiner, P.; Graham, D.Y.; Wong, V.W.S.; Wu, J.C.Y.; et al. Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis. Gastroenterology 2017, 153, 420–429. [Google Scholar] [CrossRef] [Green Version]
- Kotilea, K.; Bontems, P.; Touati, E. Epidemiology, Diagnosis and Risk Factors of Helicobacter pylori Infection. Adv. Exp. Med. Biol. 2019, 1149, 17–33. [Google Scholar] [CrossRef]
- Ozaydin, N.; Turkyilmaz, S.A.; Cali, S. Prevalence and risk factors of Helicobacter pylori in Turkey: A nationally-representative, cross-sectional, screening with the 13C-Urea breath test. BMC Public Health 2013, 13, 1215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nakaya, T.; Honjo, K.; Hanibuchi, T.; Ikeda, A.; Iso, H.; Inoue, M.; Sawada, N.; Tsugane, S.; Japan Public Health Center-based Prospective Study Group. Associations of all-cause mortality with census-based neighbourhood deprivation and population density in Japan: A multilevel survival analysis. PLoS ONE 2014, 9, e97802. [Google Scholar] [CrossRef]
- Matsuo, K.; Wakai, K.; Hirose, K.; Ito, H.; Saito, T.; Suzuki, T.; Kato, T.; Hirai, T.; Kanemitsu, Y.; Hamajima, H.; et al. A gene-gene interaction between ALDH2 Glu487Lys and ADH2 His47Arg polymorphisms regarding the risk of colorectal cancer in Japan. Carcinogenesis 2006, 27, 1018–1023. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ishioka, K.; Masaoka, H.; Ito, H.; Oze, I.; Ito, S.; Tajika, M.; Shimizu, Y.; Niwa, Y.; Nakamura, S.; Matsuo, K. Association between ALDH2 and ADH1B polymorphisms, alcohol drinking and gastric cancer: A replication and mediation analysis. Gastric. Cancer 2018, 21, 936–945. [Google Scholar] [CrossRef] [Green Version]
- Fritz, A.G.; World Health Organization. International Classification of Diseases for Oncology = ICD-O, 3rd ed.; World Health Organization: Geneva, Switzerland, 2000; p. 240. [Google Scholar]
- Nakaya, T. Evaluating socioeconomic inequalities in cancer mortality by using areal statistics in Japan: A note on the relation between the municipal cancer mortality and the areal deprivation index. Proc. Inst. Stat. Math. 2011, 59, 239–265. [Google Scholar]
- Koyanagi, Y.N.; Suzuki, E.; Imoto, I.; Kasugai, Y.; Oze, I.; Ugai, T.; Iwase, M.; Usui, Y.; Kawakatsu, Y.; Sawabe, M.; et al. Across-Site Differences in the Mechanism of Alcohol-Induced Digestive Tract Carcinogenesis: An Evaluation by Mediation Analysis. Cancer Res. 2020, 80, 1601–1610. [Google Scholar] [CrossRef] [Green Version]
- Jette, M.; Sidney, K.; Blumchen, G. Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity. Clin. Cardiol. 1990, 13, 555–565. [Google Scholar] [CrossRef]
- Willett, W. Nutritional Epidemiology, 2nd ed.; Oxford University Press: New York, NY, USA, 1998; pp. 288–291. [Google Scholar]
- Willett, W.C.; Sampson, L.; Stampfer, M.J.; Rosner, B.; Bain, C.; Witschi, J.; Hennekens, C.H.; Speizer, F.E. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am. J. Epidemiol. 1985, 122, 51–65. [Google Scholar] [CrossRef]
- Imaeda, N.; Goto, C.; Tokudome, Y.; Hirose, K.; Tajima, K.; Tokudome, S. Reproducibility of a short food frequency questionnaire for Japanese general population. J. Epidemiol. 2007, 17, 100–107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Attila, T.; Zeybel, M.; Yigit, Y.E.; Baran, B.; Ahishali, E.; Alper, E.; Aslan, F.; Ergonul, O.; Mungan, Z. Upper socioeconomic status is associated with lower Helicobacter pylori infection rate among patients undergoing gastroscopy. J. Infect. Dev. Ctries. 2020, 14, 298–303. [Google Scholar] [CrossRef] [Green Version]
Table 1.
Subject characteristics.
| Characteristics | Head and Neck | Esophagus | Stomach | Colorectal | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cases | Controls | Cases | Controls | Cases | Controls | Cases | Controls | |||||||||
| (n = 587) | % | (n = 587) | % | (n = 503) | % | (n = 503) | % | (n = 1146) | % | (n = 1146) | % | (n = 952) | % | (n = 952) | % | |
| Sex | ||||||||||||||||
| Male | 442 | 75.3% | 442 | 75.3% | 437 | 86.9% | 437 | 86.9% | 827 | 72.2% | 827 | 72.2% | 586 | 61.6% | 586 | 61.6% |
| Female | 145 | 24.7% | 145 | 24.7% | 66 | 13.1% | 66 | 13.1% | 319 | 27.8% | 319 | 27.8% | 366 | 38.4% | 366 | 38.4% |
| Age | ||||||||||||||||
| <40 | 62 | 10.6% | 58 | 9.9% | 1 | 0.2% | 4 | 0.8% | 54 | 4.7% | 55 | 4.8% | 42 | 4.4% | 50 | 5.3% |
| 40–49 | 54 | 9.2% | 62 | 10.6% | 23 | 4.6% | 24 | 4.8% | 82 | 7.2% | 106 | 9.2% | 108 | 11.3% | 113 | 11.9% |
| 50–59 | 138 | 23.5% | 142 | 24.2% | 134 | 26.6% | 138 | 27.4% | 296 | 25.8% | 290 | 25.3% | 265 | 27.8% | 262 | 27.5% |
| 60–69 | 216 | 36.8% | 203 | 34.6% | 242 | 48.1% | 215 | 42.7% | 452 | 39.4% | 429 | 37.4% | 351 | 36.9% | 345 | 36.2% |
| >70 | 117 | 19.9% | 122 | 20.8% | 103 | 20.5% | 122 | 24.3% | 262 | 22.9% | 266 | 23.2% | 186 | 19.5% | 182 | 19.1% |
| Mean age (SD) | 59.3 (12.3) | 59 (12.4) | 63.3 (7.7) | 63.0 (8.4) | 61.3 (10.4) | 60.9 (10.9) | 60.1 (10.5) | 59.75 (11.0) | ||||||||
| Alcohol consumption | ||||||||||||||||
| Never | 184 | 31.3% | 215 | 36.6% | 51 | 10.1% | 157 | 31.2% | 436 | 38.0% | 417 | 36.4% | 376 | 39.5% | 401 | 42.1% |
| Low | 138 | 23.5% | 204 | 34.8% | 64 | 12.7% | 184 | 36.6% | 339 | 29.6% | 417 | 36.4% | 298 | 31.3% | 331 | 34.8% |
| Mod | 86 | 14.7% | 87 | 14.8% | 121 | 24.1% | 90 | 17.9% | 170 | 14.8% | 159 | 13.9% | 109 | 11.4% | 109 | 11.4% |
| Heavy | 171 | 29.1% | 74 | 12.6% | 257 | 51.1% | 68 | 13.5% | 186 | 16.2% | 143 | 12.5% | 161 | 16.9% | 98 | 10.3% |
| Unknown | 8 | 1.4% | 7 | 1.2% | 10 | 2.0% | 4 | 0.8% | 15 | 1.3% | 10 | 0.9% | 8 | 0.8% | 13 | 1.4% |
| Smoking status | ||||||||||||||||
| Never | 166 | 28.3% | 237 | 40.4% | 69 | 13.7% | 176 | 35.0% | 413 | 36.0% | 483 | 42.1% | 430 | 45.2% | 470 | 49.4% |
| Former | 192 | 32.7% | 211 | 35.9% | 199 | 39.6% | 208 | 41.4% | 381 | 33.2% | 400 | 34.9% | 302 | 31.7% | 284 | 29.8% |
| Current | 225 | 38.3% | 138 | 23.5% | 234 | 46.5% | 117 | 23.3% | 347 | 30.3% | 259 | 22.6% | 216 | 22.7% | 194 | 20.4% |
| Unknown | 4 | 0.7% | 1 | 0.2% | 1 | 0.2% | 2 | 0.4% | 5 | 0.4% | 4 | 0.3% | 4 | 0.4% | 4 | 0.4% |
| Pack years | ||||||||||||||||
| Never | 168 | 28.6% | 239 | 40.7% | 70 | 13.9% | 178 | 35.4% | 414 | 36.1% | 483 | 42.1% | 433 | 45.5% | 473 | 49.7% |
| 0 ≤ PY < 20 | 85 | 14.5% | 120 | 20.4% | 59 | 11.7% | 78 | 15.5% | 160 | 14.0% | 190 | 16.6% | 145 | 15.2% | 163 | 17.1% |
| 20 ≤ PY < 40 | 125 | 21.3% | 103 | 17.5% | 131 | 26.0% | 110 | 21.9% | 224 | 19.5% | 218 | 19.0% | 179 | 18.8% | 141 | 14.8% |
| 40 ≤ PY < 60 | 109 | 18.6% | 65 | 11.1% | 138 | 27.4% | 74 | 14.7% | 193 | 16.8% | 135 | 11.8% | 109 | 11.4% | 87 | 9.1% |
| 60 ≤ PY | 77 | 13.1% | 46 | 7.8% | 88 | 17.5% | 44 | 8.7% | 111 | 9.7% | 83 | 7.2% | 68 | 7.1% | 55 | 5.8% |
| Unknown | 23 | 3.9% | 14 | 2.4% | 17 | 3.4% | 19 | 3.8% | 44 | 3.8% | 37 | 3.2% | 18 | 1.9% | 33 | 3.5% |
| BMI (body mass index) | ||||||||||||||||
| BMI < 18.5 | 86 | 14.7% | 41 | 7.0% | 78 | 15.5% | 15 | 3.0% | 119 | 10.4% | 72 | 6.3% | 66 | 6.9% | 51 | 5.4% |
| 18.5 ≤ BMI < 25 | 390 | 66.4% | 423 | 72.1% | 381 | 75.7% | 368 | 73.2% | 850 | 74.2% | 821 | 71.6% | 677 | 71.1% | 690 | 72.5% |
| 25 ≤ BMI < 30 | 100 | 17.0% | 106 | 18.1% | 40 | 8.0% | 104 | 20.7% | 157 | 13.7% | 224 | 19.5% | 182 | 19.1% | 185 | 19.4% |
| 30 ≤ BMI < 35 | 5 | 0.9% | 13 | 2.2% | 1 | 0.2% | 10 | 2.0% | 16 | 1.4% | 22 | 1.9% | 23 | 2.4% | 16 | 1.7% |
| 35 ≤ BMI < 40 | 2 | 0.3% | 0 | 0.0% | 0 | 0.0% | 1 | 0.2% | 0 | 0.0% | 1 | 0.1% | 1 | 0.1% | 3 | 0.3% |
| 40 ≤ BMI < 45 | 0 | 0.0% | 0 | 0.0% | 0 | 0.0% | 0 | 0.0% | 0 | 0.0% | 1 | 0.1% | 1 | 0.1% | 0 | 0.0% |
| Unknown | 4 | 0.7% | 4 | 0.7% | 3 | 0.6% | 5 | 1.0% | 4 | 0.3% | 5 | 0.4% | 2 | 0.2% | 7 | 0.7% |
| Diabetes | ||||||||||||||||
| Yes | 74 | 12.6% | 59 | 10.1% | 37 | 7.4% | 63 | 12.5% | 111 | 9.7% | 131 | 11.4% | 106 | 11.1% | 89 | 9.3% |
| No | 508 | 86.5% | 528 | 89.9% | 459 | 91.3% | 440 | 87.5% | 1028 | 89.7% | 1012 | 88.3% | 836 | 87.8% | 860 | 90.3% |
| Unknown | 5 | 0.9% | 0 | 0.0% | 7 | 1.4% | 0 | 0 | 7 | 0.6% | 3 | 0.3% | 10 | 1.1% | 3 | 0.3% |
| Family history of each cancer | ||||||||||||||||
| Yes | 19 | 3.2% | 11 | 1.9% | 16 | 3.2% | 9 | 1.8% | 301 | 26.3% | 202 | 17.6% | 135 | 14.2% | 117 | 12.3% |
| No | 568 | 96.8% | 576 | 98.1% | 487 | 96.8% | 494 | 98.2% | 845 | 73.7% | 944 | 82.4% | 817 | 85.8% | 835 | 87.7% |
| Physical activity (metabolic equivalent (MET)-hour per week) | ||||||||||||||||
| 0 | 199 | 33.9% | 107 | 18.2% | 125 | 24.9% | 76 | 15.1% | 268 | 23.4% | 204 | 17.8% | 223 | 23.4% | 185 | 19.4% |
| 0 < MET-hours < 10 | 223 | 38.0% | 289 | 49.2% | 207 | 41.2% | 232 | 46.1% | 488 | 42.6% | 527 | 46.0% | 428 | 45.0% | 453 | 47.6% |
| 10 ≤ MET-hours < 20 | 74 | 12.6% | 90 | 15.3% | 80 | 15.9% | 87 | 17.3% | 188 | 16.4% | 187 | 16.3% | 155 | 16.3% | 147 | 15.4% |
| 20 ≤ MET-hours | 74 | 12.6% | 94 | 16.0% | 81 | 16.1% | 94 | 18.7% | 178 | 15.5% | 201 | 17.5% | 126 | 13.2% | 142 | 14.9% |
| Unknown | 17 | 2.9% | 7 | 1.2% | 10 | 2.0% | 14 | 2.8% | 24 | 2.1% | 27 | 2.4% | 20 | 2.1% | 25 | 2.6% |
| Vegetable/fruit intake | ||||||||||||||||
| 1st quartile | 210 | 35.8% | 147 | 25.0% | 157 | 31.2% | 126 | 25.0% | 361 | 31.5% | 287 | 25.0% | 260 | 27.3% | 238 | 25.0% |
| 2nd quartile | 127 | 21.6% | 147 | 25.0% | 123 | 24.5% | 126 | 25.0% | 237 | 20.7% | 286 | 25.0% | 243 | 25.5% | 238 | 25.0% |
| 3rd quartile | 127 | 21.6% | 147 | 25.0% | 123 | 24.5% | 126 | 25.0% | 277 | 24.2% | 287 | 25.0% | 263 | 27.6% | 238 | 25.0% |
| 4th quartile | 123 | 21.0% | 146 | 24.9% | 100 | 19.9% | 125 | 24.9% | 271 | 23.6% | 286 | 25.0% | 186 | 19.5% | 238 | 25.0% |
| Beef/pork intake | ||||||||||||||||
| 1/week | 132 | 22.5% | 132 | 22.5% | 110 | 21.9% | 112 | 22.3% | 266 | 23.2% | 266 | 23.2% | 208 | 21.8% | 193 | 20.3% |
| 1–4/week | 417 | 71.0% | 421 | 71.7% | 373 | 74.2% | 368 | 73.2% | 829 | 72.3% | 814 | 71.0% | 677 | 71.1% | 704 | 73.9% |
| 5≤/week | 21 | 3.6% | 28 | 4.8% | 14 | 2.8% | 19 | 3.8% | 40 | 3.5% | 60 | 5.2% | 55 | 5.8% | 46 | 4.8% |
| Unknown | 17 | 2.9% | 6 | 1.0% | 6 | 1.2% | 4 | 0.8% | 11 | 1.0% | 6 | 0.5% | 12 | 1.3% | 9 | 0.9% |
| Processed meat intake | ||||||||||||||||
| 1/week | 266 | 45.3% | 278 | 47.4% | 251 | 49.9% | 263 | 52.3% | 571 | 49.8% | 597 | 52.1% | 453 | 47.6% | 452 | 47.5% |
| 1–4/week | 274 | 46.7% | 259 | 44.1% | 210 | 41.7% | 206 | 41.0% | 496 | 43.3% | 465 | 40.6% | 414 | 43.5% | 434 | 45.6% |
| 5≤/week | 29 | 4.9% | 40 | 6.8% | 33 | 6.6% | 29 | 5.8% | 69 | 6.0% | 74 | 6.5% | 72 | 7.6% | 55 | 5.8% |
| Unknown | 18 | 3.1% | 10 | 1.7% | 9 | 1.8% | 5 | 1.0% | 10 | 0.9% | 10 | 0.9% | 13 | 1.4% | 11 | 1.2% |
| Helicobacter pylori IgG (immunoglobulin G) test | ||||||||||||||||
| Positive | 570 | 49.7% | 333 | 29.1% | ||||||||||||
| Negative | 225 | 19.6% | 462 | 40.3% | ||||||||||||
| Unmeasured | 351 | 30.6% | 351 | 30.6% | ||||||||||||
| Atrophic gastritis defined by pepsinogen testing | ||||||||||||||||
| Positive | 369 | 32.2% | 158 | 13.8% | ||||||||||||
| Negative | 426 | 37.2% | 637 | 55.6% | ||||||||||||
| Unmeasured | 351 | 30.6% | 351 | 30.6% | ||||||||||||
Table 2.
Association between educational level and risk of subject cancers by site 4.
| Educational Level | Case | Control | Crude 1 | Model 1 2 | Model 2 3 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Odds Ratio | (95% Conf. Interval) | p > z | Odds Ratio | (95% Conf. Interval) | p > z | Odds Ratio | (95% Conf. Interval) | p > z | ||||||
| Head and Neck | ||||||||||||||
| ≤junior high school | 123 | 68 | 1 | Reference | 1 | Reference | 1 | Reference | ||||||
| ≤high school | 228 | 215 | 0.54 | 0.37 | 0.78 | 0.001 | 0.62 | 0.42 | 0.94 | 0.023 | 0.67 | 0.43 | 1.03 | 0.069 |
| higher education | 232 | 297 | 0.37 | 0.26 | 0.55 | 4.2 × 10−7 | 0.43 | 0.29 | 0.66 | 9.0 × 10−5 | 0.43 | 0.27 | 0.68 | 3.2 × 10−4 |
| trend p = | 3.0 × 10−7 | trend p = | 3.6 × 10−5 | trend p = | 1.4 × 10−4 | |||||||||
| Esophagus | ||||||||||||||
| ≤junior high school | 103 | 67 | 1 | Reference | 1 | Reference | 1 | Reference | ||||||
| ≤high school | 193 | 175 | 0.70 | 0.47 | 1.02 | 0.063 | 0.76 | 0.46 | 1.25 | 0.278 | 0.63 | 0.35 | 1.13 | 0.121 |
| higher education | 202 | 254 | 0.51 | 0.35 | 0.74 | 3.8 × 10−4 | 0.71 | 0.43 | 1.17 | 0.175 | 0.64 | 0.36 | 1.15 | 0.137 |
| trend p = | 2.7 × 10−4 | trend p = | 0.181 | trend p = | 0.172 | |||||||||
| Stomach | ||||||||||||||
| ≤junior high school | 193 | 138 | 1 | Reference | 1 | Reference | 1 | Reference | ||||||
| ≤high school | 481 | 415 | 0.80 | 0.61 | 1.03 | 0.088 | 0.79 | 0.61 | 1.04 | 0.089 | 0.79 | 0.60 | 1.05 | 0.105 |
| higher education | 453 | 583 | 0.50 | 0.38 | 0.65 | 4.3 × 10−7 | 0.52 | 0.39 | 0.69 | 5.5 × 10−6 | 0.52 | 0.38 | 0.69 | 9.9 × 10−6 |
| trend p = | 8.4 × 10−9 | trend p = | 2.8 × 10−7 | trend p = | 5.5 × 10−7 | |||||||||
| Colorectum | ||||||||||||||
| ≤junior high school | 166 | 107 | 1 | Reference | 1 | Reference | 1 | Reference | ||||||
| ≤high school | 386 | 350 | 0.70 | 0.53 | 0.94 | 0.016 | 0.70 | 0.52 | 0.93 | 0.016 | 0.69 | 0.51 | 0.93 | 0.016 |
| higher education | 390 | 484 | 0.50 | 0.37 | 0.67 | 2.3 × 10−6 | 0.51 | 0.38 | 0.69 | 1.1 × 10−5 | 0.52 | 0.38 | 0.71 | 2.9 × 10−5 |
| trend p = | 3.5 × 10−7 | trend p = | 2.6 × 10−6 | trend p = | 1.2 × 10−5 | |||||||||
1 Crude model considering matching factors (age and sex) in the conditional logistic regression model. 2 Model 1 further adjusted for alcohol intake, pack-years (PY) of smoking, and family history of subject cancer. 3 Model 2 further adjusted for BMI, past history of diabetes, physical activity (metabolic equivalent (MET)-hour per week), vegetable/fruit intake, beef/pork intake, and processed meat intake. 4 Some cases were excluded because the education status was unknown or other (11 cases of head and neck cancer, 12 cases of esophageal cancer, 29 cases of stomach cancer, and 21 cases of colorectal cancer were excluded).
Table 3.
Association between quintiles of the deprivation index and risk of subject cancers by sites 4.
Table 3.
Association between quintiles of the deprivation index and risk of subject cancers by sites 4.
| Quintile of ADI | Case | Control | Crude 1 | Model 1 2 | Model 2 3 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Odds Ratio | (95% Conf. Interval) | p > z | Odds Ratio | (95% Conf. Interval) | p > z | Odds Ratio | (95% Conf. Interval) | p > z | ||||||
| Head and Neck | ||||||||||||||
| Q1 | 97 | 117 | 1 | Reference | 1 | Reference | 1 | Reference | ||||||
| Q2 | 110 | 117 | 1.16 | 0.79 | 1.71 | 0.457 | 1.37 | 0.89 | 2.12 | 0.151 | 1.35 | 0.84 | 2.16 | 0.214 |
| Q3 | 116 | 117 | 1.22 | 0.83 | 1.78 | 0.309 | 1.22 | 0.80 | 1.87 | 0.352 | 1.07 | 0.67 | 1.69 | 0.784 |
| Q4 | 133 | 117 | 1.40 | 0.95 | 2.04 | 0.086 | 1.63 | 1.07 | 2.49 | 0.023 | 1.62 | 1.03 | 2.56 | 0.039 |
| Q5 | 129 | 117 | 1.34 | 0.92 | 1.94 | 0.123 | 1.52 | 1.00 | 2.30 | 0.048 | 1.59 | 1.02 | 2.48 | 0.041 |
| trend p = | 0.085 | trend p = | 0.047 | trend p = | 0.035 | |||||||||
| Esophagus | ||||||||||||||
| Q1 | 71 | 100 | 1 | Reference | 1 | Reference | 1 | Reference | ||||||
| Q2 | 67 | 101 | 0.95 | 0.61 | 1.47 | 0.806 | 0.75 | 0.41 | 1.38 | 0.352 | 0.72 | 0.36 | 1.44 | 0.357 |
| Q3 | 97 | 99 | 1.41 | 0.93 | 2.14 | 0.108 | 1.36 | 0.79 | 2.35 | 0.265 | 1.50 | 0.80 | 2.84 | 0.208 |
| Q4 | 130 | 100 | 1.82 | 1.22 | 2.73 | 0.004 | 1.27 | 0.75 | 2.16 | 0.378 | 1.22 | 0.66 | 2.23 | 0.525 |
| Q5 | 134 | 100 | 1.88 | 1.25 | 2.84 | 0.003 | 1.48 | 0.85 | 2.58 | 0.162 | 1.77 | 0.94 | 3.35 | 0.078 |
| trend p = | 4.7 × 10−5 | trend p = | 0.038 | trend p = | 0.021 | |||||||||
| Stomach | ||||||||||||||
| Q1 | 156 | 230 | 1 | Reference | 1 | Reference | 1 | Reference | ||||||
| Q2 | 239 | 228 | 1.56 | 1.18 | 2.05 | 0.002 | 1.60 | 1.20 | 2.13 | 0.001 | 1.71 | 1.27 | 2.30 | 3.7 × 10−4 |
| Q3 | 243 | 228 | 1.56 | 1.20 | 2.05 | 0.001 | 1.60 | 1.21 | 2.12 | 0.001 | 1.64 | 1.23 | 2.19 | 0.001 |
| Q4 | 270 | 229 | 1.77 | 1.35 | 2.33 | 4.7 × 10−5 | 1.70 | 1.28 | 2.26 | 2.5 × 10−4 | 1.77 | 1.32 | 2.37 | 1.5 × 10−4 |
| Q5 | 232 | 228 | 1.51 | 1.14 | 1.99 | 0.004 | 1.50 | 1.12 | 1.99 | 0.006 | 1.55 | 1.15 | 2.09 | 0.004 |
| trend p = | 0.005 | trend p = | 0.013 | trend p = | 0.013 | |||||||||
| Colorectum | ||||||||||||||
| Q1 | 145 | 191 | 1 | Reference | 1 | Reference | 1 | Reference | ||||||
| Q2 | 171 | 190 | 1.18 | 0.88 | 1.60 | 0.27 | 1.07 | 0.79 | 1.46 | 0.65 | 1.07 | 0.78 | 1.48 | 0.658 |
| Q3 | 223 | 190 | 1.54 | 1.15 | 2.06 | 0.003 | 1.47 | 1.10 | 1.98 | 0.01 | 1.43 | 1.06 | 1.94 | 0.02 |
| Q4 | 234 | 190 | 1.62 | 1.21 | 2.17 | 0.001 | 1.52 | 1.13 | 2.04 | 0.006 | 1.44 | 1.06 | 1.96 | 0.02 |
| Q5 | 177 | 190 | 1.22 | 0.91 | 1.64 | 0.191 | 1.10 | 0.81 | 1.49 | 0.533 | 1.04 | 0.76 | 1.42 | 0.819 |
| trend p = | 0.035 | trend p = | 0.11 | trend p = | 0.30 | |||||||||
1 Crude model considering matching factors (age and sex) in the conditional logistic regression model. 2 Model 1 further adjusted for alcohol intake, PY of smoking, and family history of subject cancer. 3 Model 2 further adjusted for BMI, past history of diabetes, physical activity (metabolic equivalent (MET)-hour per week), vegetable/fruit intake, beef/pork intake, and processed meat intake. 4 Some cases were excluded because the areal deprivation index was unknown or other (4 cases of head and neck cancer, 7 cases of esophageal cancer, 9 cases of stomach cancer, and 3 cases of colorectal cancer were excluded).
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Kawakatsu, Y.; Koyanagi, Y.N.; Oze, I.; Kasugai, Y.; Morioka, H.; Yamaguchi, R.; Ito, H.; Matsuo, K. Association between Socioeconomic Status and Digestive Tract Cancers: A Case-Control Study. Cancers 2020, 12, 3258. https://doi.org/10.3390/cancers12113258
AMA Style
Kawakatsu Y, Koyanagi YN, Oze I, Kasugai Y, Morioka H, Yamaguchi R, Ito H, Matsuo K. Association between Socioeconomic Status and Digestive Tract Cancers: A Case-Control Study. Cancers. 2020; 12(11):3258. https://doi.org/10.3390/cancers12113258
Chicago/Turabian StyleKawakatsu, Yukino, Yuriko N. Koyanagi, Isao Oze, Yumiko Kasugai, Hisayoshi Morioka, Rui Yamaguchi, Hidemi Ito, and Keitaro Matsuo. 2020. "Association between Socioeconomic Status and Digestive Tract Cancers: A Case-Control Study" Cancers 12, no. 11: 3258. https://doi.org/10.3390/cancers12113258
APA StyleKawakatsu, Y., Koyanagi, Y. N., Oze, I., Kasugai, Y., Morioka, H., Yamaguchi, R., Ito, H., & Matsuo, K. (2020). Association between Socioeconomic Status and Digestive Tract Cancers: A Case-Control Study. Cancers, 12(11), 3258. https://doi.org/10.3390/cancers12113258
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