Skip to content
naulon

No retraction, correction or concern found when last checked on Oct 27, 2026.

A 50% higher prevalence of life-shortening chronic conditions among cancer patients with low socioeconomic status

W J Louwman, M J Aarts, S Houterman and 3 more

British Journal of Cancer · 2010

doi:10.1038/sj.bjc.6605949

Abstract

BACKGROUND: Comorbidity and socioeconomic status (SES) may be related among cancer patients. METHOD: Population-based cancer registry study among 72,153 patients diagnosed during 1997-2006. RESULTS: Low SES patients had 50% higher risk of serious comorbidity than those with high SES. Prevalence was increased for each cancer site. Low SES cancer patients had significantly higher risk of also having cardiovascular disease, chronic obstructive pulmonary diseases, diabetes mellitus, cerebrovascular disease, tuberculosis, dementia, and gastrointestinal disease. One-year survival was significantly worse in lowest vs highest SES, partly explained by comorbidity. CONCLUSION: This illustrates the enormous heterogeneity of cancer patients and stresses the need for optimal treatment of cancer patients with a variety of concomitant chronic conditions.

Shown for reading. Its publisher allows quoting it, not republishing it.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 27, 2026. Paragraph numbers are ours, so an answer can point at one.

People of a lower socioeconomic status (SES) generally have poorer health status and higher mortality than people of higher SES, also with respect to cancer, with in general higher incidence rate of all cancers combined among people from lower socioeconomic groups. A differential distribution of known risk factors for specific neoplasms between SES groups seems a likely explanation for the above inequalities. For example, the prevalence of smokers has become higher among lower classes, probably resulting in higher rates of cancer of the lung, larynx, mouth, pharynx, oesophagus, and bladder. However, smoking is not only related to cancer but also to chronic obstructive pulmonary diseases (COPD) and cardiovascular diseases. Hence, the high prevalence of comorbidity among lung cancer patients. Socioeconomic status may thus be associated with comorbidity among cancer patients. Thus, medical doctors are presented with a heterogeneous group of cancer patients, for whom appropriate individual treatment must be chosen, taking concomitant conditions into account.

We studied in a large population-based group of cancer patients the prevalence of comorbidity according to SES, not only by number of concomitant diseases, but also for specific diseases that affect patients with the various tumour sites.

Materials and methods

The Eindhoven Cancer Registry records data on all patients newly diagnosed with cancer in the south of the Netherlands (2.4 million inhabitants, 15% of the Dutch population); it also records serious comorbidity according to an adaptated list. Chronic obstructive pulmonary diseases, cardio- and cerebrovascular diseases, peripheral arterial disease, other malignancies, and diabetes mellitus, connective tissue diseases, rheumatoid arthritis, kidney, bowel, and liver diseases, dementia, tuberculosis and other chronic infections were also recorded. For most analyses peripheral arterial disease was included in the cardiovascular diseases, although gastrointestinal diseases were grouped (gastric diseases, Crohn's disease, ulcerative colitis, liver cirrhosis, and hepatitis). Comorbidity was defined as life-shortening disease that was present at the time of cancer diagnosis and/or received treatment or surveillance. Trained registry personnel actively collect data on diagnosis, staging, and treatment from the medical records after notification by pathologists and medical registration offices. Previous admissions, letters from and to general practitioners and other specialists, the medical history and preoperative screening were used as sources.

Patients with cancer of the oesophagus, stomach, colon or rectum, pancreas, lung, melanoma, breast, cervix uteri, corpus uteri, ovary, prostate, bladder, kidney, and non-Hodgkin's lymphoma (NHL), newly diagnosed between 1997 and 2006 (n=72 153), were included in this study; cancers diagnosed at autopsy (n=369) were excluded.

Statistics Netherlands developed an indicator of SES, using individual fiscal data on the economic value of the home and household income, and is provided at aggregated level for each postal code (covering an average of 17 households). Socioeconomic status was categorised as low (deciles 1–3), medium (deciles 4–7), or high social class (deciles 8–10), and a separate class for postal codes for a long-term care providing institution (such as a nursing home; ). We calculated the distribution of cancer patients across socioeconomic strata according to tumour localisation, also by gender and age. Patients for whom the SES was unknown (n=766, 1%) or for whom the postal code included a care providing institution (n=3569, 5%), as well as those with unknown comorbidity (n=8399, 12%) were excluded from the analyses of SES and comorbidity. Differences in distribution were tested with the χ2 test. Logistic regression analyses of the odds of having a specific concomitant disease were performed age- and gender-adjusted for all tumour sites combined, and according to tumour site for four concomitant diseases separately; cardiovascular disease, COPD, diabetes mellitus, and gastrointestinal disease. Statistical significance of an overall effect of SES on the prevalence of a specific condition was tested using the χ2-likelihood ratio test. Crude 1-year survival rates were calculated for all studied tumours combined and for the most important tumour sites separately. Cox's regression models were used to compute multivariate rates (hazard ratio=HR) and 95% confidence intervals (95% CI). The relative contribution (%) of adding comorbidity to the model was calculated as follows: ((HR model A−HR model B)/(HR model A−1)) × 100, where A is the basic model (age- and gender-adjusted) and in model B comorbidity is added to model A. All statistical analyses were performed using SAS V9.12 (SAS Institute Inc., Cary, NC, USA).

Results

Male cancer patients were older than female patients (Table 1), the median age being 69 and 64 years, respectively (P<0.0001). At the time of the diagnosis of the cancer 71% of male and 58% of female cancer patients had at least one concomitant disease. The most frequent concomitant condition for males with cancer was cardiovascular disease (23%), for women hypertension (20%), among cancer patients older than 70 the prevalence of these diseases was 34% and 31%, respectively. In the subgroup of cancer patients with two or more concomitant diseases, the most frequent combination of diseases among males was cardiovascular disease with hypertension (14%) and in females diabetes with hypertension (21%).

The proportion of patients by SES varied for the different tumour sites (Table 2). Patients under age 70 with stomach, lung, bladder, or cervical cancer more often had low SES. High SES was more frequent among patients with melanoma or breast, colorectal, or prostate cancer in this age group.

Among patients aged 70+ with cancer of the oesophagus, stomach, or lung, low SES was clearly over-represented. High SES was more frequent among patients with prostate cancer or NHL.

For all tumour localisations the proportion of patients without comorbidity was highest in the high SES group (Figure 1). A gradient towards more concomitant conditions appeared in lower SES groups (P<0.001), which had a significantly higher risk of cardiovascular disease (ORlow vs high SES=1.4, 95% CI: 1.3–1.5), COPD (OR=1.8 (1.7–1.9)), diabetes mellitus (OR=1.5 (1.4–1.6)), cerebrovascular disease (OR=1.5 (1.4–1.7)), tuberculosis (OR=1.3 (1.1–1.6)), dementia (OR=1.3 (1.0–1.8)), gastrointestinal disease (OR=1.5 (1.4–1.6)), and two or more concomitant conditions (OR=1.8 (1.7–1.9)) in addition to their cancer (Table 3). The risk of having cancer and also at least one other serious concomitant disease was 50% higher in the low SES than in the high SES group (OR=1.5 (1.4–1.6)).

For four concomitant conditions we stratified by tumour localisation (Figure 2). The risk of cardiovascular disease among low compared with high SES patients was significantly higher (1.4–1.6 times) for patients with stomach, colorectal, lung, breast, prostate, and bladder cancer. The risk of COPD was elevated among low SES patients with cancer of the stomach, colorectum, pancreas, lung, breast, corpus uteri, prostate, and kidney (OR's ranging from 1.4 to 2.2). The risk of diabetes mellitus was highest among people from low SES with breast cancer (OR=2.0 (1.2–2.4)) and the risk of gastrointestinal diseases was highest among patients with oesophageal cancer (OR=2.0 (1.2–3.4)).

Crude 1-year survival of cancer patients from lower SES was worse compared with the highest SES for all tumour sites combined and for the major sites separately (Table 4). The age-adjusted risk of death was significantly elevated for both men (HRlow vs high SES=1.40, 95% CI: 1.3–1.4) and women (HR 1.40 (1.3–1.5)). Adding comorbidity to the model reduced HR to 1.35 for men and 1.34 for women. The relative contribution of comorbidity in explaining the inequality in 1-year survival varied from 0% for lung cancer to 33% among female colorectal cancer patients.

Discussion

To our knowledge, this is the first large population-based study to demonstrates the impact of SES on the prevalence of concomitant diseases among cancer patients, with increased prevalence of comorbidity in lower socioeconomic strata for each type of cancer. Cancer patients with low SES had a 50% higher risk of suffering from at least one other serious disease compared with those with high SES. The prevalence of comorbidity was significantly higher with newly diagnosed cancer of lower compared with higher SES for all 14 cancer sites studied. The diseases significantly related to SES among cancer patients were cardiovascular disease, COPD, diabetes mellitus, cerebrovascular disease, tuberculosis, diseases of the central nervous system, and gastrointestinal disease. Although both the prevalence of comorbidity and the proportional distribution of SES vary significantly among tumour types, the gradient of more comorbidity from high to low SES was apparent among all tumour types.

Smoking is probably responsible for the higher risk of cardiovascular disease, COPD, and cerebrovascular disease among low SES groups. This is confirmed by the higher prevalence of those diseases among patients with smoking-related tumours: cancers of the stomach, lung, bladder, and kidney. Diabetes was more frequent among low SES for patients with cancers of the colorectum, pancreas, lung, breast, corpus uteri or prostate, or melanoma or NHL. Diabetes has been linked to pancreas cancer either as a risk factor or as the clinical manifestation of the cancer itself. Diabetes has also been associated with an increased risk for breast, endometrial, and colorectal cancer probably because of a relation with obesity. Substantial evidence exists for the association of obesity with low SES.

The prevalence of gastrointestinal diseases was highest for low SES patients with oesophageal, colorectal, lung, breast, prostate or kidney cancer, or NHL. Oesophageal cancer has also been associated with gastrointestinal diseases. A lower consumption of vegetables, fruit, and fibres, which may protect from oesophageal and colorectal cancer, has been reported among lower SES.

We used an indicator of SES based on the postal code of a residential area. This aggregate covers a very small geographical area, and thus represents a reliable approximation of individual SES. Furthermore, routinely collected income tax data (no questionnaires or interviews) have been found to provide reliable estimates of household income. Previous studies have proven that socioeconomic differences based on neighbourhood data tend to reflect such differences well at the individual level. Furthermore, this objective measure of SES is also applicable to older women (born before 1955), whose occupation or education does not always properly reflect their social class.

Previously, we found that patients with comorbidity were often treated less aggressively, if alternative treatment strategies were available. Except for patients with a tumour with poor survival, comorbidity has an independent prognostic effect. This negative impact of comorbidity on survival of cancer might have several mechanisms: the increased risk of death due to the comorbid condition itself, more contra-indications for the cancer treatment, more indications for dose reduction and a higher rate of treatment-related complications such as infections and cardiovascular events. In several of our recent studies, the adverse effects of comorbidity on survival appeared to be independent of treatment, so less aggressive treatment could not (fully) account for the observed differences in survival between patients with and without comorbidity. As SES represents a combination of lifestyle, health, and risk of suboptimal treatment, cancer patients with comorbidity could also (partly) explain the poorer prognosis. Although an in-depth study remains necessary to reveal whether stage at diagnosis and treatment contributed to the SES gradient in survival, also for longer survival periods, our preliminary analyses demonstrated a clear gradient in 1-year survival rates, which could partly be attributed to comorbidity.

Our study shows considerable variation in comorbidity by tumour type and a higher risk of concomitant disease among patients from lower SES. Given the aetiology of the type of tumours as well as the aetiology of the concomitant diseases that occur more frequently among patients from low SES background, a lot can probably be gained from preventive measures related to lifestyle (such as smoking and obesity). Considering survival is worse for patients of low SES, our results stress the need for reduction of socioeconomic differences in health.

References 47

  1. 1.

    Ayanian JZ, Zaslavsky AM, Fuchs CS, Guadagnoli E, Creech CM, Cress RD, O’Connor LC, West DW, Allen ME, Wolf RE, Wright WE (2003 ) Use of adjuvant chemotherapy and radiation therapy for colorectal cancer in a population-based cohort. J Clin Oncol 21: 1293–1300

    Not in our library10.1200/jco.2003.06.178

  2. 2.

    Berkman LF, Macintyre S (1997 ) The measurement of social class in health studies: old measures and new formulations. IARC Sci Publ 138: 51–64

    Not in our library

  3. 3.

    Bos V, Kunst AE, Mackenbach JP (2000 ) Nationale gegevens over sociaal-economische sterfteverschillen op basis van informatie over kleine geografische eenheden. Verslag aan de programmacommissie Sociaal-economische gezondheidsverschillen II. Instituut Maatschappelijke Gezondheidszorg, Erasmus Universiteit: Rotterdam

    Not in our library

  4. 4.

    Bos V, Kunst AE, Mackenbach JP (2001 ) De omvang van sociaal-economische sterfteverschillen gemeten op buurtniveau: vergelijking met schattingen op basis van informatie op individueel niveau. In Sociaal-economische gezonheidsverschillen: Van verklaren naar verkleinen, Stronks K (ed) Vol. 5, pp 8–20. Zon/MW: Den Haag

    Not in our library

  5. 5.

    Bueno-de-Mesquita HB, Ferrari P, Riboli E (2002 ) Plant foods and the risk of colorectal cancer in Europe: preliminary findings. IARC Sci Publ 156: 89–95

    Not in our library

  6. 6.

    Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987 ) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40: 373–383

    Not in our library10.1016/0021-9681(87)90171-8

  7. 7.

    Dalton SO, Schuz J, Engholm G, Johansen C, Kjaer SK, Steding-Jessen M, Storm HH, Olsen JH (2008 ) Social inequality in incidence of and survival from cancer in a population-based study in Denmark, 1994–2003: summary of findings. Eur J Cancer 44(14): 2074–2085

    Not in our library10.1016/j.ejca.2008.06.018

  8. 8.

    Doll R, Peto R, Wheatley K, Gray R, Sutherland I (1994 ) Mortality in relation to smoking: 40 years’ observations on male British doctors. Br Med J 309: 901–911

    Not in our library10.1136/bmj.309.6959.901

  9. 9.

    Flood A, Velie EM, Chaterjee N, Subar AF, Thompson FE, Lacey Jr JV, Schairer C, Troisi R, Schatzkin A (2002 ) Fruit and vegetable intakes and the risk of colorectal cancer in the Breast Cancer Detection Demonstration Project follow-up cohort. Am J Clin Nutr 75: 936–943

    Not in our library10.1093/ajcn/75.5.936

  10. 10.

    Houterman S, Janssen-Heijnen ML, Hendrikx AJ, Berg HA, Coebergh JW (2006 ) Impact of comorbidity on treatment and prognosis of prostate cancer patients: A population-based study. Crit Rev Oncol Hematol 58: 60–67

    Not in our library10.1016/j.critrevonc.2005.08.003

  11. 11.

    Hulshof KF, Brussaard JH, Kruizinga AG, Telman J, Lowik MR (2003 ) Socio-economic status, dietary intake and 10 y trends: the Dutch National Food Consumption Survey. Eur J Clin Nutr 57: 128–137

    Not in our library10.1038/sj.ejcn.1601503

  12. 12.

    Jain M, Howe GR, St Louis P, Miller AB (1991 ) Coffee and alcohol as determinants of risk of pancreas cancer: a case-control study from Toronto. Int J Cancer 47: 384–389

    Not in our library10.1002/ijc.2910470313

  13. 13.

    Janssen-Heijnen ML, Houterman S, Lemmens VE, Louwman MW, Maas HA, Coebergh JW (2005 ) Prognostic impact of increasing age and co-morbidity in cancer patients: a population-based approach. Crit Rev Oncol Hematol 55: 231–240

    Not in our library10.1016/j.critrevonc.2005.04.008

  14. 14.

    Janssen-Heijnen ML, Schipper RM, Razenberg PP, Crommelin MA, Coebergh JW (1998 ) Prevalence of co-morbidity in lung cancer patients and its relationship with treatment: a population-based study. Lung Cancer 21: 105–113

    Not in our library10.1016/s0169-5002(98)00039-7

  15. 15.

    Janssen-Heijnen ML, Smulders S, Lemmens VE, Smeenk FW, van Geffen HJ, Coebergh JW (2004 ) Effect of comorbidity on the treatment and prognosis of elderly patients with non-small cell lung cancer. Thorax 59: 602–607

    Not in our library10.1136/thx.2003.018044

  16. 16.

    Jemal A, Thun MJ, Ward EE, Henley SJ, Cokkinides VE, Murray TE (2008 ) Mortality from leading causes by education and race in the United States, 2001. Am J Prev Med 34: 1–8

    Not in our library10.1016/j.amepre.2007.09.017

  17. 17.

    Kalapothaki V, Tzonou A, Hsieh CC, Toupadaki N, Karakatsani A, Trichopoulos D (1993 ) Tobacco, ethanol, coffee, pancreatitis, diabetes mellitus, and cholelithiasis as risk factors for pancreatic carcinoma. Cancer Causes Control 4: 375–382

    Not in our library10.1007/bf00051341

  18. 18.

    Koppert LB, Janssen-Heijnen ML, Louwman MW, Lemmens VE, Wijnhoven BP, Tilanus HW, Coebergh JW (2004 ) Comparison of comorbidity prevalence in oesophageal and gastric carcinoma patients: a population-based study. Eur J Gastroenterol Hepatol 16: 681–688

    Not in our library10.1097/01.meg.0000108331.52416.f1

  19. 19.

    Lahelma E, Rahkonen O, Berg MA, Helakorp S, Prattala R, Puska P, Uutela A (1997 ) Changes in health status and health behavior among Finnish adults 1978–1993. Scand J Work Environ Health 23(Suppl 3): 85–90

    Not in our library

  20. 20.

    Lash TL, Thwin SS, Horton NJ, Guadagnoli E, Silliman RA (2003 ) Multiple informants: a new method to assess breast cancer patients’ comorbidity. Am J Epidemiol 157: 249–257

    Not in our library10.1093/aje/kwf193

  21. 21.

    Lemmens VE, Janssen-Heijnen ML, Verheij CD, Houterman S, Repelaer van Driel OJ, Coebergh JW (2005 ) Co-morbidity leads to altered treatment and worse survival of elderly patients with colorectal cancer. Br J Surg 92: 615–623

    Not in our library10.1002/bjs.4913

  22. 22.

    Louwman WJ, Janssen-Heijnen ML, Houterman S, Voogd AC, van der Sangen MJ, Nieuwenhuijzen GA, Coebergh JW (2005 ) Less extensive treatment and inferior prognosis for breast cancer patient with comorbidity: a population-based study. Eur J Cancer 41: 779–785

    Not in our library10.1016/j.ejca.2004.12.025

  23. 23.

    Mackenbach JP, Stirbu I, Roskam AJ, Schaap MM, Menvielle G, Leinsalu M, Kunst AE (2008 ) Socioeconomic inequalities in health in 22 European countries. N Engl J Med 358: 2468–2481

    Not in our library10.1056/nejmsa0707519

  24. 24.

    McLaren L (2007 ) Socioeconomic status and obesity. Epidemiol Rev 29: 29–48

    Not in our library10.1093/epirev/mxm001

  25. 25.

    Michels KB, Edward G, Joshipura KJ, Rosner BA, Stampfer MJ, Fuchs CS, Colditz GA, Speizer FE, Willett WC (2000 ) Prospective study of fruit and vegetable consumption and incidence of colon and rectal cancers. J Natl Cancer Inst 92: 1740–1752

    Not in our library10.1093/jnci/92.21.1740

  26. 26.

    Parazzini F, La Vecchia C, Bocciolone L, Franceschi S (1991 ) The epidemiology of endometrial cancer. Gynecol Oncol 41: 1–16

    Not in our library10.1016/0090-8258(91)90246-2

  27. 27.

    Pietinen P, Malila N, Virtanen M, Hartman TJ, Tangrea JA, Albanes D, Virtamo J (1999 ) Diet and risk of colorectal cancer in a cohort of Finnish men. Cancer Causes Control 10: 387–396

    Not in our library10.1023/a:1008962219408

  28. 28.

    Polednak AP (2006 ) Comorbid diabetes mellitus and risk of death after diagnosis of colorectal cancer: a population-based study. Cancer Detect Prev 30: 466–472

    Not in our library10.1016/j.cdp.2006.07.003

  29. 29.

    Post PN, Hansen BE, Kil PJ, Janssen-Heijnen ML, Coebergh JW (2001 ) The independent prognostic value of comorbidity among men aged <75 years with localized prostate cancer: a population-based study. BJU Int 87: 821–826

    Not in our library10.1046/j.1464-410x.2001.02189.x

  30. 30.

    Reeves GK, Pirie K, Beral V, Green J, Spencer E, Bull D (2007 ) Cancer incidence and mortality in relation to body mass index in the Million Women Study: cohort study. Br Med J 335: 1134

    Not in our library10.1136/bmj.39367.495995.ae

  31. 31.

    Siemiatycki J, Krewski D, Franco E, Kaiserman M (1995 ) Associations between cigarette smoking and each of 21 types of cancer: a multi-site case-control study. Int J Epidemiol 24: 504–514

    Not in our library10.1093/ije/24.3.504

  32. 32.

    Smits J, Keij I, Westert G (2001 ) Effecten van sociaal-economische status van kleine, middelgrote en grote geografische eenheden op de sterfte. Mndstat bevolking 11: 4–10

    Not in our library

  33. 33.

    Sobal J, Stunkard AJ (1989 ) Socioeconomic status and obesity: a review of the literature. Psychol Bull 105: 260–275

    Not in our library10.1037/0033-2909.105.2.260

  34. 34.

    Stellman SD, Resnicow K (1997 ) Tobacco smoking, cancer and social class. IARC Sci Publ 229–250

    Not in our library

  35. 35.

    Stronks K, van de Mheen HD, Looman CW, Mackenbach JP (1997 ) Cultural, material, and psychosocial correlates of the socioeconomic gradient in smoking behavior among adults. Prev Med 26: 754–766

    Not in our library10.1006/pmed.1997.0174

  36. 36.

    Terry P, Giovannucci E, Michels KB, Bergkvist L, Hansen H, Holmberg L, Wolk A (2001a ) Fruit, vegetables, dietary fiber, and risk of colorectal cancer. J Natl Cancer Inst 93: 525–533

    Not in our library10.1093/jnci/93.7.525

  37. 37.

    Terry P, Lagergren J, Hansen H, Wolk A, Nyren O (2001b ) Fruit and vegetable consumption in the prevention of oesophageal and cardia cancers. Eur J Cancer Prev 10: 365–369

    Not in our library10.1097/00008469-200108000-00010

  38. 38.

    Tyczynski JE, Bray F, Parkin DM (2003 ) Lung cancer in Europe in 2000: epidemiology, prevention, and early detection. Lancet Oncol 4: 45–55

    Not in our library10.1016/s1470-2045(03)00960-4

  39. 39.

    Tzonou A, Lipworth L, Garidou A, Signorello LB, Lagiou P, Hsieh C, Trichopoulos D (1996 ) Diet and risk of esophageal cancer by histologic type in a low-risk population. Int J Cancer 68: 300–304

    Not in our library10.1002/(sici)1097-0215(19961104)68:3<300::aid-ijc6>3.0.co;2-5

  40. 40.

    van Duijn C, Keij I (2002 ) Sociaal-economische status indicator op postcode niveau. Maandstatistiek van de bevolking 50: 32–35

    Not in our library

All 47 references

Cited by papers we hold 1

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.