Socio-economic status and oesophageal cancer: results from a population-based case-control study in a high-risk area

Abstract

Background: Cancer registries in the 1970s showed that parts of Golestan Province in Iran had the highest rate of oesophageal squamous cell carcinoma (OSCC) in the world. More recent studies have shown that while rates are still high, they are approximately half of what they were before, which might be attributable to improved socio-economic status (SES) and living conditions in this area. We examined a wide range of SES indicators to investigate the association between different SES components and risk of OSCC in the region.

Methods: Data were obtained from a population-based case-control study conducted between 2003 and 2007 with 300 histologically proven OSCC cases and 571 matched neighbourhood controls. We used conditional logistic regression to compare cases and controls for individual SES indicators, for a composite wealth score constructed using multiple correspondence analysis, and for factors obtained from factors analysis.

Results: We found that various dimensions of SES, such as education, wealth and being married were all inversely related to OSCC. The strongest inverse association was found with education. Compared with no education, the adjusted odds ratios (95% confidence intervals) for primary education and high school or beyond were 0.52 (0.27-0.98) and 0.20 (0.06-0.65), respectively.

Conclusions: The strong association of SES with OSCC after adjustment for known risk factors implies the presence of yet unidentified risk factors that are correlated with our SES measures; identification of these factors could be the target of future studies. Our results also emphasize the importance of using multiple SES measures in epidemiological studies.

Figures

Figure 1

Visualization of the coordinates of wealth variables in the MCA among 571 controls, Golestan Province, Northern Iran, 2003–07. Appliance ownership: Bath = bath in the residence; Car = automobile; Motor = motorbike; BW = B/W TV; ColTV = colour TV; Refri = refrigerator; Freez = freezer; Vac = vacuum; Wash = washing machine; suffix ‘0’ is the indicator of not owning the appliance; suffix ‘1’ is the indicator of owning the appliance. Housing: Res0 = not owned a residence; Res1 = owned a residence; Struc1 = house made of burned brick; Struc2 = house made of mud brick/clay; Struc3 = other house structures; Size1 = first tertile of house size (m2); Size2 = second tertile of house size; Size3 = third tertile of house size. Occupation: Job1 = farmers; Job2 = non-skilled manual worker; Job3 = skilled manual occupations; Job4 = service (white-collar); Job5 = supported by aid organizations. The indicators of higher wealth are located on the right side of the plot. The first dimension explains the majority (86.8%) of the chi-square variation of the data. The wealth score was calculated by weighing each variable by weights reported in the first dimension and then summing. For example, the weights for owning or not owning a refrigerator in the first dimension were 0.038 and –0.733, and for owning or not owning a freezer were 0.542 and –0.105, respectively. If a subject owned a refrigerator but did not have a freezer, the corresponding weights (0.038 and –0.105) were summed up; this procedure was continued until the weights of all MCA variables were included in this calculation.