Socio-economic status and overall and cause-specific mortality in Sweden

  • Marianne Weires1Email author,

    Affiliated with

    • Justo Lorenzo Bermejo1,

      Affiliated with

      • Kristina Sundquist2,

        Affiliated with

        • Jan Sundquist2 and

          Affiliated with

          • Kari Hemminki1, 2

            Affiliated with

            BMC Public Health20088:340

            DOI: 10.1186/1471-2458-8-340

            Received: 04 March 2008

            Accepted: 30 September 2008

            Published: 30 September 2008

            Abstract

            Background

            Previous studies have reported discrepancies in cause-specific mortality among groups of individuals with different socio-economic status. However, most of the studies were limited by the specificity of the investigated populations and the broad definitions of the causes of death. The aim of the present population-based study was to explore the dependence of disease specific mortalities on the socio-economic status in Sweden, a country with universal health care. Another aim was to investigate possible gender differences.

            Methods

            Using the 2006 update of the Swedish Family-Cancer Database, we identified over 2 million individuals with socio-economic data recorded in the 1960 national census. The association between mortality and socio-economic status was investigated by Cox's proportional hazards models taking into account the age, time period and residential area in both men and women, and additionally parity and age at first birth in women.

            Results

            We observed significant associations between socio-economic status and mortality due to cardiovascular diseases, respiratory diseases, to cancer and to endocrine, nutritional and metabolic diseases. The influence of socio-economic status on female breast cancer was markedly specific: women with a higher socio-economic status showed increased mortality due to breast cancer.

            Conclusion

            Even in Sweden, a country where health care is universally provided, higher socio-economic status is associated with decreased overall and cause-specific mortalities. Comparison of mortality among female and male socio-economic groups may provide valuable insights into the underlying causes of socio-economic inequalities in length of life.

            Background

            Socio-economic inequalities in overall and cause-specific mortality have been previously reported for several populations [111]. Low socio-economic status has been generally associated with a higher mortality due to cardiovascular and respiratory disease, diabetes and several types of cancer, independently of the socio-economic indicator used (for example, occupation, educational level, income or a combination of these factors) [1, 3, 12]. By contrast, an excess of breast cancer mortality among women with a high socio-economic status has been noticed in different countries [13, 1316].

            The direction and magnitude of the difference in length of life and mortality depends on two different components: the time to diagnosis of the disease (age of onset) and the time from diagnosis to death (survival time). Several factors associated with socio-economic disparities in survival have been identified, including treatment discrepancies among socio-economic groups and lower screening compliance in deprived persons, thus leading to socio-economic differentials in the stage of disease and the subsequent prognosis [1719]. However, data on socio-economic status and mortality are sparse and most studies are limited by the specificity of the investigated populations and by the broad definitions of the causes of death [3, 4, 68, 10, 16, 20]. This study investigates socio-economic differences among the most common causes of death taking advantage of the nationwide Swedish Family-Cancer Database. Although there is some evidence that a socio-economic gradient in cancer survival is present in Sweden, it is still unclear to what extent the socio-economic status influences other disease specific mortalities in a country with universal health care [21, 22, 3, 8, 11]. It is important to notice here that the structure of the Database (Swedes born after 1931 with their biological parents), together with the restriction of the analyses to individuals aged 30–60 years in the Swedish census of 1960, resulted in a study population where all individuals were parents.

            Methods

            The present study was based on the 2006 update of the Swedish Family-Cancer Database [23]. Statistics Sweden created this family database in 1995 by linking information from national censuses, the Multigeneration Register and the Swedish Causes of Death Register, to the Swedish Cancer Registry using an individually unique national registration number. The present study included women and men who were residing in Sweden in 1960, i.e. present at the 1960 national census. In order to adequately describe the socio-economic status, the individuals' age at the beginning of the follow-up period ranged from 30 to 60 years (birth year between 1900 and 1930). Information on socio-economic status was available for 95% of the individuals. In the Swedish census of population of 1960, socio-economic status was categorized into nine levels [24, 25]: blue-collar worker, service worker, farmhand (employee in agriculture), farmer (employer in agriculture), white-collar worker, employer, entrepreneur (company owners in industrial, trade, transport and service sectors), professional (physicians, solicitors etc.) and military personnel.

            Information on the main underlying and up to ten contributing causes of death was derived from the Swedish Causes of Death Register. The main underlying causes of death were classified in the four major groups: cardiovascular, respiratory, endocrine-nutritional-metabolic diseases and cancer. Underlying causes of death were coded according to the International Classification of Disease (ICD) revision seven to ten, depending on the year of death [2629]. In contrast with cause-specific mortality, overall mortality included any cause of death. Mortality due to cardiovascular disease included all deaths due to any cardiovascular disease and was further subdivided into ischaemic heart, cerebrovascular and other forms of heart diseases. Death due to cancer included all deaths due to any cancer and was further subdivided into the following types of cancer: lung, colorectal, stomach, pancreatic, breast and prostate cancers. Respiratory system specific mortality included all deaths due to any respiratory disease and was further subdivided into chronic obstructive pulmonary disease (COPD), influenza and pneumonia. Diabetes was considered a separate group within endocrine, nutritional and metabolic diseases.

            The association between overall and disease specific mortality and socio-economic status was investigated by comparing mortality rates/hazard rates between socio-economic groups. These results were summarized by mortality rate ratios/hazard ratios (HRs) using a Cox regression model with age as the underlying time scale (for simplicity later in the text also referred to as mortality) [30]. Analyses were implemented using the PHREG procedure of SAS Version 9.1. Follow-up started for each individual in January 1, 1961. Censoring events were defined as emigration, end of the study (December 31, 2003) or death of any other cause than the investigated disease. Regression models were separately fitted for each cause of death and each sex, and they included the covariates residential area (big cities, south or north of Sweden), time period (by five year categories from 1961 to 2004) and, for women, fertility history (parity and age at first birth). Female and male blue-collar workers constituted the largest socio-economic group and were therefore used as reference category. Spearman correlation coefficients were used to determine the degree of similarity between findings of female and male socio-economic groups separately for each disease.

            Spearman correlation coefficients were used as a measure of the within-group similarity in HRs related to different diseases. An estimated correlation value close to one would indicate homogeneous HRs for the investigated causes of deaths within a determined socio-economic group. Relationships between the socio-economic groups were visualized in a hierarchical clustering dendrogram of their average profiles (HCLUST function in R Version 2.5.1[31], average linkage method with 1 – Spearman correlation as distance).

            Appropriate human subjects approval and consent forms for the group to construct the database have been secured from the Ethics Committee of the Huddinge University Hospital, Karolinska Institutet. Permit number: Dnr 12/00, March 27, 2000.

            Results

            The number of women and men at risk and the number of fatalities by socio-economic status are shown in Table 1. Overall, more than one million women and more than one million men were followed-up; 579,288 women and 760,964 men died during the follow-up period. Female and male overall and cause-specific mortality rate ratios/HRs according to socio-economic status are presented in Table 1 to Table 6.
            Table 1

            Eligible population and overall mortality in the Swedish population from 1960 to 2004: Number of women and men at risk of mortality, number of fatalities, age at death distribution and HRs for overall mortality according to socio-economic status in the Swedish population from 1960 to 20041.

            Overall mortality

            Women

            Men

             

            Population

            Fatalities

            Age at death3

            HR4 (95% CI)

            Population

            Fatalities

            Age at death3

            HR5 (95% CI)

            All combined 2

            1025856

            579288

            78 (56;92)

            NA

            1060370

            760964

            75 (54;89)

            NA

            Blue-collar

            worker

            356190

            208011

            78 (55;91)

            Ref.

            465577

            341027

            74 (54;89)

            Ref.

            Service worker

            73132

            48862

            79 (57;92)

            1.02 (1.00;1.03)

            12892

            10214

            74 (54;84)

            1.04 (1.02;1.06)

            Farmhand

            35122

            22297

            78 (56;92)

            1.07 (1.05;1.08)

            42407

            31741

            76 (56;90)

            0.94 (0.93;0.95)

            Farmer

            108518

            70149

            80 (58;93)

            0.93 (0.93;0.94)

            109169

            84544

            77 (58;91)

            0.85 (0.85;0.86)

            White-collar worker

            323311

            156288

            78 (54;92)

            0.83 (0.83;0.84)

            283398

            182977

            74 (54;89)

            0.87 (0.86;0.87)

            Employer

            22585

            11880

            80 (55;93)

            0.78 (0.76;0.79)

            27644

            19532

            75 (55;90)

            0.85 (0.83;0.86)

            Entrepreneur

            90628

            53430

            79 (56;92)

            0.90 (0.89;0.91)

            97719

            76068

            75 (55;89)

            0.99 (0.98;1.01)

            Professional

            10121

            5668

            80 (55;93)

            0.83 (0.80;0.85)

            11869

            8849

            75 (54;90)

            0.93 (0.91;0.95)

            Military personnel

            6249

            2703

            77 (53;91)

            0.83 (0.80;0.86)

            9695

            6012

            74 (52;88)

            0.83 (0.81;0.85)

            1 Bold type, 95% CI does not include 1.00

            2 Any cause of death

            3 Median age at death with 5% and 95% quantiles

            4 Adjusted for residential area, time period and fertility history

            5 Adjusted for residential area and time period

            HR Hazard Ratio, CI Confidence Interval, NA not applicable

            Table 2

            Cardiovascular disease mortality: Number of fatalities and HRs for cardiovascular disease according to socio-economic status for women and men in Sweden from 1960 to 20041

            Cardiovascular disease

            Women

            Men

             

            Fatalities

            HR3 (95% CI)

            Fatalities

            HR4 (95% CI)

            Overall cardiovascular disease 2

                

               Blue-collar worker

            102389

            Ref.

            178383

            Ref.

               Service worker

            24416

            1.01 (0.99;1.02)

            5088

            0.98 (0.96;1.01)

               Farmhand

            11641

            1.10 (1.08;1.12)

            17428

            0.96 (0.94;0.97)

               Farmer

            37276

            0.95 (0.93;0.96)

            47133

            0.88 (0.87;0.89)

               White-collar worker

            69740

            0.78 (0.77;0.78)

            92567

            0.86 (0.85;0.87)

               Employer

            5208

            0.68 (0.66;0.70)

            9860

            0.83 (0.81;0.84)

               Entrepreneur

            26247

            0.88 (0.87;0.89)

            40052

            0.99 (0.98;1.01)

               Professional

            2544

            0.74 (0.71;0.77)

            4378

            0.88 (0.86;0.91)

               Military personnel

            1093

            0.73 (0.69;0.78)

            2873

            0.78 (0.76;0.81)

            Ischaemic heart disease

                

               Blue-collar worker

            50393

            Ref.

            116367

            Ref.

               Service worker

            12172

            1.03 (1.01;1.05)

            3232

            0.96 (0.93;0.99)

               Farmhand

            5808

            1.10 (1.07;1.13)

            11213

            0.94 (0.92;0.96)

               Farmer

            17915

            0.92 (0.91;0.94)

            29381

            0.85 (0.84;0.86)

               White-collar worker

            32657

            0.75 (0.74;0.76)

            59120

            0.85 (0.84;0.86)

               Employer

            2310

            0.63 (0.60;0.66)

            6155

            0.81 (0.79;0.83)

               Entrepreneur

            12777

            0.88 (0.86;0.90)

            25884

            0.99 (0.98;1.01)

               Professional

            1192

            0.72 (0.68;0.76)

            2745

            0.86 (0.83;0.90)

               Military personnel

            507

            0.69 (0.64;0.76)

            1878

            0.80 (0.76;0.84)

            Cerebrovascular disease

                

               Blue-collar worker

            26634

            Ref.

            29697

            Ref.

               Service worker

            6134

            0.98 (0.95;1.01)

            850

            0.99 (0.92;1.05)

               Farmhand

            3083

            1.10 (1.06;1.14)

            3031

            0.98 (0.94;1.02)

               Farmer

            10084

            0.98 (0.96;1.01)

            8610

            0.94 (0.92;0.96)

               White-collar worker

            18556

            0.80 (0.78;0.81)

            15861

            0.89 (0.87;0.90)

               Employer

            1409

            0.72 (0.68;0.76)

            1759

            0.87 (0.83;0.91)

               Entrepreneur

            6952

            0.90 (0.87;0.92)

            6868

            1.02 (0.99;1.05)

               Professional

            656

            0.74 (0.68;0.80)

            775

            0.93 (0.86;1.00)

               Military personnel

            301

            0.77 (0.69;0.87)

            408

            0.67 (0.61;0.74)

            Other forms of heart diseases

                

               Blue-collar worker

            12925

            Ref.

            15155

            Ref.

               Service worker

            3050

            0.98 (0.94;1.02)

            496

            1.11 (1.02;1.22)

               Farmhand

            1475

            1.14 (1.08;1.20)

            1637

            1.08 (1.02;1.13)

               Farmer

            5153

            1.02 (0.99;1.05)

            4882

            1.02 (0.99;1.06)

               White-collar worker

            9355

            0.80 (0.78;0.83)

            7903

            0.83 (0.81;0.86)

               Employer

            775

            0.75 (0.70;0.81)

            902

            0.81 (0.76;0.87)

               Entrepreneur

            3380

            0.88 (0.84;0.91)

            3501

            1.00 (0.96;1.04)

               Professional

            373

            0.81 (0.73;0.89)

            406

            0.91 (0.82;0.99)

               Military personnel

            138

            0.74 (0.63;0.87)

            271

            0.82 (0.73;0.92)

            1 Bold type, 95% CI does not include 1.00

            2 ICD codes: ICD-7: 400–468, ICD-8: 390–458, ICD-9: 390–459, ICD-10: I00-I99

            3 Adjusted for residential area, time period and fertility history

            4 Adjusted for residential area and time period

            HR Hazard Ratio, CI Confidence Interval, NA not applicable

            Table 3

            Cancer mortality: Number of fatalities and HRs for cancer according to socio-economic status for women and men in Sweden from 1960 to 20041

            Cancer

            Women

            Men

             

            Fatalities

            HR3 (95% CI)

            Fatalities

            HR4 (95% CI)

            Overall cancer 2

                

               Blue-collar worker

            54394

            Ref.

            85328

            Ref.

               Service worker

            12204

            1.03 (0.99;1.04)

            2633

            1.09 (1.05;1.13)

               Farmhand

            5176

            0.99 (0.96;1.02)

            7126

            0.88 (0.86;0.91)

               Farmer

            16570

            0.94 (0.92;0.96)

            19145

            0.82 (0.81;0.84)

               White-collar worker

            47175

            0.93 (0.92;0.94)

            50442

            0.93 (0.92;0.94)

               Employer

            3432

            0.90 (0.87;0.94)

            5345

            0.93 (0.91;0.96)

               Entrepreneur

            14096

            0.96 (0.94;0.97)

            18937

            1.02 (0.99;1.04)

               Professional

            1572

            0.94 (0.89;0.99)

            2347

            1.00 (0.96;1.04)

               Military personnel

            890

            0.94 (0.87;1.01)

            1807

            0.96 (0.91;1.01)

            Lung cancer

                

               Blue-collar worker

            4410

            Ref.

            16263

            Ref.

               Service worker

            1152

            1.25 (1.17;1.33)

            495

            1.06 (0.97;1.16)

               Farmhand

            329

            0.84 (0.75;0.94)

            948

            0.66 (0.62;0.70)

               Farmer

            773

            0.63 (0.58;0.68)

            1773

            0.43 (0.41;0.45)

               White-collar worker

            4201

            0.93 (0.89;1.00)

            8500

            0.80 (0.78;0.82)

               Employer

            297

            0.92 (0.82;1.04)

            825

            0.79 (0.77;0.81)

               Entrepreneur

            1104

            0.96 (0.90;1.03)

            3315

            0.95 (0.92;0.99)

               Professional

            136

            0.98 (0.83;1.17)

            406

            0.89 (0.81;0.98)

               Military personnel

            89

            1.01 (0.82;1.25)

            355

            0.96 (0.87;1.07)

            Stomach Cancer

                

               Blue-collar worker

            2944

            Ref.

            7014

            Ref.

               Service worker

            719

            1.08 (0.99;1.18)

            212

            1.03 (0.90;1.19)

               Farmhand

            330

            1.10 (0.98;1.23)

            711

            1.00 (0.93;1.08)

               Farmer

            1071

            1.03 (0.96;1.11)

            1942

            0.96 (0.91;1.01)

               White-collar worker

            2092

            0.80 (0.76;0.85)

            3082

            0.74 (0.71;0.78)

               Employer

            145

            0.72 (0.61;0.85)

            265

            0.58 (0.52;0.66)

               Entrepreneur

            753

            0.93 (0.86;1.00)

            1444

            0.92 (0.87;0.98)

               Professional

            77

            0.85 (0.67;1.06)

            107

            0.55 (0.46;0.67)

               Military personnel

            28

            0.61 (0.42;0.88)

            119

            0.86 (0.71;1.03)

            1 Bold type, 95% CI does not include 1.00

            2 ICD codes: ICD-7: 140–239, ICD-8: 140–239, ICD-9: 140–239, ICD-10: C00-D48

            3 Adjusted for residential area, time period and fertility history

            4 Adjusted for residential area and time period

            HR Hazard Ratio, CI Confidence Interval, NA not applicable

            Table 4

            Cancer mortality (continued): Number of fatalities and HRs for cancer according to socio-economic status for women and men in Sweden from 1960 to 20041

            Cancer

            Women

            Men

             

            Fatalities

            HR2 (95% CI)

            Fatalities

            HR3 (95% CI)

            Colorectal Cancer

                

               Blue-collar worker

            6527

            Ref.

            9186

            Ref.

               Service worker

            1367

            0.96 (0.91;1.02)

            297

            1.14 (1.02;1.28)

               Farmhand

            620

            0.98 (0.91;1.07)

            854

            0.98 (0.92;1.05)

               Farmer

            2080

            0.94 (0.90;0.99)

            2201

            0.86 (0.82;0.90)

               White-collar worker

            5705

            0.95 (0.91;1.00)

            5789

            1.00 (0.97;1.04)

               Employer

            434

            0.95 (0.86;1.05)

            635

            1.04 (0.96;1.13)

               Entrepreneur

            1736

            0.97 (0.92;1.02)

            2155

            1.07 (1.02;1.12)

               Professional

            188

            0.92 (0.80;1.07)

            284

            1.13 (0.99;1.27)

               Military personnel

            119

            1.06 (0.88;1.27)

            185

            0.92 (0.79;1.06)

            Pancreatic Cancer

                

               Blue-collar worker

            3922

            Ref.

            5179

            Ref.

               Service worker

            918

            1.08 (0.99;1.16)

            166

            1.13 (0.97;1.32)

               Farmhand

            368

            0.97 (0.87;1.08)

            466

            0.94 (0.85;1.03)

               Farmer

            1250

            0.96 (0.91;1.03)

            1184

            0.86 (0.80;0.91)

               White-collar worker

            3501

            0.96 (0.92;1.02)

            3260

            1.00 (0.96;1.05)

               Employer

            279

            1.00 (0.88;1.13)

            357

            1.07 (0.96;1.19)

               Entrepreneur

            1010

            0.94 (0.88;1.01)

            1255

            1.12 (1.05;1.19)

               Professional

            103

            0.85 (0.70;1.03)

            142

            1.01 (0.85;1.20)

               Military personnel

            60

            0.88 (0.68;1.14)

            111

            0.98 (0.81;1.19)

            Breast Cancer

               

            NA

               Blue-collar worker

            7686

            Ref.

              

               Service worker

            1578

            0.96 (0.91;1.01)

              

               Farmhand

            660

            0.91 (0.84;0.98)

              

               Farmer

            2450

            1.00 (0.96;1.05)

              

               White-collar worker

            7708

            1.05 (1.01;1.08)

              

               Employer

            579

            1.09 (1.00;1.18)

              

               Entrepreneur

            2086

            1.01 (0.96;1.06)

              

               Professional

            264

            1.12 (0.99;1.27)

              

               Military personnel

            127

            0.98 (0.82;1.14)

              

            Prostate Cancer

             

            NA

              

               Blue-collar worker

              

            14844

            Ref.

               Service worker

              

            473

            1.12 (1.02;1.23)

               Farmhand

              

            1456

            1.00 (0.94;1.05)

               Farmer

              

            4783

            1.10 (1.06;1.14)

               White-collar worker

              

            9578

            1.02 (1.00;1.05)

               Employer

              

            1091

            1.05 (0.99;1.14)

               Entrepreneur

              

            3415

            1.03 (0.99;1.07)

               Professional

              

            465

            1.12 (1.00;1.23)

               Military personnel

              

            338

            1.02 (0.92;1.14)

            1 Bold type, 95% CI does not include 1.00

            2 Adjusted for residential area, time period and fertility history

            3 Adjusted for residential area and time period

            HR Hazard Ratio, CI Confidence Interval, NA not applicable

            Table 5

            Respiratory disease mortality: Number of fatalities and HRs for respiratory disease according to socio-economic status for women and men in Sweden from 1960 to 20041

            Respiratory disease

            Women

            Men

             

            Fatalities

            HR3 (95% CI)

            Fatalities

            HR4 (95% CI)

            Overall respiratory disease 2

                

               Blue-collar worker

            12654

            Ref.

            23233

            Ref.

               Service worker

            3211

            1.06 (1.02;1.10)

            759

            1.09 (1.01;1.17)

               Farmhand

            1361

            1.09 (1.03;1.16)

            2031

            0.88 (0.84;0.92)

               Farmer

            4057

            0.88 (0.85;0.92)

            5449

            0.76 (0.74;0.78)

               White-collar worker

            9259

            0.79 (0.79;0.81)

            10901

            0.75 (0.73;0.77)

               Employer

            797

            0.80 (0.75;0.86)

            1168

            0.67 (0.64;0.72)

               Entrepreneur

            3216

            0.88 (0.84;0.91)

            4727

            0.88 (0.86;0.91)

               Professional

            398

            0.90 (0.81;0.99)

            573

            0.82 (0.76;0.89)

               Military personnel

            214

            1.10 (0.96;1.26)

            352

            0.72 (0.64;0.80)

            COPD

                

               Blue-collar worker

            3559

            Ref.

            8552

            Ref.

               Service worker

            944

            1.21 (1.12;1.30)

            303

            1.22 (1.09;1.37)

               Farmhand

            299

            0.93 (0.83;1.05)

            688

            0.87 (0.80;0.94)

               Farmer

            539

            0.51 (0.47;0.56)

            1418

            0.58 (0.55;0.62)

               White-collar worker

            2888

            0.80 (0.76;0.84)

            3967

            0.71 (0.68;0.73)

               Employer

            251

            0.90 (0.79;1.03)

            387

            0.61 (0.55;0.68)

               Entrepreneur

            831

            0.86 (0.80;0.93)

            1613

            0.85 (0.80;0.89)

               Professional

            115

            0.95 (0.79;1.15)

            194

            0.78 (0.67;0.89)

               Military personnel

            82

            1.17 (0.94;1.46)

            126

            0.64 (0.54;0.76)

            Influenza and Pneumonia

                

               Blue-collar worker

            6800

            Ref.

            10845

            Ref.

               Service worker

            1716

            1.01 (0.96;1.07)

            348

            1.03 (0.93;1.15)

               Farmhand

            832

            1.17 (1.09;1.26)

            1048

            0.92 (0.87;0.99)

               Farmer

            2793

            1.00 (0.96;1.05)

            3191

            0.87 (0.84;0.91)

               White-collar worker

            4728

            0.79 (0.76;0.82)

            5317

            0.81 (0.79;0.84)

               Employer

            417

            0.78 (0.70;0.86)

            620

            0.75 (0.69;0.81)

               Entrepreneur

            1801

            0.88 (0.83;0.93)

            2373

            0.93 (0.88;0.97)

               Professional

            222

            0.90 (0.79;1.03)

            298

            0.89 (0.79;0.99)

               Military personnel

            93

            1.05 (0.86;1.29)

            158

            0.75 (0.64;0.88)

            1 Bold type, 95% CI does not include 1.00

            2 ICD codes: ICD-7: 470–527, ICD-8: 460–519, ICD-9: 460–519, ICD-10: J00-J99

            3 Adjusted for residential area, time period and fertility history

            4 Adjusted for residential area and time period

            HR Hazard Ratio, CI Confidence Interval, NA not applicable

            Table 6

            Endocrine, nutritional and metabolic disease mortality: Number of fatalities and HRs for endocrine, nutritional and metabolic disease according to socio-economic status for women and men in Sweden from 1960 to 20041

            Endocrine nutritional and metabolic diseases

            Women

            Men

             

            Fatalities

            HR3 (95% CI)

            Fatalities

            HR4 (95% CI)

            Overall endocrine nutritional and metabolic diseases 2

                

               Blue-collar worker

            5473

            Ref.

            5458

            Ref.

               Service worker

            1159

            0.94 (0.88;1.00)

            195

            1.28 (1.11;1.48)

               Farmhand

            697

            1.21 (1.12;1.31)

            582

            1.06 (0.97;1.15)

               Farmer

            1797

            0.86 (0.81;0.91)

            1440

            0.91 (0.87;0.98)

               White-collar worker

            2674

            0.56 (0.54;0.59)

            2754

            0.81 (0.77;0.85)

               Employer

            163

            0.42 (0.36;0.49)

            288

            0.81 (0.72;0.91)

               Entrepreneur

            1153

            0.74 (0.69;0.79)

            1347

            1.12 (1.06;1.19)

               Professional

            91

            0.51 (0.42;0.63)

            132

            0.91 (0.77;1.08)

               Military personnel

            45

            0.54 (0.40;0.72)

            67

            0.56 (0.44;0.72)

            Diabetes Mellitus

                

               Blue-collar worker

            4651

            Ref.

            4463

            Ref.

               Service worker

            976

            0.93 (0.87;1.00)

            167

            1.35 (1.15;1.57)

               Farmhand

            586

            1.20 (1.10;1.30)

            486

            1.09 (0.99;1.19)

               Farmer

            1525

            0.85 (0.80;0.90)

            1191

            0.93 (0.87;0.99)

               White-collar worker

            2056

            0.51 (0.49;0.54)

            2211

            0.79 (0.75;0.83)

               Employer

            109

            0.33 (0.27;0.40)

            232

            0.80 (0.70;0.91)

               Entrepreneur

            935

            0.70 (0.65;0.75)

            1133

            1.16 (1.08;1.24)

               Professional

            69

            0.46 (0.36;0.58)

            107

            0.91 (0.75;1.10)

               Military personnel

            30

            0.42 (0.30;0.61)

            52

            0.53 (0.40;0.69)

            1 Bold type, 95% CI does not include 1.00

            2 ICD codes: ICD-7: 240–289, ICD-8: 240–279, ICD-9: 240–279, ICD-10: E00-E90

            3 Adjusted for residential area, time period and fertility history

            4 Adjusted for residential area and time period

            HR Hazard Ratio, CI Confidence Interval, NA not applicable

            For both overall and cause-specific mortalities, the mortality of women showed a statistically significant association with socio-economic status. Compared with blue-collar workers, the HR for overall mortality was significantly elevated among farmhands (HR = 1.07, 95% CI 1.05–1.08) and it was particularly decreased in employers (HR = 0.78, 95% CI 0.76–0.79) and professionals (HR = 0.83, 95% CI 0.80–0.85). For men, service workers showed a significantly increased overall mortality (HR = 1.04, 95% CI 1.02–1.06) compared to the reference group blue-collar workers, whereas farmhands (HR = 0.94, 95% CI 0.93–0.95), farmers (HR = 0.85, 95% CI 0.85–0.86), white-collar workers (HR = 0.87, 95% CI 0.86–0.87), employers (HR = 0.85, 95% CI 0.83–0.86), professionals (HR = 0.93, 95% CI 0.91–0.95) and military personnel (HR = 0.83, 95% CI 0.81–0.85) showed a significantly decreased mortality (Table 1).

            For overall cardiovascular disease mortality in women, farmhands were at a significantly increased risk (HR = 1.10, 95% CI 1.08–1.12) and employers showed the lowest risk (HR = 0.68, 95% CI 0.66–0.70). For men, farmhands, farmers, white-collar workers, employers, professionals and military personnel showed significantly decreased overall cardiovascular disease mortality. A similar pattern among women was observed for ischaemic heart disease. In men, compared to blue-collar workers, service workers, farmhands, farmers, white-collar workers, employers, professionals and military personnel showed significantly decreased ischaemic heart disease mortality. For cerebrovascular disease in women, farmhand showed the highest mortality compared to the lowest mortality among employers. In men, farmers, white-collar workers, employers, professionals and military personnel showed significantly decreased mortality. A socio-economic gradient was also noticeable for other forms of heart disease in women and in men (Table 2).

            For overall cancer mortality in women, farmers, white-collar workers, employers, entrepreneurs and professionals showed significantly decreased mortality. For overall cancer mortality, we observed the highest mortality in men among service workers and the lowest mortality among farmers. A socio-economic gradient was also noticeable for lung cancer and stomach cancer in men and women. In women, only farmers were at a significantly decreased mortality due to colorectal cancer. Similarly in men, farmers showed the only decreased mortality and entrepreneurs the only increased mortality. For pancreatic cancer, no significant association between socio-economic status and mortality among women was observed. In men, farmers showed the only decreased mortality and entrepreneurs the only increased mortality due to pancreatic cancer. In contrast to other causes of death, increased mortality due to breast cancer in women was observed among white-collar workers and decreased mortality among farmhands. Increased mortality due to prostate cancer in men was observed among farmers and service workers (Table 3 and Table 4).

            For overall respiratory disease, the highest mortality in women was observed among farmhand and the lowest in white-collar workers and employers. A socio-economic gradient was also observed among men. COPD showed large differences in mortality among female socio-economic groups (a 21% increase for service workers versus a 49% decrease in farmers) and male socio-economic groups (a 22% increase for service workers versus a 42% decrease in farmers). For influenza and pneumonia in women, farmhands showed the highest mortality and employers showed the lowest mortality. In men, farmhands, farmers, white-collar workers, employers, entrepreneurs, professionals and military personnel showed significantly decreased mortality (Table 5).

            The socio-economic status was also significantly associated with mortality related to overall endocrine, nutritional and metabolic diseases in women and men. For example in women, employers showed a 58% decrease in mortality and farmhands an increase in mortality of 21%, both compared to blue-collar workers. In men, service workers showed a 28% mortality increase compared to a 44% mortality decrease for military personnel. A similar pattern was observed for diabetes mellitus in women with a 67% decreased mortality for employers and a 20% increased mortality for farmhands. For men, service workers showed a 35% increase in mortality and military personnel showed a 47% decrease in mortality (Table 6).

            Spearman correlation coefficients between HRs for women and men according to disease from Tables 1 to Table 6 are shown in Table 7. Only socio-economic groups with significant HRs in at least one gender were included. Furthermore, the reference category of blue-collar workers was not included in the analysis. Significant correlation coefficients were observed for overall cardiovascular disease, ischaemic heart disease, cerebrosvascular disease and other forms of heart disease. Further, only lung cancer, overall endocrine, nutritional and metabolic diseases showed a significant correlation between men and women (Table 7).
            Table 7

            Comparison between women and men: Correlation between HRs for women and men according to disease1

            Disease

            Spearman coefficient

            p value

            Overall mortality

            0.60

            0.115

            Cardiovascular disease

            0.77

            0.041

               Ischaemic heart disease

            0.76

            0.028

               Cerebrovascular disease

            0.71

            0.048

               Other forms of heart diseases

            0.92

            0.003

            Cancer

            0.57

            0.136

               Lung cancer

            0.96

            <0.001

               Colorectal cancer

            0.50

            0.667

               Stomach cancer

            0.87

            0.873

               Pancreatic cancer

            -0.50

            0.667

            Respiratory disease

            0.34

            0.405

               COPD

            0.55

            0.160

               Influenza and pneumonia

            0.27

            0.558

            Endocrine, nutritional and metabolic diseases

            0.69

            0.05

               Diabetes Mellitus

            0.76

            0.028

            1 Socio-economic groups with significant HRs in at least one gender are included

            The results of the Spearman correlation analysis and the dendrograms showing average-linkage hierarchical clustering of female and male socio-economic groups are shown in Figure 1 and Figure 2. The distance metric used was 1-r, where r is the Spearman correlation coefficient between socio-economic groups.
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2458-8-340/MediaObjects/12889_2008_Article_1310_Fig1_HTML.jpg
            Figure 1

            Spearman correlation and clustering of HRs for women. Spearman correlation analysis between HRs (r, p value) and hierarchical clustering dendogram with correlation-based distance (1-r) for women.

            http://static-content.springer.com/image/art%3A10.1186%2F1471-2458-8-340/MediaObjects/12889_2008_Article_1310_Fig2_HTML.jpg
            Figure 2

            Spearman correlation and clustering of HRs for men. Spearman correlation analysis between HRs (r, p value) and hierarchical clustering dendogram with correlation-based distance (1-r) for men.

            Discussion

            This population-based study showed significant differences in mortality by socio-economic status for both men and women in the Swedish population. Socio-economic mortality differences in women were the most profound for lung cancer, COPD, overall endocrine, nutritional and metabolic disease, and diabetes mellitus, with the lowest HR for diabetes mellitus among female employers and the highest HR for lung cancer among female service workers. In men mortality differences were the most profound for lung cancer, COPD, overall endocrine, nutritional and metabolic disease, and diabetes mellitus, with the lowest HR for lung cancer among male farmers and the highest HR for diabetes mellitus among male service workers.

            In our analysis we used overall and cause-specific mortality as the outcome. Some other studies have investigated socio-economic variation in survival following the diagnosis of a specific disease, such as cancer [17, 21, 3239], ischaemic heart disease [4042] or stroke [43, 42]. These studies generally reported improved survival in many countries for individuals with higher socio-economic status, compared to individuals with lower socio-economic status. Several possible explanations for the observed survival differences among socio-economic groups have been proposed, such as earlier diagnosis in individuals with higher socio-economic status, consequently leading to a longer interval between diagnosis and death (lead time bias) [19, 44, 18, 17], and differences in treatment among socio-economic groups [38, 17]. On the other hand, some studies showed no or only a small relationship between socio-economic status and stage at diagnosis [4547]. Additionally, adverse behavioural and lifestyle aspects have been reported to be more common among more deprived groups [48, 49].

            Table 8 provides a short summary of the causes of death discussed in the following section along with the socio-economic groups with the highest and lowest observed mortality risk and potential determinants. The socio-economic status was strongly related to overall cardiovascular disease and ischaemic heart disease, showing decreased mortality risk for both male and female farmers, white-collar workers, employers, entrepreneurs, professionals and military personnel. In contrast to female service workers and farmhands, male service workers and farmhands were at decreased mortality risk from overall cardiovascular disease and ischaemic heart disease. Socio-economic inequalities in mortality from overall cardiovascular disease and ischaemic heart disease were also reported by previous studies [1, 4, 3, 41, 7]. Our results might reflect different distributions of risk factors for overall cardiovascular disease and ischaemic heart disease, varying among female and male socio-economic groups.
            Table 8

            Relationship between causes of death and socio-economic groups: Summary of the relationship between causes of death, female and male socio-economic groups with highest and (↑) and lowest (↓) mortality and potential determinants. Please refer to the text for a more detailed discussion.

            Cause of death

            Socio-economic group with highest/lowest mortality risk

            Potential determinates

            Cardiovascular disease

            female farmhand ↑

            female employer ↓

            male military personnel ↓1

            Risk factors (high blood pressure, smoking, physical inactivity and obesity) varying among female and male socio-economic groups

            Lung cancer

            female service worker ↑

            female farmer ↓

            male farmer ↓1

            Smoking

            Prostate cancer

            male service worker ↑1

            Low PSA screening

            Breast cancer

            female white-collar worker ↑

            female farmhand ↓

            Access to screening, stage at diagnosis, reproductive history, age at first parturition, hormone replacement therapy

            Overall respiratory disease

            female farmhand ↑

            female white-collar worker ↓

            male service worker ↑

            male military personnel ↓

            Smoking, air pollution, allergens

            COPD

            female/male service worker ↑

            female/male farmer ↓

            Smoking, exposure to pollutants, allergies and asthma

            Overall endocrine nutritional and metabolic disease/diabetes mellitus

            female farmhand ↑

            female employer ↓

            male service worker ↑

            male military personnel ↓

            Overweight, smoking, physical inactivity

            1 With blue-collar worker as reference category no other group showed increased/decreased mortality risk

            The association between lung cancer and socio-economic status differed for women and men. Female service workers were at an increased risk and female farmhands and farmers were at a decreased risk of lung cancer mortality. We equally observed a decreased risk among men except for service worker and military personnel. As smoking is a major risk factor for lung cancer [60], our observed differences might reflect changing patterns of smoking among socio-economic groups and among men and women in Sweden [12, 6].

            We observed increased mortality due to prostate cancer among male farmers and service workers, compared to male blue-collar workers. No further socio-economic group was at a significant risk of prostate cancer mortality. For prostate and breast cancer higher rates in incidence and mortality has been reported among more advantageous groups [13, 4953]. It was argued that greater take up of PSA screening and earlier detection of lesions among men with higher socio-economic status or education (lead time bias) could at least partly explain the inverse gradient observed in prostate cancer incidence [51, 38]. In an earlier case-control study in Sweden, socio-economic status was not associated with risk of prostate cancer [54], and also other studies reported no clear association [55, 56]. Many factors have been associated with prostate cancer that were not recorded in the national censuses, including obesity, physical exercise, diet, tobacco use, and alcohol use [57, 58], which could partly explain our results.

            Although delayed childbearing and nulliparity are known to be high risk factors for breast cancer [59], only a few studies have assessed the impact of fertility history on the inverse socio-economic gradient observed in breast cancer incidence and mortality [3, 53, 13, 2]. After adjusting for fertility history we observed an increased mortality due to breast cancer among white-collar workers and a decreased mortality among service workers and farmhands. A previous Swedish study reported a significant inverse socio-economic gradient, still being significant after controlling for fertility history [3]. In a recent Danish study breast cancer incidence was higher in women with higher socio-economic status than in women with lower socio-economic status, which also persisted after adjusting for fertility history. However, there was a less pronounced gradient in breast cancer mortality and after controlling for fertility history none of the variations by socio-economic group remained significant [53]. On the other hand, Strand et al. observed that after adjusting for fertility history the educational gradient in breast cancer mortality among parous women disappeared. They concluded that fertility history or factors associated with it can fully explain the educational differences in breast cancer mortality among parous women in Norway. Further on they concluded that for nulliparous women, other factors than fertility history must explain the educational gradient observed [13].

            Large differences in mortality between socio-economic groups were also found for overall respiratory disease and COPD among men and women. While female farmhands showed a slightly increased mortality due to overall respiratory disease, male farmhands showed a decreased mortality due to overall respiratory disease and COPD. A marked socio-economic gradient in overall respiratory disease and COPD, which was stronger in males and independent of smoking, was also previously reported by two Danish studies [10, 20]. The gradient may be partly explained by differences in environmental and occupational exposure of women and men and among different socio-economic groups [10, 61]. Lung cancer and COPD share the same common risk factors, which include smoking, genetic predisposition, and environmental and occupational exposures [62, 63]. Among women we observed a significant correlation between mortality risk due to COPD and lung cancer (Spearman correlation coefficient = 0.79, p = 0.021), but not among men (Spearman correlation coefficient = 0.50, p = 0.207). This suggests a similar influence of the socio-economic status on COPD and lung cancer mortality among women, but not among men.

            A marked socio-economic gradient was also observed for overall endocrine, nutritional and metabolic diseases, and diabetes mortality among women and men. Differences in HRs between socio-economic groups were more pronounced for women than for men. Large socio-economic inequalities in the prevalence and mortality of diabetes were also previously reported [6467]. The explanation for this socio-economic gradient is unclear, but probably reflects increased exposure to lifestyle and environmental risk factors of diabetes for people with lower socio-economic status [64, 66]. As type 2 diabetes and cardiovascular disease share common risk factors such as body mass index, physical activity, alcohol intake, and cigarette smoking [64, 68, 66], a significant correlation between mortality risk due to diabetes mellitus and cardiovascular disease was observed in women (Spearman correlation coefficient = 1.00, p < 0.001) and men (Spearman correlation coefficient = 0.95, p < 0.001), suggesting that socio-economic status is associated with a certain lifestyle in women and men.

            The correlation analysis encompassed the most common diseases in the Swedish Family-Cancer Database, suggesting similar influence of the socio-economic status on cause-specific mortality, such as cardiovascular diseases, lung cancer, endocrine, endocrine, nutritional and metabolic diseases. The cluster analysis grouped female and male socio-economic groups with similar mortality risks. For both women and men farmer and farmhand as well as white-collar worker and employer were considered the most similar in mortality. These results might help to identify further differences in cause-specific mortalities between female and male socio-economic groups.

            The present population-based study had several methodological strengths. First, it encompassed over 2 million men and women, and covered a follow-up period of over 40 years. Second, reporting bias was eliminated as all variables were based on register data from the Swedish Family-Cancer Database. As census forms are individually filled out, however, they may contain small inaccuracies. Further, registration of causes of death was highly complete and was obtained from death certificates from the Swedish Causes of Death Register. However, some limitations must be considered when interpreting the present results. Although the socio-economic group blue-collar worker was used as reference category in each model, the job/function description might differ by sex. This issue should be considered when comparing findings for women and men. Most importantly, the lack of potential risk factors not included in the national registries, such as use of hormonal contraceptives, age at menarche, alcohol consumption, body mass index, tobacco use, physical activity etc., preclude the estimation of their effect on overall and cause-specific mortality. Further, it is important to point out here that the structure of the Swedish Family-Cancer Database (Swedes born after 1931 with their biological parents), together with the restriction of the analyses to individuals aged 30–60 years in 1960, resulted in a study population were all individuals were parents and probably excluded adults with severe health problems. Furthermore, we were not able to investigate the socio-economic influence for nulliparous women. In order to account for changes in treatment over time, we adjusted our models for time period. Comparisons with other results on mortality must also take into account that our study population only consisted of individuals with a documented socio-economic status and probably did not include unemployed individuals.

            The examination of the Database showed that about 70% of the individuals belonged to the same socio-economic group in the 1960 and the 1970 censuses. To evaluate the impact of the change in socio-economic position on the calculated HRs, we replicated the analyses for the first 10 years of follow-up (1960 to 1970). As expected, the relationship between socio-economic status and mortality was stronger during the first 10 years of follow-up for most causes of death. For example, the HR for overall respiratory disease in female farmhand was HR = 1.44, 95% CI 1.07–1.95 in the first 10 years, compared to HR = 1.09 (95% CI 1.03–1.16) for the whole period of follow-up. The HR for any cause of death in male service worker was 1.12 (95% CI 1.05–1.19) for the first 10 years, compared to HR = 1.04 (95% CI 1.02–1.06) for the whole period. The correlation between the HRs restricted to the first ten years and the estimates for the overall period were 0.7 (p < 0.001) for women and 0.4 (p = 0.02) for men. Another limitation due to space was the consideration of any age at death. It is important to mention here that the HRs may vary with age due to changes in incidence, age-related prognosis and treatment. For example, in contrast to mortality due to breast cancer at any age, no significant relationship was observed between socio-economic status and mortality before age 50 years (results not shown here). A recent article [16] shows that socio-economic inequalities in breast cancer mortality disappeared between 1968 and 1996 in France. The investigation of the temporal trends in socio-economic differences of mortality warrants further investigation.

            Conclusion

            The present study shows that in Sweden, a country with in principal universal access to health care, socio-economic status is significantly associated with overall and cause-specific mortality risk and social inequalities exist. Using the Swedish Family-Cancer Database we were able to investigate more specific causes of death than have been typically reported. Our results might reflect different behavioral and lifestyle aspects and different exposure to occupational and environmental factors among socio-economic groups with elevated overall and cause-specific mortality. Comparison of overall and cause-specific mortality among female and male socio-economic groups may provide helpful insights into the underlying causes of socio-economic inequalities in mortality. In addition, further research is needed to confirm our results and to identify specific factors related to increased mortality in specific socio-economic groups. These factors will help to prevent higher mortality among more deprived socio-economic groups in Sweden.

            Abbreviations

            HR: 

            Hazard Ratio

            CI: 

            Confidence Interval

            COPD: 

            Chronic Obstructive Pulmonary Disease

            ICD: 

            International Classification of Disease.

            Declarations

            Acknowledgements

            Supported by the Baden-Württemberg Network of Aging Research (NAR), Deutsche Krebshilfe, the Swedish Cancer Society and The Swedish Council for Working Life and Social Research. The used database was created by linking registers maintained at Statistics Sweden and the Swedish Cancer Registry.

            Authors’ Affiliations

            (1)
            Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ)
            (2)
            Center for Family and Community Medicine, Karolinska Institute

            References

            1. Huisman M, Kunst AE, Bopp M, Borgan JK, Borrell C, Costa G, Deboosere P, Gadeyne S, Glickman M, Marinacci C, Minder C, Regidor E, Valkonen T, Mackenbach JP: Educational inequalities in cause-specific mortality in middle-aged and older men and women in eight western European populations. Lancet 2005, 365 (9458) : 493–500.PubMed
            2. Strand BH, Kunst A, Huisman M, Menvielle G, Glickman M, Bopp M, Borell C, Borgan JK, Costa G, Deboosere P, Regidor E, Valkonen T, Mackenbach JP, on Socioeconomic Inequalities in Health EUWG: The reversed social gradient: higher breast cancer mortality in the higher educated compared to lower educated. A comparison of 11 European populations during the 1990s. Eur J Cancer 2007, 43 (7) : 1200–1207.View ArticlePubMed
            3. Cabrera C, O H, Wedel H, Björkelund C, Bengtsson C, Lissner L: Socioeconomic status and mortality in Swedish women: opposing trends for cardiovascular disease and cancer. Epidemiology 2001, 12 (5) : 532–536.View ArticlePubMed
            4. Mackenbach JP, Cavelaars AE, Kunst AE, Groenhof F: Socioeconomic inequalities in cardiovascular disease mortality; an international study. Eur Heart J 2000, 21 (14) : 1141–1151.View ArticlePubMed
            5. Mackenbach JP, Bos V, Andersen O, Cardano M, Costa G, Harding S, Reid A, Hemström O, Valkonen T, Kunst AE: Widening socioeconomic inequalities in mortality in six Western European countries. Int J Epidemiol 2003, 32 (5) : 830–837.View ArticlePubMed
            6. Mackenbach JP, Huisman M, Andersen O, Bopp M, Borgan JK, Borrell C, Costa G, Deboosere P, Donkin A, Gadeyne S, Minder C, Regidor E, Spadea T, Valkonen T, Kunst AE: Inequalities in lung cancer mortality by the educational level in 10 European populations. Eur J Cancer 2004, 40: 126–135.View ArticlePubMed
            7. Kunst AE, Groenhof F, Mackenbach JP, Health EW: Occupational class and cause specific mortality in middle aged men in 11 European countries: comparison of population based studies. EU Working Group on Socioeconomic Inequalities in Health. BMJ 1998, 316 (7145) : 1636–1642.PubMed
            8. Rosvall M, Chaix B, Lynch J, Lindström M, Merlo J: Contribution of main causes of death to social inequalities in mortality in the whole population of Scania, Sweden. BMC Public Health 2006, 6: 79.View ArticlePubMed
            9. Howard G, Anderson RT, Russell G, Howard VJ, Burke GL: Race, Socioeconomic Status, and Cause-Specific Mortality. Annals of Epidemiology 2000, 10 (4) : 214–223.View ArticlePubMed
            10. Prescott E, Godtfredsen N, Vestbo J, Osler M: Social position and mortality from respiratory diseases in males and females. Eur Respir J 2003, 21 (5) : 821–826.View ArticlePubMed
            11. Hemminki K, Zhang H, Czene K: Socioeconomic factors in cancer in Sweden. Int J Cancer 2003, 105 (5) : 692–700.View ArticlePubMed
            12. Adler NE, Ostrove JM: Socioeconomic status and health: what we know and what we don't. Ann N Y Acad Sci 1999, 896: 3–15.View ArticlePubMed
            13. Strand BH, Tverdal A, Claussen B, Zahl PH: Is birth history the key to highly educated women's higher breast cancer mortality? A follow-up study of 500,000 women aged 35–54. Int J Cancer 2005, 117 (6) : 1002–1006.View ArticlePubMed
            14. Heck KE, Wagener DK, Schatzkin A, Devesa SS, Breen N: Socioeconomic status and breast cancer mortality, 1989 through 1993: an analysis of education data from death certificates. Am J Public Health 1997, 87 (7) : 1218–1222.View ArticlePubMed
            15. Wagener DK, Schatzkin A: Temporal trends in the socioeconomic gradient for breast cancer mortality among US women. Am J Public Health 1994, 84 (6) : 1003–1006.View ArticlePubMed
            16. Menvielle G, Leclerc A, Chastang JF, Luce D: Social inequalities in breast cancer mortality among French women: disappearing educational disparities from 1968 to 1996. Br J Cancer 2006, 94: 152–155.View ArticlePubMed
            17. Woods LM, Rachet B, Coleman MP: Origins of socio-economic inequalities in cancer survival: a review. [http://​dx.​doi.​org/​10.​1093/​annonc/​mdj007] Ann Oncol 2006, 17: 5–19.View ArticlePubMed
            18. Banks E, Beral V, Cameron R, Hogg A, Langley N, Barnes I, Bull D, Reeves G, English R, Taylor S, Elliman J, Harris CL: Comparison of various characteristics of women who do and do not attend for breast cancer screening. Breast Cancer Res 2002, 4: R1.View ArticlePubMed
            19. Schwartz KL, Crossley-May H, Vigneau FD, Brown K, Banerjee M: Race, socioeconomic status and stage at diagnosis for five common malignancies. Cancer Causes Control 2003, 14 (8) : 761–766.View ArticlePubMed
            20. Prescott E, Vestbo J: Socioeconomic status and chronic obstructive pulmonary disease. Thorax 1999, 54 (8) : 737–741.View ArticlePubMed
            21. Hussain SK, Lenner P, Sundquist J, Hemminki K: Influence of education level on cancer survival in Sweden. Ann Oncol 2008, 19: 156–162.View ArticlePubMed
            22. Hussain SK, Altieri A, Sundquist J, Hemminki K: Influence of education level on breast cancer risk and survival in Sweden between 1990 and 2004. Int J Cancer 2008, 122: 165–169.View ArticlePubMed
            23. Hemminki K, Granström C, Sundquist J, Bermejo JL: The updated Swedish family-cancer database used to assess familial risks of prostate cancer during rapidly increasing incidence. Hereditary Cancer in Clinical Practice 2006, 4 (4) : 186–192.View ArticlePubMed
            24. Sweden S: Socioekonomisk indelning. Meddelande i Samordningsfrågor 1982[Swedish socio-economic classification, SEI. Reports on Statistical Coordination, in Swedish with an English summary]. [http://​www.​scb.​se/​gemensamma_​filer/​_​Dokument/​SEI-AGG_​Eng.​pdf] Tech Rep 4, Statistics Sweden, Stockholm 1982.
            25. Moradi T, Adami HO, Bergström R, Gridley G, Wolk A, Gerhardsson M, Dosemeci M, Nyrén O: Occupational physical activity and risk for breast cancer in a nationwide cohort study in Sweden. Cancer Causes Control 1999, 10 (5) : 423–430.View ArticlePubMed
            26. WHO: [http://​www.​health.​nsw.​gov.​au/​public-health/​icd/​icd7.​htm] Manual of the international statistical classification of diseases, injuries and causes of death: seventh revision Geneva: World Health Organization 1957.
            27. WHO: [http://​www.​health.​nsw.​gov.​au/​public-health/​icd/​icd8.​htm] Manual of the international statistical classification of diseases, injuries and causes of death: eighth revision Geneva: World Health Organization 1967.
            28. WHO: [http://​www.​health.​nsw.​gov.​au/​public-health/​icd/​icd9.​htm] Manual of the international statistical classification of diseases, injuries and causes of death: ninth revision Geneva: World Health Organization 1977.
            29. WHO: [http://​www.​who.​int/​classifications/​apps/​icd/​icd10online/​] Manual of the international statistical classification of diseases, injuries and causes of death: tenth revision Geneva: World Health Organization 1993.
            30. Cox D: Regression models and life-tables. J R Stat Soc Ser B Stat Methodol 1972, 34: 187–202.
            31. R Development Core Team: [http://​www.​R-project.​org] R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing, Vienna, Austria 2007. [ISBN 3–900051–07–0]
            32. Kogevinas M, Porta M: Socioeconomic differences in cancer survival: a review of the evidence 1997., 138:
            33. Hall SE, Holman CDJ, Wisniewski ZS, Semmens J: Prostate cancer: socio-economic, geographical and private-health insurance effects on care and survival. BJU Int 2005, 95: 51–58.View ArticlePubMed
            34. Sloggett A, Young H, Grundy E: The association of cancer survival with four socioeconomic indicators: a longitudinal study of the older population of England and Wales 1981–2000. BMC Cancer 2007, 7: 20.View ArticlePubMed
            35. Coleman MP, Rachet B, Woods LM, Mitry E, Riga M, Cooper N, Quinn MJ, Brenner H, Estève J: Trends and socioeconomic inequalities in cancer survival in England and Wales up to 2001. Br J Cancer 2004, 90 (7) : 1367–1373.View ArticlePubMed
            36. Vågerö D, Persson G: Cancer survival and social class in Sweden. J Epidemiol Community Health 1987, 41 (3) : 204–209.View ArticlePubMed
            37. Harvei S, Kravdal O: The importance of marital and socioeconomic status in incidence and survival of prostate cancer. An analysis of complete Norwegian birth cohorts. Prev Med 1997, 26 (5 Pt 1) : 623–632.View ArticlePubMed
            38. Steenland K, Henley J, Calle E, Thun M: Individual- and area-level socioeconomic status variables as predictors of mortality in a cohort of 179,383 persons. Am J Epidemiol 2004, 159 (11) : 1047–1056.View ArticlePubMed
            39. Coleman MP, Gatta G, Verdecchia A, Estève J, Sant M, Storm H, Allemani C, Ciccolallo L, Santaquilani M, Berrino F, Group EUROCAREW: EUROCARE-3 summary: cancer survival in Europe at the end of the 20th century. Ann Oncol 2003, 14 (Suppl 5) : v128-v149.View ArticlePubMed
            40. Wilhelmsen L, Rosengren A: Are there socio-economic differences in survival after acute myocardial infarction? Eur Heart J 1996, 17 (11) : 1619–1623.PubMed
            41. Rosengren A, Wedel H, Wilhelmsen L: Coronary heart disease and mortality in middle aged men from different occupational classes in Sweden. BMJ 1988, 297 (6662) : 1497–1500.View ArticlePubMed
            42. Peltonen M, Rosén M, Lundberg V, Asplund K: Social patterning of myocardial infarction and stroke in Sweden: incidence and survival. Am J Epidemiol 2000, 151 (3) : 283–292.PubMed
            43. Kapral MK, Wang H, Mamdani M, Tu JV: Effect of socioeconomic status on treatment and mortality after stroke. Stroke 2002, 33: 268–273.View ArticlePubMed
            44. Tarman GJ, Kane CJ, Moul JW, Thrasher JB, Foley JP, Wilhite D, Riffenburgh RH, Amling CL: Impact of socioeconomic status and race on clinical parameters of patients undergoing radical prostatectomy in an equal access health care system. Urology 2000, 56 (6) : 1016–1020.View ArticlePubMed
            45. Brewster DH, Thomson CS, Hole DJ, Black RJ, Stroner PL, Gillis CR: Relation between socioeconomic status and tumour stage in patients with breast, colorectal, ovarian, and lung cancer: results from four national, population based studies. BMJ 2001, 322 (7290) : 830–831.View ArticlePubMed
            46. Schrijvers CT, Mackenbach JP, Lutz JM, Quinn MJ, Coleman MP: Deprivation, stage at diagnosis and cancer survival. Int J Cancer 1995, 63 (3) : 324–329.View ArticlePubMed
            47. Kaffashian F, Godward S, Davies T, Solomon L, McCann J, Duffy SW: Socioeconomic effects on breast cancer survival: proportion attributable to stage and morphology. Br J Cancer 2003, 89 (9) : 1693–1696.View ArticlePubMed
            48. Lynch JW, Kaplan GA, Cohen RD, Tuomilehto J, Salonen JT: Do cardiovascular risk factors explain the relation between socioeconomic status, risk of all-cause mortality, cardiovascular mortality, and acute myocardial infarction? Am J Epidemiol 1996, 144 (10) : 934–942.PubMed
            49. Ortiz CAR, Goodwin JS, Freeman JL: The effect of socioeconomic factors on incidence, stage at diagnosis and survival of cutaneous melanoma. Med Sci Monit 2005, 11 (5) : RA163-RA172.PubMed
            50. Nilsen TL, Johnsen R, Vatten L: Socio-economic and lifestyle factors associated with the risk of prostate cancer. British Journal of Cancer 2000, 82 (7) : 1358–1363.View Article
            51. Liu L, Cozen W, Bernstein L, Ross RK, Deapen D: Changing relationship between socioeconomic status and prostate cancer incidence. J Natl Cancer Inst 2001, 93 (9) : 705–709.View ArticlePubMed
            52. Braaten T, Weiderpass E, Kumle M, Lund E: Explaining the socioeconomic variation in cancer risk in the Norwegian Women and Cancer Study. Cancer Epidemiol Biomarkers Prev 2005, 14: 2591–2597.View ArticlePubMed
            53. Danø H, Hansen KD, Jensen P, Petersen JH, Jacobsen R, Ewertz M, Lynge E: Fertility pattern does not explain social gradient in breast cancer in denmark. Int J Cancer 2004, 111 (3) : 451–456.View ArticlePubMed
            54. Andersson SO, Baron J, Bergström R, Lindgren C, Wolk A, Adami HO: Lifestyle factors and prostate cancer risk: a case-control study in Sweden. Cancer Epidemiol Biomarkers Prev 1996, 5 (7) : 509–513.PubMed
            55. Vecchia CL, Negri E, Franceschi S: Education and cancer risk. Cancer 1992, 70 (12) : 2935–2941.View ArticlePubMed
            56. Wynder EL, Mabuchi K, Whitmore WF: Epidemiology of cancer of the prostate. Cancer 1971, 28 (2) : 344–360.View ArticlePubMed
            57. Yu H, Harris RE, Wynder EL: Case-control study of prostate cancer and socioeconomic factors. Prostate 1988, 13 (4) : 317–325.View ArticlePubMed
            58. Fincham SM, Hill GB, Hanson J, Wijayasinghe C: Epidemiology of prostatic cancer: a case-control study. Prostate 1990, 17 (3) : 189–206.View ArticlePubMed
            59. Ewertz M, Duffy SW, Adami HO, Kvåle G, Lund E, Meirik O, Mellemgaard A, Soini I, Tulinius H: Age at first birth, parity and risk of breast cancer: a meta-analysis of 8 studies from the Nordic countries. Int J Cancer 1990, 46 (4) : 597–603.View ArticlePubMed
            60. Research on Cancer IA: Cancer: Causes, Occurrence, and Control Lyon: IARC Scientific Publications 1990., 100:
            61. Bakke PS, Hanoa R, Gulsvik A: Educational level and obstructive lung disease given smoking habits and occupational airborne exposure: a Norwegian community study. Am J Epidemiol 1995, 141 (11) : 1080–1088.PubMed
            62. Skillrud DM, Offord KP, Miller RD: Higher risk of lung cancer in chronic obstructive pulmonary disease. A prospective, matched, controlled study. Ann Intern Med 1986, 105 (4) : 503–507.PubMed
            63. Tockman MS, Anthonisen NR, Wright EC, Donithan MG: Airways obstruction and the risk for lung cancer. Ann Intern Med 1987, 106 (4) : 512–518.PubMed
            64. Chaturvedi N, Jarrett J, Shipley MJ, Fuller JH: Socioeconomic gradient in morbidity and mortality in people with diabetes: cohort study findings from the Whitehall Study and the WHO Multinational Study of Vascular Disease in Diabetes. BMJ 1998, 316 (7125) : 100–105.PubMed
            65. Connolly V, Unwin N, Sherriff P, Bilous R, Kelly W: Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas. J Epidemiol Community Health 2000, 54 (3) : 173–177.View ArticlePubMed
            66. Agardh EE, Ahlbom A, Andersson T, Efendic S, Grill V, Hallqvist J, Ostenson CG: Explanations of socioeconomic differences in excess risk of type 2 diabetes in Swedish men and women. Diabetes Care 2004, 27 (3) : 716–721.View ArticlePubMed
            67. Dalstra JAA, Kunst AE, Borrell C, Breeze E, Cambois E, Costa G, Geurts JJM, Lahelma E, Oyen HV, Rasmussen NK, Regidor E, Spadea T, Mackenbach JP: Socioeconomic differences in the prevalence of common chronic diseases: an overview of eight European countries. Int J Epidemiol 2005, 34 (2) : 316–326.View ArticlePubMed
            68. Perry IJ, Wannamethee SG, Walker MK, Thomson AG, Whincup PH, Shaper AG: Prospective study of risk factors for development of non-insulin dependent diabetes in middle aged British men. BMJ 1995, 310 (6979) : 560–564.PubMed
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              1. The pre-publication history for this paper can be accessed here:http://​www.​biomedcentral.​com/​1471-2458/​8/​340/​prepub

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