Study
Reference
Data (country)
Size
Adjusted for
SES variables
Results
Leigh 1991
Leigh and Fries (1991a)
NHANES-I, QES73, SDW78, NHIS86 (USA)
Hannan 1992
Hannan et al. (1992)
NHANES-I (USA)
6880
Income, BMI, smoking, knee injury
Education: HS, <HS
Greater risk among <HS than ≥HS
Income
Wang 2000
Peter Wang et al. (2000)
NPHS (Canada)
39,240
Education, BMI
Income
Greater prevalence in group with low income based on family size (e.g., <$15,000 for one- or two-person family) vs. not-low income participants
Education
Busija 2007
Busija et al. (2007)
VPHS (Australia)
7500
BMI
Callahan 2008
Callahan et al. (2008)
NC-FM-RN (NC, USA)
7306
Cañizares 2008
Cañizares et al. (2008)
Community health survey (Canada)
127,513
Grotle (2008)
Grotle et al. (2008)
Community survey (Norway)
3266
Cunningham 2011
Cunningham (2011)
NATSIHS (Australia)
18,340
Brennan 2012
Brennan and Turrell (2012)
HABITAT (Australia)
10,757
Education
Income
Area SES
Baldassari 2013
Baldassari et al. (2013a)
NC-FM-RN (NC, USA)
Age, sex, race
Count of low education, non-homeowner-ship and low SES occupation
Count of low maternal: education, non-parental homeownership and low-SES paternal occupation
Anderson 1988
Anderson and Felson (1988)
NHANES-I (USA)
5193
Income: (continuous scale)
KNEE OA
Decreased risk with $5000/year income increments, similarly for both men and women (OR = 0.8). Not significant in fully adjusted models
Education: <8 years, 9–12 years, >12 years
Decreased risk per increase in education category. OR = 0.7 for men, OR = 0.8 for women. Not significantin fully adjusted models
Hannan 1992
Hannan et al. (1992)
NHANES-I (USA)
6880
Age, race, sex, smoking
Education
KNEE OA
Greater prevalence with fewer years of education,for r-kOA (lowest vs. highest OR = 1.5) and s-kOA (lowest vs. highest OR = 1.3). Explained by BMI for r-kOA but not s-kOA
Tepper 1993
Tepper and Hochberg (1993)
NHANES-I (USA)
2358
Age, race, sex, marital status, other SES variable
Education
HIP OA
Greater prevalence in <12 years of schooling vs. ≥12 years, slightly short of significance at α = 0.05 (OR = 1.64). Possibly stronger association among men
Income
No significant associations, imprecise estimates show possible U-shaped relation between higher family income and hip OA
Andrianakos 2006
Andrianakos et al. (2006)
Population-based study (Greece)
8740
Age, gender,
Education: ≤12 years, >12 years
KNEE, HIP, AND HAND OA
Greater risk of knee OA for ≤12 years vs. >12 years (OR = 2.8) but not for hand or hip OA
Occupation: non-manual, others
Lower risk for hand OA for non-manual occupations vs. others (OR = 0.6), but not hip OA or knee OA
Callahan 2008
Callahan, Cleveland, et al., (2011)
Johnston County OA project (USA)
3552
Age, gender, race, BMI, physical activity
Education: <12 years, ≥12 years
KNEE OA
Greater risk in <12 years of schooling vs. ≥12 years for unilateral (OR = 2.1) and bilateral r-kOA, and symptomatic unilateral (OR = 2.2) and bilateral r-kOA
Occupation: managerial, others
Greater risk for non-managerial vs. managerial, for r-kOA (OR = 1.4) and s-kOA (OR = 1.5). Explained by other SES variables
Area poverty: high, medium, low
Greater risk in medium and high poverty vs. low poverty. OR for High vs. low poverty = 1.9 (r-kOA) and 1.4 (s-kOA)
Callahan 2010
Callahan et al. (2010)
Johnston County OA project (USA)
2627
Age, race/ethnicity, stratified by gender
Education: <12 years, ≥12 years
KNEE OA
Greater prevalence for <12 years vs. ≥12 years, for r-kOA (women only: OR = 1.6) and s-kOA (Men: OR = 1.55, Women: OR = 1.9). Unexplained by BMI, smoking, alcohol use, previous knee injury, hormone replacement therapy
Cleveland 2013
Cleveland et al. (2013)
Johnston County OA project (USA)
3087
Age, gender, race, BMI, smoking, prior hip injury, workplace physical demands
Education: <12 years, ≥12 years
HIP OA
Greater risk in <12 years of schooling vs. ≥12 years for unilateral (OR = 1.4) and bilateral s-hOA (OR = 2.0), and for bilateral s-hOA (OR = 1.3)
Occupation: managerial, others
Greater risk for non-managerial vs. managerial, for r-hOA (OR = 1.4) and s-hOA (OR = 1.5). Explained by other SES variables
Area poverty: high, medium, low
Greater risk in medium and high poverty vs. low poverty. Unilateral/bilateral High vs. low OR = 1.3/2.0 (r-kOA) and 1.6/NS (s-kOA)
Rodriguez-Amado 2014
Rodriguez-Amado et al. (2014)
Population-based study (Mexico)
17,566
ANY OA
Self-Reported Arthritis
Ascertainment of arthritis can be challenging in large-scale studies, and self-reported case definitions are often used to facilitate the evaluation of disease burden. Case definitions used in most studies involve participant reports of any prior receipt of an arthritis or rheumatic disease diagnosis from a health professional; self-reported chronic arthritis symptoms have also been used, rarely as a stand-alone criterion and typically together with a self-reported doctor diagnosis. There is evidence that self-reported case definitions for arthritis have satisfactory sensitivity for population surveillance purposes, but suffer from low specificity, particularly among individuals less than 65 years old (Bombard, Powell, Martin, Helmick, & Wilson, 2005). Nevertheless, there is no evidence that instrument validity for self-reported arthritis definitions varies by SES, as could systematically bias estimates of the SES and self-reported arthritis association. To the extent that most self-reported arthritis involves OA of the hips, knees, or hands, the evidence for its differential prevalence by SES complements that presented in our section on radiographic OA.
We summarize some of the work on socioeconomic differences in the risk of self-reported arthritis in Table 6.2. These studies span decades, geographic regions, and demographic groups, yet almost unanimously find meaningful differences in the risk of self-reported arthritis by a number of socioeconomic characteristics, including education, income, neighborhood disadvantage, or occupational class. There is equivocal evidence for effect-measure modification by race/ethnicity. Cunningham and colleagues notably found gradient patterns of increased self-reported arthritis susceptibility with lower SES, but only among non-indigenous participants in a sample of Australian participants (Cunningham, 2011); in comparison, data from a cohort study in rural North Carolina (USA) did not suggest significantly different patterns of inequities between Whites and African Americans (Callahan et al., 2008).
Table 6.2
Overview of socioeconomic disparities in self-reported doctor-diagnosed arthritis prevalence
Study | Reference | Data (country) | Size | Adjusted for | SES variables | Results |
---|---|---|---|---|---|---|
Hannan 1992 | Hannan et al. (1992) | Population health survey: NHANES-I (USA) | 6880 | Age, gender, race, smoking | Education: 0–8 years, 9–11 years, 12 years, ≥13 years | Increasing risk with lower educational category. Lowest vs. highest OR = 1.51. Unchanged by further adjustments for BMI and knee injury |
Wang 2000 | Peter Wang et al. (2000) | Population health survey: NPHS (Canada) | 39,240 | Age, gender, ethnic group, other SES variables | Education: <secondary, ≥secondary | No difference by low education status |
Income: low for, family size, not low | Greater risk in low income group than not-low group (OR = 1.14) | |||||
Occupation: unskilled, semiskilled, professional | No meaningful difference in risk by occupation | |||||
Busija 2007 | Busija et al. (2007) | Population health survey: VPHS (Australia) | 7500 | Age, gender, BMI, area of residence, other SES variables | Education: primary, secondary, tertiary | No meaningful independent associations |
Income: <$A 20 K, 20–40 K, 40–60 K, >60 K | Increasing risk with decreasing income; vs. highest category, ORs = 0.8, 0.6 and 0.5 respectively | |||||
Occupation: professional, others | No meaningful independent associations | |||||
Callahan 2008 | Callahan et al. (2008) | Primary-care patients: NC-FM-RN (NC, USA) | 7306 | Age, gender, BMI, stratified by race. | Education: <12 years, 12 years, ≥12 years | Greater risk in <12 years vs. >12 years; OR = 1.55 for Whites and 1.92 for African Americans |
Area poverty: low %, medium %, high % | No independent main effects, but meaningful interaction of greatest poverty with highest education category: OR = 1.55 for Whites, 2.06 for African Americans | |||||
Cañizares 2008 | Cañizares et al. (2008) | Community health survey (Canada) | 127,513 | Age, gender, race, smoking, physical activity, immigration status,, area characteristic, other SES variables | Education: <secondary, secondary, >secondary | No meaningful independent association |
Income: <$A 20 K, 20–40 K, 40–60 K, ≥60 K | Increasing risk in decreasing income categories; vs. ≥60 K, OR = 1.49, 1.26, and 1.13, respectively | |||||
Area poverty: % low-SES | Greater risk with increasing % of low-income families (OR = 1.3 for 10 % increase), but not with increasing % of low-education families | |||||
Grotle 2008 | Grotle et al. (2008) | Community survey (Norway) | 3266 | Age, gender | Education: ≤9 years, 9–12 years, >12 years | Uses self–reported osteoarthritis rather than any arthritis |
Evenly greater self-reported OA risk for lower two education categories vs. >12 years, for any OA (OR ≈ 2.1), hip OA (OR ≈ 2.8), knee OA (OR ≈ 2.3), and hand OA (OR ≈ 1.5) | ||||||
Cunningham 2011 | Cunningham (2011) | Population health survey NATSIHS (Australia) | 18,340 | Age, gender, stratified by indigenous status | Education: <12 years, ≥12 years, or no degree, certificate, diploma, university | Associations only found among non–indigenous participants, except for greater risk for lowest vs. highest income quartile among indigenous (OR = 1.6) |
Lower risk for ≥12 years than less than years (OR = 0.6) | ||||||
Decreasing risk with higher categories of education than no degree. University vs. no degree OR = 0.5 | ||||||
Income: quintiles | Increasing risk with lower income quintiles than Q3-5. Q1 vs. Q3-5 OR = 2.1 | |||||
Area poverty: quintiles | Increasing risk with lower area poverty. Q1 (poorest) vs. Q5 (richest) OR = 1.7 | |||||
Housing tenure | No meaningful association | |||||
Brennan 2012 | Brennan and Turrell (2012) | Population-based cross-sectional study: HABITAT (Australia) | 10,757 | Education: Secondary, Certificate, Diploma, Bachelor | Greater disease in categories lower than highest education, similar in all three lower categories (OR ≈ 1.3) | |
Income: <$A 26 K, 26–42 K, 42–73 K, 73–130 K, >130 K | Progressively higher risk with decreasing income category: respectively, OR = 1.96, 1.75, 1.51, 1.28 | |||||
Occupation: blue collar, white collar, professional | No meaningful independent association with work status among the employed | |||||
Area poverty: quintiles | Greater risk among people in quintile 2 or 3 (OR ≈ 1.2) and quintile 1 (lowest SES, OR = 1.42) vs. quintile 5 (highest SES) | |||||
Baldassari 2013 | Baldassari, Cleveland, and Callahan (2013b) | Primary-care patients: NC-FM-RN (NC, USA) | 1276 | Age, sex, race | Current SES: Count of low education, non-homeowner-ship and low SES occupation | Increasing risk of self-reported arthritis with increasing count of low-SES factors. Not explained by childhood SES or BMI: lowest vs. highest OR = 2.08 in fully adjusted models |
Count of low maternal: education, non-parental homeownership and low-SES paternal occupation | Increasing risk with higher count of childhood low-SES factors. Lowest vs. highest SES difference remained significant after adjusting for current SES and BMI (OR = 1.39) |
Rheumatoid Arthritis
With a prevalence between 0.5 and 1 %, rheumatoid arthritis (RA) is the most common autoimmune disorder; women represent two thirds of all cases (Gabriel, 2001) and the disease typically occurs between the age of 50 and 75. Current genetic heritability estimates for RA do not exceed 50 % (Frisell et al., 2013), leaving ample room for modifiable determinants which, with the exception of smoking (Silman, Newman, & Macgregor, 1996), remain largely unknown.
We provide an overview of studies on socioeconomic differences in RA risk in Table 6.3. While early studies found no clear associations of socioeconomic status with RA development (Bankhead et al., 1996; Uhlig et al., 1999), more recent work consistently suggests that people with low SES are at an increased susceptibility for RA. Intriguing data further indicate that socioeconomic disadvantage chiefly increases susceptibility to seropositive disease (Bengtsson et al., 2005; Pedersen et al., 2006), as characterized by rheumatoid factors, supporting the increasingly adopted notion that seropositive and seronegative RA constitute etiologically distinct phenotypes.
Table 6.3
Overview of studies of socioeconomic disparities in rheumatoid arthritis risk
Study | Reference | Data (country) | Size | Adjusted for | SES variables | Results |
---|---|---|---|---|---|---|
Bankhead 1996 | Bankhead et al. (1996) | Population-based register NOAR (USA) | 687 | Stratified by gender | Five census-level variables | No strong correlation between SES variables and arthritis incidence (r s between 0 and 0.3) |
Uhlig 1999 | Uhlig, Hagen, and Kvien (1999) | County RA register (Norway) | 361 cases, 5851 controls | Age, gender, smoking | Education | Not associated with RA risk |
Olsson 2001 | Olsson, Skogh, and Wingren (2001) | Single hospital records (Denmark) | 281 cases, 507 controls | Age, smoking, high-risk occupation | Education | Lower incidence for secondary/upper secondary and university vs. compulsory school only (RR ≈ 0.5). Similar disparities among men and women |
Jaakola and Gissler 2004 | Jaakkola and Gissler (2005) | 1987 national birth cohort, (Finland) | 56,632 | Age (birth cohort), gender, birth order, maternal age, marital status, current SES index | Maternal occupation | Increased risk of inflammatory polyarthropathies (incl. RA) (OR = 2.11) and juvenile RA (OR = 2.12) for blue-collar vs. upper white-collar maternal occupation. Imprecise estimates; not statistically significant |
Bengtsson 2005 | Bengtsson et al. ( 2005) | Population-based case-control study EIRA (Sweden) | 930 cases, 1126 controls | Age, residential area, smoking | Education | Greater incidence of RA among participants in both educational categories below university degree (RR ≈ 1.7), similarly among men and women. Associations stronger for RF-positive RA than for RF-negative RA |
Occupation | Higher RA incidence among women in the manual occupational class vs. non-manual (RR ≈ 1.3). Associations stronger for RF-positive RA than for RF-negative RA | |||||
Pedersen 2006 | Pedersen et al. (2006) | Nationwide hospital records over 5 years (Denmark) | 515 cases, 769 controls | Gender, year of birth, year of diagnosis, place of residence | Education | Lower risk with greater education in graded pattern (linear trend p < 0.001, highest vs. lowest education RR ≈ 0.4). Only observed for RF-positive RA |
Current affluence | Lower risk in middle and most affluent thirds vs. least affluent thirds (OR 0.9 and 0.66), not significant at α = 0.05 | |||||
Lower risk in middle and most affluent thirds vs. least affluent thirds (OR 0.90 and 0.66), not significant at α = 0.05 | ||||||
Affluence during childhood | ||||||
Schneider 2006 | Schneider, Schmitt, and Richter (2006) | National Health survey (Germany) | 6491 | Age, sex | Occupation | Top-level office workers, and white-collar vs. manual OR = 0.27 and 0.54. Unexplained by lifestyle factors and other sociodemographic variables |
Education | Highest category and high school vs. no high school OR = 0.5 and 0.72 | |||||
Social status | Upper class and middle class vs. Lower class OR = 0.54 and 0.86 | |||||
Bergstrom 2011 | Bergström et al. (2011) | Citywide Preventative screening program (Sweden)
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