- Top of page
- PATIENTS AND METHODS
- AUTHOR CONTRIBUTIONS
Arthritis, a term encompassing many different types of arthritis, the most often diagnosed being osteoarthritis and rheumatoid arthritis (1), is a common and often debilitating disease. In developed countries it is the most common single cause of disability, impacting both the individual and population levels (2). Australian public health expenditure for arthritis and other musculoskeletal disorders was recently estimated at approximately $4 billion (3), and accounted for the largest proportion of direct health expenditure (31%), amounting to $1.2 billion (3). Individuals with these diseases experience a substantially lower quality of life compared to those without arthritis (1). In addition, and compared to any other disease, arthritis accounts for greater increased dependency to perform activities of daily living (1). Currently there is no known cure for arthritis; however, the promotion of modifiable risk factors to specific at-risk populations offers the potential to reduce arthritis onset, or at least to slow the progression of this disease.
With few exceptions, an inverse relationship exists between social disadvantage and disease (4, 5). Attention is now increasingly being directed toward examining the relationship between socioeconomic status (SES) and arthritis; however, these data still remain sparse. It is well documented that individuals of lower SES, measured by individual parameters such as income, education, and occupation, have lifestyles that are less protective of arthritis, including, but not limited to, greater levels of physical inactivity and obesity (6) and a greater likelihood of smoking (7). Occupation also influences the onset and/or progression of arthritis, whereby individuals with physically demanding occupations such as miners and dockers have a higher prevalence of knee arthritis (8), and physically strenuous jobs that require knee bending, squatting, or heavy lifting significantly increase the risk of knee arthritis (9). Furthermore, data examining area-based parameters of SES show that individuals who reside in areas of greater disadvantage are more likely to participate in less protective lifestyle behaviors that predispose them to arthritis (10).
There is an increasing body of work employing multilevel statistical techniques aimed at disentangling the relative contribution of socioeconomic factors at the individual and neighborhood levels to various diseases, but none to date have examined arthritis. Multilevel analyses have shown that residents of disadvantaged neighborhoods, independent of individual factors, are more likely to be overweight (11) and have lower levels of physical activity (12), both significant modifiable lifestyle-related risk factors that are associated with the onset and progression of arthritis (13, 14). Our primary hypothesis was that there would be differential contributions to arthritis associated with individual- and neighborhood-level disadvantage. A secondary aim was to examine, regardless of individual socioeconomic position (SEP), whether individuals who resided within a more advantaged neighborhood may experience a protective effect against arthritis, or conversely, whether the likelihood of reporting arthritis may be exacerbated if residing in a more disadvantaged neighborhood.
Significance & Innovations
These are the first multilevel data to examine the relationship between individual- and neighborhood-level disadvantage upon arthritis.
We observed arthritis prevalence to be greater in neighborhoods of increased social disadvantage.
We provide novel and important information regarding specific population groups at an increased risk of arthritis and the importance of focusing on both people and places for disease intervention.
- Top of page
- PATIENTS AND METHODS
- AUTHOR CONTRIBUTIONS
Table 1 shows the proportion of respondents who reported experiencing arthritis as a chronic condition. The overall rate of reported arthritis was 23.3% (95% CI 22.4–24.1), and the rates were highest for women (27.1%; 95% CI 26.0–28.3), those ages 60–65 years (40.0%; 95% CI 37.8–42.2), the least educated (28.2%; 95% CI 26.8–29.5), those who were permanently unable to work (58.8%; 95% CI 53.3–64.3), respondents from low-income families (42.2%; 95% CI 39.1–45.3), and residents of the most disadvantaged neighborhoods (31.4%; 95% CI 29.1–33.7).
Table 1. Number of respondents in the study sample and the proportion (95% CI) who reported experiencing arthritis as a long-term chronic condition*
| ||N||% reporting arthritis (95% CI)|
|All persons||10,757||23.3 (22.4–24.1)|
|Sex|| || |
| Male||4,752||18.4 (17.3–19.5)|
| Female||6,005||27.1 (26.0–28.3)|
|Age, years|| || |
| 40–44||2,207||10.3 (9.0–11.6)|
| 45–49||2,388||16.1 (14.6–17.6)|
| 50–54||2,207||23.1 (21.3–24.8)|
| 55–59||2,037||30.2 (28.2–32.2)|
| 60–65||1,918||40.0 (37.8–42.2)|
|Educational attainment|| || |
| Bachelor's degree or higher||3,391||17.0 (15.8–18.3)|
| Diploma/associate diploma||1,251||23.3 (20.9–25.6)|
| Certificate (trade/business)||1,914||23.6 (21.7–25.5)|
| Secondary school||4,201||28.2 (26.8–29.5)|
|Occupation|| || |
| Manager/professional||3,610||16.7 (15.4–17.9)|
| White collar||2,365||23.2 (21.5–24.9)|
| Blue collar||1,520||20.6 (18.6–22.6)|
| Home duties||667||27.6 (24.1–31.0)|
| Retired||942||41.1 (37.9–44.2)|
| Permanently unable to work||311||58.8 (53.3–64.3)|
| Other (not easily classifiable)||426||26.5 (22.3–30.7)|
| Missing||916||19.0 (16.5–21.5)|
|Household income|| || |
| ≥$130,000||1,861||13.3 (11.7–14.8)|
| $72,800–129,999||2,795||18.2 (16.7–19.6)|
| $41,600–72,799||2,387||24.0 (22.3–25.7)|
| $26,000–41,599||1,165||30.6 (28.0–33.3)|
| <$25,999||988||42.2 (39.1–45.3)|
| Missing||1,561||25.7 (23.5–27.9)|
|Neighborhood disadvantage|| || |
| Quintile 5 (least disadvantaged)||2,515||18.5 (16.9–20.0)|
| Quintile 4||2,654||21.1 (19.5–22.7)|
| Quintile 3||2,215||24.6 (22.8–26.4)|
| Quintile 2||1,795||24.4 (22.4–26.4)|
| Quintile 1 (most disadvantaged)||1,578||31.4 (29.1–33.7)|
Table 2 shows the multilevel association between individual- and household-level sociodemographic factors and neighborhood disadvantage and the likelihood of reporting arthritis. The results for the null model (model 1) show that the probability of reporting arthritis differed significantly across the neighborhoods (P ≤ 0.001). The differences were successively reduced with adjustment for the individual- and household-level factors (by 79.2% for model 2 relative to the null model) and neighborhood disadvantage (92.2%), after which there was no longer between-neighborhood variation in self-reported arthritis (P = 0.204). Similarly, in the fully adjusted model (model 3), there was no significant between-neighborhood variation in the probability of reporting arthritis (P = 0.602).
Table 2. Individual- and neighborhood-level effects on self-reported arthritis (n = 10,757)*
| ||Model 1†||Model 2‡||Model 3§|
|Fixed effects|| || || |
| Sex|| || || |
| Male (referent)|| ||1.00||1.00|
| Female|| ||1.50 (1.35–1.67)||1.51 (1.36–1.68)|
| P|| ||≤ 0.001||≤ 0.001|
| Age, years|| || || |
| 40–44 (referent)|| ||1.00||1.00|
| 45–49|| ||1.62 (1.35–1.93)||1.62 (1.36–1.94)|
| 50–54|| ||2.49 (2.09–2.96)||2.50 (2.10–2.97)|
| 55–59|| ||3.33 (2.80–3.95)||3.36 (2.83–3.99)|
| 60–65|| ||4.38 (3.66–5.23)||4.42 (3.70–5.28)|
| P|| ||≤ 0.001||≤ 0.001|
| Educational attainment|| || || |
| Bachelor's degree or higher (referent)|| ||1.00||1.00|
| Diploma/associate diploma|| ||1.31 (1.11–1.55)||1.30 (1.10–1.54)|
| Certificate (trade/business)|| ||1.29 (1.10–1.51)||1.26 (1.08–1.47)|
| Secondary school|| ||1.29 (1.13–1.47)||1.25 (1.10–1.44)|
| P|| ||≤ 0.001||0.002|
| Occupation|| || || |
| Manager/professional (referent)|| ||1.00||1.00|
| White collar|| ||1.01 (0.87–1.17)||1.01 (0.87–1.17)|
| Blue collar|| ||1.02 (0.86–1.22)||1.00 (0.84–1.19)|
| Home duties|| ||1.13 (0.91–1.39)||1.12 (0.91–1.39)|
| Retired|| ||1.29 (1.07–1.56)||1.29 (1.07–1.56)|
| Permanently unable to work|| ||4.02 (3.05–5.29)||3.85 (2.93–5.07)|
| Other (not easily classifiable)|| ||1.22 (0.95–1.56)||1.20 (0.94–1.54)|
| P|| ||≤ 0.001||≤ 0.001|
| Household income|| || || |
| ≥$130,000 (referent)|| ||1.00||1.00|
| $72,800–129,999|| ||1.32 (1.11–1.57)||1.28 (1.08–1.53)|
| $41,600–72,799|| ||1.59 (1.33–1.89)||1.51 (1.26–1.80)|
| $26,000–41,599|| ||1.86 (1.53–2.27)||1.75 (1.43–2.14)|
| <$25,999|| ||2.14 (1.73–2.64)||1.96 (1.58–2.43)|
| P|| ||≤ 0.001||≤ 0.001|
| Neighborhood disadvantage|| || || |
| Quintile 5 (referent)¶|| || ||1.00|
| Quintile 4|| || ||1.09 (0.94–1.26)|
| Quintile 3|| || ||1.23 (1.05–1.43)|
| Quintile 2|| || ||1.17 (0.99–1.38)|
| Quintile 1|| || ||1.42 (1.20–1.68)|
| P|| || ||≤ 0.001|
|Random effects#||0.077 (0.019)||0.016 (0.012)||0.006 (0.011)|
| P**||≤ 0.001||0.204||0.602|
After simultaneously adjusting for all sociodemographic factors (model 2), statistically significant associations (P ≤ 0.001) were observed between self-reported arthritis and sex, age, education, occupation, and household income, with rates being highest for women (OR 1.50, 95% CI 1.35–1.67), those ages 60–65 years (OR 4.38, 95% CI 3.66–5.23), respondents with a diploma/associate diploma (OR 1.31, 95% CI 1.11–1.55), those permanently unable to work (OR 4.02, 95% CI 3.05–5.29), and those living in households where the annual income was less than $25,999 (OR 2.14, 95% CI 1.73–2.64). When neighborhood disadvantage was added to the model (model 3), associations between each of the sociodemographic factors and arthritis remained largely unchanged or were slightly attenuated. Independent of sex, age, education, occupation, and household income, residents of the most disadvantaged neighborhoods were 42% (OR 1.42, 95% CI 1.20–1.68) more likely than their counterparts in the least disadvantaged neighborhoods to report that they experienced arthritis as a chronic condition.
Tests of cross-level interactions were not statistically significant between neighborhood disadvantage and education (P = 0.164), occupation (P = 0.628), and household income (P = 0.389) (Figure 1).
Figure 1. Cross-level interactions between A, education, B, occupation, C, household income, and neighborhood disadvantage and the probability of reporting arthritis in men and women ages 40–65 years in 2007. Models include age, sex, education, occupation, household income, and neighborhood disadvantage. Dis. = disadvantaged; Dip/Assoc = diploma/associate diploma; Mgr/Prof = manager/professional.
Download figure to PowerPoint
- Top of page
- PATIENTS AND METHODS
- AUTHOR CONTRIBUTIONS
This study shows novel insights into the social patterning of arthritis. Individuals from disadvantaged neighborhoods were more likely to report arthritis compared to individuals residing in less disadvantaged neighborhoods, independent of education, occupation, and household income. Patterns of association also suggested that individual- and neighborhood-level socioeconomic factors may combine to either amplify the susceptibility to disadvantage or offer additional protection against arthritis. In light of a paucity of data, our current understanding of why advantaged and disadvantaged neighborhoods differ in their rates of self-reported arthritis is limited; therefore, we provide a speculative discussion to inform future research.
When examining the role played by social disadvantage in health outcomes, we provide evidence for biologic plausibility, a criterion clearly demonstrated within the widely documented social gradient of health (4). Biologic plausibility is also suggested as being inherent in the role played by social capital, a complex construct concerned with “features of social organization such as trust, norms, and networks that can improve the efficiency of society by facilitating coordinated actions” (24) upon health outcomes. Social capital may contribute to differences in rates of arthritis between advantaged and disadvantaged neighborhoods, potentially resulting from 3 processes of 1) concomitant differences in mutual concern for others, 2) the extent of social networks, and 3) their normative environments. Preventive health care and increased participation in medical checkups may also be a characteristic of a neighborhood with greater social capital, thereby enabling residents to identify potential health problems prior to onset. Social capital has the potential to reduce individuals' focus on disease and illness, potentially explaining why individuals from disadvantaged neighborhoods are more likely to self-rate their general health as poor, compared to their counterparts residing in less disadvantaged areas (25). Neighborhoods of greater social capital share “norms” that influence preventive health behaviors such as physical activity and lower caloric intake, both of which are key predictors of body habitus and obesity. Furthermore, neighborhoods with greater social capital potentially have greater involvement with social networks, community activities, and social engagement (26). Individuals in those neighborhoods may be more able to secure health-promoting resources (27, 28), which may include safe walking paths with good lighting and even surfaces, and this may reduce the likelihood of traumatic injury to the knees or hips related to falls, high-impact forces, or malalignment of the joint during uneven or altered biomechanical loading (29). Trauma to joints increases the likelihood of developing bone marrow lesions, a factor well documented as being associated with pain in those with osteoarthritis (30).
Positive mental well-being is a fundamental element that enables individuals to cope with adversity, operationalized through factors such as resilience and positive adaptation (31). By residing in a socially disadvantaged area, the individual not only experiences their individual-level dimension of inequality, but may also perceive an unequal distribution of economic and social resources compared to other areas, potentially resulting in an internalization of the emotional and cognitive effects of insecurity at many levels (31). Indeed, the importance of social and psychological dimensions of deprivation at multiple levels is gaining increased international recognition due to efforts to address poorer health outcomes experienced by those of greater social disadvantage (4). Considering that resilience and positive adaptation are likely to act as protective factors with regard to symptoms associated with poorer health in general, positive mental well-being may improve an individual's ability to cope with pain, resulting in an improved quality of life. However, pain is but one characteristic of arthritis, with radiologic diagnosis being another. Should the individual be less affected by pain, there is less likelihood that a medical practitioner may determine a need for radiology services, reducing the likelihood of diagnosis.
Obesity is the strongest modifiable predictor of arthritis onset and progression, especially knee osteoarthritis (13), and increases the likelihood of reduced cartilage volume (32), the hallmark characteristic of arthritis. Given that physical activity is an important predictor of obesity, this is worthy of discussion in the context of social determinants. The built environment plays a key role in terms of influencing physical activity in neighborhoods, with recent data showing a greater propensity for individuals residing in less disadvantaged areas to participate in more physically active lifestyles (12). However, given that our current study did not examine the role of physical activity on arthritis, we are only able at this point to speculate about the complexity between disadvantage measured at the neighborhood and individual levels and arthritis. For instance, undertaking a higher-skilled occupation may likely result in lower levels of work-related physical activity, and the opposite could be suggested of lower-skilled occupations; however, individuals who have a physically demanding occupation may not recognize or report those physical demands in a self-administered survey. Clearly, this area of inquiry begs immediate attention to elucidate by use of multilevel analyses the role played by physical activity in the onset and progression of arthritis.
Income and education are inextricably linked, with one influencing the other, and it is well documented that individuals with greater education and/or income are more likely to be health literate compared to individuals with less education and/or income (33). Given that education and income reflect material and intellectual resources, there is an inherent assumption of an important dose-response relationship between those individual measures of SES and improved health literacy (33). Taken in context, social capital and health literacy skills combined may explain the nonsignificant patterns of association we observed that suggested a potential protective effect of residing in a less disadvantaged neighborhood for individuals with lower educational attainment or household income.
We observed a greater likelihood of self-reported arthritis in women compared to men, which is indicative of the well-documented sex bias observed for this disease. Although the mechanisms by which female sex increases the risk of arthritis are unknown, they may be related to a combination of hormonal, heritability, and/or body habitus factors (13). However, differential variation in arthritis between the sexes when accounting for social disadvantage may also be partly explained by a potential dose-response aspect of social capital, whereby the well-documented dual workload of women (34) may limit the amount of time spent interacting with their immediate neighborhood, and thus results in an increased likelihood of reporting ill health (34).
The increased risk of arthritis in association with age is not surprising, given that this is a well-documented predisposing risk factor for this disease (35). Furthermore, the observed association between arthritis and being permanently unable to work is also not surprising, given that arthritis is associated with reduced mobility and increased difficulty in performing acts of daily living (1, 3). We are unable to comment further on this observed association due to the cross-sectional nature of our analyses.
This study has some limitations. First, the categorization of occupations may have limited our ability to determine an association with arthritis due to the inability to assign occupation type to 26.5% of participants, including those who were volunteers, held numerous part-time positions, were retired, were current students, or were home based, and the 19% for whom data were missing. Furthermore, and as previously reported, the areal units employed for analysis within the HABITAT cohort were selected based on sampling convenience (36), and not reasons specifically related to arthritis prevalence; given this, we hypothesize that the potential impact of living in a disadvantaged neighborhood on the prevalence of self-reported arthritis would be underestimated. Nonresponse for the baseline HABITAT study was 31.5%, with greater nonresponse from individuals in more disadvantaged neighborhoods (12). The population was identified from the Commonwealth Electoral Roll, which has been reported as underrepresenting individuals who are disadvantaged, transient or homeless, and migrant (15). Given this, and the fact that lifestyle behaviors associated with arthritis are more likely to be observed in individuals of lower SES (10), it is possible that the relationship we report between neighborhood disadvantage and arthritis may be underestimated. However, we also recognize that the OR may overestimate the relative risks of arthritis (37). Our findings are based on a research design that achieved a moderate individual-level response rate of 68.5%, and a response rate that followed an inverse association across the deciles of neighborhood disadvantage. We therefore need to consider the likely bias attributable to nonresponse and how this might affect this study's inferences to the wider population. Previous studies show that persons from socioeconomically disadvantaged backgrounds (38) and residents of more deprived neighborhoods (39) are the least likely to respond to or participate in survey research. As a result, population-based samples typically underrepresent the most disadvantaged and overrepresent the advantaged, the likely consequence of which is a socioeconomically truncated sample resulting in an underestimation of the magnitude of socioeconomic variability in self-reported arthritis. Therefore, the neighborhood- and individual-level socioeconomic differences in arthritis shown in this study, although significant, may be an underestimate of the “true” magnitude of socioeconomic differences in the population. Also, our finding of an association between neighborhood disadvantage and self-reported arthritis might be confounded by individual-level socioeconomic factors not included in the models. However, we included 3 of the most widely used indicators of an individual's socioeconomic characteristics (education, occupation, and income), and given the correlation among these measures (40), it is likely that these socioeconomic indicators are capturing some of the unmeasured influences of other socioeconomic factors not included in the models. Alternatively, it may be that the inclusion of individual-level measures of SES resulted in “overadjustment,” which provides evidence for the possibility of an even stronger contextual effect on self-reported arthritis than was observed in this study. If education, occupation, and household income represent part of the pathway via which neighborhood disadvantage influences the likelihood of experiencing arthritis, then simultaneously modeling individual-level socioeconomic variables may have inappropriately attenuated the variation that was more correctly attributable to neighborhood disadvantage (41). Finally, we were unable to determine the proportions of osteoarthritis and rheumatoid arthritis within our measure of arthritis due to the method of disease identification; however, of the overall arthritis prevalence, we may expect to observe ∼8% of the former and ∼2% of the latter (1). In the absence of published data examining this question and in light of the current limited understanding examining social disadvantage and arthritis, our findings encourage further research into this area of inquiry.
We conclude that arthritis prevalence is greater in neighborhoods of increased social disadvantage. This study is the first to examine the association between social disadvantage and arthritis using a multilevel analysis, and therefore provides novel and important information regarding specific population groups at an increased risk of arthritis. These data also suggest the importance of focusing on both people and places for disease intervention. Without this information, we are limited in our ability to intervene to reduce the prevalence of arthritis, and to identify potential target groups for preventive health programs. Given that arthritis is not yet curable and little is currently known about the social determinants of arthritis diseases, our study prompts further research into examining this association, and potentially encourages increased attention from health practitioners toward clients from socially disadvantaged groups and neighborhoods.