Determinants of MSK health and disability – Social determinants of inequities in MSK health




Abstract


Even in most egalitarian societies, disparities in care exist to the disadvantage of some people with chronic musculoskeletal (MSK) disorders and related disability. These situations translate into inequality in health and health outcomes. The goal of this chapter is to review concepts and determinants associated with health inequity, and the effect of interventions to minimize their impact.


Health inequities are avoidable, unnecessary, unfair and unjust. Inequities can occur across the health care continuum, from primary and secondary prevention to diagnosis and treatment. There are many ways to define and identify inequities, according for instance to ethical, philosophical, epidemiological, sociological, economic, or public health points of view. These complementary views can be applied to set a framework of analysis, identify determinants and suggest targets of action against inequity.


Most determinants of inequity in MSK disorders are similar to those in the general population and other chronic diseases. People may be exposed to inequity as a result of policies and rules set by the health care system, individuals’ demographic characteristics (e.g., education level), or some behavior of health professionals and of patients.


Osteoarthritis (OA) represents a typical chronic MSK condition. The PROGRESS-Plus framework is useful for identifying the important role that place of residence, race and ethnicity, occupation, gender, education, socioeconomic status, social capital and networks, age, disability and sexual orientation may have in creating or maintaining inequities in this disease. In rheumatoid arthritis (RA), a consideration of international data led to the conclusion that not all RA patients who needed biologic therapy had access to it. The disparity in care was due partly to policies of a country and a health care system, or economic conditions. We conclude this chapter by discussing examples of interventions designed for reducing health inequity.


1


Introduction


Even in most egalitarian societies, disparities in care exist and may affect people with chronic musculoskeletal (MSK) disorders and related disability. These may translate into inequality in health and health outcomes. Access to appropriate prevention and care for chronic MSK disorders is not only necessary, but is a basic human right. The insufficient integration of this concept in research on determinants of disease progression and prevention underlines the need for research. To measure inequity and its determinants, it is essential to have indicators of inequity. In the past 10 years, there has been an increase in initiatives to promote awareness about inequity in health and health care. The Cochrane methodology group introduced a framework to detect and report determinants of health inequity in all systematic reviews . A developing area of measurement is Patient Reported Outcome (PRO). The Outcome Measures in Rheumatology (OMERACT) initiative introduced health equity considerations in development of such instruments in 2012 . This includes a specific attention dedicated to measuring health literacy and ensuring cross-cultural equivalence of PRO, irrespective of socioeconomic status and language. The goal of this chapter is to review concepts and determinants associated with health inequity, and the effect of interventions to minimize their impact.




Inequality versus inequity


Whitehead asserted that health inequalities are inequity if they are avoidable, unnecessary, unfair and unjust. In 2003, Braveman proposed that health equity was a state of absence of systematic disparities in health (or its social determinants) between the advantaged and disadvantaged social groups. As such, health equity is a fundamental ethical principle of social justice. Norheim pointed out in 2009 that health inequalities that are amenable to human intervention were unfair and should be mitigated.


Differences in care occur between countries and are determined by several factors including the wealth of the countries and their health system. These differences do not make one necessarily more inequitable than the other, though unequal. When differences occur within a country, within a specific health care system, it leads to more perceived inequities, as people become more aware of the discrepancy. This phenomenon usually broadens with increasing public discourse and media attention.


One should distinguish between inequalities in needs and inequity in supply of health services and treatment. Health inequalities in needs refer to a situation where individuals are in need of more care when severely ill or less care when less ill. Health inequity, on the other hand, refers to the availability of health services and treatment based on individuals’ personal, financial and socioeconomic characteristics rather than their health status and needs. Hence, health inequity is a fundamental injustice in delivery of care to people with similar health conditions.


Much confusion exists in the literature between inequalities and inequities. Often, researchers are unable to distinguish the two concepts because they don’t know the actual needs of the population of interest. The approach is reduced to considering subgroups with more or less precise limits of identification/definition, and to surmise that because there is imbalance, the inequality is almost synonymous to inequity. Unfortunately, it will not be possible to solve the issue, unless considering patient’s situations individually, and then identifying the person to a subgroup at risk of inequity. This may apply to clinical practice, but cannot generalize to a population, particularly in epidemiological or intervention studies.


A good measure of inequality in a population is the concentration index, derived from the Gini coefficient, a measure of statistical dispersion intended to represent the income distribution of a nation’s residents. The concentration index applied to ill-health provides a means of quantifying the degree of income-related inequality in a specific health variable . It relates income distribution in this population to the distribution of their needs. It can also illustrate how one can measure if the curve relating needs to resources used departs from linearity and concentrates in some groups of population who become advantaged groups. If the supply of health care concentrates in wealthy categories, it describes a pro-rich income-related inequality ( Fig. 1 ). A horizontal inequity describes how resources are unequally distributed to people with equal needs. It is a form of inequity measured when all needs of a population are equal.




Fig. 1


Ill-health concentration curve.




Inequality versus inequity


Whitehead asserted that health inequalities are inequity if they are avoidable, unnecessary, unfair and unjust. In 2003, Braveman proposed that health equity was a state of absence of systematic disparities in health (or its social determinants) between the advantaged and disadvantaged social groups. As such, health equity is a fundamental ethical principle of social justice. Norheim pointed out in 2009 that health inequalities that are amenable to human intervention were unfair and should be mitigated.


Differences in care occur between countries and are determined by several factors including the wealth of the countries and their health system. These differences do not make one necessarily more inequitable than the other, though unequal. When differences occur within a country, within a specific health care system, it leads to more perceived inequities, as people become more aware of the discrepancy. This phenomenon usually broadens with increasing public discourse and media attention.


One should distinguish between inequalities in needs and inequity in supply of health services and treatment. Health inequalities in needs refer to a situation where individuals are in need of more care when severely ill or less care when less ill. Health inequity, on the other hand, refers to the availability of health services and treatment based on individuals’ personal, financial and socioeconomic characteristics rather than their health status and needs. Hence, health inequity is a fundamental injustice in delivery of care to people with similar health conditions.


Much confusion exists in the literature between inequalities and inequities. Often, researchers are unable to distinguish the two concepts because they don’t know the actual needs of the population of interest. The approach is reduced to considering subgroups with more or less precise limits of identification/definition, and to surmise that because there is imbalance, the inequality is almost synonymous to inequity. Unfortunately, it will not be possible to solve the issue, unless considering patient’s situations individually, and then identifying the person to a subgroup at risk of inequity. This may apply to clinical practice, but cannot generalize to a population, particularly in epidemiological or intervention studies.


A good measure of inequality in a population is the concentration index, derived from the Gini coefficient, a measure of statistical dispersion intended to represent the income distribution of a nation’s residents. The concentration index applied to ill-health provides a means of quantifying the degree of income-related inequality in a specific health variable . It relates income distribution in this population to the distribution of their needs. It can also illustrate how one can measure if the curve relating needs to resources used departs from linearity and concentrates in some groups of population who become advantaged groups. If the supply of health care concentrates in wealthy categories, it describes a pro-rich income-related inequality ( Fig. 1 ). A horizontal inequity describes how resources are unequally distributed to people with equal needs. It is a form of inequity measured when all needs of a population are equal.




Fig. 1


Ill-health concentration curve.




Points of view


There are many ways to define and identify inequities according to ethical, philosophical, sociological, epidemiological, economic, or public health points of view. Researchers with ethical and philosophical points of view differentiate between inequality, which identifies difference in care provision based on needs, and inequity, which focuses on justice in care provision. On the other hand, sociologists focus on identifying inequities and developing hypotheses for studying relevant determinants. Epidemiologists take the point of view that focuses on identifying determinants of inequity and their impact using experimental designs, quantitative analysis, and risk indicators. In contrast, economists approach the subject by analyzing resource consumption and economical modeling of health care systems at the macro (national) and micro (patient) levels. Researchers in public health are interested in identifying sources of inequity by means of population data, system-based approach, and these analyses are oriented towards developing action to reduce inequities in vulnerable groups. The points of view of the different disciplines propose a comprehensive framework of analysis for identifying determinants and providing evidence to guide actions against inequity.




Determinants


Most determinants of inequity in MSK disorders are similar to those in the general population and other chronic diseases. In this chapter, we will focus on osteoarthritis (OA), a model of chronic MSK disease, and rheumatoid arthritis (RA), a common chronic inflammatory disorder, in which access to expensive biological therapies is crucial to some patients.


Population data


People in society are exposed to inequity by demographic characteristics, societal factors and living conditions that determine to some degree the risk of disease, disability and lower quality of life. These factors are common to the general population, and therefore apply to people suffering from chronic MSK disorders, independently of other more specific factors.


There are many determinants of inequity currently identified. First, the health care system may be a source of inequity. In Ireland the evolution over the last 15 years has documented a change from a pro-rich inequity distribution of care in 1997 to a pro-poor distribution for accessing specialist and general practitioners in 2001. A similar trend was observed in Italy, caused mainly by income and regional variations and by educational attainment and insurance . In Thailand, health care use tends to favor the poor, especially at the primary care level after implementing a universal coverage policy .


Personal factors may also play a role. For example, access to hip and knee replacement surgery is different between men and women, with women over 3 times less likely to undergo arthroplasty despite equal willingness and need . Access to hip and knee OA treatment is also associated with education level, income, place of residence and personal factors. Overall, the highest access group are men between the ages of 60 and 84. On the other hand, access tends to be low among people living in rural communities and of non-white ethnicity .


Education is a strong determinant of differences in health. It has been shown to determine a remarkably homogeneous gradient of prevalence at the disadvantage of low educated people across age classes for a large number of chronic disease groups in Europe ( Table 1 ) .



Table 1

Education differences (low vs high education) for chronic disease groups in Europe.




































































































































Chronic disease groups OR (95% CI)
Total Men (Aged 25–79) Women (Aged 25–79) Men and women (25–59 years) Men and women (60–79 years)
Stroke 1.64 (1.40–1.93) a 1.70 (1.35–2.14) a 1.56 (1.25–1.96) a 1.89 (1.43–2.51) a 1.53 (1.27–1.86) a
Diseases of the nervous system 1.63 (1.51–1.77) a 1.57 (1.40–1.77) a 1.57 (1.41–1.75) a 1.81 (1.64–1.99) a 1.33 (1.17–1.52) a
Diabetes mellitus 1.60 (1.43–1.80) a 1.30 (1.11–1.51) a 2.19 (1.82–2.63) a 1.64 (1.38–1.94) a 1.57 (1.34–1.84) a
Arthritis 1.56 (1.40–1.73) a 1.50 (1.27–1.77) a 1.46 (1.26–1.68) a 2.04 (1.76–2.36) a 1.17 (1.01–1.36) a
Hypertension 1.42 (1.34–1.50) a 1.10 (1.00–1.22) 1.52 (1.42–1.62) a 1.55 (1.43–1.67) a 1.30 (1.20–1.40) a
Stomach/duodenum ulcer 1.40 (1.22–1.60) a 1.41 (1.19–1.67) a 1.56 (1.25–1.95) a 1.37 (1.15–1.62) a 1.46 (1.16–1.83) a
Genitourinary diseases 1.35 (1.24–1.47) a 1.29 (1.13–1.48) a 1.53 (1.36–1.72) a 1.51 (1.35–1.69) a 1.15 (1.00–1.31)
Headache/migraine 1.35 (1.27–1.43) a 1.18 (1.06–1.32) a 1.29 (1.20–1.39) a 1.28 (1.20–1.37) a 1.62 (1.42–1.84) a
Osteoarthrosis 1.34 (1.21–1.49) a 1.32 (1.12–1.55) a 1.29 (1.12–1.48) a 1.51 (1.30–1.75) a 1.20 (1.03–1.38) a
Liver/gall diseases 1.26 (1.08–1.46) a 1.10 (0.87–1.40) 1.30 (1.07–1.58) a 1.31 (1.07–1.60) a 1.19 (0.95–1.49)
Chronic respiratory diseases 1.24 (1.15–1.33) a 1.33 (1.20–1.48) a 1.19 (1.07–1.33) a 1.13 (1.03–1.25) a 1.42 (1.26–1.61) a
Heart disease 1.22 (1.10–1.35) a 1.18 (1.04–1.34) a 1.51 (1.28–1.79) a 1.29 (1.09–1.53) a 1.18 (1.04–1.33) a
Back and spinal cord disorders 1.19 (1.11–1.29) a 1.33 (1.19–1.49) a 1.05 (0.94–1.16) 1.29 (1.18–1.41) a 0.98 (0.86–1.13)
Cancer 1.13 (0.98–1.30) 0.96 (0.78–1.20) 1.22 (1.02–1.46) a 1.64 (1.36–1.99) a 0.77 (0.64–0.93) a
Kidney stones and other kidney diseases 1.11 (0.95–1.31) 1.03 (0.83–1.27) 1.34 (1.04–1.72) a 1.17 (0.95–1.45) 1.03 (0.80–1.33)
Skin diseases 0.99 (0.91–1.08) 0.99 (0.86–1.14) 0.98 (0.87–1.11) 0.98 (0.88–1.09) 1.03 (0.86–1.23)
Allergy 0.73 (0.66–0.81) a 0.67 (0.57–0.79) a 0.72 (0.63–0.82) a 0.69 (0.61–0.78) a 0.82 (0.68–0.99) a

a Confidence interval excludes 1.



Health professionals and patients may have an influence on each other’s behaviors . This sometimes may be a catalyst of inequity. A decline in inequality for hip replacement over 1990–2003 was observed in Finland despite structural features (uneven availability, co-payments, plurality of provisions) due to directed service expansion and the behavior changes of health professionals and patients . In Ontario Canada, Glazier found no issue in access to primary care physicians based on people’s level of education, but they noted an inequity in access to specialist care in frequency of visit and bypassing primary care in a system where there is universal health care coverage.


The EUMUSC.net project has recently proposed facilitators to implementing standards of care in OA and RA. It examined the following: accessibility of recommendations, knowledge, agreement with the content of the recommendations, cultural background, personal attitude towards recommendations, motivation, organization, environmental factors, time resources, economical resources and outcome expectancy. This was built on the model developed by Cabana in 1999 . The results showed that economical resources, time resources, environment and culture were not very valued as facilitators but that outcome expectancy, belief of the doctors, motivation, attitude, agreement, knowledge of patient and accessibility were contributing the most to positive physician behaviors .


Inequity can affect a variety of patient outcomes. Among them, the concept of health-related quality of life (HRQoL) considers the patient’s perspective as an essential component of the health care relationship. Patients’ reports of their health is, however, diverse, as social factors such as age, gender and professional status are likely to impact health self-perception and reporting. Social aspects of HRQoL have been explored using epidemiological HRQoL data issued from two national surveys in France which showed the existence of social determinants of HRQoL. Four social indicators have been identified, independent of age and gender, as determinants of HRQoL, including: 1) married or living with a common law partner, 2) level of education, 3) occupational status and 4) net household income .


OA: a model of chronic disease


OA represents a typical chronic MSK condition. The effectiveness of interventions for OA, however, investigated in clinical trials or observational studies, has been limited in some subgroups of less advantaged populations.


Disparities in health and health care have been demonstrated in a wide range of chronic diseases, including OA. OA is a major cause of chronic pain and disability contributing to 3.4% and 1.7% of the total disease burden in the developed and developing world . This burden is expected to increase in most countries because of the obesity epidemic and the aging population. In developed countries, the prevalence of knee OA in people age 60 and above is projected to grow to 35% by 2020 in obese persons . Both prevalence and severity are higher in populations with lower socioeconomic status, non-white groups and women, after accounting for the effect of obesity .


In 2011, Borkhoff et al. published a systematic review of determinants of accessing appropriate treatments and interventions to reduce the burden of OA . The PROGRESS framework and the further developed PROGRESS-Plus framework nicely summarize multidimensional components that may affect health and health care equity (see Target actions against inequity below). This framework is useful for identifying populations at higher risk of inequity, and has been used successfully to identify disparity in care for people with OA .


Place of residence


People living in inner-city or rural areas have higher prevalence of OA than in urban populations in the US and in Russia, but not in Australia (OR = 1.0 [0.8–1.2] in men and 0.9 [0.7–1.1] in women) .


Race and ethnicity


Compared to Caucasians, the prevalence of OA in African-Americans and Hispanics was found to be almost two times higher (Caucasians: 25%, African-Americans: 44%, Hispanics: 40%) , especially for severe OA . In the US and Canada, the white-to-black ratio for total joint arthroplasty (TJA) was approximately 3.0–5.1 in men and 1.5 to 2.0 in women .


Non-infection-related and infection-related complications after knee arthroplasty were higher among black patients compared with white patients (RR = 1.50 [1.08–2.10] and RR = 1.42 [1.06–1.90], respectively). Hispanic patients had a significantly higher risk of infection-related complications after knee arthroplasty (RR = 1.64 [1.08–2.49]) relative to otherwise similar white patients. No difference was observed for hip arthroplasty . There were also death-rate racial disparities as seen in the national data in the US .


Occupation


The prevalence and severity of OA was higher in unemployed vs employed populations, in unprofessional vs professional occupations, and 1.2 to 2.9 times higher in blue-collar vs all other workers in Canada, Russia, and France.


Gender/sex


Several studies, including the recent Global Burden of Disease (GDB) study 2010 , report that women have a higher prevalence of OA , but lower rates of TJA and a greater unmet need for TJA compared to men . One study showed that women have a greater reliance on the use of acetaminophen, suggesting that the pain due to OA may be undertreated among women . Interestingly, among people who are overweight, men are less likely to receive weight-loss advice as a part of OA management .


More strikingly, compared to a women, a man with similar symptoms was found 22 times more likely to receive a recommendation for total knee arthroplasty (TKA) from an orthopedic surgeon and twice more likely to receive the recommendation from a family physician . It should be noted that women tended to receive TJA at a more advanced stage of their disease and so they benefited less from the procedure.


Education


This determinant has been evidenced in a number of developed countries and is associated with access to health care services in developing countries . In people with severe OA, those who had less than high school education were less likely to be offered TJA , compared to those who completed post-secondary education (OR = 1.57 [1.17-2.11]) . Also, people with lower education were less likely to receive advice on exercise , and when it was received, instruction and monitoring were delivered in a less comprehensive manner .


Socioeconomic status


The same was reported for socioeconomic status and lower income (OR = 1.83 [1.24–2.70] for ≤ $20,000 versus >$40,000), which were independently associated with a greater likelihood of having the potential need for arthroplasty .


Social capital and networks


A study conducted in the US showed that individuals without a partner were less likely to consult an orthopedic surgeon for TJA .


Age


The same study observed the same disparities in people aged over 80 years compared to their younger counterparts . There is a well documented effect of age on prevalence, as well as increasing severity with aging, but no specific time trend or generation effect has been evidenced, after accounting for the obesity epidemic.


Disability


Disability has been integrated in previous factors as severity. It is here more a consequence of the disease. A study in Canada found that people who were overweight but experienced less disability from hip/knee OA were less likely to receive weight management advice, compared to their more disabled counterparts, after adjusting for confounding variables .


Sexual orientation


A recent study in the US Hispanic population from the BFFRS System showed that bisexual women were at 4 times higher odds (OR = 4) of arthritis, and lesbians at 5 times higher risk of disability .


Barriers to equitable health care are multifactorial and can exist at the system level, physician level and patient level. The system level includes health insurance, geographic proximity and transportation; the physician level includes cultural competence and bias, and patient–physician interaction; and the patient level includes factors such as patient preferences and health literacy.


Importance of developing interventions


A major challenge is that interventions with good evidence of effectiveness are most likely to benefit those who are more amenable to follow them (i.e. less exposed to disadvantage, since they are of higher socioeconomic status, education, and wealth). Hence, much effort should be placed on sustaining interventions targeting those most in need, which means specific design, recruitment, and attention.


A review on the effectiveness of self-management and education interventions found 10 relevant studies that demonstrated reduction of inequity in reaching the target population . These interventions targeted 9 disadvantaged groups with OA and demonstrated improvement in their disease knowledge, self-help skills, exercise behavior, symptom management, pain, disability and overall health. Seven studies compared disadvantaged groups to a control group or in a one-group pre-post study. One study compared the effect of a video patient decision aid about treatment options in African-American men and white men and found the aid helped reduce the disparities in knowledge and expectation between the two groups (gap intervention research).


RA: review on access to biologic therapy


Research from around the world has shown that not all RA patients receive and have access to biologic therapy equitably. Since the year 2000, a number of biologic therapies have been launched in the market and have progressively become a landmark in the treatment of early aggressive inflammatory rheumatic diseases, such as RA, because of their strong efficacy in the short and midterm. In the early years, little was known about the overall practices of prescriptions of these drugs.


In 2009, the first large study, the QUEST-RA study, was conducted in 25 countries including European countries and the USA, and the authors drew attention to the semi-ecological correlation between gross domestic product per capita and DAS28 disease activity scores of RA patients ( Fig. 2 ). This cross-sectional study comparing the types of treatment in high GDP countries to low GDP countries, found that biologics had different effect in different countries. For instance, in high GDP countries patients who took biologics and those who did not had the same average DAS28 score of 3.7, suggesting that the disease activity of biologic users was as well controlled as the non-users. This indicated that access to biologics was adequate. When looking at the low GDP countries, however, there were two striking differences. First, patients who did not receive biologic treatment had significantly higher disease activity (DAS28 = 5.2) than those who did receive biologic treatment (DAS28 = 4.4). Second, people in low GDP countries had higher DAS28 scores than those in the high GDP countries. Since patients’ response to medications is associated with the baseline health status, findings form this study suggests that people in low GDP countries may not have access to biologics until the disease is at a more advanced stage.


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Nov 10, 2017 | Posted by in RHEUMATOLOGY | Comments Off on Determinants of MSK health and disability – Social determinants of inequities in MSK health

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