There has been a resurgence of interest in the relation between health and socioeconomic position (SEP). SEP encompasses two important notions: the influence of the structural location of individuals and groups in a society and the cumulative effects of time. It addresses the context in which health-damaging exposures and health-protective resources act at different stages of the life course to influence adult health (1, 2). Such an approach provides a broad framework in which to think about and understand how both recent and remote socioeconomic factors interact to affect adult health. A substantial body of literature demonstrates that in the general population, material and social deprivation are directly related to disease incidence and prevalence and inversely related to health status (1, 3–8). Various studies have addressed the relation between lower SEP and mortality (9) or the development of chronic conditions (10–13) such as diabetes mellitus, cardiovascular disease, and cancer. Although some compelling evidence exists for an association between low SEP and adverse health outcomes for persons with diabetes and other chronic conditions, the pathways through which SEP and health are related in persons with chronic illness are poorly understood.
Because type 2 diabetes is common in all populations in industrialized nations but disproportionately affects socially and materially disadvantaged adults (14–24), it may serve as a model condition for evaluating the associations between SEP and health among persons with chronic disease (25, 26). Although effective therapies are available for managing diabetes and preventing or treating its complications, these therapies are underutilized, particularly among persons of low SEP (27–29). For someone with diabetes, SEP may influence access to and quality of care, social support, and community resources. It may also influence diabetes-related knowledge, communication with providers, ability to adhere to recommended medication, exercise, and dietary regimens, and treatment choices. Correspondingly, the reduction of socioeconomic disparities in health may have a profound impact on the morbidity and mortality associated with diabetes.
In this paper, we present a conceptual framework for the mechanisms linking SEP to the health of persons with diabetes (figure 1). We present findings from a review of the literature characterizing the association between SEP and health in persons with diabetes and discuss the main mechanisms posited to influence this relation: health behaviors, access to care, and processes of care, which we refer to as proximal mediators/moderators. We then discuss more distal mediators and moderators of the relation between SEP and health outcomes that often act through their relations to health behaviors, access, and process. Among these distal mediators and moderators are characteristics of persons with diabetes, their health care providers, their communities or neighborhoods, and their health care systems. We conclude this paper with a discussion of unanswered questions about relations between SEP and health in persons with diabetes and a research agenda for clarifying these associations.
MECHANISMS THROUGH WHICH SEP INFLUENCES HEALTH AMONG PERSONS WITH DIABETES
SEP and diabetes outcomes
Pathway 1 in figure 1 illustrates the relation between SEP and health outcomes in persons with diabetes. Research on the relation between SEP and health has often focused on individual characteristics such as income, wealth, education, and occupation. However, SEP encompasses not only current individual socioeconomic status but also social relationships (30), community-level characteristics (31, 32), and gradients of SEP at the individual and community levels (33), and it can be conceptualized and measured over the life course (34). Use of this broader framework may provide greater insights into the relation between SEP and health. For example, because the progression of both type 1 diabetes and type 2 diabetes can be influenced by behavior over the life course, SEP in childhood may have profound consequences for long-term health, even if SEP changes during adulthood. In addition, neighborhoods or communities may play an instrumental role in the health status of their residents through the availability of health care services, neighborhood characteristics that promote health (e.g., access to stores that sell healthy foods and places to exercise) or disease (e.g., toxic environments), and the prevailing attitudes toward health and health behaviors in those communities (32). The strengths and limitations of using present, childhood/life course, and community measures of SEP have been described elsewhere (35–37) and will not be a focus of this paper; however, a fuller picture of the relation between SEP and health will require studies that use different approaches to the measurement of SEP.
The health outcomes included in the model are both general and diabetes-specific. Among the general outcomes are health status, quality of life, and mortality. The diabetes-specific outcomes include intermediate outcomes (such as blood pressure management, hemoglobin A1c and lipid concentrations, and the presence of proteinuria or microalbuminuria) and their long-term consequences (such as cardiovascular disease, retinopathy, nephropathy, cerebrovascular disease, and peripheral vascular disease).
Proximal mediators/moderators: health behaviors, access, and process
SEP may influence health outcomes through individual health behaviors, access to care, and processes of care (see pathways 2a, 2b, and 2c in figure 1). These factors are called mediators if they are the means through which SEP influences health outcomes (or, in other contexts, access, quality, or health behaviors); they are called moderators if the effect of SEP differs according to levels of the factor.
A complex set of personal health care behaviors is critical to diabetes control (38). Among the behaviors of particular importance are self-monitoring of blood glucose concentrations, adjustment of insulin and oral antidiabetic agents in response to blood glucose readings and intercurrent illnesses, management of comorbid medical conditions (e.g., hypertension and hyperlipidemia), dietary adherence, exercise, and smoking.
“Access to care” encompasses the availability of health care services (potential access) and use of those services by patients (realized access). In managed care systems and health care systems that provide “universal coverage,” realized access may still be constrained by financial and organizational barriers to the use of benefits, such as required copayments, restrictions on specialty referrals, or lack of proximity to health care facilities (39). Access to care can be measured by identification of a regular source of care and the number of primary care visits, ease of specialty referrals and the number of specialty visits, travel time to appointments, and appointment waiting times.
The “process of care” is the technical and interpersonal care provided to patients within the health care setting (40). Among the processes important for persons with diabetes are regular measurement of blood pressure, hemoglobin A1c, and cholesterol levels; assessment of nephropathy; regularly conducted dilated eye and foot examinations; and counseling on smoking cessation. The process of care may directly influence health outcomes (41). A causal pathway between lower socioeconomic status and poor health care outcomes has been postulated that includes poorer access to care (resulting in inadequate treatment and increased risk of complications), poorer quality of care, and worse self-care behaviors (such as diet and exercise behaviors), which may contribute to other diseases and further decreased function (42).
Distal mediators/moderators
Distal mediators and moderators potentially explain the relation between SEP and access, process, health behaviors, and health outcomes for persons with diabetes. Figure 2 highlights some of these important potential distal mediators and moderators.
Critical covariates
Age, sex, and race/ethnicity are important covariates that should be considered in any analysis of the relation between SEP and health. Evidence suggests that although many of the observed age, sex, and racial disparities in health may be explained by differences in SEP, each of these demographic characteristics may exert an independent influence as well (2, 34, 43). Although they are not the focus of this paper, we highlight findings on these covariates, where appropriate, from studies that evaluated their relation to SEP among persons with diabetes.
Endogeneity or reverse causality
An underlying assumption of our model is that low SEP leads to poorer health outcomes. However, just as absolute or relative material or social disadvantage may lead to worse health outcomes, poorer health may result in lower SEP. We recognize that endogeneity, or reverse causality, is an important concern in studies of SEP and health (44), but it should be less of a problem in studies that define SEP by measures determined early in life (e.g., education), before the onset of most health problems. Our decision to examine how SEP might influence health is strengthened by empirical evidence from longitudinal (45) and instrumental-variables (46) analyses which reveals that at least part of the strong association between SEP and health can be attributed to the causal effects of SEP. Thus, our emphasis in the discussion below is on the mechanisms through which SEP might influence health.
EVIDENCE ON THE PROXIMAL PATHWAYS
SEP and diabetes outcomes
We have considerable evidence that the social status of persons with diabetes and the characteristics of their communities or neighborhoods may determine their risk of mortality and diabetes-related complications such as cardiovascular disease, retinopathy, end-stage renal disease, and amputation, as well as their quality of life (see pathway 1 in figure 1). Lower individual SEP, as measured by individual or household income, education, employment, occupation, or living in an underprivileged area, has been associated with poorer physical or emotional health (47–50), all-cause mortality or higher rates of fatal and nonfatal cardiovascular disease (50–56), poorer glycemic control (57–60), and increased risk of microvascular disease (58, 61–63). However, some US studies have found no association between SEP and glycemic control (64, 65).
SEP, health behaviors, and diabetes outcomes
Among persons with diabetes, factors such as low income, less education, and living in a high-poverty area have been associated with higher rates of smoking (23, 57, 58), lower rates of blood glucose monitoring (29, 66–68), and lower rates of vigorous exercise (23, 57) (see pathway 2a in figure 1). Unfortunately, no studies have evaluated the complex range of self-management behaviors required to control blood sugar and to manage lipid levels, blood pressure, and associated chronic conditions among persons with diabetes. These behaviors include not only blood glucose monitoring, examination of the feet, dietary restrictions, and regular exercise but also management of multiple medications, dosing schedules, and the maintenance of glycemic control during intercurrent illness.
SEP, access to care, and diabetes outcomes
The relation between higher socioeconomic status and better health outcomes is partly explained by better access to primary and specialty care (see pathway 2b in figure 1). Observational studies suggest that improved access, measured by a greater number of primary care providers in a region or the availability of facilities that provide high-quality primary care, may reduce the negative association between income inequality and self-reported health (69–71). For persons with diabetes, access to health insurance is also important for the receipt of high-quality care (68, 72–75). Compared with the uninsured, insured adults with diabetes have been shown to have three times the odds of having undergone a dilated eye examination (68). Those without insurance receive fewer foot examinations and preventive health care services (68), have poorer glycemic control (75), and have almost seven times the odds of having diabetic eye disease (73). Studies of patients with diabetes in managed care (76–78) indicate that socioeconomic disparities are reduced in settings of improved access. The mechanism is probably multifactorial; however, one study suggests that educational barriers may be reduced in managed care settings (79). Nonetheless, there is also evidence from managed care (80) and from countries with universal health insurance coverage (53, 59–61, 66, 81–85) that some socioeconomic disparities in health persist despite improved access to care.
SEP and the process of care
Although there have been exceptions (65, 86, 87), reports from diverse settings indicate that the process of diabetes care for persons of low SEP is inferior to that of more affluent persons (59, 66, 68, 88–91) (see pathway 2c in figure 1). Income (59, 88–91), education (68, 73, 92), and area of residence (88) have all been associated with gradients in the process of care. Having less education has been associated with lower rates of hemoglobin A1c and lipid measurement, fewer ophthalmologic visits, and lower rates of preventive services such as influenza shots and mammograms (92). Less educated persons also have less understanding of hemoglobin A1c testing (68) and receive fewer foot examinations and dilated eye examinations (73).
EVIDENCE ON THE DISTAL PATHWAYS
Individual-level pathways between SEP and health for persons with diabetes
The health inequalities observed for persons with diabetes at different levels of the socioeconomic hierarchy may be explained by differences in one or more individual-level characteristics, including patient-provider communication, culture and acculturation, mental health, social support, and stress (see pathway 3a in figure 1).
Patient and provider communication
Effective communication between patients and providers (93, 94) and shared decision-making (95, 96) influence health behaviors and the process and outcomes of care for persons with diabetes. Communication barriers may significantly decrease a patient’s ability to appropriately obtain health care and may inhibit the degree to which the patient benefits from such care.
Less effective communication (as measured by agreement between patients and clinicians on symptoms, test results, therapy, and prognosis) has been observed among patients of lower occupational status (97) and may pose a significant barrier to good care. Physicians are more likely to adopt a more directive approach with less-educated patients, who are then less likely to have their expectations met (98). Provider communication style has also been shown to influence diabetes outcomes. Patients who interact with less controlling, more informative physicians (99) and nurses (100) achieve better metabolic control. Indigent patients whose physicians facilitate participation in decision-making are more satisfied with their care (101). Moreover, satisfaction with both the effectiveness of the provider’s communication and participatory decision-making styles are important predictors of diabetes self-care behavior, an outcome that appears to be mediated by enhanced patient understanding of his/her diabetes care and confidence in his/her self-management skills and knowledge (102).
In one study, diabetic patients who were more assertive, who expressed more emotion during health care encounters, and who were more adept at obtaining information from health care providers also had better metabolic control than persons who were less assertive or expressive (94). Another small study, however, found no association between a patient’s behavior during health care visits and metabolic control and only a nonsignificant reduction in blood sugar levels among persons who more frequently elicited information from providers, though power may have been inadequate to detect clinically important differences between the groups (103). There is some evidence suggesting that efforts to enhance communication may mitigate the observed detrimental effects of low SEP. In a randomized trial designed to increase patient participation in medical-care decision-making, intervention patients were more effective at eliciting information from physicians and had significantly lower hemoglobin A1c levels than control patients (93).
Additional communication barriers of particular importance for persons with diabetes are inadequate functional health literacy and language discordance.
Health literacy
Functional health literacy, “the ability to read and comprehend prescription bottles, appointment slips, and the other essential health-related materials required to successfully function as a patient,” may mediate the relation between low SEP and health (104, p. 552). Lower educational status is strongly associated with worse health literacy (105, 106), and inadequate health literacy has been linked to poorer health status (100) and more hospital admissions (107). Persons with diabetes who have inadequate or marginal literacy are less likely to know the symptoms of hypoglycemia (108), and they have higher hemoglobin A1c levels and higher rates of retinopathy even when exposed to traditional diabetes education (109).
Language barriers
Like health literacy, language barriers appear to influence self-care behaviors and outcomes specific to diabetes. Patients with type 2 diabetes who report difficulty with English monitor their blood sugar levels less frequently than persons with no language barriers (29). Spanish-speaking Latinos are less likely than those who speak English to have a regular source of health care, and they receive less screening, report lower rates of use of preventive services (110–112), and are less satisfied with their health care (113) than Whites and English-speaking Latinos. In another study, among persons with diabetes or hypertension, language concordance between patient and provider was associated with greater physical and emotional well-being, better health perceptions, and less pain (114). Two other studies observed no difference in glycemic control between English-speaking and Spanish-speaking Latinos (115, 116). In one study, however, patients had established primary care providers, and interpreters were consistently and widely used (115), while in the other study, patients appeared to self-select Spanish-speaking providers (116); this suggests that the impact of language barriers may be attenuated by either language concordance or the availability of translators in the setting of an established primary care relationship.
Culture and acculturation
In the United States, medicine and medical care have a strong cultural context that has been inadequately explored in the medical literature. Cultural factors that represent shared norms, values, and attitudes of an ethnic or racial group may influence health beliefs, treatment preferences, and health behaviors and ultimately health outcomes. Although little work has been done on the association between culture, acculturation, and health among persons with diabetes (117), research in the general population suggests that cultural factors have a profound impact on health, including conditions that may worsen diabetes outcomes, such as obesity, depression, and cardiovascular disease. Moreover, there is evidence that these effects vary by place of origin, gender, and SEP. For example, among Japanese-American men in Hawaii, acculturation to a Westernized lifestyle was associated with higher rates of obesity and cardiovascular disease in addition to diabetes (118–120). In contrast, acculturation among Mexican Americans in Texas has been associated with lower rates of diabetes (120, 121), obesity (122), and depression (123) and higher rates of leisure-time physical activity (124). The relation between acculturation and health for Mexican Americans appears to be strongest among women and to be closely linked to socioeconomic status. Among Mexican-American women, residence in more affluent and more acculturated neighborhoods is associated with higher rates of regular exercise and lower rates of obesity in comparison with women in less acculturated and poorer neighborhoods (120, 121). In the studies that have examined how acculturation may influence the process of care, acculturation among Latinos was not associated with use of or receipt of preventive care (125, 126).
It is not clear whether the associations observed in the general population are the same for persons with diabetes, since there have been few studies of the impact of culture or acculturation on health among persons with diabetes. However, one small study of Latino and White veterans suggested that a number of sociocultural factors, including religiosity and family structure, may play a more important role than ethnicity per se in use of health care services and quality of life (117). In persons with diabetes, culturally appropriate interventions that address dietary, language, and social norms and health beliefs have been associated with better glycemic control among Mexican Americans (127) and enhanced physical activity among African Americans (128, 129). There has been little research on the roles that the cultural norms and backgrounds of clinicians play in their decision-making.
Mental health
Persons with diabetes are more likely than the general population to have depression (130–132), eating disorders (133), and other psychological conditions (132). Of these problems, depression is the most common and the one most extensively studied (131, 134–138). Persons with diabetes have double the odds of depression of the general population (136, 137), and in the general population there is a consistent inverse relation between income and depression (139). Finally, adults with diabetes who have relatively less education have significantly higher unadjusted rates of depression (138).
Depression may influence communication with health care providers, self-management behaviors, use of health care services, and metabolic control. Among patients with diabetes in one study, those with more depressive symptoms were found to have higher rates of nonadherence to oral antidiabetes medications than those with the fewest symptoms—15 percent versus 7 percent (134). These behaviors may translate into worse glycemic control, greater use of health care services, and higher costs. A meta-analysis demonstrated a positive association between depression and hemoglobin A1c levels (135). However, it is not known whether treatment of depression is associated with better management of diabetes. Depressed persons with diabetes also report more primary care and emergency department visits, more hospitalizations, and higher costs of care, and their total costs were found to be over four times those of persons with diabetes who were not depressed (134, 137).
Social support, social integration, and competing demands
Evidence is strong for a relation between supportive social ties and better physical and mental health (140–142) and, conversely, between social isolation and greater morbidity and mortality (143, 144). Poorer persons are at higher risk of social isolation and of having fewer supportive social ties (145, 146), and among persons with diabetes, low SEP may have a larger negative effect for people with lower levels of social support. Researchers in one small study found that under conditions of high stress, which tend to be more prevalent among people of lower SEP, persons with less social support had higher hemoglobin A1c levels than persons with more social support; however, under low-stress conditions, level of social support was not associated with differences in glycemic control (147).
In the aggregate, among persons with diabetes, higher levels of social support have been associated with better self-management, including adherence to recommended diet and exercise regimens and better glycemic control (148–153). Although empirical data on how social support influences health outcomes in persons with diabetes are sparse, better self-management skills and improved access (through the health-seeking behaviors of the patients themselves or through their social networks) and quality (due to better communication with physicians) probably play a role.
However, certain kinds of social ties, such as obligations that entail financial expenditures, demands on one’s time, and criticism from social contacts, may have detrimental effects (154–158). The health care needs of persons who are caregivers for disabled elders often compete with the physical and emotional needs of their dependents (159), and the caregivers often fail to obtain adequate health care for themselves (160, 161). Adults with diabetes who are the primary caregivers for young children or dependent adults may have worse access to care, a lower quality of care, and worse health behaviors because they lack the time to obtain care or look after themselves. A study of African-American adults with diabetes suggests that the caregiver role is not uncommon: 29 percent had a dependent child at home and 13 percent were caring for an ill person at home, and almost 20 percent of their visits to a case manager or community health worker addressed family responsibilities such as child care or elder care (162). Competing demands for time may also exacerbate the negative effect of low SEP on health care access, quality, and behaviors for these persons, who are less able to purchase child care or elder care, prepared meals, and other goods and services that could reduce the demands on their time.
There is contradictory evidence on whether even satisfactory social relationships may have detrimental effects on diabetes management. One small study found that greater satisfaction with social relationships was associated with better glycemic control among diabetic women but worse control among diabetic men (163). Although another small study did not support this finding (164), the influence of the content of social relationships on health among persons with diabetes deserves further study.
Stress
Control of blood glucose levels has been closely linked to stress in studies of type 1 and type 2 diabetes (165–169). The mechanisms for this association are probably multifactorial (170–172), but they remain poorly understood. It has been suggested that stress may impair glucose control through two different pathways: behaviors, such as a reduction in medication adherence, and neurohumoral pathways, notably the counter-regulatory hormones (170). Allostatic load, the cumulative biologic burden associated with the body’s adaptation to chronic stress, may be a particularly important mechanism through which low SEP affects health outcomes for persons with diabetes (173). There is evidence from one small study that stress management can result in modest but sustained reductions in glycemic control that are not associated with changes in health behaviors such as diet or exercise (174).
Stress may be a common mechanism through which SEP and several other individual-level variables influence health. However, the specific biologic markers of stress among persons with diabetes and the relation between health behaviors and the biologic mediators are not well characterized. Additionally, much prior research has focused on the acute effects of stress on diabetes management and glycemic control, while the long-term impact of sustained exposure to socioeconomic deprivation and its association with detrimental health behaviors and biologic mediators remains poorly understood.
Provider characteristics
Social influences on clinician decision-making include the clinician’s relationship with the patient and characteristics such as clinician specialty and decision-making style (175), as well as the SEP of the patient and the patient’s community (176, 177) (see pathway 3b in figure 1). SEP has been shown to influence physicians’ perceptions of the intelligence and personality characteristics of patients with coronary heart disease (178). These perceptions may influence the provider’s willingness or ability to provide counseling on healthy behaviors and disease management, which in turn would reinforce the tendency of patients of low SEP not to engage in these behaviors.
Persons of low SEP who have diabetes have been shown to have lower rates of seeing an endocrinologist for their care (67 percent of endocrinologists’ patients, as compared with less than 50 percent of generalists’ patients, had 12 or more years of education) (179), and diabetes specialists were more likely to provide indicated components of care (180–182). Only one study did not find such a difference between generalist care and specialty care (183).
Characteristics of communities or neighborhoods
Characteristics of communities or neighborhoods, such as the availability and accessibility of health services, infrastructure deprivation, prevailing attitudes toward health, levels of stress and social support, and environmental conditions, may influence general health outcomes (32, 184, 185) (see pathway 3c in figure 1). The SEP of a community may determine the educational, employment, and income opportunities of community residents, and it may also directly influence the social environment (e.g., crime rates, social capital, social organization, social isolation), the types of services provided, and the physical environment (31).
Analyses that incorporate data at both the individual level and the community level (e.g., multilevel modeling) may provide some assessment of whether communities and neighborhoods exert an independent contextual effect on individual health, health behaviors, access to care, and processes of care. Although some research has found no independent effect of the community on health and mortality after adjustment for individual socioeconomic characteristics (186–192), several studies have used multilevel models to demonstrate an independent influence of communities’ socioeconomic context on the health of their residents (5, 12, 22, 35, 187, 189, 193–204).
Poor persons are more likely to experience multiple dimensions of poor environmental quality, including higher-priced yet poorer-quality foods, high crime rates, poor-quality housing and schools, and toxic environments (205–207). Perhaps it is the accumulation of exposures, rather than any one exposure, that explains the SEP-health gradient at the community level (208).
Availability and accessibility of healthy foods
A lack of access to healthy foods may contribute to disparities in health care by SEP. Barriers to healthy eating are greater for poorer persons (209–214), and there are neighborhood effects on the purchase and consumption of healthy foods. For example, price disincentives to eating healthy food are greater in poor neighborhoods than in wealthier neighborhoods (211), and low-income communities have one third the number of supermarkets found in more affluent neighborhoods (215). This means that residents of low-income communities must purchase food in smaller stores that tend to have higher prices and a more limited selection of products (212, 216).
Access to places to exercise and neighborhood safety
Access to parks and recreational facilities is often described as an important domain in the social environment at the community level (32, 184), but this variable has rarely been incorporated into multilevel studies (185, 195, 217). Neighborhood safety may be an important element in people’s ability or willingness to engage in exercise, and it may affect stress levels, which have been implicated as a cause of poor health outcomes among the disadvantaged (32, 43, 184, 197, 198, 218, 219).
Transportation
Lack of transportation is an important barrier to receipt of appropriate health services (220–223), and it may influence other environmental factors, such as access to food, health care, and social networks (224). The care of persons with diabetes, who are asked to use numerous health services, may be very sensitive to the provision of transportation. To our knowledge, no US studies have characterized barriers to transportation and evaluated their influence on health outcomes. How the results of studies conducted elsewhere might apply to US settings and people with diabetes is not clear (201, 217, 224).
Environmental exposures
Toxic environmental exposures have been linked to poor-quality housing, as well as proximity to industrial centers and landfills (225). Several toxic exposures have been associated with the incidence of diabetes (226–230) or with health outcomes associated with diabetes, such as cardiovascular disease, renal disease, and hypertension (230–233). However, toxin levels in a given area can be difficult to quantify systematically, and very few studies have incorporated such measures into multilevel analyses (217).
Health care organizations
Several characteristics of health care organizations have been shown to influence diabetes outcomes (see pathway 3d in figure 1). These characteristics include the practices of health care providers or the organization of diabetes care (234), identification of persons with diabetes and the tracking of their care (180, 181, 235–252), and patient education and empowerment training (38, 66, 245, 253, 254). However, it is not known whether the impact of these interventions varies by SEP.
Although health care organizations can promote access (for example, by reducing financial barriers to care), other characteristics of these systems have the potential to negatively affect access, process, and health behaviors and worsen health outcomes. One of these is the management of referral care. Although management of specialty referrals may lead to more appropriate referral patterns (255–257), restrictions on referrals to specialists that are differentially applied to poorer or less-educated persons may adversely influence health in this patient cohort. Physicians practicing in managed-care settings report that financial incentives in the form of bonuses limit care (258, 259), and they express concern about compromising patient care when bonuses are linked to the volume of referrals, hospitalizations, or the use of expensive tests or procedures (258).
Because persons of low SEP may be clustered in certain health care systems (80), the gradient of the relation between SEP and health may be explained by the kinds of systems in which less affluent persons receive care. These relations can become quite complex. In one study carried out among hospitalized Medicare beneficiaries, poor patients received worse care than wealthier patients in all hospitals, but because they were more likely to be hospitalized in better-quality hospitals (urban teaching facilities), their aggregate level of care was comparable to that of more affluent patients (260).
Some financial and organizational arrangements may pose greater obstacles for persons of lower SEP. Patient copayments have been the most closely studied, and low-income persons are particularly sensitive to cost-sharing issues, since they spend a greater proportion of their income on out-of-pocket expenses than do higher-income patients (261). A reduction in the use of essential medications among Medicaid beneficiaries was observed after a copayment increase of only 50 cents (262, 263). Physician financial incentives in managed care may also have a differential impact on low-SEP patients, since clinicians may be influenced by the patient’s income or education. More educated patients and those who do not face language barriers may be more effective at negotiating with clinicians or health insurers to get their needs met in the face of potential disincentives to provision of care, and wealthier patients have a greater ability to purchase alternate health care.
CONCLUSIONS
Inequalities in health are a major challenge for the US health care system, yet we have a very limited understanding of the mechanisms by which social gradients are related to health disparities. Attempts to reduce these disparities should be guided by an understanding of the individual and contextual factors that may influence health outcomes and the associations between these factors. In the model presented here, we have attempted to integrate the many dimensions that might explain how SEP influences diabetes outcomes. The mechanisms proposed are likely to be complementary rather than competing and to vary at different levels of the social hierarchy rather than act uniformly across social strata.
Many questions remain, and many issues need further exploration:
• Is the gradient in health outcomes for persons with diabetes consistent across levels of SEP, or is there a threshold above which additional income has less of an impact on health and well-being?
• In health care systems in which access to care is comparable or uniform, is there substantial marginal benefit to be obtained by targeting interventions toward socioeconomically disadvantaged groups?
• Are the relations observed between different measures of SEP and health the same in developing nations and industrialized nations?
• What are the greatest socioeconomic influences on diabetes-related health behaviors? Are there certain periods during the life course when people are particularly sensitive to socioeconomic factors that may influence the development and course of diabetes?
• How might neighborhood characteristics be altered to improve health and health care for persons with diabetes and other chronic conditions?
• Does reducing financial or psychosocial strain improve health outcomes for persons with diabetes?
• What are the biologic markers associated with stress in persons with diabetes? Through what physiologic mechanisms do these markers exert their influence? Do they differ by SEP?
• Are there unrecognized financial and social costs to the adoption of healthy behaviors?
These questions have direct implications for the types of interventions that might be developed to reduce health inequalities among persons with diabetes and other chronic conditions, the populations that should be targeted by these interventions, and the barriers to their successful implementation. Critical to the success of such efforts is the realization of the multifaceted nature of socioeconomic influences on health and the need to examine individual, system-level, and area-level factors and their relation to access to care, health behaviors, and quality of care. We have attempted to simplify and focus the discussion by concentrating on diabetes, but we believe that our framework is broadly applicable to many other chronic conditions and is a step toward reducing inequalities in health for all of the chronically ill.
ACKNOWLEDGMENTS
This study was funded by the Division of Diabetes Translation of the Centers for Disease Control and Prevention (grant U-48-CCU916373) and the National Institute of Diabetes and Digestive and Kidney Diseases. Dr. Arleen Brown also received support from the Robert Wood Johnson Foundation (grant 037214).
The authors acknowledge the assistance of the investigators and staff of the Translating Research into Action for Diabetes (TRIAD) project.
FIGURE 1. Conceptual framework for the relation between socioeconomic position and health among persons with diabetes mellitus. Numbers and letters refer to pathways mentioned in the text.
FIGURE 2. Distal mediators and moderators of the relation between socioeconomic position and health behaviors, access to care, processes of care, and health among persons with diabetes mellitus.
References
Lynch JW, Kaplan GA, Shema SJ. Cumulative impact of sustained economic hardship on physical, cognitive, psychological, and social functioning.
Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines.
Kaplan GA, Pamuk ER, Lynch JW, et al. Inequality in income and mortality in the United States: analysis of mortality and potential pathways.
Kaplan GA. People and places: contrasting perspectives on the association between social class and health.
Haan M, Kaplan GA, Camacho T. Poverty and health: prospective evidence from the Alameda County Study.
Marmot MG, Fuhrer R, Ettner SL, et al. Contribution of psychosocial factors to socioeconomic differences in health.
Backlund E, Sorlie PD, Johnson NJ. The shape of the relationship between income and mortality in the United States: evidence from the National Longitudinal Mortality Study.
Haan MN, Weldon M. The influence of diabetes, hypertension, and stroke on ethnic differences in physical and cognitive functioning in an ethnically diverse older population.
Wei M, Valdez RA, Mitchell BD, et al. Migration status, socioeconomic status, and mortality rates in Mexican Americans and non-Hispanic whites: The San Antonio Heart Study.
Lynch JW, Kaplan GA. Socioeconomic factors. In: Berkman LF, Kawachi I, eds. Social epidemiology. New York, NY: Oxford University Press, 2000:13–35.
Smith GD, Hart C, Blane D, et al. Adverse socioeconomic conditions in childhood and cause specific adult mortality: prospective observational study.
Diez Roux AV, Merkin SS, Arnett D, et al. Neighborhood of residence and incidence of coronary heart disease.
Blackwell DL, Hayward MD, Crimmins EM. Does childhood health affect chronic morbidity in later life?
Barker DJ, Gardner MJ, Power C. Incidence of diabetes amongst people aged 18–50 years in nine British towns: a collaborative study.
La Vecchia C, Negri E, Pagano R, et al. Education, prevalence of disease, and frequency of health care utilization: the 1983 Italian National Health Survey.
Pincus T, Callahan LF, Burkhauser RV. Most chronic diseases are reported more frequently by individuals with fewer than 12 years of formal education in the age 18–64 United States population.
Scragg R, Baker J, Metcalf P, et al. Prevalence of diabetes mellitus and impaired glucose tolerance in a New Zealand multiracial workforce.
Burke JP, Williams K, Gaskill SP, et al. Rapid rise in the incidence of type 2 diabetes from 1987 to 1996: results from the San Antonio Heart Study.
Rich-Edwards JW, Colditz GA, Stampfer MJ, et al. Birthweight and the risk for type 2 diabetes mellitus in adult women.
Winkleby MA, Cubbin C, Ahn DK, et al. Pathways by which SES and ethnicity influence cardiovascular disease risk factors.
Robbins JM, Vaccarino V, Zhang H, et al. Excess type 2 diabetes in African-American women and men aged 40–74 and socioeconomic status: evidence from the Third National Health and Nutrition Examination Survey.
Cubbin C, Hadden WC, Winkleby MA. Neighborhood context and cardiovascular disease risk factors: the contribution of material deprivation.
Forsen T, Eriksson J, Tuomilehto J, et al. The fetal and childhood growth of persons who develop type 2 diabetes.
Glasgow RE, Wagner EH, Kaplan RM, et al. If diabetes is a public health problem, why not treat it as one? A population-based approach to chronic illness.
McKinlay J, Marceau L. US public health and the 21st century: diabetes mellitus.
Cowie CC, Eberhardt MS. Sociodemographic characteristics of persons with diabetes. In: Harris MI, Cowie CC, Stern MP, et al, eds. Diabetes in America. 2nd ed. Bethesda, MD: National Institutes of Health, 1995:85–116.
Brown AF, Gross AG, Gutierrez PR, et al. Income-related differences in the use of evidence-based therapies in older persons with diabetes mellitus in for-profit managed care.
Karter AJ, Ferrara A, Darbinian JA, et al. Self-monitoring of blood glucose: language and financial barriers in managed care population with diabetes.
Seeman TE, Crimmins E. Social environment effects on health and aging: integrating epidemiologic and demographic approaches and perspectives.
Robert SA. Community-level socioeconomic status effects on adult health.
Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review.
Kennedy BP, Kawachi I, Glass R, et al. Income distribution, socioeconomic status, and self rated health in the United States: multilevel analysis.
House JS, Williams DR. Understanding and reducing socioeconomic and racial/ethnic disparities in health. In: Smedley BD, Syme SL, eds. Promoting health: intervention strategies from social and behavioral research. Washington, DC: National Academy Press, 2000:81–124.
Duncan TE, Duncan SC, Hops H. Latent variable modeling of longitudinal and multilevel alcohol use data.
Braveman P, Cubbin C, Marchi K, et al. Measuring socioeconomic status/position in studies of racial/ethnic disparities: maternal and infant health.
Oakes JM, Rossi PH. The measurement of SES in health research: current practice and steps toward a new approach.
Anderson RM, Funnell MM, Butler PM, et al. Patient empowerment: results of a randomized controlled trial.
Gold M. Beyond coverage and supply: measuring access to healthcare in today’s market.
Donabedian A. Methods for deriving criteria for assessing the quality of medical care.
Fleming BB, Greenfield S, Engelgau MM, et al. The Diabetes Quality Improvement Project: moving science into health policy to gain an edge on the diabetes epidemic.
Kington RS, Smith JP. Socioeconomic status and racial and ethnic differences in functional status associated with chronic diseases.
Smith GD. Learning to live with complexity: ethnicity, socioeconomic position, and health in Britain and the United States.
Smith JP. Healthy bodies and thick wallets: the dual relation between health and economic status.
McLeod JD, Kessler RC. Socioeconomic status differences in vulnerability to undesirable life events.
Ettner SL. Measuring the human cost of a weak economy: does unemployment lead to alcohol abuse?
Olivera EM, Duhalde EP, Gagliardino JJ. Costs of temporary and permanent disability induced by diabetes.
Robinson N, Yateman NA, Protopapa LE, et al. Employment problems and diabetes.
Robinson N, Stevens LK, Protopapa LE. Education and employment for young people with diabetes.
Ware JE Jr, Bayliss MS, Rogers WH, et al. Differences in 4-year health outcomes for elderly and poor, chronically ill patients treated in HMO and fee-for-service systems: results from the Medical Outcomes Study.
Dorman JS, Tajima N, LaPorte RE, et al. The Pittsburgh Insulin-Dependent Diabetes Mellitus (IDDM) Morbidity and Mortality Study: case-control analyses of risk factors for mortality.
Matsushima M, Shimizu K, Maruyama M, et al. Socioeconomic and behavioural risk factors for mortality of individuals with IDDM in Japan: population-based case-control study. Diabetes Epidemiology Research International (DERI) US-Japan Mortality Study Group.
Chaturvedi N, Jarrett J, Shipley MJ, et al. 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.
Nielsson P, Johansson S-E, Sundqvist J. Diabetes mortality and social class in Sweden—influence of age and gender.
Cabrera C, Helgesson O, Wedel H, et al. Socioeconomic status and mortality in Swedish women: opposing trends for cardiovascular disease and cancer.
Robinson N, Lloyd CE, Stevens LK. Social deprivation and mortality in adults with diabetes mellitus.
Caddick SL, McKinnon M, Payne N, et al. Hospital admissions and social deprivation of patients with diabetes mellitus.
Connolly V, Kesson CM. Socio-economic status and membership of the British Diabetic Association in Scotland.
Weng C, Coppini DV, Sonksen PH. Geographic and social factors are related to increased morbidity and mortality rates in diabetic patients.
Roper NA, Ballous RW, et al. Excess mortality in a population with diabetes and the impact of material deprivation: longitudinal, population based study.
Chaturvedi N, Stephenson JM, Fuller JH. The relationship between socioeconomic status and diabetes control and complications in the EURODIAB IDDM complications study.
Unwin N, Binns D, Elliott K, et al. The relationships between cardiovascular risk factors and socio-economic status in people with diabetes.
Klein R, Klein BE, Jensen SC, et al. The relationship of socioeconomic factors to the incidence of proliferative diabetic retinopathy and loss of vision.
Haffner SM, Hazuda HP, Stern MP, et al. Effects of socioeconomic status on hyperglycemia and retinopathy levels in Mexican Americans with NIDDM.
Harris MI. Racial and ethnic differences in health care access and health outcomes for adults with type 2 diabetes.
Muhlhauser I, Overmann H, Bender R, et al. Social status and the quality of care for adult people with type 1 (insulin-dependent) diabetes mellitus: a population-based study.
Harris MI, Cowie CC, Howie LJ. Self-monitoring of blood glucose by adults with diabetes in the United States population.
Beckles GL, Engelgau MM, Narayan KM, et al. Population-based assessment of the level of care among adults with diabetes in the U.S.
Shi L, Starfield B. Primary care, income inequality, and self-rated health in the United States: a mixed-level analysis.
Shi L, Starfield B. The effect of primary care physician supply and income inequality on mortality among blacks and whites in US metropolitan areas.
Shi L, Starfield B, Politzer R, et al. Primary care, self-rated health, and reductions in social disparities in health.
Burge MR, Lucero Rassam AG, Schade DS. What are the barriers to medical care for patients with newly diagnosed diabetes mellitus?
Baker RS, Watkins NL, Wilson MR, et al. Demographic and clinical characteristics of patients with diabetes presenting to an urban public hospital ophthalmology clinic.
Piette JD. Perceived access problems among patients with diabetes in two public systems of care.
Gregg EW, Geiss LS, Saaddine J, et al. Use of diabetes preventive care and complications risk in two African-American communities.
Martin TL, Selby JV, Zhang D. Physician and patient prevention practices in NIDDM in a large urban managed-care organization.
Karter AJ, Ferrara A, Liu JY, et al. Ethnic disparities in diabetic complications in an insured population.
Karter AJ, Stevens MR, Herman WH, et al. Out of pocket costs and diabetes preventive services. The TRIAD Study Group.
Fiscella K, Franks P, Doescher MP, et al. Do HMOs affect educational disparities in health care?
Schneider EC, Zaslavsky AM, Epstein AM. Racial disparities in the quality of care for enrollees in Medicare managed care.
Booth GL, Hux JE. Relationship between avoidable hospitalizations for diabetes mellitus and income level.
Eachus J, Williams M, Chan P, et al. Deprivation and cause specific morbidity: evidence from the Somerset and Avon survey of health.
Ellemann K, Sorenson JN, Pedersen L, et al. Epidemiology and treatment of diabetic ketoacidosis in a community population.
Larsson D, Lager I, Nilsson PM. Socio-economic characteristics and quality of life in diabetes mellitus: relation to metabolic control.
Van der Meer JB, Mackenbach JP. The care and course of diabetes: differences according to level of education.
Ahluwalia HK, Miller CE, Pickard SP, et al. Prevalence and correlates of preventive care among adults with diabetes in Kansas.
Arday DR, Fleming BB, Keller DK, et al. Variation in diabetes care among states: do patient characteristics matter?
Kelly WF, Mahmood R, Kelly MJ, et al. Influence of social deprivation on illness in diabetic patients.
Kelly WF, Mahmood R, Turner S, et al. Geographical mapping of diabetic patients from the deprived inner city shows less insulin therapy and more hyperglycaemia.
Peters AL, Legorreta AP, Ossorio RC, et al. Quality of outpatient care provided to diabetic patients: a health maintenance organization experience.
Connolly V, Unwin N, Sherriff P, et al. Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas.
Chin MH, Zhang JX, Merrell K. Diabetes in the African-American Medicare population: morbidity, quality of care, and resource utilization.
Greenfield S, Kaplan SH, Ware JE Jr, et al. Patients’ participation in medical care: effects on blood sugar control and quality of life in diabetes.
Kaplan SH, Greenfield S, Ware JE. Assessing the effects of physician-patient interactions on the outcomes of chronic disease.
Olivarius NF, Beck-Nielsen H, Andreasen AH, et al. Randomised controlled trial of structured personal care of type 2 diabetes mellitus.
Epstein AM, Taylor WC, Seage GR 3rd. Effects of patients’ socioeconomic status and physicians’ training and practice on patient-doctor communication.
Fiscella K, Goodwin MA, Stange KC. Does patient educational level affect office visits to family physicians?
Kaplan SH, Gandek B, Greenfield S, et al. Patient and visit characteristics related to physicians’ participatory decision-making style: results from the Medical Outcomes Study.
Street RL Jr, Piziak VK, Carpentier WS, et al. Provider-patient communication and metabolic control.
Golin C, DiMatteo MR, Duan N, et al. Impoverished diabetic patients whose doctors facilitate their participation in medical decision-making are more satisfied with their care.
Heisler M, Bouknight RR, Hayward RA, et al. The relative importance of physician communication, participatory decision-making, and patient understanding in diabetes self- management.
Rost KM, Flavin KS, Cole K, et al. Change in metabolic control and functional status after hospitalization: impact of patient activation intervention in diabetic patients.
Ad Hoc Committee on Health Literacy for the Council on Scientific Affairs, American Medical Association. Health literacy: report of the Council on Scientific Affairs.
Baker DW, Parker RM, Williams MV, et al. Health literacy and the risk of hospital admission.
Gazmararian JA, Parker RM, Baker DW. Reading skills and family planning knowledge and practices in a low-income managed-care population.
Baker DW, Parker RM, Williams MV, et al. The relationship of patient reading ability to self-reported health and use of health services.
Williams MV, Baker DW, Parker RM, et al. Relationship of functional health literacy to patients’ knowledge of their chronic disease: a study of patients with hypertension and diabetes.
Schillinger D, Grumbach K, Piette J, et al. Association of health literacy with diabetes outcomes.
Hu DJ, Covell RM. Health care usage by Hispanic outpatients as function of primary language.
Schur CL, Albers LA. Language, sociodemographics, and health care use of Hispanic adults.
Fiscella K, Franks P, Doescher MP, et al. Disparities in health care by race, ethnicity, and language among the uninsured.
Morales LS, Cunningham WE, Brown JA, et al. Are Latinos less satisfied with communication by health care providers?
Perez-Stable EJ, Napoles-Springer A, Miramontes JM. The effects of ethnicity and language on medical outcomes of patients with hypertension or diabetes.
Tocher TM, Larson E. Quality of diabetes care for non- English-speaking patients: a comparative study.
Lasater LM, Davidson AJ, Steiner JF, et al. Glycemic control in English- vs Spanish-speaking Hispanic patients with type 2 diabetes mellitus.
Walsh ME, Katz MA, Sechrest L. Unpacking cultural factors in adaptation to type 2 diabetes mellitus.
Huang B, Rodriguez BL, Burchfiel CM, et al. Acculturation and prevalence of diabetes among Japanese-American men in Hawaii.
Reed D, McGee D, Cohen J, et al. Acculturation and coronary heart disease among Japanese men in Hawaii.
Stern MP, Knapp JA, Hazuda HP, et al. Genetic and environmental determinants of type II diabetes in Mexican Americans: is there a “descending limb” to the modernization/diabetes relationship?
Hazuda HP, Haffner SM, Stern MP, et al. Effects of acculturation and socioeconomic status on obesity and diabetes in Mexican Americans: The San Antonio Heart Study.
Sundquist J, Winkleby M. Country of birth, acculturation status, and abdominal obesity in a national sample of Mexican-American women and men.
Gonzalez HM, Haan MN, Hinton L. Acculturation and the prevalence of depression in older Mexican Americans: baseline results of the Sacramento Area Latino Study on Aging.
Crespo CJ, Smit E, Carter-Pokras O, et al. Acculturation and leisure-time physical inactivity in Mexican American adults: results from NHANES III, 1988–1994.
Marks G, Solis J, Richardson JL, et al. Health behavior of elderly Hispanic women: does cultural assimilation make a difference?
Solis JM, Marks G, Garcia M, et al. Acculturation, access to care, and use of preventive services by Hispanics: findings from HHANES 1982–84.
Brown SA, Garcia AA, Kouzekanani K, et al. Culturally competent diabetes self-management education for Mexican Americans: The Starr County Border Health Initiative.
Keyserling TC, Ammerman AS, Samuel-Hodge CD, et al. A diabetes management program for African American women with type 2 diabetes.
Keyserling TC, Samuel-Hodge CD, Ammerman AS, et al. A randomized trial of an intervention to improve self-care behaviors of African-American women with type 2 diabetes.
Lustman PJ. Anxiety disorders in adults with diabetes mellitus.
Peyrot M, Rubin RR. Levels and risks of depression and anxiety symptomatology among diabetic adults.
Rubin RR, Peyrot M. Psychological issues and treatments for people with diabetes.
Rydall AC, Rodin GM, Olmsted MP, et al. Disordered eating behavior and microvascular complications in young women with insulin-dependent diabetes mellitus.
Ciechanowski PS, Katon WJ, Russo JE. Depression and diabetes: impact of depressive symptoms on adherence, function, and costs.
Lustman PJ, Anderson RJ, Freedland KE, et al. Depression and poor glycemic control: a meta-analytic review of the literature.
Anderson RJ, Freedland KE, Clouse RE, et al. The prevalence of comorbid depression in adults with diabetes.
Egede LE, Zheng D, Simpson K. Comorbid depression is associated with increased health care use and expenditures in individuals with diabetes.
Tellez-Zenteno JF, Cardiel MH. Risk factors associated with depression in patients with type 2 diabetes mellitus.
Muntaner C, Eaton WW, Diala C, et al. Social class, assets, organizational control and the prevalence of common groups of psychiatric disorders.
Seeman TE, Syme SL. Social networks and coronary artery disease: a comparison of the structure and function of social relations as predictors of disease.
Uchino BN, Cacioppo JT, Kiecolt-Glaser JK. The relationship between social support and physiological processes: a review with emphasis on underlying mechanisms and implications for health.
Glasgow RE, Strycker LA, Toobert DJ, et al. A social-ecologic approach to assessing support for disease self-management: The Chronic Illness Resources Survey.
Berkman LF, Syme SL. Social networks, host resistance, and mortality: a nine-year follow-up study of Alameda County residents.
Balfour JL, Kaplan GA. Neighborhood environment and loss of physical function in older adults: evidence from the Alameda County Study.
Oakley A, Rajan L. Social class and social support: the same or different?
Turner RJ, Marino F. Social support and social structure: a descriptive epidemiology.
Griffith LS, Field BJ, Lustman PJ. Life stress and social support in diabetes: association with glycemic control.
Wilson W, Ary DV, Biglan A, et al. Psychosocial predictors of self-care behaviors (compliance) and glycemic control in non-insulin-dependent diabetes mellitus.
Murphy DJ, Williamson PS, Nease DE Jr. Supportive family members of diabetic adults.
Garay-Sevilla ME, Nava LE, Malacara JM, et al. Adherence to treatment and social support in patients with non-insulin dependent diabetes mellitus.
Ford ME, Tilley BC, McDonald PE. Social support among African-American adults with diabetes, part 2: a review.
Fukunishi I, Akimoto M, Horikawa N, et al. Stress coping and social support in glucose tolerance abnormality.
Wang CY, Fenske MM. Self-care of adults with non-insulin-dependent diabetes mellitus: influence of family and friends.
Wellman B. From social support to social network. Dordrecht, The Netherlands: Martinus Nijhoff, 1985:205–24.
Kessler RC, McLeod JD, Wethington E. The costs of caring: a perspective on the relationship between sex and psychological distress. In: Sarason IG, Sarason BR, eds. Social support: theory, research and applications. Dordrecht, The Netherlands: Martinus Nijhoff, 1985:491–506.
Rook KS. The negative side of social interaction: impact on psychological well-being.
Schuster TL, Kessler RC, Aseltine RH Jr. Supportive interactions, negative interactions, and depressed mood.
Finch JF, Okun MA, Barrera M Jr, et al. Positive and negative social ties among older adults: measurement models and the prediction of psychological distress and well-being.
Zarit SH, Reever KE, Bach-Peterson J. Relatives of the impaired elderly: correlates of feelings of burden.
Batts ML, Gary TL, Huss K, et al. Patient priorities and needs for diabetes care among urban African American adults.
Heitzmann CA, Kaplan RM. Interaction between sex and social support in the control of type II diabetes mellitus.
Eriksson BS, Rosenqvist U. Social support and glycemic control in non-insulin dependent diabetes mellitus patients: gender differences.
Lloyd CE, Dyer PH, Lancashire RJ, et al. Association between stress and glycemic control in adults with type 1 (insulin-dependent) diabetes.
Kramer JR, Ledolter J, Manos GN, et al. Stress and metabolic control in diabetes mellitus: methodologic issues and an illustrative analysis.
Aikens JE, Wallander JL, Bell DS, et al. Daily stress variability, learned resourcefulness, regimen adherence, and metabolic control in type 1 diabetes mellitus: evaluation of a path model.
Jaber LA, Lweis NJ, Slaughter RL, et al. The effect of stress on glycemic control in patients with type 2 diabetes during glyburide and glipizide therapy.
Inui A, Kitaoka H, Majima M, et al. Effect of the Kobe earthquake on stress and glycemic control in patients with diabetes mellitus.
Barglow P, Hatcher R, Edidin DV, et al. Stress and metabolic control in diabetes: psychosomatic evidence and evaluation of methods.
Surwit RS, Schneider MS. Role of stress in the etiology and treatment of diabetes mellitus.
Peyrot M, McMurry JF, Kruger DF. A biopsychosocial model of glycemic control in diabetes: stress, coping, and regimen adherence.
Seeman TE, McEwen BS, Rowe JW, et al. Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging.
Surwit RS, van Tilburg MA, Zucker N, et al. Stress management improves long-term glycemic control in type 2 diabetes.
Eisenberg JM. Sociologic influences on decision-making by clinicians.
Pendleton DA, Bochner S. The communication of medical information in general practice consultations as a function of patients’ social class.
Komaromy M, Lurie N, Osmond D, et al. Physician practice style and rates of hospitalization for chronic medical conditions.
Van Ryn M, Burke J. The effect of patient race and socio- economic status on physicians’ perceptions of patients.
Chin MH, Zhang JX, Merrell K. Specialty differences in the care of older patients with diabetes.
Weiner JP, Parente ST, Garnick DW, et al. Variation in office-based quality: a claims-based profile of care provided to Medicare patients with diabetes.
Aubert RE, Herman WH, Waters J, et al. Nurse case management to improve glycemic control in diabetic patients in a health maintenance organization: a randomized, controlled trial.
Lafata JE, Martin S, Morlock R, et al. Provider type and the receipt of general and diabetes-related preventive health services among patients with diabetes.
Greenfield S, Rogers W, Mangotich M, et al. Outcomes of patients with hypertension and non-insulin dependent diabetes mellitus treated by different systems and specialties: results from the Medical Outcomes Study.
Diez-Roux AV. Bringing context back into epidemiology: variables and fallacies in multilevel analysis.
Yen IH, Kaplan GA. Neighborhood social environment and risk of death: multilevel evidence from the Alameda County Study.
Ecob R, Smith GD. Income and health: what is the nature of the relationship?
Sloggett A, Joshi H. Deprivation indicators as predictors of life events 1981–1992 based on the UK ONS longitudinal study.
Reijneveld SA. The impact of individual and area characteristics on urban socioeconomic differences in health and smoking.
Muller A. Education, income inequality, and mortality: a multiple regression analysis.
Osler M, Prescott E, Gronbaek M, et al. Income inequality, individual income, and mortality in Danish adults: analysis of pooled data from two cohort studies.
Shibuya K, Hashimoto H, Yano E. Individual income, income distribution, and self rated health in Japan: cross sectional analysis of nationally representative sample.
Sturm R, Gresenz CR. Relations of income inequality and family income to chronic medical conditions and mental health disorders: national survey in USA.
Krieger N. Women and social class: a methodological study comparing individual, household, and census measures as predictors of black/white differences in reproductive history.
Diez-Roux AV, Nieto FJ, Muntaner C, et al. Neighborhood environments and coronary heart disease: a multilevel analysis.
O’Campo P, Xue X, Wang MC, et al. Neighborhood risk factors for low birthweight in Baltimore: a multilevel analysis.
Roberts EM. Neighborhood social environments and the distribution of low birthweight in Chicago.
Waitzman NJ, Smith KR. Phantom of the area: poverty area residence and mortality in the United States.
LeClere FB, Rogers RG, Peters K. Neighborhood social context and racial differences in women’s heart disease mortality.
Malmstrom M, Sundquist J, Johansson SE. Neighborhood environment and self-reported health status: a multilevel analysis.
Soobader MJ, LeClere FB. Aggregation and the measurement of income inequality: effects on morbidity.
Hart C, Hole DJ, Smith GD. The contribution of risk factors to stroke differentials, by socioeconomic position in adulthood: The Renfrew/Paisley Study.
Kalff AC, Kroes M, Vles JS, et al. Neighbourhood level and individual level SES effects on child problem behaviour: a multilevel analysis.
Wilson DK, Kliewer W, Plybon L, et al. Socioeconomic status and blood pressure reactivity in healthy black adolescents.
Bosma H, van de Mheen HD, Borsboom GJ, et al. Neighborhood socioeconomic status and all-cause mortality.
Macintyre S, Maciver S, Sooman A. Area, class and health: should we be focusing on places or people?
Sampson R, Raudenbush S, Earls F. Neighborhoods and violent crime: a multilevel study of collective efficacy.
Steptoe A, Feldman P. Neighborhood problems as sources of chronic stress: development of a measure of neighborhood problems, and associations with socioeconomic status and health.
Evans GW, Kantrowitz E, for the John D. and Catherine T. MacArthur Research Network on Socioeconomic Status and Health. Socioeconomic status and health: the potential role of suboptimal physical environments. San Francisco, CA: University of California, San Francisco, 1999. (Revised September 18, 2001). (World Wide Web URL: http://www.macses.ucsf.edu/Research/Social%20Environment/notebook/suboptimalEnv.html).
Mooney C. Cost and availability of healthy food choices in a London health district.
Sallis JF, Nader R, Rupp JW, et al. San Diego surveyed for heart-healthy foods and exercise facilities.
Sooman A, Macintyre S, Anderson A. Scotland’s health—a more difficult challenge for some? The price and availability of healthy foods in socially contrasting localities in the west of Scotland.
Curtis KA, McClellan S. Falling through the safety net: poverty, food assistance and shopping constraints in an American city.
Turrell G. Structural, material and economic influences of the food purchasing choices of socioeconomic groups.
Mackerras D. Disadvantage and the cost of food. (Letter).
Morland K, Wing S, Diez Roux A. The contextual effect of the local food environment on residents’ diets: The Atherosclerosis Risk in Communities Study.
Horowitz CR, Colson KA, Hebert PL. Disparities in access to healthy foods for people with diabetes. (Abstract).
Ahmed SM, Lemkau JP, Nealeigh N, et al. Barriers to healthcare access in a non-elderly urban poor American population.
Lepore SJ, Evans GW, Palsane MN. Social hassles and psychological health in the context of chronic crowding.
Krieger J, Higgins DL. Housing and health: time again for public health action.
Rittner B, Kirk AB. Health care and public transportation use by poor and frail elderly people.
Melnikow J, Paliescheskey M, Stewart GK. Effect of a transportation incentive on compliance with the first prenatal appointment: a randomized trial.
Williamson D, Fast JE. Poverty and medical treatment: when public policy compromises accessibility.
Takano T, Nakamura K. An analysis of health levels and various indicators of urban environments for Healthy Cities projects.
Bostock L. Pathways of disadvantage? Walking as a mode of transport among low-income mothers.
Baum A, Garofalo JP, Yali AM. Socioeconomic status and chronic stress: does stress account for SES effects on health?
Lai MS, Hsueh YM, Chen CJ, et al. Ingested inorganic arsenic and prevalence of diabetes mellitus.
Van Maanen JM, Albering HJ, de Kok TM, et al. Does the risk of childhood diabetes mellitus require revision of the guideline values for nitrate in drinking water?
Tseng CH, Tai TY, Chong CK, et al. Long-term arsenic exposure and incidence of non-insulin-dependent diabetes mellitus: a cohort study in arseniasis-hyperendemic villages in Taiwan.
Longnecker MP, Daniels JL. Environmental contaminants as etiologic factors for diabetes.
Bener A, Obineche E, Gillett M, et al. Association between blood levels of lead, blood pressure and risk of diabetes and heart disease in workers.
Gustavsson P, Plato N, Hallqvist J, et al. A population-based case-referent study of myocardial infarction and occupational exposure to motor exhaust, other combustion products, organic solvents, lead, and dynamite. Stockholm Heart Epidemiology Program (SHEEP) Study Group.
Yaqoob M, Patrick AW, McClelland P, et al. Occupational hydrocarbon exposure and diabetic nephropathy.
Chen CJ, Chiou HY, Chiang MH, et al. Dose-response relationship between ischemic heart disease mortality and long-term arsenic exposure.
Renders CM, Valk GD, Griffin SJ, et al. Interventions to improve the management of diabetes in primary care, outpatient, and community settings: a systematic review.
Warren-Boulton E, Anderson BJ, Schwartz NL, et al. A group approach to the management of diabetes in adolescents and young adults.
Tattersall RB, McCulloch DK, Aveline M. Group therapy in the treatment of diabetes.
Coulter A, Noone A, Goldacre M. General practitioners’ referrals to specialist outpatient clinics. I. Why general practitioners refer patients to specialist outpatient clinics.
Maxwell AE, Hunt IF, Bush MA. Effects of a social support group, as an adjunct to diabetes training, on metabolic control and psychosocial outcomes.
Wasson J, Gaudette C, Whaley F, et al. Telephone care as a substitute for routine clinic follow-up.
Kirkman MS, Weinberger M, Landsman PB, et al. A telephone-delivered intervention for patients with NIDDM: effect on coronary risk factors.
Osman LM, Abdalla MI, Beattie JA, et al. Reducing hospital admission through computer supported education for asthma patients. Grampian Asthma Study of Integrated Care (GRASSIC).
Peters AL, Davidson MB, Ossorio RC. Management of patients with diabetes by nurses with support of subspecialists.
Weinberger M, Kirkman MS, Samsa GP, et al. A nurse-coordinated intervention for primary care patients with non-insulin-dependent diabetes mellitus: impact on glycemic control and health-related quality of life.
Glasgow RE, Toobert DJ, Hampson SE. Effects of a brief office-based intervention to facilitate diabetes dietary self-management.
Verlato G, Muggeo M, Bonora E, et al. Attending the diabetes center is associated with increased 5-year survival probability of diabetic patients: The Verona Diabetes Study.
Beck A, Scott J, Williams P, et al. A randomized trial of group outpatient visits for chronically ill older HMO members: the Cooperative Health Care Clinic.
McCulloch DK, Price MJ, Hindmarsh M, et al. A population-based approach to diabetes management in a primary care setting: early results and lessons learned.
Piette JD, McPhee SJ, Weinberger M, et al. Use of automated telephone disease management calls in an ethnically diverse sample of low-income patients with diabetes.
Sikka R, Waters J, Moore W, et al. Renal assessment practices and the effect of nurse case management of health maintenance organization patients with diabetes.
Simon LP, Albright A, Belman MJ, et al. Risk and protective factors associated with screening for complications of diabetes in a health maintenance organization setting.
Norris SL, Engelgau MM, Narayan KM. Effectiveness of self-management training in type 2 diabetes: a systematic review of randomized controlled trials.
Norris SL, Lau J, Smith SJ, et al. Self-management education for adults with type 2 diabetes: a meta-analysis of the effect on glycemic control.
Moore SH, Martin DP, Richardson WC. Does the primary-care gatekeeper control the costs of health care? Lessons from the SAFECO experience.
Martin DP, Diehr P, Price KF, et al. Effect of a gatekeeper plan on health services use and charges: a randomized trial.
Ferris TG, Perrin JM, Manganello JA, et al. Switching to gatekeeping: changes in expenditures and utilization for children.
Grumbach K, Osmond D, Vranizan K, et al. Primary care physicians’ experience of financial incentives in managed-care systems.
Gold M. Financial incentives: current realities and challenges for physicians.
Kahn KL, Pearson ML, Harrison ER, et al. Health care for black and poor hospitalized Medicare patients.
Gross DJ, Alecxih L, Gibson MJ, et al. Out-of-pocket health spending by poor and near-poor elderly Medicare beneficiaries.
Nelson AA Jr, Reeder CE, Dickson WM. The effect of a Medicaid drug copayment program on the utilization and cost of prescription services.
