As noted in Chapter 1, the committee developed a conceptual framework to guide its approach to the inclusion of social risk factors in Medicare payment programs. The committee agreed to employ the phrase social risk factors to broadly characterize a set of constructs that capture the key ways in which social processes and social relationships could influence key health-related outcomes in Medicare beneficiaries. The conceptual model is broadly grounded in many models articulating the social determinants of health, but it is also tailored and made specific to the health-related processes and outcomes that are of interest in understanding and evaluating the performance of the health care system among Medicare beneficiaries.
The five domains of social risk factors are
- Socioeconomic position (SEP);
- Race, ethnicity, and cultural context;
- Social relationships; and
- Residential and community context.
The five social risk factors may influence health care and health through a number of potential pathways. These include (1) direct effects of social risk factors on behavioral and clinical disease risk factors (as well as on the prevalence and development of disease), (2) direct effects of social risk factors on access to care and on the process of care, and (3) direct effects of social risk factors on the quality of health care received and on the outcomes of this care. These social risk factors may also directly affect satisfaction with care and adverse health care effects, as well as the cost of care if, for example, additional effort on the part of the health care system is required to achieve a given outcome.
The five social risk factors may also influence health literacy, the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions (NASEM, 2015). Health literacy in turn has an important impact on the patients’ interaction with the health care system and may affect access to care and the process of care, which in turn has consequences for quality of care,
outcomes of care, satisfaction and patient safety, and cost. Health literacy may also directly affect quality of care, outcomes of care, adverse effects, satisfaction, and cost.
It is important to note that social risk factors may affect the outcomes of interest through many interrelated pathways, some of which may be indirect or mediated through clinical or behavioral risk factors, disease prevalence, and behaviors or mediated through access to care and the process of care (e.g., the types of facilities and providers where patients are seen and the processes followed in the health care system). In addition, social risk factors may affect the outcomes of care through direct pathways by influencing the outcomes of the care received independently of effects on clinical or behavioral risk factors, access to care, or the process of care (e.g., the effectiveness of a blood pressure control using a certain drug may be modified by the persons social context even if the treatment is high quality and appropriate). Feedback loops may also be present.
In this section, the committee defines each of these five social risk factor domains, as well as health literacy, and summarizes the results of the literature search linking effects of each domain on health care outcomes and quality measures. Within each factor, results of review articles are discussed first, followed by results from individual studies. Individual studies are organized by outcome domain (e.g., health care use), subdomain (e.g., clinical processes of care), and measure (e.g., receipt of recommended care).
Socioeconomic position (SEP) is an indicator of an individual’s absolute and relative position in a society’s stratification system. SEP captures a combination of access to material and social resources as well as relative status, meaning prestige- or rank-related characteristics (Krieger et al., 1997). To that end, the committee employs the term socioeconomic position, rather than the more commonly used phrase socioeconomic status, because socioeconomic status blurs distinctions between the two different aspects of SEP (actual resources and status), and privileges status over the material and social resources (Krieger et al., 1997; Lynch and Kaplan, 2000).
SEP is commonly measured through indicators such as income and wealth (with wealth being of special relevance for older individuals and disabled persons out of the paid workforce), education, and occupation (including occupational history and employment status) (Braveman et al., 2005; Krieger et al., 1997; Lynch and Kaplan, 2000). SEP over the life course is a powerful predictor of many health-related processes and outcomes and is often related to outcomes in a dose–response manner (Adler et al., 1994; Krieger et al., 1997; Lynch and Kaplan, 2000). In the medical field, insurance status (whether an individual has insurance and insurance type) is also used as a proxy for SEP—for example, dual Medicare–Medicaid eligibility among the Medicare population is often used as a proxy for low income. However, insurance status is generally a very imperfect proxy, because it does not capture the continuum of SEP, may capture dimensions of health status unmeasured by other data sources, and because it represents insurance status itself, which is distinct from SEP.
Several review articles examined the influence of SEP on health care use and health care outcomes, but each found only a small number of studies. Two reviews examined the effect of
SEP on readmissions, one of which found no association between education and readmissions after acute myocardial infarction (AMI) and insufficient but suggestive evidence that income negatively affects readmissions (Damiani et al., 2015). By contrast, the other study found substantial inconsistencies about which patient characteristics, including indicators of SEP and other measures, were predictive of readmissions for heart failure and no patterns emerged (Ross et al., 2008). Three articles examined the effect of SEP on outcomes after surgery. A review of socioeconomic factors and kidney transplant outcomes reported that higher educational attainment, higher income, and being employed are associated with better outcomes after kidney transplantation (Hod and Goldfarb-Rumyantzev, 2014). A review of patient characteristics and outcome after hip replacement surgery (Young et al., 1998) reported that education and employment were likely to influence outcomes, although the review was limited by few studies with inconsistent findings. A review examining patient factors and outcomes after orthopedic surgery involving implantable devices found only one study examining SEP and outcomes, and this study found that only individual income was associated with better outcomes (Waheeb et al., 2015).
Individual income is strongly associated with morbidity and mortality (Ecob and Smith, 1999). Moreover, this relationship is graded such that increases in income are associated with increases in health status even above a threshold of material deprivation (Adler et al., 1994). Income can affect health outcomes directly as a means of purchasing health care and indirectly as a means of acquiring health promoting resources, such as better education, housing, and nutrition (Adler and Newman, 2002). Relative income may also affect health through psychosocial mechanisms (Marmot and Wilkinson, 2001). Wealth can affect health in similar ways, although it is less frequently studied (Braveman et al., 2005). Wealth can also provide economic resources during periods of low income, and as such may be more relevant for older adults and persons with disabilities who are out of the paid labor force (Braveman et al., 2005). A number of articles examined the independent effect of individual-level income (typically measured by annual household income) on health care use, health care outcomes, and costs. Most studies examined utilization and clinical processes of care.
In terms of utilization, studies examined the influence of income on readmissions and hospitalizations. With regard to readmissions, one study found a significant income gradient in which lower income was associated with increased readmissions (Philbin et al., 2001), while others reported that low income was not significantly associated with readmission within 30 days (Maniar et al., 2014; Moore et al., 2013), 60 days (Arbaje et al., 2008), or 1 year (Bernheim et al., 2007). With regard to other types of hospitalizations, one study found that lower income was associated with significantly greater preventable hospitalizations for ambulatory care–sensitive conditions (Blustein et al., 1998), one study found that low income was significantly associated with chronic obstructive pulmonary disease (COPD) exacerbations requiring hospitalization or an emergency department (ED) visit (Eisner et al., 2011), and one study examining hospital admissions (including readmissions) found no association with income (Sattler et al., 2015). In terms of clinical processes of care, one study found that patients with the highest incomes had significantly higher overall quality scores, and when examined by type of care, wealthier patients had significantly higher scores for preventive care and screenings compared to those with the lowest income (Asch et al., 2006). Another study found that low-income patients were significantly less likely to get recommended rheumatoid arthritis therapy (Yazdany et al., 2014).
One study examining medication adherence found that low income was associated with poorer adherence related to cost (Billimek and August, 2014).
Fewer articles examined health care outcomes, including health outcomes and patient experiences. No studies examining the effect of income on inpatient safety were identified. In terms of health outcomes, one study reported a significant income gradient where functional health outcomes increased with higher income (Bierman et al., 2001), one study found that low income was not associated with 1-year mortality after AMI (Bernheim et al., 2007) and another study found no significant differences in health outcomes after lower-limb revascularization by income (Durham et al., 2010). In terms of patient experience, one study found that excellent ratings of care were significantly lower among colorectal cancer patients, but not among lung cancer patients, and also reported no differences in experiences of interpersonal care by income (Ayanian et al., 2010). Another study found that income was not significantly associated with perceived care coordination or patient satisfaction among breast cancer patients (Hawley et al., 2010). Two studies found that low income was associated with significantly higher costs from lower-limb revascularization (Durham et al., 2010) and from cardiovascular disease (Shaw et al, 2008).
Although numerous studies have examined the impact of insurance coverage on health outcomes (e.g., IOM, 2009), this literature search restricted studies to those examining insurance as a proxy for income. As with income and education, most articles on insurance as a proxy for income assessed health care utilization, of which most also focused on hospital readmissions. Three articles found that patients on Medicaid (as a proxy for low income) had significantly higher odds of readmissions (Aujesky et al., 2009; Jiang et al., 2003; Oronce et al., 2015), while one found that among low-income elderly adults (those with incomes under 200 percent of the federal poverty level), not having Medicaid coverage was significantly associated with increased early readmissions (Iloabuchi et al., 2014). One study found that Medicare beneficiaries in need of food assistance with managed care were more likely to be readmitted compared to those without managed care, but that there was no association among Part D coverage, Medicare–Medicaid dual eligibility status, and other subsidies and readmissions (Sattler et al., 2015).
One study reported a significant interaction between Medicaid coverage and comorbidities, such that Medicaid recipients with a low level of comorbidities had increased risk of 1-year readmissions compared to non-Medicaid recipients with a low level of comorbidities (Foraker et al., 2011). One study reported no significant differences in time to readmission or death by insurance status among patients with left ventricular assist devices (Smith et al., 2014), and one study reported no association between Medicaid coverage or uninsured status and 30-day readmissions for community-acquired pneumonia (Jasti et al., 2008). One article looked at treatment differences and found that Medicaid patients with myocardial infarction were significantly less likely to receive revascularizations regardless of the availability of the service in their neighborhood, but if available, revascularization rates were slightly higher among Medicaid patients (Fang and Alderman, 2003).
Several articles looked at other utilization measures, and one that found that Medicaid patients had significantly longer lengths of stay for incident heart failure compared to non-Medicaid patients (Foraker et al., 2014). One found no association between public insurance (excluding Medicare) and avoidable hospitalizations among patients with lupus (Ward, 2008), and one that found that among Medicare beneficiaries, Medicare–Medicaid dual eligible
beneficiaries were less likely to have a follow-up visit and more likely to have either an ED visit or a readmission after hospital discharge compared to those without Medicaid coverage (DeLia et al., 2014). One study reported no association between Medicaid or other state insurance coverage and perceptions of care coordination or patient satisfaction among breast cancer patients (Hawley et al., 2010). The committee made the following findings:
- The committee identified literature indicating that income may influence health care utilization, clinical processes of care, costs, health outcomes, and patient experience.
- The committee identified literature indicating that when measured by a proxy of insurance status, income may influence health care utilization, clinical processes of care, and patient experience.
Education is important for health, because it shapes future economic resources, including income and occupation (Adler and Newman, 2002; IOM, 2014a). Education level has been shown to have a strong relationship with health behaviors, health status, morbidity, and mortality—in particular, life expectancy (IOM, 2014a). Literature on the independent effects of education on health care utilization, health care outcomes, and costs typically measures education using categories of educational attainment (e.g., years of schooling or credentials achieved). As with the literature on income, most of the literature on education and health care focuses on utilization outcomes.
Several studies examined the influence of education on readmissions, among which two found that low education was associated with increased readmissions (Arbaje et al., 2008; Jasti et al., 2008), one found that higher education was associated with decreased readmissions (Maniar et al., 2014), and three found that low education was not associated with readmissions (Bernheim et al., 2007; Iloabuchi et al., 2014; Sattler et al., 2015). One study found that education was not significantly associated with preventable hospitalizations for ambulatory care–sensitive conditions (Blustein et al., 1998) and one found that low education was associated with significantly increased COPD exacerbations requiring hospitalization or an ED visit (Eisner et al., 2011). With regard to health outcomes, one study found that high education was associated with better glycemic control among diabetes patients (Maney et al., 2007) and one found a strong, consistent, and significant gradient where functional health outcomes improved with increasing educational attainment (Bierman et al., 2001). Finally, several articles examined patient experience, among which one found no difference in the likelihood of excellent ratings of care or in experiences with interpersonal care by education among lung and colorectal cancer patients (Ayanian et al., 2010), one reported that low education was significantly associated with better experiences reported through Consumer Assessment of Healthcare Providers and Systems (CAHPS) (O’Malley et al., 2005), and one found a significant inverse gradient between education and ratings of care coordination among breast cancer patients (Hawley et al., 2010). The committee made the following finding:
- The committee identified literature indicating that education may influence health care utilization, health outcomes, and patient experience.
Occupation covers both employment status (i.e., whether or not and to what degree an individual participates in the paid labor force) as well as the type of occupation among the employed (Adler and Newman, 2002). Occupation can affect health by exposing workers to hazardous health exposures as well as through psychosocial risks related to job strain, lack of control, and increased stress (Kasl and Jones, 2000; Theorell, 2000). Among Medicare beneficiaries, relatively fewer of whom remain in the work force, employment status may be more salient than occupational type. While a large literature has demonstrated the negative health effects of unemployment, job insecurity, and flexible employment on unhealthy behaviors, morbidity (including physical and mental health), and mortality (IOM, 2014a), fewer studies were identified that examined the influence of employment on health care utilization and outcomes. One article found that unemployment significantly increased odds of 30-day readmissions among patients hospitalized with community-acquired pneumonia (Jasti et al., 2008). One article reported that being retired was significantly associated with variations in glycemic control among diabetes patients across medical centers, but it did not specify the direction of the association (Maney et al., 2007). One article found that unemployment was associated with lower orthopedic outpatient satisfaction, but this association was no longer significant after adjustment (Abtahi et al., 2015). The committee made the following finding:
- The committee identified literature indicating that occupation may influence health care utilization, health outcomes, and patient experience.
Other Measures of SEP
Given the challenge of measuring income, a small number of studies examined access to economic resources through other types of measures. For example, two studies examined the effect of food sufficiency as a proxy. One found that being worried about food sufficiency was significantly associated in variations in glycemic control among diabetes patients across medical centers, but it did not specify the direction of the association (Maney et al., 2007), while the other reported that food insecurity was not associated with hospital admissions among Medicare beneficiaries in need of food assistance (Sattler et al., 2015). A third study examined the effect of self-reported financial burden among cancer patients and found that it was associated with some but not other measures of patient experience (Chino et al., 2014). Similarly, one study reported that individuals who reported financial barriers to medication were more likely to report poorer self-rated health and have higher hazard for readmissions at 1-year follow-up after AMI (Rahimi et al., 2007).
The committee made the following finding:
- The committee identified no literature indicating that socioeconomic position may influence patient safety outcomes.
Race, Ethnicity, and Community Context
Race and ethnicity are another key social factor. Race and ethnicity are dimensions of a society’s stratification system by which resources, risks, and rewards are distributed. As such, racial/ethnic categories capture a range of dimensions relevant to health, especially those related
to social disadvantage (IOM, 2014a; Williams, 1997). These dimensions include access to key social institutions and rewards; behavioral norms and other sociocultural factors; inequality and injustice in the distribution of power, status, and material resources; and psychosocial exposures like discrimination (Williams, 1997). In the health care setting, salient psychosocial mechanisms may include both provider discrimination and trust or mistrust between patients and providers (IOM, 2003). It is well established that race and ethnic background is often predictive of health care and health outcomes even after accounting for traditional measures of SEP like income and education (Krieger, 2000; LaVeist, 2005; Williams, 1999; Williams et al., 2010).
A number of factors likely contribute to this “independent” effect of race/ethnicity, including the following:
- Lack of comparability of a given SEP measure across race/ethnic groups (e.g., income returns to education are well known to vary by race, and income is differentially correlated with wealth by race);
- Importance of other exposures such as neighborhood environments that are patterned differently by race even among individuals of apparently similar SEP;
- The importance of race- or ethnic-specific factors such as discrimination and immigration-related factors, including time living in the United States and language proficiency; and
- Measurement error in SEP.
Although race and ethnicity reflect many different social circumstances, there can also be important heterogeneity in health within race and ethnic groups, driven for example by SEP heterogeneity or heterogeneity in English language proficiency, country of origin, time in the United States, or other cultural dimensions.
Race and Ethnicity
Race and ethnicity are typically identified through self-reported categories. Although race and ethnicity are conceptually distinct and federal standards recommend using separate items for collecting the two (whitehouse.gov, 1995), investigators use different classifications for both collecting and analyzing race and ethnicity. In health services research, Hispanic ethnicity is frequently combined with racial categories, such that the most frequently used “racial” categories are non-Hispanic white, non-Hispanic black, Hispanic, and Asian. This scheme conceals tremendous heterogeneity across Asian groups from different countries, as well as heterogeneity within the Hispanic group with regard to country of origin and racial classifications from other countries that represent different sociopolitical constructs. Given these measurement issues, it can be challenging to compare studies on race and ethnicity. Nevertheless, vast literature shows substantial racial and ethnic health disparities and health care disparities (Escarce and Goodell, 2007; IOM, 2003). Several review articles examined race and ethnicity effects on health care use and health care outcomes. One overarching review of racial and ethnic disparities in access to and quality of health care found that black and Hispanics are much less likely to have had an ambulatory care visit within the year and less likely to receive certain preventive services (e.g., flu shots among the elderly). Of three review articles examining effects of race and ethnicity on readmissions, one reported that non-whites had higher readmission rates for both pneumonia and heart failure (Calvillo-King et al., 2013), one reported that studies suggest race/ethnicity is positively related to readmission in the short term (30 days and 90 days) and suggestive but
inconclusive for the longer term (6 months and 1 year) (Damiani et al., 2015), and one found substantial inconsistencies (Ross et al., 2008).
With respect to health care outcomes, three reviews examined surgical outcomes. One found that blacks are more likely to have poor surgical outcomes and Hispanics have comparable or better mortality outcomes compared to whites but inconsistent evidence on other outcomes (Haider et al., 2013). The study also reported comparable or better outcomes among Asians compared with whites, but noted that potential disparities within the heterogeneous Asian population remained unexplored. One review found that black women were more likely to die or suffer an adverse cardiac event after undergoing a percutaneous coronary intervention (Kamble and Boyd, 2008), and found only one study assessing race and postsurgical outcomes, which found no association between race and patient-reported outcomes after orthopedic surgery involving implantable devices (Waheeb et al., 2015).
Two reviews found that blacks were more likely to experience complications (Haider et al., 2013; Kamble and Boyd, 2008). One review examining patient experience outcomes found that the magnitude of racial/ethnic disparities in pain management was small, despite also finding problematic classification and lack of definition of racial and ethnic groups (Ezenwa et al., 2006).
A relatively substantial literature examined effects of race and ethnicity on health care use, health care outcomes, and costs. Much of the literature focuses on health care utilization and processes of care. In terms of utilization, many studies focused on readmissions. Five studies found that race was not associated with readmissions—two for all causes (Iloabuchi et al., 2014; Moore et al., 2013), one for pneumonia (Jasti et al., 2008), one for heart failure (Vaccarino et al., 2002), and one for orthopedic surgery (Hunter et al., 2015). Eleven studies found a significant association between black race and readmissions, among which 10 found that blacks had higher risk of readmission (Aujesky et al., 2009; Girotti et al., 2014; Joynt et al., 2011; Kim et al., 2010; Kroch et al., 2015; McHugh et al., 2010; Oronce et al., 2015; Silber et al., 2015; Tsai et al., 2014; Vivo et al., 2014), while one found that blacks had lower risk of readmission (Spertus et al., 2009). Three studies found that Hispanics had higher risk of readmission compared to whites (Rodriguez et al., 2011; Stahler et al., 2009; Vivo et al., 2014), while one did not (Oronce et al., 2015). One study reported mixed results and interactions between payer and black race or Hispanic ethnicity: Hispanic Medicare patients had significantly higher 30-day readmissions and Hispanics of all payers had significantly higher 180-day readmissions compared to whites. Black Medicare patients had significantly higher 180-day readmissions compared to whites (Jiang et al., 2005). One study reported that Asians did not have significantly different odds of readmission compared to whites (Oronce et al., 2015). One study found that there were no significant differences in readmissions among whites compared to non-whites (Kennedy et al., 2007), while another study found that non-white race was slightly significantly protective against readmissions (Singh et al., 2014). In terms of other utilization outcomes, one study found that blacks had significantly increased all-cause hospitalization over 2.5 year follow up of heart failure patients (Mentz et al., 2013).
A number of studies also examined differences in clinical process of care by race and ethnicity. Among articles investigating receipt of recommended preventive care, one study found no significant differences in the likelihood of having a prostate-specific antigen (PSA) screening in the past year between blacks and whites (Thomas et al., 2010). Three studies found that blacks were less likely to get recommended preventive care (Schneider et al., 2002; Trivedi et al., 2005, 2006), among which one found that racial disparities had decreased over time (Trivedi et al.,
2005). One study found a significant interaction between race/ethnicity and comorbidity among Medicare beneficiaries, where lower rates of flu and pneumonia immunization among racial/ethnic minorities decreases relative to white beneficiaries as the burden of comorbidity increases (Orr et al., 2013) One study of socially-assigned race (Macintosh et al., 2013) reported mixed results. Contrasted with self-identified race/ethnicity, socially-assigned race/ethnicity describes the racial/ethnic categories others ascribe to a person through social interactions (Macintosh et al., 2013). The authors found that whites socially-assigned as whites and minorities socially-assigned as whites had significantly higher odds of having flu and pneumonia vaccinations, compared to minorities socially-assigned as minorities, no differences in cancer screening by socially-assigned race. Whites socially-assigned as whites were significantly less likely to receive cancer screenings compared with minorities socially-assigned as minorities (Macintosh et al., 2013). Two studies found that blacks had significantly higher odds of receiving recommended ambulatory care (Asch et al., 2006; Thorpe et al., 2013), among which one also reported that Hispanics also received more recommended care (Asch et al., 2006).
Among articles about clinical processes in the inpatient setting, one article reported that non-white stroke patients had significantly higher rates of inappropriate surgery (carotid endarterectomy) and significantly lower rates of appropriate surgery compared to whites (Halm et al., 2009). Another study found that blacks were significantly less likely to receive laparoscopy for appendicitis compared to whites (Lee et al., 2011a). Three studies found that blacks were significantly less likely to receive invasive cardiac procedures compared to whites (Fang and Alderman, 2003; Popescu et al., 2007; Shen et al., 2007). With regard to differences among Hispanics, one article reported that Hispanics were significantly more likely to receive laparoscopy for appendicitis compared to whites (Lee et al., 2011a), while three found that Hispanics were less likely to undergo invasive cardiac procedures (Fang and Alderman, 2003; Parikh et al., 2009; Shen et al., 2007). One article reported that whites received more recombinant tissue plasminogen activator therapy after stroke compared to blacks and Hispanics, and Asians under age 65 but not age 65 and older (Nasr et al., 2013). One study found no association between race and recommended AMI treatment (Shah et al., 2007), and another found no association between race and colorectal cancer treatment (Zullig et al., 2013). One study of clinical processes in the nursing home setting reported that having a higher proportion of black nursing home residents was protective against restraint use and receipt of antipsychotic medications, although this effect was attenuated for nursing home facilities in counties with a high proportion of black residents (Miller et al., 2006).
Most of the literature on race and ethnicity and health care outcomes examined differences in mortality, while several other studies also looked at functional outcomes and ambulatory care outcomes. As with other areas, much of the literature investigated mortality differences in blacks compared to whites. Several articles found no significant differences between blacks and whites in in-hospital mortality (Khambatta et al., 2013; Silber et al., 2015), 30-day mortality (Silber et al., 2015; Stamou et al., 2012), 1-year mortality (Stamou et al., 2012), and in time from surgery for colorectal cancer to death (Zullig et al., 2013). One article found no association between black race and 2-year mortality after AMI (Spertus et al., 2009) and another found no differences between blacks and whites in 2.5-year follow up after ischemic heart disease treatment (Cromwell et al., 2005), while one found that blacks had significantly increased mortality over 2.5-year follow up of heart failure patients (Mentz et al., 2013). Several studies reported significantly higher odds of in-hospital mortality (Nietert et al., 2005), 30-day mortality (Halm et al., 2009), 6-month mortality (Vaccarino et al., 2002), and 1-year mortality
(Popescu et al., 2007), while others found that blacks had significantly lower odds of in-hospital mortality (LaPar et al., 2011; Shen et al., 2007), 30-day mortality (Barnato et al., 2005; Popescu et al., 2007; Vivo et al., 2014), and 1-year mortality (Barnato et al., 2005; Popescu et al., 2007; Vivo et al., 2014). One article found that white patients but not black patients had significantly lower odds of death at teaching hospitals compared to non-teaching hospitals, suggesting a benefit accrued by whites but not blacks (Silber et al., 2009).
In terms of differences in mortality among Hispanics, several articles found no significant association between Hispanic ethnicity and in-hospital mortality (LaPar et al., 2011; Shen et al., 2007), 30-day mortality (Stamou et al., 2012; Vivo et al., 2014), or 1-year mortality (Parikh et al., 2009; Stamou et al., 2012; Vivo et al., 2014), while two articles found that Hispanics had significantly higher odds of mortality in hospital (Nasr et al., 2013) and at 30 days (Halm et al., 2009). One article found significantly higher rates of 2.5-year mortality among Hispanic patients undergoing medical management for ischemic heart disease compared to whites, but there were no differences by race for patients who underwent revascularization (Cromwell et al., 2005). Three articles reported no association between Asians and mortality in hospital (LaPar et al., 2011; Nasr et al., 2013) or at 30 days or 1 year (Vivo et al., 2014). One article found significantly higher rates of 2.5-year mortality among Asian patients undergoing medical management for ischemic heart disease compared to whites, but no differences by race for patients who underwent revascularization (Cromwell et al., 2005).
A small number of articles examined mortality differences among whites compared to non-whites. Of these, one article reported significantly higher risk of both in-hospital and 30-day mortality among non-whites compared to whites (Rangrass et al., 2014), while another found that non-whites had significantly lower rates of in-hospital death (Zacharia et al., 2010). One article reported no association between whites and non-whites and in-hospital, 30-day, 1-year, or 3-year mortality after AMI among Medicare beneficiaries 65 years or older (Shah et al., 2007), and another reported no significant difference between whites and non-whites in in-hospital mortality (Kennedy et al., 2007).
Studies examining racial and ethnic differences in functional outcomes examined differences after acute care or surgery and among the elderly. In terms of post-acute outcomes, one study reported that blacks, Hispanics, and other non-whites had significantly worse functional outcomes after stroke (Ottenbacher et al., 2008), but another study found no significant differences in functional status at discharge between black and whites after a moderate or severe stroke (Putman et al., 2010). One study reported that non-whites, especially blacks, had worse functional outcomes after primary total joint arthroplasty (hip and knee) (Lavernia et al., 2011). One article reported that after acute illness hospitalization, there were no differences in activities of daily living (ADLs) improvement by discharge and by 90 days between blacks and whites, but blacks were significantly less likely to improve instrumental activities of daily living (IADLs) functioning by discharge and 90 days compared to white patients (Sands et al., 2005).
In terms of functional outcomes among the elderly, one study of home health care patients age 65 and older reported mixed findings, where whites experienced significantly better outcomes compared to patients of other races, and this effect was especially pronounced compared to black patients (Brega et al., 2005). Another study of older Medicare managed care beneficiaries reported that blacks, American Indians/Alaskan Natives, and multiracial individuals had significantly greater ADL impairment compared to whites (Ng et al., 2014). Whites were also significantly more likely to experience positive change in ADLs than African Americans;
differences between whites and Hispanics on change in functional outcomes were not significant. Another study reported no differences in functional decline between blacks and whites among community-dwelling adults age 70 and older, except among those age 80 and older, among whom blacks had significantly lower risk of ADL decline (Moody-Ayers et al., 2005).
With respect to ambulatory care outcomes, two studies found that blacks had worse control of cardiovascular disease risk factors (Rooks et al., 2008; Wendel et al., 2006). Of these, one also found interactions by income, such that there were no differences in hypertension among those with low income, but blacks with higher income had greater odds of hypertension, while the reverse was true for left ventricular hypertrophy (Rooks et al., 2008). The other study examined both cardiovascular disease and type 2 diabetes risk factors and found that both Hispanics and blacks had significantly lower daily insulin doses but no differences in lipid or blood control (Wendel et al., 2006).
Several studies examined the relationship between race and patient experience using CAHPS data. In terms of inpatient care, one study reported that black and Asian lung and colorectal cancer patients and Hispanic colorectal cancer (but not lung cancer) patients were significantly less likely to report excellent care compared to white patients (Ayanian et al., 2010). One study reported that Hispanics and Asians consistently reported less positive ratings compared to non-Hispanic whites, and blacks and American Indians had some more positive and some more negative ratings compared to non-Hispanic whites, but after adjusting for hospital differences, Hispanics and blacks reported significantly more positive ratings than whites and Asians consistently reported less positive ratings (Goldstein et al., 2010). American Indian ratings were not substantially different compared to whites. Consistent with this study, another study found that blacks and Hispanics reported more positive overall experiences in U.S. Department of Veterans Affairs (VA) hospitals (Hausmann et al., 2014). One study found that only non-Hispanic black race was predictive of overall nurse, physician, and hospital ratings (O’Malley et al., 2005). Hispanic, Asian, and Native American race/ethnicity was not predictive of provider ratings. In terms of ambulatory care, one study reported that blacks and Hispanics reported more positive patient experiences at VA facilities compared to whites (Hausmann et al., 2013), and another found that patients reporting discrimination on the basis of race or ethnicity reported significantly poorer experiences of care (Weech-Maldonado et al., 2012). One study found that black, Hispanic, and Asian/Pacific Islander Medicare beneficiaries reported significantly poorer experiences with Part D prescription drug plans (Haviland et al., 2012). While some of these differences in patient experiences may be genuine, some evidence also suggests that differences in experience may be artefactual and due to differences in scale use by race (Elliott et al., 2009; Mayer et al., In press; Weech-Maldonado et al., 2008; Weinick et al., 2011).
A small number of articles examined patient safety outcomes. Compared to whites, blacks were found to have significantly higher rates of complications after general surgery (Silber et al., 2009, 2015) and prostate cancer surgery (Jayadevappa et al., 2011), but significantly decreased rates of complications after lung cancer resections (LaPar et al., 2011). One study reported no significant differences between blacks and whites in complications after percutaneous coronary intervention (Khambatta et al., 2013), and another reported no significant differences in complications from appendicitis treatment across white, black, Hispanic, or Asian patients (Lee et al., 2011b).
Several articles examined effects of race and ethnicity on costs. In terms of inpatient hospital costs, two studies found that blacks had higher total charges compared to whites
(Jayadevappa et al., 2011; Shen et al., 2007), whereas one study found that blacks had low costs (Dowell et al., 2004). One study found that Hispanics had higher costs to whites (Shen et al., 2007), but two found that Hispanics had significantly lower costs compared to whites (Dowell et al., 2004; Jayadevappa et al., 2011). One study reported that Native Americans incurred the highest costs of all racial groups (Dowell et al., 2004). One study examining renal dialysis costs found that black patients had significantly higher costs compared to non-black patients (Roach et al., 2010). The committee made the following finding:
- The committee identified literature indicating that race and ethnicity may influence health care utilization, clinical processes of care, costs, health outcomes, patient safety, and patient experience.
Language typically represents language barriers, such as speaking a primary language other than English, having limited English proficiency (LEP), and otherwise needing interpreter services. Language barriers have been shown to be associated with poorer health care access; poorer health status; poorer quality care, including less recommended care (e.g., preventive services) and more adverse health effects (e.g., drug complications); and higher rates of diagnostic testing (Flores, 2005). One review found that use of professional interpreters improved clinical care, especially processes of care, among patients with a language barrier compared to patients with language-concordant care (Karliner et al., 2007). Another review similarly found that professional interpreter services are associated with increased office visits and prescriptions being written and refilled, while patients with no interpreter or an ad hoc interpreter have more tests resulting in more test costs and a higher risk of hospitalization (Flores, 2005). This review also found that interpreter services improve care processes, although whether interpreter use is associated with increased duration of visits remains unclear (Flores, 2005).
Individual studies echo review findings of generally worse outcomes for patients with language barriers. One study reported that deaf American Sign Language (ASL) users concordant providers (i.e., providers who sign) were more likely to receive an influenza vaccination but not a colon or cholesterol screening compared to deaf ASL users reporting discordant providers (McKee et al., 2011). Regarding health outcomes, one study found that among patients on warfarin, LEP was associated with less time in therapeutic range, but had no differences in risk of spending time in danger range. There was also a significant interaction with use of a communication surrogate, such that both LEP and non-LEP patients who used a surrogate spent less time in therapeutic range and more time in danger range (Rodriguez et al., 2013). A study of Latino diabetes patients found that LEP Latinos with language-discordant physicians had greater odds of poor glycemic control compared to Latino English speakers, but there were no differences between LEP Latinos with language-concordant physicians and Latino English speakers (Fernandez et al., 2011). Another study found that having English as the primary language spoken was associated with significantly lower risk of in-hospital, 30-day, 90-day, and 1-year mortality among critically ill patients (Mendu et al., 2013).
Several studies examined language and patient experiences of care. Among studies examining Spanish language, one study found that Spanish language was associated with significantly lower CAHPS ratings of nurses, doctors, and hospitals (O’Malley et al., 2005) one found that English-speaking Hispanics reported greater satisfaction with provider
communication compared to Spanish-speaking Hispanics (Villani and Mortensen, 2014), and one reported no significant differences in provider ratings by Spanish language (Ayanian et al., 2010). Among studies examining experiences of Asian-language-speaking patients, one study found that there were significantly fewer excellent and excellent or very good ratings of providers among Chinese-speaking lung and colorectal patients compared to whites (Ayanian et al., 2010), and another reported that among LEP Asian Americans (Chinese and Vietnamese immigrants) there were no differences in provider communication or overall satisfaction with care between patients who used an interpreter compared to those who had language-concordant care (Green et al., 2005). However, significantly more patients who used interpreters reported having questions about their care and questions about their mental health that they wanted to ask but did not. Two studies examined patient experience among Asian language-speaking patients. One found that Chinese-speaking lung and colorectal cancer patients reported significantly fewer excellent and excellent or very good ratings of providers compared to whites (Ayanian et al., 2010), and one found that among limited English proficient Chinese and Vietnamese immigrants, there were no differences regarding provider communication and overall satisfaction between patients who used an interpreter compared to those who had language concordant care, but significantly more patients who used interpreters reported having questions about their care and questions about their mental health that they wanted to ask but did not (Green et al., 2005). In terms of utilization, one study found that patients needing interpreter services had significantly higher risk of at least one ED visit and of at least one hospitalization during the 12-month study period compared to patients not needing interpreter services (Njeru et al., 2015) and a study of Russian immigrants found that language difficulty was not significantly associated with health care use compared to non-immigrants (Aroian and Vander Wal, 2007). The committee made the following finding:
- The committee identified literature indicating that language may influence health care utilization, clinical processes of care, health outcomes, and patient experience.
Nativity covers country of origin, immigration status (including refugee and documentation status), duration in the United States, as well as acculturation, or the extent to which an individual adheres to the social norms, values, and practices of his own ethnic group or home country or to those of the United States (IOM, 2014a). Nativity may affect health status through access to health care, language barriers (as described in the previous section), and deleterious health exposures such as communicable diseases from an individual’s country of origin (IOM, 2014a). The relationship between immigration and health is complex, in particular due to the heterogeneity across immigrant communities, but studies have shown that country of origin and immigration status are associated with health behaviors, morbidity, and mortality (Abraido-Lanza et al., 1999; IOM, 2014a; Singh and Hiatt, 2006). One review of immigrants and health care found that most studies of immigrants and quality of health examined predominantly self-reported outcomes, in particular related to patient experience (Derose et al., 2009). In terms of health care outcomes, the review found that foreign-born Americans generally report poorer experiences with health care, including poorer satisfaction, although experiences may differ by immigrant subgroup. The review also found that immigrant adults had substantially lower overall health care costs.
A small number of studies examined effects of nativity and health care utilization and outcomes. In terms of utilization, one study of Latino adults found that foreign-born citizens, foreign-born permanent residents, and undocumented Latinos were significantly less likely to receive preventive care compared to U.S.-born Latinos (Rodriguez et al., 2009), and one found that nativity was not associated with lung or colorectal cancer treatment after adjusting for language (Nielsen et al., 2010). One study reported that Russian immigrants had significantly higher health service use compared to non-immigrants (Aroian and Vander Wal, 2007). Regarding health care outcomes, one small study of Mexican and Mexican American adults with type 2 diabetes reported intermediate health outcomes, finding that acculturation was not significantly related to glycemic control (Ross et al., 2011). Several articles examined nativity and patient experience. One study found that non-immigrants reported a significantly greater number of problems with providers than Russian immigrants (Aroian and Vander Wal, 2007). Another study found that, after adjusting for language, being foreign born increased odds of reporting poorer interactions with physicians in some areas but not others (Dallo et al., 2008). For example, all foreign-born individuals had greater odds of reporting that their physician did not involve them in their care as much as they would have liked, but there were no significant differences in other areas of patient–physician interaction (e.g., physician not listening or understanding, distrust in physician, patient treated with respect, patient had unanswered questions).
By contrast, Nielsen et al. (2010) found that foreign-born patients were less likely than U.S.-born patients to report excellent quality of care, but after adjustment for language, the effect attenuated for the overall foreign-born sample and for Hispanic foreign-born patients and was no longer significant. However, this was not true of Asians, who still had significantly lower odds of reporting excellent care. Finally, one article reported that foreign-born citizens, foreign-born permanent residents, and undocumented Latinos were more likely to report that they received no health care information from doctors compared to U.S.-born patients (Rodriguez et al., 2009). Foreign-born citizens and permanent residents but not undocumented Latinos were also less likely to report receiving care in their language of preference, and undocumented Latinos were less likely to report excellent or good quality care. The committee made the following finding:
- The committee identified literature indicating that nativity may influence clinical processes of care and patient experience.
Gender is associated with many health and health care–related outcomes (IOM, 2014a). The committee used the term gender broadly to capture the social dimensions of gender and distinguish these from biological effects of sex. Gender is known to affect a number of health outcomes as well as interactions with the health care system, health care–related processes, and outcomes of health care.
Parsing the effects of gender from sex is challenging because investigators frequently do not specify which construct they are measuring, they use the terms interchangeably (often erroneously referring to sex differences as gender differences), and because gender differences and sex-linked biology may interact to produce health outcomes (Krieger, 2003). A small number of articles examined effects of gender on patient experience. Gender may affect patient experience because men and women presenting the same symptoms may behave differently and because providers may act differently toward men and women (Elliott et al., 2012). Several
studies reported that, compared to men, women reported significantly worse experiences of care—in the inpatient setting (Elliott et al., 2012), at VA hospitals (Hausmann et al., 2014), and for COPD (Martinez et al., 2012). Among these, one study also found a significant interaction with age, where women age 18 to 24 report significantly better experiences of inpatient care than men, but women age 85 and older report significantly worse experiences than men (Elliott et al., 2012). One study found that men gave significantly more positive ratings of nurses and hospitals compared to women, but there were no significant differences in physician ratings by gender (O’Malley et al., 2005). One study reported no significant differences between men and women in reported pain or in the satisfaction with pain management and response to pain among ED staff (Patel et al., 2014).
Gender or sexual minorities may also experience differences in health and health care. Gender and sexual minorities include individuals who identify as lesbian, gay, bisexual, transgender, intersex, queer, and questioning. Health disparities among gender and sexual minorities may be related to exposure to stigma, discrimination, and violence on the basis of their non-normative identity; barriers to accessing health care, including fear of discrimination from providers; and unhealthy behaviors, especially increased rates of smoking, alcohol use, and substances (IOM, 2011). Conducting research on gender and sexual minority populations can be challenging with respect to defining sexual orientation and gender nonconformity operationally, collecting sensitive information, and due to the relatively small size of these populations (IOM, 2011). Despite these challenges, some evidence suggests that lesbians and bisexual women may be less likely to receive preventive services (e.g., breast cancer screenings and Pap tests) compared to heterosexual women (Buchmueller and Carpenter, 2010; IOM, 2011). The committee made the following finding:
- The committee identified literature indicating that gender may influence clinical processes of care and patient experience.
Social relationships are another important social risk factor. It is well established that many dimensions of social relationships, including access to social networks that can provide access to resources (including material and instrumental support), as well as the emotional support available through social relationships, can be important to health, health care use, and outcomes of care (Berkman and Glass, 2000; Cohen, 2004; Eng et al., 2002; House et al., 1988a). Social isolation and loneliness have been shown to have important consequences for health (Berkman and Glass, 2000; Brummett et al., 2001; Cohen, 2004; Eng et al., 2002; House et al., 1988a; Wilson et al., 2007). Social relationships may be of special importance to health care access, processes, and outcomes among older individuals (Cornwell and Waite, 2009; Hawton et al., 2011; Seeman et al., 2001; Tomaka et al., 2006) and persons with ADL and IADL limitations (AARP Public Policy Institute, 2010). Social relationships are most frequently assessed in the health care and health services research literature with three constructs: marital status, living alone, and social support.
Marriage is a foundational structural element of social relationships that can convey substantial health benefits among the elderly. For example, marriage has been shown to be
protective against injury (e.g., osteoporotic fractures, which mostly occur in the elderly) (Brennan et al., 2009) and mortality (Manzoli et al., 2007). Given demographic shifts in household composition and marriage in the past several decades, indicators assessing marital status not only include dichotomous measures of whether someone is married or not, but sometimes also include other measures of partnership (e.g., partnered or lacks a partner), as well as individuals who are single, widowed, or divorced. Several review articles each assessing a small number of articles found that being married is associated with better health care outcomes, including better outcomes after hip replacement surgery (Young et al., 1998), and lower rates of readmissions (Calvillo-King et al., 2013). Similarly, being unmarried, widowed, or otherwise lacking a partner is associated with worse outcomes, such as graft loss after heart transplantation (Coglianese et al., 2015) and increased risk of readmissions (Damiani et al., 2015). One review found that marriage was also associated with better medication adherence (Wu et al., 2008). Looking at individual studies, the effect of marital status on health care use and health care outcomes is somewhat more mixed.
Regarding utilization, several studies found an association between marital status and readmissions (Arbaje et al., 2008; Garrison et al., 2013; Howie-Esquivel and Spicer, 2012; Hu et al., 2014; Moore et al., 2013), while others did not (Iwashyna and Christakis, 2003; Jasti et al., 2008; Metersky et al., 2012; Watkins et al., 2013). Two studies found that marital status was significantly associated with hospital length of stay (Iwashyna and Christakis, 2003; Metersky et al., 2012). In terms of health outcomes, one study found that marital status was associated with both in-hospital and 90-day mortality among pneumonia patients, while another found that it was not associated with in-hospital mortality among heart failure patients (Watkins et al., 2013). Another study (Maney et al., 2007) found that marital status was significantly associated with the variance in glycemic control among diabetes patients, but it was not specifically associated with high or low control. One study found that there were significantly fewer excellent ratings of care among unmarried lung and colon cancer patients (Ayanian et al., 2010). The committee made the following finding:
- The committee identified literature indicating that marital status may influence health care utilization, clinical processes of care, costs, health outcomes, and patient experience.
Living alone is a structural element of social relationships and an indicator of social isolation or loneliness in health care and health services research. Living alone can be a dichotomous measure (living alone or not) or cover more finely graded household composition (e.g., living alone, living with one other, living with two others, and so on). Literature examining the influence of living alone on health care outcomes is sparse. Two reviews examining the relationship between living alone and health outcomes found just one article each. In a review of psychological variables that may affect recovery after surgery, Mavros and colleagues (2011) found one study that showed no association between loneliness and wound healing. In a review of literature on medication adherence among heart failure patients, Wu and colleagues (2008) identified just one meta-analysis, which found that living alone was positively associated with nonadherence. One slightly older review identified living alone as a risk factor for poor outcomes of elderly patients presenting to EDs (Aminzadeh and Dalziel, 2002).
A small number of individual studies examined the influence of living alone on health care use. In terms of utilization, two studies found that living alone significantly increased risk of readmissions (Hamner and Ellison, 2005; Iloabuchi et al., 2014). One study found that living alone was significantly associated with getting a flu shot but not getting a pneumonia vaccination among adults age 85 and older (Farmer et al., 2010). Another study found that living alone was not significantly associated with hospitalization, except among adults age 85 and older for whom living alone was protective against hospitalization (Ennis et al., 2014). The authors suggested that living alone among this older population may be a sign of healthy aging in place, rather than isolation. The committee made the following finding:
- The committee identified literature indicating that living alone may influence health care utilization, clinical processes of care, and health outcomes.
Social support is a key function of social relationships and includes the provision of emotional and appraisal support through caring and concern, as well as more tangible instrumental and informational support such as the provision of material or other practical support (House et al., 1988b). Reviews examining the relationship between social support and health care outcomes mostly supports a positive effect of social support on health, finding that higher levels of social support are associated with better medication adherence (Dunbar et al., 2008; Wu et al., 2008), fewer readmissions (Calvillo-King et al., 2013; Dunbar et al., 2008; Luttik et al., 2005), better diabetes outcomes (Strom and Egede, 2012), and better outcomes after hip replacement surgery (Young et al., 1998). One review (Pelle et al., 2008) reported mixed evidence about the effect of social support on both inpatient and outpatient mortality among heart failure patients, while another (Mookadam and Arthur, 2004) reported a significant association between social support and both 6-month and 6-year mortality among patients after AMI. The reviews are limited by a small number of studies.
Individual articles looking at the influence of social support on other health care use and health care outcomes is mixed. With respect to health outcomes, Theiss and colleagues (2011) reported a significant association between social support and outcomes after joint operations. Platinga and colleagues (2010) reported no association between social support and mortality among chronic kidney disease patients. In terms of utilization, Platinga and colleagues (2010) found that more social support decreased likelihood of hospital admissions, while Perry and colleagues (2008) found no association between social support and health services use. Thomas and colleagues (2010) found that informational support in the form of a family member having cancer was associated with lower likelihood of having a PSA test, while other measures of informational and instrumental support were not significant. Regarding patient experiences, Platinga and colleagues (2010) found that higher levels of social support were associated with better quality of care ratings and increased likelihood that patients would recommend their dialysis center, and Rosland et al. (2011) found that patients who had a regular companion participate in primary care visits were more likely to have high satisfaction with their primary care provider. On the other hand, Perry and colleagues (2008) found that social support was not associated with satisfaction with care or the quality of provider communication among low-income individuals. One explanation for mixed findings is that because social support covers multiple, heterogeneous types of support, these different types of social support may have different effects on patient experiences, which may not be well captured using a global social
support measure. To that end, Han and colleagues (2005) found that among breast cancer patients some types of social support but not others were associated with satisfaction with their physician and problems interacting with their medical team. The committee made the following findings:
- The committee identified literature indicating that social support may influence heath care utilization, clinical processes of care, health outcomes, and patient experience.
- The committee identified no literature indicating that social relationships may influence patient safety.
Residential and Community Context
The committee uses the term community context to refer to a set of broadly defined characteristics of residential environments that could be important to health and the health care process and its outcomes. Dimensions include the physical environments (e.g., housing, walkability, transportation options, and proximity to services) as well as the social environment (e.g., safety and violence, social disorder, presence of social organizations, and social cohesion) (Diez Roux, 2001; Diez Roux and Mair, 2010). Community context also references the policies, infrastructural resources, and opportunity structures that influence individuals’ everyday lives. The SEP or racial and ethnic composition of an area is sometimes used as a proxy for some of these attributes, although it is an imperfect proxy and can also capture unmeasured or imperfectly measured individual-level SEP. Community context may also have special relevance for older persons due to decreases in mobility with age and for persons with mobility disabilities. One review found “limited evidence” that neighborhood environment is a primary influence on older adults health and functioning (Yen et al., 2009).
Community Socioeconomic Composition
A community’s compositional characteristics may include dimensions of SEP (income, poverty, educational attainment, and employment), as well as the proportion of racial/ethnic minority residents, foreign-born residents, single parent households, or English language proficient residents. Studies may examine individual characteristics or composite indices covering multiple characteristics grouped into an overall measure, such as a deprivation index or segregation index. Community composition has been shown to affect health behaviors and other risk factors, morbidity, and mortality (Diez Roux and Mair, 2010; IOM, 2014a). As described above, community composition can be used to measure both group- and individual-level effects. Although measured in similar ways, the literature described below makes a conceptual distinction between studies that use community composition as a proxy for individual-level effects and those that use community composition as a genuine group-level exposure.
Community composition as a proxy for individual-level effects
Income Studies examined effects of neighborhood-level income, typically assessed using median household income of a patient’s residence’s zip code, as a proxy for individual income on utilization, health outcomes, patient safety, and costs. In terms of utilization, median household income has been associated with both treatment differences and readmissions. Regarding the former, one article found that high and middle income was significantly associated with higher use of laparoscopic appendectomy compared to low-income patients (Lee et al.,
2011a), while another found that low-income elderly patients were less likely to get timely care for AMI (Agarwal et al., 2014). Four studies found that low income was associated with significantly increased odds of readmission (Jiang et al., 2005; Kim et al., 2010; Kroch et al., 2015; Oronce et al., 2015), while one found no association between household income and readmissions (Hunter et al., 2015). With respect to health outcomes, three articles found that median household income was inversely related to in-hospital mortality (Agarwal et al., 2014; Bennett et al., 2010; LaPar et al., 2011). Two studies examined patient safety outcomes, among which one found that income was not significantly associated with complications after lung cancer resections (LaPar et al., 2011), whereas the other found that higher incomes were protective against complications after elective ventral hernia repair (Novitsky and Orenstein, 2013). One article reported that the higher three income quartiles had significantly higher ST-segment elevation myocardial infarction (STEMI) costs compared to the lowest income quartile (Agarwal et al., 2014)
In addition to income, some studies use alternative measures of economic resources. For example, one study assessed area-level poverty relative to the federal poverty level and the type or rheumatoid arthritis therapy received, and found no significant differences (Yazdany et al., 2014). Another study found that the percent of residents receiving public assistance was significantly associated with receipt of certain preventive services (Zaslavsky and Epstein, 2005).
Education Few studies examined the influence of neighborhood educational attainment as a proxy for individual education on health care utilization and health care outcomes. One study found that areas with medium educational attainment (areas where 50 to 75 percent of households had individuals who achieved greater than a high school education) was significantly associated with a longer length of stay compared to areas with low levels of educational attainment (areas where less than 50 percent of households had an individual who achieved greater than a high school education) (Lee et al., 2011a). Interestingly, this study found no differences in length of stay among high education areas (more than 75 percent of households with someone who achieved greater than a high school education) compared to low education areas. One study reported no differences in the use of laparoscopic appendectomy among adults with appendicitis by education (Lee et al., 2011b).
Composite measures A small number of articles used neighborhood compositional measures as a proxy for individual SEP to examine health care utilization and health care outcomes. One study found that low neighborhood SEP was associated with significantly greater odds of operative death (Birkmeyer et al., 2008), and another found that below-average neighborhood SEP composition was associated with increased mortality 1-year after heart failure, but not with 30-day mortality (Rathore et al., 2006). Regarding utilization, one study found that lupus patients in the lowest SEP quartile had a higher risk of avoidable hospitalizations, but there were no differences in the higher three SEP quartiles (Ward, 2008). One article found that neighborhood SEP was associated with decreased likelihood of undergoing left ventricular systolic function assessment for heart failure, but not with prescription of angiotensin-converting-enzyme (ACE) inhibitors or 30-day readmissions (Rathore et al., 2006). Another article reported that neighborhood deprivation was significantly associated with medication non-adherence due to beliefs, but not with non-adherence due to costs among Mexican Americans with type 2 diabetes (Billimek and August, 2014).
Community composition as a measure of group-level effects
Income A number of studies examined the effects of area-level income, measured using median household income, and poverty, measured as relative to the federal poverty level, on health care utilization and health care outcomes. Of articles examining median household income and health care use, one study found that area-level income was not significantly associated with getting recommended rheumatoid arthritis therapy (Yazdany et al., 2014). Among studies examining readmissions outcomes, one found that income was not associated with readmissions (Hu et al., 2014), one found that medium but not high-median household income was associated with a lower hazard of readmission (Smith et al., 2014), and one reported a significant interaction between comorbidity and neighborhood income, such that patients with high comorbidity burden living in low-income areas had significantly higher rehospitalizations for all causes compared to those with a high comorbidity burden living in high-income areas (Foraker et al., 2011). This study reported similar effects for death and rehospitalization or death. In a separate study, these investigators also found a significant interaction between race and income, where blacks living in low-income neighborhoods had significantly higher 28-day and 1-year mortality compared to whites living in high-income neighborhoods (Foraker et al., 2013). On the other hand, Smith and colleagues (2014) found that income was not significantly associated with death over 6-year follow up.
Among studies that examined the effect of poverty on health care use and health care outcomes, one reported that poverty level was not significantly associated with mortality in hospital, or within 30 days, 90 days, or 1 year among patients receiving critical care (Villanueva and Aggarwal, 2013; Zager et al., 2011). A slightly greater number of studies examined health care utilization outcomes, among which one found that high poverty was associated with increased 30-day readmissions (Hu et al., 2014), while one found that poverty level was not significantly associated with either 30-day or 1-year readmission (Villanueva and Aggarwal, 2013). One study found that town-level poverty was predictive of AMI and heart failure hospitalizations (Harris et al., 2008). Of three studies examining clinical processes of care, one reported that higher poverty areas were associated with decreased odds of colon and rectal cancer treatments (Hines et al., 2014), and one found that poverty level was not significantly associated with receipt of recommended rheumatoid arthritis therapy (Yazdany et al., 2014). One study reported no association between county-level poverty and receipt of antipsychotic drugs among black or white nursing home residents, no association between poverty level and restraint use on black nursing home patients, and a small but significant protective effect of poverty on restraint use among white nursing home residents (Miller et al., 2006).
Two studies found that the proportion of households on public assistance was associated with decreased likelihood of getting recommended preventive care (Zaslavsky and Epstein, 2005; Zaslavsky et al., 2000).
Education Several articles examined the relationship between neighborhood education and health care use. Two articles found that neighborhood education was the strongest predictor of getting recommended preventive care (Zaslavsky and Epstein, 2005; Zaslavsky et al., 2000). Three articles examining readmissions reported inconsistent findings, with one finding that residing in a low education area was associated with significantly higher readmissions for AMI, heart failure, or pneumonia(Herrin et al., 2015), and one study reported increased likelihood of 30-day or 1-year readmission among patients living in an area with low educational attainment with comorbid mental health and substance use disorders who had been discharged from acute patient care (Stahler et al., 2009). One study reported no significant association between
neighborhood education and 30-day all-cause readmissions (Hu et al., 2014). The latter article also found no association between neighborhood educational attainment and keeping a follow-up appointment after discharge (Stahler et al., 2009). One article found that educational attainment was not associated with hospitalization for heart failure or AMI (Harris et al., 2008).
Occupation Two articles examined area-level employment and health care utilization. One found that being in a retirement area significantly decreased risk of readmission (Herrin et al., 2015), and the other found that high unemployment was predictive of hospitalizations for AMI and heart failure (Harris et al., 2008).
Racial/ethnic composition Two articles examined neighborhood racial/ethnic composition and health care utilization. Zaslavsky and colleagues (2000) found that the proportion of black residents was negatively associated with getting recommended preventive care, while the proportion of Asian residents was positively associated, and the proportion of Hispanic residents was not significantly associated. Another study found that county-level racial composition may interact with nursing home facility-level racial composition on nursing home quality outcomes, blunting the protective effect of having a higher proportion of black residents on restraint use and receipt of antipsychotic drugs (Miller et al., 2006).
Composite measures A small number of articles examined composite measures of community composition and health care use and health care outcomes. Two studies found that area-level SEP was associated with increased readmissions from heart failure (Bikdeli et al., 2014) from all causes (Kind et al., 2014). One study found that lower area-level SEP was associated with poorer glycemic control even after controlling for individual SEP, but there was no association between area-level SEP and lipid control (Geraghty et al., 2010). One study reported a significant interaction between individual SEP and area-level SEP (Taylor et al., 2006). Specifically, the authors found that individuals with low SEP residing in high-SEP areas had shorter time to hospitalization, higher rates of hospitalization, and higher rates of uncontrolled blood pressure after accounting for other individual and neighborhood SEP factors, and compared to individuals of low SEP in low- or moderate SEP areas, and moderate- or high-SEP individuals from all-level SEP areas.
Other compositional factors Two studies examined the effect of other compositional factors on health care use. One article found that the percent of residents never married, the number of Medicare beneficiaries per capita, the number of nursing home residents with pressure sores, and the number of nursing home residents with increased need for help were associated with increased readmissions for AMI, heart failure, and pneumonia, whereas the number of nursing home patients who were depressed or anxious was associated with decreased risk of readmission (Herrin et al., 2015). Another study found no association between the county-level nursing home occupancy rate and receipt of antipsychotic drugs among black and white nursing home patients. No significant association between the county-level nursing home occupancy rate and restraint use on black nursing home patients was found, but a small, significant protective effect on white nursing home residents was observed (Miller et al., 2006). The committee made the following finding:
- The committee identified literature indicating that neighborhood composition may influence health care utilization, clinical processes of care, health outcomes, and patient safety.
Contextual Community Effects
Contextual community effects include a variety of heterogeneous elements of a community’s physical and social environments. Unlike compositional characteristics that aggregate individual-level characteristics, contextual characteristics cannot be disaggregated into individual-level characteristics, but are rather emergent properties of the place or the community itself. Evidence suggests that both physical and social environments may affect health behaviors (in particular, nutrition and physical activity), morbidity, and mortality (Diez Roux and Mair, 2010; IOM, 2002).
Built environment The built environment encompasses man-made aspects of the physical environment and may include transportation, walkability, sanitation, buildings and housing, and other elements of infrastructure and urban planning (IOM, 2002). Transportation and walkability may be especially relevant for the health outcomes of older adults and persons with mobility disabilities.
Housing Elements of housing include housing stability, homelessness, and quality and safety. Homelessness and housing instability are associated with poor health care access, increased physical and mental morbidity, and mortality (Fazel et al., 2008, 2014; Kushel et al., 2006). Poor housing can negatively affect health through exposure to environmental hazards such as lead or poor air quality, infectious disease, poor sanitation, and injury (IOM, 2002; Krieger and Higgins, 2002). Studies examining the association between housing status (namely, post-discharge residence—e.g., private residence, institutional residence such as skilled care or assisted living) found no association with readmissions in either the short term (30 days) or longer term (1 year) (Garrison et al., 2013; Jasti et al., 2008; Stahler et al., 2009).
Transportation Transportation can be a barrier to health care access and may include both availability of public transportation and travel distance; identified studies examining the influence of transport on health care utilization and health care outcomes focused on the latter. One study found no association between distance traveled and readmissions (Chou et al., 2014), while another reported that distance traveled relative to the patient mean distance was significantly associated with increased likelihood of 30-day readmission (Kroch et al., 2015). One article examined influence on patient experience and found that patients with a smaller travel distance were less satisfied with their care compared to patients living farther away (Abtahi et al., 2015).
Two articles examined influence of travel distance on mortality. One found that patients traveling further were significantly more likely to die in surgery (Chou et al., 2014). There was a significant interaction with disease severity such that travel distance had no effect on mortality among healthier patients, but high-severity patients traveling further had significantly higher rates of operative mortality compared to patients traveling less far. One article found no effect of travel distance measured by both point distance and driving distance and survival to discharge (Cudnik et al., 2010). However, the authors also reported that survival to discharge was higher in patients taken to a further, more specialized hospital, bypassing closer, but less specialized facilities, compared to those simply taken to the closest hospital (Cudnik et al., 2010).
Health care resources The availability of health care services is not evenly distributed in either number or quality. This uneven distribution has consequences for health care access and ultimately health status. Two studies examined the influence of area-level health care resources and health care use and outcomes. Herrin and colleagues (2015) found that a higher number of specialists per capita and the number of hospital beds per capita significantly increased risk of
readmissions, while designation as a retirement area, the number of general practitioners per capita, and having more nursing homes per capita was associated with decreased risk of readmission. Nyweide and colleagues (2011) found that physician supply was not associated with Medicare beneficiaries’ satisfaction with care.
Other elements of the built environment One study reported that, for patients diagnosed with comorbid mental health and substance use disorders discharged from acute inpatient care, living in an area with high levels of vacant housing and living relatively far from an Alcoholics Anonymous meeting location significantly decreased likelihood of keeping a 30-day follow-up appointment (Stahler et al., 2009). Another study reported that towns closer to a hospital had significantly higher hospitalization rates for heart failure (Harris et al., 2008).
Social environment While many elements of a social environment are compositional, or derived from the individuals who make up a social group, other elements such as economic inequality, urbanization, safety and violence, and social mobility are emergent properties of the groups as a whole (IOM, 2002).
Income inequality Income inequality, or the distribution of income across societies, has been shown to be associated with worse population health (e.g., Kawachi and Kennedy, 1999; Lynch and Kaplan, 2000; Subramanian and Kawachi, 2004; Wilkinson and Pickett, 2006). One study examined income inequality and found that it was associated with increased 30-day readmissions for AMI, heart failure, and pneumonia, even after adjustment of individual patient SEP (Lindenauer et al., 2013). The authors reported no association with 30-day mortality for any condition.
Neighborhood disadvantage One study reported that disadvantaged neighborhoods had both lower availability of and reduced use of revascularization services for AMI (Fang and Alderman, 2003). While the selected disadvantaged neighborhoods were more likely to have residents living under the poverty line, who were unemployed, had lower incomes, and less education compared to residents in other neighborhoods of the city under study, the authors did not assess “neighborhood disadvantage” using a specified measure.
Urbanization Urbanization describes where a place falls on the spectrum from urban to rural. Many studies categorize urbanization as dichotomous (i.e., urban or rural) or trichotomous (e.g., urban, suburban, or rural), while some use a more graded spectrum (e.g., percent urban). Rural areas present challenges related to health care access due to both the availability and distance to health care resources and may also increase risks from environmental hazards associated with rural industries, such as pesticides from farming (IOM, 2002). Individuals in urban areas may also experience negative environmental exposures such as air pollution and safety hazards of old buildings. Furthermore, urban areas may have concentrated areas of disadvantage that may expose residents to negative health effects of poverty and decay, as well as unique social, political, and economic contexts that converge with a city’s physical attributes to shape health behaviors (e.g., physical activity and healthy eating) (IOM, 2002).
A review of the influence of social factors on readmission and mortality among pneumonia and heart failure patients found only a small number of studies that examine the effect of urban or rural residence (Calvillo-King et al., 2013). The review found that rural residence was associated with significantly fewer readmissions for heart failure, but not associated with readmission for pneumonia, and that urban residence was not significantly associated with increased mortality for either condition. Most studies of health care use and health care outcomes focused on utilization. One study found that rural residence was associated with decreased risk of readmission (Herrin et al., 2015), and another study found that urban
residence was associated with increased risk of unscheduled readmission but not scheduled readmissions (Kim et al., 2010). Njeru and colleagues (2015) reported a significant interaction between rural residence and need for an interpreter, such that patients in need of interpreter services from rural areas had significantly increased risk of hospitalization. Ward (2008) reported no association between urban–rural status and avoidable hospitalization among lupus patients. In terms of treatment differences, one study reported that among colorectal cancer patients, rural residents were significantly less likely to receive chemotherapy and suburban patients were significantly less likely to receive radiotherapy (Hines et al., 2014). Another study found that percent urban was associated with receiving recommended childhood and adolescent immunizations, but no recommended care for adults (Zaslavsky et al., 2000). One study reported that urban residents reported significantly worse provider communication (Wallace et al., 2008). The committee made the following findings:
- The committee identified literature indicating that community context may influence health care utilization, health outcomes, and patient experience
- The committee identified literature indicating that urbanization may influence health care utilization, clinical processes of care, costs, and patient experience.
Although an individual risk factor and not a social factor, the committee includes health literacy in the framework. It does so because it is specifically mentioned in the Improving Medicare Post-Acute Care Transformation Act of 2014, and is thus of interest to Congress. It does so also because it is affected by social risk factors, and the literature supports a role for health literacy in health care outcomes and quality measures. The committee also included numeracy as a related concept. Numeracy is the ability to understand information presented in mathematical terms, as health and medical information often is, and to use mathematical knowledge and skills in a variety of applications across different settings (IOM, 2014b). Adults with limited health literacy have lower levels of knowledge about health, poorer health status, and may receive fewer preventive services but have higher rates of ED use and hospitalizations, which may be associated with higher costs (IOM, 2004). Health literacy can be especially relevant for adults with certain disabilities, such as individuals who are deaf, hard of hearing, blind, or have low vision, who have communication barriers and for whom health care information is often not available in accessible formats (IOM, 2004).
Several review articles examined the association between health literacy and health care use and health care outcomes. A review of health literacy and ED outcomes found limited evidence, but the small number of studies identified suggest that inadequate health literacy may be associated with higher ED use and higher costs among Medicare beneficiaries age 65 and older (Herndon et al., 2011). A review of low health literacy and health outcomes found insufficient and inconsistent evidence on the effect of health literacy and numeracy on clinical processes of care (including immunizations, mammography screenings, medication adherence among patients with HIV), health outcomes (including medication adherence, asthma control, diabetes control and complications, and hypertension control), costs, and disparities (Berkman et al., 2011). Similarly, a review of health literacy and diabetes outcomes reported inconsistent and insufficient evidence on the effect of health literacy and numeracy on diabetes risk factors, diabetes complications, and patient experiences (Al Sayah et al., 2013).
Evidence from individual studies echoes the review findings. With respect to utilization, two studies found that higher health literacy was associated with lower utilization. One article found that patients with above basic health literacy had significantly lower risk and lower incidence of all-cause 30-day readmissions after AMI (Bailey et al., 2015). Another study reported a significant, graded, negative association, such that poorer health literacy was associated with significantly higher odds of COPD exacerbations requiring ED visits or hospitalizations (Omachi et al., 2013). Two articles examined effects of health literacy on patient experience. Aboumatar and colleagues (2013) found that among patients with hypertension, there were no differences between patients with high and low health literacy in patient ratings of care, including measures of trust, satisfaction, the likelihood of recommending their doctor, and reporting participatory decision making. Hawley and colleagues (2010) reported that breast cancer patients with moderate or low health literacy were significantly more likely to report poor satisfaction with their care coordination compared to patients with high health literacy. The committee made the following finding:
- The committee identified literature indicating that health literacy may influence health care utilization, clinical processes of care, cost, and patient experience.
It is important to note that although often correlated (e.g., SEP is correlated with race/ethnicity and both race/ethnicity and income are correlated with community context) the different social risk factors also capture distinct dimensions that may need to be considered in understanding the social determinants of health care processes and outcomes in Medicare beneficiaries.
The conceptual framework implies that social risk factors may influence the health care process as well as the outcomes of care among Medicare beneficiaries in many interrelated ways. Thus, all other things being equal, the performance of a given health care system (in terms of quality, outcomes, and cost) can undoubtedly be affected by the social composition of the population it serves. At the same time, there are mechanisms through which the health care system can itself ameliorate the impact of social risk factors on quality, outcomes, and cost. As a simple example, through its action to control clinical risk factors the health care system can reduce the impact of social factors on health. As an example of more complex mechanisms, the health care system can partner with social services to improve health literacy or enhance the effectiveness of clinical interventions by, for example, ensuring local access to healthy foods. These strategies will of course require extra effort (and cost) on the part of the system, and there is still relatively limited evidence on the effectiveness of various strategies to achieve this goal.
What is clear at this point in time, however, is that health literacy and social risk factors (SEP; race, ethnicity, and cultural context; gender; social relationships; and residential and community context) have been shown to influence health care use, costs, and health care outcomes in Medicare beneficiaries. However, some specific factors were found not to influence one or more outcomes. The committee has not yet evaluated the literature for the purpose of identifying those factors that could be incorporated into measures used in Medicare payment programs; that is the focus of the third report from the committee.
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