4

Health-Care Utilizations as Proxies for Listing-Level Severity

This chapter examines health-care utilizations for particular medical conditions and how they might be related to impairment severity and disability. Chapter 2 described the many factors associated with health-care utilization. This chapter describes what is known about how utilization might be associated with the ability to work and the many confounding factors that make this association difficult to measure. It examines the feasibility of using utilization measures other than the ones currently in use in the determination process. For each body system that appears in the Social Security Administration (SSA) Listing of Impairments, the chapter focuses on the following elements of the committee’s statement of task on the basis of available data:

• Explain how types of utilizations are more or less probable for particular medical conditions or combinations of medical conditions.
• Identify health-care utilizations that represent and are good indicators of impairment severity.
• Explain how intervals between utilizations and duration of utilizations affect whether health-care utilization is a good indicator of impairment severity.

Finally, the committee briefly discusses how health-care utilizations might interfere with the ability to work.

DEFINITIONS

The committee’s task is to analyze the relationship of health-care utilizations to “impairment severity” and “SSA’s definition of disability,” so it is important to define these terms. SSA defines a severe impairment as one that “significantly limits an individual’s physical or mental abilities to do basic work activities” (SSR 96-3p); according to the committee’s statement of task, “listing-level” severity refers to an impairment that is “severe enough to prevent a person from doing any gainful activity.” SSA defines disability as “the inability to engage in any substantial gainful activity (SGA) by reason of any medically determinable physical or mental impairment(s) which can be expected to result in death or which has lasted or

can be expected to last for a continuous period of not less than 12 months”¹ (SSA, 2017a). SSA thus defines severe impairment and disability similarly, the difference being the extent of a person’s inability to perform over some period, that is, to perform any gainful activity in the case of severe impairment as opposed to performing substantial gainful activity in the case of disability.²

Other organizations define the terms differently. Although the committee’s statement of task asks it to focus on SSA’s definitions of the terms because the body of evidence that links health-care utilizations with inability to perform any or substantial gainful activity is small, the committee did not limit its search to these definitions. It also considered related concepts, such as disease severity and impairment, according to definitions used by other authoritative bodies.

Disease severity refers to the presence and extent of disease. It can be objectively evaluated through diagnostic testing and physiologic examination (Finlayson et al., 2004). Specific evaluation criteria might include such information as disease progression, likelihood of death, likelihood of high inpatient expenses, likelihood of length of stay, and disease burden (e.g., presence or absence of comorbidities). Other measurement schemes include stage of the disease, complications of the principal conditions, concurrent interacting conditions that affect the hospital course, dependence on hospital staff, extent of non–operating-room life-support procedures, rate of response to therapy or rate of recovery, and impairment remaining after therapy for acute aspect of the hospitalization (Horn et al., 1984).

With regard to impairment, the World Health Organization defines it as any loss or abnormality of psychologic, physiologic, or anatomic structure or function. According to the American Medical Association (AMA), impairment is defined as “a significant deviation, loss, or loss of use of any body structure or body function in an individual with a health condition, disorder, or disease” (AMA, 2008).

The term impairment severity appears in the literature primarily in the context of Social Security disability. It is also mentioned by AMA in its Guides to the Evaluation of Permanent Impairment (AMA, 2008) as degree of loss of body structure or function, which “can vary according to discrete (i.e., level of amputation) or continuous (i.e., degrees of motion lost) criteria.” According to AMA’s definition, severity of impairment is closely related to disease severity but distinct from disability, which is defined as “activity limitations and/or participation restrictions in an individual with a health condition, disorder, or disease.” This distinction can be contrasted with how SSA defines both impairment severity and disability in terms of activity limitations.

DATA

Determination of disability would, in theory, require the use of longitudinal or continuing data gathered over some period to assess the continuing nature of the condition. Longitudinal event-level health-care utilization data at the person level are sparse. Even data on episodes of utilization are limited in that continuing collection of utilization data over a given period is required. (The Agency for Healthcare Research and Quality [AHRQ] Medical Expenditure Panel Survey can be used to assess episodes of care but not at the diagnosis level.) Longitudinal

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¹ A few health-care utilizations are used by SSA as criteria in step 3 of its disability-determination process, for example, “exacerbations or complications requiring three hospitalizations within a 12-month period and at least 30 days apart.”

² See CFR § 404.1505 for the full definition of disability.

collection of data is expensive and often involves small samples. Followup of sample respondents is often difficult. Federal data therefore tend to focus on individual events, and surveys are not granular enough to link specific utilizations with specific conditions. Analyses at the episode or person level have been conducted with Medicare or private-insurer all-payer data; there is no comparable nationally representative dataset for all working-age adults.

Because nationally representative diagnosis-specific episode or person-level data are not readily available, the committee chose to use Healthcare Cost and Utilization Project (HCUP) data to provide some insight into the relationship between specific diagnoses and health-care utilizations associated with severe illness, disease, or injury. Hospitalizations and emergency department (ED) visits generally indicate more severe illness or conditions than do ambulatory care visits. The committee tabulates ED visits and hospitalizations for selected medical conditions relevant to disability determination. It created tables by using HCUPnet, an online query system that exports user-specified descriptive statistics from HCUP data (AHRQ, 2017a). HCUP is the largest collection of longitudinal hospital care data in the United States and includes data on inpatient visits in 44 states and the District of Columbia and on ED visits in 33 states and the District of Columbia (AHRQ, 2017b). It includes data on all patients regardless of payer. ED and inpatient data are weighted national estimates of data from 2014. The committee chose to include patients 18–64 years old inasmuch as most adults who receive disability insurance are in this age range. The committee selected several Clinical Classifications Software (CCS) codes, categories developed by AHRQ that collapse the International Classification of Diseases (ICD) codes into clinically meaningful groups of diagnoses and procedures that reflect diagnoses and procedures for which people receive disability insurance.

Health-care utilization is often not associated with one specific condition. A patient might have multiple comorbid conditions but present to a medical provider for a specific symptom or problem. For each HCUP hospital or ED event, up to five diagnoses are recorded. One of them is coded as the principal, or first listed. In analyzing the data, selection of the principal diagnosis avoids duplication of events, whereas analyzing all listed diagnoses would duplicate events if several diagnoses were compared. Analyzing all listed diagnoses, however, shows the importance of comorbid conditions, as shown in Table 4-1. Among people who were 45–64 years old, there were more than 170,000 hospitalizations with a diagnosis of rheumatoid arthritis, but fewer than 5,000 of the people who had that diagnosis had rheumatoid arthritis as the principal reason for hospitalization. Similarly, among people who were 45–64 years old, there were almost 2 million hospitalizations with a diagnosis of uncomplicated diabetes mellitus, but fewer than 4,000 of them had it as the principal diagnosis. Other comorbid conditions appear to be less prevalent; of hospitalizations with a diagnosis of schizophrenia or related psychoses, about 300,000 had this diagnosis listed, and about 200,000 of them had it listed as the principal diagnosis.

As described in Chapter 2, many factors other than severity of a condition are associated with health-care utilization. Age is one such factor that is available for tabulation in the HCUP software. As shown in Table 4-1, the number of hospitalizations for many diagnoses is larger for people 45–64 years old than for younger adults. That is consistent with the increasing prevalence of several chronic conditions with age, including diabetes, hypertension and heart disease, and arthritis (NCHS, 2017a). For other diagnoses, such as mental health disorders, prevalence and associated hospitalizations do not increase with age. When a diagnosis incorporates some measure of severity, such as the diagnosis of diabetes mellitus with complications, it makes other characteristics, such as age, less important to consider. Most younger adults with a diagnosis of

diabetes mellitus with complications had it listed as the principal diagnosis. Older adults with any diagnosis of diabetes mellitus with complications were less likely than younger adults to have it listed as the principal condition; this indicates that they were more likely to be hospitalized for a comorbid condition. That the number of comorbid conditions increases with age makes the relationship of an individual health event, such as a hospitalization or ED visit, to a specific condition less straightforward. Utilization is more related to sets of conditions, but analysis of which specific conditions should be grouped is extremely complex and faces many data limitations because of limitations in the number of conditions coded for each event and the lack of person-level data on the association between multiple specific groups of comorbid conditions and outcomes.

TABLE 4-1 Hospitalizations and Emergency Department Visits for Selected Diagnoses by Age Group, 2014

NOTES: This data table was produced by using HCUPnet (AHRQ, 2017a), sorting by CCS codes, and stratifying by age group. CCS codes are diagnostic categories developed by AHRQ that collapse ICD codes into clinically meaningful groups. ED data are weighted national estimates from the HCUP National Emergency Department Sample (NEDS), 2014, with an undefined population sample size; inpatient data are weighted national estimates from the HCUP National Inpatient Sample, 2014, N = 35,358,818.

SOURCE: AHRQ, 2017a.

Tables 4-2 and 4-3 present more detailed utilization data, showing length of hospital stays and percentages of ED visits that led to hospital admissions. They also present the rate of hospitalizations and ED visits per 100,000 people. Separate tables are presented for the populations 18–44 and 45–64 years old due to restrictions on the HCUP software, which produces hospital length of stay and rates per 100,000 people for those age groups. For these tables the principal diagnosis at admission is used because it is most strongly related to the reason for the admission or visit. These tables show the wide variation in use of hospitals and EDs for specific diagnoses and differences in severity as measured by the need for long hospital stays or for hospital admission from the ED.

TABLE 4-2 Hospitalizations and Emergency Department Visits of People 18–44 Years Old for Selected Health Conditions in the United States in 2014

NOTES: ALS = amyotrophic lateral sclerosis; CCS = Clinical Classification Software; COPD = chronic obstructive pulmonary disease; ED = emergency department; ICD = International Classification of Diseases. This table was produced by using HCUPnet (AHRQ, 2017a), sorting by CCS codes, and stratifying by age group. ED data are weighted national estimates from the HCUP National Emergency Department Sample, 2014, with an undefined population sample size; inpatient data are weighted national estimates from the HCUP National Inpatient Sample, 2014, N = 35,358,818.

SOURCE: AHRQ, 2017a.

High rates of hospitalization for the selected diseases provide some information on the severity of the diseases if it is assumed that diagnoses with high rates are more severe and require more hospitalizations. Cardiac disease and arthritis increase with patient age, and rates per 100,000 people are higher among people 45–64 years old (see Table 4-3) than among their younger working-age counterparts (see Table 4-2). In 2016, however, about 6 percent of people who were 45–64 years old had self-reported coronary heart disease of any kind (NCHS, 2017a), and about 8 percent had asthma.

TABLE 4-3 Hospitalizations and Emergency Department Visits of People 45–64 Years Old for Selected Health Conditions in the United States in 2014

NOTES: ALS = amyotrophic lateral sclerosis; CCS = Clinical Classification Software; COPD = chronic obstructive pulmonary disease; ED = emergency department; ICD = International Classification of Diseases. This table was produced by using HCUPnet (AHRQ, 2017a), sorting by CCS codes, and stratifying by age group. ED data are weighted national estimates from the HCUP National Emergency Department Sample, 2014, with an undefined population sample size; inpatient data are weighted national estimates from the HCUP National Inpatient Sample, 2014, N = 35,358,818.

SOURCE: AHRQ, 2017a.

Large percentages of ED visits for some diagnoses, such as hypertension with complications and congestive heart failure, result in hospitalization. Very few ED visits for other diagnoses, such as rheumatoid arthritis and related conditions, result in hospitalization, even when rheumatoid diagnosis is coded as the principal diagnosis. But, that information says little about the severity of a disease overall; to know that, one must also know the prevalence of the disease in the population. Asthma, for example, has a relatively high incidence per 100,000 people but is much more prevalent than congestive heart failure overall and can be controlled in most cases. Congestive heart failure is usually not diagnosed until it is serious, so rates of hospitalization after ED visits are relatively high. The data appear to show that the diagnosis is more related to the probability of a hospitalization, ED visit, or hospitalization after an ED visit than the other way around; that is, hospitalization or an ED visit is not indicative of the severity of the diagnosis.

The remainder of the chapter discusses what is known about the relationship between specific health-care utilizations and impairment severity, by organ system, and then discusses what is known about the relationship between utilization and the ability to work. The public-access SSA data do not provide statistics on particular medical conditions. Finally, this chapter summarizes the committee’s findings from the literature review. The evidence review strategy is outlined in Appendix B, and the details of each study cited in this chapter can be found in Appendix C.

MUSCULOSKELETAL SYSTEM

Disorders of the musculoskeletal system accounted for 28.2 percent of diagnoses of disability insurance beneficiaries in 2014, making them the most common type of impairment among disabled workers who were awarded disability insurance payments (SSA, 2015).

Disorders and impairments of the musculoskeletal system might result from hereditary, infectious, inflammatory, neoplastic, degenerative, and traumatic processes. Musculoskeletal impairments can cause an inability to walk or perform fine or gross movements effectively on a sustained basis. Musculoskeletal disorders are the number one cause of severe long-term pain and physical disability worldwide. According to self-reported data, one-fourth of adults who were 18–64 years old had low back pain that lasted a day or more in the past 3 months (NCHS, 2017a); if other types of musculoskeletal conditions are included, the fraction is even higher. Their burden on people and health systems is expected to grow dramatically as the population ages; osteoarthritis is predicted to be the fourth-leading cause of disability in 2020 (Woolf and Pfleger, 2003).

There are ways of grading the severity of musculoskeletal disorders medically. For instance, the severity level of osteoarthritis is based on the degree of narrowing of joint spaces and the location and frequency of pain (Woolf and Pfleger, 2003).

In general, although health-care utilization might have been discussed to some extent in the research examined, its use as a proxy for impairment severity meets various barriers. One such barrier is that utilization varies with nonmedical factors, including social factors (Neumatitis et al., 2016), hospital factors (Pendleton et al., 2007), insurance (Menendez and Ring, 2015; Neumatitis et al., 2016), and economic and geographic factors (Young et al., 2009, 2015; Menendez and Ring, 2015). Much of the current research available on musculoskeletal injury and illness that lead to disability does not look at health-care utilization as a function of disability (see Appendix C). Research that includes it to any extent does not consistently note that health-care utilization increases or decreases as a function of disability.

Health-care utilizations alone are not shown to predict disease severity or disability, and other factors arose as predictive. Health-care utilizations are found to increase for nonclinical reasons not related to severity of injury or impairment, and these nonclinical factors might also drive down health-care utilization. Hence, any relationship is complex and depends on other factors that are nonclinical and might not be easily measured.

SPECIAL SENSES AND SPEECH

Disorders related to special senses and speech include visual disorders, hearing impairment, vertigo, and loss of speech. It is estimated that in 2011 uncorrectable vision loss resulted in a social burden of 283,000 disability-adjusted life-years lost. The prevalence of blindness, according to 2005–2008 National Health and Nutrition Examination Survey data, was 0.1–0.15 percent in people who were 18–64 years old (NORC, 2013). About 12 percent of people who were 45–64 years old reported being blind or having trouble seeing even with glasses in 2014 (NCHS, 2017a). Disabling hearing loss has a prevalence of 2 percent in people who are 45–54 years old; this increases to 8.5 percent in people who are 55–65 years old (NIDCD, 2016).

Various tests are used to measure the clinical severity of this group of disorders. For example, visual-acuity efficiency for best corrected vision is used to measure the severity of vision loss. There are special rules for disability insurance applicants who are blind (whose vision cannot be corrected to better than 20/200). Blind claimants might receive up to $1,950/month without their work being considered substantial gainful activity compared with a limit of $1,170/month for nonblind applicants in 2017 (SSA, 2017b). Hearing thresholds in decibels and speech-discrimination scores are used to measure hearing impairments.

Few studies have examined associations between health-care utilizations and the severity of vision loss, hearing loss, and other disorders of the special senses. The one study included in Appendix C (McKee et al., 2015) did not discuss health-care utilization with respect to impairment severity but suggested that deafness could be a comorbid condition that increases the likelihood of ED visits. The evidence does not appear to suggest any health-care utilization that is a good indicator of impairment severity in this body system.

RESPIRATORY DISORDERS

Respiratory impairments can lead to disability and severe impairment. The most common impairing respiratory diseases are asthma, chronic obstructive pulmonary disease (COPD), emphysema, interstitial fibrosis, chronic bronchitis, bronchiectasis, reactive airway disease, and carcinoma. In addition, vascular diseases of the respiratory tract can be life-threatening because of pulmonary embolism and pulmonary hypertension. Autoimmune diseases, vasculitis, and pneumoconiosis lead to fibrosis, scarring, and restriction. Various defining criteria are necessary for diagnosis and further definition of the impairment status of each of those diseases. Chronic lower respiratory diseases, especially COPD, constituted the third-leading cause of death in the United States in 2014. More than 6.4 percent of Americans reported having had a diagnosis of COPD, and the prevalence is higher in people over 65 years old. The prevalence of COPD varies considerably by state, from less than 4 percent in Colorado, Hawaii, and Utah to greater than 9 percent in Alabama, Kentucky, Tennessee, and West Virginia (CDC, 2017a). About 8 percent of working-age adults reported having asthma in 2015 (NCHS, 2017b).

The traditional means of establishing function of and diagnoses related to the respiratory tract involve chest x-ray and computed tomography/magnetic resonance imaging, pulmonary-function tests, diffusion capacity, bronchial-reactivity testing, cardiopulmonary exercise testing, and arterial blood gases. The AMA Guides to the Evaluation of Permanent Impairment states that ambulatory measurements, such as maximum postbronchodilator forced expiratory volume (FEV), are important for severity impairment ratings (AMA, 2008). Reactive airway challenge testing, such as methacholine challenge tests at below 0.50 PC(20) mg/mL, where PC—provocative concentration—(20) indicates a 20 percent fall in FEV, place patients or claimants who have asthma or other respiratory conditions at 25 percent impairment (Class 3 or 4) or worse with respect to the respiratory tract (AMA, 2008). As noted in the AMA Guides and SSA Listings, substantial reductions in activities of daily living (ADLs), inability to find gainful employment, terminal disease, multiple hospitalizations in a year, severe frequent exacerbations of oxygen supplementation, or ventilator assistance mark greater severity and possible disability. Respiratory disorders accounted for 2.6 percent of diagnoses of disability insurance beneficiaries in 2014 (SSA, 2015). “COPD and bronchiectasis” ranked in the top 10 conditions with the most all-causes, 30-day readmissions for Medicaid patients in 2011 with a readmissions rate of 25.2 per 100 admissions (AHRQ, 2014). They did not rank in the top 10 readmissions for privately insured patients in 2011.

Exacerbations of COPD requiring hospital admission have a major effect on progression of disease. COPD patients experience an average of one to two exacerbations per year. Patients who have more than three exacerbations per year demonstrate faster fall in FEV1 (forced expiratory volume in 1 second) (Celli et al., 2008) and greater airway inflammation when clinically stable (Bhowmik et al., 2000).

The committee found many reports of studies that examined health-care utilizations for COPD. A few associated health-care utilizations for COPD with disease severity by using such indicators as SpO2 and BODE index score (Ekberg-Aronsson et al., 2008; Alcazar et al., 2012). Several studies show that COPD exacerbations that require hospitalizations predict severity of illness, but they do not indicate whether the hospitalizations predict ability to work (Fan et al., 2007; Mullerova et al., 2015). Using hospitalizations to predict severity of COPD seems to be a research subject of interest. In fact, Omachi et al. (2008) developed a dynamic prediction tool to use a COPD-severity score with sociodemographics, medical comorbidity, and tobacco history to

predict respiratory-specific health-care utilizations, including hospitalizations, ED visits, and outpatient visits. They found that adding a COPD-severity score to the basic model substantially increased the predictive value of the model. One study that looked at patients who had asthma found a relationship between hospitalizations for asthma exacerbation and lung-function decline (Bai et al., 2007).

CARDIOVASCULAR SYSTEM

Cardiovascular diseases (CVDs) accounted for 7.5 percent of diagnoses of disability insurance beneficiaries in 2014 (SSA, 2015). The most common impairing CVDs—excluding stroke—involve coronary heart disease, heart failure (HF), rhythm disorders, valvular disease, venous disease, and peripheral arterial disease (PAD) (Benjamin et al., 2017). The costs of CVDs to both the individual and the nation are great. More than one-third of US adults (92.1 million people) have CVDs (including hypertensive disease), which accounted for 807,775 deaths in 2014 (Benjamin et al., 2017). In 2015, about 6 percent of people who were 45–64 years old had self-reported coronary disease; about one-third reported having hypertensive disease (NCHS, 2017a). The 2017 Heart Disease and Stroke Statistics Update of the American Heart Association (Benjamin et al., 2017), which used national Medical Expenditure Panel Survey (MEPS) data, estimated the annual direct and indirect cost of CVDs in the United States as $316.1 billion (note, however, that the association’s definition of CVD includes stroke).

The AMA Guides (AMA, 2008) define cardiovascular dysfunction on the basis of elements of cardiac function: history and physical examination findings, findings of objective tests of cardiac function, proven target-organ damage from CVDs, and proven established diagnoses. SSA Listings use established diagnoses, results of functional testing, hospitalizations, and arrhythmia assessments and treatment as criteria in disability determination.

Although the cardiovascular literature contains many studies of risk factors for specific diseases or factors related to improvements in care, there is little on utilizations that might serve as proxies for disability in working-age adults. The prevalence of CVDs increases with age, so the vast majority of studies involve people who are 65 years old or older, especially because of the availability of Medicare data on this population. In its review of the literature on the cardiovascular system, the committee concentrated on studies involving young or middle-aged adults in high-income countries. However, several studies involving older people were included if a factor deemed important for understanding utilization was found (e.g., Bibbins-Domingo et al., 2009; Bengtson et al., 2014; Mizutani et al., 2017). Of the studies involving adults under 65 years old, utilizations or functionality metrics were occasionally included, but none provided direct evidence for determining inability to return to work. Information that might provide guidance for addressing the SSA Listings, however, was not covered.

Studies that used a health-care utilization as a metric often evaluated trends in hospitalizations (Towfighi et al., 2011; Chamberlain et al., 2013; Badheka et al., 2015) or patient readmissions after a CVD event (Kim et al., 2009; Foraker et al., 2011; Yamada et al., 2012; Betihavas et al., 2015). All those studies, either directly via comparisons with people free of CVD or indirectly through other means, documented the higher rates of inpatient admissions associated with a specific cardiovascular condition. In some cases, the length of time between hospitalizations might be helpful in inferring the incapacity to work. Many epidemiologic investigations provided data to elucidate the factors inherent in subjects that predicted either utilization or functionality outcomes. The most striking finding in studies that used that approach

was the adverse effect of comorbidities on health (Fan et al., 2009; Foraker et al., 2011; Calvillo-King et al., 2013; Chamberlain et al., 2013; Agarwal et al., 2014; Badheka et al., 2015; Thorpe et al., 2016). Psychologic distress and depression were identified most consistently as predictors of disability or other functional outcomes. With respect to other comorbidities, people who had concomitant cardiovascular conditions (hypertension, diabetes, HF, arrhythmias, or PAD) or noncardiovascular diseases (asthma, COPD, arthritis, or renal disease) were also at higher risk for CVD progression. Many studies included demographic and social variables in their analyses. Most studies that included race as a covariate found that being nonwhite was a strong predictor of CVD outcomes (Bibbins-Domingo et al., 2009; Thomas et al., 2011; Calvillo-King et al., 2013; Thorpe et al., 2016), although not all studies agreed (Gambassi et al., 2008). Results on the effect of sex were mixed; studies generally reported no differences in adjusted models with the exception of myocardial infarction (Towfighi et al., 2011; Agarwal et al., 2014). The adverse influence of a multitude of socioeconomic factors has been documented. As in other body systems, many of the selected variables in CVD studies that examined socioeconomic status correlated, but studies generally found associations with such factors as low income, less education, unmarried status, rural residence, zip code or neighborhood value, home instability, and lack of social support (Foraker et al., 2011; Calvillo-King et al., 2013; Schofield et al., 2013; Agarwal et al., 2014).

Finally, although this review did not intend to focus on biomarkers as predictors of disease severity, the value of brain natriuretic peptide as a marker for CVD was identified (Allen et al., 2011; Mizutani et al., 2017). In conclusion, although the cardiovascular literature examined here was not exhaustive and this review was unable to provide utilizations as definitive markers by which SSA determinations of disability can be made, important factors to address during disability evaluations were revealed.

DIGESTIVE SYSTEM

Digestive diseases accounted for 1.5 percent of diagnoses of disability insurance beneficiaries in 2014 (SSA, 2015). Digestive system disorders include gastrointestinal (GI) hemorrhage, liver dysfunction, inflammatory bowel disease, short bowel syndrome, and malnutrition. They might lead to such complications as obstruction or have manifestations in other body systems. Digestive diseases are a leading cause of ambulatory care visits (Everhart and Ruhl, 2009), ED utilizations (Myer et al., 2013), and hospitalizations (Everhart and Ruhl, 2009). Everhart and Ruhl (2009) and Myer et al. (2013) demonstrated rising trends in GI-related hospitalizations and ED utilizations, respectively. However, despite the increase in ED visits, only 21 percent of those visits resulted in hospitalization (Myer et al., 2013). In addition to physician services, ED visits, and hospitalizations, health-care utilizations for digestive diseases include prescription drugs, diagnostic testing, nursing home care, and home health care (Everhart and Ruhl, 2009). In 2009, digestive diseases accounted for $85.2 billion in direct costs for personal health care measured by MEPS and the AHRQ (NHLBI, 2012).

Disease severity is determined primarily through laboratory findings and physical signs and symptoms, such as diarrhea, nausea and vomiting, and weight loss. The disorders often are treated with medication, therapy, and surgery.

Although the specific impact varies by digestive disease, factors associated with increased health-care utilization include the presence of psychologic comorbidities (Allegretti et

al., 2015), overlapping somatic complaints (Dudekula et al., 2011), symptom severity (Reilly et al., 2004; Wahlqvist et al., 2008), and response to disease-related therapy (Buono et al., 2014).

Factors associated with disability in digestive diseases have not been well studied except for inflammatory bowel disease. In Crohn’s disease, disease-related characteristics—such as disease activity (Allen et al., 2013), fistulizing disease, duration of disease, number of relapses. and response to therapy—have been associated with loss of work productivity and disability (Siebert et al., 2013).

In some diseases, such as chronic pancreatitis (Mullady et al., 2011) and gastroparesis (Dudekula et al., 2011), pain frequency more than pain severity is associated with decreased quality of life (QOL), decreased work productivity, and increased health-care utilization. In patients who have chronic pancreatitis, the characteristic of pain (constant versus intermittent) but not severity of pain is associated with greater absenteeism, hospitalizations, and disability (Mullady et al., 2011). High illness-related absenteeism (defined as more than 15 absences/year) is associated with a greater risk of job termination, unemployability, or disability (Virtanen et al., 2006).

Overall, disease severity and presence of comorbidities are associated with greater impairment in QOL, health-care utilization, and disability. As with the other body systems, it is challenging to identify specific health-care utilizations that could serve as accurate proxies for disability. However, such disease-related characteristics as frequency, severity, and response to therapy are objectively quantifiable measures that have been associated with impairment in work productivity and QOL, which might serve as better proxies for disability. In addition to disease-specific characteristics, the presence of psychiatric disorders, sleep disturbance, and multiple somatic comorbidities is associated with a greater risk of disability in patients who have gastrointestinal diagnoses.

GENITOURINARY DISORDERS

Genitourinary diseases accounted for 1.6 percent of diagnoses of disability insurance beneficiaries in 2014. For the purposes of assessing disability, the three main categories of genitourinary disorders are chronic kidney disease (CKD), complications of CKD, and nephrotic syndrome. Genitourinary diseases account for $66.6 billion in direct costs for personal health care as measured by MEPS and AHRQ (NHLBI, 2012).

Two key health-care utilizations by which SSA determines the presence of severe CKD are dialysis and kidney transplantation. People who are undergoing dialysis are automatically classified as having Listing-level severity if dialysis has lasted or is expected to last for a continuous period of at least 12 months (SSA, 2017b). People who receive a kidney transplant are automatically classified as having Listing-level severity for 1 year after transplantation; the classification is revised later, depending on clinical status. Other measures of CKD used by SSA include reduced glomerular filtration (measured by serum creatinine, creatinine clearance, or estimated glomerular filtration rate) and the presence of comorbid conditions (renal osteodystrophy, peripheral neuropathy, fluid overload syndrome, or anorexia with weight loss). Tools used to identify CKD include laboratory, blood pressure, and anthropometric measurements and imaging studies. SSA uses laboratory findings to identify nephrotic syndrome, defined by proteinuria, serum albumin, urine protein:creatinine ratio, and anasarca (extreme generalized edema). To identify complications of CKD, SSA uses hospitalizations due

to stroke, congestive heart failure, hypertensive crisis, or acute kidney failure that requires a short course of hemodialysis.

There is some evidence that numbers of physician visits and hospitalizations can predict severity of CKD (Alexander et al., 2009). Although there is strong evidence in the literature that worsening of renal function is a predictor of mortality in acute decompensated heart failure (Damman et al., 2014; Ueda et al., 2014), fewer studies have examined this comorbidity as a predictor of longer-term outcomes.

HEMATOLOGIC DISORDERS

Hematologic diseases accounted for 0.3 percent of diagnoses of disability insurance beneficiaries in 2014 (SSA, 2015). Hematologic disorders include such nonmalignant conditions as hemolytic anemias (e.g., sickle-cell disease and the more specific sickle-cell anemia, a form of sickle-cell disease in which there are two sickle-cell genes), disorders of thrombosis and hemostasis, and disorders of bone-marrow failure (e.g., myelodysplastic syndromes, aplastic anemia, granulocytopenia, and myelofibrosis). Such malignant hematologic disorders as lymphoma, leukemia, and multiple myeloma are covered under other Listings. One exception is that disability resulting from bone marrow transplantation for any of the above conditions is covered under the hematologic disorders Listing. SSA uses health-care utilizations in addition to clinical measures to determine severity in this Listing. For instance, the Listing for hemolytic anemias includes a painful vaso-occlusive crisis requiring parenteral narcotic medication, occurring at least six times within a 12-month period at least 30 days apart or a complication that requires at least three hospitalizations within a 12-month period at least 30 days apart (each hospitalization lasting at least 48 hours) or hemoglobin measurements of 7 g/dL or less that occur at least three times within a 12-month period at least 30 days apart or beta-thalassemia major that requires life-long transfusions at least once every 6 weeks. Compared with diseases of other body systems, hematologic diseases are rare in the United States; for example, although the exact prevalence of sickle-cell anemia is unknown, the Centers for Disease Control and Prevention estimates that sickle-cell disease affects about 100,000 Americans (CDC, 2017b).

The AMA Guides (AMA, 2008) note that the severity of sickle-cell anemia depends on the cardiovascular system’s compensatory response. AMA also notes that in persistent refractory anemia, the degree of impairment is related to the need for transfusion.

The committee found no literature relevant to determination of disability associated with other hematologic conditions on the basis of a health-service use. Hematologic Listings use hospitalizations as a criterion for receiving disability benefits. The literature suggests that not only frequent hospitalizations but also frequent ED visits are typical for sickle-cell patients (Brousseau et al., 2010). In addition, many factors other than the severity of the disease or disability resulting from the disease might predict hospital use and ED visits. They include the availability of good outpatient care (Leschke et al., 2012), distance from outpatient care (Wolfson et al., 2011), and insurance coverage (Wolfson et al., 2011). Changes in the health-care delivery system might render the three-hospital-stays-per-year criterion less valuable in the future inasmuch as hospital stays will undoubtedly decrease in length to less than 48 hours for crisis management. There might also be a shift to ED and urgent-care referral to outpatient care to manage a crisis.

SKIN DISORDERS

Skin diseases and burns accounted for 0.2 percent of diagnoses of disability insurance beneficiaries in 2014. Skin disorders might result from hereditary, congenital, or acquired pathologic processes. They include such impairments as ichthyosis, dermatitis, chronic infections of the skin or mucous membranes, and burns. The cost of skin disease in the United States 20 years ago was $35.9 billion, which included $7.2 billion in hospital inpatient charges (Dehkharghani et al., 2003). The indirect costs due to labor loss were estimated at $1.6 billion. Although skin disorders are seldom fatal, they can be terminal in some circumstances of burn, infection, cancer, inflammation, and severe allergic conditions.

Disease severity is generally determined according to the extent and frequency of flareups of skin lesions and the extent of treatment. The AMA Guides (AMA, 2008) call for evaluating impairment severity in skin conditions on the basis of the degree to which a condition persists after medical treatment and rehabilitation and affects ADLs.

The rate of hospitalization related to skin infection has increased over the past 20 years: hospitalization for skin infections has increased by 52 percent and for sepsis-related skin infections by 190 percent (Martin et al., 2003). Edelsberg et al. (2009) found in the 2000–2004 HCUP data that admissions for skin and soft-tissue infections increased by 29 percent over that 5-year period. Suaya and colleagues (2014) reported that hospitalization for soft-tissue infection due to community-associated methicillin-resistant Staphylococcus aureus increased by 122 percent (from 161,000 to 358,000) from 2001 to 2009 according to HCUP and Bureau of the Census.

In an analysis of data from the National Electronic Injury Surveillance System—Occupational Supplement for 1999–2008, Reichard et al. (2015) found that scalds and thermal burns made up greater than 60 percent of all burns. Most burns occurred in occupations related to accommodation and food service, manufacturing, and construction.

The committee found no evidence that particular utilizations are good indicators of impairment severity associated with skin diseases. The two studies summarized in Appendix C indicate that comorbid conditions are important determinants of health-care utilization for diseases related to this body system. There is evidence that obesity is a comorbid condition that impairs functional ability in burn patients (Farrell et al., 2008) and that psoriasis might increase risk of hospitalization of lymphoma patients (Kimball et al., 2014).

ENDOCRINE DISORDERS

Endocrine diseases accounted for 0.3 percent of diagnoses of disability insurance beneficiaries in 2014. Impairments of the endocrine system are caused by hormone overproduction or underproduction that results in structural or functional changes in the body. Examples are diabetes mellitus (DM), thyroid and parathyroid disorders, and pituitary disorders. In the United States, several endocrine disorders, such as DM and thyroiditis, have prevalences greater than 5 percent in adults (Golden et al., 2009). Changes in the body that indicate endocrine disorders can be determined through laboratory findings or imaging, such as x-ray imaging, computed axial tomography, and magnetic resonance imaging.

The committee found no evidence that links health-care utilization to severity of endocrine conditions, but it did find evidence that diabetes as a comorbid condition can affect the

frequency of health-care utilization by people who have cardiac disease (Jang et al., 2016; Nadjiri et al., 2016).

CONGENITAL DISORDERS THAT AFFECT MULTIPLE BODY SYSTEMS

The primary congenital disorder that affects multiple body systems is nonmosaic Down syndrome, which is diagnosed with a laboratory test. Down syndrome is generally diagnosed before or shortly after birth and only rarely in adulthood. There are about 6,000 diagnoses of Down syndrome in the United States each year (Parker et al., 2010). The syndrome is associated with distinctive physical characteristics, intellectual disability, higher risk of congenital heart defects, and higher incidences of infection, respiratory problems, vision and hearing impairments, and thyroid disorders. The committee’s search on the use of health-care utilizations as indicators of impairment did not yield any articles related to congenital disorders that affect multiple body systems as defined by SSA.

NEUROLOGIC DISORDERS

Neurologic conditions accounted for 9.4 percent of diagnoses of disability insurance beneficiaries in 2014. Neurologic conditions encompass a wide array of disorders and injuries, and many people who have these conditions present with a continuum of impairment severity that can differentially affect their ability to work. Some examples of neurologic conditions are epilepsy, amyotrophic lateral sclerosis, parkinsonian syndrome, stroke, and brain injury. In 2011, nearly 100 million Americans had at least one neurologic disease. The number of years of life lost because of disability associated with neurologic and musculoskeletal disorders is greater than that associated with any other body system (Gooch et al., 2017). The prevalence of dementia, including Alzheimer dementia, is cited as 7.5 million, and the cost of dementia increases dramatically with increased severity because of the great need for daily care and assistance. The annual incidence of traumatic brain injury (TBI) in the United States is cited as 1.4–1.7 million. The risks of dementia and TBI increase with age (Gooch et al., 2017).

According to the AMA Guides (AMA, 2008), neurologic impairments should be assessed as they affect ADLs. Minimal impairment might be seen in a person who experiences epileptic seizures once every 2 months despite optimal medical intervention. Moderate impairment might be seen in a person who requires moderate assistance with ADLs, whereas severe impairment might be seen in a person who needs extensive assistive care throughout the day.

The relationship between health-care utilization for neurologic disorders and functional abilities, including ability to work, has received little study. Multiple sclerosis (MS) has probably been the most frequently studied neurologic disorder relevant to health-care utilization and disability. The committee found that people who had MS were more likely to use the hospital, ED, and rehabilitation therapy than those who did not have MS (Asche et al., 2010) and that patients who had more severe MS were more likely to be unemployed (Jones et al., 2016).

Overall, studies suggested that greater health-care utilization was associated with decreased likelihood of employment of people who had neurologic disorders. However, the direct relationship between health-care utilization and impairment severity or employment is rarely studied, and the types of health-care utilization most likely to be associated with SSA’s definition of impairment severity have not been sufficiently evaluated.

MENTAL DISORDERS

Mental disorders accounted for 34 percent of diagnoses of disability insurance beneficiaries in 2014 (SSA, 2015). Mental disorders include schizophrenia, paranoia, psychotic disorders, autism and related disorders, personality disorders, substance addiction, depression, and intellectual disability. However, SSA does not consider a claimant to be disabled if drug addiction or alcoholism is a contributing factor material to the determination (SSA, 2017c). Mental disorders are among the most common diagnoses of disability insurance beneficiaries and are often seen in combination with disorders of other body systems. The disease burden of mental disorders is among the highest of all body systems. An estimated 18 percent of US adults suffer from any mental illness, including 4.2 percent who suffer from a seriously debilitating mental illness in any given year (HHS, 2014).

Two studies that examine mental disorders and their relationship to impairment severity are discussed in detail in Appendix C. One study of patients who had major depressive disorder found that utilization of prehospital resources predicted remission, whereas medication use was not associated with remission (Naz et al., 2007). A study of patients who had schizophrenia found that severity of illness predicted disability compensation (Rosenheck et al., 2017). The committee found many studies that focused on mental disorders as comorbidities. Specifically, they examined whether comorbid mental-health conditions affect health-care utilization, whether they affect disability, and whether they affect readmissions or duration of utilization. Those are summarized below. Finally, the committee gives an example of a prescription drug whose use could indicate impairment severity; this example is based on the committee’s expert opinion, not on evidence found in the literature.

Comorbid Mental Disorders

In patients who had heart failure or other cardiac diseases, comorbid depression and anxiety were found to increase health-services utilization in a prospective study of 402 patients (Moraska et al., 2013). Bansil and colleagues (2009) found by using the 1994–2004 National Inpatient Sample that depression and anxiety were common reasons for hospitalization of women who had HIV and that the number of hospitalizations of such women for a diagnosis of psychiatric disorder increased from 1994 to 2004 while the overall number of hospitalizations of women who had HIV remained constant. An analysis of the 2012 National Emergency Department Sample concluded that HIV patients who had mental health and substance use disorders were more likely to be admitted to the hospital and ED than ones who did not (Choi et al., 2016). McMorris and colleagues (2010), in a cross-sectional survey of patients in seven states, found that comorbid bipolar I disorder was significantly associated with increased resource utilization (particularly office visits and ED visits) and with losses in work productivity.

In a longitudinal study of 1,632 subjects, comorbid anxiety and depression were found to lead to more work disability and absenteeism over 4 years (Hendriks et al., 2015). Another multisite longitudinal cohort study of 715 patients found that comorbid anxiety and depression led to increased risk of disability at 12 months after injury (O’Donnell et al., 2013). Both studies assessed disability at 12 months after injury by using the World Health Organization Disability Assessment Schedule 2.0.

In patients who had heart failure and other cardiac diseases, comorbid depression and anxiety were found to predict increased 30-day readmission in an analysis of the HMO Research

Network Virtual Data Warehouse (N = 160,169) (Ahmedani et al., 2015). In diabetes patients, comorbid serious mental illness was found in two studies to increase risk of 30-day readmission (Albrecht et al., 2012; Chwastiak et al., 2014). Comorbid serious mental illness was suggested to lead to increased risk of repeat hospitalization in general medical patients in a longitudinal cohort study of 925,705 adults in Washington state (Daratha et al., 2012).

Stephens and colleagues (2014) found in a case-control study of an urban trauma center that some mental health patients have extremely long stays. Factors that contribute to long stays include insurance status, admission to inpatient care, transfer to a remote facility, and suicidal ideation. The population consisted mostly of substance-abuse patients. Wolff and colleagues (2015) examined predictors of length of stay in psychiatry by looking at medical records at a psychiatric hospital and found that the most significant factors in predicting longer stays were affective disorders as a main diagnosis followed by disease severity and chronicity. Boaz et al. (2013) found that shorter inpatient stays led to greater readmission risk in patients who had serious mental illness. Tulloch (2011, 2012) found length of stay of patients who had mental-health disorders to be affected by several factors: length of stay was greater in larger medical centers, for homeless patients, and for psychotic patients and was lower for those who had insurance coverage limits, for those in areas that had fewer physicians per capita, and for those who had recent evidence of self-harm. Becerra et al. (2016) found that having any mental illness was associated with a 10 percent increase in length of hospital stay.

Douzenis et al. (2012) concluded in their analysis of factors that affect hospital stay of psychiatric patients that having a physical comorbidity as determined by referral to a medical subspecialty was a significant determinant. Depression led to poorer outcomes of colorectal surgery, including longer stay and requirement of skilled nursing assistance after discharge (Balentine et al., 2011). Zhang et al. (2011) noted that length of stay is multifactorially determined. It could be related to good clinical practice, social support, or location. Other factors that predict longer inpatient stays of people who have serious mental illness include stays in psychiatric hospitals, rather than general medical hospitals (Lee et al., 2012), and having Medicare or Medicaid (Lee et al., 2012; Masters et al., 2014).

Prescription Drug Use

If depression is comorbid with other health conditions, treatment for it could be a healthcare utilization that indicates impairment severity associated with other health conditions. There are three domains of treatment for major depression: antidepressant medications, including medications approved for treatment-resistant depression; psychotherapy; and stimulation–convulsive therapies, such as electroconvulsive therapy. Evidence on sustained treatment of those kinds would support the presence of depression, which should then be considered as a contributory factor in overall impairment across conditions or evaluated as a stand-alone contributor to impairment. In any case, those utilizations do not provide a curative treatment for depression.

An example of use of a prescription drug as a potential indicator of severity is the use of clozapine. Clozapine is a treatment for schizophrenia that is reserved for two special circumstances: treatment resistance and persistent suicidal ideation or behavior. Treatment resistance is defined for the purposes of clozapine treatment as failure to respond, completely or nearly completely, to two standard courses of antipsychotic treatment. Thus, anyone who is receiving clozapine treatment has been determined to have evidence of functionally relevant

periods of extended decompensation, whose functional consequences have been judged to be extreme. Suicidal ideation or behavior has not been defined with the same level of precision.

Clozapine as a utilization entails various demands on the patient and the prescriber. Because of its potential side effects, clozapine requires biweekly blood monitoring, and prescriptions for it can be refilled only if there is evidence of compliance with the monitoring process. Although the actual time demands of the blood monitoring are not enough to preclude labor-force participation, the demands on clinicians tend to lead to clozapine being prescribed only in specialty clinics wherein regular attendance is required and medication is dispensed in person every 2 weeks.

Empirical evidence suggests that only about one-third of patients who are treated with clozapine experience successful treatment (Lieberman et al., 1994; McEnvoy et al., 2006; Friedman et al., 2011). Even in patients who achieve clinical stability, clozapine treatment does not directly improve functional disability. It should be noted that absence of clozapine use is not evidence of lower severity.

In summary, for mental health disorders, the committee’s literature review found no evidence that health-care utilizations are a good indicator of impairment severity for the purposes of the disability program. The majority of the literature that the committee reviewed concluded that comorbid mental disorders can indicate disease severity in people who present with other diseases. That could be because people who have comorbid mental disorders are especially vulnerable to the consequences of disease and might have more difficulty in accessing healthcare services. That is particularly true for depression as a mental disorder and for a cardiac condition as the physical condition. Length of stay tends to be greater overall for mental disorders than for disorders of other body systems (AHRQ, 2017a), and that is influenced by many factors, including behavioral manifestations of illness, lack of social support, quality of clinical care, and changing structures of payment systems. Downey and Zun (2015) suggested that in all the comorbid mental health conditions, a two-pronged approach to treatment, focusing on both mental illness and physical illness, might be developed to reduce readmission rates in this population.

Although the committee found no evidence in the literature to suggest health-care utilizations as indicators of severity, psychiatry experts on the committee suggested that use of the prescription drug clozapine might be an indicator of severity. Clozapine is a unique medication used to treat a subset of the schizophrenia population, that is, those who have already demonstrated consistent failures to respond to other treatments. In contrast with other treatments that are used for mental illness in general, the prescription of clozapine provides information about treatment and functional history. Furthermore, because it is regulated and cannot be dispensed without agreement on the part of clinicians and patients to have treatment monitored, the decision to prescribe it comes with more planning and consideration than are required for any similar treatment.

CANCER

Neoplastic diseases (cancers) accounted for 2.8 percent of diagnoses of disability insurance beneficiaries in 2014. Health-care utilization related to a cancer diagnosis can depend heavily on the site of the cancer, the stage at diagnosis, and the treatment provided. The cancer itself, depending on symptoms through progression of the disease, can cause substantial morbidity that might have important implications for the ability to work. Depending on site and

stage, various treatment options might be offered to patients that provide, if not a cure, a remission. However, treatment in the form of surgery, radiation therapy, chemotherapy, and other procedures might also result in adverse effects that elicit new symptoms that affect functionality. More than 1.5 million people receive a diagnosis of cancer each year in the United States, and the number of new cases is expected to increase to 2 million per year in 2020 (CDC, 2016).

Cancer severity is often described in stages that reflect tumor size, location, growth, and spread. Cancers originating in different organ systems and with different cell types differ in prognosis and course of treatment. Some cancer types have very short life expectancies; most people who receive a diagnosis of lung or pancreatic cancer die well before they can qualify for Social Security disability insurance.

The literature provides minimal insights into the relationship between specific types of cancers and disability. Although it has been acknowledged that the “long-term effects of cancer and its treatment on employment and productivity are a major concern for the 40 percent of cancer survivors in the US who are of working age” (Short et al., 2008), there have been relatively few attempts to measure the increase in disability attributable to cancer in the years after initial treatment (Bradley, 2002; Chirikos, 2002; Hewitt, 2003; Yabroff, 2004; Short et al., 2005). The prevalence of disability in cancer survivors cannot be denied.

Overall, the articles identified in the committee’s literature review, although not comprehensive, revealed the effects of increased disability and reduced functionality on cancer survivors; trends in modern screening and diagnosis might allow more working-age adults to return to employment after a diagnosis of cancer. There is a paucity of studies that documented utilization related to disabilities. In addition to the variability inherent in different cancers and effects of treatment, there is evidence that race, socioeconomic status, and access to care in connection with different forms of cancer can be different (Parsons et al., 2012; Simpson et al., 2013). Data on hospitalization frequency and duration associated with advanced tumors and cancers vary considerably by type and stage of cancer. Any relationship between utilization and severity is contingent on the stage of disease at the time of diagnosis. There is little in the literature to permit generalizations on health-care utilization that can predict disability in adult cancer survivors who are under 65 years old.

IMMUNE-SYSTEM DISORDERS

Immune-system disorders include immune-deficiency disorders, such as HIV, and autoimmune disorders, such as systemic lupus erythematosus (SLE). Immune-deficiency disorders are characterized by congenital or acquired recurrent infections that respond poorly to treatment. Autoimmune disorders are caused by dysfunctional immune responses directed against the body’s own tissues. It is estimated that in 2014 there were 37,600 new cases of HIV in the United States. The annual number of new cases has declined by 10 percent since 2010. In 2014, 62 percent of people who were living with HIV received medical treatment; 48 percent of them received continuous HIV care (CDC, 2017c). The extent of HIV infection can be characterized by CD4 count.

The committee found several studies that suggested an association between utilization and disease severity in people who had HIV/AIDS or SLE. The studies suggest that frequency of ED visits might reflect disease severity in patients who have SLE (Panopalis et al., 2010) and HIV (Josephs et al., 2010), that greater clinical severity is associated with increased hospital

utilization by HIV and AIDS patients (Yehia et al., 2010; Kerr et al., 2012), that hospital readmission could be associated with disease severity in SLE patients (Yazdany et al., 2014), and that frequency of outpatient-clinic use might reflect disease severity in HIV patients (Palma et al., 2015). Details of these studies can be found in Appendix C. It should be noted that the studies were designed to evaluate clinical measures of severity, such as CD4 count, SLAQ scores, and the Ward Index rather than SSA’s definition of disability.

HOW HEALTH-CARE UTILIZATIONS INTERFERE WITH THE ABILITY TO WORK

As severity of disease increases or as the number of conditions increases, overall health declines and utilization increases (NCHS, 2017a). Therefore, increased utilization is, on average, associated with less ability to work because people are sicker or more impaired. However, utilization is only indirectly related to ability to work, and the periods between utilization and work or work disability vary with the specific disease and with conditions of employment. Utilization is directly related to a specific diagnosis or condition, not to a person’s ability to function overall. Each disease or condition has its trajectory, which interacts with the patient’s characteristics. Ability to work is a continuing state, whereas utilization takes place at a particular point in time. Ability to work is a function of numerous factors. Health is also a function of a number of factors, of which health-care utilization is only one, as discussed in Chapter 2.

Workplaces are becoming more accommodating for people who receive health care, but people undergoing, for example, chemotherapy or radiation therapy, dialysis, or supplemental oxygen therapy might have difficulty in maintaining a work schedule. The act of managing one’s health—including the utilization of health care—can itself interfere with one’s ability to work. Walter Oi (1992) noted that disability “steals time” from people by increasing the demands for personal care or rehabilitative services. In the case of health care specifically, the frequency of use (e.g., if a person is hospitalized every few weeks or months or requires weekly dialysis) has traditionally been recognized as a barrier to employment. That would be particularly true for acute hospitalizations, which can occur unexpectedly during the work week and in the workplace. However, it is also important to acknowledge that other factors—such as the duration of utilization (e.g., length of hospital stay), the time spent in receiving treatment, or the side effects of treatment (e.g., for many types of cancer)—can affect one’s ability to work.

Even if a person does not require hospital confinement, a health condition might require treatment that precludes employment or at least hampers the ability to be employed. The committee reviewed recent papers that examined the ability to work while undergoing chemotherapy, dialysis, and supplemental oxygen therapy.

A study by Mujahid et al. (2010) examined results of a survey of women who had nonmetastatic breast cancer and noted that many women stop work altogether after a diagnosis of breast cancer, particularly if they belong to racial or ethnic minorities, receive chemotherapy, or are in an unsupportive work setting. Women who received a mastectomy and those receiving chemotherapy were more likely to stop working independently of sociodemographic and treatment factors.

Patients who were undergoing final cycles of adjuvant chemotherapy for breast or colorectal cancer or first-line chemotherapy for lymphoma in two cancer treatment centers were approached to take part in a cross-sectional survey. Some 64 percent of respondents were

working when cancer was diagnosed; 54 percent were working when chemotherapy began; and only 29 percent continued to work in any capacity as treatment progressed.

Another study, by Jagsi et al. (2014), demonstrated that unemployment is common among breast-cancer survivors 4 years after diagnosis and appears to be related to the receipt of chemotherapy during initial treatment. The authors conducted a longitudinal multicenter cohort study of women who had diagnoses of nonmetastatic breast cancer in 2005–2007, as reported to Los Angeles and Detroit Surveillance, Epidemiology, and End Results program registries.³ Some 76 percent (746) of the women worked for pay before diagnosis; 68 percent (507) of those were working 4 years later.

Munir et al. (2009) reviewed 19 studies published in 1999–2008 on cancer and ability to work. The authors noted that people undergoing treatment for cancer are likely to have poorer ability to work than healthy people and those who have such chronic conditions as depression, heart disease, and diabetes. They also noted that type of treatment, particularly chemotherapy, has a substantial effect on ability to work.

A study of the differences between employed and nonemployed dialysis patients found that education correlated significantly with employment. The authors (Curtin et al., 1996) noted that neither mode of dialysis, length of time on dialysis, number of comorbid conditions, nor cause of renal failure was associated with employment status. Functional status was positively associated with employment. The authors also noted that patients who believed that dialysis patients should work and had that notion reinforced by significant others were more likely to be employed.

Muehrer et al. (2011) conducted a study to understand factors associated with maintaining employment among working-age patients who had advanced kidney failure. The authors conducted a retrospective review of the US Renal Data System database (1992–2003) and selected all patients (N = 102,104) who were of working age and employed 6 months before dialysis initiation. Large numbers of patients stopped working or reduced their work hours before or after initiating dialysis. However, the authors maintained that loss of employment is not inevitable, and some patients continued to work as their kidneys failed. The authors posited that one factor in discontinuing work might be anemia, which if untreated could lead to fatigue and adversely affect a person’s ability to work. Another possible factor is the dialysis modality. For example, hemodialysis in a center usually requires three sessions per week for 3–4 hours, but peritoneal dialysis provides patients with more options for treatment. Finally, the type of health insurance that a patient has can influence whether employment continues; patients covered by employer group-health plans might be motivated to keep working.

The committee reviewed several studies that were related to the need for supplemental oxygen therapy for COPD. Owing to its progressive and debilitating nature, COPD has the potential to interfere with a person’s ability to work. Adhering to oxygen therapy can be complex and difficult for some, including the physical difficulty of using oxygen, self-consciousness, and a sense of social stigma (Earnest, 2002). Other studies have revealed an association between COPD hospitalizations and work loss (e.g., Sin et al., 2002; Fletcher et al., 2011; Dhamane et al., 2016).

The committee notes that health-care utilizations, particularly frequent and time-intensive utilizations, interfere with a person’s ability to work. In addition, the length of time and the

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³ The Surveillance, Epidemiology, and End Results program of the National Cancer Institute provides information on cancer statistics.

number of treatments, the severity of side effects, self-consciousness, and social stigma compound a person’s ability to work.

The committee’s extensive literature review found no studies that addressed the usefulness of health-care utilizations in determining disability or impairment severity and few that addressed the association of health-care utilization with disability. The question of whether health-care utilizations can be proxies for disability or impairment severity according to SSA’s definition has not been extensively researched in the health sciences. In the absence of such data, the committee reviewed literature that links health-care utilizations to similar concepts, such as disease severity and ability to work. The committee’s review led it to draw several conclusions.

The committee found no evidence that health-care utilizations alone can predict disability, impairment severity, or disease severity. For several medical conditions, including COPD and CKD, there is some evidence that increased hospitalizations, ED visits, and outpatient physician visits might predict disease severity for some specific diagnoses. However, their relevance to the committee’s task is limited in that disease severity does not fit SSA’s definition of impairment severity and statistical modeling in the supporting papers involved more factors than health-care utilization. The other factors could be social factors, insurance, hospital factors, geographic factors, and personal factors. Those factors, many of which are discussed in Chapter 2, limit the classifying power of health-care utilizations in determining disability and impairment severity.

Another intervening factor that complicates the picture is the presence of comorbid conditions. Many of the studies that the committee reviewed discussed the influence of comorbidities in predicting health-care utilizations and health outcomes. In particular, psychiatric disorders were found to increase the likelihood of disability associated with and use of healthcare services to address medical conditions of several body systems. Most of the literature found on mental disorders was related to their use as comorbid conditions that can predict increased resource utilization by, greater disability of, and greater length of hospital stay of patients who have various health conditions.

SUMMARY AND CONCLUSIONS

With few exceptions the health-care utilizations featured in the committee’s literature review were hospitalizations, ED visits, and outpatient physician visits. Given that annual data on hospitalizations have been collected in the United States since 1965, hospital data are easy to capture and are more likely than data on other utilizations to constitute a reliable measure of impairment severity associated with some diseases. However, care is given in many other settings, such as dialysis centers, urgent care centers, and ambulatory care centers, and healthcare laws encourage the use of health-care delivery sites other than hospitals. This fragmented health-care delivery system makes it difficult to capture all the different types and locations of utilizations for purposes of determining disease severity.

The committee’s review of HCUP data corroborated its literature findings that numbers and rates of hospitalizations and ED visits alone do not indicate severity of a condition; they only suggest that a hospitalization or ED visit appeared necessary. Event-level data tell little about the continuing severity of a condition. The committee did not find the data useful in determining how types of utilizations are more or less probable for particular medical conditions, but it found that utilization is more related to sets of conditions, and analysis of which specific conditions should be grouped is extremely complex and faces many data limitations. The committee

suggests that specific diagnoses are more predictive of patterns of utilization than vice versa. The HCUP data could provide insight into which health conditions prevent a person from working, if only because the act of using a health-care service prevents a person from being at work. However, they are not informative with respect to whether these utilizations are useful for indicating disease severity or inability to work.

Little research has attempted to account for the relationships among employment, health, and disability. Some studies explore effects of health on disability or vice versa but rarely in a manner that acknowledges that health interferes with the ability to work. Different types of employment are more or less supportive of particular health conditions because of the availability of sick leave or other working conditions. For example, white-collar places of employment might be more supportive of musculoskeletal conditions than places of employment where physical labor is required. In addition, most studies rely on data collected over short timeframes and have little or delayed followup because of the cost of continuing data collection and of following up with respondents.

The committee concludes that types of health-care utilizations vary with combinations of health conditions. Although there might be a connection between some utilizations and impairment severity or disability, the committee could not make that specific connection on the basis of available data. It was not possible for the committee to link disability (the restriction of the ability to perform “substantial gainful activity”) in the context of its statement of task universally to a single health-care utilization. Linking health-care utilizations to impairment severity and disability is complex. Although datasets on utilization and function exist, it is not possible to associate a specific type of utilization with a specific condition without longitudinal data on all types of utilization, multiple specific diagnoses, and multiple levels of function.

REFERENCES

Alcazar, B., C. Garcia-Polo, A. Herrejon, L. A. Ruiz, J. de Miguel, J. A. Ros, P. Garcia-Sidro, G. T. Conde, J. L. Lopez-Campos, C. Martinez, J. Costan, M. Bonnin, S. Mayoralas, and M. Miravitlles. 2012. Factors associated with hospital admission for exacerbation of chronic obstructive pulmonary disease. Archivos de Bronconeumologia 48(3):70–76.

Alexander, M., B. D. Bradbury, R. Kewalramani, A. Barlev, S. A. Mohanty, and D. Globe. 2009. Chronic kidney disease and US healthcare resource utilization in a nationally representative sample. American Journal of Nephrology 29(5):473–482.

Allegretti, J. R., L. Borges, M. Lucci, M. Chang, B. Cao, E. Collins, B. Vogel, E. Arthur, D. Emmons, and J. R. Korzenik. 2015. Risk factors for rehospitalization within 90 days in patients with inflammatory bowel disease. Inflammatory Bowel Diseases 21(11):2583–2589.

Allen, L. A., M. Gheorghiade, K. J. Reid, S. M. Dunlay, P. S. Chan, P. J. Hauptman, F. Zannad, M. A. Konstam, and J. A. Spertus. 2011. Identifying patients hospitalized with heart failure at risk for unfavorable future quality of life. Circulation: Cardiovascular Quality and Outcomes 4(4):389–398.

Allen, P. B., M. A. Kamm, L. Peyrin-Biroulet, C. Studd, C. McDowell, B. C. Allen, W. R. Connell, P. P. De Cruz, S. J. Bell, R. P. Elliot, S. Brown, P. V. Desmond, M. Lemann, and J. F. Colombel. 2013. Development and validation of a patient-reported disability measurement tool for patients with inflammatory bowel disease. Alimentary Pharmacology & Therapeutics 37(4):438–444.

AMA (American Medical Association). 2008. AMA guides to the evaluation of permanent impairment, sixth edition. Chicago, IL: American Medical Association.

Asche, C. V., M. E. Singer, M. Jhaveri, H. Chung, and A. Miller. 2010. All-cause health care utilization and costs associated with newly diagnosed multiple sclerosis in the United States. Journal of Managed Care & Specialty Pharmacy 16(9):703–712.

Badheka, A. O., V. Singh, N. J. Patel, S. Arora, N. Patel, B. Thakkar, S. Jhamnani, S. Pant, A. Chothani, C. Macon, S. S. Panaich, J. Patel, S. Manvar, C. Savani, P. Bhatt, V. Panchal, N. Patel, A. Patel, D. Patel, S. Lahewala, A. Deshmukh, T. Mohamad, A. A. Mangi, M. Cleman, and J. K. Forrest. 2015. Trends of hospitalizations in the United States from 2000 to 2012 of patients >60 years with aortic valve disease. American Journal of Cardiology 116(1):132–141.

Bai, T. R., J. M. Vonk, D. S. Postma, and H. M. Boezen. 2007. Severe exacerbations predict excess lung function decline in asthma. The European Respiratory Journal 30(3):452–456.

Balentine, C. J., J. Hermosillo-Rodriguez, C. N. Robinson, D. H. Berger, and A. D. Naik. 2011. Depression is associated with prolonged and complicated recovery following colorectal surgery. Journal of Gastrointestinal Surgery 15(10):1712–1717.

Bansil, P., D. J. Jamieson, S. F. Posner, and A. P. Kourtis. 2009. Trends in hospitalizations with psychiatric diagnoses among HIV-infected women in the USA, 1994-2004. AIDS Care 21(11):1432–1438.

Becerra, B. J., J. E. Banta, M. Ghamsary, L. R. Martin, and N. Safdar. 2016. Burden of mental illness on hospital and patient outcomes among asthma hospitalizations. Journal of Asthma 53(4):392–397.

Bengtson, L. G., P. L. Lutsey, L. R. Loehr, A. Kucharska-Newton, L. Y. Chen, A. M. Chamberlain, L. M. Wruck, S. Duval, S. C. Stearns, and A. Alonso. 2014. Impact of atrial fibrillation on healthcare utilization in the community: The atherosclerosis risk in communities study. Journal of the American Heart Association 3(6):e001006.

Benjamin, E. J., M. J. Blaha, S. E. Chiuve, M. Cushman, S. R. Das, R. Deo, S. D. de Ferranti, J. Floyd, M. Fornage, C. Gillespie, C. R. Isasi, M. C. Jiménez, L. C. Jordan, S. E. Judd, D. Lackland, J. H. Lichtman, L. Lisabeth, S. Liu, C. T. Longenecker, R. H. Mackey, K. Matsushita, D. Mozaffarian, M. E. Mussolino, K. Nasir, R. W. Neumar, L. Palaniappan, D. K. Pandey, R. R. Thiagarajan, M. J. Reeves, M. Ritchey, C. J. Rodriguez, G. A. Roth, W. D. Rosamond, C. Sasson, A. Towfighi, C. W. Tsao, M. B. Turner, S. S. Virani, J. H. Voeks, J. Z. Willey, J. T. Wilkins, J. H. Y. Wu, H. M. Alger, S. S. Wong, and P. Muntner. 2017. Heart disease and stroke statistics—2017 update: A report from the American Heart Association. Circulation 135(10):e146–e603.

Betihavas, V., S. A. Frost, P. J. Newton, P. Macdonald, S. Stewart, M. J. Carrington, Y. K. Chan, and P. M. Davidson. 2015. An absolute risk prediction model to determine unplanned cardiovascular readmissions for adults with chronic heart failure. Heart, Lung and Circulation 24(11):1068–1073.

Bhowmik, A., T. A. Seemungal, R. J. Sapsford, and J. A. Wedzicha. 2000. Relation of sputum inflammatory markers to symptoms and lung function changes in COPD exacerbations. Thorax 55(2):114–120.

Bibbins-Domingo, K., M. J. Pletcher, F. Lin, E. Vittinghoff, J. M. Gardin, A. Arynchyn, C. E. Lewis, O. D. Williams, and S. B. Hulley. 2009. Racial differences in incident heart failure among young adults. The New England Journal of Medicine 360(12):1179–1190.

Boaz, T. L., M. A. Becker, R. Andel, R. A. Van Dorn, J. Choi, and M. Sikirica. 2013. Risk factors for early readmission to acute care for persons with schizophrenia taking antipsychotic medications. Psychiatric Services 64(12):1225–1229.

Bradley, C. J., and H. L. Bednarek. 2002. Employment patterns of long-term cancer survivors. Psychooncology 11(3):188–198.

Brousseau, D. C., P. L. Owens, A. L. Mosso, J. A. Panepinto, and C. A. Steiner. 2010. Acute care utilization and rehospitalizations for sickle cell disease. JAMA 303(13):1288–1294.

Buono, J. L., S. Tourkodimitris, P. Sarocco, J. M. Johnston, and R. T. Carson. 2014. Impact of linaclotide treatment on work productivity and activity impairment in adults with irritable bowel syndrome with constipation: Results from 2 randomized, double-blind, placebo-controlled phase 3 trials. American Health & Drug Benefits 7(5):289–297.

Calvillo-King, L., D. Arnold, K. J. Eubank, M. Lo, P. Yunyongying, H. Stieglitz, and E. A. Halm. 2013. Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: Systematic review. Journal of General Internal Medicine 28(2):269–282.

CDC (Centers for Disease Control and Prevention). 2016. Cancer: Preventing one of the nation’s leading causes of death at a glance 2016. Atlanta, GA: CDC. https://www.cdc.gov/chronicdisease/resources/publications/aag/dcpc.htm (accessed October 31, 2017).

CDC. 2017a. Chronic obstructive pulmonary disease (COPD). Atlanta, GA: CDC. https://www.cdc.gov/copd/index.html (accessed October 31, 2017).

CDC. 2017b. Sickle cell disease (SCD). Atlanta, GA: CDC. https://www.cdc.gov/ncbddd/sicklecell/data.html (accessed October 31, 2017).

CDC. 2017c. HIV in the United States: At a glance. Atlanta, GA: CDC. https://www.cdc.gov/hiv/statistics/overview/ataglance.html (accessed October 31, 2017).

Celli, B. R., N. E. Thomas, J. A. Anderson, G. T. Ferguson, C. R. Jenkins, P. W. Jones, J. Vestbo, K. Knobil, J. C. Yates, and P. M. Calverley. 2008. Effect of pharmacotherapy on rate of decline of lung function in chronic obstructive pulmonary disease: Results from the Torch Study. American Journal of Respiratory and Critical Care Medicine 178(4):332–338.

Chamberlain, A. M., S. J. Bielinski, S. A. Weston, W. Klaskala, R. M. Mills, B. J. Gersh, A. Alonso, and V. L. Roger. 2013. Atrial fibrillation in myocardial infarction patients: Impact on health care utilization. American Heart Journal 166(4):753–759.

Chirikos, T. N., A. Russell-Jacobs, and P. B. Jacobsen. 2002. Functional impairment and the economic consequences of female breast cancer. Women’s Health 36(1):1–20.

Choi, B. Y., D. M. DiNitto, C. N. Marti, and N. G. Choi. 2016. Impact of mental health and substance use disorders on emergency department visit outcomes for HIV patients. The Western Journal of Emergency Medicine 17(2):153–164.

Chwastiak, L. A., D. S. Davydow, C. L. McKibbin, E. Schur, M. Burley, M. G. McDonell, J. Roll, and K. B. Daratha. 2014. The effect of serious mental illness on the risk of rehospitalization among patients with diabetes. Psychosomatics 55(2):134–143.

Curtin, R. B., E. T. Oberley, P. Sacksteder, and A. Friedman. 1996. Differences between employed and nonemployed dialysis patients. American Journal of Kidney Disease 27(4):533–540.

Damman, K., M. A. E. Valente, A. A. Voors, C. M. O’Connor, D. J. van Veldhuisen, and H. L. Hillege. 2014. Renal impairment, worsening renal function, and outcome in patients with heart failure: An updated meta-analysis. European Heart Journal 35(7):455–469.

Daratha, K. B., C. Barbosa-Leiker, M. H. Burley, R. Short, M. E. Layton, S. McPherson, D. G. Dyck, B. H. McFarland, and K. R. Tuttle. 2012. Co-occurring mood disorders among hospitalized patients and risk for subsequent medical hospitalization. General Hospital Psychiatry 34(5):500–505.

Dehkharghani, S., J. Bible, J. G. Chen, S. R. Feldman, and A. B. Fleischer, Jr. 2003. The economic burden of skin disease in the United States. Journal of the American Academy of Dermatology 48(4):592–599.

Dhamane, A. D., E. A. Witt, and J. Su. 2016. Associations between COPD severity and work productivity, health-related quality of life, and health care resource use: A cross-sectional analysis of national survey data. Journal of Occupational and Environmental Medicine 58(6):e191–e197.

Douzenis, A., D. Seretis, S. Nika, P. Nikolaidou, A. Papadopoulou, E. N. Rizos, C. Christodoulou, C. Tsopelas, D. Mitchell, and L. Lykouras. 2012. Factors affecting hospital stay in psychiatric patients: The role of active comorbidity. BMC Health Services Research 12:166.

Downey, L. V., and L. S. Zun. 2015. Reasons for readmissions: What are the reasons for 90-day readmissions of psychiatric patients from the ED? American Journal of Emergency Medicine 33(10):1489–1491.

Dudekula, A., M. O’Connell, and K. Bielefeldt. 2011. Hospitalizations and testing in gastroparesis. Journal of Gastroenterology and Hepatology 26(8):1275–1282.

Earnest, M. A. 2002. Explaining adherence to supplemental oxygen therapy: The patient’s perspective. Journal of General Internal Medicine 17(10):749–755.

Edelsberg, J., C. Taneja, M. Zervos, N. Haque, C. Moore, K. Reyes, J. Spalding, J. Jiang, and G. Oster. 2009. Trends in US hospital admissions for skin and soft tissue infections. Emerging Infectious Diseases Journal 15(9):1516–1518.

Ekberg-Aronsson, M., K. Lofdahl, J. A. Nilsson, C. G. Lofdahl, and P. M. Nilsson. 2008. Hospital admission rates among men and women with symptoms of chronic bronchitis and airflow limitation corresponding to the gold stages of chronic obstructive pulmonary disease—a population-based study. Respiratory Medicine 102(1):109–120.

Everhart, J. E., and C. E. Ruhl. 2009. Burden of digestive diseases in the United States part iii: Liver, biliary tract, and pancreas. Gastroenterology 136(4):1134–1144.

Fan, A. Z., T. W. Strine, R. Jiles, J. T. Berry, and A. H. Mokdad. 2009. Psychological distress, use of rehabilitation services, and disability status among noninstitutionalized US adults aged 35 years and older, who have cardiovascular conditions, 2007. International Journal of Public Health 54(Suppl 1):100–105.

Fan, V. S., S. D. Ramsey, B. J. Make, and F. J. Martinez. 2007. Physiologic variables and functional status independently predict COPD hospitalizations and emergency department visits in patients with severe COPD. Journal of Chronic Obstructive Pulmonary Disease 4(1):29–39.

Farrell, R. T., R. L. Gamelli, R. F. Aleem, and J. M. Sinacore. 2008. The relationship of body mass index and functional outcomes in patients with acute burns. Journal of Burn Care & Research 29(1):102–108.

Finlayson, T. L., C. A. Moyer, and S. S. Sonnad. 2004. Assessing symptoms, disease severity, and quality of life in the clinical context: A theoretical framework. American Journal of Managed Care 10(5):336–344.

Fletcher, M. J., J. Upton, J. Taylor-Fishwick, S. A. Buist, C. Jenkins, J. Hutton, N. Barnes, T. Van Der Molen, J. W. Walsh, P. Jones, and S. Walker. 2011. COPD uncovered: An international survey on the impact of chronic obstructive pulmonary disease [COPD] on a working age population. BMC Public Health 11:612.

Foraker, R. E., K. M. Rose, C. M. Suchindran, P. P. Chang, A. M. McNeill, and W. D. Rosamond. 2011. Socioeconomic status, Medicaid coverage, clinical comorbidity, and rehospitalization or death after an incident heart failure hospitalization: Atherosclerosis risk in communities cohort (1987 to 2004). Circulation: Heart Failure 4(3):308–316.

Friedman, J. I., J.-P. Lindenmayer, F. Alcantara, S. Bowler, M. Parak, L. White, A. Iskander, M. Parrella, D. N. Adler, N. D. Tsopelas, W.-Y. Tsai, V. Novakovick, P. D. Harvey, and K. L. Davis. 2011. Pimozide augmentation of clozapine inpatients with schizophrenia and schizoaffective disorder unresponsive to clozapine monotherapy. Neuropsychopharmacology 36(6):1289–1295.

Gambassi, G., S. A. Agha, X. Sui, C. W. Yancy, J. Butler, G. Giamouzis, T. E. Love, and A. Ahmed. 2008. Race and the natural history of chronic heart failure: A propensity-matched study. Journal of Cardiac Failure 14(5):373–378.

Golden, S. H., K. A. Robinson, I. Saldanha, B. Anton, and P. W. Ladenson. 2009. Prevalence and incidence of endocrine and metabolic disorders in the United States: A comprehensive review. The Journal of Clinical Endocrinology and Metabolism 94(6):1853–1878.

Gooch, C. L., E. Pracht, and A. R. Borenstein. 2017. The burden of neurological disease in the United States: A summary report and call to action. Annals of Neurology 81(4):479–484.

Hendriks, S. M., J. Spijker, C. M. Licht, F. Hardeveld, R. de Graaf, N. M. Batelaan, B. W. Penninx, and A. T. Beekman. 2015. Long-term work disability and absenteeism in anxiety and depressive disorders. Journal of Affective Disorders 178:121–130.

Hewitt, M., J. H. Rowland, and R. Yancik. 2003. Cancer survivors in the United States: Age, health, and disability. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 58(1):82–91.

Horn, S. D., R. A. Horn, and P. D. Sharkey. 1984. The severity of illness index as a severity adjustment to diagnosis-related groups. Health Care Financing Review 1984(Suppl):33-45.

HHS (US Department of Health and Human Services). 2014. Healthy people 2020: Mental health and mental disorders. Washington, DC: HHS. https://www.healthypeople.gov/2020/topics-objectives/topic/mental-health-and-mental-disorders (accessed October 31, 2017).

Jagsi, R., S. T. Hawley, P. Abrahamse, Y. Li, N. K. Janz, J. J. Griggs, C. Bradley, J. J. Graff, A. Hamilton, and S. J. Katz. 2014. Impact of adjuvant chemotherapy on long-term employment of survivors of early-stage breast cancer. Cancer 120(12):1854–1862.

Jang, D. B., S. D. Shin, Y. S. Ro, K. J. Song, K. O. Ahn, S. S. Hwang, Y. T. Kim, S. O. Hong, and J. A. Choi. 2016. Interaction of the diabetes mellitus and cardiac diseases on survival outcomes in out-of-hospital cardiac arrest. American Journal of Emergency Medicine 34(4):702–707.

Jones, E., J. Pike, T. Marshall, and X. Ye. 2016. Quantifying the relationship between increased disability and health care resource utilization, quality of life, work productivity, health care costs in patients with multiple sclerosis in the US. BMC Health Services Research 16:294.

Josephs, J. S., J. A. Fleishman, P. T. Korthuis, R. D. Moore, and K. A. Gebo. 2010. Emergency department utilization among HIV-infected patients in a multisite multistate study. HIV Medicine 11(1):74–84.

Kerr, J. C., T. G. Stephens, J. J. Gibson, and W. A. Duffus. 2012. Risk factors associated with inpatient hospital utilization in HIV-positive individuals and relationship to HIV care engagement. Journal of Acquired Immune Deficiency Syndromes 60(2):173–182.

Kim, M. H., J. Lin, M. Hussein, and D. Battleman. 2009. Incidence and economic burden of suspected adverse events and adverse event monitoring during af therapy. Current Medical Research and Opinion 25(12):3037–3047.

Kimball, A. B., J. Schenfeld, N. A. Accortt, M. S. Anthony, K. J. Rothman, and D. Pariser. 2014. Incidence rates of malignancies and hospitalized infectious events in patients with psoriasis with or without treatment and a general population in the U.S.A.: 2005–09. British Journal of Dermatology 170(2):366–373.

Lee, S., A. B. Rothbard, and E. L. Noll. 2012. Length of inpatient stay of persons with serious mental illness: Effects of hospital and regional characteristics. Psychiatric Services 63(9):889–895.

Leschke, J., J. A. Panepinto, M. Nimmer, R. G. Hoffmann, K. Yan, and D. C. Brousseau. 2012. Outpatient follow-up and rehospitalizations for sickle cell disease patients. Pediatric Blood Cancer 58(3):406–409.

Lieberman, J. A., A. Z. Safferman, S. Pollack, S. Szymanski, C. Johns, A. Howard, M. Kronig, P. Bookstein, and J. M. Kane. 1994. Clinical effects of clozapine in chronic schizophrenia: Response to treatment and predictors of outcome. American Journal of Psychiatry 151(12):1744–1752.

Martin, G. S., D. M. Mannino, S. Eaton, and M. Moss. 2003. The epidemiology of sepsis in the United States from 1979 through 2000. The New England Journal of Medicine 348(16):1546–1554.

Masters, G. A., R. J. Baldessarini, D. Ongur, and F. Centorrino. 2014. Factors associated with length of psychiatric hospitalization. Comprehensive Psychiatry 55(3):681–687.

McEnvoy, J. P., J. A. Lieberman, T. S. Stroup, S. M. Davis, H. Y. Meltzer, R. A. Rosenheck, M. S. Swartz, D. O. Perkins, R. S. Keefe, C. E. Davis, J. Severe, and J. K. Hsiao. 2006. Effectiveness of clozapine versus olanzapine, quetiapine, and risperidone in patients with chronic schizophrenia who did not respond to prior atypical antipsychotic treatment. American Journal of Psychiatry 163(4):600–610.

McKee, M. M., P. C. Winters, A. Sen, P. Zazove, and K. Fiscella. 2015. Emergency department utilization among deaf American sign language users. Disability and Health Journal 8(4):573–578.

McMorris, B. J., K. E. Downs, J. M. Panish, and R. Dirani. 2010. Workplace productivity, employment issues, and resource utilization in patients with bipolar I disorder. Journal of Medical Economics 13(1):23–32.

Menendez, M. E., and D. Ring. 2015. Factors associated with hospital admission for proximal humerus fracture. American Journal of Emergency Medicine 33(2):155–158.

Mizutani, K., M. Hara, S. Iwata, T. Murakami, T. Shibata, M. Yoshiyama, T. Naganuma, F. Yamanaka, A. Higashimori, N. Tada, K. Takagi, M. Araki, H. Ueno, M. Tabata, S. Shirai, Y. Watanabe, M. Yamamoto, and K. Hayashida. 2017. Elevation of b-type natriuretic peptide at discharge is associated with 2-year mortality after transcatheter aortic valve replacement in patients with severe aortic stenosis: Insights from a multicenter prospective ocean-tavi (optimized transcatheter valvular intervention-transcatheter aortic valve implantation) registry. Journal of the American Heart Association 6(7).

Moraska, A. R., A. M. Chamberlain, N. D. Shah, K. S. Vickers, T. A. Rummans, S. M. Dunlay, J. A. Spertus, S. A. Weston, S. M. McNallan, M. M. Redfield, and V. L. Roger. 2013. Depression, healthcare utilization, and death in heart failure: A community study. Circulation: Heart Failure 6(3):387–394.

Muehrer, R. J., D. Schatell, B. Witten, R. Gangnon, B. N. Becker, and R. M. Hofmann. 2011. Factors affecting employment at initiation of dialysis. Clinical Journal of the American Society of Nephrology 6(3):489–496.

Mujahid, M. S., N. K. Janz, S. T. Hawley, J. J. Griggs, A. S. Hamilton, and S. J. Katz. 2010. The impact of sociodemographic, treatment, and work support on missed work after breast cancer diagnosis. Breast Cancer Research and Treatment 119(1):213–220.

Mullady, D. K., D. Yadav, S. T. Amann, M. R. O’Connell, M. M. Barmada, G. H. Elta, J. M. Scheiman, E. J. Wamsteker, W. D. Chey, M. L. Korneffel, B. M. Weinman, A. Slivka, S. Sherman, R. H. Hawes, R. E. Brand, F. R. Burton, M. D. Lewis, T. B. Gardner, A. Gelrud, J. DiSario, J. Baillie, P. A. Banks, D. C. Whitcomb, M. A. Anderson, and N. Consortium. 2011. Type of pain, pain-associated complications, quality of life, disability and resource utilisation in chronic pancreatitis: A prospective cohort study. Gut 60(1):77–84.

Mullerova, H., D. J. Maselli, N. Locantore, J. Vestbo, J. R. Hurst, J. A. Wedzicha, P. Bakke, A. Agusti, and A. Anzueto. 2015. Hospitalized exacerbations of COPD: Risk factors and outcomes in the eclipse cohort. Chest 147(4):999–1007.

Munir, F., J. Yarker, and H. McDermott. 2009. Employment and the common cancers: Correlates of work ability during or following cancer treatment. Occupational Medicine (Oxford, England) 59(6):381–389.

Myer, P. A., A. Mannalithara, G. Singh, G. Singh, P. J. Pasricha, and U. Ladabaum. 2013. Clinical and economic burden of emergency department visits due to gastrointestinal diseases in the United States. American Journal of Gastroenterology 108(9):1496–1507.

Nadjiri, J., J. Hausleiter, S. Deseive, A. Will, E. Hendrich, S. Martinoff, and M. Hadamitzky. 2016. Prognostic value of coronary CT angiography in diabetic patients: A 5-year follow up study. International Journal of Cardiovascular Imaging 32(3):483–491.

Naz, B., T. J. Craig, E. J. Bromet, S. J. Finch, L. J. Fochtmann, and G. A. Carlson. 2007. Remission and relapse after the first hospital admission in psychotic depression: A 4-year naturalistic follow-up. Psychological Medicine 37(8):1173–1181.

NCHS. 2017a. Health, United States, 2016: With chartbook on long-term trends in health. Hyattsville, MD: National Center for Health Statistics.

NCHS. 2017b. National Health Interview Survey (NHIS) 2015 Data. https://www.cdc.gov/asthma/most_recent_data.htm (accessed October 31, 2017).

Nemunaitis, G., M. J. Roach, J. Claridge, and M. Mejia. 2016. Early predictors of functional outcome after trauma. PM&R: The Journal of Injury, Function, and Rehabilitation 8(4):314–320.

NHLBI (National Heart, Lung, and Blood Institute). 2012. National Heart, Lung, and Blood Institute fact book: Chapter 4–disease statistics. Washington, DC: HHS. https://www.nhlbi.nih.gov/about/documents/factbook/2012/chapter4 (accessed October 31, 2017).

NIDCD (National Institute on Deafness and Other Communication Disorders). 2016. Quick statistics about hearing. Bethesda, MD: NIH. https://www.nidcd.nih.gov/health/statistics/quick-statistics-hearing (accessed October 31, 2017).

NORC (National Opinion Research Center). 2013. The economic burden of vision loss and eye disorders in the United States. Bethesda, MD: NORC. https://www.preventblindness.org/sites/default/files/national/documents/Economic%20Burden%20of%20Vision%20Final%20Report_130611.pdf (accessed October 31, 2017).

O’Donnell, M. L., T. Varker, A. C. Holmes, S. Ellen, D. Wade, M. Creamer, D. Silove, A. McFarlane, R. A. Bryant, and D. Forbes. 2013. Disability after injury: The cumulative burden of physical and mental health. Journal of Clinical Psychiatry 74(2):e137–e143.

Omachi, T. A., E. H. Yelin, P. P. Katz, P. D. Blanc, and M. D. Eisner. 2008. The COPD severity score: A dynamic prediction tool for health-care utilization. Journal of Chronic Obstructive Pulmonary Disease 5(6):339–346.

Palma, A., D. W. Lounsbury, L. Messer, and E. B. Quinlivan. 2015. Patterns of HIV service use and HIV viral suppression among patients treated in an academic infectious diseases clinic in North Carolina. AIDS and Behavior 19(4):694–703.

Panopalis, P., J. Z. Gillis, J. Yazdany, L. Trupin, A. Hersh, L. Julian, L. A. Criswell, P. Katz, and E. Yelin. 2010. Frequent use of the emergency department among persons with systemic lupus erythematosus. Arthritis Care and Research: The Official Journal of the Arthritis Health Professions Association 62(3):401–408.

Parker, S. E., C. T. Mai, M. A. Canfield, R. Rickard, Y. Wang, R. E. Meyer, P. Anderson, C. A. Mason, J. S. Collins, R. S. Kirby, and A. Correa. 2010. Updated national birth prevalence estimates for selected birth defects in the United States, 2004–2006. Birth Defects Research. Part A, Clinical and Molecular Teratology 88(12):1008–1016.

Parsons, H. M., E. B. Habermann, S. C. Stain, S. M. Vickers, and W. B. Al-Refaie. 2012. What happens to racial and ethnic minorities after cancer surgery at American College of Surgeons national surgical quality improvement program hospitals? Journal of the American College of Surgeons 214(4):539–547; discussion 547–549.

Pendleton, A. M., L. K. Cannada, and M. Guerrero-Bejarano. 2007. Factors affecting length of stay after isolated femoral shaft fractures. Journal of Trauma and Acute Care Surgery 62(3):697–700.

Reichard, A. A., S. Konda, and L. L. Jackson. 2015. Occupational burns treated in emergency departments. American Journal of Industrial Medicine 58(3):290–298.

Reilly, M. C., A. Bracco, J. F. Ricci, J. Santoro, and T. Stevens. 2004. The validity and accuracy of the work productivity and activity impairment questionnaire—irritable bowel syndrome version (WPAI:Ibs). Alimentary Pharmacology & Therapeutics 20(4):459–467.

Rosenheck, R. A., S. E. Estroff, K. Sint, H. Lin, K. T. Mueser, D. G. Robinson, N. R. Schooler, P. Marcy, and J. M. Kane. 2017. Incomes and outcomes: Social Security disability benefits in first-episode psychosis. American Journal of Psychiatry. https://ajp.psychiatryonline.org/doi/10.1176/appi.ajp.2017.16111273 (accessed March 22, 2018).

Schofield, D., R. Shrestha, R. Percival, M. Passey, E. Callander, and S. Kelly. 2013. The personal and national costs of CVD: Impacts on income, taxes, government support payments and GDP due to lost labour force participation. International Journal of Cardiology 166(1):68–71.

Short, P. F., J. J. Vasey, and K. Tunceli. 2005. Employment pathways in a large cohort of adult cancer survivors. Cancer 103(6):1292–1301.

Short, P. F., J. J. Vasey, and R. Belue. 2008. Work disability associated with cancer survivorship and other chronic conditions. Psychooncology 17(1):91–97.

Siebert, U., J. Wurm, R. M. Gothe, M. Arvandi, S. R. Vavricka, R. von Kanel, S. Begre, M. C. Sulz, C. Meyenberger, and M. Sagmeister. 2013. Predictors of temporary and permanent work disability in patients with inflammatory bowel disease: Results of the Swiss inflammatory bowel disease cohort study. Inflammatory Bowel Diseases 19(4):847–855.

Simpson, D. R., M. E. Martinez, S. Gupta, J. Hattangadi-Gluth, L. K. Mell, G. Heestand, P. Fanta, S. Ramamoorthy, Q. T. Le, and J. D. Murphy. 2013. Racial disparity in consultation, treatment, and the impact on survival in metastatic colorectal cancer. Journal of the National Cancer Institute 105(23):1814–1820.

Sin, D. D., T. Stafinski, Y. C. Ng, N. R. Bell, and P. Jacobs. 2002. The impact of chronic obstructive pulmonary disease on work loss in the United States. American Journal of Respiratory and Critical Care Medicine 165(5):704–707.

SSA (Social Security Administration). 2015. Annual statistical report on the Social Security Disability Insurance program, 2014. Woodlawn, MD: SSA. https://www.ssa.gov/policy/docs/statcomps/di_asr/2014/index.html (accessed May 27, 2017).

SSA. 2017a. Substantial gainful activity. https://www.ssa.gov/oact/cola/sga.html (accessed May 27, 2017).

SSA. 2017b. Disability evaluation under Social Security: Listing of impairments-adult listings (part A). Woodlawn, MD: SSA. https://www.ssa.gov/disability/professionals/bluebook/AdultListings.htm (accessed March 1, 2017).

SSA. 2017c. Di 90070.050 adjudicating a claim involving drug addiction or alcoholism (DAA). https://secure.ssa.gov/poms.nsf/lnx/0490070050 (accessed December 13, 2017).

Stephens, R. J., S. E. White, M. Cudnik, and E. S. Patterson. 2014. Factors associated with longer length of stay for mental health emergency department patients. Journal of Emergency Medicine 47(4):412–419.

Suaya, J. A., R. M. Mera, A. Cassidy, P. O’Hara, H. Amrine-Madsen, S. Burstin, and L. G. Miller. 2014. Incidence and cost of hospitalizations associated with staphylococcus aureus skin and soft tissue infections in the United States from 2001 through 2009. BMC Infectious Diseases 14:296.

Thomas, K. L., A. F. Hernandez, D. Dai, P. Heidenreich, G. C. Fonarow, E. D. Peterson, and C. W. Yancy. 2011. Association of race/ethnicity with clinical risk factors, quality of care, and acute outcomes in patients hospitalized with heart failure. American Heart Journal 161(4):746–754.

Thorpe, R. J., Jr., A. J. Wynn, J. L. Walker, J. R. Smolen, M. P. Cary, S. L. Szanton, and K. E. Whitfield. 2016. Relationship between chronic conditions and disability in African American men and women. Journal of the National Medical Association 108(1):90–98.

Towfighi, A., D. Markovic, and B. Ovbiagele. 2011. National gender-specific trends in myocardial infarction hospitalization rates among patients aged 35 to 64 years. American Journal of Cardiology 108(8):1102–1107.

Tulloch, A. D., P. Fearon, and A. S. David. 2011. Length of stay of general psychiatric inpatients in the United States: Systematic review. Administration and Policy in Mental Health and Mental Health Services Research 38(3):155–168.

Tulloch, A. D., M. R. Khondoker, P. Fearon, and A. S. David. 2012. Associations of homelessness and residential mobility with length of stay after acute psychiatric admission. BMC Psychiatry 12:121.

Ueda, T., R. Kawakami, Y. Sugawara, S. Okada, T. Nishida, K. Onoue, T. Soeda, S. Okayama, Y. Takeda, M. Watanabe, H. Kawata, S. Uemura, and Y. Saito. 2014. Worsening of renal function during 1 year after hospital discharge is a strong and independent predictor of all-cause mortality in acute decompensated heart failure. Journal of the American Heart Association 3(6):e001174.

Virtanen, M., M. Kivimaki, J. Vahtera, M. Elovainio, R. Sund, P. Virtanen, and J. E. Ferrie. 2006. Sickness absence as a risk factor for job termination, unemployment, and disability pension among temporary and permanent employees. Occupational and Environmental Medicine 63(3):212–217.

Wahlqvist, P., M. Karlsson, D. Johnson, J. Carlsson, S. C. Bolge, and M. A. Wallander. 2008. Relationship between symptom load of gastro-oesophageal reflux disease and health-related quality of life, work productivity, resource utilization and concomitant diseases: Survey of a US cohort. Alimentary Pharmacology & Therapeutics 27(10):960–970.

Wolff, J., P. McCrone, A. Patel, K. Kaier, and C. Normann. 2015. Predictors of length of stay in psychiatry: Analyses of electronic medical records. BMC Psychiatry 15:238.

Wolfson, J. A., S. M. Schrager, T. D. Coates, and M. D. Kipke. 2011. Sickle-cell disease in California: A population-based description of emergency department utilization. Pediatric Blood & Cancer 56(3):413–419.

Woolf, A. D., and B. Pfleger. 2003. Burden of major musculoskeletal conditions. Bulletin of the World Health Organization 81(9):646–656.

Yabroff, K. R., W. F. Lawrence, S. Clauser, W. W. Davis, and M. L. Brown. 2004. Burden of illness in cancer survivors: Findings from a population-based national sample. Journal of the National Cancer Institute 96(17):1322–1330.

Yamada, S., Y. Shimizu, M. Suzuki, and T. Izumi. 2012. Functional limitations predict the risk of rehospitalization among patients with chronic heart failure. Circulation Journal 76(7):1654–1661.

Yazdany, J., B. J. Marafino, M. L. Dean, N. S. Bardach, R. Duseja, M. M. Ward, and R. A. Dudley. 2014. Thirty-day hospital readmissions in systemic lupus erythematosus: Predictors and hospital- and state-level variation. Arthritis & Rheumatology 66(10):2828–2836.

Yehia, B. R., J. A. Fleishman, P. L. Hicks, M. Ridore, R. D. Moore, and K. A. Gebo. 2010. Inpatient health services utilization among HIV-infected adult patients in care 2002–2007. Journal of Acquired Immune Deficiency Syndromes 53(3):397–404.

Young, A. E., M. Cifuentes, R. Wasiak, and B. S. Webster. 2009. Urban-rural differences in work disability following occupational injury: Are they related to differences in healthcare utilization? Journal of Occupational and Environmental Medicine 51(2):204–212.

Young, A., S. Muhlner, A. Kurowski, and M. Cifuentes. 2015. The association between physical medicine and rehabilitation service utilization and disability duration following work-related fracture. Work 51(2):327–336.

Zhang, J., C. Harvey, and C. Andrew. 2011. Factors associated with length of stay and the risk of readmission in an acute psychiatric inpatient facility: A retrospective study. Australian & New Zealand Journal of Psychiatry 45(7):578–585.

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