Barriers to Obtaining Diagnostic Testing for Coronary Artery Disease Among Veterans

Abstract

Objectives. We sought to identify factors associated with appointment nonattendance for diagnostic testing of coronary artery disease among veterans. For patients with possible heart disease, appointment nonattendance may seriously compromise short- and long-term outcomes. Understanding factors associated with nonattendance may help improve care while reducing inefficiency in service delivery.

Methods. We surveyed patients who attended (n = 240) or did not attend (n = 139) a scheduled cardiac appointment at a midwestern Veterans Administration medical center. Multivariable regression models were used to assess factors associated with nonattendance.

Results. Younger age, lower income, unemployment, and longer wait times for appointments were predictive of nonattendance. Nonattenders reported fewer cardiac symptoms and were more likely to attribute their symptoms to something other than heart disease. Nonattendance was also associated with a coping style characterized by avoidance of aversive information. Logistical issues, fear of diagnostic procedures, disbelief that one had heart disease, and medical mistrust were some of the reasons given for missed appointments.

Conclusions. Appointment nonattendance among veterans scheduled for cardiology evaluation was associated with several important cognitive factors. These factors should be considered when one is designing clinical systems to reduce patient nonattendance.

Missed appointments, or nonattendance, by patients are common in most health systems. Estimates of nonattendance vary, but in general, rates range from 5% to 30% for primary care.^1–8 Given that patient nonattendance poses problems for both practitioners and patients, extensive research efforts have been focused on identifying factors that are associated with missed appointments. Numerous studies have been undertaken to examine nonattendance and its negative impact on patients and practitioners,^1–28 but almost none have focused on patients who miss appointments for testing or treatment for coronary artery disease (CAD). One study that examined nonattendance among patients with various chronic diseases found that patients with CAD were more likely to miss appointments compared with patients with other chronic diseases.²³ For patients with possible heart disease, a failure to follow through with appointments may seriously compromise patient short- and long-term outcomes.^27,28 Identifying the reasons underlying nonattendance among this at-risk population is therefore important.

We examined whether patient features (e.g., sociodemographic characteristics, health status, attitudes, symptom perceptions, health-seeking behaviors, and coping styles) and clinic features (e.g., length of time between scheduling and time to appointment) were associated with nonattendance among patients scheduled for CAD diagnostic testing whose primary care was received through the Veterans Administration (VA). The study was guided by the existing literature on patient nonattendance as well as by attribution theory.

On the basis of past research, we posited that individuals of lower socioeconomic status, those who were younger, and those who had better general health would be more likely to miss appointments. As well, the authors of several studies have reported that patients with stronger social support networks are more likely to keep appointments.^29,30 We also hypothesized that nonattendance would be more common among patients who reported less trust in health care³¹ and differences in health-seeking behaviors. Specifically, individuals who avoided information would be less likely to keep appointments or adhere to screening regimens.^32,33 This same finding has also been reported in individuals who prefer a more active role in health care decisionmaking.³⁴ We therefore measured treatment decisionmaking preferences.

Attribution theory suggests several additional variables possibly related to nonattendance based on individuals' fundamental assumptions and beliefs about their world, which form the basis of their attitudes and interpretations of events.^35,36 This theory is suggestive that individuals who are more fatalistic in their attitudes, meaning that they believe events in life are predetermined, will be more likely to be nonattenders. This hypothesis is consistent with previous findings that fatalistic individuals were less likely to be screened for cancer.³⁷ On the basis of similar reasoning, we also hypothesized that individuals who expressed higher religiosity would be more likely to be nonattenders.³⁸ Finally, we measured individuals' experiences of CAD symptoms and their beliefs about the causes of those symptoms. Specifically, nonattendance was expected to be more frequent among patients who experienced fewer symptoms, less severe symptoms, or those who attributed their symptoms to something other than CAD.

METHODS

Sample

Patients scheduled for cardiac testing (noninvasive pharmacologic or nonpharmacologic assessment of cardiac ischemia as well as invasive testing such as coronary angiography) at a regional VA medical center were recruited for a study of treatment decisionmaking in cardiac patients. People were eligible for this study if they were new to the cardiovascular clinic, received all their cardiac care at the VA, had never undergone cardiac catheterization, and had never had an invasive cardiac procedure. Exclusion criteria included having a past history of myocardial infarction, having had prior invasive tests or procedures, or having a pacemaker. Patients were excluded if their reason for referral for testing was evaluation for mitral valve prolapse, congenital abnormality, or cardiac infection. Patients were also excluded if they were using the VA cardiac clinic for a second opinion or consultation. Patients who did not keep their clinic appointments after missing a third rescheduled appointment over the course of 3 months were logged as “nonattenders.” Patients who kept an appointment, even if they had not been able to keep up to 2 prior appointments, were logged as “attenders.” We tracked nonattenders throughout the study and saw no crossover into the attender category.

Patients were recruited from March 2001 to September 2003. During that period, a total of 1306 patients were initially scheduled for testing. Of these, 712 (54.5%) were eligible for entry into this study, meaning they were naive to cardiac testing and care and had a probable diagnosis of CAD. Eligible patients were identified through patient schedules weekly and then assessed for study eligibility. Of the 712 study-eligible patients, we randomly selected up to 15 patients per week for recruitment into the study from the available list of eligible patients by using a random number generator. The number of patients selected each week was determined on the basis of study recruitment goals and availability of research staff. Patients were sent a letter informing them about the study and received a follow-up telephone call for recruitment and consent into the study. The VA institutional review board approved this study and all patients consented according to approved procedures.

Of the 415 patients asked to participate, 388 (93.5%) consented; 240 (61.8%) of the patients recruited to the study kept their scheduled appointments within 3 months or by the third scheduled appointment (designated as “attenders”), and 139 (38.2%) patients never kept their appointments (designated as “nonattenders”). In addition, 11 patients (2.8%) who did not keep their appointments were lost to follow-up, which resulted in a final sample of 379 participants.

Measurement

Patients completed several interviewer-administered standardized measures. The following patient sociodemographic and background characteristics were measured: gender, race, educational attainment, marital status, income, employment status, type of medical insurance, and self-reported difficulty of getting medical care.

Fatalism was measured with the Powe Fatalism Inventory, a 15-item measure that assesses 4 attributes of fatalism: fear, pessimism, inevitability of death, and predetermination. Scores range from 0 to 15; higher scores show higher levels of fatalism.^39,40

Religiosity was measured with the Multidimensional Measure of Religious Involvement.⁴¹ This 4-item scale measures organizational religiosity (i.e., church attendance), nonorganizational religiosity (i.e., personal prayer), and subjective, self-rated religiosity.

Social support, comorbitity, and general health status were measured with well- accepted health services outcomes measures, specifically the 14-item Duke–UNC Functional Social Support Questionnaire⁴²; the Charlson Comorbidity Index,⁴³ which provides an estimate of disease burden; and the Short Form–12 (SF-12) Health Survey,⁴⁴ the shortest version of the medical outcomes survey.

Patients' decisionmaking preferences were assessed with the 5-item Decision Making Preference Questionnaire, which measures the amount of autonomy patients prefer when making medical decisions.⁴⁵

Coping was measured with the Monitor–Blunter Style Scale.⁴⁶ The scale was developed to address differences in processing disease-relevant information and permits identification of patients who are predominantly blunters (people who tend to prefer distraction from, and minimization of, threatening information) or monitors (people who tend to prefer scanning for, and amplification of, threatening information).^47,33 Scores ranged from −7 to 7, with negative scores indicating a more avoidant coping style and positive scores indicating a more information-seeking coping style.

Patient trust in their physician was measured with the 11-item Trust in Physician Scale,⁴⁸ which assesses dependability of the physician, confidence in physician knowledge and skills, and confidentiality and reliability of information received. Scores for each item ranged from 1 to 5, with higher scores indicating higher levels of trust.

The number of days from the initial request for an appointment to the actual appointment date was calculated from VA scheduling records.

Interview Procedures

Attenders completed 3 interviewer- administered semistructured instruments. The first 2 interviews were conducted in clinic prior to and immediately after meeting with a physician. The first survey collected baseline information and assessment of patient symptoms and understanding of symptoms. The second survey examined what patients understood and remembered from their consultation with the physician and their decisions concerning testing and treatment. The third interview was conducted no later than 3 months after the patient had completed CAD testing and any invasive treatment procedures to examine satisfaction with decisionmaking.

Our report focuses on data obtained from the first 2 interviews. During these 2 interviews, patients were asked if they had each of 8 common risk factors for heart disease, how often they experienced each of 13 heart disease symptoms, and the severity of each symptom experienced (Table 1). Frequency was measured with 7-choice responses divided into time periods ranging from less than once per month to at least 4 times per day. Severity was measured on a scale from 0 (not at all bothersome) to 10 (extremely bothersome). Patients were also asked to report the extent to which they believed their symptoms were related to their heart, with options ranging from 0 (not at all related to the heart) to 10 (completely related to the heart).

TABLE 1.

Types of Coronary Artery Disease Symptoms Reported by Patients Who Attended or Did Not Attend a Scheduled Cardiac Appointment at a Midwestern Veterans Administration Medical Center: 2001–2003

Symptoms, No. (%)	Nonattenders (n = 139)	Attenders (n = 240)	Pa
Patient has symptoms	121 (87.1)	231 (96.3)	.001
Shortness of breath on exertion	82 (59.0)	168 (70.0)	.03
Fatigue	70 (50.4)	157 (65.4)	.005
Chest pain	66 (47.5)	153 (63.7)	.002
Lightheadedness	58 (41.7)	118 (49.2)	.17
Shortness of breath	53 (38.1)	100 (41.7)	.51
Numbness, radiating pain	51 (36.7)	98 (40.8)	.45
Difficulty breathing	51 (36.7)	93 (38.8)	.74
Coughing	39 (28.1)	95 (39.6)	.03
Swelling	36 (25.9)	85 (35.4)	.07
Palpitations	36 (25.9)	84 (35.0)	.07
Dizziness, fainting	37 (26.6)	72 (30.0)	.56
Lameness not attributable to arthritis	23 (16.5)	68 (28.3)	.01
Nausea	25 (18.0)	46 (19.2)	.89

^aSignificance level for a 2-tailed χ² test.

Nonattenders were interviewed with a modified survey instrument that included all of the previously described measures plus questions to assess why they failed to keep their appointments. Verbal responses were recorded and transcribed by interviewers. These responses were double coded by trained coders with a set of categories developed by the coding team led by the first author. Discrepancies were resolved through discussion among the coding team.

Statistical Procedures

Whether patients attended appointments for recommended testing for CAD was the outcome variable of interest. Preliminary analyses were done to explore the bivariate associations with each of the surveyed constructs. Continuous variables were compared with a t test, and categorical variables were explored with the Fisher exact test or χ² test. Ordered scales were analyzed with Kendall's τ-b. All tests of significance were 2-tailed.

Multivariable analyses consisted of logistic regression in which all variables that were associated with appointment attendance at the bivariate level with a significance level of .10 or less were included. Beginning with this set of variables, a backward stepwise regression analysis based on the likelihood ratio statistic was conducted. Inclusion and exclusion criteria were set at .05 and .15, respectively. For the final model, goodness of fit was examined with results as follows: −2 log likelihood = 349.724; Cox and Snell R² = 0.324; Nagelkerke R² = 0.443; and Hosmer–Lemeshow test = 12.413 (df = 8; P = .134).

Individual symptoms, total number of symptoms, symptom frequency, and symptom severity were each tested in the multivariable analyses. In the final model, only the symptom summary variables were included because they were the superior predictors. Cross products were tried for several factors that were correlated. Cross products did not materially improve model fit. Missing values (less than 3% of data on average) for retained variables were interpolated with the SPSS version 15 module (SPSS Inc, Chicago, Illinois). The reported results are for models tested with the interpolated data.

RESULTS

Study Sample

The study included 379 veterans whose cardiac specialty care was provided by a VA medical center. The sample was 98.4% male and 66.0% non-Hispanic White. The racial/ethnic composition of the non-White participants was largely African American (91.5%). Because of the small number of participants from other racial/ethnic groups (n = 11), race was dichotomized in the analyses as non-Hispanic White and other race/ethnicity. Most participants had attained at least a high school education (80.7%). Patient ages ranged from 31 to 91 years, with a mean age of 60.35 years (standard deviation [SD] = 11.46). The sample was largely low-income with a median income level of $10 000 to $14 999. A total of 53.8% only had medical insurance through the VA.

Patients who did not keep their appointments differed from their appointment-keeping counterparts on only a few characteristics (Table 2). There were no differences by group on race/ethnicity, education, marital status, or income. Groups also did not differ on fatalism, religiosity, or social support. Patients who attended their clinic appointments were slightly older than nonattenders (61.4 vs 58.4 years; P = .01) and more likely to be either employed or retired. Nonattenders reported they were not working or working less frequently than attenders (40.3% vs 14.6%; P = .04).

TABLE 2.

Demographic and Attitudinal Characteristics Among Patients Who Attended or Did Not Attend a Scheduled Appointment for Coronary Artery Disease Testing at a Midwestern Veterans Administration Medical Center: 2001–2003

	Nonattenders (n = 139)	Attenders (n = 240)	Pa
Age, mean (SD)	58.4 (11.80)	61.5 (11.12)	.01
Race/ethnicity, no. (%)
Non-Hispanic White	87 (62.6)	163 (67.9)	.31
Other race/ethnicity	52 (37.4)	77 (32.1)
Education, no. (%)
Less than high school diploma	21 (15.1)	52 (21.7)	.29
High school diploma	64 (46.0)	104 (43.3)
More than high school	54 (38.8)	84 (35.0)
Marital status, no. (%)
Married/cohabitate	53 (31.8)	119 (49.6)	.11
Widowed	9 (6.5)	19 (7.9)
Divorced	55 (39.6)	71 (29.6)
Never married	22 (15.8)	31 (12.9)
Household income, $, no. (%)
< 10 000	52 (37.4)	67 (27.9)	.08
10 000–29 999	74 (53.2)	136 (56.7)
≥ 30 000	13 (9.4)	37 (15.4)
Current employment, no. (%)
Yes	26 (18.7)	63 (26.3)	.002
No	75 (54.0)	85 (35.4)
Retired	38 (27.3)	92 (38.3)
Not working, or working less because of heart disease, no. (%)
Yes	56 (40.3)	35 (14.6)	.04
No	83 (59.7)	205 (85.4)
Attitudinal characteristics,b mean (SD)
Fatalism	36.9 (30.3)	38.20 (29.1)	.63
Religiosity	59.8 (28.8)	58.60 (26.7)	.70
Social supportc	62.9 (27.6)	67.04 (26.1)	.30

^aSignificance level for a 2-tailed test; the Fisher exact test was used for factors with 2 levels and the χ² test for more than 2 levels. The t test was used for continuous variables.

^bPossible scores for fatalism, religiosity, and social support ranged from 0 to 100, with higher scores reflecting more of each variable. See “Methods” section for details on scales used.

^cFor social support, the sample sizes were 61 for nonattenders and 183 for nonattenders.

Table 3 compares access to health care, general and CAD-specific health status and CAD symptomology, and attitudes for nonattenders and attenders. Nonattenders were significantly more likely than were attenders to have VA insurance as their only source of insurance and to report that obtaining medical care was more difficult. Nonattenders also experienced much longer delays in obtaining appointments for cardiac testing (an average of 136 vs 54 days for nonattenders and attenders, respectively).

TABLE 3.

Access to Health Care, Health Status, and Medical Attitudes Among Patients Who Attended or Did Not Attend a Scheduled Appointment for Coronary Artery Disease (CAD) Testing at a Midwestern Veterans Administration Medical Center: 2001–2003

	Nonattenders (n = 139)a	Attenders (n = 240)b	Pc
Access to health care
Insurance status, no. (%)
VA insurance only	84 (60.4)	120 (50.0)	.05
VA insurance plus additional coverage	55 (39.6)	120 (50.0)
Difficulty getting medical care, no. (%)
Extremely difficult	12 (8.6)	10 (4.2)	.001
Very difficult	9 (6.5)	7 (2.9)
Somewhat difficult	46 (33.1)	62 (25.8)
Not too difficult	32 (23.0)	62 (25.8)
Not at all difficult	40 (28.8)	99 (41.3)
Days waited for appointment, mean (SD)	136 (102)	54 (58)	<.001
Range	0–406	0–330
Health status
Overall health status, mean (SD)
Comorbidity scored	8.5 (9.4)	7.5 (7.4)	.26
SF-12 mental healthe	49.5 (17.5)	50.6 (17.3)	.59
SF-12 physical healthf	52.3 (27.2)	49.9 (27.1)	.39
CAD-specific health status
Number of CAD risk factorsg	3.9 (1.8)	4.0 (1.9)	.69
Number of CAD symptomsh	4.5 (3.2)	5.6 (3.0)	.002
Average symptom frequencyi	3.7 (2.5)	4.5 (2.4)	.002
Average symptom severityj	4.4 (3.0)	5.6 (3.2)	.001
Patient's belief that symptoms are related to heartk	3.5 (3.2)	5.62 (3.5)	.001
Medical attitudes
Trust in physician,l mean (SD)	67.1 (17.9)	70.6 (14.9)	.04
Coping style,m mean (SD)	–0.036 (0.94)	1.00 (2.6)	.001
Medical decisionmaking preference, no. (%)
Primarily the doctor	42 (30.4)	76 (32.3)	.94
Joint decision	50 (36.2)	75 (31.9)
Primarily the patient	46 (33.3)	84 (35.7)

Notes. VA = Veterans Administration; SF-12 = Short Form–12 survey.

^aSF-12 mental health, n = 135; SF-12 physical health, n = 136; trust in physician and medical decisionmaking preference, n = 138.

^bSF-12 mental health, n = 231; SF-12 physical health, n = 233; trust in physician and medical decisionmaking preference, n = 235.

^cFrom Kendall's τ-b for categorical variables; t test for continuous variables.

^dRange = 0–100; higher scores indicate more comorbidity.

^eRange = 0–100; higher scores indicate better mental health.

^fRange = 0–100; higher scores indicate better physical health.

^gRange = 0–8.

^hRange = 0–13.

ⁱRange = 0–7; higher scores indicate greater frequency.

^jRange = 0–10; higher scores indicate greater severity.

^kRange = 0–10; higher scores indicate greater belief that symptoms are heart-related.

^lRange = 0–100; higher scores indicate more trust.

^mMonitor–Blunter Style Scale range = −7 to 7; lower scores indicate a more avoidant coping style.

Overall health status, as measured by the Charlson and SF-12 scores for physical and mental health, did not differ by group (Table 2). The CAD-related health status, as defined by number of risk factors for CAD; number, frequency, and severity of symptoms; and type of symptoms, was also compared. The total number of risk factors reported by patients did not differ for nonattenders and attenders. However, nonattenders reported fewer, less frequent, and less severe CAD symptoms than did attenders. In addition, the attributions patients made about the source of their symptoms differed across the 2 groups; nonattenders were less likely to attribute their symptoms to heart disease than were attenders.

As shown in Table 3, nonattenders were more likely than were attenders to be completely asymptomatic, but the number of patients who reported at least 1 symptom was high for both groups (> 87%). Table 3 also shows that certain individual symptoms were less likely to be reported by nonattenders than attenders. Specifically, fewer nonattenders reported shortness of breath on exertion, fatigue, chest pain, lightheadedness, coughing, and lameness not attributable to arthritis. There was also a marginal trend for nonattenders to report less swelling and palpitations than attenders.

Three medical-related attitudes were compared for nonattenders and attenders (Table 2). There was a significant difference in trust in physician, with nonattenders reporting less trust. There was also a significant difference in coping style. The average coping style score for nonattenders (–0.036; SD = 0.94) compared with attenders (1.00; SD = 2.57) indicated that nonattenders were more information-avoidant about aversive events compared with attenders, who were more likely to seek out such information. Decisionmaking preference was not significantly different between the 2 groups.

Multivariable Model

A backward stepwise logistic regression analysis with appointment nonattendance as the test variable (Table 4) was used to examine variables chosen from Tables 1 and 2 that had a bivariate association with appointment attendance at a significance level of .10 or less.

TABLE 4.

Results of Final Logistic Regression Model Predicting Appointment Nonattendance for Coronary Artery Disease (CAD) Testing Among Patients at a Midwestern Veterans Administration Medical Center: 2001–2003

	ORa (95% CI)	P
Age	0.97 (0.95, 0.99)	.03
Household income, $		.35
< 10 000	3.50 (1.40, 9.00)	.02
10 000–29 999	2.70 (1.10, 6.60)	.03
≥ 30 000 (Ref)	1.00
Not working or working less because of heart disease	4.30 (2.30, 8.00)	<.001
Access to healthcare: days waited for appointment	1.01 (1.01, 1.01)	<.001
CAD-specific health status
Number of symptoms	0.89 (0.80, 0.98)	.02
Belief that symptoms are related to heart	0.87 (0.80, 0.95)	.003
Medical attitudes: coping style	0.78 (0.68, 0.88)	<.001

Notes. OR = odds ratio; CI = confidence interval.

^aORs adjusted for other covariates in the multivariate model. Nonattenders were the test group; attenders were the comparison group.

Table 4 displays the final step of the regression analysis. Variables retained in the final model included age, household income, whether patients were working less because of heart disease, days to appointment, number of CAD symptoms, belief that symptoms were related to heart, and coping style. Patients who were more likely to be nonattenders were younger, poorer, less likely to report not working or working less because of heart disease, had to wait longer for an appointment, had fewer symptoms, were less likely to believe that their symptoms were attributable to heart disease, and were more apt to cope with problems through avoidance.

Reasons for Missing Appointments

The 139 nonattenders were asked why they did not keep their appointments. Most patients (64%) reported that they had intended to keep their appointments but did not because they forgot or were confused about their appointment date. More than half of this group thought they could just have testing done at some unspecified future time. Structural barriers such as distance or transportation were cited as the reason for not keeping appointments by 33.1% of nonattenders. Almost a quarter (24.5%) of patients reported that they had failed to obtain scheduled CAD testing because other personal issues took precedence. Fear of the recommended diagnostic tests was also given as a reason for nonattendance (22.3%).

Patients reported several less common but significant reasons. Eighteen percent of patients did not obtain testing because they did not believe that they had heart disease or thought their symptoms were related to another health condition. Dissatisfaction with VA care and lack of trust in the physicians or hospital were reported among 16.5% of nonattenders. Finally, 13.7% reported that other health problems took precedence, and 10.8% reported communication and scheduling problems and complained that staff failed to remind them of appointments that had been made months earlier.

DISCUSSION

Our study was one of the first to examine frequency of and reasons for missed appointments among patients being screened for cardiac disease and one of the first to examine reasons for missed appointments among patients seeking care in the VA health care system. Our findings that nonattendance was associated with younger age, lower socioeconomic status, and longer wait times for appointments were consistent with past studies that have examined appointment nonattendance in primary care patients from non-VA or private health care systems.^{1–8,10–22} In our study, race/ethnicity was not associated with nonattendance, nor did we find lack of social support to be associated with nonattendance. This suggests that providing access through comprehensive health insurance can ameliorate racial disparities.

We also identified 3 new factors associated with nonattendance: CAD-specific symptoms, attributions about symptoms, and coping style. Compared with attenders, nonattenders reported fewer symptoms consistent with CAD, attributed these symptoms to something other than heart disease, and generally coped with potentially adverse medical events by avoiding additional information about them. Interventions to decrease nonattendance for CAD testing could be designed with these factors in mind. For example, improving communication with asymptomatic patients about why CAD testing may benefit them and explaining to patients how symptoms can have several etiologies (i.e., related to their heart as well as to other preexisting conditions) may encourage patients to keep appointments. Furthermore, assessing patients' coping style to identify those who avoid information about aversive events may help target patients at higher risk for missed appointments and help tailor communication approaches.

Public Health Impact

Cardiovascular disease, of which heart disease is a major component, is a leading cause of morbidity and mortality in the United States.^49–51 For the nation at large, cardiovascular morbidity and mortality has been improving largely because of diagnostic and therapeutic advances.^49,52 Timely access to these cardiac diagnostic and therapeutic advances is an important part of the national effort to improve heart disease outcomes. A missed appointment for diagnostic testing could delay treatment until an acute event, such as a myocardial infarction, has occurred. These events are likely to result in decreased quality of life even when the patient survives the event. Early detection and treatment can improve patient outcomes and delay or prevent incapacitating cardiac events.

Prentice and Pizer found that veterans who attended a VA hospital with an average wait of at least 31 days had a 21% higher mortality rate than those with an average wait of less than 31 days.⁵³ This finding underscored the importance of appointment attendance and the effects of not obtaining proper treatment or diagnostic screening in a timely manner. Furthermore, the VA, which is the largest integrated health care system in the nation, serves as a safety net to millions of American veterans who have no or inadequate access to private health care. Barriers to timely medical care, especially among patients from low socioeconomic strata, have implications for the delivery of high-quality medical care in an equitable fashion. Lastly, it is reassuring that, in the face of well-documented racial/ethnic cardiovascular health and health care disparities,^54–57 race/ethnicity did not appear to be a factor in missed appointments for cardiac diagnostic testing in the VA medical center we studied.

Study Limitations

Several limitations should be considered when one interprets the findings of this study. First, the sample was drawn from a single VA medical center and, consistent with the makeup of the veteran population, contained mostly men. Therefore, the results may not generalize to women and patients from other hospitals or regions of the country. Second, although we considered several system-level reasons for nonattendance, other factors not observed in this study may also have aggravated or ameliorated patient reasons for missed appointments. Finally, like all studies of patients who fail to keep their appointments, this study was challenged with the need to contact patients who consistently opt out of regular medical treatment. In the case of this study, the major problem was finding these patients and making contact with them. Once located, however, these patients were as likely to participate in this study as other patients.

Conclusion

In this study of patients scheduled for diagnostic testing for CAD in a VA healthcare setting, we found that nearly 40% did not keep their appointments, indicating that nonattendance in this patient population is a significant problem. In addition to finding several factors associated with nonattendance in our sample that were consistent with those identified for primary care, non-VA populations, we also identified previously neglected factors that should be considered in research regarding patient appointment-keeping behavior. Specifically, we found that patients' beliefs about their symptoms and how they tended to cope with potentially unpleasant events were associated with nonattendance. Additional studies are needed to design and test specific interventions that might reduce nonattendance among patients whose cardiac risk is unknown or inadequately determined. Future work should also explore the long-term health impact on patients of nonattendance at CAD diagnostic appointments.