Data source
Data was obtained from the Medical Expenditure Panel Survey (MEPS), a nationally representative survey of noninstitutionalized US citizens. Since 1996 the MEPS has collected data annually from families, individuals, medical providers, and employers in the United States on access to, utilization of, and costs for health care services. Although MEPS has a household component, an insurance component, and a medical provider component, analyses for this study will focus on 2012 data from the household component, which includes respondents’ answers to questions about their satisfaction with their medical care. The household component uses an overlapping panel design to survey households that were used in the previous year for the
National Health Interview Survey (NHIS), a survey conducted by the National Center for Health Statistics. Household interviews were conducted over a period of two years to provide some individual longitudinal data for information such as health condition, health status, and
satisfaction with care. In 2012, 14,763 families were surveyed, totaling to 37,182 respondents. Racial/ethnic minorities were oversampled. The combined overall response rates for the two- year 2012 household components of MEPS (which drew from NHIS respondents at a response rate of 82.9%), was 56.3%.
For this study, I merged data from the Medical Conditions File and Panel 16 of the
Longitudinal Data File, both of which were obtained from the MEPS’ Household Component. In the Medical Conditions File, when respondents reported no medical condition, their condition did not meet the criteria for a current condition, or the condition was not one experienced in the
past 12 months, no condition was included in the dataset and these patients were excluded from the final sample. Panel 16 of the Longitudinal Data File included questions regarding ratings of health care and, consequently, was only applicable to those who had experiences with health care. Therefore, respondents in the final sample were those who had a medical condition as reported in one of the 15 ICD-9 classifications (Table 1) and had completed questionnaires regarding health satisfaction regarding visits that took place within the past year. The final sample included 5,817 respondents.
Dependent Variables: Measures of health care satisfaction
I include an outcome variable of global health care satisfaction as well as four variables that pertain more specifically to satisfaction with one’s provider and with the doctor-patient relationship. In this way, my analyses include outcomes that refer to specific components of the clinical experience. All measures of health care satisfaction were self-administered to
respondents who had an experience with health care in the past year. The survey questions were taken from the Consumer Assessment of Health Plans Survey, which was used to measure consumer’s ratings of the quality of health care (MEPS 2012). Answers to the five questions asked in this survey have been used in prior research on health care satisfaction and health care quality (Fenton et al. 2012). The first item, global health care satisfaction, was obtained through a question asking how satisfied respondents were with their health care over the past 12 months, on a scale from 0 (worst health care possible) to 10 (best health care possible). Due to low sample sizes for the lowest ratings of health care satisfaction, I consolidated reports into three categories which I label poor/fair, good, and very good/excellent. The remaining four items measured health care satisfaction specific to the patient encounter. These included questions about provider respect (how often providers showed respect for what you had to say), provider’s
attentiveness (how often providers listened carefully to you), provider’s explanations (how often health providers explained things in a way that was easy to understand), and provider’s time (how often health providers spent enough time with you). Again, due to low sample size for the lowest health care ratings, outcomes were consolidated into three categories of never/sometimes, usually, and always.
All outcome variables were tested for ordinality using stereotype logistic regression to ensure dependent variables that were monotonically increasing. For the final analyses, ordinal logistic regression analyses were used. Interpretations of results in odds ratios follow the standard comparison across groups. For global health care satisfaction, odds ratio coefficients report the odds of rating very good/excellent compared to good or poor/fair, and the odds of rating very good/excellent and good compared to poor/fair. Provider-specific satisfaction ratings provide the odds of reporting always compared to usually or sometimes/never, and the odds of reporting always and usually compared to sometimes/never.
Independent Variables:
Medical Conditions
Patients’ medical conditions were selected based on their diagnostic uncertainty, which was determined by the availability of valid and reliable diagnostic tests. Medical conditions were reported to interviewers verbatim by respondents during the household component surveys. Conditions were later coded by professional coders into the ICD-9-CM codes. I selected three classes of medical conditions based on their diagnostic uncertainty: conditions with low or no diagnostic uncertainty, those with moderate diagnostic uncertainty, and those with high
diagnostic uncertainty. Five ICD-9 classifications were chosen based on the presence or absence of diagnostic tests for each of the three levels of diagnostic uncertainty.
Conditions were placed into categories based on the diagnostic criteria found on National Institute websites for each disease.1 Conditions with low diagnostic uncertainty include
conditions that are diagnosed using a positive diagnostic test that is considered objective and verifiable using quantitative medical technologies. For example, a patient with a dysrhythmia receives a diagnosis when findings on an electrocardiogram (ECG) indicate that a dysrhythmia is present.
Conditions of moderate diagnostic uncertainty fall in the middle, when medical
technologies can be used to aid in a diagnosis but are not required for a diagnosis to take place. Diagnostic technology may provide unreliable results, and so these conditions often rely on patient symptom report as well as providers’ physical examination of patients to aid in the diagnosis. The diagnosis for Parkinson’s disease, for example, often occurs based on clinical examination alone. When medical testing is performed and shows no indication for disease, providers can still diagnose Parkinson’s disease based on other criteria, such as noting the presence of a resting tremor on clinical examination.
Conditions of high diagnostic uncertainty include those commonly diagnosed by physicians when no other organic cause is found; this grouping of ICD-9 codes includes
diagnostic labels applied to vague and ill-defined complaints such as nausea and fatigue. These types of medical complaints are often labeled medically unexplained [physical] symptoms (Creed et al. 2011). No formal criteria for labeling and studying MUPS exist to date, which hinders replicability of such studies (Dirkzwager and Verhaak 2007). Within the MEPS, condition codes were grouped so that there were no codes for individual medically unexplained syndromes. However, I selected conditions based on past research of common medically unexplained symptoms (Creed et al. 2011). No MUPS patient also had a medical condition that
was considered of moderate or high diagnostic uncertainty. Therefore, this category of patients – although limited – represents patients who have presented to their doctors over the last year with symptoms that did not fit within any other diagnostic category and instead fell into an ICD-9 classification for symptoms that could not be coded elsewhere.
Condition categories can be found in Table 1.
Table 1. Patient-reported Health Conditions by Degree of Diagnostic Ambiguity
Low Moderate High
Multiple Sclerosis Parkinson’s Disease Headache, including Migraine
Cardiac Dysrhythmias Esophageal Disorders Nonspecific Chest Pain Essential Hypertension Gastritis and Duodenitis Nausea and Vomiting
COPD2 Rheumatoid Arthritis and related
disorders
Abdominal Pain
Diabetes Mellitus w/o Complications
Systemic Lupus Erythematosis and connective tissue disorders
Malaise and Fatigue
Other independent variables
Other variables included in the analysis are age, gender, race/ethnicity, health insurance, family income, education, self-rated health, and self-rated mental health. Age was a continuous variable ranging in ages from 18 to 85. Gender was dichotomized to male or female. A dummy variable for race/ethnicity was created in which there were four categories: Whites who did not report Hispanic ethnicity, non-Hispanic Blacks, non-Hiispanic Asian, and non-Hispanic
other/multiple races. Health insurance status was reported as three categories: public insurance only, any private insurance, and uninsured. Family income, which was a measure of income in relation to poverty status, was divided into four categories of poor or near poor, low income, middle income, and high income. A dummy variable for education was created which consolidated education into five categories indicating if the respondent had less than a high school education, had some high school, was a high school graduate, had some college, or had a college degree or higher. Variables for self-rated health included respondents’ self-reported physical health and self-reported mental health. Both variables were coded into a five-point Likert scale associated with the responses of poor, fair, good, very good, and excellent. The average amount of missing cases across variables was between 1 and 2% (with the exception of education at 10.9%). These were eliminated using list-wide deletion.
RESULTS
Basic descriptive statistics can be found in Table 2. Each of the variables used in my analyses were also stratified by the diagnostic uncertainty level of respondents’ medical conditions, as shown in Table 3.
The mean age of respondents in my sample was 58 (Table 2). When stratified by the level of conditions’ diagnostic uncertainty, respondents who had a condition of low diagnostic uncertainty were substantially older than those whose conditions were moderately or highly uncertain (Table 3). Respondents with conditions of low diagnostic uncertainty had a mean age of 60, which is 11 years older than the mean of patients with moderately uncertain conditions (mean age of 49) and 23 years older than the mean of patients with highly uncertain conditions (mean age of 37).
The final sample included a higher number of women (58%) than men (Table 2), and a slightly higher percentage of women reported conditions of both moderate and high diagnostic uncertainty when compared to men. This is consistent with the literature, which states that a larger number of women than men present with medically unexplained symptoms (Hausteiner- Wiehle et al. 2011); Johnson 2008).
Around 48% of the samplewas White. 26% were Black, 18% were Hispanic, 5% were Asian, and the remainder were other/multiple races (see Table 2). More Hispanics and Asians (between 5 and 6% compared to 3-4% for Whites or Blacks) reported a medical condition that was considered of high diagnostic uncertainty (Table 3).
According to prior research, medically unexplained syndromes are more common among those of lower socioeconomic status (Creed et al. 2011). In this study, the reverse was found: reported conditions of moderate or high diagnostic uncertainty were slightly more likely in respondents who had higher educational attainment. For example, 3% of those with less than a high school education reported a condition of moderate diagnostic uncertainty, compared to over 4% of those with college or more. The difference is more apparent for those reporting conditions of high diagnostic uncertainty. Under 2% of those with less than a high school education
reported conditions of high diagnostic uncertainty, compared to 6% of those who attended college or more (Table 3). When looking at family income, the distribution of diagnostic conditions is similar across income categories. Therefore, the finding that MUPS are more common among those of a lower social class is not supported in my study.
Younger age historically has been thought to be associated with higher rates of somatoform disorders, of which functional somatic syndromes – or medically unexplained syndromes – are a part. It is hypothesized within psychology that medically unexplained symptoms are underdiagnosed and underreported due to the difficulty differentiating medically unexplained and medically explained symptoms in an age group where organic diseases are common (Hausteiner-Wiehle et al. 2011; Wijeratne and Hickie 2001). My finding that
respondents with conditions of high diagnostic uncertainty were younger in age than those with conditions that were more diagnostically certain is in support of the finding that younger age groups either have more medically unexplained symptoms or that younger patients’ symptoms are less likely to receive an organic disease classification than a MUPS diagnosis code.
Table 2. Basic Descriptive Statistics: Continuous and Categorical Variables. N=5,817. Percent Age (Mean/SD) 58.53/15.21 Race/Ethnicity non-Hispanic White 48.44 non-Hispanic Black 25.96 Hispanic 18.22 non-Hispanic Asian 5.16 non-Hispanic Other/Multiple 2.22 Gender Male 41.96 Female 58.04 Education Less than HS 10.18 Some HS 11.47 HS grad 32.22 Some College 25.31 College+ 20.84 Insurance Coverage Any Private 57.14 Public Only 34.74 Uninsured 8.11 Income/Poverty Status Poor/Near Poor 24.63 Low Income 16.31 Middle Income 28.83 High Income 30.22
Self-rated Physical Health
Poor 7.43
Fair 21.35
Good 36.22
Very Good 25.96
Excellent 9.04
Self-rated Mental Health
Poor 2.73
Fair 10.71
Good 32.92
Very Good 28.66
Table 3. Variables by Diagnostic Category. N=5,817.
Diagnostic Uncertainty Level
Low Moderate High
Total (%) 92.42 3.54 4.04 Age (mean) 60 49 37 Gender Male 94.84 2.87 2.29 Female 90.67 4.03 5.3 Race/Ethnicity non-Hispanic White 92.87 3.51 3.62 non-Hispanic Black 93.58 3.11 3.31 Hispanic 90.28 3.87 5.85 non-Hispanic Asian 91 3.67 5.33 Non-Hispanic Other/Multiple 89.92 6.2 3.88 Education Less than HS 95.27 2.87 1.86 Some HS 92.05 3.9 4.05 HS grad 93.7 3.09 3.2 Some College 92.12 3.6 4.28 College+ 89.6 4.29 6.11 Insurance Coverage Any Private 91.73 3.55 4.72 Public Only 94.9 3.32 1.78 Uninsured 86.65 4.45 8.9 Income/Poverty Status Poor/Near Poor 91.97 4.4 3.63 Low Income 93.26 2.32 4.43 Middle Income 92.49 3.22 4.29 High Income 92.26 3.81 3.92
Self-rated Physical Health
Poor 94.68 3.7 1.62
Fair 94.93 3.22 1.85
Good 93.45 3.18 3.37
Very Good 90.86 3.58 5.56
Excellent 84.98 5.51 9.51
Self-rated Mental Health
Poor 94.34 4.4 1.26
Fair 94.22 3.69 2.09
Good 93.47 3.39 3.13
Very Good 92.26 3.48 4.26
Regression results by measure of satisfaction
I used ordered logistic regression to estimate the effects of diagnostic uncertainty on health care satisfaction. I expand on previous research of health care satisfaction by including four additional measures beyond global yearly satisfaction. These added measures intend to capture patient perceptions of specific components of the health care experience, from providers'
communication patterns to providers' behaviors such as time allocation and displays of respect. I include results on global health care satisfaction (Table 4) and provider respect (Table 5). The remaining models can be found in the Appendix.
Table 4 displays the ordered logit results with the outcome "Health Care Satisfaction over the past 12 months." Model 1 shows the bivariate model. In this model, conditions of high diagnostic uncertainty were associated with a 37% lower odds of health care satisfaction over the past year when compared to conditions that are of low diagnostic uncertainty (OR=.633; p<.01). Although there was a significant difference in patient satisfaction between conditions considered low and high in terms of their diagnostic uncertainty, there was no statistically significant difference in patient satisfaction between conditions considered moderate and low in their diagnostic uncertainty. Model 2 adds sociodemographic variables to the bivariate model. These include gender, age, race/ethnicity, education, insurance status, and family income. Inclusion of these variables made the bivariate relationship insignificant. The relationship remains insignificant after inclusion of the self-rated health variables in the full model, Model 3. Model fit was best for the full model, Model 3, which had a slightly lower BIC than the other models (BIC=7240).
In both Model 2 and Model 3, females had higher odds of being satisfied with their health care than men, controlling for other variables. The relationship between gender and satisfaction
strengthens after addition of self-rated health variables in the full model. Women had a 1.19 times higher odds of being satisfied with their health care than men, controlling for other factors (p<.01).
Both Blacks and Asians were less satisfied with their health care than Whites, net of other factors. This held true in Models 2 and 3. For example, in the full model the odds of Blacks reporting higher health care satisfaction was nearly 30% less likely than Whites, net of other factors; similarly, the odds of Asians’ expressing higher satisfaction were 39% lower than that of Whites (p<.001). Hispanics ratings of health care satisfaction did not differ significantly from that of Whites.
Having attended some college increased respondents’ odds of being satisfied with health care compared to those who had less than a high school degree, net of other factors. However, this relationship becomes insignificant with the addition of self-rated health variables in Model 3. Type of insurance coverage was significantly associated with health care satisfaction in both models. Compared to respondents with private insurance coverage, those with public insurance had .774 times lower odds of being satisfied, and being uninsured lowered the odds of being satisfied even further to .485, net of other factors (p<.001; Model 3). Higher family income was associated with higher satisfaction in Model 2, but this became insignificant after inclusion of self-rated health variables in the full model.
Higher ratings of physical health were strongly associated with higher satisfaction, net of other factors. Similarly, self-rated mental health was associated with higher satisfaction, but only when respondents selected their mental health as very good or excellent as compared to poor. Previous research has been variable on the association between self-rated health and MUPS (Hausteiner-Wiehle et al. 2011; Johnson 2008; Lian and Hansen 2016). Therefore, I
included a model that only added self-rated physical health and self-rated mental health to the bivariate model (Model 4). Compared to Model 1, the effect of diagnostic uncertainty on satisfaction increases in Model 4, which indicates that self-rated health is suppressing its
influence. In this model, individuals with a condition considered highly diagnostically uncertain had nearly half the odds of being satisfied with their health care over the past 12 months than individuals with conditions considered low in diagnostic uncertainty, net of self-rated health (OR=.51; p<.001). Self-rated physical and mental health showed the same trends as seen in Model 3: higher ratings of health were associated with higher odds of beings satisfied with one's health care over the past 12 months. Self-rated physical health was more strongly associated with satisfaction than self-rated mental health, and this held true for the other outcomes evaluating provider satisfaction.
There are some important differences in findings when examining the four measures of satisfaction with one’s provider as opposed to the measure of global satisfaction. Included in Table 5 are regression results from one of these measures of provider satisfaction – that of feeling respected by one’s provider. The remaining three measures of provider listening, explanations, and time can be found in the Appendix. The primary relationship is only
significant in the final model for provider time (Table 10 in Appendix). Respondents with highly diagnostically uncertain conditions had 25% lower odds of being satisfied with the time their providers give to them, compared with respondents who had conditions considered low in diagnostic uncertainty, and controlling for other factors (p<.05).
Of note in all four of the final models for provider satisfaction, Blacks no longer report lower satisfaction than Whites and instead had around 1.2 to 1.3 times higher odds of being satisfied on all four measures of provider respect, listening, explanations, and time, net of other
factors (p<.05 to .01). Asians no longer reported lower satisfaction; instead, their odds of being satisfied on all four provider measures did not differ from Whites. Hispanic satisfaction
remained insignificantly different from Whites.
Higher education was now significantly associated with lower odds of satisfaction with one's provider when it came to ratings of provider respect, listening, and time in the full models. The higher one's education above high school, the lower the odds of feeling respected by one's provider, net of other factors. College graduates and above were the least satisfied, with odds of feeling respected over 30% lower than that of individuals with less than a high school education