Medical Care Avoidance Among Older Adults

Abstract

Objective: To examine factors and reasons associated with medical care avoidance among older adults in the United States. Method: Analysis of data on 2,155 adults aged 65 and older, who completed the 2008 Health Information National Trends Survey. Results: Nearly one fourth of older adults reported medical care avoidance (n = 449; weighted % = 22.5%). Of these, more than one third reported doing so because of feeling uncomfortable when their body is examined (34.5%) or fearing a serious illness (35.9%) and with fewer reporting avoiding care because it made them think of dying (14.3%). Likelihood of avoidance was higher among those with worse self-reported health status, severe psychological distress, lower health self-efficacy, lower confidence in obtaining health information, lower trust in doctors, less patient-centered communication, lower perceived health care quality, and those who were current smokers. Qualitative reasons for avoidance corroborated quantitative results. Conclusion: Avoidance by older adults appears to be largely associated with evaluations of the quality of care and provider communication.

Keywords

medical care avoidance patient delay quality of care patient-centered communication

Introduction

Despite the potential health benefits of modern medicine, many adults avoid medical care. Over one third of participants in a national survey reported avoiding visiting the doctor even when they suspected they should go (Kannan & Veazie, 2014). Although it may seem paradoxical that people avoid medical care that could potentially save their lives or ameliorate suffering, the phenomenon of doctor avoidance has been well documented in the medical literature (Byrne, 2008; Kannan & Veazie, 2014; Lund-Nielsen, Midtgaard, Rorth, Gottrup, & Adamsen, 2011; Persoskie, Ferrer, & Klein, 2014; Spleen, Lengerich, Camacho, & Vanderpool, 2014). People may avoid medical care at any point in the disease continuum. Such avoidance contributes to delays in symptom presentation and treatment and is associated with increased morbidity and mortality across a range of diseases (American Heart Association, 2005; Moser et al., 2006), including cancer (Caplan, 2014; Richards, Westcombe, Love, Littlejohns, & Ramirez, 1999).

Medical care avoidance may be more likely to yield adverse health consequences for “high risk” patients such as older adults, who often have complex medical and social needs that require greater physician involvement (Prentice & Pizer, 2007). Older adults are generally sicker, have worse health-related quality of life (Luo, Johnson, Shaw, Feeny, & Coons, 2005; Zack, 2013), take more prescription and over-the-counter medications (e.g., 29% report using at least five prescription medications concurrently; Kaufman, Kelly, Rosenberg, Anderson, & Mitchell, 2002; Qato et al., 2008), and have a higher prevalence of multiple chronic conditions (including “silent” conditions such as hypertension) than their younger counterparts (Ward & Schiller, 2013). A study using data from the 2010 U.S. National Health Interview Survey estimated that 46.5% of older adults had two to three chronic conditions and 15.6% had four or more chronic conditions (Ward & Schiller, 2013). Together, the increased medical needs of older adults may make them particularly vulnerable to the adverse health consequences of avoiding medical care.

Few studies have examined medical care avoidance specifically among older adults. Although some studies have examined patient delay among older adults, these studies typically focus on specific diseases or patients who already have a health condition. Prior studies have examined characteristics of and reasons for avoidance among both older and younger adults who avoid medical care (Kannan & Veazie, 2014, 2015; Moser et al., 2014; Persoskie et al., 2014; Taber, Leyva, & Persoskie, 2015; Vanderpool & Huang, 2010; Ye, Shim, & Rust, 2012). One study (using the same dataset as the present study) examined correlates of avoidance among adults who were older versus younger than 50 years of age; in the over-50 group, medical care avoidance was higher among people who were less educated, had lower income, were relatively younger, lacked health insurance, and had high perceived risk and worry about cancer (Persoskie et al., 2014). However, altogether, the factors examined in that study accounted for a relatively small proportion of the observed variance in doctor avoidance (Cox & Snell R² below .05) and the study did not examine people’s qualitative reasons for avoiding medical care. Similarly, another study using this same survey data examined factors associated with avoidance among six groups of adults segmented by age (i.e., 59 to 68 years and greater than 68 years) and education (i.e., less than high school, high school graduate, and college graduate; Kannan & Veazie, 2015); however, this study is not informative as to the factors that are associated with avoidance among the broader group of adults 65 and older.

With older adults now comprising 13.0% of the U.S. population (40.3 million people in 2010; West, Cole, Goodkind, & He, 2014), it is important to determine the extent to which older adults avoid medical care, the factors associated with avoidance in this population, and the reasons older adults have for avoiding doctors. This information may enhance our understanding of health care access and utilization among this growing segment of the population, and inform the development of targeted health communication messages and interventions for this group. Toward that end, using data collected in a nationally representative U.S. survey, we employed a mixed-methods approach to (a) examine factors associated with avoidance among older adults and (b) characterize reasons reported by older adults for avoiding medical care.

Method

Data Source

Data were from the Health Information National Trends Survey (HINTS) 3, a nationally representative cross-sectional survey conducted by the National Cancer Institute to assess health behaviors and health information seeking in the U.S. public. Data were collected between January 2008 and April 2008 by computer-assisted telephone interview and by mail to maximize response rates (24.2% and 31.0%, respectively). Participants were selected using random-digit dialing (RDD) for the telephone component and using random sampling of households from the U.S. Postal Service for the mail component. In the RDD sample, the adult in the household having the next birthday was asked to complete the survey. In the mail sample, all adults in the household at a sampled address were asked to complete a survey. The decision to not subsample the adults in sampled households was the result of an evaluation study conducted by Battaglia, Link, Frankel, Osborn, & Mokdad (2008), which found that the next birthday and all-adults methods yielded household-level completion rates that were comparable with the any-adult method. Hence, the mail sample was a stratified cluster sample in which the household was the cluster. Census blocks with a higher proportion of minorities were oversampled. Details of the survey design are available elsewhere (Cantor et al., 2009). The survey was completed by 7,674 participants. We analyzed data from only the 2,155 participants who were 65 years of age or older and provided a valid response for the medical care avoidance item. When weighted, the data are representative of the non-institutionalized U.S. adult population.

Measures

Medical care avoidance, our dependent variable, was assessed with the item, “Some people avoid visiting their doctor even when they suspect they should. Would you say this is true for you, or not true for you?” Participants who responded “true” were asked to what extent they endorsed three specific researcher-identified reasons for avoiding the doctor: “I avoid seeing my doctor because . . . ” (a) I feel uncomfortable when my body is being examined, (b) I fear I may have a serious illness, and (c) it makes me think about dying, on scales from 1 (strongly agree) to 4 (strongly disagree). These variables were reverse-coded. Results concerning these three latter items in the full sample have been published elsewhere (Kannan & Veazie, 2014; Moser et al., 2014; Vanderpool & Huang, 2010; Ye et al., 2012). Participants who reported ever avoiding their doctor were then asked if there were “any other reasons why you avoid seeing your doctor” and wrote their response in a small box if completing a mail survey or verbally responded to an interviewer who summarized their response if completing a phone survey. Responses typically consisted of a short phrase or sentence.

Our independent variables are described as follows and reflect characteristics that we hypothesized may predispose or enable an individual to seek/avoid medical care. We included demographic, socioeconomic and access factors, health status indicators (e.g., self-reported health status, body mass index [BMI]), health-related practices (e.g., exercise, health information seeking), and psychosocial factors (e.g., perceived health care quality).

Demographic factors

Demographic factors included gender (coded as male vs. female), race/ethnicity (categorized into non-Hispanic White, non-Hispanic Black or African American, Hispanic or Latino/a, and biracial or Other), nativity (coded as born in the United States vs. not born in the United States), and marital status (coded as “married” or “living as married” = 1 vs. “divorced,” “widowed,” “separated,” or “single, never been married” = 0).

Socioeconomic factors

Socioeconomic factors included household income (coded into five categories: less than US$20,000/year, US$20,000 to US$34,999, US$35,000 to US$49,999, US$50,000 to US$74,999, or US$75,000 or more) and education (coded into four categories: less than high school, high school graduate, some college, and college graduate). Employment status was assessed with the item, “What is your current occupational status?” (coded as “employed” = 1 vs. “unemployed,” “homemaker,” “student,” “retired,” “disabled,” or “other” = 0).

Urbanization level was assessed using Rural Urban Continuum Codes. These codes placed participants along a 9-point rural–urban continuum based on the population size of their metropolitan county, or for participants living in nonmetropolitan counties, by the degree of urbanization and adjacency to a metropolitan area (U.S. Department of Agriculture Economic Research Service, 2013). For our analyses, we used a standard dichotomization of this scale consistent with prior research (Zahnd, Goldfarb, Scaife, & Francis, 2010), with scores from 1 to 3 categorized as urban and from 4 to 9 as rural.

Access factors

Access to health care was assessed with two separate items: health insurance status (“Do you have any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare?”; yes vs. no), and usual source of care (“Not including psychiatrists and other mental health professionals, is there a particular doctor, nurse, or other health professional that you see most often?”; yes vs. no).

Health status indicators

Health status indicators assessed included self-reported health status, BMI, and psychological distress. Self-reported health status was assessed with, “In general, would you say your health is . . . ” from 1 (excellent) to 5 (poor); this variable was reverse-coded. BMI was calculated using self-reported weight and height and was coded into four categories specified by the Centers for Disease Control and Prevention: underweight, normal, overweight, and obese.

Psychological distress was measured with the Kessler-6 (Kessler et al., 2002; Kessler et al., 2003). Participants completed six items in response to “How often did you feel each of the following during the past 30 days”: (a) so sad that nothing could cheer you up, (b) nervous, (c) restless or fidgety, (d) hopeless, (e) that everything was an effort, and (f) worthless on scales from 0 (none of the time) to 4 (all of the time). The six items were summed and this score was then dichotomized into severe distress (score of 13-24) or not severely distressed (score of 0-12).

Health-related practices

Health-related practices assessed included exercise, fruit and vegetable consumption, smoking, health information seeking, and use of complementary and alternative medicine. Exercise in the past month was dichotomized as yes versus no based on the question, “During the past month, did you participate in any physical activities or exercises such as running, yoga, golf, gardening, or walking for exercise?” We also used two additional questions about exercise to determine whether participants met the recommendations of 150 weekly minutes of moderate intensity physical activity: “In a typical week, how many days do you do any physical activity or exercise of at least moderate intensity, such as brisk walking, bicycling at a regular pace, swimming at a regular pace, and heavy gardening? Moderate-intensity activities make you breathe somewhat harder than normal,” and “On the days that you do any physical activity or exercise of at least moderate intensity, how long are you typically doing these activities?” Participants who did not provide responses to exercise in the past month, days of exercise, or length of exercise were excluded from analyses; impossible values (e.g., more than 25 hrs of exercise per day) were also coded as missing. Those who indicated no physical activity in the past month were coded as not meeting recommendations. For those who did indicate some physical activity in the past month, we multiplied the number of days by number of minutes per session to determine the number of weekly minutes. Participants with values of 150 minutes or higher were coded as meeting recommendations; those with 149 minutes or less were coded as not meeting recommendations.

Fruit and vegetable intake was assessed using two variants of a two-item fruit and vegetables screener that asked participants about the quantity of fruit and vegetables consumed daily. On the mail survey, participants were asked, “About how many cups of fruit (including 100% pure fruit juice) do you eat or drink each day?” and “About how many cups of vegetables (including 100% vegetable juice) do you eat or drink each day?” Seven response options were presented from “none” to “4 cups or more.” Participants were provided with examples of 1 cup equivalents. For the phone surveys, participants were asked two open-ended questions: “How many servings of fruit [vegetables] do you usually eat or drink each day?” Participants were given examples of one serving of fruits and vegetables. Responses provided on the mail survey were converted into servings based on the average of the response range for each option and the ½ cup = 1 serving metric as follows: none = 0; ½ cup or less = 1; ½ to 1 cup = 1.5; 1 to 2 cups = 3; 2 to 3 cups = 5; 3 to 4 cups = 7; 4 cups or more = 8. Number of servings of fruits and vegetables were then summed, and a score of 7 or more was coded as meeting fruit/vegetable consumption recommendations at the time of survey completion (Erinosho et al., 2015).

Smoking status was categorized as current smoker, former smoker, or never smoker based on responses to two items: “Have you smoked at least 100 cigarettes in your entire life?” and “How often do you now smoke cigarettes?” Participants who responded “no” to the former question were categorized as never smokers. Those who responded “yes” to the former question and “not at all” to the latter were categorized as former smokers. Those who responded “yes” to the former question and “every day” or “some days” to the latter question were categorized as current smokers.

Health information seeking was assessed with the item “Have you ever looked for information about health or medical topics from any source?” (yes vs. no). Use of complementary and alternative medicine was measured with “During the past 12 months, did you use any complementary, alternative, or unconventional therapies such as herbal supplements, acupuncture, chiropractic, homeopathy, meditation, yoga, or Tai Chi?” (yes vs. no).

Psychosocial factors

Psychosocial factors assessed included health self-efficacy, confidence in obtaining health information, ratings of patient-centered communication by health care providers, perceived health care quality, and trust in information from doctors. Health self-efficacy (“Overall, how confident are you about your ability to take good care of your health?”) and confidence in getting health information (“Overall, how confident are you that you could get health-related advice or information if you needed it?”) were assessed on scales from 1 (completely confident) to 5 (not confident at all); responses were reverse scored.

Patient-centered communication was measured using six items (Epstein & Street, 2007). Participants who indicated that they had gone to a doctor, nurse, or other health professional to get care for themselves at least one time in the past 12 months were asked a series of questions related to communication with health professionals. They were then asked, “Within the past 12 months, how often did doctors, nurses, or other providers”: (a) “provide you with the chance to ask all of the health-related questions that you had?” (b) “give you the attention that you needed for your feelings and emotions?” (c) “involve you in decisions about your general health care?” (d) “ensure that you understood the things you needed to do to take care of yourself?” and (e) “help you deal with feelings of uncertainty about your health?” Finally, participants were asked how often, over the past 12 months, they felt they could rely on doctors, nurses, or other health professionals to take care of their health care needs, on scales from 1 (always) to 4 (never); these responses were reverse scored. Responses to these six items were averaged to create a score representing the overall degree of patient-centeredness of provider communication (α = .895). This scoring procedure is consistent with previous research (Silk, Westerman, Strom, & Andrews, 2008; Underhill & Kiviniemi, 2012). Respondents who were missing responses to three or more of the individual questions were treated as missing for the scale score.

Perceived health care quality was assessed with the item, “Overall, how would you rate the quality of health care you received in the past 12 months?” from 1 (excellent) to 5 (poor); this item was reverse scored. Trust in information from doctors was assessed with the item, “In general, how much would you trust information about health or medical topics from each of the following? A doctor” (dichotomized as 1 = “a lot” vs. 0 = “some” to “not at all” due to skew).

Overview of Analyses

For all variables, “don’t know” and “refused” responses were coded as missing and individuals with missing data on a particular item were excluded from those analyses. Statistical analyses were conducted in SAS 9.3® and SAS-callable SUDAAN software version 11.0 (SAS Institute Inc., 2009) to account for the complex sampling procedure used to collect HINTS data.

To achieve the first study objective (i.e., to examine factors associated with avoidance), we first tested whether demographic, socioeconomic, and access variables were associated with doctor avoidance at the bivariate level using t tests or chi-square analyses depending on whether the independent variable was continuous or dichotomous, respectively. We next conducted logistic regressions to test whether additional factors (i.e., health status, health-related factors, and psychosocial factors) were significantly associated with doctor avoidance controlling for the factors previously identified as associated with avoidance at p < .10 (a liberal significance value was used for the selection of covariates). These analyses also controlled for mode of survey administration (coded as “mail” = 1, “phone” = 2) and age. Each predictor variable was entered in a separate logistic regression analysis along with covariates. For regression and all subsequent analyses, the significance value was set at the more conservative p < .05. The HINTS uses a multistage probability sampling design; thus, we incorporated a set of 50 jackknife replicate weights to adjust for the complex design so the results would be generalizable to the non-institutionalized U.S. population. Listwise deletion was used for analyses such that reported analyses represent only those individuals who provided valid responses for the covariates included in a particular analysis (if applicable) and independent variable; sample sizes for each regression analysis are noted in Table 2 and range from 2,021 to 1,919.

To achieve the second study objective (i.e., to characterize reasons reported for avoidance), we used descriptive statistics to determine the extent to which participants endorsed the three researcher-identified reasons for avoiding the doctor (i.e., feeling uncomfortable when body is examined, fear of serious illness, and makes them think of dying). We then used a general inductive qualitative analytic approach (Glaser & Strauss, 2017; Thomas, 2006) to examine participant-generated responses from those who answered “yes” to the question, “Are there any other reasons why you avoid seeing your doctor?” Detailed information about our qualitative approach is provided elsewhere (Taber et al., 2015). This process yielded several overarching, conceptually distinct categories of reasons for avoiding medical care based on whether participants perceived seeking medical care to be needed, available to them as a course of action, and favorable or beneficial.

Results

Sample Characteristics

Characteristics of the sample are shown in Table 1. Nearly one fourth of the older adult sample reported avoiding seeking needed medical care (n = 449; weighted % = 22.5%).

Table 1.

Characteristics of Participants Who Reported Avoiding or Not Avoiding the Doctor.

	Doctor avoidance		χ² value, p value
	Yes (n = 449)	No (n = 1,706)
	n (%)	n (%)
Age	75.1 (0.14)^a	74.2 (0.40)^a	t(49) = 1.80, p < .078^b
Gender			χ²(1) = 1.42, p = .240
Male	178 (45.3)	641 (41.4)
Female	271 (54.7)	1,065 (58.6)
Marital status			χ²(1) = 2.93, p = .093
Married or living as married	202 (50.7)	866 (56.7)
Not married	228 (49.3)	766 (43.3)
Education			χ²(3) = 2.55, p = .066
Less than high school education	91 (30.2)	196 (22.6)
High school graduate	148 (31.6)	516 (32.4)
Some college	104 (23.9)	439 (25.5)
College graduate	86 (14.3)	482 (19.5)
Household income (in US$)			χ²(4) = 1.03, p = .400
<20,000/year	106 (30.0)	309 (26.9)
20,000-34,999	93 (29.3)	320 (26.4)
35,000-49,999	43 (13.3)	200 (15.7)
50,000-74,999	58 (15.9)	206 (15.7)
75,000 or more	46 (11.5)	235 (15.3)
Employment status			χ²(1) = 1.30, p = .260
Employed	57 (13.4)	184 (10.6)
Not employed	372 (86.6)	1,448 (89.4)
Race/ethnicity			χ²(3) = 0.42, p = .738
Hispanic or Latino	25 (5.9)	64 (5.6)
White	350 (82.9)	1,379 (81.9)
Black or African American	31 (7.4)	99 (10.1)
Other	16 (3.8)	47 (2.5)
Nativity			χ²(1) = 0.37, p = .548
Born in the United States	405 (93.5)	1,511 (92.6)
Not born in the United States	25 (6.5)	122 (7.4)
Health insurance status			χ²(1) = 0.20, p = .659
Yes	426 (97.5)	1,655 (97.9)
No	13 (2.5)	24 (2.1)
Usual source of care			χ²(1) = 8.21, p = .006
Yes	374 (81.2)	1,524 (89.1)
No	68 (18.8)	160 (10.9)
Population density			χ²(1) = 2.17, p = .147
Urban	331 (73.7)	1,340 (80.0)
Rural	118 (26.3)	336 (20.0)
Survey response mode			χ²(1) = 0.94, p < .337
Mail	198 (53.3)	640 (49.7)
Telephone	251 (46.7)	1,066 (50.3)

Variable is continuous and values indicate mean (standard error).

Test of comparison is t test.

Factors Associated With Avoidance Among Older Adults

Table 1 shows the unadjusted associations observed among medical care avoidance with sociodemographic and health care access variables. Those who avoided the doctor were less likely to have a usual source of care, reported lower education, were less likely to be married, and were older than those who did not avoid the doctor (all ps < .10). Gender, race/ethnicity, nativity, and insurance status were not associated with doctor avoidance.

All subsequent analyses controlled for age, marital status, education, usual source of care, and sample mode. Results of hierarchical logistic regressions are shown in Table 2 (the associations of the covariates with doctor avoidance in each analysis are not shown); each row represents a separate regression analysis. The following factors were significantly associated with greater likelihood of reporting doctor avoidance: worse self-reported health status, lower health self-efficacy, lower confidence in getting health information, less patient-centered communication, lower perceived health care quality, less trust in information from one’s doctor, and reporting severe psychological distress. Current smokers were more likely to avoid the doctor compared with former smokers and marginally more likely than never smokers.

Table 2.

Logistic Regression Analyses Testing Predictors of Doctor Avoidance When Controlling for Select Sociodemographic Factors, and Item Means and Standard Deviations.

	M (SE)	OR	95% CI of the OR	p	Cox & Snell R²
Self-reported health status (n = 2,021)	3.31 (0.02)	0.85	[0.75, 0.95]	.006	.024
Health self-efficacy (n = 2,014)	3.92 (0.02)	0.62	[0.52, 0.73]	<.001	.049
Confidence in getting health information (n = 2,000)	3.67 (0.03)	0.73	[0.63, 0.84]	<.001	.040
Patient-centered communication (n = 1,919)	3.41 (0.02)	0.44	[0.35, 0.57]	<.001	.065
Perceived health care quality (n = 1,921)	4.10 (0.03)	0.54	[0.46, 0.64]	<.001	.065
	% (SE of %)
Trust information from doctor (n = 2,015)					.047
A lot	67.5 (1.4)	REF
Not at all to some	35.5 (1.4)	2.32	[1.74, 3.10]	<.001
Health information seeking (n = 2,018)					.021
Yes	63.4 (1.2)	REF
No	36.6 (1.2)	0.93	[0.68, 1.28]	.658
BMI (n = 1,987)					.026
Underweight	2.1 (0.4)	1.10	[0.34, 3.52]	.878
Normal	34.9 (1.5)	REF
Overweight	37.7 (1.4)	1.12	[0.79, 1.58]	.523
Obese	25.3 (1.1)	0.77	[0.53, 1.11]	.156
Severe psychological distress (n = 1,940)					.031
Yes	4.0 (0.6)	REF
No	96.0 (0.6)	0.28	[0.15, 0.53]	<.001
Meets recommendations of 150 min of exercise per week (n = 2,014)					.021
Yes	29.3 (1.3)	REF
No	70.7 (1.3)	0.98	[0.71, 1.36]	.920
Meets recommendations of seven servings of fruit and vegetables (n = 1,973)					.022
Yes	22.0 (0.01)	REF
No	78.0 (0.01)	1.25	[0.90, 1.74]	.174
	% (SE of %)
Smoking status (n = 1,991)					.029
Never smoker	47.5 (1.2)	0.64	[0.39, 1.06]	.080
Former smoker	42.7 (1.1)	0.54	[0.33, 0.88]	.014
Current smoker	9.8 (0.8)	REF
Complementary and alternative medicine use (n = 2,013)					.023
Yes	19.3 (1.1)	REF
No	80.7 (1.1)	0.75	[0.53, 1.07]	.113

Note. Analyses control for age, marital status, education, usual source of care, and survey response mode. Cox & Snell R² for the set of five covariates alone was .0211; 2,029 respondents had complete data for this set of five covariates. Each row represents a separate regression analysis. OR = odds ratio; CI = confidence interval; BMI = body mass index.

Reasons Reported by Older Adults for Avoiding Seeking Medical Care

Researcher-identified reasons

Of the 449 respondents who reported avoiding the doctor, more than one third reported avoiding medical care because of feeling uncomfortable when their body is being examined (n = 155, 34.5%) or fearing a serious illness (n = 161, 35.9%), with markedly fewer reporting avoiding medical care because it made them think of dying (n = 64, 14.3%).

Participant-generated reasons

Of the 449 respondents who reported avoiding the doctor, 200 participants (40.1%) provided a qualitative “other” reason for avoidance. Of these 200, 18 participants listed more than one reason. Compared with participants who did not provide a participant-generated reason for avoiding care, participants who did were more likely to be born in the United States (96.7% vs. 91.2%), χ²(1) = 5.05, p = .03, and to have completed the survey by phone (54.3% vs. 41.5%), χ²(1) = 4.71, p = .04. Participants who did and did not provide a qualiative “other” response did not differ in income, χ²(4) = 2.51, p = .054; age, 73.2 years versus 74.9 years; t = 1.91, p = .062; race, χ²(3) = 0.78, p = .51; gender, χ²(1) = 0.001, p = .97; marital status, χ²(1) = 0.54, p = .46; health insurance status, χ²(1) = 0.72, p = .40; or education, χ²(3) = 1.65, p = .19.

Participant-generated reasons for medical avoidance in the three main categories were (a) unfavorable evaluations of seeking medical care (n = 95/200, 47.5%), which includes responses that regarded some aspect of health care seeking as negative; (b) traditional barriers to medical care (n = 66/200, 33.0%), which includes responses that indicated that seeking medical care was not an option because of a lack of resources, or that circumstances or obstacles limited access to care; and (c) low perceived need to seek medical care (n = 41/200, 20.5%), which includes responses that indicated a determination that seeking medical care was unnecessary. A fourth category, labeled personality traits (n = 8/200, 4.0%), was added to accommodate some responses that did not fit into the other categories. Table 3 lists these four categories and any subcategories that emerged, specific reasons reported for avoiding medical care within each category, and representative quotations.

Table 3.

Qualitative Reasons for Avoiding Medical Care Among Older Adults (n = 200).

Category	n (%)	Reasons	Representative quotations
Unfavorable evaluations of seeking medical care	95 (47.5%)
Physician factors	35 (17.5%)	Interpersonal factors (n = 25)• Poor relationship with doctor (n = 10)• Does not like doctors (n = 8)• Communication issues (n = 5)• Does not trust doctors (n = 5)• Believes doctors are too busy (n = 3)Medical factors (n = 12)• Low confidence in doctors’ expertise (n = 11)• Believes doctor will do unnecessary tests or prescribe unnecessary medication (n = 1)	• “[Doctors] don’t listen to problems and do not do what you want them to”• “[My] doctor rushes [me] and doesn’t pay any attention to [my] complaints”• “I don’t particularly care for my doctor”• “They’re always in a rush; only get 15 minutes”• “They don’t know enough, [I] usually [do] not get firm answers”
Organizational factors	24 (12%)	• Long waiting times (n = 11)• Hassle (n = 11)• Avoiding sick people (n = 2)• Dislikes HMOs (n = 1)	• “[I don’t] like being stuck in doctor’s office for simple things”• “Wait time is too long at their offices”• “[I] usually can’t see [the] doctor at the time of a problem”• “Difficulty getting a timely appointment”
Affective concerns	13 (6.5%)	• Fear of bad news (n = 7)• Fear, unspecified (n = 3)• Fear of pain (n = 2)• Feels guilty about unhealthy behavior (n = 1)	• “There always seems to be something that they find”• “[I] don’t want to be hurt”• “Afraid [I’ll] hear something bad”
Expected negative outcomes	9 (4.5%)	• Dislikes or is avoiding medical recommendations (n = 5)• Cannot take or dislikes medication (n = 3)• Believes will leave feeling worse (n = 1)	• “[My] doctor always says to lose weight”• “Don’t like what he tells me”• “Sometimes [I] leave feeling worse after doctor’s visit”
Other reasons	16 (8.0%)	• Dislikes going, unspecified (n = 12)• Not a priority (n = 3)• Denial (not explained further; n = 1)• Has an untreatable illness (n = 1)	• “Just don’t want to go”• “Seen my share of doctors”• “Have better things to do”
Traditional barriers to medical care	66 (33.0%)	• Cost/co-pay is too high (n = 32)• Not enough time/too busy (n = 30)• Transportation/distance (n = 10)• Lacking/inadequate health insurance (n = 3)• Inconvenient clinic hours (i.e., during work; n = 2)• Too sick to travel to doctors’ office (n = 1)• Doctor is inaccessible (n = 1)• Does not have a doctor (n = 1)	• “[I] don’t have money for the co-pay”• “Not enough time”• “[I] have trouble getting around”• “Because of lack of transportation”• “The VA is 100 miles away”• “We live so [many] miles from the doctor”
Low perceived need to seek medical care	41 (20.5%)	• Not sick enough (n = 14)• Does not have health problems (n = 11)• Believes illness will improve with time/on its own (n = 10)• Tries to take care of self (n = 3)• Concerned that doctor will think they are a hypochondriac (n = 2)• Family provides care (n = 1)	• “I can help myself [and] will go to the hospital if needed”• “I can cure myself over time”• “Just go when I have to”• “If [I’m] not hurting then [I] will not see the doctor”• “I hate to waste time if not seriously sick”• “Don’t want to bother [my doctor]”
Personality traits	8 (4.0%)	• Procrastination (n = 5)• Laziness (n = 3)	• “I’m a procrastinator”• “Too lazy”

Note. Because 18 participants listed more than one reason, the number of responses given for specific reasons may total more than the overall number of responses for a particular category.

Discussion

In the present study, nearly one fourth of older adults in the United States reported avoiding needed medical care. Older adults who lacked a usual source of care, had worse self-reported health status, had severe psychological distress and who smoked cigarettes were more likely to report avoiding needed medical care. This is concerning because medical avoidance may come at a greater health cost for older adults who, compared with younger individuals, have more chronic health problems and disability (Ward & Schiller, 2013), take more medications (Kaufman et al., 2002; Qato et al., 2008), and have greater vulnerability to acute stress due to age-related reduction of physiologic reserves (Navaratnarajah & Jackson, 2017).

Notably, the prevalence of older adults who reported avoiding the doctor was lower than that observed in the general population: 22.5% of older adults in our study reported avoiding the doctor, as compared with 36% of adults in the full HINTS sample (which included both older and younger adults; Persoskie et al., 2014). However, some of the factors associated with avoidance among older adults were similar to those found in the general population. Using this same dataset, Kannan and Veazie (2014) found that avoiding needed care in the general population was associated with lower self-efficacy for taking care of one’s health, less experience with both quality care and getting help with uncertainty about health, having your feelings attended to by your provider, no usual source of care, serious psychological distress, smoking daily, and fatalistic attitudes toward cancer. Although this article did not test the same set of factors and the analytic strategy differed somewhat, many of these same characteristics were associated with avoidance among older adults in our analyses, suggesting that interventions to promote care seeking among middle-aged adults may also be effective (at least to some degree) among older adults.

This study showed that individuals who reported more negative evaluations of the health care system (i.e., less patient-centered communication, lower perceived health care quality) and less trust in information from doctors were more likely to report avoiding medical care. Although these data are cross-sectional, these results speak to the importance of strong patient–provider relationships and high quality of care in promoting effective use of the health care system. In addition, we found that individuals who reported greater health self-efficacy (i.e., ability to take care of oneself) and greater confidence in obtaining health information were less likely to avoid doctors, consistent with prior research (Kannan & Veazie, 2015). Targeting perceptions of health self-efficacy and information self-efficacy may promote use of medical services among older adults.

Although factors associated with avoidance may be similar across age groups, the distribution of reasons people have for avoiding needed medical care may differ by age group. Interestingly, the researcher-provided reasons were endorsed at higher rates among the older sample compared with the general population (Vanderpool & Huang, 2010). For example, in the present article, more than one third of older adults reported fearing a serious illness and feeling uncomfortable during the clinical exam as reasons for avoiding medical care; in the general population, only about one quarter of avoiders endorsed each of these reasons (Vanderpool & Huang, 2010). Future studies might explore how fear and other affective responses affect the quality of care and subsequent behavior, and whether these processes and/or their effects differ by age group. In addition, research on clinical interactions between older adults and providers may reveal aspects of physician behavior or the clinical examination that contribute to feelings of discomfort in older patients. On a more practical level, providing clinicians with strategies to assuage unwanted feelings may reduce future avoidance.

A prior study of the full HINTS sample found that traditional barriers to medical care (e.g., costs, health insurance) were more commonly reported reasons for avoidance than were unfavorable evaluations of seeking medical care (Taber et al., 2015). In the current article, unfavorable evaluations constituted nearly half of the responses reported by older adults, whereas traditional barriers comprised less than a third. The near universal enrollment in Medicare at age 65 (West et al., 2014; indicated by the data showing that nearly 98% of the older adult sample was insured) can help to explain why barriers such as cost and lack of health insurance—which are major reasons for avoidance among the general population (Taber et al., 2015)—are less prominent in an older sample. Nevertheless, the overall frequency in which cost and other access barriers were mentioned suggests that active monitoring and removal of access barriers is still needed to ensure that the benefits of medical care extend to all people.

The qualitative reasons provided for avoidance among older adults were diverse, although physician- and organization-related factors (such as negative prior experiences with physicians, communication issues, and long waiting times) were mentioned most frequently. This provides some evidence that clinicians and the medical system as a whole should continue to search for more effective ways to communicate and interact with older adults.

Health care seeking is associated with a number of factors, including the physician–patient relationship. In a community sample of 1,106 adults, participants who felt their physicians were more attuned to their feelings and needs were less likely to avoid treatment for both medical and psychological problems during the previous 12 months (Moore et al., 2004). The authors suggested, “that patients’ perceptions of how they are treated by physicians may help to explain why many people avoid healthcare treatment, even when faced with a significant problem” (p. 421). Federman and colleagues (2001) found that patients’ intentions not to return to a health care practice for primary care were mainly related to two aspects of their experience with their physicians: (a) dissatisfaction with visit duration and (b) perceptions that physicians had not listened to them. Together, these studies suggest that patients’ experiences with physicians have a significant effect on patient perceptions and intentions, and may influence patients’ decisions to seek (or not seek) medical care.

Effective and empathic communication is essential for the care of older adults as it can promote patient satisfaction and comfort (McGilton et al., 2009; Rao, Anderson, Inui, & Frankel, 2007; Stewart, 1995), and improve health outcomes (Heisler, Cole, Weir, Kerr, & Hayward, 2007; Tamblyn et al., 2010). Effective physician–patient communication with older adults extends beyond exchanging biomedical information; it also encompasses psychosocial information and care. It involves providers allowing patients to explain their own history and current illness experience, communicating the right amount of information at the right time, giving patients choices in care, understanding what patients wish to convey, focusing on patients’ point of view, engaging patients in joint decision making about medication regimens, involving family members and caregivers when appropriate, attending to patients’ cultural values and preferences, and having empathy. A patient-centered communication style that embraces these elements can help physicians be an important source of support for older adults who are navigating challenges associated with aging (Charles & Carstensen, 2010; Epstein & Street, 2007), including the changes in social structure (e.g., decrease in size and strength of social networks), cognitive and psychological challenges (e.g., depression, stress from serious illness, or chronic conditions), financial changes (e.g., reductions in income, depletion of savings), and the physical manifestations (e.g., loss of mobility, decline in physical health, disability).

A number of interventions to improve communication between patients and providers have been developed in the past decade (McGilton et al., 2009; Rao et al., 2007; Stewart, 1995). For example, interventions that utilize communication skills training, simulation, individualized feedback, video feedback, and small group education have demonstrated to be particularly effective (Dwamena et al., 2012; Epstein et al., 2005; Hulsman, Ros, Winnubst, & Bensing, 1999). Results from the present article suggest that assessing the effects of these interventions on medical care avoidance may be a worthwhile area for future research.

Limitations and Strengths

The present study has several limitations. The use of cross-sectional data limits the ability to establish causality or rule-out reciprocal causation. For example, people who avoid doctors may tend to rationalize their behavior by downgrading their evaluations of the quality of previous medical care. In some cases, poorer physical and mental health—as measured by indicators of perceived general health status, tobacco use, and serious psychological distress—may be the result of doctor avoidance, as opposed to the reverse (Lund-Nielsen et al., 2011). Furthermore, the present article relied on self-reported information, which may be subject to selective recall. We are also limited by available measures in the dataset. For example, the measure of medical care avoidance is dichotomous, limiting our ability to examine medical care avoidance within specific time frames or at various thresholds of symptomatology. Moreover, although the term avoidance suggests some choice, the measure of medical avoidance captured both voluntary avoidance of medical care and non-voluntary avoidance (i.e., avoidance resulting from a lack of access). This is evidenced by the qualitative data in which a large number of respondents reported “traditional barriers to care” such as cost and time, although we do not know whether these barriers make care inaccessible or merely more difficult to procure. Finally, the data for this study predate the Affordable Care Act, which had a number of provisions that affect the care of older adults, including effects on prescription and out-of-pocket drug costs, access to preventive services, Medicare premiums, and Medicare delivery and spending (Bartels, Gill, & Naslund, 2015; National Council on Aging, n.d.). These and other recent policy changes in health care may limit the applicability of study findings to the present day.

Strengths of the study include the nationally representative sample and mixed-methods design, which allowed us to explore participant-generated reasons and examine factors associated with doctor avoidance. The latter is especially important given that people may not always know their motivations (Nisbett & Wilson, 1977). We also considered a broad range of factors, including sociodemographic characteristics, health behaviors, health beliefs, and perceptions of the health care system. Future studies should explore other factors that are potentially associated with avoidance and are unique to an older population, such as the effect of perceived stereotypes about aging and old age, the impact of multi-morbidities and symptom perception on care seeking, or the effect of future time perspectives.

Conclusion

This national study found that nearly one fourth of older adults in the United States reported avoiding needed medical care in 2008. Likelihood of avoidance was higher among those with worse health status, severe psychological distress, lower health self-efficacy, lower confidence in obtaining health information, lower trust in doctors, less patient-centered communication, lower health care quality, and those who were current smokers. An analysis of qualitative reasons reported by older adults for avoiding medical care corroborated quantitative results.

Although medical care avoidance among older adults involves many factors, it appears to be especially related to negative evaluations of the quality of care and provider communication. Interventions to improve patient-centered communication may help to reduce avoidance in this population by helping physicians to understand, relate to, and provide better care to their older patients. Patient-centered communication is essential to establishing a patient–provider therapeutic alliance, and can help providers to assess and address the psychosocial, cognitive, and other barriers to medical care often experienced by older adults. Unfortunately, older adults tend to report experiencing less patient-centered communication than their younger counterparts, making it even more important for health systems to invest resources in improving the clinical experiences of these patients.

Footnotes

Acknowledgements

We would like to thank Alexander Persoskie, PhD, for his valuable contributions to the design, implementation, and execution of this project, including his extensive revisions to prior drafts of this article.

Authors’ Note

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Informed Consent

Informed consent was obtained from all individual participants included in the article.

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