ArticlePDF Available

Abstract and Figures

BACKGROUND: Much research has examined barriers to health care utilization, with the majority conducted in the context of specific populations and diseases. Less research has focused on reasons people avoid seeking medical care, even when they suspect they should go. OBJECTIVE: To present a comprehensive description and conceptual categorization of reasons people avoid medical care. DESIGN: Data were collected as part of the 2008 Health Information National Trends Survey, a cross-sectional national survey. PARTICIPANTS: Participant-generated reasons for avoiding medical care were provided by 1,369 participants (40% male; Mage=48.9; 75.1% Non-Hispanic White, 7.4% Non-Hispanic Black, 8.5% Hispanic or Latino/a). MAIN MEASURES: Participants first indicated their level of agreement with three specific reasons for avoiding medical care; these data are reported elsewhere. We report responses to a follow-up question in which participants identified other reasons they avoid seeking medical care. Reasons were coded using a general inductive approach. KEY RESULTS: Three main categories of reasons for avoiding medical care were identified. First, over one third of participants (33.3% of 1,369) reported unfavorable evaluations of seeking medical care, such as factors related to physicians, health care organizations, and affective concerns. Second, a subset of participants reported low perceived need to seek medical care (12.2%), often because they expected their illness or symptoms to improve over time (4.0%). Third, many participants reported traditional barriers to medical care (58.4%), such as high cost (24.1%), no health insurance (8.3%), and time constraints (15.6%). We developed a conceptual model of medical care avoidance based on these results. CONCLUSIONS: Reasons for avoiding medical care were nuanced and highly varied. Understanding why people do not make it through the clinic door is critical to extending the reach and effectiveness of patient care, and these data point to new directions for research and strategies to reduce avoidance.
Content may be subject to copyright.
Why do People Avoid Medical Care? A Qualitative Study Using
National Data
Jennifer M. Taber, Ph.D., Bryan Leyva, B.A, and Alexander Persoskie, Ph.D.
National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
BACKGROUND: Many studies have examined barriers to
health care utilization, with the majority conducted in the
context of specific populations and diseases. Less re-
search has focused on why people avoid seeking medical
care, even when they suspect they should go.
OBJECTIVE: The purpose of the study was to present a
comprehensive description and conceptual categorization
of reasons people avoid medical care.
DESIGN: Data were collected as part of the 2008 Health
Information National TrendsSurvey, a cross-sectional na-
tional survey.
PARTICIPANTS: Participant-generated reasons for avoid-
ing medical care were provided by 1,369 participants
(40% male; M
age
=48.9; 75.1% non-Hispanic white,
7.4% non-Hispanic black, 8.5% Hispanic or Latino/
a).
MAIN MEASURES: Participants first indicated their level
of agreement with three specific reasons for avoiding
medical care; these data are reported elsewhere. We
report responses to a follow-up question in which
participants identified other reasons they avoid seek-
ing medical care. Reasons were coded using a gener-
al inductive approach.
KEY RESULTS: Three main categories of reasons for
avoiding medical care were identified. First, over
one-third of participants (33.3% of 1,369) reported
unfavorable evaluations of seeking medical care,
such as factors related to physicians, health care
organizations, and affective concerns. Second, a sub-
set of participants reported low perceived need to
seek medical care (12.2%), often because they
expected their illness or symptoms to improve over
time (4.0%). Third, many participants reported tradi-
tional barriers to medical care (58.4%), such as high
cost (24.1%), no health insurance (8.3%), and time
constraints (15.6%). We developed a conceptual mod-
el of medical care avoidance based on these results.
CONCLUSIONS: Reasons for avoiding medical care were
nuanced and highly varied. Understanding why people do
not make it through the clinic door is critical to extending
the reach and effectiveness of patient care, and these data
point to new directions for research and strategies to re-
duce avoidance.
KEYWORDS: Medical care avoidance; Health care barriers; Health care
utilization; Qualitative.
J Gen Intern Med
DOI: 10.1007/s11606-014-3089-1
© Society of General Internal Medicine 2014
INTRODUCTION
People often avoid seeking medical care even when they
suspect it may be necessary;
14
nearly one-third of respond-
ents in a recent national United States (U.S.) survey reported
avoiding the doctor.
57
Even individuals with major health
problems
4,8,9
or who are experiencing symptoms
1012
avoid
seeking medical care. For example, in one study, 17% of
patients diagnosed with rectal tumors reported that they waited
a year or more to seek medical consultation after noticing
symptoms, with some waiting up to five years.
12
Avoiding
medical care may result in late detection of disease, reduced
survival, and potentially preventable human suffering.
1,8,13,14
In the present study, we sought to understand why people
avoid seeking medical care. Avoidance of medical care has
been defined as keeping away from something [in a medical
context] that is thought to cause mental or physical distress.
8
Avoidance can also occur as a result of barriers, which can be
defined as factors that limit access to or ease of obtaining
quality health care (e.g., financial concerns, time con-
straints).
1,15
Avoidance of medical care can occur at any point
on the disease continuum, including preventing and detecting
asymptomatic disease, noticing symptoms and interpreting
their significance, seeking care after determining a potential
need, and complying with recommended treatment.
1,2,16
Of
note, the term patient delayhas also been used to describe
phenomena related to avoidance, but guidelines for research
on early cancer diagnosis have suggested instead using the
more informative terms appraisal interval(the time taken to
interpret symptoms) and help-seeking interval(the time
taken to seek care after determining a need).
17
To date, research on avoidance of medical care has been
limited in the extent to which it examines the broad spectrum
of reasons for avoidance (but see
7
), often focusing on specific
factors such as barriers or psychological characteristics (e.g.,
lack of insurance, fear of a diagnosis).
46,15,1824
A conceptual
review of reasons people avoid medical care identified only
six qualitative or mixed-methods studies assessing participant-
generated reasons, all of which used convenience samples
with predominately white participants.
1
Moreover, five of
the six studies reviewed assessed avoidance of specific
Received May 23, 2014
Revised September 24, 2014
Accepted October 20, 2014
procedures.
1
The exception was a focus group study among a
sample of Hispanics that explored reasons for avoiding med-
ical visits in response to warning signs of heart disease, cancer,
and diabetes.
11
This qualitative study identified factors such as
low trust in doctors, low perceived severity of symptoms,
emotional factors (e.g., denial, avoiding worry, embarrass-
ment), practical barriers, and prior negative experiences as
contributing to avoidance.
11
Given the significance and prevalence of medical care
avoidance in the U.S.,
1,5
there is a need for continued basic
qualitative research that can uncover the reasons underlying
this phenomenon. Simply put, why do people avoid medical
visits that could save lives or reduce suffering, whether
through early detection of disease or timely treatment? To help
answer this question, the present study used data collected
from a large national sample. The purpose of the study was to
identify the reasons people avoid seeking medical care and to
classify these reasons into conceptually distinct categories
reflecting underlying factors contributing to avoidance. Ulti-
mately, we sought to develop a model of medical care avoid-
ance that can inform efforts to promote care-seeking, help
providers reduce avoidance in their patient populations, and
promote theoretical advancement in this area of research.
METHODS
Data Source
Data were obtained from the National Cancer Institutes2008
Health Information National Trends Survey (HINTS). This
cross-sectional survey collects data from a nationally repre-
sentative sample of civilian non-institutionalized adults aged
18 and over in order to assess trends and patterns in health
communication. Data were collected from January through
April 2008. Phone and mail surveys were administered to
maximize response rates (24.2 and 31.0 %, respectively).
The survey was completed by 7,674 participants. Details of
the study design have been published elsewhere.
2527
Measures
Participants were first asked whether they avoid visiting their
doctor even when they suspect they should.Participants who
responded yes(n=2,327) were then asked to what extent they
endorsed three researcher-identified reasons for avoiding the
doctor (i.e., feeling uncomfortable when their body is being
examined, fear of having a serious illness, and because it makes
them think of dying); results concerning these items have been
published elsewhere.
5,7,18,24
Next, participants were asked
whether there were any other reasons why you avoid seeing
your doctor,and either wrote their response in a small box if
completing a mail survey or stated their response to an inter-
viewer, who summarized their response, if completing a phone
survey. Responses were brief and typically consisted of a short
phrase or sentence. Interpretable responses were provided by
1,369 participants (58.8% of those who reported avoidance in
response to the initial question). Eight participants provided
uninterpretable responses either because they failed to provide
a reason (e.g., dont know) or because it was impossible to
determine the motivation (e.g., ambivalenceor family tradi-
tion), with 164 participants listing more than one reason.
Data Management and Analytic Approach
An independent research company was contracted to preliminar-
ily clean the participant-generated responses (n=1,377; see 7 for a
report of these uncoded responses) by using short phrases to
standardize responses (e.g., Busyand Im too busy to go to
the doctorwere recoded into Too busy). A prior study reported
the top five of these uncoded responses (i.e., preference for self-
care or alternative care, dislike or distrust doctors, fear or dislike
of medical treatments, time, and money) and predictors of these
responses.
7
For the present study, the three study authors analyzed
these short phrases provided by the research company in con-
junction with participantsraw responses using a general induc-
tive data analysis approach, a method in which a theory or
conceptual model is developed through an iterative process of
coding, grouping codes into categories based on underlying con-
cepts, and forming a model or generating hypotheses based on the
data.
28,29
Coding was conducted by discussion of each participant
response among all three authors; in rare cases when two authors
disagreed, the third author acted as arbiter. Through this process,
the authors identified emergent codes and collapsed and re-
conceptualized existing codes as necessary. After assigning
codes, all authors participated in an iterative process of placing
codes into sub- and superordinate categories. The goal of the
coding and categorization was to identify conceptually distinct
factors underlying reasons for avoiding medical care, and to
organize these factors into a conceptual framework that could
provide targets for intervention and stimulate further research on
avoidance. Upon completing the coding and categorizing, we
reviewed existing theory on care-seeking and avoidance to deter-
mine whether our data provided support for a pre-existing theory
or whether we should develop a new theory and/or model. We
provide quantitative counts of the number of respondents, listing
each reason in order to convey the frequency of responses, and
qualitatively describe themes to provide context and explanation.
RESULTS
Among participants who indicated avoiding medical care,
characteristics of those who provided qualitative responses
(n=1,369) compared to those who did not (n=958) are shown
in Table 1. Participants who provided responses were more
likely to be white, female, younger, married, born in the U.S.,
to have completed the survey by phone, and to have higher
income and education, but were less likely to have health
insurance. Of the 1,369 participants who provided interpret-
able otherresponses, fewer than half (43.5%, n=595) en-
dorsed at least one researcher-identified reason (answered
Taber et al.: Why do people avoid medical care? JGIM
"agree/strongly agree" versus "disagree/strongly disagree") for
avoiding medical care. Approximately one-fourth reported
avoiding medical care because of feeling uncomfortable
(26.8%, n=369) or fearing a serious illness (26.4%, n=363),
with substantially fewer reporting avoiding medical care be-
cause it made them think of dying (8.2%, n=113).
From the analysis of participant-generated qualitative rea-
sons for avoiding the doctor, we identified three overarching,
conceptually distinct categories of reasons for avoiding med-
ical care based on whether participants perceived seeking
medical care to be necessary, available to them as a course of
action, and favorable or beneficial. In the first category, "low
perceived need to seek medical care," responses indicated a
determination that seeking medical care was unnecessary. In
the second category, "traditional barriers to medical care,"
responses indicated that seeking medical care was not an
option because of a lack of resources. In the third category,
"unfavorable evaluations of seeking medical care," people
evaluated some aspect of the care-seeking process as negative.
A fourth category, labeled personality traits,was also iden-
tified as a reason for avoidance that did not fall into any of the
three major categories. Each category and relevant subcatego-
ries are described in detail below and outlined in Fig. 1.
Low Perceived Need to Seek Medical Care
Many responses, coded as low perceived need,indicated the
belief that seeking medical care was unnecessary (n=167). The
most common reasons were that medical problems would
either improve over timeor improve on their own
(n=55; e.g., Whatever the symptoms, time will make it bet-
ter;I believe the body will heal itself in most cases).
Participants often indicated that this was contingent on the
problem not being very serious (e.g., What I have will pass. I
only go if I think it is serious), with many stating not being
sick enoughas a reason for avoiding medical care (n=40;
Table 1 Characteristics of Participants Who Reported Avoiding the Doctor and Either Did or Did Not Provide an Interpretable Qualitative
OtherReason for Avoidance
Provided qualitative otherreason χ2value, pvalue
Ye s ( n=1,369) No (n=958)
n(%) n(%)
Age 48.9 (14.9)
*
52.7 (17.8)
*
t(2301)=5.63, p<0.001
Gender χ2(1)=5.49, p=0.02
Male 547 (40.0) 429 (45.8)
Female 822 (60.0) 528 (55.1)
Marital status χ2(1)=4.95, p=0.03
Married or living as married 781 (57.1) 496 (44.8)
Not married 555 (40.5) 427 (44.6)
Education χ2(3)=96.84, p<0.001
Less than high school 115 (8.4) 165 (8.3)
High school graduate 302 (22.1) 299 (31.2)
Some college 442 (32.3) 254 (26.5)
College graduate 478 (34.9) 204 (21.3)
Household income χ2(5)=37.41, p<0.001
$0 to $9,999 76 (5.6) 75 (7.8)
$10,000 to $19,999 139 (10.2) 135 (14.1)
$20,000 to $49,999 362 (26.4) 284 (29.7)
$50,000 to $74,999 242 (17.7) 132 (13.8)
$75,000 to $99,999 154 (11.3) 85 (8.9)
$100,000 or more 224 (16.4) 98 (10.2)
Race/ethnicity χ2(6)=40.56, p<0.001
Hispanic or Latino 116 (8.5) 115 (12.0)
White 1028 (75.1) 597 (62.3)
Black or African American 101 (7.4) 113 (11.8)
American Indian or Alaska Native 12 (0.9) 12 (1.3)
Asian 28 (2.0) 34 (3.6)
Native Hawaiian or other Pacific lslander 3 (0.2) 5 (0.5)
Biracial 27 (2.0) 26 (2.7)
Nativity χ2(1)=8.53, p=0.003
Born in the United States 1206 (88.1) 795 (83.0)
Not born in the United States 131 (9.6) 127 (13.3)
Health insurance status χ2(1)=15.72, p<0.001
Yes 1073 (78.4) 805 (84.0)
No 281 (20.5) 134 (14.0)
Personal history of cancer χ2(1)=4.77, p=0.03
Yes 114 (8.3) 104 (10.9)
No 1227 (89.6) 820 (85.6)
Survey response mode χ2(1)=38.92, p<0.001
Mail 642 (46.9) 575 (60.0)
Telephone 727 (53.1) 383 (40.0)
Note: Percentages do not sum to 100 because of missing data
*
Variable is continuous and values indicate mean (standard deviation)
Test of comparison is t test
Taber et al.: Why do people avoid medical care?JGIM
e.g., Dont go unless there is a real need). Despite the
question stem referring to avoiding the doctor when you
think you should go,many participants said they avoided
medical care because they did not have health problems
(n=40; e.g., Not sick. If not brokendontfix). A small subset
of participants also reported avoiding medical care because
they try to take care of themselves(n=13; e.g., by using
over-the-counter medication), were either a doctor or worked
in a health care setting (n=9), were afraid to be labeled a
hypochondriac (n=5), or preferred to rely on spiritual healing
(n=3) or to use natural remedies (n=2).
Traditional Barriers to Medical Care
The largest overarching category of reasons for avoidance of
medical care may be best described as traditional barriers to
medical care(n=800, 58.4%). In this category, we included
responses indicating circumstances or obstacles limiting ac-
cess to medical care. Participants reported having too little
time or being too busy to seek medical care (n=214), that clinic
hours were inconvenient (n=57; e.g., Have to take time off
from work), that transportation was difficult (n=18) or the
distance was too far (n=7), that they were too sick to travel to
the doctorsoffice(n=6), or that an existing physical (n=5;
e.g., multiple sclerosis) or mental health (e.g., depression,
severe anxiety) problem prevented them from going. Financial
reasons included concerns about overall cost (n=330), co-pays
(n=35), and health insurance (n=151). Few reported not hav-
ing a doctor (n=13), that their doctor was inaccessible (n=5;
e.g., I dont see him, I just see nurses, he is never there), not
having childcare (n=3), or language barriers (n=2).
Unfavorable Evaluations of Seeking Medical
Care
Approximately one-third of participants (n=456, 33.3%) pro-
vided responses that demonstrated unfavorable evaluations of
the process or outcomes of seeking medical care.
Physician Factors. The most frequently reported reasons for
unfavorable evaluations were factors related to physicians
(n=178). There were two major categories of physician
factors: interpersonal concerns (n=98) and concerns about
the quality of medical care (n=81). The most frequent
interpersonal concerns involved communication concerns
(n=34), including perceptions that doctors do not follow-up,
that communication is difficult, disliking how doctors
communicate (e.g., Doctors often make you feel like youre
stupid), disliking the manner in which doctors provide advice
or recommendations (e.g., Tired of being chewed out for not
following medical advice), perceiving that doctors do not
listen to patients (e.g., They are impersonalpaying more
attention to computers;My experience is one of not being
heard/considered), and perceiving that doctors do not take
patientsconcerns seriously. Other interpersonal reasons
Figure 1 Participant-generated reasons for avoiding medical care (n=1,369).
Taber et al.: Why do people avoid medical care? JGIM
included general mistrust of doctors (e.g. I just donttrust
them;n=25), believing that doctors do not care about patients
(e.g., Idont always feel that they truly care;n=8), and
perceiving that doctors are too busy (n=8). Participants also
reported a broad dislike of doctors, without elaboration
(n=21).
The most frequent reason concerning the quality of medical
care was that participants had low confidence in doctors
expertise (n=61), which included beliefs that doctors would
not be able to diagnose patients (e.g., Fear that they wont
know whats wrong either), that doctors would provide in-
correct diagnoses (e.g., They usually make the wrong diag-
nosis), and that doctors simply make things worse.This
category also included more general statements about a lack of
confidence in medical providers (e.g., No confidence in
todays medical field). Participants also expressed concerns
that doctors would prescribe unnecessary tests or medication
(n=13), and several participants stated that doctors care more
about money than patients(n=9).
Organizational Factors. Many reasons for unfavorable
evaluations concerned aspects of the medical system
(n=108), such as long waiting times (n=52) and hassle
(n=51), which included the hassle of making timely
appointments (e.g., Usually cant see doctor at the time of a
problem) or even making appointments at all (e.g., Difficult
to get appointment, office too busy), as well as general hassle
(e.g., Its a big bother). Several participants reported not
wanting to be around sick people (n=6). Additional reasons
are shown in Fig. 1.
Affective Concerns. Some participants reported that anticipated
fear, embarrassment, or guilt kept them from seeking medical
care (n=76). Responses concerning fear included the fear of
receiving bad news (n=31) such as a medical diagnosis or a
prognosis concerning an already diagnosed condition (e.g.,
Afraid they might say my diabetes is worse). Participants
also reported fear of needles (n=7), pain (n=5), and specific
procedures such as surgery or prostate exams (n=5), or simply
reported fear(n=12). Relatedly, participants reported the
specific emotion of embarrassment (n=15), including
embarrassment about weight (n=4), health issues (n=2), or
general feelings of discomfort (n=9). Finally, some
participants reported feeling guilty about potentially disclosing
engagement in unhealthy behavior (n=2).
Expected Negative Outcomes. Some responses pertained to
beliefs that the outcome of seeking medical care would be
negative, including dislike of a providersmedical
recommendations or the perception that recommendations
would not be useful (n=42). These responses included
avoidance of specific recommendations to change behavior
(n=19); participantsoften disliked the emphasis on weight loss
(n=10; e.g., Hearing the same oldlose weightand
Always have to hear about how fat I am) or other health
problems such as alcohol consumption, smoking, or high
blood pressure. Some participants indicated they disliked or
could not take medication (n=12; e.g., I hate Rx drugsthe
side effects scare me) or that they would not follow a
physicians recommendations (n=7). Additional responses
are reported in Fig. 1.
Figure 2 Conceptual model of medical care avoidance.
Taber et al.: Why do people avoid medical care?JGIM
Other Reasons. Several additional reasons were reported that
were either nonspecific or did not fall into another category
(n=67). The majority of these responses included generally not
liking or wanting to go to the doctor (n=46). Participants also
reported having had past negative experiences but not
specifying the nature of these experiences (n=11), denial
(n=4), and not viewing seeking medical care as a priority
(n=3). Fig. 1presents other reasons reported by few
participants.
Self-Ascribed Personality Traits
A fourth category of reasons for avoiding medical care
concerned personality traits (n=45). Specifically, participants
responded that they were lazy(n=23) or that they procras-
tinate(n=20), with little elaboration. Two additional
responses are shown in Fig. 1.
ConceptualModelofMedicalCare
Avoidance
Fig. 2presents the conceptual model of medical care avoidance
that emerged from our categorization of participant-generated
reasons. The language used to describe this model, as well as the
conceptualization of avoidance at different stages of the care-
seeking process, was influenced by Crisis Decision Theory,
which describes how people respond to negative events more
generally.
30
Our conceptual model proposes that avoidance may
begin prior to noticing a need (e.g., avoidance of early detection
or preventive services) or in the process of evaluating symptoms,
or that avoidance can occur after a need is identified if people
perceive a lack of resources, evaluate medical care unfavorably,
or have a personality trait that discourages care-seeking. The
model also proposes that avoiding medical care for any of these
reasons would lead to a lack of medical and preventive care and,
ultimately, poorer health outcomes. A specific comparison of our
model of medical care avoidance to the more general Crisis
Decision Theory
30
is presented in the Discussion.
DISCUSSION
This study presents the first comprehensive qualitative analysis
of reasons for avoiding medical care among the general U.S.
public. Using a diverse nationally representative sample and
participant-generated responses, we applied inductive qualitative
research methods to identify and categorize reasons for and to
develop a conceptual model of medical care avoidance. Three
overarching categories of reasons emerged based on the neces-
sity, availability, and desirability of care-seeking: 1) low per-
ceived need to seek medical care; 2) traditional barriers to
medical care, in which people may want to seek care but are
limited in their ability to do so; and 3) unfavorable evaluations of
seeking medical care, in which people may perceive care-
seeking as necessary and an available option, but not desirable.
Notably, unlike much of the prior research, the reasons identified
here are applicable across a broad range of clinical settings and
are particularly relevant for primary care. Primary care settings
are patientsfirst point of contact for most health issues, and
increasingly function as the hub of all medical care.
31
Under-
standing why people fail to make it through the clinic door is
critical to extending the reach and effectiveness of patient care.
Many of the reasons identified here are consistent with factors
previously described in prior research, including studies of
smaller patient and community samples, and reflected in theories
of health behavior and health care use.
14,7,10,11,3237
Interesting-
ly, the categories of reasons that emerged from the present study
mapped almost directly onto a general psychological model of
responses to negative eventsCrisis Decision Theory
30
which
has not previously been used as a framework for understanding
medical care avoidance. Crisis Decision Theory posits that peo-
ple respond to negative events first by appraising the severity of
threat, next by identifying available response options, and lastly
by evaluating available response options.
30
Putting our results
into the language of this framework, participants who reported
low perceived need to seek medical care may have appraised little
threat or perceived high control to respond to the crisisthem-
selves. Participants who reported factors limiting access may
have felt that their response options were limited and that seeking
medical care was not an option. Participants who reported unfa-
vorable evaluations of medical care may have moved beyond
both of these stagesthey may have recognized a need to seek
care (sufficient threat) and perceived seeking care to be a feasible
option, butin the language of Crisis Decision Theorydid not
expect the gains of seeking care to outweigh the costs.
Our conceptual model proposes that perceptions of the ne-
cessity, availability, and desirability of seeking medical care
may be prime intervention targets for reducing medical care
avoidance. Although in some cases participants may have cor-
rectly assessed that their symptoms would go away with time or
heal on their own, low perceived need to seek care suggests a
need to educate patients on how to recognize symptoms for
common health problems and the value of medical screening for
asymptomatic conditions. For example, many people falsely
believe they can tell when their blood pressure is high.
38
Edu-
cation about the importance of seeking preventive health care
and regular checkups is critical. Public health efforts might
include telephone or printed client reminders that medical visits
are vital to health maintenance, that regular checkups can
identify risk factors and problems before they become serious,
and that treatments are often more effective when disease is
caught early.
39
Interventions utilizing technology such as tele-
medicine and eHealth (e.g., patient portals) may increase patient
engagement with health care, provided they facilitate awareness
of health care services and disease management.
40,41
In terms of
symptom appraisal, research should assess whether people must
reach certain thresholds prior to seeking care.
Traditional barriers limiting access to or ease of seeking
medical care, such as lack of health insurance and time
Taber et al.: Why do people avoid medical care? JGIM
constraints, were the most commonly cited reasons for avoid-
ing medical care, consistent with prior research.
7,21,22,4244
With the advent of the Affordable Care Act (ACA), lack of
health insurance may become less of a barrier, but our results
indicate that inadequate health insurance and high co-pays are
also reasons for avoiding medical care, as well as numerous other
reasons that may not be abated by the ACA. Interventions
targeting these barriers are an important area for continued re-
search. Strategies designed to tackle multiple barriers simulta-
neously (e.g., case management, financial incentives such as cost-
reduction strategies or efforts to limit out-of-pocket costs) and
comprehensive approaches addressing multiple patient needs
(e.g., multidisciplinary team care
45
) may be more effective in
reducing avoidance than strategies that target only one barrier.
Finally, many people reported unfavorable evaluations of
seeking medical care (e.g., communication problems, concerns
about physicianstrustworthiness and expertise), consistent with
prior research showing the impact of the patient-physician rela-
tionship and medical trust on medication adherence, health care
utilization, and health outcomes.
7,32,4650
Much intervention
research is focused on improving patient experiences and com-
munication,
5155
and the frequency of responses indicating dis-
like of both physicians and the health care system confirms that
this intervention focus is well-deserved. However, we also ob-
served a variety of other reasons, such as avoiding specific
recommendations or procedures, which could also be addressed
through interventions aimed at changing negative perceptions
about specific aspects of medical care.
Limitations and Future Directions
There are several important limitations of the present study.
Medical care avoidance due to discomfort with physical
examinations, fear of having a serious illness, and associating
doctors with death may have been underestimated because
these factors were assessed with closed-ended questions im-
mediately prior to the open-ended question analyzed here. The
pattern of differences in demographic factors among individ-
uals who did and did not provide written reasons for avoiding
seeking medical care suggest that responses were provided
more often by people who may have been more favorably
disposed to participate in research (e.g., those with higher
incomes and education levels). Given the subjective nature
of qualitative coding, alternate categorizations of the data are
possible. In particular, reasons categorized here as unfavor-
able evaluations of seeking medical carehave been concep-
tualized elsewhere as cognitive barriers.
15
Few participants
self-identified as members of racial and ethnic minority groups
or were born outside the U.S., which is important because the
breadth and distribution of reasons for medical care avoidance
may be different among these populations. For example, al-
though language is a strong barrier for many immigrant pop-
ulations,
56
only two participants identified language as a rea-
son for avoiding care. Therefore, care should be taken to study
reasons for avoidance among these specific populations.
Further, we cannot be certain that all respondents understood
or paid attention to the exact item wording. We interpreted
responses concerning no health problemsas evidence for
avoiding preventive screening or routine checkups. However,
this is an extrapolation, and participantsintentions cannot be
known.
In addition to informing intervention development, the present
results are intended to generate hypotheses for future research.
Participants tended to list only one response and were not en-
couraged to report all reasons that were important to them. If
some people avoid medical care for multiple reasons, this may
have reduced the overall reported prevalence of many reasons.
Research is also necessary to test whether there is a linear
decision-making strategy as suggested by Crisis Decision Theo-
ry, as it is possible that various reasons may interact and co-occur.
For example, traditional barriers may exert more influence, or
symptoms may be interpreted as less severe, when people nega-
tively evaluate some aspect of care-seeking, (e.g., fearing bad
news). Prospective research in which participants report symp-
toms and behavioral responses as they unfold would provide
valuable insight into the process of decision-making surrounding
avoidance. Researchers can also follow up on specific reasons
that have been understudied and develop validated scales of
reasons for medical care avoidance, and future research should
test the predictive validity of these reasons for actual avoidance.
Quantitative analyses are necessary because people cannot al-
ways accurately report their motivations
57
and might not be fully
aware of the specific reasons they avoid seeking medical care.
The frequency of specific reasons reported here might overesti-
mate or underestimate the impact of these reasons on actual
avoidance. Finally, asking people to explain why they choose
to seek medical care in some instances but not in others might
provide better understanding of the potentially nuanced and
dynamic patterns and processes of decision-making.
2
Conflict of Interest: The authors declare that they do not have a
conflict of interest.
Corresponding Author: Jennifer M. Taber, Ph.D.; National Cancer
InstituteNational Institutes of Health, 9609 Medical Center Drive,
Bethesda, MD 20892-9761, USA (e-mail: Jennifer.taber@nih.gov).
REFERENCES
1. Byrne SK. Healthcare avoidance: a critical review. Holist Nurs Pract.
2008;22:28092.
2. Scott S, Walter F. Studying help-seeking for symptoms: The challenges of
methods and models. Soc Personal Psychol Compass. 2010;4:53147.
3. Yousaf O, Grunfeld EA, Hunter MS. A systematic review of the factors
associated with delays in medical and psychological help-seeking among
men. Health Psychol Rev. 2013:1-13
4. Smith LK, Pope C, Botha JL. Patientshelp-seeking experiences and
delay in cancer presentation: a qualitative synthesis. Lancet.
2005;366:82531.
5. Vanderpool RC, Huang B. Cancer risk perceptions, beliefs, and physician
avoidance in Appalachia: results from the 2008 HINTS Survey. J Health
Commun. 2010;15(Suppl 3):7891.
Taber et al.: Why do people avoid medical care?JGIM
6. Persoskie A, Ferrer RA, Klein WM. Association of cancer worry and
perceived risk with doctor avoidance: an analysis of information avoidance
in a nationally representative US sample. J Behav Med. 2014;37:977-87.
7. Kannan VD, Veazie PJ. Predictors of avoiding medical care and reasons
for avoidance behavior. Med Care. 2014;52:336-45.
8. Lund-Nielsen B, Midtgaard J, Rorth M, Gottrup F, Adamsen L. An
avalanche of i gnoringa qualitative study of health careavoidance in women
with malignant breast cancer wounds. Cancer Nurs. 2011;34:27785.
9. Kiefe CI, Funkhouser E, Fouad MN, May DS. Chronic disease as a
barrier to breast and cervical cancer screening. J Gen Intern Med.
1998;13:35765.
10. Barbour JB, Rintamaki LS, Ramsey JA, Brashers DE. Avoiding health
information. J Health Commun. 2012;17:21229.
11. Larkey LK, Hecht ML, Miller K, Alatorre C. Hispanic cultural norms for
health-seeking behaviors in the face of symptoms. Health Educ Behav.
2001;28:6580.
12. Ristvedt SL, Trinkaus KM. Psychological factors related to delay in
consultation for cancer symptoms. Psychooncology. 2005;14:33950.
13. Richards MA, Westcombe AM, Love SB, Littlejohns P, Ramirez AJ.
Influence of delay on survival in patients with breast cancer: a systematic
review. Lancet. 1999;353:111926.
14. OhlM,TateJ,DuggalM,etal.Rural residence is associated with delayed
care entry and increased mortality among veterans with human immuno-
deficiency virus infection. Med Care. 2010;48:106470.
15. Carrillo JE, Carrillo VA, Perez HR, Salas-Lopez D, Natale-Pereira A,
Byron AT. Defining and targeting health care access barriers. J Health
Care Poor Underserved. 2011;22:56275.
16. Reynolds LM, Consedine NS, Pizarro DA, Bissett IP. Disgust and
behavioral avoidance in colorectal cancer screening and treatment: a
systematic review and research agenda. Cancer Nurs. 2013;36:12230.
17. Weller D, Vedsted P, Rubin G, et al. The Aarhus statement: improving
design and reporting of studies on early cancer diagnosis. Br J Cancer.
2012;106:12627.
18. Moser RP, Arndt J, Han PK, Waters EA, Amsellem M, Hesse BW.
Perceptions of cancer as a death sentence: prevalence and consequences. J
Health Psychol. Jul 17 2013.
19. Capp R, Rooks S, Wiler J, Zane R, Ginde A. National study of health
insurance type and reasons for emergency department use. J Gen Intern
Med. 2014;29:6217.
20. Cheung PT, Wiler JL, Lowe RA, Ginde AA. National study of barriers to
timely primary care and emergency department utilization amongmedicaid
beneficiaries. Ann Emerg Med. 2012;60:410.e12.
21. Baker DW, Shapiro MF, Schur CL. Health insurance and access to care for
symptomatic conditions. Arch Intern Med. 2000;160:126974.
22. DeVoe JE, Fryer GE, Phillips R, Green L. Receipt of preventive care
among adults: insurance status and usual source of care. Am J Public
Health. 2003;93:78691.
23. Goins RT, Williams KA, Carter MW, Spencer M, Solovieva T. Perceived
barriers to health care access among rural older adults: a qualitative study.
J Rural Health. 2005;21:20613.
24. Ye J, Shim R, Rust G. Health care avoidance among people with serious
psychological distress: analyses of 2007 Health Information National
Trends Survey. J Health Care Poor Underserved. 2012;23:16209.
25. Cantor D, Coa K, Crystal-Mansour S, Davis T, Dipko S, Sigman R.
Health Information National Trends Survey (HINTS) 2007 Final Report.
Rockville, MD: Westat; 2009.
26. Nelson DE, Kreps GL, Hesse BW, et al. The Health Information National
Trends Survey (HINTS): development, design, and dissemination. J Health
Commun. 2004;9:44360.
27. Rutten LF, Moser RP, Beckjord EB, Hesse BW, Croyle RT. Cancer
Communication: Health Information National Trends Survey. Washington,
DC.: National Cancer Institute; 2007.
28. Glaser BG, Strauss AL. The discovery of grounded theory: strategies for
qualitative research. Transaction Publishers; 2009
29. Thomas DR. A general inductive approach for analyzing qualitative
evaluation data. Am J Eval. 2006;27:23746.
30. Sweeny K. Crisis decision theory: decisions in the face of negative events.
Psychol Bull. 2008;134:6176.
31. Rittenhouse DR, Shortell SM. The patient-centered medical home: Will it
stand the test of health reform? JAMA. 2009;301:203840.
32. Moore PJ, Sickel AE, Malat J, Williams D, Jackson J, Adler NE. Psychosocial
factors in medical and psychological treatment avoidance: the role of the doctor-
patient relationship. J Health Psychol. 2004;9:42133.
33. Andersen RM. Revisiting the behavioral model and access to medical care:
does it matter? J Health Soc Behav. 1995;36:110.
34. Iskandarsyah A, de Klerk C, Suardi DR, Soemitro MP, Sadarjoen SS,
Passchier J. Psychosocial and cultural reasons for delay in seeking help
and nonadherence to treatment inIndonesian women with breast cancer: a
qualitative study. Health Psychol. 2014;33:21421.
35. Ramirez AJ, Westcombe AM, Burgess CC, Sutton S, Littlejohns P,
Richards MA. Factors predicting delayed presentation of symptomatic
breast cancer: a systematic review. Lancet. 1999;353:112731.
36. Scott SE, Walter FM, Webster A, Sutton S, Emery J. The model of
pathways to treatment: conceptualization and integration with existing
theory. Br J Health Psychol. 2013;18:4565.
37. Walter F, Webster A, Scott S, Emery J. The Andersen Model of Total
Patient Delay: a systematic review of its application in cancer diagnosis. J
Health Serv Res Policy. 2012;17:1108.
38. Meyer D, Leventhal H, Gutmann M. Common-sense models ofillness: the
example of hypertension. Health Psychol. 1985;4:11535.
39. Guide to Community Preventive Services. Increasing cancer screening:
client reminders. http://www.thecommunityguide.org/cancer/screening/
client-oriented/reminders.html. (Accessed 5/21/14).
40. Kreps GL, Neuhauser L. New directions in eHealth communica-
tion: opportunities and challenges. Patient Educ Couns.
2010;78:32936.
41. Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health
information technology on quality, efficiency, and costs of medical care.
Ann Intern Med. 2006;144:74252.
42. Blewett LA, Johnson PJ, Lee B, Scal PB. When a usual source of care
and usual provider matter: adult prevention and screening services. J Gen
Intern Med. 2008;23:135460.
43. Okoro CA, Strine TW, Young SL, Balluz LS, Mokdad AH. Access to health
care among older adults and receipt of preventive services. Results from the
Behavioral Risk Factor Surveillance System, 2002. Prev Med.
2005;40:337343.
44. Rezayatmand R, Pavlo va M, Groot W. The impact of out-of-pocket
payments on prevention and health-related lifestyle: a systematic literature
review. Eur J Public Health. 2013;23:749.
45. Fennell ML, Das IP, Clauser S, Petrelli N, Salner A. The
organization of multidisciplinary care teams: modeling internal and
external influences on cancer care quality. JNCI Monographs.
2010;7280.
46. Bynum SA, Davis JL, Green BL, Katz RV. Unwillingness to participate in
colorectal cancer screening: examining fears, attitudes, and medical
mistrust in an ethnically diverse sample of adults 50 years and older. Am
J Health Promot. 2012;26:295300.
47. Thompson HS, Valdimarsdottir HB, Winkel G, Jandorf L, Redd W. The
Group-Based Medical Mistrust Scale: psychometric properties and associ-
ation with breast cancer screening. Prev Med. 2004;38:20918.
48. LaVeist TA, Isaac LA, Williams KP. Mistrust of health care organizations
is associated with underutilization of health services. Health Serv Res.
2009;44:2093105.
49. Hammond WP, Matthews D, Mohottige D, Agyemang A, Corbie-Smith
G. Masculinity, medical mistrust, and preventive health services delays
among community-dwelling African-American men. J Gen Intern Med.
2010;25:13008.
50. Arora NK. Interacting with cancer patients: the significance of physicians
communication behavior. Soc Sci Med. 2003;57:791806.
51. Rao JK, Anderson LA, Inui TS, Frankel RM. Communication interven-
tions make a difference in conversations between physicians and patients:
a systematic review of the evidence. Med Care. 200 7;45:3409.
52. Penner LA, Gaertner S, Dovidio JF, et al. A social psychological approach
to improving the outcomes of racially discordant medical interactions. J
Gen Intern Med. 2013;28:11439.
53. FawoleOA,DySM,WilsonRF,etal.A systematic review of communi-
cation quality improvement interventions for patients with advanced and
serious illness. J Gen Intern Med. 2013;28:5707.
54. Anderson LA, Sharpe PA. Improving patient and provider communication:
a synthesis and review of communication interventions. Patient Educ
Couns. 1991;17:99134.
55. Griffin SJ, Kinmonth AL, Veltman MW, Gillard S, Grant J, Stewart M.
Effect on health-related outcomes of interventions to alter the interaction
between patients and practitioners: a systematic review of trials. Ann Fam
Med. 2004;2:595608.
56. Clough J, Lee S, Chae DH. Barriers to health care among Asian
immigrants in the United States: a traditional review. J Health Care Poor
Underserved. 2013;24:384403.
57. Nisbett RE, Wilson TD. Telling more than we can know: verbal reports on
mental processes. Psychol Rev. 1977;84:23159.
Taber et al.: Why do people avoid medical care? JGIM
... This is a substantial number; however, based on their response, we did not consider them in our primary outcome of long COVID, and considered them as having made a full recovery and no longer being troubled by symptoms. Although we cannot be certain, they may correspond to a subset of persons with lower perceived need for services who believe that the symptoms will go away (Taber et al., 2015). ...
... which focuses on the need aspect. Although many persons may still have symptoms (as reflected by our results), they may not be troubled by these and still consider that they had made a full recovery (i.e., low perceived need) (Taber et al., 2015). Nevertheless, we cannot be certain that those who responded that they made a full recovery and were no longer troubled by symptoms but still had lingering symptoms, may require services. ...
Article
Objectives: The impact of long COVID among persons hospitalized and discharged home is unknown. We aimed to (1) report the prevalence of long COVID in persons hospitalized for COVID-19 and discharged home; (2) estimate the prevalence of physical, sensory, and psychological/mental health impairments; and (3) explore associated factors. Methods: We conducted a telephone survey of adult residents in Laval, Quebec, who were discharged home ≥ 2 months post-hospitalization for COVID-19. Participants responded to a standard questionnaire regarding persistent symptoms. We calculated the prevalence of long COVID and of persistent types of symptoms and evaluated associated factors using bivariate analysis and multivariable logistic regression. Results: In our sample (n = 398), 70% reported physical symptoms, 58% psychological problems, and 16% sensory impairments. 31.5% reported being troubled by persistent symptoms (long COVID). Factors associated with long COVID were a greater number of symptoms (odds ratio (OR) = 1.97, 95% confidence interval (CI) = 1.69-2.28) and increased hospital stay (OR = 1.03, 95% CI = 1.01-1.06). Other factors associated with physical and psychological symptoms were female sex (OR = 2.17, 95% CI = 1.27-3.71 and OR = 2.06, 95% CI = 1.25-3.39; respectively), higher education level (OR = 2.10, 95% CI = 1.20-3.68 and OR = 2.43, 95% CI = 1.44-4.14; respectively), and obesity (OR = 1.95, 95% CI = 1.15-3.34 and OR = 1.70, 95% CI = 1.05-2.77; respectively). Conclusion: In this population-based study of persons hospitalized for COVID-19 and discharged home, nearly one third were troubled by symptoms for 2 months or more post-discharge. There was a high proportion with persistent physical and psychological/mental health symptoms. Further research will assess the specific needs of these patients to inform health policy makers on service requirements for these persons.
... The value of health information undoubtedly varies as a function of the nature of the information. For example, information about a disease is likely more valuable, and thus elicit more information seeking, if the disease has severe consequences than if it does not (Barth et al., 2002;Denison et al., 2017;Taber et al., 2015), or if the disease is curable or treatable than if it is not (Dawson et al., 2006;Howell & Shepperd, 2012;Melnyk & Shepperd, 2012). Presumably, people seek information about severe diseases and curable diseases because the information has greater utility in terms of making decisions and intervening (Backonja et al., 2014). ...
... Most notably, we excluded disease controllability. Evidence suggests that people are less likely to undergo screening for uncontrollable diseases because they are untreatable or incurable(Dawson et al., 2006;Howell & Shepperd, 2012;Melnyk & Shepperd, 2012;Taber et al., 2015). To address these limitations, in Study 2, we randomly assigned participants to either the mild or severe disease condition and manipulated whether the disease was curable.Participants were 400 undergraduates in the U.S. (Mage = 18.96; ...
Full-text available
Article
Rationale and objective Delay discounting is the devaluation of an outcome as a function of delay until receiving that outcome. In two studies, we used a delay discounting approach to examine how wait times for a medical diagnosis can affect people's decision to undergo medical testing. Methods: In Study 1 (N = 151), participants rated the likelihood they would get tested for a severe and a mild disease with wait times ranging from 0 to 180 days (within persons). Study 2 (N = 400) randomized disease severity (severe vs. mild) between persons and manipulated disease curability (curable vs. incurable). Results: Likelihood of testing decreased as delay until receiving test results increased. This effect of delay on testing was stronger for the mild than for the severe disease, and for the curable than for the incurable disease. Conclusions: We found strong evidence for a delay discounting effect, an effect that varied depending on aspects of diseases. The findings illustrate how delay discounting can affect screening uptake and how it is moderated by disease characteristics.
... Time constraint is one of the reasons why almost 16% of people avoid medical care. 38 By incorporating the dollar value of peoples' waiting costs into the staff planning model, they can experience reasonable waiting times that balance the trade-off between resource idle times and customer waiting times. ...
Preprint
Waiting times can be a huge concern for companies, as long waiting times can result in lost sales, unsatisfied customers, and a bad reputation for organizations. Staff requirement planning can help reduce the average time in queue. We propose a capacity planning model that considers the dollar value of customers’ waiting time to determine the optimal production (service) rate that maximizes the total profit of organizations. The model balances the trade-off between customers’ waiting costs and staff’s idle time costs. This model introduces a delay discount factor that quantifies customers’ time in dollar value. We used two “inferred” models from healthcare and compared the current settings with the optimal ones resulting from the proposed model. The optimal setting significantly reduced waiting times and increased profits. The proposed model can be extremely helpful in adequate staff planning for service organizations.
... In contrast, Asian patients performed glucose monitoring better than Iraqi patients, which may be explained by the fact that fewer Iraqi patients than most Canadian people with the disease had professional instruction in diabetes management (46). Additionally, the biggest obstacles to routine doctor visits were patient-related, including lack of awareness, the expense of appointments, and time restraints; a survey among patients in the United States revealed similar results (47). Additionally, the difficulties with daily foot care, managing stress, controlling hypoglycemia on one's own, and managing diabetes while ill was primarily attributed to a lack of knowledge, demonstrating that only a small number of participants were aware of the seriousness of diabetes complications (48). ...
Full-text available
Article
Type 2 diabetes mellitus (DM) is a persistent metabolic condition illness with a rapidly rising prevalence in the entire world. The management of T2DM varies greatly across the government and industry sectors in Iraq; Using this variant brought on by unequal access to treatment. The goal of this agreement is to present uniform advice about the process of treat patients with T2DM for the initial time. while taking into account regional challenges in Iraq. A group of Iraqi internists and diabetologists from throughout the nation gave their approval to these consensus statements. The recommendation was based entirely on the most recent and current level of evidence. T2DM makes up about 90% of all diabetes cases. As a result of the reduced insulin response in T2DM, This condition is referred to as insulin resistance. In this condition, To keep glycemic control, insulin is ineffectual, which is first countered by an elevation in the synthesis of insulin; however, over time, the secretion of insulin falls, which results in T2DM.
... For many years, healthcare for psychiatric care was inaccessible to many. For the most part, distance to a mental healthcare facility, transportation, and costs were the main factors preventing many from access to care [7]. It is no surprise that telehealth evolved to bridge the gap in the global mental health crisis. ...
Article
The age of technology and smart devices has paved the way for a current and encouraging method to address mental healthcare that benefits from global connectivity: telehealth. According to the American Telemedicine Association, telehealth or telemedicine is defined as the usage of medical information from one site to another through electronic communication with the goal of improving a client’s overall health through emails, cellular phones, two-way videos, and conference calls. With the current Covid-19 pandemic, hospitals (especially those with mental health units or free-standing psychiatry facilities) are seldomly able to keep up with the influx of mental health patients without being turned away or having to wait for an extended period of time. Through telemedicine, those barriers have been lifted providing more efficient and enhanced access to care for everyone, especially those seeking mental health services. This review paper attempts to establish that despite the observations that telehealth has a positive impact on improved access to mental healthcare, it has not flourished to the extent it could have prior to the COVID-19 pandemic. We also try to provide long-term telehealth solutions, some of which are already being implemented in the current pandemic to improve the quality of mental healthcare access to a larger majority of Americans and those in other countries as well. With people being advised to stay-at-home coupled with the fear of cross-contamination in public places, people are resorting to telehealth for psychiatric visits and follow-ups. Before this pandemic, however, certain laws and rules have been a barrier to more telehealth options becoming available or feasible to the public. Telehealth is changing the conventional standard of psychiatric medicine by enhancing access to care, reducing re-admission rates, and enhancing the quality of life. Those that have tested positive for the COVID-19 virus and are quarantined at home can still meet with their mental healthcare provider periodically to discuss progress and prognosis. Research has shown that telemedicine has neutralized the impacts of delayed care, hospital admissions, and complications from psychiatric conditions globally. Telehealth has proven to provide steady benefits to patients, psychiatrists, and mental health providers through round-the-clock access remotely.
... Taber et al., indicated that lower perceived need for treatment could be attributed to patients cognitive perceptions, i.e., the symptoms could improve with time, ability of their bodies to heal themselves and fear of creating a burden on their spouses or families. [29] Almost forty five percent of the participants do use (Miswak), a wooden piece that is used for oral practices by majority of the Saudi population. However, various studies have found Miswak to be inferior in maintain favorable oral hygiene. ...
Article
Objectives: To assess the relationship between self-rated mental health (SRMH) and infrequent routine care among Medicare beneficiaries and to investigate the roles of managed care and having a personal doctor. Study design: Cross-sectional analysis of data from the 2018 Medicare Consumer Assessment of Healthcare Providers and Systems survey. Methods: Logistic regression was used to predict infrequent routine care (having not made an appointment for routine care in the last 6 months) from SRMH, Medicare coverage type (fee-for-service [FFS] vs Medicare Advantage [MA], the managed care version of Medicare), and the interaction of these variables. Models that did and did not include having a personal doctor were compared. All models controlled for demographics and physical health. Results: Overall, 14.9% of beneficiaries did not make a routine care appointment in the last 6 months, with rates adjusted for demographics and physical health ranging from 14.5% for those with "excellent" SRMH to 19.2% for those with "poor" SRMH. Beneficiaries with poor SRMH were less likely to make a routine care appointment in FFS than in MA (20.1% vs 16.4%, respectively, had not done so in the last 6 months; P < .05). Accounting for having a personal doctor reduced the association between SRMH and infrequent routine care by about a third. Conclusions: Extra efforts are needed to ensure receipt of routine care by beneficiaries with poor mental health-particularly in FFS, where more should be done to ensure that beneficiaries have a personal doctor.
Full-text available
Preprint
Background Avoiding deemed necessary healthcare needs may worsen prognosis and treatment options, and damage people’s ability to perform their roles in society. Our study investigates why people avoid healthcare services in an upper-middle-income country, Turkey. Methods We apply TurkStat’s 2012 Health Survey Data that includes a comprehensive health and social-demographic information of 28,055 survey participants who were 15 + aged. We prefer to use bivariate probit model to analyze the avoiding behaviour in inpatient level in accordance with outpatient level because of the observed significant correlation between people’s avoiding behaviour under tertiary and lower level of health care. Results According to our descriptive analysis, we see that 2.6% of 15 + aged population were avoiding deemed necessary hospital services. Furthermore, it is found that high cost (31%), organizational factors (21%) and fear (12%) are prominent reasons of avoiding tertiary care. Thereafter, in our bivariate probit model findings, we figure out that being covered by social security schemes decreases the probability of avoiding both outpatient and inpatient health services by 6.9%. Moreover, being female, living in rural area, having lower income increase the chance of being avoider in both stages of healthcare. Conclusions We conclude that social inequalities were main underlying determinants of the avoiding behavior. As health and effective provision of health care are vital for the smooth functioning of society, we suggest that improve health care protection of people from disadvantaged social groups and develop better organizational factors to prevent difficulty of having treatment at policlinics.
Article
Background While no hospitalization is inexpensive, some are extremely costly. Learning from these exceptions is critical. The patients and conditions that ultimately translate into the most exorbitant adult hospitalizations have not been characterized. Objective To analyze and detail characteristics of extreme high cost adult hospitalizations in the United States using the most recently available Nationwide Inpatient Sample (NIS) data. Design/Setting/participants The NIS 2018 database was queried for the all adult hospitalizations with hospital charges greater than $333,000. Multivariable linear regression was used in the analyses. Measures The main outcome measures were total charges, mortality, length of stay, admitting diagnosis, and procedures. Results There were 538,121 adults age ≥ 18 years with total hospital charges ≥ $333,333. Among these patients 481,856 (89.5%) survived their hospitalization and 56,265 (10.4%) died. Males, older patients, being insured by Medicare, having more comorbid illness, and those who were transferred from another hospital were significantly more likely to die during the incident hospitalization (all p<0.01). Patients who died had even more costly hospitalizations with more procedures (mean [SD]: 10.7 [±6.4] versus 7.0 [± 5.9], p<0.01), and longer lengths of stay after adjustment for confounders (p=0.01). Conclusions Hundreds of thousands of adult patients are hospitalized in the US each year with extremely high costs. For both those who survive and the 10% who die, there may be opportunities for reducing the expense. Interventions, such as predictive modeling and systematic goals of care discussions with all patients, deserve further study.
Full-text available
Article
Background: Adults over 50 have high healthcare needs, but also face high coronavirus disease 2019 (COVID-19)-related vulnerability. This may result in reluctance to enter public spaces, including healthcare settings. Here, we examined factors associated with healthcare delays among adults over 50 early in the COVID-19 pandemic. Methods: Using data from the 2020 wave of the Health and Retirement Study (N=7615), we evaluated how race/ethnicity, age, geographic region, and pandemic-related factors were associated with healthcare delays. Results: In our sample, 3 in 10 participants who were interviewed from March 2020 to June 2021 reported delays in medical or dental care in the early stages of the COVID-19 pandemic. Non-Hispanic Whites (OR: 1.37; 95% CI: 1.19-1.58) and those of other racial/ethnic backgrounds (OR: 1.31; 95% CI: 1.02-1.67) delayed care more than Non-Hispanic Blacks. Other factors associated with delayed care included younger age, living in the Midwest or West, knowing someone diagnosed with or who died from COVID-19, and having high COVID-19-related concerns. There were no differences in care delays among adults aged >70; however, among those ≤70, those who knew someone diagnosed with COVID-19 were more likely to delay care than those who did not. Additionally, among those ≤70, Non-Hispanic Whites and those of other racial/ethnic backgrounds delayed care more than Non-Hispanic Blacks and Hispanics. Conclusions: There is considerable heterogeneity in care delays among older adults based on age, race/ethnicity, and pandemic-related factors. As the pandemic continues, future studies should examine whether these patterns persist.
Full-text available
Article
Despite a growing literature on the factors associated with men's low rates of medical and psychological help-seeking, a systematic review of these is missing. Such an overview can help to inform health psychologists of the barriers to the performance of adaptive health behaviours, such as prompt help-seeking, and could inform theoretical advancements and the development of targeted interventions to facilitate prompt help-seeking among men. We systematically reviewed quantitative and qualitative empirical papers on factors associated with delays in men's medical and psychological help-seeking. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and we used the databases PsycINFO, Medline, Embase and PsycARTICLES (with keywords: men/male*/gender*, help*/seek* and health*/service*/utili*[sation]) for papers in English. 41 citations (amounting to 21,787 participants aged 15-80 + ) met the inclusion criteria. Approximately half of these used qualitative methodologies (i.e., semi-structured interviews and focus groups), while half used quantitative methodologies (i.e., questionnaires). We identify a number of recurring cognitive, emotional, health-service related and socio-demographic help-seeking factors/predictors from the 41 papers. Of these, the most prominent barriers to help-seeking were disinclination to express emotions/concerns about health, embarrassment, anxiety and fear, and poor communication with health-care professionals.
Full-text available
Article
abstract --- Background: Delayed medical care has negative health and economic consequences; interventions have focused on appraising symptoms, with limited success in reducing delay. Objective: To identify predictors of care avoidance and reasons for avoiding care. Methods: Using the Health Information National Trends Survey (2007), we conducted logistic regressions to identify predictors of avoiding medical visits deemed necessary by the respondents; and, we then conducted similar analyses on reasons given for avoidance behavior. Independent variables included geographic, demographic, socioeconomic, personal health, health behavior, health care system, and cognitive characteristics. Results: Approximately one third of adults avoided doctor visits they had deemed necessary. Although unadjusted associations existed, avoiding needed care was not independently associated with geographic, demographic, and socioeconomic characteristics. Avoidance behavior is characterized by low health self-efficacy, 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, negative affect, smoking daily, and fatalistic attitude toward cancer. Reasons elicited for avoidance include preference for self-care or alternative care, dislike or distrust of doctors, fear or dislike of medical treatments, time, and money; respondents also endorsed discomfort with body examinations, fear of having a serious illness, and thoughts of dying. Distinct predictors distinguish each of these reasons. Conclusions: Interventions to reduce patient delay could be improved by addressing the health-related behavioral, belief, experiential, and emotional traits associated with delay. Attention should also be directed toward the interpersonal communications between patients and providers.
Full-text available
Article
Fear of receiving bad news about one's health can lead people to avoid seeking out health information that, ironically, may be crucial for health maintenance. Using a nationally representative US sample, the present study examined whether perceived likelihood of developing cancer and worry about cancer were associated with reports of avoiding visits to one's doctor, in respondents under and over age 50. Cancer worry, but not perceived risk of cancer, predicted doctor avoidance in respondents aged 50 and older, whereas the opposite pattern held for respondents under age 50. Moreover, in respondents aged 50 and older, cancer worry and perceived cancer risk interacted such that cancer worry was linked to doctor avoidance only when respondents also perceived a high likelihood of cancer. The latter result is consistent with the notion that worry may motivate information seeking when people expect information to dispel worry and information avoidance when the information is seen as highly likely to confirm one's fears. Findings suggest a need for communication strategies that can influence worry and perceived risk differentially. Research should also assess the effectiveness of other behavioral strategies (e.g., automatic scheduling of appointments) as a means for reducing doctor avoidance.
Full-text available
Article
Research suggests that perceiving cancer as a death sentence is a critical determinant of health care-seeking behaviors. However, there is limited information regarding the prevalence of this perception in the US population. Cross-sectional analysis of data (n = 7674 adults) from the 2007-2008 administration of the nationally representative Health Information National Trends Survey (HINTS 3) was performed. A majority (61.6%) of respondents perceived cancer as death sentence, and more than one-third (36%) of respondents reported that they avoid seeing their physicians. In the adult US population, perceiving cancer as a death sentence is common and is associated with education level and avoidance of physicians.
Full-text available
Article
Using data of 2007 Health Information National Trends Survey, we investigated the association between individuals' psychological distress and their reported avoidance of medical care and assessed whether people with serious psychological distress (SPD) were more likely to report psychosocial barriers to care. After controlling for demographic and health characteristics, individuals with SPD were more likely than those without SPD to report having avoided visiting a doctor even when they suspected they should (OR=1.64, 95% CI=1.08-2.48). The distressed individuals were also more likely to agree that they avoided a doctor because of fear of having a serious illness (OR=1.99, 95% CI=1.15-3.44) or thinking about dying (OR=2.15, 95% CI=1.12-4.11). Further understanding of the mechanism under which an individuals' mental health status may influence their perceived need for health and their use of medical services would improve the interface between mental health and primary care services.
Full-text available
Article
Background: Medical interactions between Black patients and non-Black physicians are less positive and productive than racially concordant ones and contribute to racial disparities in the quality of health care. Objective: To determine whether an intervention based on the common ingroup identity model, previously used in nonmedical settings to reduce intergroup bias, would change physician and patient responses in racially discordant medical interactions and improve patient adherence. Iintervention: Physicians and patients were randomly assigned to either a common identity treatment (to enhance their sense of commonality) or a control (standard health information) condition, and then engaged in a scheduled appointment. Design: Intervention occurred just before the interaction. Patient demographic characteristics and relevant attitudes and/or behaviors were measured before and immediately after interactions, and 4 and 16 weeks later. Physicians provided information before and immediately after interactions. Participants: Fourteen non-Black physicians and 72 low income Black patients at a Family Medicine residency training clinic. Main measures: Sense of being on the same team, patient-centeredness, and patient trust of physician, assessed immediately after the medical interactions, and patient trust and adherence, assessed 4 and 16 weeks later. Key results: Four and 16 weeks after interactions, patient trust of their physician and physicians in general was significantly greater in the treatment condition than control condition. Sixteen weeks after interactions, adherence was also significantly greater. Conclusions: An intervention used to reduce intergroup bias successfully produced greater Black patient trust of non-Black physicians and adherence. These findings offer promising evidence for a relatively low-cost and simple intervention that may offer a means to improve medical outcomes of racially discordant medical interactions. However, the sample size of physicians and patients was small, and thus the effectiveness of the intervention should be further tested in different settings, with different populations of physicians and other health outcomes.
Article
Evidence is reviewed which suggests that there may be little or no direct introspective access to higher order cognitive processes. Subjects are sometimes (a) unaware of the existence of a stimulus that importantly influenced a response, (b) unaware of the existence of the response, and (c) unaware that the stimulus has affected the response. It is proposed that when people attempt to report on their cognitive processes, that is, on the processes mediating the effects of a stimulus on a response, they do not do so on the basis of any true introspection. Instead, their reports are based on a priori, implicit causal theories, or judgments about the extent to which a particular stimulus is a plausible cause of a given response. This suggests that though people may not be able to observe directly their cognitive processes, they will sometimes be able to report accurately about them. Accurate reports will occur when influential stimuli are salient and are plausible causes of the responses they produce, and will not occur when stimuli are not salient or are not plausible causes.
Article
The rates of emergency department (ED) utilization vary substantially by type of health insurance, but the association between health insurance type and patient-reported reasons for seeking ED care is unknown. We evaluated the association between health insurance type and self-perceived acuity or access issues among individuals discharged from the ED. This was a cross-sectional analysis of the 2011 National Health Interview Survey. Adults whose last ED visit did not result in hospitalization (n = 4,606) were asked structured questions about reasons for seeking ED care. We classified responses as 1) perceived need for immediate evaluation (acuity issues), or 2) barriers to accessing outpatient services (access issues). We analyzed survey-weighted data using multivariable logistic regression models to test the association between health insurance type and reasons for ED visits, while adjusting for sociodemographic characteristics. Overall, 65.0 % (95 % CI 63.0-66.9) of adults reported ≥ 1 acuity issue and 78.9 % (95 % CI 77.3-80.5) reported ≥ 1 access issue. Among those who reported no acuity issue leading to the most recent ED visit, 84.2 % reported ≥ 1 access issue. Relative to those with private insurance, adults with Medicaid (OR 1.05; 95 % CI 0.79-1.40) and those with Medicare (OR 0.98; 95 % CI 0.66-1.47) were similarly likely to seek ED care due to an acuity issue. Adults with Medicaid (OR 1.50; 95 % CI 1.06-2.13) and Medicaid + Medicare (dual eligible) (OR 1.94; 95 % CI 1.18-3.19) were more likely than those with private insurance to seek ED care for access issues. Variability in reasons for seeking ED care among discharged patients by health insurance type may be driven more by lack of access to alternate care, rather than by differences in patient-perceived acuity. Policymakers should focus on increasing access to alternate sites of care, particularly for Medicaid beneficiaries, as well as strategies to increase care coordination that involve ED patients and providers.