INTRODUCTION
Older adults can face challenges attending medical office visits.1 Those who have trouble getting to the doctor’s office—including but not limited to adults who are homebound2—may access health care differently, with potential consequences including caregiver burden and deferred care. These adults may also benefit from targeted solutions. Yet this population has not been characterized. We used a new item in the nationally representative Medicare Current Beneficiary Survey (MCBS) to understand which older adults may face this barrier and how they access visits.
METHODS
The MCBS is a rotating panel survey covering an annual statistical sample of Medicare beneficiaries in the continental USA. We performed weighted cross-sectional analyses of 2019 MCBS data for ≥ 65-year-old Traditional Medicare and Medicare Advantage beneficiaries who were community-dwelling, continuously enrolled without death in 2019, and responded to: “Because of a health or physical problem, have you had trouble getting places, like the doctor’s office, a supermarket, or a friend’s house since [Reference Date]?”
We used multivariable logistic regression to determine sociodemographic and health characteristics (listed in Table 1) associated with reporting trouble getting places. For those reporting trouble, we described presence of functional impairment (reported difficulty with any of 6 activities of daily living: walking, dressing, bathing, eating, toileting, getting out of bed).
Table 1.
Sociodemographic and Health Characteristics of Older Adults Who Report Trouble Getting Places Like the Doctor’s Office
| Respondents reporting trouble getting places N = 8,308,041 Weighted no. (%) |
Adjusted odds ratio, 95%CI* | ||
|---|---|---|---|
| Age | 65–69 | 1,865,495 (13.9) | Ref |
| 70–74 | 1,910,904 (13.9) | 1.04 (0.83 to 1.30) | |
| 75–79 | 1,535,305 (16.1) | 1.23 (0.96 to 1.58) | |
| 80–84 | 1,212,640 (21.3) | 1.68 (1.30 to 2.17) | |
| 85+ | 1,783,699 (33.0) | 2.97 (2.30 to 3.85) | |
| Sex | Male | 1,960,134 (13.8) | Ref |
| Female | 5,347,907 (20.3) | 1.54 (1.35 to 1.76) | |
| Race† | Asian | 204,143 (15.7) | 0.86 (0.54 to 1.36) |
| African American | 848,061 (19.4) | 0.84 (0.65 to 1.10) | |
| Native Hawaiian or Pacific Islander | 6,417 (10.7) | 0.34 (0.02 to 5.27) | |
| White | 6,596,660 (16.8) | Ref | |
| American Indian or Alaska Native | 103,167 (26.0) | 1.08 (0.69 to 1.69) | |
| More than one | 247,686 (19.0) | 1.00 (0.75 to 1.35) | |
| Hispanic ethnicity‡ | Yes | 910,054 (24.6) | 1.12 (0.89 to 1.41) |
| No | 7,320,657 (16.7) | Ref | |
| Education§ | Did not graduate high school | 1,552,283 (25.1) | 0.88 (0.71 to 1.09) |
| High school/some college | 4,134,390 (18.2) | 1.01 (0.86 to 1.17) | |
| College or above | 2,589,759 (13.8) | Ref | |
| Income | ≤100% FPL | 1,582,602 (28.9) | 1.29 (1.03 to 1.62) |
| >100% to ≤200% FPL | 2,484,079 (22.6) | 1.16 (1.01 to 1.34) | |
| > 200% FPL | 4,241,360 (13.5) | Ref | |
| Lives alone | No | 5,470,158 (16.2) | Ref |
| Yes | 2,837,883 (20.2) | 1.11 (0.97 to 1.27) | |
| ResidenceII | Metropolitan | 6,886,431 (17.6) | Ref |
| Micropolitan | 765,496 (17.0) | 0.81 (0.64 to 1.02) | |
| Small town | 319,175 (14.1) | 0.63 (0.44 to 0.91) | |
| Rural | 33,528 (17.2) | 0.80 (0.56 to 1.13) | |
| Area Deprivation Index¶ | 1st quartile (least deprivation) | 2,033,304 (15.6) | Ref |
| 2nd quartile | 1,867,441 (16.1) | 0.92 (0.78 to 1.09) | |
| 3rd quartile | 1,795,536 (17.3) | 0.90 (0.75 to 1.07) | |
| 4th quartile (most deprivation) | 2,141,071 (20.4) | 0.93 (0.74 to 1.17) | |
| Medicare type# | Medicare Advantage | 3,166,735 (17.6) | 0.88 (0.78 to 0.99) |
| Traditional Medicare | 4,879,325 (17.0) | Ref | |
| Medicaid dual eligibility | Yes | 1,622,070 (32.8) | 1.34 (1.08 to 1.66) |
| No | 6,685,971 (15.6) | Ref | |
| Self-rated health** | Fair or poor | 3,462,584 (42.6) | 3.68 (3.21 to 4.22) |
| Not fair or poor | 4,794,300 (12.1) | Ref | |
| Heart disease†† | Present | 4,120,571 (24.7) | 1.45 (1.27 to 1.65) |
| Not present | 4,187,470 (13.5) | Ref | |
| Stroke | Present | 1,575,245 (34.0) | 1.48 (1.28 to 1.71) |
| Not present | 6,732,797 (15.6) | Ref | |
| Diabetes | Present | 2,321,115 (23.6) | 1.17 (0.98 to 1.39) |
| Not present | 5,986,926 (15.8) | Ref | |
| Non-skin cancer | Present | 1,879,100 (19.3) | 1.00 (0.87 to 1.15) |
| Not present | 6,428,940 (16.9) | Ref | |
| Rheumatoid arthritis | Present | 2,648,197 (28.3) | 1.48 (1.30 to 1.69) |
| Not present | 5,659,843 (14.7) | Ref | |
| Pulmonary disease | Present | 2,308,392 (27.4) | 1.35 (1.20 to 1.52) |
| Not present | 5,999,649 (15.2) | Ref | |
| Obesity | Present | 3,875,349 (19.3) | 1.23 (1.08 to 1.39) |
| Not present | 4,432,692 (16.0) | Ref | |
| Paralysis or amputation | Present | 670,952 (45.2) | 2.79 (2.07 to 3.75) |
| Not present | 7,637,089 (16.5) | Ref | |
| Mental illness | Present | 3,151,091 (28.3) | 1.63 (1.43 to 1.85) |
| Not present | 5,156,949 (14.1) | Ref | |
| Dementia | Present | 966,745 (50.5) | 2.54 (1.91 to 3.37) |
| Not present | 7,341,296 (16.0) | Ref | |
SOURCE: Centers for Medicare & Medicaid Services, Medicare Current Beneficiary Survey [2019]. Data were collected in the winter, summer, and fall of 2019. Table presents weighted numbers and row percentages of respondents in a given subcategory who reported trouble getting places. We applied MCBS cross-sectional sampling weights to construct national estimates and used balanced repeated replication weights for variance estimation
FPL, Federal Poverty Level. *Multivariable model includes all variables in the table. Missing values were included in the models using an indicator variable. †Race don’t know or refused for 313 respondents. ‡Ethnicity don’t know or refused for 56 respondents. §Education don’t know or refused for 42 respondents. IIResidence (based on Rural-Urban commuting area (RUCA) codes) missing for 7 respondents. ¶Area Deprivation Index (for census block group with quartiles based on national-level percentile) missing for 626 respondents. #249 respondents had both MA and TM enrollment in the study period. **Self-rated health don’t know or refused for 50 respondents. ††For chronic conditions, don’t know/refused was counted as “no.”
Among respondents reporting a usual place of care outside the home, we used chi-square tests to compare visit access approaches (mode and time to get to doctor’s office, accompaniment to office, appointment in past year, missed appointments) between those with and without trouble.
The study followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. The Mass General Brigham institutional review board waived review. Analyses were performed using SASv9.4 (SAS Institute).
RESULTS
Among 11,518 respondents (representing 47,792,980 beneficiaries), 17.4% reported trouble getting places like the doctor’s office. Beneficiaries were more likely to report trouble if they were older (adjusted OR 2.97 [95%CI 2.30, 3.85] for 85+ years vs 65–69), female sex (aOR 1.54 [95%CI 1.35, 1.76] vs male), had lower income (aOR 1.29 [95%CI 1.03, 1.62] for ≤ 100% Federal Poverty Level vs > 200% FPL), had dual eligibility (aOR 1.34 [95%CI 1.08, 1.66]), reported fair or poor health (aOR 3.68 [95%CI 3.21, 4.22]), or reported having conditions such as dementia (aOR 2.54 [95%CI 1.91, 3.37]), mental illness (aOR 1.63 [95%CI 1.43, 1.85]), and paralysis or amputation (aOR 2.79 [95%CI 2.07, 3.75]). In a model including chronic condition count rather than individual conditions, each additional condition increased odds of reporting trouble by 1.41 (95%CI 1.35, 1.46).
Among respondents with trouble getting places, 63.3% noted functional impairment and 1.0% reported their usual place of care was home.
Compared to those without trouble, respondents with trouble were more likely to get to their usual doctor’s office by being driven (40.6% vs 11.2%), by public transportation (3.5% vs 2.0%), or by ambulance (2.1% vs 0.3%); to report that it usually takes ≥ 30 minutes to get to the office (22.4% vs 18.2%); to usually be accompanied by someone to the office (54.3% vs 26.1%); and to miss appointments at least sometimes (13.4% vs 6.2%) (all p < 0.001) (Table 2).
Table 2.
Visit Access Approaches of Older Adults Who Do and Do Not Report Trouble Getting Places Like the Doctor’s Office, Among Those with a Usual Source of Care Outside the Home
| Respondents reporting trouble getting places N = 7,401,106 Weighted no. (%) |
Respondents not reporting trouble getting places N = 36,622,284 Weighted no. (%) |
P value for chi-square | ||
|---|---|---|---|---|
| How do you usually get to your usual doctor’s office* | Walking | 75,106 (1.0) | 620,363 (1.7) | < .001 |
| Driving | 3,592,166 (48.5) | 30,729,595 (83.9) | ||
| Being driven | 3,004,053 (40.6) | 4,119,485 (11.2) | ||
| Ambulance | 153,359 (2.1) | 118,537 (0.3) | ||
| Taxi | 138,863 (1.9) | 56,180 (0.2) | ||
| Other public transportation | 258,747 (3.5) | 724,008 (2.0) | ||
| Doctor comes to home | 12,309 (0.2) | 45,138 (0.1) | ||
| Other | 82,324 (1.1) | 168,604 (0.5) | ||
| How long does it usually take for you to get to your usual doctor’s office† | < 15 min | 2,489,805 (33.6) | 314,492,917 (39.6) | < .001 |
| 15 to < 30 min | 3,104,050 (41.9) | 15,276,796 (41.7) | ||
| 30 to < 45 | 1,063,673 (14.4) | 4,274,370 (11.7) | ||
| 45 to < 60 | 313,028 (4.2) | 1,163,516 (3.2) | ||
| 60 or more | 284,292 (3.8) | 1,240,912 (3.4) | ||
| Do you usually have someone accompany you there? | Yes | 4,016,780 (54.3) | 26,968,278 (26.1) | < .001 |
| No | 3,285,970 (44.4) | 26,968,278 (73.6) | ||
| Don’t know/refused | 98,356 (1.3) | 92,890 (0.3) | ||
| Have you seen your usual doctor in last 12 months | Yes | 6,957,521 (94.0) | 34,036,528 (92.9) | 0.16 |
| No | 410,933 (5.6) | 2,506,003 (6.8) | ||
| Don’t know/refused | 32,652 (0.4) | 79,752 (0.2) | ||
| In the past 12 months, how often did you miss an appointment with your usual doctor‡ | Never | 5,990,260 (86.1) | 31,875,654 (93.7) | < .001 |
| At least sometimes | 934,331 (13.4) | 2,096,504 (6.2) | ||
| Don’t know/refused | 32,930 (0.5) | 64,370 (0.2) |
Table presents weighted numbers and column percentages of responses to each survey item. We applied MCBS cross-sectional sampling weights to construct national estimates and used balanced repeated replication weights for variance estimation. Numbers may not add up to 100% due to missing data. *Inapplicable for those reporting usual place of care was home (20 with trouble, 6 without trouble), don’t know/refused (1 with trouble, 1 without trouble). †Missing for those reporting doctor comes to home or if mode of getting to usual office inapplicable or don’t know/refused (39 with trouble, 34 without trouble). ‡Only among those who saw a usual doctor in last 12 months.
DISCUSSION
In this nationally representative study, nearly 1 in 5 Medicare beneficiaries reported trouble getting places like the doctor’s office—more commonly women and those in the fastest-growing 85+ age bracket, those with lower incomes, and those with conditions including dementia and mental illness. Many but not all who had this trouble also reported functional impairment. These results suggest barriers to attending office visits are not restricted to physical limitations and align with evidence that homebound individuals have greater risk of dementia and depression.2 Older adults with social risk factors including female sex, Medicaid dual eligibility, and lower income are also more often homebound2 and may face additional barriers as cities gentrify and health care facilities move out of low-income areas.3,4
Those reporting trouble took more time to attend visits, often relying on others or on public transportation—demonstrating substantial opportunity costs of office visits for these adults and their caregivers.5 Few received primarily home-based care. While this descriptive, cross-sectional study does not assess causation and assesses a survey item that is not specific to office visit barriers, these results suggest that many patients may benefit from clinicians using office visits more selectively6 and from expanding alternatives like telemedicine and home-based care. These results can also inform Centers for Medicare and Medicaid Services decision-making on whether, and how, to target telemedicine reimbursement to individuals or specific conditions most likely to benefit from telemedicine.
Role of the Funder/Sponsor
The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Funding
This article was supported by grant K23AG068240 from the National Institute on Aging of the National Institutes of Health.
Declarations
Conflict of Interest
Dr. Ganguli reports a prior grant from The Commonwealth Fund and consulting fees from FPrime outside of the submitted work.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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